Results publication

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-11-06

Checks: 7 0

Knit directory: emlr_obs_analysis/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210412)

The command set.seed(20210412) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 820fff8

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 820fff8. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/
    Ignored:    output/other/
    Ignored:    output/presentation/
    Ignored:    output/publication/

Untracked files:
    Untracked:  code/visualization_equal_earth.R
    Untracked:  code/visualization_equal_earth_cleaned.R
    Untracked:  code/visualization_equal_earth_cleaned_jdm.R

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   code/Workflowr_project_managment.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/results_publication.Rmd) and HTML (docs/results_publication.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	7275091	jens-daniel-mueller	2022-11-05	Build site.
Rmd	ba55b15	jens-daniel-mueller	2022-11-05	include run with C*(P only)
html	99610ed	jens-daniel-mueller	2022-11-01	Build site.
Rmd	c972beb	jens-daniel-mueller	2022-11-01	updated reoccupation run
html	7060801	jens-daniel-mueller	2022-10-31	Build site.
html	ecb84c1	jens-daniel-mueller	2022-10-30	Build site.
html	2345935	jens-daniel-mueller	2022-10-29	Build site.
html	0b40366	jens-daniel-mueller	2022-10-26	Build site.
Rmd	0465788	jens-daniel-mueller	2022-10-26	implemented additive uncertainty assesment
html	91052ae	jens-daniel-mueller	2022-10-20	Build site.
Rmd	33486a1	jens-daniel-mueller	2022-10-20	updated plots
html	084a41c	jens-daniel-mueller	2022-10-13	Build site.
Rmd	ffef558	jens-daniel-mueller	2022-10-13	land sink assesment
html	46c163a	jens-daniel-mueller	2022-10-10	Build site.
Rmd	78161f9	jens-daniel-mueller	2022-10-10	revised figures
html	8105380	jens-daniel-mueller	2022-10-07	Build site.
Rmd	d663118	jens-daniel-mueller	2022-10-07	penetration depth analysis
html	e93fdfd	jens-daniel-mueller	2022-09-23	Build site.
Rmd	f01db23	jens-daniel-mueller	2022-09-23	added uncertainty to dcant maps and section
html	8dc5a3b	jens-daniel-mueller	2022-09-09	Build site.
Rmd	25bff3d	jens-daniel-mueller	2022-09-09	added global section location to maps
html	77e6a80	jens-daniel-mueller	2022-09-08	Build site.
Rmd	188dc63	jens-daniel-mueller	2022-09-08	added uncertainty to global section
html	7cf954e	jens-daniel-mueller	2022-09-08	Build site.
Rmd	6f53c28	jens-daniel-mueller	2022-09-08	column inventory uncertainty stippling added
html	051b911	jens-daniel-mueller	2022-09-07	Build site.
Rmd	c87415f	jens-daniel-mueller	2022-09-07	testrun integration 3000
html	75bd58c	jens-daniel-mueller	2022-09-07	Build site.
Rmd	1024099	jens-daniel-mueller	2022-09-07	testrun integration 1000
html	1c6de0d	jens-daniel-mueller	2022-09-07	Build site.
Rmd	4106aa4	jens-daniel-mueller	2022-09-07	testrun integration 10000
html	5ca8778	jens-daniel-mueller	2022-09-07	Build site.
Rmd	784849d	jens-daniel-mueller	2022-09-07	included uncertainty assesment for column inventories
html	4eb9ed2	jens-daniel-mueller	2022-08-29	Build site.
html	392f3d7	jens-daniel-mueller	2022-08-29	Build site.
Rmd	282421f	jens-daniel-mueller	2022-08-29	added contour line to global section
html	c975141	jens-daniel-mueller	2022-08-29	Build site.
Rmd	8f5c3c2	jens-daniel-mueller	2022-08-29	finalized global section
html	cf69673	jens-daniel-mueller	2022-08-29	Build site.
html	4810db6	jens-daniel-mueller	2022-08-26	Build site.
Rmd	88ce2d9	jens-daniel-mueller	2022-08-26	added global section
html	74dca9d	jens-daniel-mueller	2022-08-17	Build site.
Rmd	37ab61f	jens-daniel-mueller	2022-08-17	added adjustment delta map
html	ec4f1d0	jens-daniel-mueller	2022-08-11	Build site.
Rmd	d1cd39a	jens-daniel-mueller	2022-08-11	revised figure aspect ratio
html	78dbb27	jens-daniel-mueller	2022-08-11	Build site.
Rmd	2155209	jens-daniel-mueller	2022-08-11	rebuild with 3 basin separation as standard case
html	302d289	jens-daniel-mueller	2022-08-11	Build site.
Rmd	77719db	jens-daniel-mueller	2022-08-11	rebuild with 5 basin separation as standard case
html	318fefe	jens-daniel-mueller	2022-08-11	Build site.
Rmd	60dae4b	jens-daniel-mueller	2022-08-11	delta dcant budget analysis
html	5b77c4f	jens-daniel-mueller	2022-08-10	Build site.
Rmd	c25333a	jens-daniel-mueller	2022-08-10	bias sections and maps added
html	9c3be27	jens-daniel-mueller	2022-08-09	Build site.
Rmd	a940880	jens-daniel-mueller	2022-08-09	improve delta dcant bias assesment
html	a691b29	jens-daniel-mueller	2022-08-09	Build site.
Rmd	d136a86	jens-daniel-mueller	2022-08-09	include delta dcant bias assesment
html	910f96e	jens-daniel-mueller	2022-08-08	Build site.
Rmd	e064958	jens-daniel-mueller	2022-08-08	include bias assesment
html	e99640e	jens-daniel-mueller	2022-07-29	Build site.
html	f68fcfa	jens-daniel-mueller	2022-07-26	Build site.
Rmd	abec0de	jens-daniel-mueller	2022-07-26	changed color scale back
html	f2615fb	jens-daniel-mueller	2022-07-26	Build site.
Rmd	8da318e	jens-daniel-mueller	2022-07-26	changed color scale
html	99a82db	jens-daniel-mueller	2022-07-25	Build site.
Rmd	1d6985d	jens-daniel-mueller	2022-07-25	stat analysis of layer budgets and CIs
html	4af29f9	jens-daniel-mueller	2022-07-24	Build site.
Rmd	b2c4ff6	jens-daniel-mueller	2022-07-24	revised GCB plot
html	e2adac6	jens-daniel-mueller	2022-07-22	Build site.
Rmd	a9c8af4	jens-daniel-mueller	2022-07-22	revised section plots and tables
html	9bbc6a4	jens-daniel-mueller	2022-07-20	Build site.
Rmd	f937de2	jens-daniel-mueller	2022-07-20	revised plots
html	0c6db30	jens-daniel-mueller	2022-07-20	Build site.
Rmd	a42e52d	jens-daniel-mueller	2022-07-20	revised plots
html	b18b250	jens-daniel-mueller	2022-07-20	Build site.
Rmd	36691bf	jens-daniel-mueller	2022-07-20	revised plots
html	a80b59b	jens-daniel-mueller	2022-07-20	Build site.
Rmd	f9c0545	jens-daniel-mueller	2022-07-20	revised budget stats
html	fea41c1	jens-daniel-mueller	2022-07-20	Build site.
Rmd	b27e42f	jens-daniel-mueller	2022-07-20	rerun with corrected Canyon-B talk gap-filling in standard case
html	9ce772d	jens-daniel-mueller	2022-07-19	Build site.
Rmd	aaf37cf	jens-daniel-mueller	2022-07-19	coverage maps with gap filling
html	d803308	jens-daniel-mueller	2022-07-19	Build site.
Rmd	933285c	jens-daniel-mueller	2022-07-19	revised plots
html	b1f7ab3	jens-daniel-mueller	2022-07-18	Build site.
Rmd	ab5220b	jens-daniel-mueller	2022-07-18	revised budget over atm pCO2 plot
html	d2ae54c	jens-daniel-mueller	2022-07-18	Build site.
Rmd	85f482c	jens-daniel-mueller	2022-07-18	include scaled Sabine data for budgets
html	2695085	jens-daniel-mueller	2022-07-17	Build site.
Rmd	8624c93	jens-daniel-mueller	2022-07-17	use global output from MLR basins
html	535196a	jens-daniel-mueller	2022-07-17	Build site.
html	d20faeb	jens-daniel-mueller	2022-07-17	Build site.
Rmd	b6ef86b	jens-daniel-mueller	2022-07-17	revised budget plots
html	0160c40	jens-daniel-mueller	2022-07-16	Build site.
Rmd	d2b1090	jens-daniel-mueller	2022-07-16	cleaned code
html	efa414b	jens-daniel-mueller	2022-07-16	Build site.
Rmd	e84b169	jens-daniel-mueller	2022-07-16	added bias global analysis
html	7267b08	jens-daniel-mueller	2022-07-16	Build site.
Rmd	b91f619	jens-daniel-mueller	2022-07-16	analysed dcant budget stats
html	08c00b4	jens-daniel-mueller	2022-07-16	Build site.
html	692c937	jens-daniel-mueller	2022-07-16	Build site.
html	afb27ad	jens-daniel-mueller	2022-07-15	Build site.
Rmd	53e63d5	jens-daniel-mueller	2022-07-15	plot no surface data equi
html	b492b46	jens-daniel-mueller	2022-07-15	Build site.
Rmd	31f29fe	jens-daniel-mueller	2022-07-15	plot decadal sink trends as boxplots
html	bd24a0f	jens-daniel-mueller	2022-07-15	Build site.
Rmd	efbb1dc	jens-daniel-mueller	2022-07-15	include no surface equi cases
html	022fd60	jens-daniel-mueller	2022-07-14	Build site.
Rmd	5a9e419	jens-daniel-mueller	2022-07-14	implemented beta budget analysis
Rmd	d2092c1	jens-daniel-mueller	2022-07-14	implemented beta budget analysis
html	f1d7f80	jens-daniel-mueller	2022-07-13	Build site.
Rmd	6811ae4	jens-daniel-mueller	2022-07-13	refined profil plots
html	ea46812	jens-daniel-mueller	2022-07-13	Build site.
Rmd	defdcfe	jens-daniel-mueller	2022-07-13	additional analyis
html	17cd1d1	jens-daniel-mueller	2022-07-13	Build site.
Rmd	1bf1708	jens-daniel-mueller	2022-07-13	rerun reoccupation
html	26e9496	jens-daniel-mueller	2022-07-12	Build site.
Rmd	5d121f0	jens-daniel-mueller	2022-07-12	applied dcant scaling and improved ensemble member analysis
html	8fb595c	jens-daniel-mueller	2022-07-12	Build site.
Rmd	e2d97ef	jens-daniel-mueller	2022-07-12	excluded no adjustment from ensemble
html	003b161	jens-daniel-mueller	2022-07-12	Build site.
Rmd	54a0d14	jens-daniel-mueller	2022-07-12	added cruise based adjustment
html	b44c72a	jens-daniel-mueller	2022-07-03	Build site.
html	37f56b3	jens-daniel-mueller	2022-07-01	Build site.
Rmd	8c4a9f8	jens-daniel-mueller	2022-07-01	added basin separation analysis
html	232909e	jens-daniel-mueller	2022-07-01	Build site.
Rmd	7f85db3	jens-daniel-mueller	2022-07-01	added basin separation analysis
html	df21d31	jens-daniel-mueller	2022-07-01	Build site.
Rmd	2bbfba0	jens-daniel-mueller	2022-07-01	rebuild
html	6be73e0	jens-daniel-mueller	2022-06-30	Build site.
Rmd	6e173bf	jens-daniel-mueller	2022-06-30	updated regional budget plots
html	6e173bf	jens-daniel-mueller	2022-06-30	updated regional budget plots
html	8ab4a87	jens-daniel-mueller	2022-06-29	Build site.
Rmd	d8c7cb9	jens-daniel-mueller	2022-06-29	ensemble with unadjusted data
html	7629c78	jens-daniel-mueller	2022-06-29	Build site.
Rmd	4758911	jens-daniel-mueller	2022-06-29	ensemble without unadjusted data
html	f6786c8	jens-daniel-mueller	2022-06-29	Build site.
Rmd	6f694a1	jens-daniel-mueller	2022-06-29	included Cstar N in ensemble
html	f09080e	jens-daniel-mueller	2022-06-28	Build site.
Rmd	d457610	jens-daniel-mueller	2022-06-28	save figures for publication
html	ee30748	jens-daniel-mueller	2022-06-28	Build site.
Rmd	fe42644	jens-daniel-mueller	2022-06-28	GCB emissions ratio included
html	9393c07	jens-daniel-mueller	2022-06-28	Build site.
Rmd	2a3cf97	jens-daniel-mueller	2022-06-28	included basin-hemisphere bias, and bias contributions
html	0825298	jens-daniel-mueller	2022-06-28	Build site.
Rmd	d35ddb1	jens-daniel-mueller	2022-06-28	included GCB ocean sink data as boxplot
html	a13a7cf	jens-daniel-mueller	2022-06-28	Build site.
html	fb59a6f	jens-daniel-mueller	2022-06-27	Build site.
Rmd	7ff568c	jens-daniel-mueller	2022-06-27	included GCB ocean sink data
html	a26a21d	jens-daniel-mueller	2022-06-27	Build site.
Rmd	9425709	jens-daniel-mueller	2022-06-27	scaled sabine 2004 to full area
html	748aa43	jens-daniel-mueller	2022-06-27	Build site.
Rmd	82d5793	jens-daniel-mueller	2022-06-27	ensemble scatter plot
html	457e640	jens-daniel-mueller	2022-06-27	Build site.
Rmd	426c5bf	jens-daniel-mueller	2022-06-27	cleaned read-in section
html	16dc3af	jens-daniel-mueller	2022-06-27	Build site.
Rmd	dd2063a	jens-daniel-mueller	2022-06-27	ensemble member analysis added
html	87e9eb8	jens-daniel-mueller	2022-06-27	Build site.
Rmd	09a3348	jens-daniel-mueller	2022-06-27	1 as standard case and new ensemble members
html	b52b159	jens-daniel-mueller	2022-06-27	Build site.
html	09b0780	jens-daniel-mueller	2022-05-24	Build site.
html	25da2fb	jens-daniel-mueller	2022-05-24	Build site.
html	1d73ec9	jens-daniel-mueller	2022-05-16	Build site.
Rmd	e117b7b	jens-daniel-mueller	2022-05-16	rerun w/o data adjustments
html	2ffbdda	jens-daniel-mueller	2022-05-16	Build site.
Rmd	6fc8438	jens-daniel-mueller	2022-05-16	plot individual zonal sections
html	c71227f	jens-daniel-mueller	2022-05-16	Build site.
Rmd	55e9ac6	jens-daniel-mueller	2022-05-16	plot individual zonal sections
html	3c1100c	jens-daniel-mueller	2022-05-16	Build site.
Rmd	fdb111d	jens-daniel-mueller	2022-05-16	plot individual zonal sections
html	2ca0109	jens-daniel-mueller	2022-05-02	Build site.
Rmd	63d51bd	jens-daniel-mueller	2022-05-02	rerun with adjusted data
html	dcf2eaf	jens-daniel-mueller	2022-05-02	Build site.
Rmd	a3a2cf9	jens-daniel-mueller	2022-05-02	IO sections seperate
html	eff4fd7	jens-daniel-mueller	2022-05-02	Build site.
Rmd	39e920a	jens-daniel-mueller	2022-05-02	IO sections seperate
html	08607eb	jens-daniel-mueller	2022-05-02	Build site.
Rmd	0791c1b	jens-daniel-mueller	2022-05-02	modified plots
html	b018a9a	jens-daniel-mueller	2022-04-29	Build site.
Rmd	02ede93	jens-daniel-mueller	2022-04-29	standard case uncorrected data
html	e09320d	jens-daniel-mueller	2022-04-12	Build site.
Rmd	0dec180	jens-daniel-mueller	2022-04-12	3 data adjustment procedures implemented
html	8dca96a	jens-daniel-mueller	2022-04-12	Build site.
Rmd	e5e9288	jens-daniel-mueller	2022-04-12	3 data adjustment procedures implemented
html	2f20ea6	jens-daniel-mueller	2022-04-11	Build site.
html	209c9b6	jens-daniel-mueller	2022-04-10	Build site.
Rmd	537aff7	jens-daniel-mueller	2022-04-10	no data adjustment implemented
html	acad2e2	jens-daniel-mueller	2022-04-09	Build site.
html	3d81135	jens-daniel-mueller	2022-04-07	Build site.
html	0f5d372	jens-daniel-mueller	2022-04-04	Build site.
Rmd	ac5121f	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution new figure
html	1b5a309	jens-daniel-mueller	2022-04-04	Build site.
Rmd	8b96a9d	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution new figure
html	a74e341	jens-daniel-mueller	2022-04-04	Build site.
Rmd	c0432be	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution ensemble sd
html	b599680	jens-daniel-mueller	2022-04-04	Build site.
Rmd	e406c39	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution
html	ca7e590	jens-daniel-mueller	2022-03-22	Build site.
Rmd	8cb4b78	jens-daniel-mueller	2022-03-22	use 1800 as tref for sabine estimates
html	5a6be34	jens-daniel-mueller	2022-03-22	Build site.
Rmd	cee203c	jens-daniel-mueller	2022-03-22	rerun with NP 2021 talk correction
html	bd9e11d	jens-daniel-mueller	2022-03-22	Build site.
html	2501978	jens-daniel-mueller	2022-03-21	Build site.
Rmd	9e12898	jens-daniel-mueller	2022-03-21	use 1800 as tref for sabine estimates
html	c3a6238	jens-daniel-mueller	2022-03-08	Build site.
Rmd	775eb4f	jens-daniel-mueller	2022-03-08	moving eras analysis implemented
html	094bfa0	jens-daniel-mueller	2022-02-18	Build site.
Rmd	fa258cc	jens-daniel-mueller	2022-02-18	updated plots
html	ba2d62e	jens-daniel-mueller	2022-02-17	Build site.
Rmd	5420131	jens-daniel-mueller	2022-02-17	added Seaflux data
html	192504c	jens-daniel-mueller	2022-02-17	Build site.
Rmd	2394302	jens-daniel-mueller	2022-02-17	added Seaflux data
html	251c7cf	jens-daniel-mueller	2022-02-17	Build site.
Rmd	9ea0d8c	jens-daniel-mueller	2022-02-17	adapted beta factor
html	565224d	jens-daniel-mueller	2022-02-17	Build site.
Rmd	33422e3	jens-daniel-mueller	2022-02-17	scaled budgets to global coverage
html	2116dd3	jens-daniel-mueller	2022-02-09	Build site.
Rmd	e281bf3	jens-daniel-mueller	2022-02-09	updated plots
html	6fe70a1	jens-daniel-mueller	2022-02-05	Build site.
Rmd	8955c85	jens-daniel-mueller	2022-02-05	cleaned plots
html	a6b33aa	jens-daniel-mueller	2022-02-04	Build site.
Rmd	73efef8	jens-daniel-mueller	2022-02-04	atm co2 time series plotted, uncertainties revised
html	4b48475	jens-daniel-mueller	2022-02-04	Build site.
Rmd	6afd7b1	jens-daniel-mueller	2022-02-04	calculated uncertainty of basin budget changes
html	fec5a1e	jens-daniel-mueller	2022-02-04	Build site.
Rmd	325da3a	jens-daniel-mueller	2022-02-04	calculated uncertainty of basin budget changes
html	d2191ad	jens-daniel-mueller	2022-02-04	Build site.
Rmd	79a54fe	jens-daniel-mueller	2022-02-04	new coaverage map
html	4c5b079	jens-daniel-mueller	2022-02-03	Build site.
Rmd	66d89a9	jens-daniel-mueller	2022-02-03	added surface flux model products
html	4077397	jens-daniel-mueller	2022-02-03	Build site.
Rmd	c0c3be1	jens-daniel-mueller	2022-02-03	added surface flux products
html	0d0f790	jens-daniel-mueller	2022-02-02	Build site.
Rmd	049346f	jens-daniel-mueller	2022-02-02	shifted year labels
html	4673df5	jens-daniel-mueller	2022-02-02	Build site.
Rmd	1185397	jens-daniel-mueller	2022-02-02	rearranged plots
html	60727e6	jens-daniel-mueller	2022-02-02	Build site.
Rmd	db5308b	jens-daniel-mueller	2022-02-02	rearranged plots
html	c7b4984	jens-daniel-mueller	2022-02-02	Build site.
Rmd	a3d8469	jens-daniel-mueller	2022-02-02	ensemble uncertainties in global buget
html	7fb28a2	jens-daniel-mueller	2022-02-02	Build site.
Rmd	a332fe6	jens-daniel-mueller	2022-02-02	ensemble uncertainties in time series
html	49097e8	jens-daniel-mueller	2022-02-02	Build site.
Rmd	f98c33b	jens-daniel-mueller	2022-02-02	incl ensemble uncertainties in plot
html	fe11bfd	jens-daniel-mueller	2022-02-02	Build site.
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Rmd	86b711c	jens-daniel-mueller	2022-01-18	plot hemisphere budgets and publication results

---

1 Libraries

2 Read files

2.1 integration depth

params_global$inventory_depth_standard <- 1000

2.2 Paths and Versions

version_id_pattern <- "1"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_x <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "o"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_o <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "d"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_d <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "g"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_g <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "n"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_n <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "c"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_c <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "e"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_e <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "a"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_a <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

Version_IDs_ensemble <- c(Version_IDs_x, Version_IDs_o, Version_IDs_d, Version_IDs_g, Version_IDs_n, Version_IDs_c, Version_IDs_e, Version_IDs_a)

rm(Version_IDs_x, Version_IDs_o, Version_IDs_g, Version_IDs_n, Version_IDs_c, Version_IDs_e)

# subset Standard case
Version_IDs <- Version_IDs_ensemble[str_detect(Version_IDs_ensemble, "103")]

2.3 Parameters

for (i_Version_IDs in Version_IDs_ensemble) {
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  params_local <-
    read_rds(paste(path_version_data,
                   "params_local.rds",
                   sep = ""))
  
  params_local <- bind_cols(
    Version_ID = i_Version_IDs,
    tref1 = params_local$tref1,
    tref2 = params_local$tref2,
    MLR_basins = params_local$MLR_basins
  )
  
  tref <- read_csv(paste(path_version_data,
                         "tref.csv",
                         sep = ""))
  
  params_local <- params_local %>%
    mutate(
      median_year_1 = sort(tref$median_year)[1],
      median_year_2 = sort(tref$median_year)[2],
      duration = median_year_2 - median_year_1,
      period = paste(median_year_1, "-", median_year_2)
    )
  
  if (exists("params_local_all_ensemble")) {
    params_local_all_ensemble <- bind_rows(params_local_all_ensemble, params_local)
  }
  
  if (!exists("params_local_all_ensemble")) {
    params_local_all_ensemble <- params_local
  }
  
  
}

rm(params_local,
   tref)


params_local_all_ensemble <- params_local_all_ensemble %>%
  select(Version_ID, period, MLR_basins, tref1, tref2)

params_local_all_ensemble <-
  params_local_all_ensemble %>%
  mutate(
    Version_ID_group = str_sub(Version_ID, 4, 4),
    Version_ID_group = case_when(
      Version_ID_group == "1" ~ "Bulk adjustment",
      Version_ID_group == "c" ~ "Cruise adjustment",
      Version_ID_group == "d" ~ "No adjustment",
      Version_ID_group == "g" ~ "No gap filling",
      Version_ID_group == "o" ~ "Reoccupation filter",
      Version_ID_group == "e" ~ "Surface eMLR(C*)",
      Version_ID_group == "n" ~ "C*(N,AT)",
      Version_ID_group == "a" ~ "C*(P)",
      TRUE ~ Version_ID_group
    )
  )

params_local_all_ensemble <-
  params_local_all_ensemble %>%
  mutate(
    MLR_basins = case_when(
      MLR_basins == "AIP" ~ "3",
      MLR_basins == "SO_AIP" ~ "3+SO",
      MLR_basins == "SO_5" ~ "5+SO",
      TRUE ~ MLR_basins
    )
  )

params_local_all <- params_local_all_ensemble %>% 
  filter(Version_ID %in% Version_IDs)

path_version_data     <-
  paste(path_observations,
        Version_IDs[1],
        "/data/",
        sep = "")

params_local <-
  read_rds(paste(path_version_data,
                 "params_local.rds",
                 sep = ""))

2.4 eMLR stats

for (i_Version_IDs in Version_IDs) {
  # i_Version_IDs <- Version_IDs[1]
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  # Model performance: RMSE, n_predictors, VIF
  GLODAP_glanced <-
    read_csv(paste(path_version_data,
                   "lm_model_metrics.csv",
                   sep = ""))
  
  GLODAP_glanced <- GLODAP_glanced %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("GLODAP_glanced_all")) {
    GLODAP_glanced_all <-
      bind_rows(GLODAP_glanced_all, GLODAP_glanced)
  }
  
  if (!exists("GLODAP_glanced_all")) {
    GLODAP_glanced_all <- GLODAP_glanced
  }
  
  # The chosen models
  lm_best_target <-
    read_csv(paste(path_version_data,
                   "lm_best_target.csv",
                   sep = ""))
  
  lm_best_target <- lm_best_target %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("lm_best_target_all")) {
    lm_best_target_all <-
      bind_rows(lm_best_target_all, lm_best_target)
  }
  
  if (!exists("lm_best_target_all")) {
    lm_best_target_all <- lm_best_target
  }
  
  lm_best_predictor_counts <-
    read_csv(paste(path_version_data,
                   "lm_best_predictor_counts.csv",
                   sep = ""))
  
  lm_best_predictor_counts <- lm_best_predictor_counts %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("lm_best_predictor_counts_all")) {
    lm_best_predictor_counts_all <-
      bind_rows(lm_best_predictor_counts_all, lm_best_predictor_counts)
  }
  
  if (!exists("lm_best_predictor_counts_all")) {
    lm_best_predictor_counts_all <- lm_best_predictor_counts
  }
  
  lat_residual <-
    read_csv(paste(path_version_data,
                   "lm_lat_residual.csv",
                   sep = ""))
  
  lat_residual <- lat_residual %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("lat_residual_all")) {
    lat_residual_all <-
      bind_rows(lat_residual_all, lat_residual)
  }
  
  if (!exists("lat_residual_all")) {
    lat_residual_all <- lat_residual
  }
  
  lat_residual_offset <-
    read_csv(paste(path_version_data,
                   "lm_lat_residual_offset.csv",
                   sep = ""))
  
  lat_residual_offset <- lat_residual_offset %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("lat_residual_offset_all")) {
    lat_residual_offset_all <-
      bind_rows(lat_residual_offset_all, lat_residual_offset)
  }
  
  if (!exists("lat_residual_offset_all")) {
    lat_residual_offset_all <- lat_residual_offset
  }
  
  
  spatial_residual <-
    read_csv(paste(path_version_data,
                   "lm_spatial_residual.csv",
                   sep = ""))
  
  spatial_residual <- spatial_residual %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("spatial_residual_all")) {
    spatial_residual_all <-
      bind_rows(spatial_residual_all, spatial_residual)
  }
  
  if (!exists("spatial_residual_all")) {
    spatial_residual_all <- spatial_residual
  }
  
  spatial_residual_offset <-
    read_csv(paste(path_version_data,
                   "lm_spatial_residual_offset.csv",
                   sep = ""))
  
  spatial_residual_offset <- spatial_residual_offset %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("spatial_residual_offset_all")) {
    spatial_residual_offset_all <-
      bind_rows(spatial_residual_offset_all, spatial_residual_offset)
  }
  
  if (!exists("spatial_residual_offset_all")) {
    spatial_residual_offset_all <- spatial_residual_offset
  }
  
  

  
}

rm(GLODAP_glanced,
   lat_residual,
   lat_residual_offset,
   spatial_residual,
   spatial_residual_offset,
   lm_best_predictor_counts,
   lm_best_target)

2.5 Budgets

for (i_Version_IDs in Version_IDs_ensemble) {
  # i_Version_IDs <- Version_IDs[1]
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_budget_global <-
    read_csv(paste(path_version_data,
                   "dcant_budget_global.csv",
                   sep = ""))
  
  dcant_budget_global_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_global_mod_truth.csv",
      sep = ""
    ))
  
  dcant_budget_global <- bind_rows(dcant_budget_global,
                                   dcant_budget_global_mod_truth)
  
  dcant_budget_global <- dcant_budget_global %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("dcant_budget_global_all")) {
    dcant_budget_global_all <-
      bind_rows(dcant_budget_global_all, dcant_budget_global)
  }
  
  if (!exists("dcant_budget_global_all")) {
    dcant_budget_global_all <- dcant_budget_global
  }
  
}

rm(dcant_budget_global,
   dcant_budget_global_mod_truth)

for (i_Version_IDs in Version_IDs_ensemble) {
  # i_Version_IDs <- Version_IDs[1]
  
  # print(i_Version_IDs)
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_budget_basin_MLR <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_MLR.csv",
                   sep = ""))
  
  dcant_budget_basin_MLR_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_MLR_mod_truth.csv",
      sep = ""
    ))
  
    
  dcant_budget_basin_MLR <- bind_rows(dcant_budget_basin_MLR,
                                      dcant_budget_basin_MLR_mod_truth)
  
  dcant_budget_basin_MLR <- dcant_budget_basin_MLR %>%
    mutate(Version_ID = i_Version_IDs)
  

  if (exists("dcant_budget_basin_MLR_all")) {
    dcant_budget_basin_MLR_all <-
      bind_rows(dcant_budget_basin_MLR_all, dcant_budget_basin_MLR)
  }
  
  if (!exists("dcant_budget_basin_MLR_all")) {
    dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR
  }
  
  
  dcant_budget_basin_MLR_bias_decomposition <-
    read_csv(
      paste(
        path_version_data,
        "dcant_budget_basin_MLR_bias_decomposition.csv",
        sep = ""
      )
    )
  
  
  dcant_budget_basin_MLR_bias_decomposition <-
    dcant_budget_basin_MLR_bias_decomposition %>%
    mutate(Version_ID = i_Version_IDs)
  
  
  if (exists("dcant_budget_basin_MLR_bias_all_decomposition")) {
    dcant_budget_basin_MLR_bias_all_decomposition <-
      bind_rows(
        dcant_budget_basin_MLR_bias_all_decomposition,
        dcant_budget_basin_MLR_bias_decomposition
      )
  }
  
  if (!exists("dcant_budget_basin_MLR_bias_all_decomposition")) {
    dcant_budget_basin_MLR_bias_all_decomposition <-
      dcant_budget_basin_MLR_bias_decomposition
  }

}

rm(
  dcant_budget_basin_MLR,
  dcant_budget_basin_MLR_mod_truth,
  dcant_budget_basin_MLR_bias_decomposition
)

dcant_budget_global_all <- dcant_budget_global_all %>%
  filter(estimate == "dcant", 
         method == "total") %>% 
  select(-c(estimate, method)) %>% 
  rename(dcant = value)

dcant_budget_global_all <- dcant_budget_global_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
  filter(estimate == "dcant", 
         method == "total") %>% 
  select(-c(estimate, method)) %>% 
  rename(dcant = value)

dcant_budget_basin_MLR_all_1000 <- dcant_budget_basin_MLR_all %>%
  filter(inv_depth == 1000)

dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_basin_MLR_bias_all_decomposition <- 
  dcant_budget_basin_MLR_bias_all_decomposition %>%
  filter(inv_depth == params_global$inventory_depth_standard)

2.6 Column inventories

for (i_Version_IDs in Version_IDs_ensemble) {
  # i_Version_IDs <- Version_IDs_ensemble[19]
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_inv <-
    read_csv(paste(path_version_data,
                   "dcant_inv.csv",
                   sep = ""))
  
  p_map_cant_inv(dcant_inv %>% filter(data_source == "obs"))
  
  
  dcant_inv_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_inv_mod_truth.csv",
                   sep = "")) %>%
    filter(method == "total") %>%
    select(-method)
  
  dcant_inv_bias <-
    read_csv(paste(path_version_data,
                   "dcant_inv_bias.csv",
                   sep = "")) %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_inv <- bind_rows(dcant_inv,
                         dcant_inv_mod_truth) %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_lat_grid <-
    read_csv(paste(path_version_data,
                   "dcant_budget_lat_grid.csv",
                   sep = "")) %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_lon_grid <-
    read_csv(paste(path_version_data,
                   "dcant_budget_lon_grid.csv",
                   sep = "")) %>%
    mutate(Version_ID = i_Version_IDs)
  if (exists("dcant_inv_all")) {
    dcant_inv_all <- bind_rows(dcant_inv_all, dcant_inv)
  }
  
  if (!exists("dcant_inv_all")) {
    dcant_inv_all <- dcant_inv
  }
  
  if (exists("dcant_inv_bias_all")) {
    dcant_inv_bias_all <- bind_rows(dcant_inv_bias_all, dcant_inv_bias)
  }
  
  if (!exists("dcant_inv_bias_all")) {
    dcant_inv_bias_all <- dcant_inv_bias
  }
  
  if (exists("dcant_budget_lat_grid_all")) {
    dcant_budget_lat_grid_all <- bind_rows(dcant_budget_lat_grid_all, dcant_budget_lat_grid)
  }
  
  if (!exists("dcant_budget_lat_grid_all")) {
    dcant_budget_lat_grid_all <- dcant_budget_lat_grid
  }
  
  if (exists("dcant_budget_lon_grid_all")) {
    dcant_budget_lon_grid_all <- bind_rows(dcant_budget_lon_grid_all, dcant_budget_lon_grid)
  }
  
  if (!exists("dcant_budget_lon_grid_all")) {
    dcant_budget_lon_grid_all <- dcant_budget_lon_grid
  }
}

rm(dcant_inv,
   dcant_inv_bias,
   dcant_inv_mod_truth,
   dcant_budget_lat_grid,
   dcant_budget_lon_grid)

dcant_inv_all <- dcant_inv_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>% 
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>% 
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>%
  filter(period != "1994 - 2014",
         method == "total")

dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>%
  filter(period != "1994 - 2014",
         method == "total")

dcant_inv_all %>% 
  filter(Version_ID == "v_1103",
         data_source == "obs") %>% 
  select(lon, lat, dcant) %>% 
  write_csv(here::here("output/dcant_column_inventor_example.csv"))

2.7 Sections / profiles

for (i_Version_IDs in Version_IDs_ensemble) {

  path_version_data     <-
  paste(path_observations,
        i_Version_IDs,
        "/data/",
        sep = "")
  
  # load and join data files
  
  dcant_zonal <-
    read_csv(paste(path_version_data,
                   "dcant_zonal.csv",
                   sep = ""))
  
  dcant_zonal_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_mod_truth.csv",
                   sep = ""))
  
  dcant_zonal <- bind_rows(dcant_zonal,
                           dcant_zonal_mod_truth)
  
  dcant_global_section <-
    read_csv(paste(path_version_data,
                   "dcant_global_section.csv",
                   sep = ""))
  
  dcant_profile <-
    read_csv(paste(path_version_data,
                   "dcant_profile.csv",
                   sep = ""))
  
  dcant_profile_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_profile_mod_truth.csv",
                   sep = ""))
  
  dcant_profile <- bind_rows(dcant_profile,
                             dcant_profile_mod_truth)
  
  
  dcant_profile_basin_MLR <-
    read_csv(paste(path_version_data,
                   "dcant_profile_basin_MLR.csv",
                   sep = ""))
  
  
  dcant_profile_basin_MLR_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_profile_basin_MLR_mod_truth.csv",
      sep = ""
    ))

  dcant_profile_basin_MLR <- bind_rows(dcant_profile_basin_MLR,
                                       dcant_profile_basin_MLR_mod_truth)
  
  
  dcant_budget_basin_AIP_layer <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP_layer.csv",
                   sep = ""))

  dcant_budget_basin_MLR_layer <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_MLR_layer.csv",
                   
                   sep = ""))
  
  dcant_budget_basin_MLR_layer_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_MLR_layer_mod_truth.csv",
      sep = ""
    ))
  
  dcant_budget_basin_MLR_layer <-
    bind_rows(dcant_budget_basin_MLR_layer,
              dcant_budget_basin_MLR_layer_mod_truth)
  
  dcant_zonal_bias <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_bias.csv",
                   sep = ""))
  
  dcant_penetration_depth_all_lat_mean <-
    read_csv(paste0(path_version_data,
                   "dcant_penetration_depth_all_lat_mean.csv"))
  

  dcant_zonal <- dcant_zonal %>% 
    mutate(Version_ID = i_Version_IDs)

  dcant_global_section <- dcant_global_section %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_profile <- dcant_profile %>% 
    mutate(Version_ID = i_Version_IDs)

  dcant_profile_basin_MLR <- dcant_profile_basin_MLR %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_AIP_layer <- dcant_budget_basin_AIP_layer %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_MLR_layer <- dcant_budget_basin_MLR_layer %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_zonal_bias <- dcant_zonal_bias %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_penetration_depth_all_lat_mean <-
    dcant_penetration_depth_all_lat_mean %>%
    mutate(Version_ID = i_Version_IDs)
  
  
  if (exists("dcant_zonal_all")) {
    dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
  }
  
  if (!exists("dcant_zonal_all")) {
    dcant_zonal_all <- dcant_zonal
  }

  
  if (exists("dcant_global_section_all")) {
    dcant_global_section_all <- bind_rows(dcant_global_section_all, dcant_global_section)
  }
  
  if (!exists("dcant_global_section_all")) {
    dcant_global_section_all <- dcant_global_section
  }

  if (exists("dcant_profile_all")) {
    dcant_profile_all <- bind_rows(dcant_profile_all, dcant_profile)
  }
  
  if (!exists("dcant_profile_all")) {
    dcant_profile_all <- dcant_profile
  }

  if (exists("dcant_profile_basin_MLR_all")) {
    dcant_profile_basin_MLR_all <- bind_rows(dcant_profile_basin_MLR_all, dcant_profile_basin_MLR)
  }
  
  if (!exists("dcant_profile_basin_MLR_all")) {
    dcant_profile_basin_MLR_all <- dcant_profile_basin_MLR
  }

  if (exists("dcant_budget_basin_AIP_layer_all")) {
    dcant_budget_basin_AIP_layer_all <-
      bind_rows(dcant_budget_basin_AIP_layer_all,
                dcant_budget_basin_AIP_layer)
  }
  
  if (!exists("dcant_budget_basin_AIP_layer_all")) {
    dcant_budget_basin_AIP_layer_all <- dcant_budget_basin_AIP_layer
  }

  if (exists("dcant_budget_basin_MLR_layer_all")) {
    dcant_budget_basin_MLR_layer_all <-
      bind_rows(dcant_budget_basin_MLR_layer_all,
                dcant_budget_basin_MLR_layer)
  }
  
  if (!exists("dcant_budget_basin_MLR_layer_all")) {
    dcant_budget_basin_MLR_layer_all <- dcant_budget_basin_MLR_layer
  }

  if (exists("dcant_zonal_bias_all")) {
    dcant_zonal_bias_all <- bind_rows(dcant_zonal_bias_all, dcant_zonal_bias)
  }
  
  if (!exists("dcant_zonal_bias_all")) {
    dcant_zonal_bias_all <- dcant_zonal_bias
  }

  if (exists("dcant_penetration_depth_all_lat_mean_all")) {
    dcant_penetration_depth_all_lat_mean_all <-
      bind_rows(
        dcant_penetration_depth_all_lat_mean_all,
        dcant_penetration_depth_all_lat_mean
      )
  }
  
  if (!exists("dcant_penetration_depth_all_lat_mean_all")) {
    dcant_penetration_depth_all_lat_mean_all <-
      dcant_penetration_depth_all_lat_mean
  }

}

rm(dcant_zonal, dcant_global_section, dcant_zonal_bias, dcant_zonal_mod_truth,
   dcant_budget_basin_AIP_layer, dcant_budget_basin_MLR_layer,
   dcant_profile, dcant_profile_basin_MLR, dcant_penetration_depth_all_lat_mean)

2.8 Observations coverage

for (i_Version_IDs in Version_IDs) {
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  GLODAP_grid_era  <-
    read_csv(paste(
      path_version_data,
      "GLODAPv2.2020_clean_obs_grid_era.csv",
      sep = ""
    ))

  
  GLODAP_grid_era <- GLODAP_grid_era %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("GLODAP_grid_era_all")) {
    GLODAP_grid_era_all <-
      bind_rows(GLODAP_grid_era_all, GLODAP_grid_era)
  }
  
  if (!exists("GLODAP_grid_era_all")) {
    GLODAP_grid_era_all <- GLODAP_grid_era
  }
  
  
  
  GLODAP <- read_csv(paste(
    path_version_data,
    "GLODAPv2.2020_clean.csv",
    sep = ""
  ))
  
  GLODAP <- GLODAP %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("GLODAP_all")) {
    GLODAP_all <-
      bind_rows(GLODAP_all, GLODAP)
  }
  
  if (!exists("GLODAP_all")) {
    GLODAP_all <- GLODAP
  }
  
}

rm(GLODAP_grid_era, GLODAP)


GLODAP_grid_era_all <- full_join(GLODAP_grid_era_all,
                                 params_local_all)

GLODAP_all <- full_join(GLODAP_all,
                        params_local_all)


GLODAP_expocodes <-
  read_tsv(
    paste(
      "/nfs/kryo/work/updata/glodapv2_2021/",
      "EXPOCODES.txt",
      sep = ""
    ),
    col_names = c("cruise", "cruise_expocode")
  )

GLODAP_all <-
  left_join(GLODAP_all, GLODAP_expocodes)

2.9 Atm CO2

co2_atm <-
  read_csv(paste(path_preprocessing,
                 "co2_atm.csv",
                 sep = ""))

co2_atm_reccap2 <-
  read_csv(paste(path_preprocessing,
                  "co2_atm_reccap2.csv",
                  sep = ""))

2.10 Sabine total Cant

tcant_inv <-
  read_csv(paste(path_preprocessing,
                  "S04_tcant_inv.csv", sep = ""))

tcant_inv <- tcant_inv %>% 
  filter(inv_depth == 3000) %>% 
  rename(dcant = tcant,
         dcant_pos = tcant_pos) %>% 
  mutate(tref1 = 1800,
         tref2 = 1994)

2.11 GCB carbon sink

Ocean_Sink <-
  read_csv(paste(path_preprocessing,
                  "Ocean_Sink.csv",
                  sep = ""))

Global_Carbon_Budget <-
  read_csv(paste(path_preprocessing,
                  "GCB_Global_Carbon_Budget.csv",
                  sep = ""))

2.12 Periods

two_decades <- unique(params_local_all_ensemble$period)[1:2]

3 Define labels

dcant_pgc_label <- expression(Delta * C["ant"]~(PgC))
dcant_bias_pgc_label <- expression(Delta * C["ant"]~ bias ~(PgC))
delta_dcant_bias_pgc_label <- expression(Delta * Delta * C["ant"]~bias~(PgC))
dcant_pgc_scaled_label <- expression(beta~(mol~m^{-2}~µatm^{-1}))

dcant_umol_label <- expression(Delta * C[ant]~(mu * mol ~ kg ^ {-1}))
ddcant_umol_label <- expression(Delta * Delta * C[ant]~(mu * mol ~ kg ^ {-1}))

dcant_layer_budget_label <- 
  expression(Delta ~ C[ant] ~ "budget per 500m depth layer (PgC)")

dcant_CI_label <- expression(Delta * C[ant]~(mol ~ m ^ {-2}))
delta_dcant_CI_label <- expression(Delta * Delta * C[ant]~(mol ~ m ^ {-2}))
beta_CI_label <- expression(atop(beta,
                                 (mol ~ m ^ {-2} ~ µatm ^ {-1})))

4 Coverage maps

cruises_phosphate_gap_fill <-
  c("33MW19930704",
    "33RO20030604",
    "33RO20050111",
    "33RO19980123")

cruises_talk_gap_fill <-
  c("06AQ19980328")

cruises_talk_calc <-
  c("06MT19900123",
    "316N19920502",
    "316N19921006")

GLODAP_all_coverage <- GLODAP_all %>% 
  distinct(lat, lon, era, cruise_expocode) %>% 
  mutate(gap_filling = case_when(
    cruise_expocode %in% cruises_phosphate_gap_fill ~ "Phosphate from CANYON-B",
    cruise_expocode %in% cruises_talk_gap_fill ~ "TA from CANYON-B",
    cruise_expocode %in% cruises_talk_calc ~ "TA calculated from other variables",
    cruise_expocode %in% "31DS19940126" ~ "Nitrate qc-flag not met",
    TRUE ~ "All quality criteria fulfilled"
  )) %>% 
  distinct(lat, lon, era, gap_filling)

colour("bright")(7)

     blue       red     green    yellow      cyan    purple      grey 
"#4477AA" "#EE6677" "#228833" "#CCBB44" "#66CCEE" "#AA3377" "#BBBBBB" 
attr(,"missing")
[1] NA

coverage_map <- map +
  geom_tile(data = GLODAP_all_coverage,
            aes(lon, lat)) +
  facet_wrap( ~ era, ncol = 1) +
  scale_color_okabeito() +
  scale_fill_okabeito() +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.position = c(0.7,0.3),
        legend.title = element_blank())

coverage_map

Past versions of coverage_maps-1.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20
fea41c1	jens-daniel-mueller	2022-07-20
9ce772d	jens-daniel-mueller	2022-07-19
16dc3af	jens-daniel-mueller	2022-06-27
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04

ggsave(plot = coverage_map,
       path = here::here("output/presentation"),
       filename = "Fig_observations_coverage_map.png",
       height = 6,
       width = 4)


GLODAP_all_coverage <- GLODAP_all %>% 
  filter(between(depth, 250, 750)) %>% 
  group_by(lat, lon, era) %>%
  summarise(tco2 = mean(tco2, na.rm = TRUE)) %>% 
  ungroup()

library(colorspace)


coverage_map <- map +
  geom_tile(data = GLODAP_all_coverage,
            aes(lon, lat, fill = tco2, col = tco2,
                height = 2, width = 2)) +
  facet_wrap( ~ era, ncol = 1) +
  scale_fill_viridis_c(name = expression(
    atop(DIC~at~"500m",(µmol~kg^{-1})))) +
  scale_color_viridis_c(guide = "none") +
  labs(title = "Observations",
       subtitle = "Decadal coverage") +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.position = "bottom")

coverage_map

Past versions of coverage_maps-2.png

Version	Author	Date
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20

ggsave(plot = coverage_map,
       path = here::here("output/presentation"),
       filename = "Fig_observations_coverage_map_dic.png",
       height = 7,
       width = 4)

rm(coverage_map, 
   GLODAP_all, GLODAP_all_coverage, GLODAP_expocodes,
   GLODAP_grid_era_all)

rm(cruises_phosphate_gap_fill,
   cruises_talk_gap_fill,
   cruises_talk_calc)

5 Atm CO2

co2_atm_roll <- co2_atm %>% 
  mutate(pCO2_roll = zoo::rollmean(pCO2, 10, fill = NA))

p_pco2_atm <- co2_atm_roll %>% 
  ggplot() +
  geom_point(aes(year, pCO2, col="annual")) +
  geom_path(aes(year, pCO2, col="annual")) +
  geom_point(aes(year, pCO2_roll, col="10yr roll")) +
  geom_path(aes(year, pCO2_roll, col="10yr roll"))

co2_atm_roll <- co2_atm_roll %>% 
  mutate(delta_pCO2_roll = pCO2_roll - lag(pCO2_roll,10),
         delta_pCO2 = pCO2 - lag(pCO2,10))

p_delta_pCO2_atm <- co2_atm_roll %>% 
  ggplot() +
  geom_point(aes(year, delta_pCO2, col="annual")) +
  geom_path(aes(year, delta_pCO2, col="annual")) +
  geom_point(aes(year, delta_pCO2_roll, col="10yr roll")) +
  geom_path(aes(year, delta_pCO2_roll, col="10yr roll"))

p_pco2_atm / p_delta_pCO2_atm

Past versions of atm_CO2-1.png

Version	Author	Date
0160c40	jens-daniel-mueller	2022-07-16
022fd60	jens-daniel-mueller	2022-07-14

rm(p_pco2_atm, p_delta_pCO2_atm)

delta_pCO2_atm <-
  left_join(
    params_local_all %>%
      distinct(period, tref1, tref2),
    co2_atm_roll %>%
      select(
        tref1 = year,
        pCO2_1 = pCO2,
        pCO2_roll_1 = pCO2_roll
      )
  )

delta_pCO2_atm <-
  left_join(
    delta_pCO2_atm,
    co2_atm_roll %>%
      select(
        tref2 = year,
        pCO2_2 = pCO2,
        pCO2_roll_2 = pCO2_roll
      )
  )

delta_pCO2_atm <- delta_pCO2_atm %>% 
  mutate(delta_pCO2 = pCO2_2 - pCO2_1,
         delta_pCO2_roll = pCO2_roll_2 - pCO2_roll_1) %>% 
  select(period, delta_pCO2, delta_pCO2_roll)

delta_pCO2_atm <- delta_pCO2_atm %>%
  select(period, delta_pCO2)

delta_pCO2_atm <- 
  bind_rows(
    delta_pCO2_atm,
    tibble(period = "1800 - 1994",
           delta_pCO2 = co2_atm %>% filter(year==1994) %>% pull(pCO2) - 280)
  )

rm(co2_atm_roll)

6 Globale scaling

• storage beneath 3000m: +2%
• unmapped regions south of 65N: +1.5%
• Nordic Seas: +1%
• Arctic Ocean: +2%

dcant_scaling <- 1.02*1.015*1.01*1.02
dcant_scaling_uncertainty <- 0.5

7 Uncertainty factor

sd_factor <- 2

8 eMLR stats

8.1 Predictor selection

lm_best_predictor_counts_all %>% 
  filter(period %in% two_decades,
         data_source == "obs") %>% 
  pivot_longer(aou:temp,
               names_to = "predictor",
               values_to = "count") %>% 
  # mutate(count = as.factor(count)) %>% 
  group_by(predictor, count, period, Version_ID_group) %>% 
  summarise(n = n()) %>% 
  ungroup() %>% 
  ggplot(aes(x = count, y = n)) +
  scale_color_manual(values = c("#EE7733", "#009988"), name = "Decade") +
  scale_fill_manual(values = c("#EE7733", "#009988"), name = "Decade") +
  geom_path(aes(col = period)) +
    geom_point(aes(fill = period), shape = 21) +
  coord_flip() +
  scale_x_continuous(breaks = seq(0,10,2),
                     limits = c(0,10),
                     name = "Presence among 10 best models") +
  labs(y = "Counts across density slabs, regional clusters and basins") +
  facet_wrap(~predictor)

Past versions of predictors-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

8.2 Residual distribution

spatial_residual_all %>%
  filter(gamma_slab %in% params_local$plot_slabs[2],
         data_source == "obs",
         period %in% two_decades) %>%
  p_map_dcant_slab(
    var = ".resid_mean",
    col = "divergent",
    title_text = "Residual distribution"
    ) +
  facet_grid(era ~ period) +
  theme_dark()

Past versions of residual_latitude-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

spatial_residual_offset_all %>%
  filter(
    gamma_slab %in% params_local$plot_slabs[1],
    data_source == "obs",
    period %in% two_decades
  ) %>%
  p_map_dcant_slab(var = ".resid_mean_offset",
                   col = "divergent",
                   title_text = "Residual offset distribution (era 2 - era 1)") +
  facet_grid(. ~ period) +
  theme_dark()

Past versions of residual_latitude-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

lat_residual_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>%
  ggplot(aes(
    .resid_mean,
    lat_grid,
    col = era,
    group = interaction(basin, era, gamma_slab, Version_ID)
  )) +
  geom_vline(xintercept = 0) +
  # geom_point() +
  geom_path(alpha = 0.5) +
  facet_grid(. ~ period)

Past versions of residual_latitude-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

lat_residual_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>%
  select(-c(Version_ID, tref1, tref2)) %>% 
  mutate(sampling_period = if_else(period == "2004 - 2014" & 
                                     era == "2000-2009",
                                   "first",
                                   "second"),
         sampling_period = if_else(period == "1994 - 2004" & 
                                     era == "1989-1999",
                                   "first",
                                   sampling_period)) %>% 
  select(-era) %>% 
  pivot_wider(names_from = sampling_period,
              values_from = .resid_mean) %>% 
  ggplot(aes(first, second)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  geom_bin2d(binwidth = 1) +
  geom_abline(slope = 1, col = "red") +
  geom_smooth(method = "lm") +
  scale_fill_viridis_c(trans = "log10") +
  coord_fixed(ylim = c(-15, 15),
              xlim = c(-15, 15)) +
  labs(
    y = "First sampling period\nmean MLR residual in 5° latitude bin (µmol/kg)",
    x = "Second sampling period\nmean MLR residual in 5° latitude bin (µmol/kg)"
    ) +
  facet_wrap(~period)

Past versions of residual_latitude-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

lat_residual_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>%
  select(-c(Version_ID, tref1, tref2)) %>% 
  mutate(sampling_period = if_else(period == "2004 - 2014" & 
                                     era == "2000-2009",
                                   "first",
                                   "second"),
         sampling_period = if_else(period == "1994 - 2004" & 
                                     era == "1989-1999",
                                   "first",
                                   sampling_period)) %>% 
  select(-era) %>% 
  pivot_wider(names_from = sampling_period,
              values_from = .resid_mean) %>% 
  nest_by(period) %>%
  mutate(mod = list(lm(second ~ first, data = data))) %>%
  summarize(broom::tidy(mod))

# A tibble: 4 × 6
# Groups:   period [2]
  period      term        estimate std.error statistic  p.value
  <chr>       <chr>          <dbl>     <dbl>     <dbl>    <dbl>
1 1994 - 2004 (Intercept)   -0.140    0.0842     -1.66 9.75e- 2
2 1994 - 2004 first          0.647    0.0465     13.9  2.23e-36
3 2004 - 2014 (Intercept)    0.142    0.0899      1.58 1.15e- 1
4 2004 - 2014 first          0.604    0.0415     14.6  3.76e-39

lat_residual_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>%
  select(-c(Version_ID, tref1, tref2)) %>% 
  mutate(sampling_period = if_else(period == "2004 - 2014" & 
                                     era == "2000-2009",
                                   "first",
                                   "second"),
         sampling_period = if_else(period == "1994 - 2004" & 
                                     era == "1989-1999",
                                   "first",
                                   sampling_period)) %>% 
  select(-era) %>% 
  pivot_wider(names_from = sampling_period,
              values_from = .resid_mean) %>% 
  drop_na() %>% 
  group_by(period) %>%
  summarize(correlation = cor(first, second))

# A tibble: 2 × 2
  period      correlation
  <chr>             <dbl>
1 1994 - 2004       0.566
2 2004 - 2014       0.580

lat_residual_all_mean <- lat_residual_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>% 
  group_by(period, era, lat_grid) %>% 
  summarise(residual_mean = mean(abs(.resid_mean)),
            residual_sd = sd(.resid_mean),
            residual_iqr = IQR(.resid_mean)) %>% 
  ungroup()

lat_residual_offset_all_mean <- lat_residual_offset_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>% 
  group_by(period, lat_grid) %>% 
  summarise(residual_mean = mean(abs(.resid_mean_offset)),
            residual_sd = sd(.resid_mean_offset),
            residual_iqr = IQR(.resid_mean)) %>% 
  ungroup()

p_residual_lat <- 
ggplot() +
  geom_vline(xintercept = 0) +
  geom_ribbon(
    data = lat_residual_all_mean,
    aes(
      xmin = residual_mean - residual_iqr,
      xmax = residual_mean + residual_iqr,
      y = lat_grid,
      fill = era
    ),
    alpha = 0.3
  ) +
  geom_path(data = lat_residual_all_mean,
            aes(residual_mean,
                lat_grid,
                col = era)) +
  scale_fill_highcontrast() +
  scale_color_highcontrast() +
  coord_cartesian(xlim = c(-3,7)) +
  facet_grid(. ~ period)


p_residual_lat_offset <- 
ggplot() +
  geom_vline(xintercept = 0) +
  geom_ribbon(
    data = lat_residual_offset_all_mean,
    aes(
      xmin = residual_mean - residual_iqr,
      xmax = residual_mean + residual_iqr,
      y = lat_grid
    ),
    alpha = 0.3
  ) +
  geom_path(data = lat_residual_offset_all_mean,
            aes(residual_mean,
                lat_grid)) +
  coord_cartesian(xlim = c(-3,7)) +
  facet_grid(. ~ period)

p_residual_lat / p_residual_lat_offset

Past versions of residual_latitude-5.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

rm(p_residual_lat, p_residual_lat_offset)


p_residual_lat <-
ggplot() +
  geom_vline(xintercept = 0) +
  geom_path(data = lat_residual_all %>% 
              filter(data_source == "obs",
                     period %in% two_decades,
                     gamma_slab %in% params_local$plot_slabs[2]),
            aes(.resid_mean,
                lat_grid,
                col = era)) +
  scale_fill_highcontrast() +
  scale_color_highcontrast() +
  coord_cartesian(xlim = c(-3,7)) +
  facet_grid(basin ~ period)

p_residual_lat_offset <-
ggplot() +
  geom_vline(xintercept = 0) +
  geom_path(data = lat_residual_offset_all %>% 
              filter(data_source == "obs",
                     period %in% two_decades,
                     gamma_slab %in% params_local$plot_slabs[2]),
            aes(.resid_mean_offset,
                lat_grid)) +
  scale_fill_highcontrast() +
  scale_color_highcontrast() +
  coord_cartesian(xlim = c(-3,7)) +
  facet_grid(basin ~ period)


p_residual_lat / p_residual_lat_offset

Past versions of residual_latitude-6.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01

rm(p_residual_lat, p_residual_lat_offset)

lat_residual_offset_all %>% 
  ggplot(aes(.resid_mean, .resid_mean_offset)) +
  geom_bin_2d() +
  scale_fill_viridis_c(trans = "log10")

Past versions of residual_latitude-7.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

lat_residual_offset_all %>%
  pivot_longer(starts_with(".resid"),
               names_to = "type",
               values_to = "residual") %>%
  ggplot(aes(residual, fill = type, col = type)) +
  coord_cartesian(xlim = c(-5,5)) +
  geom_density(alpha = 0.5)

Past versions of residual_latitude-8.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

left_join(lm_best_target_all,
          GLODAP_glanced_all) %>% 
  summarise(mean_rmse = mean(rmse, na.rm = TRUE),
            sd_rmse = sd(rmse, na.rm = TRUE))

# A tibble: 1 × 2
  mean_rmse sd_rmse
      <dbl>   <dbl>
1      3.10    1.63

9 Inventory maps

dcant_inv_all <- bind_rows(
  dcant_inv_all,
  tcant_inv %>% mutate(period = paste(tref1, tref2, sep = " - "),
                       data_source = "obs")
)


dcant_inv_all <- left_join(dcant_inv_all,
                           delta_pCO2_atm)


dcant_inv_all <- dcant_inv_all %>% 
  mutate(beta = dcant / delta_pCO2,
         beta_pos = dcant_pos / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

dcant_inv_all_ensemble <- dcant_inv_all

dcant_inv_all <- dcant_inv_all %>% 
  filter(Version_ID %in% Version_IDs | is.na(Version_ID))

9.1 Ensemble uncertainty

dcant_inv_all_ensemble_sd <-
  dcant_inv_all_ensemble %>%
  filter(period %in% two_decades,
         data_source != "mod_truth") %>%
  group_by(data_source, period, lon, lat) %>%
  summarise(dcant_sd = sd(dcant) * sd_factor) %>%
  ungroup()


dcant_inv_all_ensemble_sd %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Synthetic data",
      data_source == "obs" ~ "Observations"
    )
  ) %>%
  p_map_cant_inv(var = "dcant_sd",
                 breaks = c(seq(0, 8, 1), Inf),
                 title_text = NULL) +
  facet_grid(period ~ data_source) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of column_inventory_standard_deviation-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
7cf954e	jens-daniel-mueller	2022-09-08
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5ca8778	jens-daniel-mueller	2022-09-07
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
f09080e	jens-daniel-mueller	2022-06-28
16dc3af	jens-daniel-mueller	2022-06-27

delta_dcant_inv_all_ensemble_RSS <-
  dcant_inv_all_ensemble_sd %>%
  arrange(period) %>%
  group_by(data_source, lat, lon) %>%
  mutate(RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  drop_na()  

delta_dcant_inv_all_ensemble_RSS %>% 
  p_map_cant_inv(var = "RSS",
                 breaks = c(seq(0, 8, 1), Inf),
                 title_text = NULL) +
  facet_grid(data_source ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of column_inventory_standard_deviation-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
7cf954e	jens-daniel-mueller	2022-09-08

delta_dcant_inv_all_ensemble_sd <-
  dcant_inv_all_ensemble %>%
  filter(period %in% two_decades,
         data_source != "mod_truth") %>%
  arrange(period) %>%
  group_by(MLR_basins, Version_ID_group, data_source, lat, lon) %>%
  mutate(delta_dcant = dcant - lag(dcant)) %>%
  ungroup()

delta_dcant_inv_all_ensemble_sd <-
  delta_dcant_inv_all_ensemble_sd %>%
  filter(!is.na(delta_dcant)) %>%
  group_by(data_source, lon, lat) %>%
  summarise(delta_dcant_sd = sd(delta_dcant) * sd_factor,
            delta_dcant = mean(delta_dcant)) %>%
  ungroup()


delta_dcant_inv_all_ensemble_sd %>% 
  p_map_cant_inv(var = "delta_dcant_sd",
                 breaks = c(seq(0, 8, 1), Inf),
                 title_text = NULL) +
  facet_grid(data_source ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of column_inventory_standard_deviation-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
7cf954e	jens-daniel-mueller	2022-09-08

dcant_inv_all_uncertainty <-
  full_join(
    dcant_inv_all %>%
      select(data_source, lon, lat, period, dcant),
    dcant_inv_all_ensemble_sd %>% select(data_source, lon, lat, period, dcant_sd)
  )

dcant_inv_all_uncertainty <-dcant_inv_all_uncertainty %>% 
  filter(period %in%two_decades,
         data_source %in% c("mod", "obs"))

dcant_inv_all_uncertainty <-
  m_grid_horizontal_coarse(dcant_inv_all_uncertainty)

dcant_inv_all_uncertainty <-
  dcant_inv_all_uncertainty %>%
  group_by(lat_grid, lon_grid, data_source, period) %>% 
  summarise(dcant = mean(dcant),
            dcant_sd = mean(dcant_sd),
            n = n()) %>% 
  ungroup() %>% 
  filter(n == 25) %>% 
  rename(lon = lon_grid,
         lat = lat_grid) %>% 
  mutate(dcant_sign = abs(dcant) - dcant_sd)

dcant_inv_all_uncertainty %>% 
  mutate(dcant = abs(dcant)) %>% 
  p_map_cant_inv(
    var = "dcant",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source~period)

Past versions of inventory_maps_absolute_dcant_vs_SD-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

dcant_inv_all_uncertainty %>% 
  p_map_cant_inv(
    var = "dcant_sd",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source~period)

Past versions of inventory_maps_absolute_dcant_vs_SD-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

dcant_inv_all_uncertainty %>% 
  p_map_cant_inv(
    var = "dcant_sign",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source~period)

Past versions of inventory_maps_absolute_dcant_vs_SD-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

map +
  geom_point(
    data = dcant_inv_all_uncertainty %>%
      filter(dcant_sign < 0),
    aes(lon, lat),
    shape = 20,
    size = 0.1
  ) +
  facet_grid(data_source ~ period)

Past versions of inventory_maps_absolute_dcant_vs_SD-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

9.2 dCant - absolute

p_CI_absolute <- dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source %in% c("obs")) %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_wrap(~ period, ncol = 1) +
  theme(axis.text.x = element_blank()) +
  geom_point(
    data = dcant_inv_all_uncertainty %>%
      filter(dcant_sign < 0,
             data_source == "obs"),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "grey"
  )

p_CI_absolute +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of inventory_maps_absolute_obs-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_column_inventory.png",
       height = 5,
       width = 6)

p_CI_absolute_mod <- dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source %in% c("mod")) %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_wrap(~ period, ncol = 1) +
  theme(axis.text.x = element_blank()) +
  geom_point(
    data = dcant_inv_all_uncertainty %>%
      filter(dcant_sign < 0,
             data_source == "mod"),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "grey"
  )

p_CI_absolute_mod +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of inventory_maps_absolute_mod-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

# ggsave(path = here::here("output/presentation"),
#        filename = "Fig_dcant_column_inventory.png",
#        height = 5,
#        width = 6)

9.3 Inventory stats

9.3.1 Basin

dcant_inv_stat_mean <-
  full_join(basinmask,
            dcant_inv_all) %>%
  mutate(surf_area = earth_surf(lat, lon)) %>%
  group_by(basin, period, data_source) %>%
  summarise(dcant = weighted.mean(dcant, surf_area)) %>%
  ungroup() %>%
  filter(!is.na(dcant))

dcant_inv_stat_ensemble <-
  full_join(basinmask,
            dcant_inv_all_ensemble) %>%
  mutate(surf_area = earth_surf(lat, lon)) %>%
  group_by(basin, data_source, period, MLR_basins, Version_ID_group) %>%
  summarise(dcant = weighted.mean(dcant, surf_area)) %>%
  ungroup() %>%
  filter(!is.na(dcant)) %>%
  group_by(basin, data_source, period) %>%
  summarise(dcant_sd = sd(dcant) * 2) %>%
  ungroup()

dcant_inv_stat <-
  full_join(dcant_inv_stat_mean,
            dcant_inv_stat_ensemble)

rm(dcant_inv_stat_mean,
   dcant_inv_stat_ensemble)

dcant_inv_stat %>%
  filter(period %in% two_decades) %>%
  group_by(data_source, basin) %>%
  mutate(delta_dcant = dcant - lag(dcant),
         RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	period	data_source	dcant	dcant_sd	delta_dcant	RSS
N. Pacific	1994 - 2004	mod	3.363703	0.6715443	NA	NA
N. Pacific	1994 - 2004	mod_truth	2.915800	0.0000000	NA	NA
N. Pacific	1994 - 2004	obs	3.076873	2.2208966	NA	NA
N. Pacific	2004 - 2014	mod	3.614817	0.7914575	0.2511143	1.037968
N. Pacific	2004 - 2014	mod_truth	3.352012	0.0000000	0.4362120	0.000000
N. Pacific	2004 - 2014	obs	3.415398	3.0147796	0.3385254	3.744500
S. Pacific	1994 - 2004	mod	4.826130	1.2679823	NA	NA
S. Pacific	1994 - 2004	mod_truth	4.931744	0.0000000	NA	NA
S. Pacific	1994 - 2004	obs	7.482479	1.8744773	NA	NA
S. Pacific	2004 - 2014	mod	6.476528	1.0607614	1.6503976	1.653177
S. Pacific	2004 - 2014	mod_truth	6.060215	0.0000000	1.1284704	0.000000
S. Pacific	2004 - 2014	obs	6.402648	1.7580644	-1.0798310	2.569914
N. Atlantic	1994 - 2004	mod	4.616514	0.8940472	NA	NA
N. Atlantic	1994 - 2004	mod_truth	4.057536	0.0000000	NA	NA
N. Atlantic	1994 - 2004	obs	9.867769	0.7502779	NA	NA
N. Atlantic	2004 - 2014	mod	5.075378	0.7758116	0.4588643	1.183725
N. Atlantic	2004 - 2014	mod_truth	4.878549	0.0000000	0.8210126	0.000000
N. Atlantic	2004 - 2014	obs	8.025765	1.6212880	-1.8420048	1.786475
S. Atlantic	1994 - 2004	mod	5.064488	1.1310498	NA	NA
S. Atlantic	1994 - 2004	mod_truth	4.706655	0.0000000	NA	NA
S. Atlantic	1994 - 2004	obs	7.252470	2.1850233	NA	NA
S. Atlantic	2004 - 2014	mod	6.393315	1.1736069	1.3288264	1.629916
S. Atlantic	2004 - 2014	mod_truth	5.251166	0.0000000	0.5445113	0.000000
S. Atlantic	2004 - 2014	obs	9.960798	2.3259181	2.7083280	3.191273
Indian	1994 - 2004	mod	5.899824	2.0053288	NA	NA
Indian	1994 - 2004	mod_truth	5.570926	0.0000000	NA	NA
Indian	1994 - 2004	obs	8.154922	3.8623952	NA	NA
Indian	2004 - 2014	mod	7.583920	1.4320692	1.6840960	2.464177
Indian	2004 - 2014	mod_truth	6.501337	0.0000000	0.9304108	0.000000
Indian	2004 - 2014	obs	6.519972	1.8028388	-1.6349503	4.262432

9.3.2 Gyres

dcant_inv_stat_mean <-
  full_join(basinmask,
            dcant_inv_all) %>%
  mutate(surf_area = earth_surf(lat, lon),
         lat_grid = cut(lat, c(-50, -20, 20, 50))) %>%
  group_by(basin, period, data_source, lat_grid) %>%
  summarise(dcant = weighted.mean(dcant, surf_area)) %>%
  ungroup() %>%
  filter(!is.na(dcant),
         !is.na(lat_grid),
         lat_grid != "(-20,20]")
  
dcant_inv_stat_ensemble <-
  full_join(basinmask,
            dcant_inv_all_ensemble) %>%
  mutate(surf_area = earth_surf(lat, lon),
         lat_grid = cut(lat, c(-50, -20, 20, 50))) %>%
  group_by(basin, data_source, period, MLR_basins, Version_ID_group, lat_grid) %>%
  summarise(dcant = weighted.mean(dcant, surf_area)) %>%
  ungroup() %>%
  filter(!is.na(dcant),
         !is.na(lat_grid),
         lat_grid != "(-20,20]") %>%
  group_by(basin, data_source, period, lat_grid) %>%
  summarise(dcant_sd = sd(dcant) * 2) %>%
  ungroup()

dcant_inv_stat <-
  full_join(dcant_inv_stat_mean,
            dcant_inv_stat_ensemble)

dcant_inv_stat %>%
  filter(period %in% two_decades) %>%
  group_by(data_source, basin, lat_grid) %>%
  mutate(delta_dcant = dcant - lag(dcant),
         RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	period	data_source	lat_grid	dcant	dcant_sd	delta_dcant	RSS
N. Pacific	1994 - 2004	mod	(20,50]	3.9084642	0.6244865	NA	NA
N. Pacific	1994 - 2004	mod_truth	(20,50]	3.4984361	0.0000000	NA	NA
N. Pacific	1994 - 2004	obs	(20,50]	3.9829040	2.1195077	NA	NA
N. Pacific	2004 - 2014	mod	(20,50]	3.9223699	1.0708606	0.0139057	1.239647
N. Pacific	2004 - 2014	mod_truth	(20,50]	3.9043518	0.0000000	0.4059158	0.000000
N. Pacific	2004 - 2014	obs	(20,50]	3.3833004	3.5827047	-0.5996036	4.162702
S. Pacific	1994 - 2004	mod	(-50,-20]	6.5209038	1.4477818	NA	NA
S. Pacific	1994 - 2004	mod_truth	(-50,-20]	6.4761945	0.0000000	NA	NA
S. Pacific	1994 - 2004	obs	(-50,-20]	9.8048932	2.5587912	NA	NA
S. Pacific	2004 - 2014	mod	(-50,-20]	8.3203549	1.1121833	1.7994510	1.825657
S. Pacific	2004 - 2014	mod_truth	(-50,-20]	7.9621161	0.0000000	1.4859216	0.000000
S. Pacific	2004 - 2014	obs	(-50,-20]	8.5667582	2.4335945	-1.2381350	3.531260
N. Atlantic	1994 - 2004	mod	(20,50]	5.6510208	1.0213838	NA	NA
N. Atlantic	1994 - 2004	mod_truth	(20,50]	4.6144491	0.0000000	NA	NA
N. Atlantic	1994 - 2004	obs	(20,50]	12.1361324	1.0459766	NA	NA
N. Atlantic	2004 - 2014	mod	(20,50]	5.6292473	0.9244144	-0.0217735	1.377595
N. Atlantic	2004 - 2014	mod_truth	(20,50]	6.1398829	0.0000000	1.5254338	0.000000
N. Atlantic	2004 - 2014	obs	(20,50]	8.4461849	1.8377634	-3.6899474	2.114578
S. Atlantic	1994 - 2004	mod	(-50,-20]	7.7366367	1.4125504	NA	NA
S. Atlantic	1994 - 2004	mod_truth	(-50,-20]	6.3774635	0.0000000	NA	NA
S. Atlantic	1994 - 2004	obs	(-50,-20]	8.4912458	2.1905872	NA	NA
S. Atlantic	2004 - 2014	mod	(-50,-20]	8.3424057	2.1467637	0.6057690	2.569804
S. Atlantic	2004 - 2014	mod_truth	(-50,-20]	7.2252841	0.0000000	0.8478205	0.000000
S. Atlantic	2004 - 2014	obs	(-50,-20]	13.1557314	3.2499600	4.6644856	3.919300
Indian	1994 - 2004	mod	(-50,-20]	8.8067366	1.9049125	NA	NA
Indian	1994 - 2004	mod	(20,50]	6.3172021	5.5056000	NA	NA
Indian	1994 - 2004	mod_truth	(-50,-20]	8.1443384	0.0000000	NA	NA
Indian	1994 - 2004	mod_truth	(20,50]	2.6253949	0.0000000	NA	NA
Indian	1994 - 2004	obs	(-50,-20]	10.7970711	3.4693880	NA	NA
Indian	1994 - 2004	obs	(20,50]	-0.3174586	8.7399190	NA	NA
Indian	2004 - 2014	mod	(-50,-20]	10.2876458	1.8622366	1.4809092	2.663948
Indian	2004 - 2014	mod	(20,50]	1.3781176	2.5857828	-4.9390844	6.082590
Indian	2004 - 2014	mod_truth	(-50,-20]	9.4334367	0.0000000	1.2890983	0.000000
Indian	2004 - 2014	mod_truth	(20,50]	3.5697855	0.0000000	0.9443906	0.000000
Indian	2004 - 2014	obs	(-50,-20]	8.2468486	2.7638471	-2.5502226	4.435708
Indian	2004 - 2014	obs	(20,50]	7.0876343	3.8995579	7.4050929	9.570409

rm(dcant_inv_stat, dcant_inv_stat_mean, dcant_inv_stat_ensemble)

delta_dcant_inv_all <-
  dcant_inv_all %>%
  filter(data_source %in% c("obs", "mod"),
         period %in% two_decades) %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
         period = "(2004 - 2014) - (1994 - 2004)")


delta_dcant_inv_all_uncertainty <-
  full_join(
    delta_dcant_inv_all %>% select(data_source, lon, lat, dcant_offset),
    delta_dcant_inv_all_ensemble_RSS %>% select(data_source, lon, lat, RSS)
  )

delta_dcant_inv_all_uncertainty <-
  m_grid_horizontal_coarse(delta_dcant_inv_all_uncertainty)

delta_dcant_inv_all_uncertainty <-
  delta_dcant_inv_all_uncertainty %>%
  group_by(lat_grid, lon_grid, data_source) %>% 
  summarise(dcant_offset = mean(dcant_offset),
            RSS = mean(RSS),
            n = n()) %>% 
  ungroup() %>% 
  filter(n == 25) %>% 
  rename(lon = lon_grid,
         lat = lat_grid) %>% 
  mutate(dcant_offset_sign = abs(dcant_offset) - RSS)

delta_dcant_inv_all_uncertainty %>% 
  mutate(dcant_offset = abs(dcant_offset)) %>% 
  p_map_cant_inv(
    var = "dcant_offset",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source~.)

Past versions of inventory_maps_absolute_delta_RSS-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

delta_dcant_inv_all_uncertainty %>% 
  p_map_cant_inv(
    var = "RSS",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source~.)

Past versions of inventory_maps_absolute_delta_RSS-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

delta_dcant_inv_all_uncertainty %>% 
  p_map_cant_inv(
    var = "dcant_offset_sign",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source~.)

Past versions of inventory_maps_absolute_delta_RSS-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

map +
  geom_point(
    data = delta_dcant_inv_all_uncertainty %>%
      filter(dcant_offset_sign < 0),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "black"
  ) + 
  facet_wrap(~data_source)

Past versions of inventory_maps_absolute_delta_RSS-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

p_CI_absolute_mod_delta <- dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Reconstruction",
      data_source == "mod_truth" ~ "Model truth"
    )
  ) %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_grid(period ~ data_source) +
  theme(axis.text.x = element_blank()) +
  geom_point(
    data = dcant_inv_all_uncertainty %>%
      filter(dcant_sign < 0,
             data_source == "mod") %>%
      mutate(data_source = case_when(data_source == "mod" ~ "Reconstruction")),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "grey"
  )

p_CI_bias_delta <-
  dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
         period = "Decadal offset") %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Reconstruction",
      data_source == "mod_truth" ~ "Model truth"
    )
  ) %>%
  p_map_cant_inv(
    var = "dcant_offset",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(period ~ data_source) +
  geom_point(
    data = delta_dcant_inv_all_uncertainty %>%
      filter(dcant_offset_sign < 0,
             data_source == "mod") %>%
      mutate(data_source = case_when(data_source == "mod" ~ "Reconstruction")),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "black"
  )

p_CI_absolute_mod_delta / p_CI_bias_delta + 
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_delta_dcant_mod_bias-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "FigS_delta_dcant_column_inventory_synthetic_data.png",
       height = 7,
       width = 8)

rm(p_CI_absolute_mod_delta, p_CI_bias_delta)

p_CI_bias_delta_delta <-
  dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
         period = "Decadal offset") %>%
  select(lon, lat, data_source, dcant_offset) %>% 
  pivot_wider(names_from = data_source,
              values_from = dcant_offset) %>% 
  mutate(delta_dcant_bias = mod - mod_truth) %>% 
  p_map_cant_inv(
    var = "delta_dcant_bias",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  )
# theme(strip.text.x = element_blank(),
#       strip.background.x = element_blank())

p_CI_bias_delta_delta

Past versions of inventory_maps_delta_dcant_mod_bias-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

rm(p_CI_bias_delta_delta)

9.4 dCant - delta

p_CI_delta <-
  delta_dcant_inv_all %>%
  filter(data_source == "obs") %>%
  p_map_cant_inv(
    var = "dcant_offset",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  geom_point(
    data = delta_dcant_inv_all_uncertainty %>%
      filter(dcant_offset_sign < 0,
             data_source == "obs"),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "black"
  ) +
  facet_wrap(~ period)

p_CI_absolute / p_CI_delta + 
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_absolute_delta-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
d6b399a	jens-daniel-mueller	2022-01-21

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_column_inventory.png",
       height = 10,
       width = 8)

p_CI_delta +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of inventory_maps_absolute_delta-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
d6b399a	jens-daniel-mueller	2022-01-21

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_column_inventory_delta.png",
       height = 3.2,
       width = 6)

rm(p_CI_absolute, p_CI_delta)

9.5 Bias

p_CI_absolute_mod <- dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Reconstruction",
      data_source == "mod_truth" ~ "Model truth"
    )
  ) %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_grid(data_source ~ period) +
  theme(axis.text.x = element_blank()) +
  geom_point(
    data = dcant_inv_all_uncertainty %>%
      filter(dcant_sign < 0,
             data_source == "mod") %>%
      mutate(
        data_source = case_when(
          data_source == "mod" ~ "Reconstruction"
        )
      ),
    aes(lon, lat),
    shape = 20,
    size = 0.1,
    col = "grey"
  )

p_CI_absolute_mod

Past versions of invetory_maps_absolute_mod-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10

p_CI_bias <-
  dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = data_source,
              values_from = dcant) %>%
  mutate(dcant_bias = mod - mod_truth,
         data_source = "Reconstruction bias") %>%
  p_map_cant_inv(
    var = "dcant_bias",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source ~ period) +
  theme(strip.text.x = element_blank(),
        strip.background.x = element_blank())

p_CI_absolute_mod / p_CI_bias + 
  plot_annotation(tag_levels = 'A')

Past versions of invetory_maps_absolute_mod_bias-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_column_inventory_synthetic_data.png",
       height = 7,
       width = 8)

rm(p_CI_absolute_mod, p_CI_bias)

p_CI_bias_decade <-
  dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = data_source,
              values_from = dcant) %>%
  mutate(dcant_bias = mod - mod_truth,
         data_source = "Reconstruction bias") %>%
  arrange(period) %>% 
  group_by(lon, lat) %>% 
  summarise(delta_dcant_bias = dcant_bias - lag(dcant_bias)) %>% 
  ungroup() %>% 
  drop_na() %>% 
  p_map_cant_inv(
    var = "delta_dcant_bias",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  theme(strip.text.x = element_blank(),
        strip.background.x = element_blank())

p_CI_bias_decade

Past versions of inventory_maps_absolute_mod_bias_decade-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/publication"),
       filename = "FigS_delta_dcant_column_inventory_synthetic_data_bias.png",
       height = 3.5,
       width = 5)

rm(p_CI_bias_decade)

9.6 Density distributions

bind_rows(
  dcant_inv_all %>%
    filter(period %in% two_decades) %>%
    mutate(
      data_source = case_when(
        data_source == "mod" ~ "Reconstruction",
        data_source == "obs" ~ "Observations",
        data_source == "mod_truth" ~ "Model truth"
      )
    ) %>%
    select(data_source, dcant, period),
  dcant_inv_bias_all %>%
    filter(period %in% two_decades) %>%
    mutate(data_source = "Reconstruction bias") %>%
    select(data_source, dcant = dcant_pos_bias, period)
) %>%
  ggplot(aes(dcant, col = data_source)) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  geom_vline(xintercept = 0) +
  geom_density(alpha = 0.3) +
  coord_cartesian(xlim = c(-2, 18)) +
  facet_grid(period ~ .) +
  labs(x = dcant_CI_label) +
  theme(legend.title = element_blank())

Past versions of dcant_column_inventory_density_distribution-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5ca8778	jens-daniel-mueller	2022-09-07

# ggsave(path = here::here("output/publication"),
#        filename = "FigS_dcant_column_inventory_density_distribution.png",
#        height = 4,
#        width = 7)

bind_rows(
  dcant_inv_all %>%
    filter(period %in% two_decades) %>%
    select(data_source, lon, lat, period, dcant) %>%
    pivot_wider(names_from = period,
                values_from = dcant) %>%
    mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
           period = "(2004 - 2014) - (1994 - 2004)")  %>%
    mutate(
      data_source = case_when(
        data_source == "mod" ~ "Reconstruction",
        data_source == "obs" ~ "Observations",
        data_source == "mod_truth" ~ "Model truth"
      )
    ) %>%
    select(data_source, dcant_offset, period),
  dcant_inv_bias_all %>%
    filter(period %in% two_decades) %>%
    select(lon, lat, period, dcant_bias) %>%
    pivot_wider(names_from = period,
                values_from = dcant_bias) %>%
    mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
           period = "(2004 - 2014) - (1994 - 2004)")  %>%
    mutate(data_source = "Reconstruction bias") %>%
    select(data_source, dcant_offset, period)
) %>%
  ggplot(aes(dcant_offset, col = data_source)) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  geom_vline(xintercept = 0) +
  geom_density(alpha = 0.3) +
  coord_cartesian(xlim = c(-10, 10)) +
  labs(x = delta_dcant_CI_label) +
  theme(legend.title = element_blank())

Past versions of delta_dcant_column_inventory_density_distribution-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5ca8778	jens-daniel-mueller	2022-09-07

# ggsave(
#   path = here::here("output/publication"),
#   filename = "FigS_delta_dcant_column_inventory_density_distribution.png",
#   height = 3,
#   width = 7
# )

9.7 Unadjusted data

dcant_inv_all_unadjusted <-
  dcant_inv_all_ensemble %>%
  filter(period %in% two_decades,
         data_source == "obs",
         MLR_basins == "3",
         Version_ID %in% Version_IDs |
           Version_ID %in% Version_IDs_d)

p_CI_unadjusted <- 
dcant_inv_all_unadjusted %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_grid(Version_ID_group ~ period)

p_CI_unadjusted_bias <-
  dcant_inv_all_unadjusted %>%
  select(Version_ID_group, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = Version_ID_group,
              values_from = dcant) %>%
  mutate(dcant_offset = `Bulk adjustment` - `No adjustment`,
         data_source = "Adjustment impact") %>%
  p_map_cant_inv(
    var = "dcant_offset",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias",
    legend_title = 
  ) +
  facet_grid(data_source ~ period) +
  theme(strip.text.x = element_blank(),
        strip.background.x = element_blank())

p_CI_unadjusted / p_CI_unadjusted_bias + 
  plot_annotation(tag_levels = 'A')

Past versions of column_inventory_unadjusted_data-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
74dca9d	jens-daniel-mueller	2022-08-17
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
9ce772d	jens-daniel-mueller	2022-07-19

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_column_inventory_unadjusted_data.png",
       height = 7,
       width = 8)

rm(p_CI_unadjusted, p_CI_unadjusted_bias)

9.8 Beta

dcant_inv_all %>%
  filter(data_source %in% c("obs")) %>% 
  p_map_cant_inv(var = "beta",
                 breaks = c(-Inf, seq(0, 1, 0.1), Inf),
                 title_text = NULL,
                 legend_title = beta_CI_label) +
  facet_grid(period ~ .)

Past versions of beta_CI_maps-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
efa414b	jens-daniel-mueller	2022-07-16

ggsave(path = here::here("output/publication"),
       filename = "FigS_beta_column_inventory.png",
       height = 8,
       width = 6)

9.9 Zonal means

dcant_inv_all <- m_grid_horizontal_coarse(dcant_inv_all)

dcant_inv_all_lat_mean <- dcant_inv_all %>% 
  group_by(basin_AIP, period, lat_grid, data_source) %>% 
  summarise(beta = mean(beta, na.rm = TRUE),
            dcant = mean(dcant, na.rm = TRUE)) %>% 
  ungroup()

dcant_inv_all_ensemble <- m_grid_horizontal_coarse(dcant_inv_all_ensemble)

dcant_inv_all_ensemble_lat_mean <- dcant_inv_all_ensemble %>% 
  group_by(basin_AIP, period, lat_grid, Version_ID, data_source) %>% 
  summarise(beta_mean = mean(beta, na.rm = TRUE),
            dcant_mean = mean(dcant, na.rm = TRUE)) %>% 
  ungroup()

dcant_inv_all_ensemble_lat_mean_average <- dcant_inv_all_ensemble_lat_mean %>% 
  group_by(basin_AIP, period, lat_grid, data_source) %>% 
  summarise(beta_sd = sd(beta_mean, na.rm = TRUE),
            beta_mean = mean(beta_mean, na.rm = TRUE),
            dcant_sd = sd(dcant_mean, na.rm = TRUE),
            dcant_mean = mean(dcant_mean, na.rm = TRUE)) %>% 
  ungroup()

dcant_inv_all_lat_mean <- full_join(dcant_inv_all_lat_mean,
                                    dcant_inv_all_ensemble_lat_mean_average)

9.9.1 dcant

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  ggplot(aes(lat_grid, dcant, fill = period)) +
  geom_hline(yintercept = c(0,10)) +
  geom_vline(xintercept = c(-10,10)) +
  geom_vline(xintercept = c(-40,-30,30,40)) +
  geom_ribbon(aes(ymin = dcant - dcant_sd,
                  ymax = dcant + dcant_sd),
              alpha = 0.3) +
  geom_path(aes(col = period)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Dark2", name = "Mean ± SD") +
  scale_fill_brewer(palette = "Dark2", name = "Mean ± SD") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = dcant_CI_label) +
  scale_x_continuous(breaks = seq(-75,75, 15))

Past versions of zonal_mean_storage_dcant-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-40,-30,-10,10,30,40))) %>% 
  group_by(lat_grid, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 18 × 3
   lat_grid  data_source dcant_mean
   <fct>     <chr>            <dbl>
 1 (-40,-30] mod              11.8 
 2 (-40,-30] mod_truth        10.9 
 3 (-40,-30] obs              14.2 
 4 (-30,-10] mod               8.72
 5 (-30,-10] mod_truth         7.73
 6 (-30,-10] obs              11.5 
 7 (-10,10]  mod               4.66
 8 (-10,10]  mod_truth         4.09
 9 (-10,10]  obs               7.42
10 (10,30]   mod               5.39
11 (10,30]   mod_truth         5.03
12 (10,30]   obs               7.23
13 (30,40]   mod               6.29
14 (30,40]   mod_truth         6.46
15 (30,40]   obs              11.0 
16 <NA>      mod               5.62
17 <NA>      mod_truth         5.59
18 <NA>      obs               7.13

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-40,-30,-10,10,30,40))) %>% 
  group_by(lat_grid, basin_AIP, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 51 × 4
   lat_grid  basin_AIP data_source dcant_mean
   <fct>     <chr>     <chr>            <dbl>
 1 (-40,-30] Atlantic  mod              11.5 
 2 (-40,-30] Atlantic  mod_truth        10.1 
 3 (-40,-30] Atlantic  obs              15.4 
 4 (-40,-30] Indian    mod              13.4 
 5 (-40,-30] Indian    mod_truth        12.6 
 6 (-40,-30] Indian    obs              14.5 
 7 (-40,-30] Pacific   mod              10.5 
 8 (-40,-30] Pacific   mod_truth         9.97
 9 (-40,-30] Pacific   obs              12.7 
10 (-30,-10] Atlantic  mod               7.58
# … with 41 more rows

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-90,0,90))) %>% 
  group_by(lat_grid, basin_AIP, period, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 54 × 5
   lat_grid basin_AIP period      data_source dcant_mean
   <fct>    <chr>     <chr>       <chr>            <dbl>
 1 (-90,0]  Atlantic  1994 - 2004 mod               4.26
 2 (-90,0]  Atlantic  1994 - 2004 mod_truth         4.06
 3 (-90,0]  Atlantic  1994 - 2004 obs               7.06
 4 (-90,0]  Atlantic  1994 - 2014 mod               9.59
 5 (-90,0]  Atlantic  1994 - 2014 mod_truth         8.72
 6 (-90,0]  Atlantic  1994 - 2014 obs              14.6 
 7 (-90,0]  Atlantic  2004 - 2014 mod               5.38
 8 (-90,0]  Atlantic  2004 - 2014 mod_truth         4.66
 9 (-90,0]  Atlantic  2004 - 2014 obs               7.59
10 (-90,0]  Indian    1994 - 2004 mod               5.84
# … with 44 more rows

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-50,-20,20,50))) %>% 
  group_by(lat_grid, basin_AIP, period, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup() %>% 
  filter(data_source == "obs") %>% 
  arrange(basin_AIP)

# A tibble: 36 × 5
   lat_grid  basin_AIP period      data_source dcant_mean
   <fct>     <chr>     <chr>       <chr>            <dbl>
 1 (-50,-20] Atlantic  1994 - 2004 obs               9.05
 2 (-50,-20] Atlantic  1994 - 2014 obs              20.4 
 3 (-50,-20] Atlantic  2004 - 2014 obs              11.2 
 4 (-20,20]  Atlantic  1994 - 2004 obs               5.56
 5 (-20,20]  Atlantic  1994 - 2014 obs              14.1 
 6 (-20,20]  Atlantic  2004 - 2014 obs               8.30
 7 (20,50]   Atlantic  1994 - 2004 obs              12.1 
 8 (20,50]   Atlantic  1994 - 2014 obs              20.5 
 9 (20,50]   Atlantic  2004 - 2014 obs               8.33
10 <NA>      Atlantic  1994 - 2004 obs               7.53
# … with 26 more rows

dcant_inv_all_ensemble_lat_mean %>% 
  ggplot(aes(lat_grid, dcant_mean, fill=period, group=Version_ID)) +
  geom_hline(yintercept = 0) +
  geom_path(aes(col=period), alpha = 0.3) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = dcant_CI_label)

Past versions of zonal_mean_storage_dcant-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

9.9.2 beta

dcant_inv_all_lat_mean %>%
  ggplot(aes(lat_grid, beta, fill = period)) +
  geom_hline(yintercept = 0) +
  geom_ribbon(aes(ymin = beta - beta_sd,
                  ymax = beta + beta_sd),
              alpha = 0.3) +
  geom_path(aes(col = period)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Dark2", name = "Mean ± SD") +
  scale_fill_brewer(palette = "Dark2", name = "Mean ± SD") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = beta_CI_label) +
  scale_x_continuous(breaks = seq(-75,75, 15))

Past versions of zonal_mean_storage_beta-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

dcant_inv_all_ensemble_lat_mean %>% 
  ggplot(aes(lat_grid, beta_mean, fill=period, group=Version_ID)) +
  geom_hline(yintercept = 0) +
  geom_path(aes(col=period), alpha = 0.3) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = beta_CI_label)

Past versions of zonal_mean_storage_beta-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

10 Global section

10.1 Ensemble uncertainty

delta_dcant_global_section_all <-
  dcant_global_section_all %>%
  filter(data_source %in% c("obs", "mod"),
         period %in% two_decades) %>%
  select(data_source, dist, depth, period, dcant) %>%
  drop_na() %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
         period = "(2004 - 2014) - (1994 - 2004)")

dcant_global_section_all_ensemble_sd <-
  dcant_global_section_all_ensemble %>%
  filter(period %in% two_decades,
         data_source != "mod_truth") %>%
  group_by(data_source, period, dist, depth) %>%
  summarise(dcant_sd = sd(dcant) * sd_factor) %>%
  ungroup()


dcant_global_section_all_uncertainty <-
  left_join(
    dcant_global_section_all %>% select(data_source, period, dist, depth, dcant),
    dcant_global_section_all_ensemble_sd %>% select(data_source, period, dist, depth, dcant_sd)
  )

dcant_global_section_all_uncertainty <-
  dcant_global_section_all_uncertainty %>%
  mutate(dist_grid = as.numeric(as.character(cut(
    dist, seq(0, 50, 2), seq(1, 49, 2)
  ))),
  depth_grid = as.numeric(as.character(cut(
    depth,
    c(seq(0, 500, 100), seq(1000, 10000, 500)),
    c(seq(50, 450, 100), seq(750, 10000, 500)),
    right = FALSE
  ))))


dcant_global_section_all_uncertainty <-
  dcant_global_section_all_uncertainty %>%
  group_by(dist_grid, period, depth_grid, data_source) %>% 
  summarise(dcant = mean(dcant),
            dcant_sd = mean(dcant_sd),
            n = n()) %>% 
  ungroup() %>% 
  rename(dist = dist_grid,
         depth = depth_grid) %>%
  mutate(dcant_sign = abs(dcant) - dcant_sd,
         band = "dummy")


dcant_global_section_all_uncertainty  %>% 
  filter(data_source == "obs",
         period == "1994 - 2004") %>% 
  p_section_global(
    var = "dcant",
    plot_slabs = "n",
    title_text = NULL,
    subtitle_text = NULL,
    col = "div"
  )

Past versions of global_section_absolute_delta_RSS-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23
77e6a80	jens-daniel-mueller	2022-09-08

dcant_global_section_all_uncertainty  %>% 
  filter(data_source == "obs",
         period == "1994 - 2004") %>% 
  p_section_global(
    var = "dcant_sd",
    plot_slabs = "n",
    title_text = NULL,
    subtitle_text = NULL,
    col = "div"
  )

Past versions of global_section_absolute_delta_RSS-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23
77e6a80	jens-daniel-mueller	2022-09-08

dcant_global_section_all_uncertainty  %>%
  filter(data_source == "obs",
         period == "1994 - 2004") %>%
  rename(delta_dcant = dcant_sign) %>%
  p_section_global(
    var = "delta_dcant",
    plot_slabs = "n",
    title_text = NULL,
    subtitle_text = NULL,
    col = "div"
  )

Past versions of global_section_absolute_delta_RSS-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23
77e6a80	jens-daniel-mueller	2022-09-08

ggplot() +
  geom_point(
    data = dcant_global_section_all_uncertainty %>%
      filter(dcant_sign < 0),
    aes(dist, depth),
    shape = 4
  ) +
  facet_grid(data_source~period) +
  scale_y_reverse()

Past versions of global_section_absolute_delta_RSS-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23
77e6a80	jens-daniel-mueller	2022-09-08

dcant_global_section_all_uncertainty <-
  dcant_global_section_all_uncertainty %>%
  filter(dcant_sign < 0)


delta_dcant_global_section_all_ensemble_RSS <-
  dcant_global_section_all_ensemble_sd %>%
  arrange(period) %>%
  group_by(data_source, dist, depth) %>%
  mutate(RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  drop_na()

delta_dcant_global_section_all_uncertainty <-
  left_join(
    delta_dcant_global_section_all %>% select(data_source, dist, depth, dcant_offset),
    delta_dcant_global_section_all_ensemble_RSS %>% select(data_source, dist, depth, RSS)
  )

delta_dcant_global_section_all_uncertainty <-
  delta_dcant_global_section_all_uncertainty %>%
  mutate(dist_grid = as.numeric(as.character(cut(
    dist, seq(0, 50, 2), seq(1, 49, 2)
  ))),
  depth_grid = as.numeric(as.character(cut(
    depth,
    c(seq(0, 500, 100), seq(1000, 10000, 500)),
    c(seq(50, 450, 100), seq(750, 10000, 500)),
    right = FALSE
  ))))

delta_dcant_global_section_all_uncertainty <-
  delta_dcant_global_section_all_uncertainty %>%
  group_by(dist_grid, depth_grid, data_source) %>% 
  summarise(dcant_offset = mean(dcant_offset),
            RSS = mean(RSS),
            n = n()) %>% 
  ungroup() %>% 
  rename(dist = dist_grid,
         depth = depth_grid) %>%
  mutate(dcant_offset_sign = abs(dcant_offset) - RSS,
         band = "dummy")


delta_dcant_global_section_all_uncertainty  %>% 
  mutate(dcant_offset = dcant_offset) %>% 
  filter(data_source == "obs") %>% 
  rename(delta_dcant = dcant_offset) %>% 
  p_section_global(
    var = "delta_dcant",
    plot_slabs = "n",
    title_text = NULL,
    subtitle_text = NULL,
    col = "div"
  )

Past versions of global_section_absolute_delta_RSS-5.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

delta_dcant_global_section_all_uncertainty  %>% 
  filter(data_source == "obs") %>% 
  rename(delta_dcant = RSS) %>% 
  p_section_global(
    var = "delta_dcant",
    plot_slabs = "n",
    title_text = NULL,
    subtitle_text = NULL,
    col = "div"
  )

Past versions of global_section_absolute_delta_RSS-6.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

delta_dcant_global_section_all_uncertainty  %>%
  filter(data_source == "obs") %>%
  rename(delta_dcant = dcant_offset_sign) %>%
  p_section_global(
    var = "delta_dcant",
    plot_slabs = "n",
    title_text = NULL,
    subtitle_text = NULL,
    col = "div"
  )

Past versions of global_section_absolute_delta_RSS-7.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

ggplot() +
  geom_point(
    data = delta_dcant_global_section_all_uncertainty %>%
      filter(dcant_offset_sign < 0),
    aes(dist, depth),
    shape = 4
  ) +
  scale_y_reverse() +
  facet_grid(data_source~.)

Past versions of global_section_absolute_delta_RSS-8.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
e93fdfd	jens-daniel-mueller	2022-09-23

delta_dcant_global_section_all_uncertainty <-
  delta_dcant_global_section_all_uncertainty %>%
  filter(dcant_offset_sign < 0)

10.2 Observations

p_dcant_global_section_dec1 <-
  dcant_global_section_all %>%
  filter(data_source == "obs",
         period %in% "1994 - 2004") %>%
  p_section_global(var = "dcant",
                   title_text = "1994 - 2004",
                   df_uncertainty = dcant_global_section_all_uncertainty %>% 
                     filter(period %in% "1994 - 2004",
                            data_source == "obs"),
                   col_uncertainty = "grey",
                   subtitle_text = NULL,
                   plot_slabs = "n",
                   contour_level = 5) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x.bottom = element_blank()
  )


p_dcant_global_section_dec2 <-
  dcant_global_section_all %>%
  filter(data_source == "obs",
         period %in% "2004 - 2014") %>%
  p_section_global(
    var = "dcant",
    title_text = NULL,
    subtitle_text = NULL,
    plot_slabs = "n",
    contour_level = 5,
    df_uncertainty = dcant_global_section_all_uncertainty %>%
      filter(period %in% "2004 - 2014",
                            data_source == "obs"),
    col_uncertainty = "grey"
  ) &
  theme(
    legend.position = "none"
  )

p_dcant_global_section_offset <-
  dcant_global_section_all %>%
  filter(data_source == "obs",
         period %in% two_decades) %>%
  arrange(depth, dist) %>%
  select(depth, dist, dcant, period, gamma_mean) %>%
  drop_na() %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  p_section_global(
    var = "delta_dcant",
    df_uncertainty = delta_dcant_global_section_all_uncertainty %>% 
      filter(data_source == "obs"),
    plot_slabs = "y",
    col = "div",
    title_text = NULL,
    subtitle_text = NULL
  )



wrap_plots(
  p_dcant_global_section_dec1,
  p_dcant_global_section_dec2 &
    theme(axis.title.x = element_blank(),
          axis.text.x = element_blank()),
  p_dcant_global_section_offset &
    theme(axis.title.x.top = element_blank(),
          axis.text.x.top = element_blank()),
  ncol = 1
)

Past versions of global_sections_obs-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_global_section.png",
       height = 10,
       width = 8)

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_global_section.png",
       height = 9,
       width = 10)

rm(p_dcant_global_section_dec1, p_dcant_global_section_dec2,
   p_dcant_global_section_offset)

10.3 Synthetic data

p_dcant_global_section_dec1 <-
  dcant_global_section_all %>%
  filter(data_source == "mod",
         period %in% "1994 - 2004") %>%
  p_section_global(var = "dcant",
                   title_text = "1994 - 2004",
                   df_uncertainty = dcant_global_section_all_uncertainty %>% 
                     filter(period %in% "1994 - 2004",
                            data_source == "mod"),
                   col_uncertainty = "grey",
                   subtitle_text = NULL,
                   plot_slabs = "n",
                   contour_level = 5) +
  theme(
    axis.title.x = element_blank(),
    axis.text.x.bottom = element_blank()
  )


p_dcant_global_section_dec2 <-
  dcant_global_section_all %>%
  filter(data_source == "mod",
         period %in% "2004 - 2014") %>%
  p_section_global(
    var = "dcant",
    title_text = NULL,
    subtitle_text = NULL,
    plot_slabs = "n",
    contour_level = 5,
    df_uncertainty = dcant_global_section_all_uncertainty %>%
      filter(period %in% "2004 - 2014",
                            data_source == "mod"),
    col_uncertainty = "grey"
  ) &
  theme(
    legend.position = "none"
  )

p_dcant_global_section_offset <-
  dcant_global_section_all %>%
  filter(data_source == "mod",
         period %in% two_decades) %>%
  arrange(depth, dist) %>%
  select(depth, dist, dcant, period, gamma_mean) %>%
  drop_na() %>%
  group_by(depth, dist) %>% 
  mutate(gamma_mean = mean(gamma_mean)) %>% 
  ungroup() %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  p_section_global(
    var = "delta_dcant",
    df_uncertainty = delta_dcant_global_section_all_uncertainty %>%
      filter(data_source == "mod"),
    col = "div",
    plot_slabs = "y",
    title_text = NULL,
    subtitle_text = NULL
  )



wrap_plots(
  p_dcant_global_section_dec1,
  p_dcant_global_section_dec2 &
    theme(axis.title.x = element_blank(),
          axis.text.x = element_blank()),
  p_dcant_global_section_offset &
    theme(axis.title.x.top = element_blank(),
          axis.text.x.top = element_blank()),
  ncol = 1
)

Past versions of global_sections_mod-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_global_section_synthetic_data.png",
       height = 10,
       width = 8)

rm(p_dcant_global_section_dec1, p_dcant_global_section_dec2,
   p_dcant_global_section_offset)

11 Zonal sections

11.1 dcant - absolut

p_dcant_zonal_absolute <-
  dcant_zonal_all %>%
  filter(
    data_source == "obs",
    period %in% two_decades,
    depth <= params_global$inventory_depth_standard
  ) %>%
  p_section_zonal_continous_depth(var = "dcant",
                                  plot_slabs = "n",
                                  title_text = NULL) +
  geom_contour(aes(lat, depth, z = dcant),
               breaks = 5,
               col = "white") +
  # geom_text_contour(
  #   aes(lat, depth, z = dcant),
  #   breaks = 5,
  #   stroke = 0.2,
  #   stroke.colour = "white",
  #   rotate = FALSE,
  #   label.placer = label_placer_n(n = 1)
  # ) +
  facet_grid(period ~ basin_AIP) +
  theme(
    axis.text.x = element_blank(),
    axis.title.x = element_blank(),
    legend.position = "top"
  ) +
  guides(fill = guide_colorsteps(
    barheight = unit(0.5, "cm"),
    barwidth = unit(7, "cm"),
    show.limits = FALSE
  ))

p_dcant_zonal_absolute

Past versions of zonal_sections-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
09b0780	jens-daniel-mueller	2022-05-24
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
e572075	jens-daniel-mueller	2022-01-21

dcant_zonal_all %>%
  filter(
    data_source == "obs",
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard
  ) %>%
  group_split(basin_AIP) %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant",
      breaks = c(-Inf, -1, 0, 1, seq(2,16,2), Inf),
      plot_slabs = "n",
      title_text = .x$basin_AIP,
      subtitle_text = NULL
    ) +
      facet_grid(period ~ .)
  )

11.2 dcant - absolute delta

dcant_zonal_all %>%
  filter(data_source %in% c("mod", "obs"),
         period != "1994 - 2014") %>%
  select(data_source, lat, depth, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "delta_dcant",
      plot_slabs = "n",
      subtitle_text = unique(.x$data_source),
      col = "bias"
    ) +
      facet_grid(basin_AIP ~ .)
  )

[[1]]

Past versions of inventory_maps_absolute_delta_zonal-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

[[2]]

Past versions of inventory_maps_absolute_delta_zonal-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

p_dcant_zonal_delta <-
  dcant_zonal_all %>%
  filter(data_source %in% c("obs"),
         period != "1994 - 2014") %>%
  select(data_source, lat, depth, basin_AIP, period, gamma_mean, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "delta_dcant",
      plot_slabs = "y",
      drop_slabs = 1,
      subtitle_text = NULL,
      title_text = NULL,
      col = "bias"
    ) +
      facet_grid("Dec. diff." ~ basin_AIP) +
      theme(
        strip.text.x = element_blank(),
        strip.background.x = element_blank(),
        legend.position = "bottom"
      ) +
      guides(fill = guide_colorsteps(
        barheight = unit(0.5, "cm"),
        barwidth = unit(10, "cm"),
        show.limits = FALSE
      ))
  )

p_dcant_zonal_absolute / p_dcant_zonal_delta + 
  plot_layout(heights = c(2,1)) +
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_absolute_delta_zonal-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_zonal_mean_section.png",
       height = 6,
       width = 8)

rm(p_dcant_zonal_absolute, p_dcant_zonal_delta)

11.3 Bias

p_dcant_zonal_absolute_mod <-
  dcant_zonal_all %>%
  filter(
    data_source != "obs",
    period %in% two_decades,
    depth <= params_global$inventory_depth_standard
  ) %>%
    mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  p_section_zonal_continous_depth(var = "dcant",
                                  plot_slabs = "n",
                                  title_text = NULL) +
  geom_contour(aes(lat, depth, z = dcant),
               breaks = 5,
               col = "white") +
  facet_nested(data_source+period ~ basin_AIP) +
  theme(
    axis.text.x = element_blank(),
    axis.title.x = element_blank(),
    legend.position = "top"
  ) +
  guides(fill = guide_colorsteps(
    barheight = unit(0.5, "cm"),
    barwidth = unit(7, "cm"),
    show.limits = FALSE
  ))

p_dcant_zonal_absolute_mod

Past versions of zonal_sections_mod-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10

p_dcant_zonal_bias <-
  dcant_zonal_all %>%
  filter(data_source != "obs",
         period %in% two_decades) %>%
  select(data_source, lat, depth, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = data_source,
              values_from = dcant) %>%
  mutate(dcant_bias = mod - mod_truth) %>%
  p_section_zonal_continous_depth(
    var = "dcant_bias",
    plot_slabs = "n",
    drop_slabs = 1,
    subtitle_text = NULL,
    title_text = NULL,
    col = "bias"
  ) +
  facet_grid(period ~ basin_AIP) +
  theme(
    strip.text.x = element_blank(),
    strip.background.x = element_blank(),
    legend.position = "bottom"
  ) +
  guides(fill = guide_colorsteps(
    barheight = unit(0.5, "cm"),
    barwidth = unit(10, "cm"),
    show.limits = FALSE
  ))

p_dcant_zonal_absolute_mod / p_dcant_zonal_bias + 
  plot_layout(heights = c(2,1)) +
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_absolute_bias-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10

ggsave(
  path = here::here("output/publication"),
  filename = "FigS_dcant_zonal_mean_section_synthetic_data.png",
  height = 9,
  width = 8
)

rm(p_dcant_zonal_absolute_mod, 
   p_dcant_zonal_bias)

12 Penetration depth

dcant_penetration_depth_all_lat_mean_all_ensemble %>%
  filter(dcant_lim == 5,
         period %in% two_decades,
         data_source != "obs") %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Reconstruction\nensemble",
      data_source == "mod_truth" ~ "Model truth"
    ),
    data_source = fct_reorder(data_source, desc(data_source))
  ) %>%
  ggplot(aes(
    lat,
    depth_mean,
    group = interaction(MLR_basins, Version_ID_group, data_source)
  )) +
  geom_path(aes(col = data_source)) +
  geom_path(
    data = dcant_penetration_depth_all_lat_mean_all %>%
      filter(dcant_lim == 5,
             period %in% two_decades,
             data_source == "mod") %>%
      mutate(
        data_source = case_when(data_source == "mod" ~ "Standard case"),
        data_source = fct_reorder(data_source, desc(data_source))
      ),
    aes(lat,
        depth_mean,
        col = data_source)
  ) +
  scale_colour_manual(values = c("red", "grey", "black")) +
  labs(y = "Depth (m)",
       x = "Latitude (°N)") +
  theme(legend.title = element_blank()) +
  scale_y_reverse() +
  facet_grid(basin_AIP ~ period, scales = "free_x") +
  coord_cartesian(ylim = c(1500,0))

Past versions of penetration_depth-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
8105380	jens-daniel-mueller	2022-10-07

ggsave(
  path = here::here("output/publication"),
  filename = "FigS_dcant_penetration_depth_synthetic_data.png",
  height = 9,
  width = 8
)


dcant_penetration_depth_all_lat_mean_all_ensemble %>%
  filter(dcant_lim == 5,
         period %in% two_decades,
         data_source == "mod") %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Reconstruction\nensemble",
      data_source == "mod_truth" ~ "Model truth"
    ),
    data_source = fct_reorder(data_source, desc(data_source))
  ) %>%
  ggplot(aes(
    lat,
    depth_mean,
    group = interaction(MLR_basins, Version_ID_group, data_source)
  )) +
  geom_path(aes(col = MLR_basins)) +
  geom_path(
    data = dcant_penetration_depth_all_lat_mean_all_ensemble %>%
      filter(
        dcant_lim == 5,
        period %in% two_decades,
        data_source == "mod_truth"
      ),
    aes(
      lat,
      depth_mean,
      group = interaction(MLR_basins, Version_ID_group, data_source)
    ),
    col = "black"
  ) +
  scale_color_vibrant() +
  labs(y = "Depth (m)",
       x = "Latitude (°N)") +
  theme(legend.title = element_blank()) +
  scale_y_reverse() +
  facet_grid(basin_AIP ~ period, scales = "free_x") +
  coord_cartesian(ylim = c(1500, 0))

Past versions of penetration_depth-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10
8105380	jens-daniel-mueller	2022-10-07

ggsave(
  path = here::here("output/publication"),
  filename = "FigS_dcant_penetration_depth_synthetic_data_MLR_basins.png",
  height = 9,
  width = 8
)


dcant_penetration_depth_all_lat_mean_all_ensemble %>%
  filter(dcant_lim == 5,
         period %in% two_decades,
         data_source != "mod_truth") %>%
  mutate(
    data_source = case_when(
      data_source == "mod" ~ "Synthetic data",
      data_source == "obs" ~ "Observations"
    ),
    data_source = fct_reorder(data_source, desc(data_source))
  ) %>%
  ggplot(aes(
    lat,
    depth_mean,
    group = interaction(MLR_basins, Version_ID_group, data_source)
  )) +
  geom_path(aes(col = data_source)) +
  scale_colour_highcontrast() +
  labs(y = "Depth (m)",
       x = "Latitude (°N)") +
  theme(legend.title = element_blank()) +
  scale_y_reverse() +
  facet_grid(basin_AIP ~ period, scales = "free_x") +
  coord_cartesian(ylim = c(1500,0))

Past versions of penetration_depth-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10

ggsave(
  path = here::here("output/publication"),
  filename = "FigS_dcant_penetration_depth_synthetic_data_vs_observations.png",
  height = 9,
  width = 8
)

dcant_penetration_depth_all_lat_mean_all_ensemble %>%
  filter(dcant_lim == 5,
         period %in% two_decades,
         data_source == "obs") %>%
  mutate(
    data_source = case_when(
      data_source == "obs" ~ "Observations"
    ),
    data_source = fct_reorder(data_source, desc(data_source))
  ) %>%
  ggplot(aes(
    lat,
    depth_mean,
    group = interaction(MLR_basins, Version_ID_group, data_source)
  )) +
  geom_path(aes(col = MLR_basins)) +
  scale_color_vibrant() +
  labs(y = "Depth (m)",
       x = "Latitude (°N)") +
  theme(legend.title = element_blank()) +
  scale_y_reverse() +
  facet_grid(basin_AIP ~ period, scales = "free_x") +
  coord_cartesian(ylim = c(1500,0))

Past versions of penetration_depth-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
46c163a	jens-daniel-mueller	2022-10-10

13 Concentration profiles

13.1 dCant, absolute

13.1.1 Standard case

dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         depth <= params_global$inventory_depth_standard) %>%
  group_split(data_source) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(dcant,
                 depth)) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(
          xmin = dcant - dcant_sd,
          xmax = dcant + dcant_sd,
          fill = period
        ),
        alpha = 0.3
      ) +
      geom_path(aes(col = period)) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        name = "mean \u00B1 regional SD",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        name = "mean \u00B1 regional SD",
        direction = -1
      ) +
      labs(
        title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.8, 0.2))
  )

[[1]]

Past versions of concentration_profiles_MLR_basins-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
4673df5	jens-daniel-mueller	2022-02-02
60727e6	jens-daniel-mueller	2022-02-02
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
c6fe495	jens-daniel-mueller	2022-01-26
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21

[[2]]

Past versions of concentration_profiles_MLR_basins-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
748aa43	jens-daniel-mueller	2022-06-27
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
4673df5	jens-daniel-mueller	2022-02-02
60727e6	jens-daniel-mueller	2022-02-02
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
c6fe495	jens-daniel-mueller	2022-01-26
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21

[[3]]

Past versions of concentration_profiles_MLR_basins-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11

13.1.2 Ensemble members

dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "Ensemble member",
          size = "Ensemble member",
          alpha = "Ensemble member"
        )
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "Standard case",
          alpha = "Standard case",
          size = "Standard case"
        )
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          group = Version_ID
        )
      ) +
      scale_size_manual(values = c(0.7, 1.5), name=" ") +
      scale_linetype_manual(values = c(1, 1), name=" ") +
      scale_alpha_manual(values = c(0.4, 1), name=" ") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        direction = -1
      ) +
      labs(
        # title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = "top")
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_individual-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
f1d7f80	jens-daniel-mueller	2022-07-13

13.1.3 Surface dCant

mean_surface_dcant <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(depth <= 50) %>%
  group_by(basin, period, data_source) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_basin_MLR_all_ensemble %>%
            filter(depth <= 50),
          mean_surface_dcant) %>%
  group_by(basin, period, data_source) %>%
  summarise(
    mean_surface_dcant = mean(mean_surface_dcant, na.rm = TRUE),
    sd_surface_dcant = sd(dcant) * sd_factor
  ) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	period	data_source	mean_surface_dcant	sd_surface_dcant
Global	1994 - 2004	mod	10.233057	0.4937456
Global	1994 - 2004	mod_truth	9.119623	0.0941725
Global	1994 - 2004	obs	10.514606	0.9893247
Global	1994 - 2014	mod	20.680491	2.2805383
Global	1994 - 2014	mod_truth	19.512012	0.1485241
Global	1994 - 2014	obs	21.476974	2.4572465
Global	2004 - 2014	mod	10.499135	2.3217247
Global	2004 - 2014	mod_truth	10.392390	0.0545478
Global	2004 - 2014	obs	11.062672	1.7246762
Indian	1994 - 2004	mod	10.013093	1.3524350
Indian	1994 - 2004	mod_truth	9.186885	0.0579891
Indian	1994 - 2004	obs	9.538497	1.6295962
Indian	1994 - 2014	mod	20.628019	1.4435803
Indian	1994 - 2014	mod_truth	19.386323	0.1378481
Indian	1994 - 2014	obs	20.779644	2.0705697
Indian	2004 - 2014	mod	10.844700	1.8647167
Indian	2004 - 2014	mod_truth	10.199438	0.0805557
Indian	2004 - 2014	obs	11.363610	3.0846136
N. Pacific	1994 - 2004	mod	10.276179	1.1192555
N. Pacific	1994 - 2004	mod_truth	9.221239	0.1726426
N. Pacific	1994 - 2004	obs	10.753290	1.5712356
N. Pacific	1994 - 2014	mod	21.011574	2.9500115
N. Pacific	1994 - 2014	mod_truth	19.988747	0.2794554
N. Pacific	1994 - 2014	obs	21.697415	3.9464635
N. Pacific	2004 - 2014	mod	10.713098	3.7085900
N. Pacific	2004 - 2014	mod_truth	10.767508	0.1072143
N. Pacific	2004 - 2014	obs	11.101775	2.5343954
S. Pacific	1994 - 2004	mod	9.818618	1.8878074
S. Pacific	1994 - 2004	mod_truth	8.898910	0.0712533
S. Pacific	1994 - 2004	obs	10.303636	0.9897165
S. Pacific	1994 - 2014	mod	20.051396	3.2910508
S. Pacific	1994 - 2014	mod_truth	19.346396	0.0775661
S. Pacific	1994 - 2014	obs	20.689650	1.6118138
S. Pacific	2004 - 2014	mod	10.186799	1.9305865
S. Pacific	2004 - 2014	mod_truth	10.447486	0.0106002
S. Pacific	2004 - 2014	obs	10.478757	1.0735422
N. Atlantic	1994 - 2004	mod	11.385040	1.2570558
N. Atlantic	1994 - 2004	mod_truth	9.693375	0.0623036
N. Atlantic	1994 - 2004	obs	11.600352	1.7598243
N. Atlantic	1994 - 2014	mod	21.747759	2.2345831
N. Atlantic	1994 - 2014	mod_truth	20.378481	0.0628048
N. Atlantic	1994 - 2014	obs	23.286845	3.6722347
N. Atlantic	2004 - 2014	mod	10.449947	2.9661843
N. Atlantic	2004 - 2014	mod_truth	10.685107	0.0125613
N. Atlantic	2004 - 2014	obs	11.721643	2.1707058
S. Atlantic	1994 - 2004	mod	10.372162	1.1679142
S. Atlantic	1994 - 2004	mod_truth	8.791151	0.0908405
S. Atlantic	1994 - 2004	obs	11.168167	1.5919138
S. Atlantic	1994 - 2014	mod	20.581578	6.9648029
S. Atlantic	1994 - 2014	mod_truth	18.459719	0.1556806
S. Atlantic	1994 - 2014	obs	22.288130	2.3707626
S. Atlantic	2004 - 2014	mod	10.278645	8.2626677
S. Atlantic	2004 - 2014	mod_truth	9.668568	0.0651656
S. Atlantic	2004 - 2014	obs	11.158470	1.1563198

13.1.4 Penetration depth

full_join(dcant_profile_basin_MLR_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(dcant >= mean_surface_dcant / 2) %>%
  group_by(period, basin, Version_ID, data_source) %>%
  summarise(max_depth = max(depth)) %>%
  ungroup() %>%
  group_by(basin, period, data_source) %>%
  summarise(sd_max_depth = sd(max_depth) * 2,
            mean_max_depth = mean(max_depth)) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	period	data_source	sd_max_depth	mean_max_depth
Global	1994 - 2004	mod	62.64907	320.5714
Global	1994 - 2004	mod_truth	0.00000	351.0000
Global	1994 - 2004	obs	59.42035	309.5238
Global	1994 - 2014	mod	39.53375	331.0476
Global	1994 - 2014	mod_truth	0.00000	351.0000
Global	1994 - 2014	obs	0.00000	300.0000
Global	2004 - 2014	mod	37.12205	335.9762
Global	2004 - 2014	mod_truth	0.00000	351.0000
Global	2004 - 2014	obs	15.43033	298.8095
Indian	1994 - 2004	mod	124.43606	408.4524
Indian	1994 - 2004	mod_truth	0.00000	483.0000
Indian	1994 - 2004	obs	199.94192	497.6190
Indian	1994 - 2014	mod	96.46457	407.5238
Indian	1994 - 2014	mod_truth	0.00000	443.0000
Indian	1994 - 2014	obs	90.93654	380.9524
Indian	2004 - 2014	mod	95.93485	379.8333
Indian	2004 - 2014	mod_truth	0.00000	443.0000
Indian	2004 - 2014	obs	106.38719	294.0476
N. Pacific	1994 - 2004	mod	76.97264	257.2143
N. Pacific	1994 - 2004	mod_truth	0.00000	268.0000
N. Pacific	1994 - 2004	obs	81.36467	242.8571
N. Pacific	1994 - 2014	mod	44.98778	258.6905
N. Pacific	1994 - 2014	mod_truth	0.00000	251.0000
N. Pacific	1994 - 2014	obs	54.74044	228.5714
N. Pacific	2004 - 2014	mod	28.32403	259.7857
N. Pacific	2004 - 2014	mod_truth	0.00000	251.0000
N. Pacific	2004 - 2014	obs	68.33000	207.1429
S. Pacific	1994 - 2004	mod	75.03243	373.7381
S. Pacific	1994 - 2004	mod_truth	0.00000	409.0000
S. Pacific	1994 - 2004	obs	137.33823	366.6667
S. Pacific	1994 - 2014	mod	67.86796	384.7381
S. Pacific	1994 - 2014	mod_truth	0.00000	409.0000
S. Pacific	1994 - 2014	obs	100.17406	357.1429
S. Pacific	2004 - 2014	mod	79.94942	401.2143
S. Pacific	2004 - 2014	mod_truth	0.00000	378.0000
S. Pacific	2004 - 2014	obs	126.87616	350.0000
N. Atlantic	1994 - 2004	mod	30.82994	294.8095
N. Atlantic	1994 - 2004	mod_truth	0.00000	327.0000
N. Atlantic	1994 - 2004	obs	43.10805	404.7619
N. Atlantic	1994 - 2014	mod	26.50076	314.5238
N. Atlantic	1994 - 2014	mod_truth	0.00000	351.0000
N. Atlantic	1994 - 2014	obs	75.43909	416.6667
N. Atlantic	2004 - 2014	mod	52.43391	333.8095
N. Atlantic	2004 - 2014	mod_truth	0.00000	351.0000
N. Atlantic	2004 - 2014	obs	89.00012	473.8095
S. Atlantic	1994 - 2004	mod	73.25987	311.8333
S. Atlantic	1994 - 2004	mod_truth	0.00000	351.0000
S. Atlantic	1994 - 2004	obs	43.10805	288.0952
S. Atlantic	1994 - 2014	mod	56.65512	326.1905
S. Atlantic	1994 - 2014	mod_truth	0.00000	378.0000
S. Atlantic	1994 - 2014	obs	100.75211	345.2381
S. Atlantic	2004 - 2014	mod	56.50395	338.8571
S. Atlantic	2004 - 2014	mod_truth	0.00000	378.0000
S. Atlantic	2004 - 2014	obs	198.71831	452.3810

rm(mean_surface_dcant)

13.1.5 Uncertainty range

dcant_profile_basin_MLR_range <- dcant_profile_basin_MLR_all_ensemble %>%
  group_by(basin, depth, period, data_source) %>% 
  summarise(dcant_sd = sd(dcant)*sd_factor) %>%
  ungroup()

dcant_profile_basin_MLR_all_ensemble <-
full_join(dcant_profile_basin_MLR_all_ensemble %>% select(-dcant_sd),
          dcant_profile_basin_MLR_range)

p_dcant_profiles <-
  dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs",
    Version_ID %in% Version_IDs
  ) %>%
  ggplot() +
  geom_vline(xintercept = 0, size = 0.1) +
  geom_hline(yintercept = 1000, size = 0.1) +
  geom_ribbon(
    aes(
      xmin = dcant - dcant_sd,
      xmax = dcant + dcant_sd,
      y = depth,
      fill = period,
      group = Version_ID
    ),
    alpha = 0.5
  ) +
  geom_path(
    aes(dcant,
        depth,
        col = period,
        group = Version_ID,)
  ) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(0, 100, 500, seq(1000, 5000, 1000))) +
  coord_cartesian(expand = 0) +
  scale_color_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2σ") +
  scale_fill_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2σ") +
  labs(y = "Depth (m)",
       x = dcant_umol_label) +
  facet_wrap(~ basin, ncol = 3, dir = "v") +
  theme(legend.position = c(0.9, 0.1))

p_dcant_profiles

Past versions of concentration_profiles_MLR_basins_ensemble_uncertainty_ribbon-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
f1d7f80	jens-daniel-mueller	2022-07-13

p_dcant_profiles_mod <-
  dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source != "obs",
    Version_ID %in% Version_IDs
  ) %>%
    mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  ggplot() +
  geom_vline(xintercept = 0, size = 0.1) +
  geom_hline(yintercept = 1000, size = 0.1) +
  geom_ribbon(
    aes(
      xmin = dcant - dcant_sd,
      xmax = dcant + dcant_sd,
      y = depth,
      fill = data_source,
      group = interaction(Version_ID,data_source)
    ),
    alpha = 0.5
  ) +
  geom_path(
    aes(dcant,
        depth,
        col = data_source,
        group = interaction(Version_ID,data_source))
  ) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(0, 100, 500, seq(1000, 5000, 1000))) +
  coord_cartesian(expand = 0) +
  scale_color_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2σ") +
  scale_fill_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2σ") +
  labs(y = "Depth (m)",
       x = dcant_umol_label) +
  facet_grid(period ~ basin) +
  theme(legend.position = "top")

p_dcant_profiles_mod

Past versions of concentration_profiles_MLR_basins_ensemble_uncertainty_ribbon_mod-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11

dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(dcant,
            depth,
            col = MLR_basins,
            group = Version_ID)
      ) +
      scale_size_manual(values = c(0.5, 2), name = "x") +
      scale_linetype_manual(values = c(2, 1), name = "x") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(palette = "Dark2", name = "regional\nclustering") +
      labs(y = "Depth (m)",
           x = dcant_umol_label) +
      facet_grid(basin ~ period)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_basin_separation-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source != "mod_truth"
  ) %>%
  group_split(data_source) %>%
  # head(1) %>%
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(
          dcant,
          depth,
          col = Version_ID_group,
          group = Version_ID
        )) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Dark2",
        name = "configuration"
      ) +
      labs(
        y = "Depth (m)",
        x = dcant_umol_label,
        title = paste("data_source", unique(.x$data_source))
      ) +
      facet_grid(basin ~ period)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_member-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30

[[2]]

Past versions of concentration_profiles_MLR_basins_ensemble_member-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30

bind_rows(
  dcant_profile_basin_MLR_all_ensemble %>%
    filter(
      period %in% two_decades,
      depth <= params_global$inventory_depth_standard,
      data_source == "obs",
      Version_ID_group == "Bulk adjustment"
    ),
  dcant_profile_basin_MLR_all_no_adj %>%
    filter(
      period %in% two_decades,
      depth <= params_global$inventory_depth_standard,
      data_source == "obs"
    )
) %>%
  group_by(depth,
           period,
           basin,
           Version_ID_group) %>% 
  summarise(dcant_sd = sd(dcant),
            dcant = mean(dcant)) %>% 
  ungroup() %>% 
  filter(basin %in% c("N. Pacific", "Indian")) %>%
  ggplot() +
  geom_hline(yintercept = params_global$inventory_depth_standard) +
  geom_vline(xintercept = 0) +
  geom_ribbon(
    aes(
      xmin = dcant - dcant_sd,
      xmax = dcant + dcant_sd,
      y = depth,
      fill = Version_ID_group
    ),
    alpha = 0.5
  )+
  geom_path(aes(dcant,
                depth,
                col = Version_ID_group,
                group = Version_ID_group)) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
  coord_cartesian(expand = 0) +
  scale_color_manual(values = colour("high contrast", names = FALSE)(3)[c(1,3)],
                     name = "Mean \u00B1 1σ") +
  scale_fill_manual(values = colour("high contrast", names = FALSE)(3)[c(1,3)],
                    name = "Mean \u00B1 1σ") +
  labs(y = "Depth (m)",
       x = dcant_umol_label) +
  facet_grid(basin ~ period) +
  theme(legend.position = c(0.85,0.15))

Past versions of concentration_profiles_data_adjustments-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
46c163a	jens-daniel-mueller	2022-10-10

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_profiles_data_adjustments.png",
       height = 6,
       width = 8)

13.2 dCant - delta

delta <- dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         Version_ID %in% Version_IDs) %>%
  select(data_source, depth, basin, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  select(-c(`2004 - 2014`, `1994 - 2004`))

delta_sd <- dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         Version_ID %in% Version_IDs) %>%
  select(data_source, depth, basin, period, dcant_sd) %>% 
  pivot_wider(names_from = period,
              values_from = dcant_sd) %>%
  mutate(delta_dcant_sd = sqrt(`2004 - 2014`^2 + `1994 - 2004`^2)) %>% 
  select(-c(`2004 - 2014`, `1994 - 2004`))

dcant_profile_basin_MLR_all_delta <- full_join(delta, delta_sd)
rm(delta, delta_sd)



dcant_profile_basin_MLR_all_delta %>%
  group_split(data_source) %>%
  # head(3) %>%
  map(
    ~ ggplot(data = .x,
             aes(delta_dcant,
                 depth)) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(
          xmin = delta_dcant - delta_dcant_sd,
          xmax = delta_dcant + delta_dcant_sd
        ),
        alpha = 0.3
      ) +
      geom_path() +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd") +
      scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd") +
      labs(
        title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = ddcant_umol_label
      ) +
      facet_wrap(~ basin, ncol = 3)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_offset-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
eccd82b	jens-daniel-mueller	2022-01-26
c6fe495	jens-daniel-mueller	2022-01-26

[[2]]

Past versions of concentration_profiles_MLR_basins_offset-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
eccd82b	jens-daniel-mueller	2022-01-26
c6fe495	jens-daniel-mueller	2022-01-26

[[3]]

Past versions of concentration_profiles_MLR_basins_offset-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11

14 Inventories

dcant_budget_ensemble_stat <-
  dcant_budget_basin_MLR_all_ensemble %>% 
  filter(period != "1994 - 2014",
         data_source != "mod_truth") %>%
  group_by(period, basin, data_source) %>%
  summarise(
    median = median(dcant),
    sd = sd(dcant),
    n = n()
  ) %>%
  ungroup() 


dcant_budget_ensemble_stat <-
  full_join(
    dcant_budget_ensemble_stat,
    dcant_budget_basin_MLR_all_ensemble %>%
      filter(
        Version_ID %in% Version_IDs,
        period != "1994 - 2014",
        data_source != "mod_truth"
      ) %>%
      select(period,
             basin,
             data_source,
             std_case = dcant)
  )

dcant_budget_ensemble_significance <- dcant_budget_ensemble_stat %>% 
  pivot_longer(c(median,std_case),
               names_to = "estimate",
               values_to = "dcant") %>%
  group_by(estimate, basin, data_source) %>%
  mutate(
    delta_dcant = dcant - lag(dcant),
    delta_dcant_uncert = sqrt(sd_factor * sd ^ 2 + lag(sd_factor * sd ^ 2)),
    error_prop_ratio = abs(delta_dcant_uncert / delta_dcant),
    ttest_p = tsum.test(
      mean.x = dcant,
      s.x = sd,
      n.x = n,
      mean.y = lag(dcant),
      s.y = lag(sd),
      n.y = lag(n)
    )$p.value
  ) %>%
  ungroup() %>% 
  drop_na()
  
dcant_budget_ensemble_stat_significance <-
  full_join(
    dcant_budget_ensemble_stat %>%
      select(-n) %>%
      pivot_wider(
        names_from = period,
        values_from = median:std_case,
        names_sep = " "
      ),
    dcant_budget_ensemble_significance %>%
      select(basin, estimate, delta_dcant, delta_dcant_uncert, ttest_p, data_source) %>%
      pivot_wider(names_from = estimate,
                  values_from = delta_dcant:ttest_p) %>%
      rename(delta_dcant_uncert = delta_dcant_uncert_std_case) %>%
      select(-delta_dcant_uncert_median)
  )


dcant_budget_ensemble_stat_significance %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	data_source	median 1994 - 2004	median 2004 - 2014	sd 1994 - 2004	sd 2004 - 2014	std_case 1994 - 2004	std_case 2004 - 2014	delta_dcant_median	delta_dcant_std_case	delta_dcant_uncert	ttest_p_median	ttest_p_std_case
Global	mod	19.2635	21.7170	1.3153522	1.3521508	18.679	23.188	2.4535	4.509	2.6677568	0.0000000	0.0000000
Global	obs	27.2165	25.5760	1.5385767	1.5309161	27.388	25.583	-1.6405	-1.805	3.0695024	0.0000048	0.0000007
Indian	mod	5.1055	5.8765	0.8588370	0.6363305	5.106	6.563	0.7710	1.457	1.5116332	0.0000126	0.0000000
Indian	obs	6.9340	5.7120	0.8501347	0.8143064	7.156	5.722	-1.2220	-1.434	1.6648267	0.0000000	0.0000000
N. Pacific	mod	3.2195	3.3385	0.3291825	0.3870243	3.133	3.367	0.1190	0.234	0.7185387	0.1329905	0.0037644
N. Pacific	obs	2.9005	3.7540	0.9488079	0.7788312	2.897	3.216	0.8535	0.319	1.7359806	0.0000226	0.0960966
S. Pacific	mod	6.1915	7.2435	0.7225522	0.6254259	5.531	7.423	1.0520	1.892	1.3514727	0.0000000	0.0000000
S. Pacific	obs	8.6760	7.5945	0.9977995	0.9258863	8.635	7.389	-1.0815	-1.246	1.9250295	0.0000018	0.0000001
N. Atlantic	mod	2.2235	2.1715	0.2141452	0.1904960	2.219	2.440	-0.0520	0.221	0.4053317	0.2431244	0.0000033
N. Atlantic	obs	4.7555	3.9160	0.1786911	0.3878363	4.805	3.908	-0.8395	-0.897	0.6038998	0.0000000	0.0000000
S. Atlantic	mod	2.6875	3.1390	0.3049506	0.3255103	2.690	3.396	0.4515	0.706	0.6307961	0.0000000	0.0000000
S. Atlantic	obs	3.9260	4.9040	0.5655845	0.6303071	3.895	5.350	0.9780	1.455	1.1976417	0.0000000	0.0000000

dcant_budget_ensemble_significance %>% 
  ggplot(aes(error_prop_ratio, ttest_p, col = basin)) +
  geom_vline(xintercept = 1) +
  geom_hline(yintercept = 0.05) +
  geom_point() +
  scale_y_log10() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(data_source ~ estimate)

Past versions of dcant_budget_stats_ensemble_SD-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26

MLR_basins_reference <- unique(params_local_all$MLR_basins)

MLR_basins_sensitivity <- unique(params_local_all_ensemble$MLR_basins)
MLR_basins_sensitivity <- MLR_basins_sensitivity[!MLR_basins_sensitivity %in% MLR_basins_reference]

unique(params_local_all_ensemble$Version_ID_group)

[1] "Bulk adjustment"     "Reoccupation filter" "No adjustment"      
[4] "No gap filling"      "C*(N,AT)"            "Cruise adjustment"  
[7] "Surface eMLR(C*)"    "C*(P)"              

dcant_budget_ensemble_stat_basin <-
  dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source != "mod_truth",
         period != "1994 - 2014") %>%
  select(-c(inv_depth, Version_ID, tref1, tref2)) %>% 
  pivot_wider(names_from = MLR_basins,
              values_from = dcant) %>% 
  pivot_longer(all_of(MLR_basins_sensitivity),
               names_to = "MLR_basins",
               values_to = "dcant") %>% 
  mutate(delta_dcant = dcant - !!sym(MLR_basins_reference))

dcant_budget_ensemble_stat_basin %>%
  filter(data_source == "obs",
         Version_ID_group == unique(params_local_all$Version_ID_group)) %>% 
  ggplot(aes(period, delta_dcant, fill = MLR_basins)) +
  geom_point(shape = 21, size = 2, alpha = 0.7) +
  scale_fill_muted(name = "Regional clustering") +
  labs(y = "Offset (PgC)",
       title = "Offset for regional clusterings schemes",
       subtitle = "Note: Only the bulk adjustment cases are shown") +
  theme(axis.title.x = element_blank()) +
  facet_wrap(. ~ basin)

Past versions of dcant_budget_stats_basin_SD_plus_sensitivity_cases-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26

dcant_budget_ensemble_stat_basin_SD <-
  dcant_budget_ensemble_stat_basin %>% 
  group_by(period, basin, Version_ID_group, data_source) %>%
  summarise(MAO = mean(abs(delta_dcant)),
            SD_AO = sd(abs(delta_dcant))) %>%
  ungroup()

dcant_budget_ensemble_stat_basin_SD %>%
  filter(data_source == "obs") %>% 
  ggplot(aes(x = period, y = MAO, fill = Version_ID_group)) +
  geom_col(position = "dodge", col = "black") +
  geom_errorbar(aes(ymin = MAO, ymax = MAO + SD_AO),
                position = "dodge") +
  scale_fill_bright(name = "Configuration") +
  labs(y = "Mean + SD of absolute offset (PgC)",
       title = "Absolute offsets for regional clusterings schemes",
       subtitle = "Note: Only offsets for the bulk adjustment cases will be used, but all configurations are shown") +
  theme(axis.title.x = element_blank()) +
  scale_y_continuous(expand = c(0, 0.1)) +
  facet_wrap(. ~ basin)

Past versions of dcant_budget_stats_basin_SD_plus_sensitivity_cases-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26

rm(MLR_basins_reference, MLR_basins_sensitivity)

# dcant_budget_ensemble_stat_basin_SD <-
#   dcant_budget_basin_MLR_all_ensemble %>%
#   filter(data_source != "mod_truth",
#          period != "1994 - 2014") %>%
#   group_by(period, basin, Version_ID_group, data_source) %>%
#   summarise(median = median(dcant),
#             sd = sd(dcant),
#             n = n()) %>%
#   ungroup() 
# 
# dcant_budget_ensemble_stat_basin_SD %>%
#   ggplot(aes(period, sd, fill = Version_ID_group)) +
#   geom_col(position = "dodge", col = "black") +
#   scale_fill_bright(name = "Configuration") +
#   labs(y = "SD (PgC)",
#        title = "Standard deviation across regional clusterings schemes",
#        subtitle = "Note: Only the SD of bulk adjustment cases will be used, but all configurations are shown") +
#   theme(axis.title.x = element_blank()) +
#   scale_y_continuous(expand = c(0, 0)) +
#   facet_grid(data_source ~ basin)
# 
ggsave(
  path = here::here("output/publication"),
  filename = "FigS_dcant_inventory_additive_uncertainty_regional_clustering_contributions.png",
  height = 6,
  width = 8
)

dcant_budget_ensemble_stat_basin_SD <-
  dcant_budget_ensemble_stat_basin_SD %>%
  filter(Version_ID_group == unique(params_local_all$Version_ID_group)) %>%
  select(-Version_ID_group)

dcant_budget_ensemble_stat_sensitvity_cases <-
  dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source != "mod_truth",
         period != "1994 - 2014") %>%
  select(basin, dcant, period, MLR_basins, Version_ID_group, data_source) %>%
  pivot_wider(names_from = Version_ID_group,
              values_from = dcant) %>%
  pivot_longer(
    `Reoccupation filter`:`Surface eMLR(C*)`,
    names_to = "Version_ID_group",
    values_to = "dcant"
  ) %>% 
  mutate(delta_dcant = `Bulk adjustment` - dcant)

dcant_budget_ensemble_stat_sensitvity_cases %>%
  filter(data_source == "mod") %>% 
  ggplot(aes(period, delta_dcant, fill = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_col(position = "dodge", col = "black") +
  scale_fill_bright(name = "Regional clustering") +
  labs(y = "Offset (PgC)",
       title = "Inventory offset compared to standard configuration | mod",
       subtitle = "Note: Only regional clustering scheme 3 will be used, but all options are shown") +
  theme(axis.title.x = element_blank()) +
  scale_y_continuous(expand = c(0, 0)) +
  facet_grid(Version_ID_group ~ basin)

Past versions of dcant_budget_stats_basin_SD_plus_sensitivity_cases-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26

dcant_budget_ensemble_stat_sensitvity_cases %>%
  filter(data_source == "obs") %>% 
  ggplot(aes(period, delta_dcant, fill = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_col(position = "dodge", col = "black") +
  scale_fill_bright(name = "Regional clustering") +
  labs(y = "Offset (PgC)",
       title = "Inventory offset compared to standard configuration | obs",
       subtitle = "Note: Only regional clustering scheme 3 will be used, but all options are shown") +
  theme(axis.title.x = element_blank()) +
  scale_y_continuous(expand = c(0, 0)) +
  facet_grid(Version_ID_group ~ basin)

Past versions of dcant_budget_stats_basin_SD_plus_sensitivity_cases-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_inventory_additive_uncertainty_configuration_contributions.png",
       height = 9,
       width = 12)


dcant_budget_ensemble_stat_sensitvity_cases <-
  dcant_budget_ensemble_stat_sensitvity_cases %>%
  filter(MLR_basins == unique(params_local_all$MLR_basins)) %>%
  select(period, basin, Version_ID_group, sd = delta_dcant, data_source)

dcant_budget_ensemble_stat_factorial <-
  bind_rows(
    dcant_budget_ensemble_stat_basin_SD %>%
      mutate(type = "regional clustering",
             Version_ID_group = "Bulk adjustment") %>%
      rename(sd = MAO) %>% 
      select(-SD_AO),
    dcant_budget_ensemble_stat_sensitvity_cases  %>% mutate(type = "configuration")
  )

dcant_budget_ensemble_stat_factorial %>%
  filter(data_source == "obs") %>%
  ggplot(aes(period, abs(sd), pattern = type, fill = Version_ID_group)) +
  # geom_col(
  #   position = position_dodge(preserve = "single")
  # ) +
  geom_col_pattern(
    position = position_dodge(preserve = "single"),
    color = "black",
    pattern_fill = "black",
    pattern_angle = 45,
    pattern_density = 0.1,
    pattern_spacing = 0.025,
    pattern_key_scale_factor = 0.6
  ) +
  scale_fill_bright(name = "Configuration") +
  scale_pattern_manual(
    values = c(`regional clustering` = "stripe", configuration = "none"),
    name = "Uncertainty\ncontribution"
  ) +
  scale_color_manual(
    values = c("white", "black"),
    name = "Uncertainty\ncontribution"
  ) +
  labs(
    y = expression("Absolute uncertainty contribution (PgC)"),
    title = "Uncertainty contributions from configuration and regional clustering",
    subtitle = "Note: The regional clustering contribution is based on bulk adjustment cases,\nand configuration changes are evaluated for the regional clustering scheme 3"
  ) +
  theme(axis.title.x = element_blank()) +
  guides(pattern = guide_legend(override.aes = list(fill = "white")),
         fill = guide_legend(override.aes = list(pattern = "none"))) +
  scale_y_continuous(
    expand = c(0, 0)
  ) +
  facet_wrap(. ~ basin)

Past versions of dcant_budget_stats_basin_SD_plus_sensitivity_cases-5.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_inventory_additive_uncertainty_combined_contributions.png",
       height = 6,
       width = 9) 
  
dcant_budget_ensemble_stat_factorial <-
  dcant_budget_ensemble_stat_factorial %>% 
  group_by(basin, period, data_source) %>% 
  summarise(sd = sqrt(sum(sd^2))) %>% 
  ungroup()

dcant_budget_ensemble_stat_factorial <-
  full_join(
    dcant_budget_ensemble_stat_factorial,
    dcant_budget_basin_MLR_all_ensemble %>%
        filter(Version_ID %in% Version_IDs,
               period != "1994 - 2014") %>%
        select(period,
               basin,
               data_source,
               std_case = dcant)
  )

# inventory scaling

dcant_budget_basin_MLR_all_ensemble <- dcant_budget_basin_MLR_all_ensemble %>% 
  mutate(dcant = if_else(basin == "Global",
                         dcant * dcant_scaling,
                         dcant))

dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>% 
  mutate(dcant = if_else(basin == "Global",
                         dcant * dcant_scaling,
                         dcant))

dcant_budget_basin_MLR_all_no_adj <- dcant_budget_basin_MLR_all_no_adj %>% 
  mutate(dcant = if_else(basin == "Global",
                         dcant * dcant_scaling,
                         dcant))
# uncertainty scaling

dcant_budget_ensemble_stat_factorial <-
dcant_budget_ensemble_stat_factorial %>%
  mutate(
    sd = if_else(basin == "Global",
      sqrt(sd ^ 2 + 
          (std_case * (dcant_scaling - 1) * dcant_scaling_uncertainty)^2),
          sd),
    std_case = if_else(basin == "Global",
                       std_case * dcant_scaling,
                       std_case)
  )

dcant_budget_ensemble_stat <-
dcant_budget_ensemble_stat %>%
  mutate(
    sd = if_else(basin == "Global",
      sqrt(sd ^ 2 + 
          (std_case * (dcant_scaling - 1) * dcant_scaling_uncertainty)^2),
          sd),
    std_case = if_else(basin == "Global",
                       std_case * dcant_scaling,
                       std_case)
  )

# estimate area scaling factor to achieve identical coverage

tcant_inv <-
  inner_join(tcant_inv, basinmask)

dcant_inv_all <-
  inner_join(dcant_inv_all, basinmask)

area_scaling <-
  bind_rows(
    tcant_inv %>% distinct(lat, lon, basin) %>%
      mutate(source = "SO4"),
    dcant_inv_all %>% distinct(lat, lon, basin) %>%
      mutate(source = "M22")
  )


map +
  geom_tile(data = area_scaling,
            aes(lon, lat, fill = basin)) +
  facet_wrap( ~ source)

Past versions of budgets_basins_hemisphere_tcant_for_scatterplot-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
5b77c4f	jens-daniel-mueller	2022-08-10
d2ae54c	jens-daniel-mueller	2022-07-18

area_scaling <- area_scaling %>% 
  mutate(area = earth_surf(lat, lon)) %>% 
  group_by(basin, source) %>% 
  summarise(area_total = sum(area)) %>% 
  ungroup() %>% 
  pivot_wider(values_from = area_total,
              names_from = source) %>% 
  mutate(scaling_factor = M22 / SO4)


tcant_budget_basin_MLR <-
  tcant_inv %>%
  mutate(method = "layer",
         data_source = "obs") %>% 
  group_by(basin) %>%
  nest() %>% 
  mutate(budget = map(.x = data, ~m_dcant_budget(.x))) %>% 
  select(-data) %>%
  unnest(budget)

tcant_budget_basin_MLR <-
  full_join(tcant_budget_basin_MLR,
            area_scaling %>% select(basin, scaling_factor)) %>%
  mutate(value = value * scaling_factor) %>%
  select(-scaling_factor)



tcant_budget_basin_MLR <-
  left_join(tcant_budget_basin_MLR %>%
              mutate(period = "1800 - 1994"),
            delta_pCO2_atm)


tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  filter(estimate == "dcant") %>% 
  select(-estimate) %>% 
  rename(dcant = value)

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(beta_1994 = dcant / delta_pCO2) %>% 
  select(basin, beta_1994)

tcant_budget_basin_MLR <-
  bind_rows(tcant_budget_basin_MLR,
            tibble(basin = "Global", beta_1994 = 118 / 78.3))

14.1 Scatterplots

14.1.1 Ensemble uncertainty

dcant_budget_basin_MLR_all_ensemble %>%
  ggplot(aes(period, dcant, col = Version_ID_group)) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ data_source, scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble_uncertainty-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

tcant_budget_basin_MLR <- expand_grid(
  tcant_budget_basin_MLR,
  delta_pCO2_atm %>% filter(period %in% two_decades)
)

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(dcant = beta_1994 * delta_pCO2)

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>%
  mutate(
    basin = fct_relevel(
      basin,
      "Global",
      "Indian",
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic"
    )
  )

dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs)) %>%
  ggplot(aes(period, dcant)) +
  geom_errorbar(
    data = tcant_budget_basin_MLR,
    aes(x = period,
        ymax = dcant, ymin = dcant,
        col = "Scaled 1994\nestimate"),
    width = 0.8,
    size = 1,
    linetype = 1,
    position = position_nudge(x = 0.05)
  ) +
  geom_jitter(
    data = dcant_budget_basin_MLR_all_no_adj %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(fill = "Unadjusted data",
        alpha = "Unadjusted data"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = dcant_budget_ensemble_stat %>% filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2*sd,
      ymax = std_case + 2*sd,
      linetype = "Ensemble SD"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = dcant_budget_ensemble_stat %>% filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Ensemble SD",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = dcant_budget_ensemble_stat %>% filter(data_source == "obs"),
    aes(x = period,
        y = std_case,
        fill = "Standard case",
        alpha = "Standard case"),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  ) +
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#DDAA33")) +
  scale_color_manual(values = c("grey50"), label = expression(Scaled~C[ant]~1994)) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.7,1,0.7)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of dcant_budget_basin_hemisphere_ensemble_uncertainty-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_inventory_ensemble_uncertainty.png",
       height = 6,
       width = 8)

dcant_budget_basin_MLR_all_ensemble %>%
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  scale_fill_brewer(palette = "Dark2", name = "ensemble group") +
  scale_color_manual(values = c("grey", "black", "red"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    data = dcant_budget_basin_MLR_all_no_adj %>%
      mutate(ensemble_role = "no adjustment") %>%
      filter(data_source == "obs",
         period != "1994 - 2014"),
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_label) +
  facet_wrap(~ basin, ncol = 3, scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble_uncertainty-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

# dcant_budget_basin_MLR_all_ensemble_summary <- dcant_budget_basin_MLR_all_ensemble %>% 
#   group_by(data_source, basin, period) %>% 
#   summarise(dcant_mean = mean(dcant),
#             dcant_sd = sd(dcant)*sd_factor) %>% 
#   ungroup()
# 
# dcant_budget_global_all_ensemble_summary <-
#   dcant_budget_basin_MLR_all_ensemble %>%
#   filter(period != "1994 - 2014") %>%
#   group_by(data_source, Version_ID, tref1, tref2, period) %>%
#   summarise(dcant = sum(dcant)) %>%
#   ungroup() %>%
#   group_by(data_source, tref1, tref2, period) %>%
#   summarise(dcant_mean = mean(dcant),
#             dcant_sd = sd(dcant)*sd_factor) %>%
#   ungroup() %>%
#   filter(data_source == "obs")

14.1.2 Additive uncertainty

dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs),
         MLR_basins == unique(params_local_all$MLR_basins)) %>%
  ggplot(aes(period, dcant)) +
  geom_errorbar(
    data = tcant_budget_basin_MLR,
    aes(x = period,
        ymax = dcant, ymin = dcant,
        col = "Scaled 1994\nestimate"),
    width = 0.8,
    size = 1,
    linetype = 1,
    position = position_nudge(x = 0.05)
  ) +
  geom_jitter(
    data = dcant_budget_basin_MLR_all_ensemble %>%
      filter(data_source == "obs",
             period != "1994 - 2014",
             Version_ID_group == "Bulk adjustment"),
    aes(fill = "Regional clustering",
        alpha = "Regional clustering"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_point(
    aes(fill = "Configuration changes",
        alpha = "Configuration changes"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = dcant_budget_ensemble_stat_factorial %>% filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2*sd,
      ymax = std_case + 2*sd,
      linetype = "Combined uncertainty"
    ),
    width = 0.1,
    fill = "#BBBBBB",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = dcant_budget_ensemble_stat_factorial %>% filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Combined uncertainty",
    ),
    width = 0.1,
    fill = "#BBBBBB",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = dcant_budget_ensemble_stat %>% filter(data_source == "obs"),
    aes(x = period,
        y = std_case,
        fill = "Standard case",
        alpha = "Standard case"),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  ) +
  scale_fill_manual(name = "group", values = c("#BB5566", "#004488", "white")) +
  scale_color_manual(values = c("grey50"), label = expression(Scaled~C[ant]~1994)) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.7,0.7,1)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of dcant_budget_basin_hemisphere_additive_uncertainty-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_inventory_additive_uncertainty.png",
       height = 6,
       width = 8)

14.1.3 Beta

# determine scaling factor from dcant -> beta inventories
# including scaling for: delta pCO2 atm, basin area, PgC -> molC

beta_scaling <-
  bind_rows(
    area_scaling,
    area_scaling %>%
      summarise(M22 = sum(M22) * dcant_scaling) %>%
      mutate(basin = "Global")
  ) %>% 
  select(basin, area = M22)


beta_scaling <- 
  full_join(delta_pCO2_atm,
            beta_scaling,
            by = character())

beta_scaling <- beta_scaling %>% 
  mutate(scaling_factor = 1e15 / 12 / delta_pCO2 / area) %>% 
  select(period, basin, scaling_factor, area)

beta_budget_basin_MLR_all_no_adj <-
  left_join(dcant_budget_basin_MLR_all_no_adj,
            beta_scaling) %>%
  mutate(beta = dcant * scaling_factor) %>% 
  select(-scaling_factor)

beta_budget_basin_MLR_all_ensemble <-
  left_join(dcant_budget_basin_MLR_all_ensemble,
            beta_scaling) %>%
  mutate(beta = dcant * scaling_factor) %>% 
  select(-scaling_factor)


tcant_budget_basin_MLR <-
  left_join(tcant_budget_basin_MLR,
            beta_scaling) %>%
  mutate(beta_1994 = beta_1994 * 1e15 / 12 / area) %>% 
  select(-c(area,scaling_factor))

beta_budget_ensemble_stat <- 
  left_join(dcant_budget_ensemble_stat,
            beta_scaling) %>% 
  mutate(
    median = median * scaling_factor,
    sd = sd * scaling_factor,
    std_case = std_case * scaling_factor
    ) %>% 
  select(-scaling_factor)

beta_budget_ensemble_stat_factorial <- 
  left_join(dcant_budget_ensemble_stat_factorial,
            beta_scaling) %>% 
  mutate(
    sd = sd * scaling_factor,
    std_case = std_case * scaling_factor
    ) %>% 
  select(-scaling_factor)

# beta_budget_ensemble_stat <-
#   beta_budget_basin_MLR_all_ensemble %>% 
#   filter(period != "1994 - 2014",
#          data_source != "mod_truth") %>%
#   select(basin, period, dcant, beta, data_source) %>% 
#   pivot_longer(c(dcant, beta),
#                values_to = "value",
#                names_to = "estimate") %>% 
#   group_by(period, basin, estimate, data_source) %>%
#   summarise(
#     median = median(value),
#     sd = sd(value),
#     n = n()
#   ) %>%
#   ungroup() 
# 
# 
# beta_budget_ensemble_stat <-
#   full_join(
#     beta_budget_ensemble_stat,
#     beta_budget_basin_MLR_all_ensemble %>%
#       filter(
#         Version_ID %in% Version_IDs,
#         period != "1994 - 2014"
#       ) %>%
#       select(period,
#              basin,
#              data_source,
#              dcant,
#              beta) %>% 
#         pivot_longer(c(dcant, beta),
#                values_to = "std_case",
#                names_to = "estimate")
#   )


dcant_beta_budget_ensemble_stat <-
  bind_rows(
    dcant_budget_ensemble_stat %>% mutate(estimate = "dcant",
                                          uncertainty = "ensemble"),
    beta_budget_ensemble_stat %>% mutate(estimate = "beta",
                                         uncertainty = "ensemble"),
    dcant_budget_ensemble_stat_factorial %>% mutate(estimate = "dcant",
                                                    uncertainty = "additive"),
    beta_budget_ensemble_stat_factorial %>% mutate(estimate = "beta",
                                                   uncertainty = "additive")
  ) %>%
  filter(data_source != "mod_truth") %>%
  select(-c(median))
  

dcant_beta_budget_ensemble_significance <- 
  dcant_beta_budget_ensemble_stat %>% 
  arrange(period) %>% 
  group_by(estimate, uncertainty, basin, data_source) %>%
  mutate(
    delta = std_case - lag(std_case),
    RSS = sqrt(sd_factor * sd ^ 2 + lag(sd_factor * sd ^ 2)),
    RSS_delta_ratio = abs(delta) / RSS,
    ttest_p = tsum.test(
      mean.x = std_case,
      s.x = sd,
      n.x = n,
      mean.y = lag(std_case),
      s.y = lag(sd),
      n.y = lag(n)
    )$p.value
  ) %>%
  ungroup() %>% 
  filter(!is.na(delta))
  

dcant_beta_budget_ensemble_stat_significance <-
  full_join(
    dcant_beta_budget_ensemble_stat %>%
      select(-c(n)) %>%
      pivot_wider(
        names_from = period,
        values_from = c(sd, std_case),
        names_sep = " "
      ),
    dcant_beta_budget_ensemble_significance %>% 
      select(-c(sd, n, std_case))
  )

dcant_beta_budget_ensemble_stat_significance <-
  dcant_beta_budget_ensemble_stat_significance %>% 
  mutate(across(where(is.numeric), signif, 2))

dcant_beta_budget_ensemble_stat_significance %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	data_source	estimate	uncertainty	area	sd 1994 - 2004	sd 2004 - 2014	std_case 1994 - 2004	std_case 2004 - 2014	period	delta	RSS	RSS_delta_ratio	ttest_p
Global	mod	dcant	ensemble	NA	1.500	1.600	20.00	25.00	2004 - 2014	4.8000	3.000	1.600	0.0e+00
Global	obs	dcant	ensemble	NA	1.800	1.800	29.00	27.00	2004 - 2014	-1.9000	3.500	0.540	3.4e-06
Indian	mod	dcant	ensemble	NA	0.860	0.640	5.10	6.60	2004 - 2014	1.5000	1.500	0.960	0.0e+00
Indian	obs	dcant	ensemble	NA	0.850	0.810	7.20	5.70	2004 - 2014	-1.4000	1.700	0.860	0.0e+00
N. Pacific	mod	dcant	ensemble	NA	0.330	0.390	3.10	3.40	2004 - 2014	0.2300	0.720	0.330	3.8e-03
N. Pacific	obs	dcant	ensemble	NA	0.950	0.780	2.90	3.20	2004 - 2014	0.3200	1.700	0.180	9.6e-02
S. Pacific	mod	dcant	ensemble	NA	0.720	0.630	5.50	7.40	2004 - 2014	1.9000	1.400	1.400	0.0e+00
S. Pacific	obs	dcant	ensemble	NA	1.000	0.930	8.60	7.40	2004 - 2014	-1.2000	1.900	0.650	1.0e-07
N. Atlantic	mod	dcant	ensemble	NA	0.210	0.190	2.20	2.40	2004 - 2014	0.2200	0.410	0.550	3.3e-06
N. Atlantic	obs	dcant	ensemble	NA	0.180	0.390	4.80	3.90	2004 - 2014	-0.9000	0.600	1.500	0.0e+00
S. Atlantic	mod	dcant	ensemble	NA	0.300	0.330	2.70	3.40	2004 - 2014	0.7100	0.630	1.100	0.0e+00
S. Atlantic	obs	dcant	ensemble	NA	0.570	0.630	3.90	5.40	2004 - 2014	1.5000	1.200	1.200	0.0e+00
Global	mod	beta	ensemble	3.6e+14	0.018	0.018	0.25	0.28	2004 - 2014	0.0340	0.036	0.930	0.0e+00
Global	obs	beta	ensemble	3.6e+14	0.023	0.020	0.37	0.31	2004 - 2014	-0.0540	0.043	1.300	0.0e+00
Indian	mod	beta	ensemble	7.3e+13	0.053	0.036	0.31	0.37	2004 - 2014	0.0540	0.090	0.610	5.0e-07
Indian	obs	beta	ensemble	7.3e+13	0.052	0.046	0.44	0.32	2004 - 2014	-0.1200	0.098	1.200	0.0e+00
N. Pacific	mod	beta	ensemble	7.8e+13	0.019	0.020	0.18	0.18	2004 - 2014	-0.0033	0.039	0.085	4.4e-01
N. Pacific	obs	beta	ensemble	7.8e+13	0.054	0.041	0.17	0.17	2004 - 2014	0.0023	0.096	0.024	8.3e-01
S. Pacific	mod	beta	ensemble	9.6e+13	0.034	0.027	0.26	0.32	2004 - 2014	0.0580	0.061	0.960	0.0e+00
S. Pacific	obs	beta	ensemble	9.6e+13	0.046	0.039	0.40	0.31	2004 - 2014	-0.0880	0.086	1.000	0.0e+00
N. Atlantic	mod	beta	ensemble	4.1e+13	0.024	0.019	0.24	0.25	2004 - 2014	0.0010	0.043	0.024	8.3e-01
N. Atlantic	obs	beta	ensemble	4.1e+13	0.020	0.039	0.53	0.39	2004 - 2014	-0.1400	0.062	2.200	0.0e+00
S. Atlantic	mod	beta	ensemble	4.5e+13	0.030	0.030	0.27	0.31	2004 - 2014	0.0410	0.060	0.680	0.0e+00
S. Atlantic	obs	beta	ensemble	4.5e+13	0.057	0.058	0.39	0.49	2004 - 2014	0.0990	0.110	0.870	0.0e+00
Global	mod	dcant	additive	NA	2.300	2.800	20.00	25.00	2004 - 2014	4.8000	5.100	0.940	NA
Global	obs	dcant	additive	NA	2.300	3.200	29.00	27.00	2004 - 2014	-1.9000	5.500	0.350	NA
Indian	mod	dcant	additive	NA	0.830	1.100	5.10	6.60	2004 - 2014	1.5000	2.000	0.750	NA
Indian	obs	dcant	additive	NA	0.980	1.500	7.20	5.70	2004 - 2014	-1.4000	2.600	0.550	NA
N. Pacific	mod	dcant	additive	NA	0.330	0.850	3.10	3.40	2004 - 2014	0.2300	1.300	0.180	NA
N. Pacific	obs	dcant	additive	NA	1.100	2.100	2.90	3.20	2004 - 2014	0.3200	3.300	0.097	NA
S. Pacific	mod	dcant	additive	NA	1.300	0.930	5.50	7.40	2004 - 2014	1.9000	2.300	0.820	NA
S. Pacific	obs	dcant	additive	NA	1.300	0.960	8.60	7.40	2004 - 2014	-1.2000	2.300	0.540	NA
N. Atlantic	mod	dcant	additive	NA	0.180	0.380	2.20	2.40	2004 - 2014	0.2200	0.600	0.370	NA
N. Atlantic	obs	dcant	additive	NA	0.170	0.330	4.80	3.90	2004 - 2014	-0.9000	0.530	1.700	NA
S. Atlantic	mod	dcant	additive	NA	0.620	0.490	2.70	3.40	2004 - 2014	0.7100	1.100	0.630	NA
S. Atlantic	obs	dcant	additive	NA	0.770	0.790	3.90	5.40	2004 - 2014	1.5000	1.600	0.930	NA
Global	mod	beta	additive	3.6e+14	0.029	0.032	0.25	0.28	2004 - 2014	0.0340	0.061	0.550	NA
Global	obs	beta	additive	3.6e+14	0.029	0.036	0.37	0.31	2004 - 2014	-0.0540	0.066	0.830	NA
Indian	mod	beta	additive	7.3e+13	0.051	0.062	0.31	0.37	2004 - 2014	0.0540	0.110	0.480	NA
Indian	obs	beta	additive	7.3e+13	0.060	0.086	0.44	0.32	2004 - 2014	-0.1200	0.150	0.800	NA
N. Pacific	mod	beta	additive	7.8e+13	0.019	0.044	0.18	0.18	2004 - 2014	-0.0033	0.068	0.049	NA
N. Pacific	obs	beta	additive	7.8e+13	0.062	0.110	0.17	0.17	2004 - 2014	0.0023	0.180	0.013	NA
S. Pacific	mod	beta	additive	9.6e+13	0.063	0.040	0.26	0.32	2004 - 2014	0.0580	0.100	0.550	NA
S. Pacific	obs	beta	additive	9.6e+13	0.061	0.041	0.40	0.31	2004 - 2014	-0.0880	0.100	0.850	NA
N. Atlantic	mod	beta	additive	4.1e+13	0.020	0.039	0.24	0.25	2004 - 2014	0.0010	0.061	0.017	NA
N. Atlantic	obs	beta	additive	4.1e+13	0.018	0.033	0.53	0.39	2004 - 2014	-0.1400	0.054	2.500	NA
S. Atlantic	mod	beta	additive	4.5e+13	0.062	0.045	0.27	0.31	2004 - 2014	0.0410	0.110	0.380	NA
S. Atlantic	obs	beta	additive	4.5e+13	0.077	0.072	0.39	0.49	2004 - 2014	0.0990	0.150	0.660	NA

dcant_beta_budget_ensemble_stat_significance %>% 
  ggplot(aes(abs(delta) / RSS, ttest_p, 
             col = basin, shape = uncertainty)) +
  geom_vline(xintercept = 1) +
  geom_hline(yintercept = 0.05) +
  geom_point() +
  scale_y_log10() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(data_source ~ estimate)

Past versions of beta_budget_stats-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16

dcant_beta_budget_ensemble_stat_significance_table <-
dcant_beta_budget_ensemble_stat_significance %>%
  filter(data_source == "obs") %>% 
  select(-data_source) %>% 
  mutate(
    `1994 - 2004` = paste(`std_case 1994 - 2004`, `sd 1994 - 2004`, sep = " ± "),
    `2004 - 2014` = paste(`std_case 2004 - 2014`, `sd 2004 - 2014`, sep = " ± "),
    `Deacadal change` = paste0(delta, " ± ", RSS, if_else(abs(delta) > RSS, " *",""))
  ) %>%
  select(Region = basin,
         Estimate = estimate,
         Uncertainty = uncertainty,
         `1994 - 2004`,
         `2004 - 2014`,
         `Deacadal change`) %>%
  arrange(Region, desc(Estimate), Uncertainty) 

dcant_beta_budget_ensemble_stat_significance_table %>% 
  write_csv(here::here("output/publication/Table_budget_stats.csv"))

beta_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs)) %>%
  ggplot(aes(period, beta)) +
  geom_errorbar(
    data = tcant_budget_basin_MLR %>% 
      rename(beta = beta_1994),
    aes(x = period,
        ymax = beta, ymin = beta,
        col = "Scaled 1994\nestimate"),
    width = 0.8,
    size = 1,
    linetype = 1,
    position = position_nudge(x = 0.05)
  ) +
  geom_jitter(
    data = beta_budget_basin_MLR_all_no_adj %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(fill = "Unadjusted data",
        alpha = "Unadjusted data"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = beta_budget_ensemble_stat %>% 
      filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2*sd,
      ymax = std_case + 2*sd,
      linetype = "Ensemble SD"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = beta_budget_ensemble_stat %>% 
      filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Ensemble SD",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = beta_budget_ensemble_stat %>% 
      filter(data_source == "obs"),
    aes(x = period,
        y = std_case,
        fill = "Standard case",
        alpha = "Standard case"),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  ) +
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_color_manual(values = c("grey50"), label = expression(Scaled~C[ant]~1994)) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_pgc_scaled_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of beta_budget_basin_hemisphere_beta_uncertainty-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "FigS_beta_budgets_ensemble_member.png",
       height = 6,
       width = 8)

beta_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs),
         MLR_basins == unique(params_local_all$MLR_basins)) %>%
  ggplot(aes(period, beta)) +
  geom_errorbar(
    data = tcant_budget_basin_MLR %>% 
      rename(beta = beta_1994),
    aes(x = period,
        ymax = beta, ymin = beta,
        col = "Scaled 1994\nestimate"),
    width = 0.8,
    size = 1,
    linetype = 1,
    position = position_nudge(x = 0.05)
  ) +
  geom_jitter(
    data = beta_budget_basin_MLR_all_ensemble %>%
      filter(data_source == "obs",
             period != "1994 - 2014",
             Version_ID_group == "Bulk adjustment"),
    aes(fill = "Regional clustering",
        alpha = "Regional clustering"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_point(
    aes(fill = "Configuration changes",
        alpha = "Configuration changes"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = beta_budget_ensemble_stat_factorial %>% filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2*sd,
      ymax = std_case + 2*sd,
      linetype = "Combined uncertainty"
    ),
    width = 0.1,
    fill = "#BBBBBB",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = beta_budget_ensemble_stat_factorial %>% filter(data_source == "obs"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Combined uncertainty",
    ),
    width = 0.1,
    fill = "#BBBBBB",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = beta_budget_ensemble_stat %>% filter(data_source == "obs"),
    aes(x = period,
        y = std_case,
        fill = "Standard case",
        alpha = "Standard case"),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  ) +
  scale_fill_manual(name = "group", values = c("#BB5566", "#004488", "white")) +
  scale_color_manual(values = c("grey50"), label = expression(Scaled~C[ant]~1994)) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.7,0.7,1)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of beta_dcant_budget_basin_hemisphere_additive_uncertainty-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05

ggsave(path = here::here("output/publication"),
       filename = "Fig_beta_inventory_additive_uncertainty.png",
       height = 6,
       width = 8)

14.1.4 Biases

dcant_budget_basin_MLR_all_ensemble_bias <- 
dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source %in% c("mod", "mod_truth")) %>% 
  pivot_wider(names_from = data_source,
              values_from = dcant) %>% 
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = 100 * dcant_bias / mod_truth)

dcant_budget_basin_MLR_all_ensemble_bias <- 
dcant_budget_basin_MLR_all_ensemble_bias %>%
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others"))

dcant_budget_basin_MLR_all_ensemble_bias %>%
  ggplot(aes(period, dcant_bias)) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 2),
    position = position_nudge(x = -0.3),
    col = "grey"
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 1),
    position = position_nudge(x = -0.3),
    size = 0.1
  ) +
  scale_fill_brewer(palette = "Dark2") +
    scale_color_manual(values = c("transparent", "black"),
                     name = "Ensemble member") +
  facet_grid(basin ~ .)

Past versions of budget_biases-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

dcant_budget_bias_ensemble_stat <-
  dcant_budget_basin_MLR_all_ensemble_bias %>%
  filter(period != "1994 - 2014") %>%
  group_by(period, basin) %>%
  summarise(
    median = median(dcant_bias),
    sd = sd(dcant_bias),
    n = n()
  ) %>%
  ungroup() 


dcant_budget_bias_ensemble_stat <-
  full_join(
    dcant_budget_bias_ensemble_stat,
    dcant_budget_basin_MLR_all_ensemble_bias %>%
        filter(Version_ID %in% Version_IDs,
               period != "1994 - 2014") %>%
        select(period,
               basin,
               std_case = dcant_bias)
  )




dcant_budget_basin_MLR_all_ensemble_bias %>%
  filter(period != "1994 - 2014") %>%
  ggplot(aes(period, dcant_bias)) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2 * sd,
      ymax = std_case + 2 * sd,
      linetype = "Ensemble SD"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Ensemble SD",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      fill = "Standard case",
      alpha = "Standard case"
    ),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  )+
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_bias_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of budget_biases-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_bias_budgets_ensemble_member.png",
       height = 6,
       width = 8)



dcant_budget_basin_MLR_all_ensemble_bias %>% 
  ggplot(aes(period, dcant_bias_rel, col = Version_ID_group)) +
  geom_hline(yintercept = 0) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ MLR_basins)

Past versions of budget_biases-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

delta_dcant_budget_basin_MLR_all_ensemble <-
dcant_budget_basin_MLR_all_ensemble_bias %>%
  filter(period %in% two_decades) %>%
  select(basin, period, MLR_basins, Version_ID_group, mod, mod_truth, Version_ID) %>%
  pivot_longer(cols = mod:mod_truth,
               names_to = "data_source",
               values_to = "dcant") %>%
  group_by(basin, MLR_basins, Version_ID_group, data_source) %>%
  arrange(period) %>%
  mutate(delta_dcant = dcant - lag(dcant)) %>%
  ungroup() %>%
  drop_na() %>%
  select(-dcant) 

delta_dcant_budget_basin_MLR_all_ensemble_bias <- 
  delta_dcant_budget_basin_MLR_all_ensemble %>%
  pivot_wider(names_from = data_source,
              values_from = delta_dcant) %>%
  mutate(delta_dcant_bias = mod - mod_truth,
         delta_dcant_bias_rel = 100 * delta_dcant_bias / mod_truth)

delta_dcant_budget_basin_MLR_all_ensemble %>% 
  ggplot(aes(data_source, delta_dcant, col = Version_ID_group)) +
  geom_hline(yintercept = 0) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ MLR_basins)

Past versions of budget_biases-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

delta_dcant_budget_basin_MLR_all_ensemble_bias %>% 
  ggplot(aes(period, delta_dcant_bias, col = Version_ID_group)) +
  geom_hline(yintercept = 0) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ MLR_basins)

Past versions of budget_biases-5.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09

delta_dcant_budget_bias_ensemble_stat <-
  delta_dcant_budget_basin_MLR_all_ensemble_bias %>%
  group_by(basin) %>%
  summarise(
    median = median(delta_dcant_bias),
    sd = sd(delta_dcant_bias),
    n = n()
  ) %>%
  ungroup() 


delta_dcant_budget_bias_ensemble_stat <-
  full_join(
    delta_dcant_budget_bias_ensemble_stat,
    delta_dcant_budget_basin_MLR_all_ensemble_bias %>%
        filter(Version_ID %in% Version_IDs,
               period != "1994 - 2014") %>%
        select(period,
               basin,
               std_case = delta_dcant_bias)
  )



delta_dcant_budget_bias_ensemble_stat_RSS <-
  dcant_budget_bias_ensemble_stat %>% 
  arrange(period) %>% 
  group_by(basin) %>% 
  mutate(delta_std_case = std_case - lag(std_case),
         RSS = sqrt(sd^2 + lag(sd)^2)) %>% 
  ungroup() %>% 
  drop_na()
  



delta_dcant_budget_basin_MLR_all_ensemble_bias %>%
  ggplot(aes(period, delta_dcant_bias)) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  # geom_crossbar(
  #   data = delta_dcant_budget_bias_ensemble_stat,
  #   aes(
  #     x = period,
  #     y = std_case,
  #     ymin = std_case - 2 * sd,
  #     ymax = std_case + 2 * sd,
  #     linetype = "Ensemble SD"
  #   ),
  #   width = 0.1,
  #   fill = "#AA3377",
  #   alpha = 0.2,
  #   position = position_nudge(x = 0.2)
  # ) +
  # geom_crossbar(
  #   data = delta_dcant_budget_bias_ensemble_stat,
  #   aes(
  #     x = period,
  #     y = std_case,
  #     ymin = std_case - sd,
  #     ymax = std_case + sd,
  #     alpha = "Ensemble SD",
  #   ),
  #   width = 0.1,
  #   fill = "#AA3377",
  #   alpha = 0.4,
  #   position = position_nudge(x = 0.2)
  # ) +
  geom_crossbar(
    data = delta_dcant_budget_bias_ensemble_stat_RSS,
    aes(
      x = period,
      y = delta_std_case,
      ymin = delta_std_case - 2 * RSS,
      ymax = delta_std_case + 2 * RSS,
      linetype = "RSS"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = delta_dcant_budget_bias_ensemble_stat_RSS,
    aes(
      x = period,
      y = delta_std_case,
      ymin = delta_std_case - RSS,
      ymax = delta_std_case + RSS,
      alpha = "RSS",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = delta_dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      fill = "Standard case",
      alpha = "Standard case"
    ),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  )+
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = delta_dcant_bias_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(), 
        panel.grid.major.x = element_blank())

Past versions of budget_biases-6.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20
46c163a	jens-daniel-mueller	2022-10-10
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09

ggsave(path = here::here("output/publication"),
       filename = "FigS_delta_dcant_bias_budgets_ensemble_member.png",
       height = 6,
       width = 6)

14.2 Flow chart

dcant_budget_basin_MLR_all <- 
  dcant_budget_basin_MLR_all %>% 
  filter(basin != "Global")

dcant_budget_basin_MLR_all <- left_join(dcant_budget_basin_MLR_all,
                           co2_atm %>% rename(tref1 = year,
                                              pCO21 = pCO2))

dcant_budget_basin_MLR_all <- left_join(dcant_budget_basin_MLR_all,
                           co2_atm %>% rename(tref2 = year,
                                              pCO22 = pCO2))


dcant_budget_basin_MLR_all_plot <- dcant_budget_basin_MLR_all %>%
  filter(period != "1994 - 2014",
         data_source == "obs")

tcant_budget_basin_MLR <-
  tcant_inv %>%
  mutate(method = "layer",
         data_source = "obs") %>% 
  group_by(basin) %>%
  nest() %>% 
  mutate(budget = map(.x = data, ~m_dcant_budget(.x))) %>% 
  select(-data) %>%
  unnest(budget)

tcant_budget_basin_MLR <-
  full_join(tcant_budget_basin_MLR,
            area_scaling %>% select(basin, scaling_factor)) %>%
  mutate(value = value * scaling_factor) %>%
  select(-scaling_factor)


tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(pCO21 = 281,
         tref1 = 1800,
         tref2 = 1994)

tcant_budget_basin_MLR <- left_join(tcant_budget_basin_MLR,
                                    co2_atm %>% rename(tref2 = year,
                                                       pCO22 = pCO2))

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(period = paste(tref1, tref2, sep = " - ")) %>% 
  filter(estimate == "dcant") %>% 
  select(-estimate) %>% 
  rename(dcant = value)

dcant_budget_basin_MLR_all_plot <- bind_rows(
  dcant_budget_basin_MLR_all_plot,
  tcant_budget_basin_MLR)

dcant_budget_basin_MLR_all_plot <-
  left_join(dcant_budget_basin_MLR_all_plot,
            dcant_budget_basin_MLR_all_ensemble_summary)

14.2.1 Absolute

g1 <- dcant_budget_basin_MLR_all_plot %>%
  filter(period != "1800 - 1994") %>% 
  ggplot(aes(
    y = dcant,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin
  )) +
  stat_alluvium(decreasing = FALSE) +
  stat_stratum(decreasing = FALSE) +
  stat_stratum(geom = "text",
               decreasing = FALSE,
               aes(label = paste(
                 round(after_stat(max-min),1)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin, label = basin),
    decreasing = FALSE
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 32), expand = c(0, 0)) +
  labs(y = dcant_pgc_label) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank()) +
  theme_classic()


newdat <- tibble(layer_data(g1))

change <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  pivot_wider(names_from = x,
              values_from = count) %>% 
  mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>% 
  select(alluvium, dcant_change)

coord <- newdat %>%
  filter(x == 2) %>% 
  select(x, y, alluvium)

new_layer <- full_join(
  change,
  coord
)

new_layer <- new_layer %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

g1 +
  geom_text(data = new_layer,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE)

g2 <- dcant_budget_basin_MLR_all_plot %>%
    filter(period != "1800 - 1994") %>%
  ggplot(aes(
    y = dcant,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  stat_stratum(geom = "text",
               aes(label = paste(
                 round(after_stat(count),1)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 33), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  pivot_wider(names_from = x,
              values_from = count) %>% 
  mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>% 
  select(alluvium, dcant_change)

coord_basin <- newdat %>%
  filter(x == 2) %>% 
  select(x, y, alluvium)

new_layer_basin <- full_join(
  change_basin,
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_change = as.character(round(dcant_change,1)),
         dcant_change = paste("global:",dcant_change))

g2 +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE) +
  geom_label(data = new_layer_total,
            aes(
              x = x,
              y = y + 1,
              label = dcant_change
            ),
            inherit.aes = FALSE)

14.2.2 Scaled

dcant_budget_basin_MLR_all_plot <- left_join(dcant_budget_basin_MLR_all_plot,
                           co2_atm %>% rename(tref1 = year,
                                              pCO21 = pCO2))

dcant_budget_basin_MLR_all_plot <- left_join(dcant_budget_basin_MLR_all_plot,
                           co2_atm %>% rename(tref2 = year,
                                              pCO22 = pCO2))

dcant_budget_basin_MLR_all_plot <- dcant_budget_basin_MLR_all_plot %>% 
  mutate(delta_pCO2 = pCO22 - pCO21,
         beta = dcant / delta_pCO2,
         dcant_sd_scaled = dcant_sd / delta_pCO2) %>% 
  select(-starts_with("pCO2"))


dcant_budget_global_all_ensemble_summary <- left_join(dcant_budget_global_all_ensemble_summary,
                           co2_atm %>% rename(tref1 = year,
                                              pCO21 = pCO2))

dcant_budget_global_all_ensemble_summary <- left_join(dcant_budget_global_all_ensemble_summary,
                           co2_atm %>% rename(tref2 = year,
                                              pCO22 = pCO2))

dcant_budget_global_all_ensemble_summary <- dcant_budget_global_all_ensemble_summary %>% 
  mutate(delta_pCO2 = pCO22 - pCO21,
         beta = dcant_mean / delta_pCO2,
         dcant_sd_scaled = dcant_sd / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

g2 <- dcant_budget_basin_MLR_all_plot %>%
  filter(period != "1800 - 1994") %>% 
  ggplot(aes(
    y = beta,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  stat_stratum(geom = "text",
               aes(label = paste(
                 round(after_stat(count),2)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  # scale_y_continuous(limits = c(0, 33), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_scaled_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  pivot_wider(names_from = x,
              values_from = count) %>% 
  mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>% 
  select(alluvium, dcant_change)

coord_basin <- newdat %>%
  filter(x == 2) %>% 
  select(x, y, alluvium)

new_layer_basin <- full_join(
  change_basin,
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_change = as.character(round(dcant_change,2)),
         dcant_change = paste("global:",dcant_change))

g2 +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE) +
  geom_label(data = new_layer_total,
            aes(
              x = x,
              y = y + 0.05,
              label = dcant_change
            ),
            inherit.aes = FALSE)

g2 <- dcant_budget_basin_MLR_all_plot %>%
  ggplot(aes(
    y = beta,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  stat_stratum(geom = "text",
               aes(label = paste(
                 round(after_stat(count),2)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 1.7), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_scaled_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(alluvium) %>% 
  arrange(x) %>% 
  mutate(dcant_change = round(100*(count - lag(count)) / lag(count))) %>% 
  ungroup()

coord_basin <- newdat %>%
  select(x, y, alluvium)

new_layer_basin <- left_join(
  change_basin %>% drop_na(),
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_change = as.character(round(dcant_change,2)),
         dcant_change = paste("global:",dcant_change))

g2 +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE) +
  geom_label(data = new_layer_total,
            aes(
              x = x,
              y = y + 0.1,
              label = dcant_change
            ),
            inherit.aes = FALSE)

g2 <- dcant_budget_basin_MLR_all_plot %>%
  ggplot(aes(
    y = beta,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 1.6), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_scaled_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

# construct budget labels

budget_basin <- 
  newdat %>% 
  select(x, y, alluvium, count)

budget_layer_basin <- budget_basin %>% 
  mutate(dcant = as.character(round(count,2)))

# construct uncertainty labels

uncertainty_basin <- 
  dcant_budget_basin_MLR_all_plot %>% 
  mutate(x = fct_recode(period,
                        "2" = "1994 - 2004",
                        "3" = "2004 - 2014")) %>% 
  select(x, alluvium = basin, dcant_sd_scaled) %>% 
  drop_na() %>% 
  arrange(x, alluvium)
  

coord_basin <- newdat %>%
  select(x, y, alluvium) %>% 
  filter(x != 1)

uncertainty_layer_basin <- bind_cols(
  coord_basin,
  uncertainty_basin %>% select(dcant_sd_scaled),
)

# construct change labels

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(alluvium) %>% 
  arrange(x) %>% 
  mutate(dcant_change_abs = count - lag(count),
         dcant_change = round(100*(dcant_change_abs) / lag(count))) %>% 
  ungroup()

coord_basin <- newdat %>%
  select(x, y, alluvium)

new_layer_basin <- left_join(
  change_basin %>% drop_na(),
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste0(dcant_change, "%"))

# construct global budget labels

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

dcant_sd_scaled <- round(c(
  0.24,
  dcant_budget_global_all_ensemble_summary %>%
    pull(dcant_sd_scaled)
), 2)

new_layer_total <- bind_cols(new_layer_total, dcant_sd_scaled = dcant_sd_scaled)

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_sd_scaled_rel = round(100 * dcant_sd_scaled/dcant_change),
         dcant_change = as.character(round(dcant_change,2)),
         dcant_sd_scaled = round(dcant_sd_scaled,1),
         dcant_change = paste0(dcant_change, "\n(±", dcant_sd_scaled, ")"))

# basin change + uncertainty

budget_layer_basin <- full_join(budget_layer_basin,
                                uncertainty_layer_basin)

budget_layer_basin <- 
  budget_layer_basin %>% 
  mutate(dcant_sd_scaled_rel = 100*dcant_sd_scaled/count,
         dcant_sd_scaled_rel = as.character(round(dcant_sd_scaled_rel)),
         dcant_sd_scaled_numeric = dcant_sd_scaled,
         dcant_sd_scaled = round(dcant_sd_scaled, 2),
         dcant_sd_scaled = paste0("(±", dcant_sd_scaled, ")"),
         dcant_label = if_else(dcant_sd_scaled != "(±NA)",
                               paste0(dcant, "\n", dcant_sd_scaled),
                               dcant))


# uncertainty changes

uncertainty_changes <- budget_layer_basin %>% 
  select(x, alluvium, dcant_sd_scaled = dcant_sd_scaled_numeric) %>% 
  group_by(alluvium) %>% 
  mutate(dcant_change_uncert = sqrt(dcant_sd_scaled^2 + lag(dcant_sd_scaled)^2)) %>% 
  ungroup()


new_layer_basin <- left_join(new_layer_basin, uncertainty_changes) %>% 
  mutate(dcant_change_uncert_rel = 100*dcant_change_uncert/abs((count + lag(count))/2),
         dcant_change_label = if_else(!is.na(dcant_change_uncert),
                                      paste0(dcant_change, "\n(±", round(dcant_change_uncert_rel), "%)"),
                                      dcant_change))


g2 +
  geom_text(data = budget_layer_basin,
            aes(
              x = x,
              y = y,
              label = dcant_label
            ),
            size = 3,
            inherit.aes = FALSE) +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change_label
            ),
            size = 4,
            inherit.aes = FALSE)
  # geom_label(data = new_layer_total,
  #           aes(
  #             x = x,
  #             y = y + 0.13,
  #             label = dcant_change
  #           ),
  #           size = 4,
  #           inherit.aes = FALSE)



# ggsave(path = here::here("output/publication"),
#        filename = "Fig_budget_beta_basin_hemisphere.png",
#        height = 6,
#        width = 8)

14.3 Depth layer

dcant_budget_change <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease"))

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant = if_else(is.na(lead(dcant)), 888, dcant),
         dcant = na_if(dcant, 888)) %>%
  fill(dcant) %>% 
  ungroup()

dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_rect(
    data = dcant_budget_change,
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = `1994 - 2004`,
      ymax = `2004 - 2014`,
      fill = sign
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1", direction = -1, name = "period") +
  scale_fill_brewer(palette = "Set1", direction = -1, name = "") +
  facet_wrap(~ basin, ncol = 3, dir = "v") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
      theme(legend.position = c(0.8,0.2))

Past versions of depth_layer_budget_basin_MLR_standard_case-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
26e9496	jens-daniel-mueller	2022-07-12

dcant_budget_range <- dcant_budget_basin_MLR_layer_all_ensemble %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  group_by(basin, inv_depth, period) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()

dcant_budget_layer <-
  full_join(dcant_budget_range,
            dcant_budget_layer)

p_dcant_budget_depth_layer <- 
dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0, size = 0.1) +
  geom_vline(xintercept = 1000, size = 0.1) +
  geom_rect(
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = dcant - dcant_sd,
      ymax = dcant + dcant_sd,
      fill = period
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500), limits = c(3000, 0)) +
  scale_color_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2 SD") +
  scale_fill_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2 SD") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_x") +
  theme(legend.position = c(0.9, 0.1))

p_dcant_budget_depth_layer

Past versions of depth_layer_budget_basin_MLR_ensemble-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12

p_dcant_profiles + p_dcant_budget_depth_layer +
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = 'A')

Past versions of depth_layer_budget_basin_MLR_ensemble-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_profiles_and_depth_layer_budget.png",
       height = 12,
       width = 8)

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source != "obs") %>%
  rename(dcant = value) %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant = if_else(is.na(lead(dcant)), 888, dcant),
         dcant = na_if(dcant, 888)) %>%
  fill(dcant) %>% 
  ungroup()

dcant_budget_range <- dcant_budget_basin_MLR_layer_all_ensemble %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source != "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  group_by(basin, inv_depth, period, data_source) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()

dcant_budget_layer <-
  full_join(dcant_budget_range,
            dcant_budget_layer)

p_dcant_budget_depth_layer_mod <- 
dcant_budget_layer %>%
      mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  ggplot() +
  geom_hline(yintercept = 0, size = 0.1) +
  geom_vline(xintercept = 1000, size = 0.1) +
  geom_rect(
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = dcant - dcant_sd,
      ymax = dcant + dcant_sd,
      fill = data_source
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = data_source), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500), limits = c(3000, 0)) +
  scale_color_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2 SD") +
  scale_fill_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2 SD") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
  facet_grid(period ~ basin, scales = "free_x") +
  theme(legend.position = "none")

p_dcant_budget_depth_layer_mod

Past versions of depth_layer_budget_basin_MLR_ensemble_mod-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11

p_dcant_profiles_mod + p_dcant_budget_depth_layer_mod +
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = 'A')

Past versions of depth_layer_budget_basin_MLR_ensemble_mod-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_profiles_and_depth_layer_budget_synthetic_data.png",
       height = 10,
       width = 7)

dcant_budget_basin_MLR_layer_all_ensemble_stat <-
dcant_budget_basin_MLR_layer_all_ensemble %>%
  filter(estimate == "dcant",
         data_source == "obs",
         inv_depth < 3000,
         period %in% two_decades) %>% 
  group_by(basin,
           period,
           inv_depth) %>%
  summarise(dcant_sd = sd(value) * 2) %>%
  ungroup()

dcant_layer_budget_stat <-
  full_join(
    dcant_budget_basin_MLR_layer_all %>%
      filter(
        estimate == "dcant",
        data_source == "obs",
        inv_depth < 3000,
        period %in% two_decades
      ) %>% 
      select(basin, period, inv_depth, dcant = value),
    dcant_budget_basin_MLR_layer_all_ensemble_stat
  )  

rm(dcant_budget_basin_MLR_layer_all_ensemble_stat)  

dcant_layer_budget_stat %>%
  filter(period %in% two_decades) %>%
  arrange(period) %>% 
  group_by(basin, inv_depth) %>%
  mutate(delta_dcant = dcant - lag(dcant),
         RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  drop_na() %>% 
  filter(abs(delta_dcant) >= RSS) %>% 
  select(basin, inv_depth, delta_dcant, RSS) %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	inv_depth	delta_dcant	RSS
Global	2750	-1.095	0.9980740
Indian	1750	-0.396	0.3688201
N. Atlantic	750	0.336	0.2694706
N. Atlantic	1750	-0.469	0.2032479
N. Atlantic	2250	-0.245	0.1563450
N. Atlantic	2750	-0.346	0.1863157
S. Atlantic	750	0.912	0.4358090
S. Atlantic	1250	0.495	0.3151858

dcant_budget_change <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  arrange(inv_depth) %>% 
  group_by(data_source, basin, period) %>% 
  mutate(dcant_cum = cumsum(dcant)) %>% 
  ungroup() %>%
  select(-c(tref2, Version_ID, dcant)) %>%
  pivot_wider(names_from = period,
              values_from = dcant_cum) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease"))

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  arrange(inv_depth) %>% 
  group_by(data_source, basin, period) %>% 
  mutate(dcant_cum = cumsum(dcant)) %>% 
  ungroup() %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant_cum = if_else(is.na(lead(dcant_cum)), 888, dcant_cum),
         dcant_cum = na_if(dcant_cum, 888)) %>%
  fill(dcant_cum) %>% 
  ungroup()



dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_rect(
    data = dcant_budget_change,
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = `1994 - 2004`,
      ymax = `2004 - 2014`,
      fill = sign
    ),
    alpha = 0.3
  ) +
  geom_step(aes(inv_depth - 250, dcant_cum, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1",
                     direction = -1,
                     name = "Decade") +
  scale_fill_brewer(palette = "Set1",
                    direction = -1,
                    name = "Decadal change") +
  
  facet_wrap( ~ basin, ncol = 3, dir = "v") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)",
       title = "Cumulative layer inventory change") +
      theme(legend.position = c(0.8,0.2))

Past versions of depth_layer_budget_basin_MLR_cumulative-1.png

Version	Author	Date
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
26e9496	jens-daniel-mueller	2022-07-12
37f56b3	jens-daniel-mueller	2022-07-01
df21d31	jens-daniel-mueller	2022-07-01
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
32e9682	jens-daniel-mueller	2022-02-02
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
ab001eb	jens-daniel-mueller	2022-01-31
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27

15 Time series

15.1 eMLR only

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs",
         period != "1994 - 2014") %>% 
  ggplot(aes(tref2, dcant, group = interaction(MLR_basins, Version_ID_group),
             col=Version_ID_group)) +
  geom_path() +
  geom_point()

Past versions of dcant_budget_time_series_member_connections-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004`, `2004 - 2014`,
             col=MLR_basins)) +
  coord_equal() +
  geom_point() +
  facet_wrap(~ Version_ID_group)

Past versions of dcant_budget_time_series_member_connections-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004`, `2004 - 2014`,
             col=Version_ID_group)) +
  coord_equal() +
  geom_point() +
  facet_wrap(~ MLR_basins)

Past versions of dcant_budget_time_series_member_connections-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004`, `1994 - 2014`,
             col=Version_ID_group)) +
  coord_equal() +
  geom_point()

Past versions of dcant_budget_time_series_member_connections-4.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004` + `2004 - 2014`, `1994 - 2014`,
             col=Version_ID_group)) +
  geom_abline(intercept = 0, slope = 1) +
  coord_equal() +
  scale_x_continuous(breaks = seq(0,100,1)) +
  scale_y_continuous(breaks = seq(0,100,1)) +
  geom_point()

Past versions of dcant_budget_time_series_member_connections-5.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18

dcant_budget_global_ts <- dcant_budget_global_all %>% 
  filter(data_source == "obs",
         period  %in% two_decades) %>% 
  select(year = tref2, dcant_mean = dcant)


dcant_budget_global_all_ensemble_summary <-
  dcant_budget_basin_MLR_all_ensemble %>%
  filter(period %in% c("1994 - 2004", "1994 - 2014"),
         data_source == "obs",
         basin == "Global") %>%
  group_by(data_source, tref2, period) %>%
  summarise(#dcant_mean = mean(dcant),
            dcant_sd = sd(dcant)*sd_factor) %>%
  ungroup()

dcant_budget_global_ts <- left_join(dcant_budget_global_ts,
                                    dcant_budget_global_all_ensemble_summary %>% 
                                      select(year = tref2, dcant_sd))


tcant_S04 <- bind_cols(year = 1994, dcant_mean = 118, dcant_sd = 19)

tcant_ts <- full_join(dcant_budget_global_ts, tcant_S04)

tcant_ts <- left_join(tcant_ts, co2_atm)

co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1800, dcant_sd = NA)

tcant_ts <- full_join(tcant_ts, co2_atm_pi)


tcant_ts <- tcant_ts %>%
  arrange(year) %>%
  mutate(
    tcant = cumsum(dcant_mean),
    tcant_sd = sqrt(dcant_sd ^ 2 + nth(dcant_sd,2) ^ 2)
  ) %>%
  mutate(
    tcant_sd = case_when(
      year == 1800 ~ 0,
      year == 1994 ~ dcant_sd,
      TRUE ~ tcant_sd
    ),
    dcant_sd_int = dcant_sd,
    dcant_sd = case_when(
      year == 1994 ~ 0,
      TRUE ~ dcant_sd
    )
  )

beta <- 1.57
co2_atm_pi_value <- 283
atm_co2_col <- "#CC3311"
tcant_col <- "#004488"

p_tcant_year <- tcant_ts %>%
  ggplot(aes(year, tcant)) +
  geom_line(data = co2_atm_reccap2 %>% filter(year > 1790),
            aes(year, (pCO2 - co2_atm_pi_value) * beta),
            col = atm_co2_col) +
  geom_linerange(aes(
    ymin = tcant - dcant_sd_int,
    ymax = tcant + dcant_sd_int,
    col = "Estimate\n±\ninterval\nuncertainty"
  ), size = 1) +
  geom_point(fill = "white", shape = 21,
             aes(col = "Estimate\n±\ninterval\nuncertainty")) +
  scale_y_continuous(name = expression(Total ~ oceanic ~ C[ant] ~ (PgC)),
                     sec.axis = sec_axis( ~ (. / beta + co2_atm_pi_value),
                                          name = expression(Atm. ~ pCO[2] ~ (µatm)))) +
  scale_x_continuous(name = "Year", breaks = seq(1800, 2000, 50)) +
  scale_color_manual(values = tcant_col) +
  theme(
    axis.title.y.right = element_text(color = atm_co2_col),
    axis.text.y.right = element_text(color = atm_co2_col),
    axis.ticks.y.right = element_line(color = atm_co2_col), 
    axis.title.y.left = element_text(color = tcant_col),
    axis.text.y.left = element_text(color = tcant_col),
    axis.ticks.y.left = element_line(color = tcant_col), 
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.85, 0.2),
    legend.title = element_blank()
  )

p_tcant_year

Past versions of mean_tcant_over_time-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
9bbc6a4	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
0825298	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
5a6be34	jens-daniel-mueller	2022-03-22
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
251c7cf	jens-daniel-mueller	2022-02-17
565224d	jens-daniel-mueller	2022-02-17
2116dd3	jens-daniel-mueller	2022-02-09
6fe70a1	jens-daniel-mueller	2022-02-05
a6b33aa	jens-daniel-mueller	2022-02-04

atm_co2_col <- "transparent"

p_tcant_year_trans <- tcant_ts %>%
  ggplot(aes(year, tcant)) +
  geom_line(data = co2_atm_reccap2 %>% filter(year > 1790),
            aes(year, (pCO2 - co2_atm_pi_value) * beta),
            col = atm_co2_col) +
  geom_linerange(aes(
    ymin = tcant - dcant_sd_int,
    ymax = tcant + dcant_sd_int,
    col = "Estimate\n±\ninterval\nuncertainty"
  ), size = 1) +
  geom_point(fill = "white", shape = 21,
             aes(col = "Estimate\n±\ninterval\nuncertainty")) +
  scale_y_continuous(name = expression(Total ~ oceanic ~ C[ant] ~ (PgC)),
                     sec.axis = sec_axis( ~ (. / beta + co2_atm_pi_value),
                                          name = expression(Atm. ~ pCO[2] ~ (µatm)))) +
  scale_x_continuous(name = "Year", breaks = seq(1800, 2000, 50)) +
  scale_color_manual(values = tcant_col) +
  theme(
    axis.title.y.right = element_text(color = atm_co2_col),
    axis.text.y.right = element_text(color = atm_co2_col),
    axis.ticks.y.right = element_line(color = atm_co2_col),
    axis.title.y.left = element_text(color = tcant_col),
    axis.text.y.left = element_text(color = tcant_col),
    axis.ticks.y.left = element_line(color = tcant_col),
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.85, 0.2),
    legend.title = element_blank()
  )

p_tcant_year_trans

Past versions of mean_tcant_over_time-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
0825298	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
5a6be34	jens-daniel-mueller	2022-03-22
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
251c7cf	jens-daniel-mueller	2022-02-17
565224d	jens-daniel-mueller	2022-02-17
2116dd3	jens-daniel-mueller	2022-02-09

atm_co2_col <- "#CC3311"

p_tcant_atm_co2 <- tcant_ts %>%
  ggplot(aes(pCO2, tcant)) +
  geom_ribbon(aes(
    ymin = tcant - tcant_sd,
    ymax = tcant + tcant_sd,
    fill = "1800 - 2014",
    alpha = "1800 - 2014"
  )) +
  geom_ribbon(aes(
    ymin = tcant - dcant_sd,
    ymax = tcant + dcant_sd,
    fill = "1994 - 2014",
    alpha = "1994 - 2014"
  )) +
  geom_line(color = tcant_col) +
  geom_point(shape = 21, fill = "white", color = tcant_col) +
  scale_x_continuous(breaks = seq(280, 400, 30)) +
  scale_fill_manual(values = c(tcant_col, tcant_col), name = "Cumulative\nuncertainty") +
  scale_alpha_manual(values = c(0.2, 0.6), name = "Cumulative\nuncertainty") +
  scale_color_manual(values = "black", name = "") +
  geom_text(aes(label = year), nudge_x = -6, nudge_y = 6) +
  labs(x = expression(Atmospheric ~ pCO[2] ~ (µatm)),
       y = expression(Total ~ oceanic ~ C[ant] ~ (PgC))) +
  theme(
    axis.title.y.left = element_text(color = tcant_col),
    axis.text.y.left = element_text(color = tcant_col),
    axis.ticks.y.left = element_line(color = tcant_col),
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.85, 0.25),
    axis.title.x = element_text(color = atm_co2_col),
    axis.text.x = element_text(color = atm_co2_col),
    axis.ticks.x = element_line(color = atm_co2_col)
  )

p_tcant_year / p_tcant_atm_co2 + 
  plot_annotation(tag_levels = 'A')

Past versions of mean_tcant_over_atm_co2-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
9bbc6a4	jens-daniel-mueller	2022-07-20
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
0825298	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
5a6be34	jens-daniel-mueller	2022-03-22
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
251c7cf	jens-daniel-mueller	2022-02-17
565224d	jens-daniel-mueller	2022-02-17
2116dd3	jens-daniel-mueller	2022-02-09
6fe70a1	jens-daniel-mueller	2022-02-05
a6b33aa	jens-daniel-mueller	2022-02-04
d2191ad	jens-daniel-mueller	2022-02-04
0d0f790	jens-daniel-mueller	2022-02-02
60727e6	jens-daniel-mueller	2022-02-02
c7b4984	jens-daniel-mueller	2022-02-02
7fb28a2	jens-daniel-mueller	2022-02-02
7655085	jens-daniel-mueller	2022-02-02
ec7fe7e	jens-daniel-mueller	2022-01-31

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_budget_global_vs_atm_pCO2.png",
       height = 6,
       width = 7)


p_tcant_year / p_tcant_atm_co2

Past versions of mean_tcant_over_atm_co2-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
0825298	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
5a6be34	jens-daniel-mueller	2022-03-22
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
251c7cf	jens-daniel-mueller	2022-02-17
565224d	jens-daniel-mueller	2022-02-17
2116dd3	jens-daniel-mueller	2022-02-09

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_budget_global_vs_atm_pCO2.png",
       height = 6,
       width = 7)

p_tcant_year_trans / p_tcant_atm_co2

Past versions of mean_tcant_over_atm_co2-3.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_budget_global_vs_atm_pCO2_trans.png",
       height = 6,
       width = 7)

tcant_ts %>% 
  mutate(beta = dcant_mean / (pCO2-lag(pCO2)))

# A tibble: 4 × 8
   year dcant_mean dcant_sd  pCO2 tcant tcant_sd dcant_sd_int  beta
  <dbl>      <dbl>    <dbl> <dbl> <dbl>    <dbl>        <dbl> <dbl>
1  1800        0      NA     280     0       0          NA    NA   
2  1994      118       0     358.  118      19          19     1.51
3  2004       27.4     3.28  377.  145.     19.3         3.28  1.47
4  2014       25.6     4.55  397.  171.     19.5         4.55  1.25

15.2 incl fluxes

Ocean_Sink <- Ocean_Sink %>%
  filter(product != "Watson")

Ocean_Sink_cum_1994 <-
  Ocean_Sink %>%
  filter(year >= 1994, year <= 2007) %>%
  mutate(fgco2_glob = if_else(year == 1994, 0, GtC_yr),
         fgco2_glob = fgco2_glob) %>% 
  arrange(year) %>%
  group_by(product, type) %>%
  mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
  ungroup()

Ocean_Sink_cum_1994 <-
  left_join(Ocean_Sink_cum_1994,
            co2_atm) 


Ocean_Sink_cum_1994 %>%
  group_split(type) %>%
  map(
    ~ ggplot(data = .x, aes(pCO2, fgco2_glob_cum, col = product)) +
      geom_line() +
      geom_point(shape = 21, fill = "white") +
      theme(legend.position = "bottom") +
      facet_grid(. ~ type)
  )

[[1]]

Past versions of GCB_carbon_sink_cum_1994_2007_reference-1.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20
8105380	jens-daniel-mueller	2022-10-07
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26

[[2]]

Past versions of GCB_carbon_sink_cum_1994_2007_reference-2.png

Version	Author	Date
8105380	jens-daniel-mueller	2022-10-07

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(fgco2_glob_cum_total = fgco2_glob_cum + 118) %>%
  select(year, pCO2, product, fgco2_glob_cum, fgco2_glob_cum_total, type)

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(type = case_when(
    type == "data_products" ~ "Observation-based\nsurface flux\nproducts",
    TRUE ~ "Global Ocean\nBiogeochemical Models\n(GOBM)"
  ))

Ocean_Sink_cum_1994 %>% 
  filter(year == 2007) %>% 
  group_by(type) %>% 
  summarise(dcant_sd = sd(fgco2_glob_cum)*2) %>% 
  ungroup()

# A tibble: 2 × 2
  type                                          dcant_sd
  <chr>                                            <dbl>
1 "Global Ocean\nBiogeochemical Models\n(GOBM)"     6.99
2 "Observation-based\nsurface flux\nproducts"       6.66

Ocean_Sink_cum_1994 <-
  Ocean_Sink %>%
  filter(year >= 1994, year <= 2014) %>%
  mutate(fgco2_glob = if_else(year == 1994, 0, GtC_yr),
         fgco2_glob = fgco2_glob) %>% 
  arrange(year) %>%
  group_by(product, type) %>%
  mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
  ungroup()

Ocean_Sink_cum_1994 <-
  left_join(Ocean_Sink_cum_1994,
            co2_atm) 

Ocean_Sink_cum_1994 %>%
  group_split(type) %>%
  map(
    ~ ggplot(data = .x, aes(pCO2, fgco2_glob_cum, col = product)) +
      geom_line() +
      geom_point(shape = 21, fill = "white") +
      theme(legend.position = "bottom") +
      facet_grid(. ~ type)
  )

[[1]]

Past versions of GCB_carbon_sink_cum_1994-1.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20
8105380	jens-daniel-mueller	2022-10-07
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
bd24a0f	jens-daniel-mueller	2022-07-15

[[2]]

Past versions of GCB_carbon_sink_cum_1994-2.png

Version	Author	Date
8105380	jens-daniel-mueller	2022-10-07

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(fgco2_glob_cum_total = fgco2_glob_cum + 118) %>%
  select(year, pCO2, product, fgco2_glob_cum, fgco2_glob_cum_total, type)

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(type = case_when(
    type == "data_products" ~ "Observation-based\nsurface flux\nproducts",
    TRUE ~ "Global Ocean\nBiogeochemical Models\n(GOBM)"
  ))

tcant_2004 <- tcant_ts %>% 
  filter(year == 2004) %>% 
  pull(tcant)

Ocean_Sink_cum_2004 <-
  Ocean_Sink %>%
  filter(year >= 2004, year <= 2014) %>%
  mutate(fgco2_glob = if_else(year == 2004, 0, GtC_yr)) %>% 
  arrange(year) %>%
  group_by(product, type) %>%
  mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
  ungroup()

Ocean_Sink_cum_2004 <-
  left_join(Ocean_Sink_cum_2004,
            co2_atm) 

Ocean_Sink_cum_2004 %>%
  group_split(type) %>%
  map(
    ~ ggplot(data = .x, aes(pCO2, fgco2_glob_cum, col = product)) +
      geom_line() +
      geom_point(shape = 21, fill = "white") +
      theme(legend.position = "bottom") +
      facet_grid(. ~ type)
  )

[[1]]

Past versions of GCB_carbon_sink_cum_2004-1.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20
8105380	jens-daniel-mueller	2022-10-07
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
bd24a0f	jens-daniel-mueller	2022-07-15

[[2]]

Past versions of GCB_carbon_sink_cum_2004-2.png

Version	Author	Date
8105380	jens-daniel-mueller	2022-10-07

Ocean_Sink_cum_2004 <- Ocean_Sink_cum_2004 %>% 
  mutate(fgco2_glob_cum_total = fgco2_glob_cum + tcant_2004) %>% 
  select(year, pCO2, product, fgco2_glob_cum, fgco2_glob_cum_total, type)

Ocean_Sink_cum_2004 <- Ocean_Sink_cum_2004 %>% 
  mutate(type = case_when(
    type == "data_products" ~ "Observation-based\nsurface flux\nproducts",
    TRUE ~ "Global Ocean\nBiogeochemical Models\n(GOBM)"
  ))

tcant_ts_zoom <- tcant_ts %>%
  filter(year != 1800)

tcant_min = tcant_ts_zoom %>% pull(tcant) %>% min()
tcant_max =
  tcant_ts_zoom %>% pull(tcant) %>% max() +
  tcant_ts_zoom %>% pull(dcant_sd) %>% max() + 0.2

p_tcant_ts_zoom <-
  tcant_ts_zoom %>%
  ggplot(aes(pCO2, tcant)) +
  geom_ribbon(
    aes(ymin = tcant - dcant_sd,
        ymax = tcant + dcant_sd),
    alpha = 0.2,
    fill = tcant_col
  ) +
  geom_ribbon(
    aes(
      ymin = tcant - dcant_sd / 2,
      ymax = tcant + dcant_sd / 2
    ),
    alpha = 0.3,
    fill = tcant_col
  ) +
  geom_line(data = Ocean_Sink_cum_1994,
            aes(pCO2, fgco2_glob_cum_total, group = product, col = type)) +
  geom_point(
    data = Ocean_Sink_cum_1994,
    aes(pCO2, fgco2_glob_cum_total, group = product, col = type),
    size = 0.3
  ) +
  geom_point(
    data = Ocean_Sink_cum_1994 %>% filter(year %in% c(2004, 2014)),
    aes(
      pCO2,
      fgco2_glob_cum_total,
      col = type
    ),
    fill = "white",
    shape = 21
  ) +
  geom_line(aes(col = "eMLR(C*)")) +
  geom_point(aes(col = "eMLR(C*)"),
             shape = 21,
             fill = "white") +
  geom_text(aes(label = year), nudge_x = -2, nudge_y = 3) +
  scale_color_highcontrast(name = "Data source") +
  scale_fill_highcontrast(name = "Data source") +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = expression(Atmospheric ~ pCO[2] ~ (µatm)),
       y = expression(Total ~ oceanic ~ carbon ~ storage ~ since ~ 1800 ~ (PgC))) +
  guides(colour = guide_legend(order = 1),
         fill = "none") +
  theme_classic() +
  theme(
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.25, 0.83),
    legend.title = element_blank(),
    legend.box.just = "top",
    legend.box = "horizontal",
    legend.key.size = unit(2.5, 'lines'),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

p_tcant_ts_zoom

Past versions of mean_tcant_over_atm_co2_incl_fluxes-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20
8105380	jens-daniel-mueller	2022-10-07
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
4af29f9	jens-daniel-mueller	2022-07-24
b18b250	jens-daniel-mueller	2022-07-20
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
9393c07	jens-daniel-mueller	2022-06-28
0825298	jens-daniel-mueller	2022-06-28
fb59a6f	jens-daniel-mueller	2022-06-27
3c1100c	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
c3a6238	jens-daniel-mueller	2022-03-08
094bfa0	jens-daniel-mueller	2022-02-18
ba2d62e	jens-daniel-mueller	2022-02-17
192504c	jens-daniel-mueller	2022-02-17
d2191ad	jens-daniel-mueller	2022-02-04
4c5b079	jens-daniel-mueller	2022-02-03
4077397	jens-daniel-mueller	2022-02-03

dcant_type_box <-
  bind_rows(
    dcant_budget_global_all_ensemble %>%
      filter(data_source == "obs") %>%
      select(period, dcant) %>%
      mutate(type = "eMLR(C*)",
             dcant = dcant + 118,
             dcant = if_else(period == "2004 - 2014",
                             dcant + tcant_2004 - 118,
                             dcant)),
    Ocean_Sink_cum_1994 %>%
      filter(year %in% c(2004, 2014)) %>%
      mutate(period = if_else(year == 2004,
                              "1994 - 2004",
                              "1994 - 2014")) %>%
      select(period, dcant = fgco2_glob_cum_total, type),
    Ocean_Sink_cum_2004 %>%
      filter(year %in% c(2014)) %>%
      mutate(period = if_else(year == 2014,
                              "2004 - 2014",
                              "other")) %>%
      select(period, dcant = fgco2_glob_cum_total, type)
  )

dcant_type_box_stat <- dcant_type_box %>% 
  group_by(period, type) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant)) %>% 
  ungroup()

dcant_type_box_stat <-
left_join(
  dcant_type_box_stat,
  dcant_budget_global_all %>%
    filter(data_source == "obs") %>%
    mutate(
      dcant = dcant + 118,
      dcant = if_else(period == "2004 - 2014",
                      dcant + tcant_2004 - 118,
                      dcant)
    ) %>%
    select(period, dcant_st_case = dcant)
)

dcant_type_box_stat <- dcant_type_box_stat %>% 
  mutate(dcant_mean = if_else(type == "eMLR(C*)",
                              dcant_st_case,
                              dcant_mean)) %>% 
  select(-dcant_st_case)

p_tcant_box <-
dcant_type_box %>%
  filter(period == "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
 geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period == "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd,
      ymax = dcant_mean + dcant_sd,
      col = type,
      fill = type
    ), width = 0.7, alpha = 0.4
  ) +
  geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period == "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd*2,
      ymax = dcant_mean + dcant_sd*2,
      col = type,
      fill = type
    ), width = 0.7, alpha = 0.2
  ) +
  geom_point(aes(fill = type),
              position = position_jitterdodge(
                dodge.width = 1,
                jitter.width = 0.1),
              shape = 21,
              alpha = 0.5) +
  scale_fill_highcontrast(
    name = "Data source",
    guide = "none"
  ) +
  scale_color_highcontrast(
    name = "Data source",
    guide = "none"
  ) +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = "20yr period") +
  theme_classic() +
  theme(
    axis.line.y.left = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

p_tcant_box_10 <-
  dcant_type_box %>%
  filter(period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period != "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd,
      ymax = dcant_mean + dcant_sd,
      col = type,
      fill = type
    ),
    width = 0.7,
    alpha = 0.4
  ) +
  geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period != "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd * 2,
      ymax = dcant_mean + dcant_sd * 2,
      col = type,
      fill = type
    ),
    width = 0.7,
    alpha = 0.2
  ) +
  geom_point(
    aes(fill = type),
    position = position_jitterdodge(dodge.width = 1,
                                    jitter.width = 0.1),
    shape = 21,
    alpha = 0.5
  ) +
  geom_point(
    data = dcant_type_box_stat %>%
      filter(period != "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(x = period,
        y = dcant_mean,
        col = type),
    fill = "white",
    shape = 21,
    size = 2
  ) +
  scale_fill_highcontrast(name = "Data source",
                          guide = "none") +
  scale_color_highcontrast(name = "Data source",
                           guide = "none") +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = "10yr periods") +
  theme_classic() +
  theme(
    axis.line.y.left = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

p_tcant_ts_zoom + p_tcant_box + p_tcant_box_10 +
  plot_layout(ncol = 3, widths = c(6,1,1.8)) + 
  plot_annotation(tag_levels = 'A')

Past versions of mean_tcant_over_atm_co2_incl_fluxes_boxplot-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20
77e6a80	jens-daniel-mueller	2022-09-08
051b911	jens-daniel-mueller	2022-09-07
75bd58c	jens-daniel-mueller	2022-09-07
1c6de0d	jens-daniel-mueller	2022-09-07
78dbb27	jens-daniel-mueller	2022-08-11
302d289	jens-daniel-mueller	2022-08-11
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
4af29f9	jens-daniel-mueller	2022-07-24
b18b250	jens-daniel-mueller	2022-07-20
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
9393c07	jens-daniel-mueller	2022-06-28
0825298	jens-daniel-mueller	2022-06-28

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_budget_global_vs_GCB_ocean_sink.png",
       height = 6,
       width = 9)

p_tcant_ts_zoom + p_tcant_box + p_tcant_box_10 +
  plot_layout(ncol = 2, widths = c(6,1.8))

Past versions of mean_tcant_over_atm_co2_incl_fluxes_boxplot-2.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_budget_global_vs_GCB_ocean_sink.png",
       height = 6,
       width = 9)

dcant_type_significance <- dcant_type_box_stat %>%
  mutate(dcant_mean = dcant_mean - 118,
         dcant_mean = if_else(period == "2004 - 2014",
                         dcant_mean - (tcant_2004 - 118),
                         dcant_mean),
         dcant_sd = dcant_sd * 2) %>%
  group_by(period) %>%
  mutate(
    dcant_diff = dcant_mean - first(dcant_mean),
    dcant_diff_rel = 100 * dcant_diff / first(dcant_mean),
    dcant_diff_sd = sqrt(dcant_sd ^ 2 + first(dcant_sd) ^ 2),
    dcant_diff_sd_comparison = dcant_diff_sd - abs(dcant_diff)
  ) %>%
  ungroup()

dcant_type_significance %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

period	type	dcant_mean	dcant_sd	dcant_diff	dcant_diff_rel	dcant_diff_sd	dcant_diff_sd_comparison
1994 - 2004	eMLR(C*)	27.38800	3.077153	0.000000	0.000000	4.351752	4.3517521
1994 - 2004	Global Ocean Biogeochemical Models (GOBM)	19.52578	5.403004	-7.862222	-28.706812	6.217824	-1.6443981
1994 - 2004	Observation-based surface flux products	21.41458	5.597734	-5.973418	-21.810347	6.387762	0.4143439
1994 - 2014	eMLR(C*)	53.12500	4.263935	0.000000	0.000000	6.030115	6.0301148
1994 - 2014	Global Ocean Biogeochemical Models (GOBM)	42.28477	10.974456	-10.840229	-20.405137	11.773692	0.9334632
1994 - 2014	Observation-based surface flux products	47.89526	7.743738	-5.229744	-9.844224	8.840058	3.6103140
2004 - 2014	eMLR(C*)	25.58300	3.061832	0.000000	0.000000	4.330085	4.3300846
2004 - 2014	Global Ocean Biogeochemical Models (GOBM)	22.75899	5.576053	-2.824007	-11.038608	6.361383	3.5373753
2004 - 2014	Observation-based surface flux products	26.48067	2.612751	0.897674	3.508869	4.025082	3.1274082

# Ocean_Sink %>% 
#   filter(between(year, 1994, 2007)) %>% 
#   arrange(year) %>% 
#   group_by(product) %>% 
#   mutate(GtC = cumsum(GtC_yr)) %>% 
#   ungroup() %>% 
#   # ggplot(aes(year, GtC, col=product)) +
#   # geom_line() %>% 
#   filter(year == 2007) %>% 
#   summarise(sd(GtC)*2)

Ocean_Sink_cum_1994_ensemble_mean <- Ocean_Sink_cum_1994 %>% 
  group_by(year, pCO2, type) %>% 
  summarise(fgco2_glob_cum_total_mean = mean(fgco2_glob_cum_total),
            fgco2_glob_cum_total_sd = sd(fgco2_glob_cum_total)) %>% 
  ungroup()

p_tcant_ts_zoom <-
tcant_ts_zoom %>%
  ggplot() +
  geom_ribbon(aes(pCO2, tcant,
                  ymin = tcant - dcant_sd,
                  ymax = tcant + dcant_sd,
              fill = "eMLR(C*)"),
              alpha = 0.2) +
  geom_ribbon(
    aes(pCO2, tcant,
        ymin = tcant - dcant_sd / 2,
        ymax = tcant + dcant_sd / 2,
              fill = "eMLR(C*)"),
    alpha = 0.3
  ) +
  # geom_ribbon(
  #   data = Ocean_Sink_cum_1994_ensemble_mean,
  #   aes(
  #     pCO2,
  #     ymin = fgco2_glob_cum_total_mean - fgco2_glob_cum_total_sd,
  #     ymax = fgco2_glob_cum_total_mean + fgco2_glob_cum_total_sd,
  #     fill = type
  #   ),
  #   alpha = 0.3
  # ) +
    geom_line(data = Ocean_Sink_cum_1994,
            aes(pCO2, fgco2_glob_cum_total, group = product, col = type), size = 0.3) +
  geom_line(data = Ocean_Sink_cum_1994_ensemble_mean,
            aes(pCO2, fgco2_glob_cum_total_mean, col = type), size = 2) +
  geom_point(
    data = Ocean_Sink_cum_1994_ensemble_mean,
    aes(pCO2, fgco2_glob_cum_total_mean, col = type), size = 1,
    shape = 21,
    fill = "white"
  ) +
  geom_point(
    data = Ocean_Sink_cum_1994_ensemble_mean %>% filter(year %in% c(2004, 2014)),
    aes(pCO2, fgco2_glob_cum_total_mean, col = type), size = 2,
    shape = 21,
    fill = "white"
  ) +
  # geom_point(
  #   data = Ocean_Sink_cum_1994 %>% filter(year %in% c(2004, 2014)),
  #   aes(
  #     pCO2,
  #     fgco2_glob_cum_total,
  #     col = type
  #   ),
  #   fill = "white",
  #   shape = 21
  # ) +
  geom_line(aes(pCO2, tcant, col = "eMLR(C*)"), size = 2) +
  geom_point(aes(pCO2, tcant, col = "eMLR(C*)"), size = 2,
             shape = 21,
             fill = "white") +
  geom_text(aes(pCO2, tcant, label = year), nudge_x = -2, nudge_y = 3) +
  scale_color_highcontrast(name = "Data source") +
  scale_fill_highcontrast(name = "Data source") +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(
    x = expression(Atmospheric ~ pCO[2] ~ (µatm)),
    y = expression(Total ~ oceanic ~ carbon ~ storage ~ since ~ 1800 ~ (PgC))
  ) +
  guides(colour = guide_legend(order = 1),
         fill = "none") +
  theme_classic() +
  theme(
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.25, 0.83),
    legend.title = element_blank(),
    legend.box.just = "top",
    legend.box = "horizontal",
    legend.key.size = unit(2.5, 'lines'),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

p_tcant_ts_zoom

Past versions of mean_tcant_over_atm_co2_incl_fluxes_mean-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

p_tcant_ts_zoom + p_tcant_box_10 +
  plot_layout(ncol = 2, widths = c(6,1.8))

Past versions of mean_tcant_over_atm_co2_incl_fluxes_boxplot_ensemble-1.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
99610ed	jens-daniel-mueller	2022-11-01
7060801	jens-daniel-mueller	2022-10-31
ecb84c1	jens-daniel-mueller	2022-10-30
2345935	jens-daniel-mueller	2022-10-29
0b40366	jens-daniel-mueller	2022-10-26
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/presentation"),
       filename = "Fig_dcant_budget_global_vs_GCB_ocean_sink_ensemble.png",
       height = 6,
       width = 9)

16 Revelle changes

delta_revelle <- co2_atm %>%
  mutate(revelle = seacarb::buffer(24,
                                   pCO2,
                                   2300 * 1e-6)$BetaD,
         DIC = seacarb::carb(24,
                                   pCO2,
                                   2300 * 1e-6)$DIC,
         delta_DIC = (1/pCO2) * DIC * (1/revelle) *1e6,
         delta_DIC_rel = delta_DIC / mean(delta_DIC),
         delta_DIC_rel_cum = delta_DIC_rel - first(delta_DIC_rel),
         delta_year = year - first(year))
  
  
delta_revelle %>% 
  ggplot(aes(delta_year, pCO2)) +
  geom_path() +
  geom_point()

Past versions of revelle_changes-1.png

Version	Author	Date
084a41c	jens-daniel-mueller	2022-10-13
fea41c1	jens-daniel-mueller	2022-07-20

delta_revelle %>% 
  ggplot(aes(delta_year,1/revelle)) +
  geom_smooth(method = "lm", aes(col="lm"), se = FALSE) +
  geom_path() +
  geom_point()

Past versions of revelle_changes-2.png

Version	Author	Date
084a41c	jens-daniel-mueller	2022-10-13

delta_revelle %>% 
  ggplot(aes(delta_year,delta_DIC_rel)) +
  geom_smooth(method = "lm", aes(col="lm"), se = FALSE) +
  geom_path() +
  geom_point()

Past versions of revelle_changes-3.png

Version	Author	Date
084a41c	jens-daniel-mueller	2022-10-13

delta_revelle %>% 
  ggplot(aes(delta_year,delta_DIC_rel_cum)) +
  geom_smooth(method = "lm", aes(col="lm"), se = FALSE) +
  geom_path() +
  geom_point()

Past versions of revelle_changes-4.png

Version	Author	Date
084a41c	jens-daniel-mueller	2022-10-13

min(delta_revelle$delta_DIC_rel_cum / max(delta_revelle$delta_year))

[1] -0.006078176

17 GCB implications

17.1 Land sink

flux_label <- expression(CO[2]~flux~(PgC~yr^{-1}))

flux_components <- Global_Carbon_Budget %>% 
  distinct(estimate) %>% 
  pull()

emission_components <- flux_components[c(1,2,6)]
sink_components <- flux_components[3:5]

estimates <- Global_Carbon_Budget %>%
  select(-GtC) %>%
  pivot_wider(names_from = estimate,
              values_from = GtC_yr) %>%
  mutate(`fossil emissions` = 
           `fossil emissions excluding carbonation` - `cement carbonation sink`) %>%
  select(-c(`fossil emissions excluding carbonation`, `cement carbonation sink`)) %>% 
  pivot_longer(-year,
               names_to = "estimate",
               values_to = "GtC_yr")

estimates <- estimates %>%
  filter(!(estimate %in% c("budget imbalance")),
         year >= 1960) %>%
  mutate(
    sign = if_else(estimate %in% sink_components,
                   "sinks",
                   "emissions"),
    GtC_yr = if_else(sign == "sinks",
                     -GtC_yr,
                     GtC_yr)
    # GtC_yr = if_else(estimate == "cement carbonation sink",
    #                  -GtC_yr,
    #                  GtC_yr)
  )

emissions_sinks <- estimates %>% 
  group_by(year, sign) %>% 
  summarise(GtC_yr = sum(GtC_yr)) %>% 
  ungroup()

estimates %>% distinct(estimate) %>% pull()

[1] "land-use change emissions" "atmospheric growth"       
[3] "ocean sink"                "land sink"                
[5] "fossil emissions"         

p_stacked_fluxes <- estimates %>%
  mutate(
    estimate = fct_relevel(
      estimate,
      "fossil emissions",
      "land-use change emissions",
      "atmospheric growth",
      "land sink",
      "ocean sink"
    )
  ) %>%
  ggplot() +
  geom_area(aes(year, GtC_yr, fill = estimate),
            col = "black",
            alpha = 0.7,
            size = 0.5) +
  # geom_line(data = emissions_sinks %>%   mutate(GtC_yr = if_else(sign == "emissions",
  #                                                                -GtC_yr,
  #                                                                GtC_yr)),
  #           aes(year, GtC_yr, linetype = sign)) +
  scale_fill_manual(values = c("#BBBBBB", "#CCBB44", "#66CCEE", "#228833", "#4477AA"),
                    name = "flux component") +
  geom_hline(yintercept = 0) +
  scale_x_continuous(expand = c(0,0)) +
  labs(y = flux_label,
       title = "Carbon emissions and sinks",
       subtitle = "Global Carbon Budget | Friedlingstein et al. (2021)") +
  theme(axis.title.x = element_blank())

p_stacked_fluxes

Past versions of GCB_land_sink_assesment-1.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/presentation"),
       filename = "Fig_GCB_emissions_sinks.png",
       height = 4,
       width = 7)

p_individual_fluxes <- estimates %>%
  ggplot(aes(year, GtC_yr, col = estimate)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(size = 0.5) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_bright(name = "flux component") +
  labs(y = flux_label,
       title = "Individual flux components from GCB") +
  theme(axis.title.x = element_blank())

p_individual_fluxes

Past versions of GCB_land_sink_assesment-2.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

BIM <- emissions_sinks %>% 
  pivot_wider(names_from = sign,
              values_from = GtC_yr) %>% 
  mutate(BIM = emissions + sinks)

p_BIM <- ggplot() +
  geom_line(data = Global_Carbon_Budget %>%
              filter(estimate == "budget imbalance",
                     year >= 1990),
            aes(year, GtC_yr, col = "reported")) +
  geom_line(data = BIM, aes(year, BIM, col = "recalculated")) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Set1") +
  scale_x_continuous(expand = c(0, 0)) +
  labs(y = flux_label,
       title = "BIM from GCB") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_BIM

Past versions of GCB_land_sink_assesment-3.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

p_land_sink_gcb <- estimates %>%
  select(year, estimate, GtC_yr) %>%
  pivot_wider(names_from = estimate,
              values_from = GtC_yr) %>%
  mutate(`land sink residual` = 
           `fossil emissions` + 
           `land-use change emissions` + 
           `atmospheric growth` + 
           `ocean sink`) %>%
  ggplot() +
  geom_line(aes(year, -`land sink residual`, col = "residual")) +
  geom_line(aes(year, `land sink`, col = "reported")) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_brewer(palette = "Set1", name = "land sink\nestimate") +
  labs(y = flux_label,
       title = "Land sink estimates from GCB",
       subtitle = "Note: The offset between reported and residual estimate equals BIM") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_land_sink_gcb

Past versions of GCB_land_sink_assesment-4.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

Ocean_Sink_annual_mean <- Ocean_Sink %>%
  filter(year >= 1990) %>%
  group_by(year, type) %>%
  summarise(GtC_yr = -mean(GtC_yr)) %>%
  ungroup()

Ocean_Sink_annual_mean <- Ocean_Sink_annual_mean %>% 
  pivot_wider(names_from = type,
              values_from = GtC_yr,
              names_prefix = "ocean_sink_")

estimates_wide <- estimates %>%
  select(year, estimate, GtC_yr) %>%
  pivot_wider(names_from = estimate,
              values_from = GtC_yr)

estimates_wide <-
  full_join(estimates_wide, Ocean_Sink_annual_mean)

dcant_interior <- tcant_ts_zoom %>% 
  filter(year > 2000) %>%
  mutate(dcant_mean = dcant_mean/10) %>% 
  pull(dcant_mean)

cnat <-
  dcant_type_significance %>%
  filter(type == "Observation-based\nsurface flux\nproducts" ,
         period %in% two_decades) %>%
  mutate(dcant_diff = dcant_diff/10) %>% 
  pull(dcant_diff)

estimates_wide <- estimates_wide %>% 
  mutate(ocean_sink_interior_Cant = case_when(
    between(year, 1994, 2003) ~ -dcant_interior[1],
    between(year, 2004, 2013) ~ -dcant_interior[2],
    TRUE ~ NaN
  )) %>% 
  mutate(ocean_sink_interior_net = case_when(
    between(year, 1994, 2003) ~ -dcant_interior[1]-cnat[1],
    between(year, 2004, 2013) ~ -dcant_interior[2]-cnat[2],
    TRUE ~ NaN
  ))


p_ocean_sinks <- estimates_wide %>%
  select(year, starts_with("ocean")) %>% 
  rename(GCB_mean = `ocean sink`) %>% 
  pivot_longer(-year,
               names_to = "estimate",
               names_prefix = "ocean_sink_",
               values_to = "GtC_yr") %>% 
  mutate(estimate = str_replace(estimate, "_", " ")) %>% 
  ggplot(aes(year, GtC_yr, col = estimate)) +
  geom_path() +
  geom_point(size = 0.5) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_brewer(palette = "Set1", name = "ocean sink\nestimate") +
  labs(y = flux_label,
       title = "Ocean sink estimates",
       subtitle = "Note: Watson et al. (2020) is included in data products and GCB mean") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_ocean_sinks

Past versions of GCB_land_sink_assesment-5.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

estimates_long <- estimates_wide %>%
  select(year, starts_with("ocean")) %>% 
  rename(GCB_mean = `ocean sink`) %>% 
  pivot_longer(-year,
               names_to = "estimate",
               names_prefix = "ocean_sink_",
               values_to = "GtC_yr") %>% 
  filter(estimate %in% c("data_products", "models"),
         year >= 1990)

p_ocean_sinks_presentation <- estimates_long %>%
  # mutate(estimate = str_replace(estimate, "_", " ")) %>% 
  ggplot() +
  geom_path(data = Ocean_Sink %>% filter(year >= 1990),
            aes(year, GtC_yr, group = product, col = type), size = 0.3) +
  geom_path(aes(year, -GtC_yr, col = estimate), size = 2) +
  geom_point(aes(year, -GtC_yr, col = estimate), size = 2, fill = "white", shape = 21) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_manual(values = c("#BB5566", "#DDAA33"),
                     name = "ocean sink\nestimate") +
  labs(y = flux_label,
       title = "Ocean Carbon sink",
       subtitle = "Global Carbon Budget | Friedlingstein et al. (2021)") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_ocean_sinks_presentation

Past versions of GCB_land_sink_assesment-6.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/presentation"),
       filename = "Fig_GCB_ocean_sinks.png",
       height = 4,
       width = 6)

p_ocean_sinks_presentation <-
  estimates_long %>%
  # mutate(estimate = str_replace(estimate, "_", " ")) %>%
  ggplot() +
  geom_path(
    data = Ocean_Sink %>% filter(year >= 1990),
    aes(year, GtC_yr, group = product, col = type),
    size = 0.3
  ) +
  geom_path(aes(year, -GtC_yr, col = estimate), size = 2) +
  geom_point(
    aes(year, -GtC_yr, col = estimate),
    size = 2,
    fill = "white",
    shape = 21
  ) +
  geom_path(
    data =             tibble(
      year = c(1994, 2007),
      estimate = "ocean_interior",
      GtC_yr = -2.6
    ),
    aes(year, -GtC_yr, col = estimate),
    size = 2
  )+
  geom_rect(
    data =             tibble(
      year_min = 1994,
      year_max = 2007,
      estimate = "ocean_interior",
      GtC_yr_min = -2.6 - 0.3,
      GtC_yr_max = -2.6 + 0.3,
    ),
    aes(xmax = year_max, xmin = year_min, ymin = -GtC_yr_min, ymax = -GtC_yr_max, col = estimate),
    fill = "transparent"
  )+
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_manual(values = c("#BB5566", "#DDAA33","#004488"),
                     name = "ocean sink\nestimate") +
  labs(y = flux_label,
       title = "Ocean Carbon sink",
       subtitle = "Global Carbon Budget | Friedlingstein et al. (2021)") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_ocean_sinks_presentation

Past versions of GCB_land_sink_assesment-7.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

ggsave(path = here::here("output/presentation"),
       filename = "Fig_GCB_ocean_sinks_eMLR.png",
       height = 4,
       width = 6)

land_sink <-
  estimates_wide %>%
  mutate(emissions_atmosphere = `fossil emissions` +
           `land-use change emissions` +
           `atmospheric growth`) %>%
  rename(ocean_sink_gcb_mean = `ocean sink`) %>%
  select(year, emissions_atmosphere, starts_with("ocean")) %>%
  pivot_longer(
    starts_with("ocean_sink"),
    names_to = "ocean_sink_estimate",
    values_to = "ocean_sink",
    names_prefix = "ocean_sink_"
  ) %>%
  mutate(land_sink = -emissions_atmosphere - ocean_sink)


p_land_sink_residual <- land_sink %>%
  mutate(ocean_sink_estimate = 
           str_replace(ocean_sink_estimate, "_", " ")) %>% 
  ggplot() +
  geom_line(aes(year, land_sink, col = ocean_sink_estimate, linetype = "residual")) +
  geom_line(data = estimates_wide %>%
              filter(year >= 1990),
            aes(year, `land sink`, linetype = "reported")) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_linetype_manual(values = c(2, 1), name = "land sink\nestimate") +
  scale_color_brewer(palette = "Set1",
                     name = "ocean sink\nestimate\nunderlying\nresidual") +
  guides(linetype = guide_legend(order = 1),
         color = guide_legend(order = 2)) +
  labs(y = flux_label,
       title = "Land sink estimates",
       subtitle = "Note: Residuals are determined for a range of ocean sink estimates") +
  theme(axis.title.x = element_blank())

p_land_sink_residual

Past versions of GCB_land_sink_assesment-8.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

land_sink <-
  full_join(
    land_sink,
    estimates_wide %>%
      select(year, land_sink_reported = `land sink`)
  )


p_land_sink_residual_delta <- land_sink %>%
  mutate(ocean_sink_estimate =
           str_replace(ocean_sink_estimate, "_", " ")) %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_line(aes(year, land_sink - land_sink_reported, col = ocean_sink_estimate)) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_brewer(palette = "Set1",
                     name = "ocean sink\nestimate\nunderlying\nresidual") +
  labs(y = flux_label,
       title = "Land sink estimates (reported - residual)",
       subtitle = "Note: Residuals are determined for a range of ocean sink estimates") +
  theme(axis.title.x = element_blank())

p_land_sink_residual_delta

Past versions of GCB_land_sink_assesment-9.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

p_land_sink_residual_decade <- land_sink %>%
  mutate(period = cut(year, c(1993, 2003, 2013),
                      c("1994 - 2004", "2004 - 2014"))) %>%
  filter(!is.na(period)) %>%
  group_by(ocean_sink_estimate, period) %>%
  summarise(across(starts_with("land_sink"), list(mean = mean, sd = sd))) %>%
  ungroup() %>%
  mutate(ocean_sink_estimate =
           str_replace(ocean_sink_estimate, "_", " ")) %>%
  ggplot() +
  geom_pointrange(
    aes(
      period,
      land_sink_reported_mean,
      ymin = land_sink_reported_mean - land_sink_reported_sd,
      ymax = land_sink_reported_mean + land_sink_reported_sd,
      shape = "reported"
    ),
    position = position_dodge2(width = 0.6)
  ) +
  geom_pointrange(
    aes(
      period,
      land_sink_mean,
      ymin = land_sink_mean - land_sink_sd,
      ymax = land_sink_mean + land_sink_sd,
      col = ocean_sink_estimate,
      shape = "residual"
    ),
    position = position_dodge2(width = 0.5)
  ) +
  scale_color_brewer(palette = "Set1",
                     name = "ocean sink\nestimate\nunderlying\nresidual") +
  scale_shape_manual(values = c(17, 16), name = "land sink\nestimate") +
  guides(shape = guide_legend(order = 1),
         color = guide_legend(order = 2)) +
  theme(axis.title.x = element_blank()) +
  labs(y = flux_label,
       title = "Decadal mean land sink estimates",
       subtitle = "Note: Errorbars indicate the StDev of the IAV") +
  theme(axis.title.x = element_blank())

p_land_sink_residual_decade

Past versions of GCB_land_sink_assesment-10.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

pdf(
  file = here::here("output/publication/GCB_land_sink_residual.pdf"),
  height = 6,
  width = 9
)

p_stacked_fluxes
p_individual_fluxes
p_BIM
p_land_sink_gcb
p_ocean_sinks
p_land_sink_residual
p_land_sink_residual_delta
p_land_sink_residual_decade

dev.off()

png 
  2 

rm(
  p_stacked_fluxes,
  p_individual_fluxes,
  p_BIM,
  p_land_sink_gcb,
  p_ocean_sinks,
  p_land_sink_residual,
  p_land_sink_residual_delta,
  p_land_sink_residual_decade,
  estimates,
  estimates_wide,
  land_sink,
  Ocean_Sink_annual_mean,
  flux_components,
  flux_label,
  emission_components,
  sink_components
)

17.2 Net land sink

flux_label <- expression(flux~(PgC~yr^{-1}))

Global_Carbon_Budget_net_land_sink <- Global_Carbon_Budget %>%
  select(year, estimate, GtC_yr) %>%
  pivot_wider(names_from = estimate,
              values_from = GtC_yr) %>%
  mutate(`net land sink` = `land sink` - `land-use change emissions`) %>%
  select(-c(`land sink`, `land-use change emissions`)) %>%
  pivot_longer(-year,
               names_to = "estimate",
               values_to = "GtC_yr")

flux_components <- Global_Carbon_Budget_net_land_sink %>% 
  distinct(estimate) %>% 
  pull()

emission_components <- flux_components[c(1,4)]
sink_components <- flux_components[c(2,3,6)]

estimates <- Global_Carbon_Budget_net_land_sink %>%
  filter(!(estimate %in% c("budget imbalance")),
         year >= 1990) %>%
  mutate(
    sign = if_else(estimate %in% sink_components,
                   "sinks",
                   "emissions"),
    GtC_yr = if_else(sign == "sinks",
                     -GtC_yr,
                     GtC_yr),
    GtC_yr = if_else(estimate == "cement carbonation sink",
                     -GtC_yr,
                     GtC_yr)
  )

emissions_sinks <- estimates %>% 
  group_by(year, sign) %>% 
  summarise(GtC_yr = sum(GtC_yr)) %>% 
  ungroup()


p_stacked_fluxes <- estimates %>%
  ggplot() +
  geom_area(aes(year, GtC_yr, fill = estimate),
            col = "white",
            alpha = 0.7,
            size = 0.5) +
  geom_line(data = emissions_sinks %>%   mutate(GtC_yr = if_else(sign == "emissions",
                                                                 -GtC_yr,
                                                                 GtC_yr)),
            aes(year, GtC_yr, linetype = sign)) +
  scale_fill_bright(name = "flux component") +
  geom_hline(yintercept = 0) +
  scale_x_continuous(expand = c(0,0)) +
  labs(y = flux_label,
       title = "Stacked emission and sink components from GCB",
       subtitle = "Note: The cement carbonation sink is accounted for in emissions") +
  theme(axis.title.x = element_blank())

p_stacked_fluxes

Past versions of GCB_net_land_sink_assesment-1.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

p_individual_fluxes <- estimates %>%
  ggplot(aes(year, GtC_yr, col = estimate)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(size = 0.5) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_bright(name = "flux component") +
  labs(y = flux_label,
       title = "Individual flux components from GCB") +
  theme(axis.title.x = element_blank())

p_individual_fluxes

Past versions of GCB_net_land_sink_assesment-2.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

BIM <- emissions_sinks %>% 
  pivot_wider(names_from = sign,
              values_from = GtC_yr) %>% 
  mutate(BIM = emissions + sinks)

p_BIM <- ggplot() +
  geom_line(data = Global_Carbon_Budget %>%
              filter(estimate == "budget imbalance",
                     year >= 1990),
            aes(year, GtC_yr, col = "reported")) +
  geom_line(data = BIM, aes(year, BIM, col = "recalculated")) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Set1") +
  scale_x_continuous(expand = c(0, 0)) +
  labs(y = flux_label,
       title = "BIM from GCB") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_BIM

Past versions of GCB_net_land_sink_assesment-3.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

p_land_sink_gcb <- estimates %>%
  select(year, estimate, GtC_yr) %>%
  pivot_wider(names_from = estimate,
              values_from = GtC_yr) %>%
  mutate(`net land sink residual` = 
           `fossil emissions excluding carbonation` + 
           `cement carbonation sink` + 
           `atmospheric growth` + 
           `ocean sink`) %>%
  ggplot() +
  geom_line(aes(year, -`net land sink residual`, col = "residual")) +
  geom_line(aes(year, `net land sink`, col = "reported")) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_brewer(palette = "Set1", name = "land sink\nestimate") +
  labs(y = flux_label,
       title = "Land sink estimates from GCB",
       subtitle = "Note: The offset between reported and residual estimate equals BIM") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_land_sink_gcb

Past versions of GCB_net_land_sink_assesment-4.png

Version	Author	Date
91052ae	jens-daniel-mueller	2022-10-20

Ocean_Sink_annual_mean <- Ocean_Sink %>%
  filter(year >= 1990) %>%
  group_by(year, type) %>%
  summarise(GtC_yr = -mean(GtC_yr)) %>%
  ungroup()

Ocean_Sink_annual_mean <- Ocean_Sink_annual_mean %>% 
  pivot_wider(names_from = type,
              values_from = GtC_yr,
              names_prefix = "ocean_sink_")

estimates_wide <- estimates %>%
  select(year, estimate, GtC_yr) %>%
  pivot_wider(names_from = estimate,
              values_from = GtC_yr)

estimates_wide <-
  full_join(estimates_wide, Ocean_Sink_annual_mean)

dcant_interior <- tcant_ts_zoom %>% 
  filter(year > 2000) %>%
  mutate(dcant_mean = dcant_mean/10) %>% 
  pull(dcant_mean)

cnat <-
  dcant_type_significance %>%
  filter(type == "Observation-based\nsurface flux\nproducts" ,
         period %in% two_decades) %>%
  mutate(dcant_diff = dcant_diff/10) %>% 
  pull(dcant_diff)

estimates_wide <- estimates_wide %>% 
  mutate(ocean_sink_interior_Cant = case_when(
    between(year, 1994, 2003) ~ -dcant_interior[1],
    between(year, 2004, 2013) ~ -dcant_interior[2],
    TRUE ~ NaN
  )) %>% 
  mutate(ocean_sink_interior_net = case_when(
    between(year, 1994, 2003) ~ -dcant_interior[1]-cnat[1],
    between(year, 2004, 2013) ~ -dcant_interior[2]-cnat[2],
    TRUE ~ NaN
  ))


p_ocean_sinks <- estimates_wide %>%
  select(year, starts_with("ocean")) %>% 
  rename(GCB_mean = `ocean sink`) %>% 
  pivot_longer(-year,
               names_to = "estimate",
               names_prefix = "ocean_sink_",
               values_to = "GtC_yr") %>% 
  mutate(estimate = str_replace(estimate, "_", " ")) %>% 
  ggplot(aes(year, GtC_yr, col = estimate)) +
  geom_path() +
  geom_point(size = 0.5) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_brewer(palette = "Set1", name = "ocean sink\nestimate") +
  labs(y = flux_label,
       title = "Ocean sink estimates",
       subtitle = "Note: Watson et al. (2020) is included in data products and GCB mean") +
  theme(axis.title.x = element_blank(),
        legend.title = element_blank())

p_ocean_sinks

Past versions of GCB_net_land_sink_assesment-5.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

land_sink <-
  estimates_wide %>%
  mutate(emissions_atmosphere = `fossil emissions excluding carbonation` +
           `atmospheric growth`) %>%
  rename(ocean_sink_gcb_mean = `ocean sink`) %>%
  select(year, emissions_atmosphere, starts_with("ocean")) %>%
  pivot_longer(
    starts_with("ocean_sink"),
    names_to = "ocean_sink_estimate",
    values_to = "ocean_sink",
    names_prefix = "ocean_sink_"
  ) %>%
  mutate(net_land_sink = -emissions_atmosphere - ocean_sink)


p_land_sink_residual <- land_sink %>%
  mutate(ocean_sink_estimate = 
           str_replace(ocean_sink_estimate, "_", " ")) %>% 
  ggplot() +
  geom_line(aes(year, net_land_sink, col = ocean_sink_estimate, linetype = "residual")) +
  geom_line(data = estimates_wide %>%
              filter(year >= 1990),
            aes(year, `net land sink`, linetype = "reported")) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_linetype_manual(values = c(2, 1), name = "net land sink\nestimate") +
  scale_color_brewer(palette = "Set1",
                     name = "ocean sink\nestimate\nunderlying\nresidual") +
  guides(linetype = guide_legend(order = 1),
         color = guide_legend(order = 2)) +
  labs(y = flux_label,
       title = "Net land sink estimates",
       subtitle = "Note: Residuals are determined for a range of ocean sink estimates") +
  theme(axis.title.x = element_blank())

p_land_sink_residual

Past versions of GCB_net_land_sink_assesment-6.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

land_sink <-
  full_join(
    land_sink,
    estimates_wide %>%
      select(year, net_land_sink_reported = `net land sink`)
  )


p_land_sink_residual_delta <- land_sink %>%
  mutate(ocean_sink_estimate =
           str_replace(ocean_sink_estimate, "_", " ")) %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_line(aes(year, net_land_sink - net_land_sink_reported, col = ocean_sink_estimate)) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_color_brewer(palette = "Set1",
                     name = "ocean sink\nestimate\nunderlying\nresidual") +
  labs(y = flux_label,
       title = "Let land sink estimates (reported - residual)",
       subtitle = "Note: Residuals are determined for a range of ocean sink estimates") +
  theme(axis.title.x = element_blank())

p_land_sink_residual_delta

Past versions of GCB_net_land_sink_assesment-7.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

p_land_sink_residual_decade <- land_sink %>%
  mutate(period = cut(year, c(1993, 2003, 2013),
                      c("1994 - 2004", "2004 - 2014"))) %>%
  filter(!is.na(period)) %>%
  group_by(ocean_sink_estimate, period) %>%
  summarise(across(starts_with("net_land_sink"), list(mean = mean, sd = sd))) %>%
  ungroup() %>%
  mutate(ocean_sink_estimate =
           str_replace(ocean_sink_estimate, "_", " ")) %>%
  ggplot() +
  geom_pointrange(
    aes(
      period,
      net_land_sink_reported_mean,
      ymin = net_land_sink_reported_mean - net_land_sink_reported_sd,
      ymax = net_land_sink_reported_mean + net_land_sink_reported_sd,
      shape = "reported"
    ),
    position = position_dodge2(width = 0.6)
  ) +
  geom_pointrange(
    aes(
      period,
      net_land_sink_mean,
      ymin = net_land_sink_mean - net_land_sink_sd,
      ymax = net_land_sink_mean + net_land_sink_sd,
      col = ocean_sink_estimate,
      shape = "residual"
    ),
    position = position_dodge2(width = 0.5)
  ) +
  scale_color_brewer(palette = "Set1",
                     name = "ocean sink\nestimate\nunderlying\nresidual") +
  scale_shape_manual(values = c(17, 16), name = "land sink\nestimate") +
  guides(shape = guide_legend(order = 1),
         color = guide_legend(order = 2)) +
  theme(axis.title.x = element_blank()) +
  labs(y = flux_label,
       title = "Decadal mean land sink estimates",
       subtitle = "Note: Errorbars indicate the StDev of the IAV") +
  theme(axis.title.x = element_blank())

p_land_sink_residual_decade

Past versions of GCB_net_land_sink_assesment-8.png

Version	Author	Date
7275091	jens-daniel-mueller	2022-11-05
2345935	jens-daniel-mueller	2022-10-29
91052ae	jens-daniel-mueller	2022-10-20

pdf(
  file = here::here("output/publication/GCB_net_land_sink_residual.pdf"),
  height = 6,
  width = 9
)

p_stacked_fluxes
p_individual_fluxes
p_BIM
p_land_sink_gcb
p_ocean_sinks
p_land_sink_residual
p_land_sink_residual_delta
p_land_sink_residual_decade

dev.off()

png 
  2 

rm(
  p_stacked_fluxes,
  p_individual_fluxes,
  p_BIM,
  p_land_sink_gcb,
  p_ocean_sinks,
  p_land_sink_residual,
  p_land_sink_residual_delta,
  p_land_sink_residual_decade,
  estimates,
  estimates_wide,
  land_sink,
  Ocean_Sink_annual_mean,
  flux_components,
  flux_label,
  emission_components,
  sink_components
)

18 Emission removal

GCB_Cant_emissions <- Global_Carbon_Budget %>%
  mutate(year = cut(year, c(1994, 2004, 2014), c("2004", "2014")),
         year = as.numeric(as.character(year))) %>%
  drop_na() %>%
  filter(estimate %in% c(
    "fossil emissions excluding carbonation",
    "land-use change emissions"
  )) %>%
  group_by(year) %>%
  summarise(cant_emissions = sum(GtC_yr)) %>%
  ungroup()

left_join(GCB_Cant_emissions,
          tcant_ts_zoom) %>%
  mutate(
    dcant_per_emission = 100 * dcant_mean / cant_emissions,
    dcant_per_emission_sd = 100 * dcant_sd / cant_emissions
  ) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

year	cant_emissions	dcant_mean	dcant_sd	pCO2	tcant	tcant_sd	dcant_sd_int	dcant_per_emission	dcant_per_emission_sd
2004	82.66365	27.388	3.281987	376.95	145.388	19.28138	3.281987	33.13185	3.970291
2014	101.81399	25.583	4.547768	397.34	170.971	19.53669	4.547768	25.12719	4.466742

18.0.0.1 Map test color palettes

breaks <- c(-Inf, seq(0, 16, 2), Inf)

legend_title <- expression(atop(Delta * C["ant"],
                                (mol ~ m ^ {
                                  -2
                                })))

breaks_n <- length(breaks) - 1

dcant_inv_all_color_test <- dcant_inv_all %>%
  filter(data_source %in% c("obs"),
         period == "2004 - 2014") %>%
  mutate(dcant_int = cut(dcant,
                         breaks,
                         right = FALSE))

scico_continous_palettes <- c(
  "acton",
  "bamako",
  "batlow",
  "bilbao",
  "buda",
  "davos",
  "devon",
  "grayC",
  "hawaii",
  "imola",
  "lajolla",
  "lapaz",
  "nuuk",
  "oslo",
  "tokyo",
  "turku"
)

p_test_scico <- function(df, i_palette) {
  p_reg <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_scico_d(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    labs(title = i_palette) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_rev <- map +
    geom_raster(data = df,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_scico_d(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      direction = -1,
      guide = "none"
    ) +
    labs(title = paste(i_palette, "rev")) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_comb <- p_reg / p_rev
  
  return(p_comb)
}


all_plots_scico <- scico_continous_palettes %>% 
  # head(1) %>% 
  map(~p_test_scico(df = dcant_inv_all_color_test,
                      i_palette = .x))

viridis_continous_palettes <- c(
  "viridis",
  "magma",
  "inferno",
  "plasma"
)

p_test_viridis <- function(df, i_palette) {
  p_reg <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_viridis_d(
      option = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    labs(title = i_palette) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_rev <- map +
    geom_raster(data = df,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_viridis_d(
      option = i_palette,
      drop = FALSE,
      name = legend_title,
      direction = -1,
      guide = "none"
    ) +
    labs(title = paste(i_palette, "rev")) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_comb <- p_reg / p_rev
  
  return(p_comb)
}

all_plots_viridis <- viridis_continous_palettes %>% 
  # head(1) %>% 
  map(~p_test_viridis(df = dcant_inv_all_color_test,
                      i_palette = .x))



library(colorspace)

colorspace_continous_palettes <- c(
  # "Purple - Blue",
  # "Red - Purple",
  # "Blue - Yellow",
  # "Heat",
  # "Heat 2",
  "Rocket"
  # "Mako",
  # "Dark",
  # "Mint",
  # "Mint",
  # "BluGrn",
  # "Teal",
  # "Sunset",
  # "SunsetDark",
  # "ag_Sunset",
  # "YlOrRd",
  # "YlGnBu",
  # "RdPu"
)

p_test_colorspace <- function(df, i_palette) {
  
  i_palette <<- i_palette
  
  p_reg <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_discrete_sequential(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    labs(title = i_palette) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_rev <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_discrete_sequential(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      rev = FALSE,
      guide = "none"
    ) +
    labs(title = paste(i_palette, "rev")) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())

  p_comb <- p_reg / p_rev
  
  return(p_comb)
  rm(i_palette)
  
}

all_plots_colorspace <- colorspace_continous_palettes %>%
  # head(2) %>%
  map(~p_test_colorspace(df = dcant_inv_all_color_test,
                           i_palette = .x))

all_plots_colorspace

all_plots <- c(all_plots_colorspace, all_plots_viridis, all_plots_scico)

pdf(here::here("output/publication/test_color_scales.pdf"),
    width = 4, height = 4)
all_plots
dev.off()

Session information

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggpattern_1.0.0    ggh4x_0.2.1        khroma_1.9.0       BSDA_1.2.1        
 [5] lattice_0.20-45    kableExtra_1.3.4   ggdist_3.1.1       ggalluvial_0.12.3 
 [9] geomtextpath_0.1.0 colorspace_2.0-2   marelac_2.1.10     shape_1.4.6       
[13] ggforce_0.3.3      metR_0.11.0        scico_1.3.0        patchwork_1.1.1   
[17] collapse_1.7.0     forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7       
[21] purrr_0.3.4        readr_2.1.1        tidyr_1.1.4        tibble_3.1.6      
[25] ggplot2_3.3.5      tidyverse_1.3.1    workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         backports_1.4.1      Hmisc_4.6-0         
  [4] systemfonts_1.0.3    plyr_1.8.6           splines_4.1.2       
  [7] digest_0.6.29        htmltools_0.5.2      fansi_1.0.2         
 [10] magrittr_2.0.1       checkmate_2.0.0      memoise_2.0.1       
 [13] cluster_2.1.2        tzdb_0.2.0           modelr_0.1.8        
 [16] vroom_1.5.7          svglite_2.0.0        jpeg_0.1-9          
 [19] rvest_1.0.2          textshaping_0.3.6    haven_2.4.3         
 [22] xfun_0.29            callr_3.7.0          crayon_1.4.2        
 [25] jsonlite_1.7.3       survival_3.2-13      zoo_1.8-9           
 [28] glue_1.6.0           polyclip_1.10-0      gtable_0.3.0        
 [31] webshot_0.5.2        distributional_0.3.0 scales_1.1.1        
 [34] DBI_1.1.2            Rcpp_1.0.8           isoband_0.2.5       
 [37] htmlTable_2.4.0      viridisLite_0.4.0    units_0.7-2         
 [40] foreign_0.8-82       bit_4.0.4            proxy_0.4-26        
 [43] Formula_1.2-4        htmlwidgets_1.5.4    httr_1.4.2          
 [46] seacarb_3.3.0        RColorBrewer_1.1-2   ellipsis_0.3.2      
 [49] pkgconfig_2.0.3      farver_2.1.0         nnet_7.3-17         
 [52] sass_0.4.0           dbplyr_2.1.1         utf8_1.2.2          
 [55] here_1.0.1           tidyselect_1.1.1     labeling_0.4.2      
 [58] rlang_1.0.2          later_1.3.0          munsell_0.5.0       
 [61] cellranger_1.1.0     tools_4.1.2          cachem_1.0.6        
 [64] cli_3.1.1            generics_0.1.1       broom_0.7.11        
 [67] evaluate_0.14        fastmap_1.1.0        yaml_2.2.1          
 [70] oce_1.5-0            processx_3.5.2       knitr_1.37          
 [73] bit64_4.0.5          fs_1.5.2             nlme_3.1-155        
 [76] whisker_0.4          xml2_1.3.3           gridpattern_1.0.1   
 [79] compiler_4.1.2       rstudioapi_0.13      png_0.1-7           
 [82] e1071_1.7-9          reprex_2.0.1         tweenr_1.0.2        
 [85] bslib_0.3.1          stringi_1.7.6        highr_0.9           
 [88] ps_1.6.0             Matrix_1.4-0         classInt_0.4-3      
 [91] vctrs_0.3.8          pillar_1.6.4         lifecycle_1.0.1     
 [94] jquerylib_0.1.4      gsw_1.0-6            data.table_1.14.2   
 [97] httpuv_1.6.5         R6_2.5.1             latticeExtra_0.6-29 
[100] promises_1.2.0.1     gridExtra_2.3        KernSmooth_2.23-20  
[103] MASS_7.3-55          assertthat_0.2.1     rprojroot_2.0.2     
[106] withr_2.4.3          SolveSAPHE_2.1.0     mgcv_1.8-38         
[109] parallel_4.1.2       hms_1.1.1            rpart_4.1-15        
[112] grid_4.1.2           class_7.3-20         rmarkdown_2.11      
[115] git2r_0.29.0         sf_1.0-5             getPass_0.2-2       
[118] lubridate_1.8.0      base64enc_0.1-3