Results publication

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-07-16

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.

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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

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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: b91f619

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 b91f619. 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/publication/

Untracked files:
    Untracked:  figure/

Unstaged changes:
    Modified:   analysis/_site.yml
    Modified:   analysis/child/budget_analysis_plot_data.Rmd
    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
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.
Rmd	d3f7f05	jens-daniel-mueller	2022-02-02	incl ensemble uncertainties
html	fa46251	jens-daniel-mueller	2022-02-02	Build site.
Rmd	33feacf	jens-daniel-mueller	2022-02-02	incl sabine budgets
html	7655085	jens-daniel-mueller	2022-02-02	Build site.
Rmd	09dd555	jens-daniel-mueller	2022-02-02	incl sabine budgets
html	226d67d	jens-daniel-mueller	2022-02-02	Build site.
Rmd	ed056e2	jens-daniel-mueller	2022-02-02	incl sabine budgets
html	ed903f7	jens-daniel-mueller	2022-02-02	Build site.
Rmd	9d11695	jens-daniel-mueller	2022-02-02	scaled budget to atm pco2 increase
html	32e9682	jens-daniel-mueller	2022-02-02	Build site.
Rmd	5cc5572	jens-daniel-mueller	2022-02-02	included Sabine column inventory as reference
html	913e42f	jens-daniel-mueller	2022-02-01	Build site.
Rmd	c4b2de9	jens-daniel-mueller	2022-02-01	updated profile plots
html	189de95	jens-daniel-mueller	2022-02-01	Build site.
Rmd	b0c17bc	jens-daniel-mueller	2022-02-01	updated profile plots
html	ab001eb	jens-daniel-mueller	2022-01-31	Build site.
Rmd	ccf6723	jens-daniel-mueller	2022-01-31	filled step plot for layer budgets
html	d2ae5fe	jens-daniel-mueller	2022-01-31	Build site.
Rmd	278523c	jens-daniel-mueller	2022-01-31	step plot for layer budgets
html	b62308d	jens-daniel-mueller	2022-01-31	Build site.
Rmd	714c5cc	jens-daniel-mueller	2022-01-31	step plot for layer budgets
html	ec7fe7e	jens-daniel-mueller	2022-01-31	Build site.
Rmd	5c948ae	jens-daniel-mueller	2022-01-31	added time series vs atm pco2
html	de557de	jens-daniel-mueller	2022-01-28	Build site.
html	5f2aed0	jens-daniel-mueller	2022-01-27	Build site.
Rmd	54c9e26	jens-daniel-mueller	2022-01-27	added layer budget profiles
html	eccd82b	jens-daniel-mueller	2022-01-26	Build site.
Rmd	c5577d3	jens-daniel-mueller	2022-01-26	added meand sd to offset mean concentrations profiles
html	c6fe495	jens-daniel-mueller	2022-01-26	Build site.
Rmd	e0e7974	jens-daniel-mueller	2022-01-26	added offset mean concentrations profiles
html	9753eb8	jens-daniel-mueller	2022-01-26	Build site.
html	b1d7720	jens-daniel-mueller	2022-01-21	Build site.
Rmd	0210ed5	jens-daniel-mueller	2022-01-21	added mean concentrations profiles per 5 basins
html	d6b399a	jens-daniel-mueller	2022-01-21	Build site.
Rmd	da17a07	jens-daniel-mueller	2022-01-21	added mean concentrations profiles
html	c499be8	jens-daniel-mueller	2022-01-21	Build site.
Rmd	d941871	jens-daniel-mueller	2022-01-21	run color map test
html	e572075	jens-daniel-mueller	2022-01-21	Build site.
Rmd	99b6c92	jens-daniel-mueller	2022-01-21	run color map test
html	4fe7150	jens-daniel-mueller	2022-01-21	Build site.
Rmd	0379e99	jens-daniel-mueller	2022-01-21	script cleaning
html	49b41cf	jens-daniel-mueller	2022-01-21	Build site.
Rmd	2c82651	jens-daniel-mueller	2022-01-21	added map of scaled absolute change
html	c0807e8	jens-daniel-mueller	2022-01-21	Build site.
Rmd	5dd3d7a	jens-daniel-mueller	2022-01-21	added map of scaled relative change
html	22b421f	jens-daniel-mueller	2022-01-21	Build site.
Rmd	2c3fa75	jens-daniel-mueller	2022-01-21	cleaned alluvial plots
html	1a35f1f	jens-daniel-mueller	2022-01-20	Build site.
Rmd	e58f510	jens-daniel-mueller	2022-01-20	added relative changes to alluvial plots
html	b503ae1	jens-daniel-mueller	2022-01-20	Build site.
Rmd	2eb2567	jens-daniel-mueller	2022-01-20	added relative changes to alluvial plots
html	cc31f4b	jens-daniel-mueller	2022-01-20	Build site.
Rmd	416e107	jens-daniel-mueller	2022-01-20	added delta dcant map
html	11a800b	jens-daniel-mueller	2022-01-20	Build site.
Rmd	81a40d5	jens-daniel-mueller	2022-01-20	updated alluvial plots
html	3087804	jens-daniel-mueller	2022-01-20	Build site.
Rmd	2ae5966	jens-daniel-mueller	2022-01-20	updated alluvial plots
html	6d566d5	jens-daniel-mueller	2022-01-20	Build site.
Rmd	4901b0f	jens-daniel-mueller	2022-01-20	updated alluvial plots
html	44796b1	jens-daniel-mueller	2022-01-20	Build site.
Rmd	cdbd92c	jens-daniel-mueller	2022-01-20	created alluvial plots
html	48ec4c6	jens-daniel-mueller	2022-01-19	Build site.
Rmd	0fb2ae5	jens-daniel-mueller	2022-01-19	printed column inv from AIP standard runs
html	f347cd7	jens-daniel-mueller	2022-01-18	Build site.
Rmd	86b711c	jens-daniel-mueller	2022-01-18	plot hemisphere budgets and publication results

---

1 Libraries

2 Read files

2.1 Paths and Versions

version_id_pattern <- "103"

# 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(Version_IDs_1, Version_IDs_2, Version_IDs_3)

print(Version_IDs)

[1] "v_1103" "v_2103" "v_3103"

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

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

2.2 Parameters

for (i_Version_IDs in Version_IDs) {
  # i_Version_IDs <- Version_IDs[1]
  
  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")) {
    params_local_all <- bind_rows(params_local_all, params_local)
  }
  
  if (!exists("params_local_all")) {
    params_local_all <- params_local
  }
  
  
}

rm(params_local,
   tref)

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

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" ~ "standard",
      Version_ID_group == "o" ~ "reoccupation filter",
      Version_ID_group == "d" ~ "no data adjustment",
      Version_ID_group == "g" ~ "no gap filling",
      Version_ID_group == "e" ~ "no surface equi",
      Version_ID_group == "n" ~ "C*(N)",
      Version_ID_group == "c" ~ "cruise adjustment",
      TRUE ~ Version_ID_group
    )
  )

2.3 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
  }

}

[1] "v_1101"
[1] "v_1102"
[1] "v_1103"
[1] "v_1104"
[1] "v_1105"
[1] "v_1106"
[1] "v_2101"
[1] "v_2102"
[1] "v_2103"
[1] "v_2104"
[1] "v_2105"
[1] "v_2106"
[1] "v_3101"
[1] "v_3102"
[1] "v_3103"
[1] "v_3104"
[1] "v_3105"
[1] "v_3106"
[1] "v_1o01"
[1] "v_1o02"
[1] "v_1o03"
[1] "v_1o04"
[1] "v_1o05"
[1] "v_1o06"
[1] "v_2o01"
[1] "v_2o02"
[1] "v_2o03"
[1] "v_2o04"
[1] "v_2o05"
[1] "v_2o06"
[1] "v_3o01"
[1] "v_3o02"
[1] "v_3o03"
[1] "v_3o04"
[1] "v_3o05"
[1] "v_3o06"
[1] "v_1d01"
[1] "v_1d02"
[1] "v_1d03"
[1] "v_1d04"
[1] "v_1d05"
[1] "v_1d06"
[1] "v_2d01"
[1] "v_2d02"
[1] "v_2d03"
[1] "v_2d04"
[1] "v_2d05"
[1] "v_2d06"
[1] "v_3d01"
[1] "v_3d02"
[1] "v_3d03"
[1] "v_3d04"
[1] "v_3d05"
[1] "v_3d06"
[1] "v_1g01"
[1] "v_1g02"
[1] "v_1g03"
[1] "v_1g04"
[1] "v_1g05"
[1] "v_1g06"
[1] "v_2g01"
[1] "v_2g02"
[1] "v_2g03"
[1] "v_2g04"
[1] "v_2g05"
[1] "v_2g06"
[1] "v_3g01"
[1] "v_3g02"
[1] "v_3g03"
[1] "v_3g04"
[1] "v_3g05"
[1] "v_3g06"
[1] "v_1n01"
[1] "v_1n02"
[1] "v_1n03"
[1] "v_1n04"
[1] "v_1n05"
[1] "v_1n06"
[1] "v_2n01"
[1] "v_2n02"
[1] "v_2n03"
[1] "v_2n04"
[1] "v_2n05"
[1] "v_2n06"
[1] "v_3n01"
[1] "v_3n02"
[1] "v_3n03"
[1] "v_3n04"
[1] "v_3n05"
[1] "v_3n06"
[1] "v_1c01"
[1] "v_1c02"
[1] "v_1c03"
[1] "v_1c04"
[1] "v_1c05"
[1] "v_1c06"
[1] "v_2c01"
[1] "v_2c02"
[1] "v_2c03"
[1] "v_2c04"
[1] "v_2c05"
[1] "v_2c06"
[1] "v_3c01"
[1] "v_3c02"
[1] "v_3c03"
[1] "v_3c04"
[1] "v_3c05"
[1] "v_3c06"
[1] "v_1e01"
[1] "v_1e02"
[1] "v_1e03"
[1] "v_1e04"
[1] "v_1e05"
[1] "v_1e06"
[1] "v_2e01"
[1] "v_2e02"
[1] "v_2e03"
[1] "v_2e04"
[1] "v_2e05"
[1] "v_2e06"
[1] "v_3e01"
[1] "v_3e02"
[1] "v_3e03"
[1] "v_3e04"
[1] "v_3e05"
[1] "v_3e06"

rm(
  dcant_budget_basin_MLR,
  dcant_budget_basin_MLR_mod_truth
)

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

2.4 Column inventories

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_inv <-
    read_csv(paste(path_version_data,
                   "dcant_inv.csv",
                   sep = ""))
  
  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")

2.5 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_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_basin_MLR <-
    read_csv(paste(path_version_data,
                   "dcant_profile_basin_MLR.csv",
                   sep = ""))
  
  dcant_profile <- bind_rows(dcant_profile,
                             dcant_profile_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_zonal_bias <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_bias.csv",
                   sep = ""))
  

  dcant_zonal <- dcant_zonal %>% 
    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)

  
  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_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
  }

}

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

2.6 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
  }
  
}

rm(GLODAP_grid_era)

GLODAP_grid_era_all <- full_join(GLODAP_grid_era_all,
                                 params_local_all)

2.7 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 = ""))

2.8 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)

2.9 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 = ""))

3 Define labels

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

4 Coverage maps

coverage_map <- map +
  geom_tile(data = GLODAP_grid_era_all,
            aes(lon, lat),
            fill = "#377eb8",
            col = "#377eb8") +
  facet_wrap( ~ era, ncol = 2) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

coverage_map

Past versions of coverage_maps-1.png

Version	Author	Date
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/publication"),
       filename = "FigS_coverage_map_observations.png",
       height = 4,
       width = 8)

5 Atm CO2

5.1

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
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)

rm(co2_atm_roll)

6 Inventory maps

6.1 Absoulte values

6.1.1 dcant - absolute

dcant_inv_all <- dcant_inv_all %>%
  filter(data_source %in% c("obs"),
         period != "1994 - 2014")

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

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


tcant_inv <- tcant_inv %>% 
  mutate(pCO21 = 280)

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

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


dcant_inv_all <- dcant_inv_all %>% 
  mutate(delta_pCO2 = pCO22 - pCO21,
         beta = dcant / delta_pCO2,
         beta_pos = dcant_pos / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

dcant_inv_all_ensemble <- dcant_inv_all

unique(dcant_inv_all$Version_ID)

 [1] "v_1101" "v_1102" "v_1103" "v_1104" "v_1105" "v_1106" "v_2101" "v_2102"
 [9] "v_2103" "v_2104" "v_2105" "v_2106" "v_1o01" "v_1o02" "v_1o03" "v_1o04"
[17] "v_1o05" "v_1o06" "v_2o01" "v_2o02" "v_2o03" "v_2o04" "v_2o05" "v_2o06"
[25] "v_1d01" "v_1d02" "v_1d03" "v_1d04" "v_1d05" "v_1d06" "v_2d01" "v_2d02"
[33] "v_2d03" "v_2d04" "v_2d05" "v_2d06" "v_1g01" "v_1g02" "v_1g03" "v_1g04"
[41] "v_1g05" "v_1g06" "v_2g01" "v_2g02" "v_2g03" "v_2g04" "v_2g05" "v_2g06"
[49] "v_1n01" "v_1n02" "v_1n03" "v_1n04" "v_1n05" "v_1n06" "v_2n01" "v_2n02"
[57] "v_2n03" "v_2n04" "v_2n05" "v_2n06" "v_1c01" "v_1c02" "v_1c03" "v_1c04"
[65] "v_1c05" "v_1c06" "v_2c01" "v_2c02" "v_2c03" "v_2c04" "v_2c05" "v_2c06"
[73] "v_1e01" "v_1e02" "v_1e03" "v_1e04" "v_1e05" "v_1e06" "v_2e01" "v_2e02"
[81] "v_2e03" "v_2e04" "v_2e05" "v_2e06" NA      

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

p_CI_absolute <- dcant_inv_all %>%
  filter(period != "1800 - 1994") %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_grid(period ~ .) +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) 

p_CI_absolute

Past versions of invetory_maps_absolute-1.png

Version	Author	Date
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
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
08607eb	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
b599680	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
d2191ad	jens-daniel-mueller	2022-02-04
32e9682	jens-daniel-mueller	2022-02-02
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

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

Past versions of invetory_maps_absolute-2.png

Version	Author	Date
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
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
b599680	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
2116dd3	jens-daniel-mueller	2022-02-09
d2191ad	jens-daniel-mueller	2022-02-04
32e9682	jens-daniel-mueller	2022-02-02
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

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



dcant_inv_all %>%
  filter(period != "1800 - 1994") %>% 
  p_map_cant_inv(var = "beta",
                 breaks = c(-Inf, seq(0, 1, 0.1), Inf),) +
  facet_grid(period ~ .)

Past versions of invetory_maps_absolute-3.png

Version	Author	Date
ea46812	jens-daniel-mueller	2022-07-13
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
b599680	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
d2191ad	jens-daniel-mueller	2022-02-04
32e9682	jens-daniel-mueller	2022-02-02

6.1.2 dcant - absolute delta

p_CI_delta <-
  dcant_inv_all %>%
  filter(data_source %in% c("obs"),
         period != "1994 - 2014") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`) %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_cant_inv(
      df = .x,
      var = "dcant_offset",
      title_text = NULL,
      subtitle_text = NULL,
      col = "bias"
    )
  )

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

Past versions of invetory_maps_absolute_delta-1.png

Version	Author	Date
f1d7f80	jens-daniel-mueller	2022-07-13
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
748aa43	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
b599680	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
32e9682	jens-daniel-mueller	2022-02-02
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

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

6.1.3 dcant - absolute delta scaled

dcant_budget_scaling <- dcant_budget_global_all %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  mutate(dcant_scaling = `2004 - 2014` / `1994 - 2004`) %>% 
  select(data_source, dcant_scaling)


left_join(dcant_inv_all %>% filter(data_source == "obs"),
          dcant_budget_scaling %>% filter(data_source == "obs")) %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant, dcant_scaling) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`*dcant_scaling) %>% 
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_cant_inv(df = .x,
                     var = "delta_dcant",
                     col = "bias") +
      # facet_grid(period ~ .) +
      theme(axis.text = element_blank(),
            axis.ticks = element_blank())
  )

[[1]]

Past versions of invetory_maps_absolute_delta_scaled-1.png

Version	Author	Date
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
b599680	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
32e9682	jens-daniel-mueller	2022-02-02
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

dcant_inv_all_ensemble_mean <-
  dcant_inv_all_ensemble %>%
  filter(period != "1800 - 1994",
         data_source == "obs") %>%
  group_by(data_source, period, lon, lat) %>%
  summarise(dcant_sd = sd(dcant)) %>%
  ungroup()


dcant_inv_all_ensemble_mean %>%
  p_map_cant_inv(var = "dcant_sd",
                 breaks = c(seq(0, 5, 0.5), Inf),
                 title_text = NULL) +
  facet_grid(period ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of column_inventory_standard_deviation-1.png

Version	Author	Date
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

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

6.2 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) %>% 
  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) %>% 
  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) %>% 
  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)

6.2.1 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(. ~ 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
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) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 6 × 2
  lat_grid  dcant_mean
  <fct>          <dbl>
1 (-40,-30]      10.8 
2 (-30,-10]       8.67
3 (-10,10]        5.63
4 (10,30]         5.60
5 (30,40]         8.14
6 <NA>            5.42

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) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 17 × 3
   lat_grid  basin_AIP dcant_mean
   <fct>     <chr>          <dbl>
 1 (-40,-30] Atlantic       11.7 
 2 (-40,-30] Indian         11.0 
 3 (-40,-30] Pacific         9.78
 4 (-30,-10] Atlantic        8.68
 5 (-30,-10] Indian          9.21
 6 (-30,-10] Pacific         8.11
 7 (-10,10]  Atlantic        6.78
 8 (-10,10]  Indian          6.08
 9 (-10,10]  Pacific         4.03
10 (10,30]   Atlantic        9.00
11 (10,30]   Indian          2.94
12 (10,30]   Pacific         4.86
13 (30,40]   Atlantic       11.8 
14 (30,40]   Pacific         4.50
15 <NA>      Atlantic        6.80
16 <NA>      Indian          5.16
17 <NA>      Pacific         4.19

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) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 12 × 4
   lat_grid basin_AIP period      dcant_mean
   <fct>    <chr>     <chr>            <dbl>
 1 (-90,0]  Atlantic  1994 - 2004       7.27
 2 (-90,0]  Atlantic  2004 - 2014       7.72
 3 (-90,0]  Indian    1994 - 2004       8.01
 4 (-90,0]  Indian    2004 - 2014       6.45
 5 (-90,0]  Pacific   1994 - 2004       7.50
 6 (-90,0]  Pacific   2004 - 2014       5.87
 7 (0,90]   Atlantic  1994 - 2004       9.77
 8 (0,90]   Atlantic  2004 - 2014       7.63
 9 (0,90]   Indian    1994 - 2004       2.59
10 (0,90]   Indian    2004 - 2014       4.94
11 (0,90]   Pacific   1994 - 2004       3.05
12 (0,90]   Pacific   2004 - 2014       3.74

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)) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() +
  facet_grid(. ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = dcant_CI_label)

Past versions of zonal_mean_storage_dcant-2.png

Version	Author	Date
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

6.2.2 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(. ~ 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
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)) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() +
  facet_grid(. ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = beta_CI_label)

Past versions of zonal_mean_storage_beta-2.png

Version	Author	Date
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(basin_AIP == "Atlantic") %>% 
  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(. ~ 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_Atlantic-1.png

Version	Author	Date
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

7 Zonal sections

7.1 dcant - absolut

p_dcant_zonal_absolute <-
  dcant_zonal_all %>%
  filter(
    data_source == "obs",
    period != "1994 - 2014",
    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) +
  # geom_hline(yintercept = seq(100,1000, 100)) +
  # geom_vline(xintercept = c(-35,0,35)) +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank())

p_dcant_zonal_absolute

Past versions of zonal_sections-1.png

Version	Author	Date
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 ~ .)
  )

7.2 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-1.png

Version	Author	Date
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

[[2]]

Past versions of inventory_maps_absolute_delta-2.png

Version	Author	Date
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

p_dcant_zonal_delta <- 
dcant_zonal_all %>%
  filter(data_source %in% c("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 = NULL,
      title_text = NULL,
      col = "bias"
    ) +
      facet_grid(. ~ basin_AIP)
      # theme(strip.text = element_blank(),
      #       strip.background = element_blank())
  )

p_dcant_zonal_absolute / p_dcant_zonal_delta + 
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_absolute_delta-3.png

Version	Author	Date
f1d7f80	jens-daniel-mueller	2022-07-13
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28

ggsave(path = here::here("output/publication"),
       filename = "Fig_zonal_mean_sections.png",
       height = 9,
       width = 15)

8 Concentration profiles

8.1 dcant - absolute

dcant_profile_global_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))) +
      # scale_y_reverse(breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        name = "mean \u00B1 sd",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        name = "mean \u00B1 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_global-1.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

[[2]]

Past versions of concentration_profiles_global-2.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

mean_surface_dcant <- dcant_profile_global_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(depth <= 50) %>%
  group_by(period) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_global_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>%
  filter(dcant >= mean_surface_dcant / 2) %>%
  group_by(period, Version_ID) %>%
  summarise(max_depth = max(depth)) %>%
  ungroup() %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth))

# A tibble: 1 × 2
  sd_max_depth mean_max_depth
         <dbl>          <dbl>
1         78.1           310.

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

[[1]]

Past versions of concentration_profiles_basin_AIP-1.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

[[2]]

Past versions of concentration_profiles_basin_AIP-2.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

[[3]]

Past versions of concentration_profiles_basin_AIP-3.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

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 sd", direction = -1) +
      scale_fill_brewer(palette = "Set1", name = "mean \u00B1 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
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
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

dcant_profile_global_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 = 0.5
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "standard case",
          size = "standard case"
        )
      ) +
      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 = "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 = c(0.85, 0.3),
            legend.title = element_blank())
  )

[[1]]

Past versions of concentration_profiles_global_ensemble-1.png

Version	Author	Date
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

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="x") +
      scale_linetype_manual(values = c(1, 1), name="x") +
      scale_alpha_manual(values = c(0.4, 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 = "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 = c(0.85, 0.3),
            legend.title = element_blank())
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_individual-1.png

Version	Author	Date
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

mean_surface_dcant <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(depth <= 50) %>%
  group_by(basin, period) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_basin_MLR_all,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 10 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004           NA            250
 2 N. Pacific  2004 - 2014           NA            200
 3 S. Pacific  1994 - 2004           NA            300
 4 S. Pacific  2004 - 2014           NA            300
 5 N. Atlantic 1994 - 2004           NA            400
 6 N. Atlantic 2004 - 2014           NA            500
 7 S. Atlantic 1994 - 2004           NA            300
 8 S. Atlantic 2004 - 2014           NA            600
 9 Indian      1994 - 2004           NA            400
10 Indian      2004 - 2014           NA            250

full_join(dcant_profile_basin_MLR_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 10 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004         75.6           250 
 2 N. Pacific  2004 - 2014         68.0           198.
 3 S. Pacific  1994 - 2004        139.            383.
 4 S. Pacific  2004 - 2014         90.9           328.
 5 N. Atlantic 1994 - 2004         89.3           403.
 6 N. Atlantic 2004 - 2014        138.            461.
 7 S. Atlantic 1994 - 2004         67.4           303.
 8 S. Atlantic 2004 - 2014        236.            435.
 9 Indian      1994 - 2004        138.            475 
10 Indian      2004 - 2014        119.            264.

dcant_profile_basin_MLR_range <- dcant_profile_basin_MLR_all_ensemble %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>%
  group_by(basin, depth, period) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()


full_join(dcant_profile_basin_MLR_all_ensemble %>% select(-dcant_sd),
          dcant_profile_basin_MLR_range) %>%
  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_ribbon(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          xmin = dcant - dcant_sd,
          xmax = dcant + dcant_sd,
          y = depth,
          fill = period,
          group = Version_ID
        ), alpha = 0.5
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          col = period,
          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 = "Set1", name = "Mean \u00B1 2 SD", direction = -1) +
      scale_fill_brewer(palette = "Set1", name = "Mean \u00B1 2 SD", direction = -1) +
      labs(
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.85, 0.3))
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_uncertainty_ribbon-1.png

Version	Author	Date
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

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



mean_surface_dcant <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(depth <= 50) %>%
  group_by(basin, period) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_basin_MLR_all,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 10 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004           NA            250
 2 N. Pacific  2004 - 2014           NA            200
 3 S. Pacific  1994 - 2004           NA            300
 4 S. Pacific  2004 - 2014           NA            300
 5 N. Atlantic 1994 - 2004           NA            400
 6 N. Atlantic 2004 - 2014           NA            500
 7 S. Atlantic 1994 - 2004           NA            300
 8 S. Atlantic 2004 - 2014           NA            600
 9 Indian      1994 - 2004           NA            400
10 Indian      2004 - 2014           NA            250

full_join(dcant_profile_basin_MLR_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 10 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004         75.6           250 
 2 N. Pacific  2004 - 2014         68.0           198.
 3 S. Pacific  1994 - 2004        139.            383.
 4 S. Pacific  2004 - 2014         90.9           328.
 5 N. Atlantic 1994 - 2004         89.3           403.
 6 N. Atlantic 2004 - 2014        138.            461.
 7 S. Atlantic 1994 - 2004         67.4           303.
 8 S. Atlantic 2004 - 2014        236.            435.
 9 Indian      1994 - 2004        138.            475 
10 Indian      2004 - 2014        119.            264.

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),
        alpha = 0.5
      ) +
      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") +
      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
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$inventory_depth_standard
    # data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  # head(1) %>% 
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(
          dcant,
          depth,
          col = Version_ID_group,
          group = Version_ID
        ), alpha = 0.5
      ) +
      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",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        direction = -1
      ) +
      labs(
        y = "Depth (m)",
        x = dcant_umol_label,
        title = unique(.x$data_source)
      ) +
      facet_grid(basin ~ period)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_member-1.png

Version	Author	Date
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
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

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

dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         depth <= params_global$inventory_depth_standard) %>%
  group_split(data_source, basin) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(dcant,
                 depth)) +
      geom_hline(yintercept = params_global$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 sd", direction = -1) +
      scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd", direction = -1) +
      labs(
        title = paste("data_source", unique(.x$data_source),
                      "|", unique(.x$basin)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      # facet_wrap(~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.8,0.2))
  )

8.2 dcant - absolute delta

delta <- dcant_profile_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, depth, basin_AIP, 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_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, depth, basin_AIP, period, dcant_sd) %>% 
  pivot_wider(names_from = period,
              values_from = dcant_sd) %>%
  mutate(delta_dcant_sd = (`2004 - 2014` + `1994 - 2004`) / 2) %>% 
  select(-c(`2004 - 2014`, `1994 - 2004`))

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

dcant_profile_all_delta %>%
  group_split(data_source) %>%
  # head(1) %>%
  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_grid(basin_AIP ~ .))

[[1]]

Past versions of concentration_profiles_basin_AIP_offset-1.png

Version	Author	Date
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
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_basin_AIP_offset-2.png

Version	Author	Date
a74e341	jens-daniel-mueller	2022-04-04
d2191ad	jens-daniel-mueller	2022-02-04
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
eccd82b	jens-daniel-mueller	2022-01-26
c6fe495	jens-daniel-mueller	2022-01-26

[[3]]

Past versions of concentration_profiles_basin_AIP_offset-3.png

Version	Author	Date
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
5f2aed0	jens-daniel-mueller	2022-01-27
eccd82b	jens-daniel-mueller	2022-01-26
c6fe495	jens-daniel-mueller	2022-01-26

delta <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  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 %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, depth, basin, period, dcant_sd) %>% 
  pivot_wider(names_from = period,
              values_from = dcant_sd) %>%
  mutate(delta_dcant_sd = (`2004 - 2014` + `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_grid(basin ~ .)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_offset-1.png

Version	Author	Date
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
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

9 Budgets

9.1 Scatterplots

9.1.1 Global

bind_rows(dcant_budget_global_all_no_adj,
          dcant_budget_global_all_ensemble) %>%
  ggplot(aes(period, dcant, col = Version_ID_group)) +
  geom_jitter() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ data_source)

Past versions of dcant_budget_global_ensemble-1.png

Version	Author	Date
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
6be73e0	jens-daniel-mueller	2022-06-30

dcant_budget_global_all_ensemble %>%
  mutate(ensemble_role = case_when(Version_ID %in% Version_IDs ~ "standard case",
                                   TRUE ~ "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("black", "red", "grey"),
                     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_global_all_no_adj %>%
      mutate(ensemble_role = "unadjusted") %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 2,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 2,
    alpha = .5,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_label, breaks = seq(0, 100, 2))

Past versions of dcant_budget_global_ensemble-2.png

Version	Author	Date
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
6be73e0	jens-daniel-mueller	2022-06-30

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


dcant_budget_global_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") +
  scale_color_manual(values = c("grey", "red"),
                     name = "ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(group = Version_ID_group,
        fill = MLR_basins,
        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)
  ) +
  scale_y_continuous(name = dcant_pgc_label, breaks = seq(0, 100, 2))

Past versions of dcant_budget_global_ensemble-3.png

Version	Author	Date
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

full_join(dcant_budget_global_all_ensemble,
          delta_pCO2_atm) %>%
  mutate(ensemble_role = case_when(Version_ID %in% Version_IDs ~ "standard case",
                                   TRUE ~ "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant/delta_pCO2)) +
  scale_fill_brewer(palette = "Dark2", name = "ensemble group") +
  scale_color_manual(values = c("black", "red", "grey"),
                     name = "ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 2,
    alpha = .5,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_scaled_label)

Past versions of beta_budget_global-1.png

Version	Author	Date
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14

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

9.1.2 basin hemisphere

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

Past versions of dcant_budget_basin_hemisphere_ensemble-1.png

Version	Author	Date
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
df21d31	jens-daniel-mueller	2022-07-01
6be73e0	jens-daniel-mueller	2022-06-30

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, breaks = seq(0, 100, 2)) +
  facet_grid(basin ~ ., scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble-2.png

Version	Author	Date
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14
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
df21d31	jens-daniel-mueller	2022-07-01
6be73e0	jens-daniel-mueller	2022-06-30

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

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") +
  scale_color_manual(values = c("grey", "red"),
                     name = "ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(fill = MLR_basins,
        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)
  ) +
  scale_y_continuous(name = dcant_pgc_label, breaks = seq(0, 100, 2)) +
  facet_grid(basin ~ ., scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble-3.png

Version	Author	Date
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14
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_ensemble_stat <-
  bind_rows(
    dcant_budget_basin_MLR_all_ensemble,
    dcant_budget_global_all_ensemble %>% mutate(basin = "global")
  ) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  group_by(period, basin) %>%
  summarise(
    mean = mean(dcant),
    median = median(dcant),
    sd = sd(dcant),
    n = n()
  ) %>%
  ungroup() 



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)*2) %>% 
  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)*2) %>%
  ungroup() %>%
  filter(data_source == "obs")

dcant_budget_ensemble_stat <-
  full_join(
    dcant_budget_ensemble_stat,
    bind_rows(
      dcant_budget_basin_MLR_all %>%
        filter(data_source == "obs",
               period != "1994 - 2014") %>%
        select(period,
               basin,
               std_case = dcant),
      dcant_budget_global_all %>%
        filter(data_source == "obs",
               period != "1994 - 2014") %>%
        select(period,
               std_case = dcant) %>%
        mutate(basin = "global")
    )
  )

dcant_budget_ensemble_stat %>% 
  pivot_longer(c(mean,median,std_case),
               names_to = "estimate",
               values_to = "dcant") %>%
  group_by(estimate, basin) %>%
  mutate(
    delta_dcant = dcant - lag(dcant),
    delta_dcant_uncert = sqrt(2 * sd ^ 2 + lag(2 * 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() %>% 
  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(. ~ estimate)

full_join(dcant_budget_basin_MLR_all_ensemble,
          delta_pCO2_atm)%>%
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "standard case",
                                 "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant / delta_pCO2)) +
  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(
    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, breaks = seq(0, 100, 2)) +
  facet_grid(basin ~ ., scales = "free_y")

Past versions of beta_budget_basin_hemisphere_ensemble-1.png

Version	Author	Date
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14

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

9.2 Flow chart

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_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")
)


area_scaling %>%
  ggplot(aes(lon, lat, fill = basin)) +
  geom_tile() +
  facet_wrap( ~ source) +
  coord_quickmap()

Past versions of budgets_basins_hemisphere_tcant-1.png

Version	Author	Date
a26a21d	jens-daniel-mueller	2022-06-27

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 <- 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)

9.2.1 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)

Past versions of budgets_basins_hemisphere_alluvial_order_budget-1.png

Version	Author	Date
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	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
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

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)

Past versions of budgets_basins_hemisphere_alluvial_order_basin-1.png

Version	Author	Date
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	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
ec7fe7e	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

9.2.2 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)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled-1.png

Version	Author	Date
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	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
2116dd3	jens-daniel-mueller	2022-02-09
d2191ad	jens-daniel-mueller	2022-02-04
ed903f7	jens-daniel-mueller	2022-02-02

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)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled_incl_sabine-1.png

Version	Author	Date
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	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
b599680	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
2116dd3	jens-daniel-mueller	2022-02-09
d2191ad	jens-daniel-mueller	2022-02-04
fa46251	jens-daniel-mueller	2022-02-02
7655085	jens-daniel-mueller	2022-02-02
226d67d	jens-daniel-mueller	2022-02-02

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)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled_incl_sabine_uncertainty-1.png

Version	Author	Date
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
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	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
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
b599680	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
2116dd3	jens-daniel-mueller	2022-02-09
a6b33aa	jens-daniel-mueller	2022-02-04
4b48475	jens-daniel-mueller	2022-02-04
fec5a1e	jens-daniel-mueller	2022-02-04
d2191ad	jens-daniel-mueller	2022-02-04
c7b4984	jens-daniel-mueller	2022-02-02
7fb28a2	jens-daniel-mueller	2022-02-02
49097e8	jens-daniel-mueller	2022-02-02
fe11bfd	jens-daniel-mueller	2022-02-02

  # 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)

9.3 Depth layer

9.3.1 Global

dcant_budget_change <- dcant_budget_global_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_global_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_global_standard_case-1.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

dcant_budget_change <- dcant_budget_global_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_global_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_global_cumulative-1.png

Version	Author	Date
17cd1d1	jens-daniel-mueller	2022-07-13

9.3.2 Basin AIP

dcant_budget_basin_AIP_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  ggplot(aes(inv_depth-250, dcant, col=period)) +
  geom_hline(yintercept = 0) +
  geom_step(direction = "vh") +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1", direction = -1) +
  facet_grid(basin_AIP ~ .) +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)")

Past versions of depth_layer_budget_basin_AIP-1.png

Version	Author	Date
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
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27

9.3.3 5 basins

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
26e9496	jens-daniel-mueller	2022-07-12

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

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)

dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  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_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_ensemble-1.png

Version	Author	Date
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

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
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

10 Time series

10.1 Scale globale

• Accumulation 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

10.2 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
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()

Past versions of dcant_budget_time_series_member_connections-2.png

Version	Author	Date
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-3.png

Version	Author	Date
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-4.png

Version	Author	Date
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_ts <- dcant_budget_global_all %>% 
  filter(data_source == "obs",
         period != "1994 - 2014") %>% 
  select(year = tref2, dcant_mean = dcant)

dcant_budget_global_ts <- dcant_budget_global_ts %>% 
  mutate(dcant_mean = dcant_mean * dcant_scaling)

dcant_budget_global_all_ensemble_summary <-
  dcant_budget_basin_MLR_all_ensemble %>%
  filter(period != "2004 - 2014") %>%
  mutate(dcant = dcant * dcant_scaling) %>% 
  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)*2) %>%
  ungroup() %>%
  filter(data_source == "obs")



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 = 0)

tcant_ts <- full_join(tcant_ts, co2_atm_pi)

tcant_ts <- tcant_ts %>% 
  arrange(year) %>% 
  mutate(tcant = cumsum(dcant_mean),
         tcant_sd = cumsum(dcant_sd))

beta <- 1.57
co2_atm_pi_value <- 283
tCant_col <- "#56B4E9"

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 = tCant_col) +
  geom_linerange(aes(
    ymin = tcant - dcant_sd,
    ymax = tcant + dcant_sd,
    col = "interval\nuncertainty"
  )) +
  geom_point(fill = "white", shape = 21) +
  geom_text(aes(label = year), nudge_x = -13, nudge_y = 6) +
  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 = "black") +
  theme(
    axis.title.y.right = element_text(color = tCant_col),
    axis.text.y.right = element_text(color = tCant_col),
    axis.ticks.y.right = 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
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

tCant_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 = tCant_col) +
  geom_linerange(aes(
    ymin = tcant - dcant_sd,
    ymax = tcant + dcant_sd,
    col = "interval\nuncertainty"
  )) +
  geom_point(fill = "white", shape = 21) +
  geom_text(aes(label = year), nudge_x = -13, nudge_y = 6) +
  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 = "black") +
  theme(
    axis.title.y.right = element_text(color = tCant_col),
    axis.text.y.right = element_text(color = tCant_col),
    axis.ticks.y.right = 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
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

dcant_uncert_2004 <- tcant_ts %>%
  filter(year %in% c(1994, 2004)) %>% 
  mutate(dcant_sd = if_else(year == 1994, 0, dcant_sd),
         period = "1994 - 2004")

dcant_uncert_2014 <- tcant_ts %>%
  filter(year %in% c(2004, 2014)) %>% 
  mutate(dcant_sd = if_else(year == 2004, 0, dcant_sd),
         period = "2004 - 2014")

dcant_uncert_ind <- bind_rows(
  dcant_uncert_2004,
  dcant_uncert_2014
)

dcant_uncert_2014_combined <-
  tcant_ts %>%
  filter(year %in% c(1994, 2004, 2014)) %>% 
  mutate(dcant_sd = if_else(year == 1994, 0, dcant_sd),
         dcant_sd_combined = sqrt(dcant_sd^2 + lag(dcant_sd, default = 0)^2),
         period = "1994 - 2014")

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 = 0.5) +
  geom_ribbon(data = dcant_uncert_2014_combined,
              aes(ymin = tcant - dcant_sd, ymax = tcant + dcant_sd, fill = "1994 - 2014"),
              alpha = 0.5) +
  # geom_linerange(aes(ymin = tcant - dcant_sd, ymax = tcant + dcant_sd, col = "interval\nuncertainty")) +
  geom_line() +
  geom_point(shape = 21, fill = "white") +
  scale_x_continuous(breaks = seq(280, 400, 30)
                     # sec.axis = dup_axis(labels =  c(1800, 1940, 1980, 2000, 2015),
                     #                     name = "Year")
                     ) +
  scale_fill_manual(values = c("grey60", "grey30"), 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(legend.background = element_rect(fill = "transparent"),
        legend.position=c(0.85, 0.2))

p_tcant_year / p_tcant_atm_co2

Past versions of mean_tcant_over_atm_co2-1.png

Version	Author	Date
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 = "FigS_budget_dcant_global_vs_atm_pCO2.png",
       height = 6,
       width = 7)

p_tcant_year_trans / p_tcant_atm_co2

Past versions of mean_tcant_over_atm_co2-2.png

Version	Author	Date
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/publication"),
#        filename = "Fig_global_dcant_budget_vs_atm_pCO2_trans.png",
#        height = 6,
#        width = 7)

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

# A tibble: 4 × 7
   year dcant_mean dcant_sd  pCO2 tcant tcant_sd  beta
  <dbl>      <dbl>    <dbl> <dbl> <dbl>    <dbl> <dbl>
1  1800        0       0     280     0       0   NA   
2  1994      118      19     358.  118      19    1.51
3  2004       29.1     2.99  377.  147.     22.0  1.56
4  2014       27.1     4.17  397.  174.     26.2  1.33

10.3 incl fluxes

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

Past versions of GCB_carbon_sink_cum_1994-1.png

Version	Author	Date
bd24a0f	jens-daniel-mueller	2022-07-15

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\nflux products",
    TRUE ~ "Hindcast models"
  ))

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

Past versions of GCB_carbon_sink_cum_2004-1.png

Version	Author	Date
bd24a0f	jens-daniel-mueller	2022-07-15

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\nflux products",
    TRUE ~ "Hindcast models"
  ))

tcant_ts_zoom <- tcant_ts %>%
  filter(year != 1800) %>% 
  mutate(dcant_sd = if_else(year == 1994,0,dcant_sd))

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


p_tcant_ts_zoom <- 
tcant_ts_zoom %>%
  ggplot(aes(pCO2, tcant)) +
  geom_ribbon(
    data = dcant_uncert_2014_combined,
    aes(
      ymin = tcant - dcant_sd,
      ymax = tcant + dcant_sd,
      fill = "1994 - 2014"
    ),
    alpha = 0.5
  ) +
  geom_line(data = Ocean_Sink_cum_1994,
            aes(pCO2, fgco2_glob_cum_total, group = product, col = type)) +
  # geom_point(
  #   data = co2_atm %>% filter(year >= 1994, year <= 2014),
  #   aes(pCO2, 118),
  #   shape = "|"
  # ) +
  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, group = product, col = type),
    shape = 21,
    fill = "white"
  )+
  geom_line(aes(col = "eMLR(C*)")) +
  geom_point(aes(col = "eMLR(C*)"),
             shape = 21, fill = "white") +
  geom_text(aes(label = year), nudge_x = -3, nudge_y = 3) +
  scale_color_manual(values = c("black", "#E69F00", "#56B4E9", "pink"),
                     name = "Data source") +
  scale_fill_manual(values = c("grey60", "grey30"), name = "Cumulative\nuncertainty") +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = expression(Atmospheric ~ pCO[2] ~ (µatm)),
       y = expression(Total ~ oceanic ~ C[ant] ~ (PgC))) +
  guides(colour = guide_legend(order = 1),
         fill = "none") +
  theme_classic() +
  theme(
    legend.background = element_rect(fill = "white"),
    legend.position = c(0.2, 0.8),
    legend.box.just = "top",
    legend.box = "horizontal",
    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
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 * dcant_scaling + 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)
  )

p_tcant_box <-
  dcant_type_box %>%
  filter(period == "1994 - 2014") %>%
  ggplot(aes(period, dcant, col = type)) +
  geom_boxplot(position = position_dodge(width = 1),
               outlier.shape = NA) +
  geom_jitter(position = position_dodge(width = 1),
              shape = 21) +
  scale_color_manual(values = c("black", "#E69F00", "#56B4E9", "pink"),
                     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, col = type)) +
  geom_boxplot(position = position_dodge(width = 1),
               outlier.shape = NA) +
  geom_jitter(position = position_dodge(width = 1),
              shape = 21) +
  scale_color_manual(values = c("black", "#E69F00", "#56B4E9", "pink"),
                     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(6,1,2))

Past versions of mean_tcant_over_atm_co2_incl_fluxes_boxplot-1.png

Version	Author	Date
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_budget_dcant_global_vs_GCB_ocean_sink.png",
       height = 6,
       width = 9)

dcant_type_box %>%
  mutate(dcant = dcant - 118,
         dcant = if_else(period == "2004 - 2014",
                         dcant - (tcant_2004 - 118),
                         dcant)) %>%
  group_by(type, period) %>%
  summarise(
    dcant_sd = sd(dcant)*2,
    dcant_mean = mean(dcant),
    dcant_median = median(dcant)
  ) %>%
  ungroup() %>% 
  group_by(period) %>% 
  mutate(dcant_diff = dcant_mean - first(dcant_mean),
         dcant_diff_sd = sqrt(dcant_sd^2 - first(dcant_sd)^2),
         dcant_diff_sd_rel = sqrt(dcant_sd^2 - first(dcant_sd)^2) / mean(dcant_mean,first(dcant_mean)),
         dcant_diff_rel = dcant_mean / first(dcant_mean)) %>% 
  ungroup() %>% 
  mutate() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

type	period	dcant_sd	dcant_mean	dcant_median	dcant_diff	dcant_diff_sd	dcant_diff_sd_rel	dcant_diff_rel
eMLR(C*)	1994 - 2004	2.990198	28.93040	28.89434	0.0000000	0.000000	0.0000000	1.0000000
eMLR(C*)	1994 - 2014	4.173842	56.41592	56.09977	0.0000000	0.000000	0.0000000	1.0000000
eMLR(C*)	2004 - 2014	3.306702	27.40553	27.25823	0.0000000	0.000000	0.0000000	1.0000000
Hindcast models	1994 - 2004	5.403004	19.52578	18.39900	-9.4046186	4.500130	0.2072678	0.6749226
Hindcast models	1994 - 2014	10.974456	42.28477	40.20600	-14.1311517	10.149765	0.2079667	0.7495184
Hindcast models	2004 - 2014	5.576053	22.75899	21.80700	-4.6465406	4.489776	0.1657167	0.8304525
Observation-based flux products	1994 - 2004	5.448186	21.71168	21.38838	-7.2187207	4.554278	0.2097617	0.7504797
Observation-based flux products	1994 - 2014	8.824335	48.80475	47.55586	-7.6111699	7.774827	0.1593047	0.8650883
Observation-based flux products	2004 - 2014	4.225215	27.09308	26.66952	-0.3124568	2.630240	0.0970816	0.9885988

11 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_per_emission	dcant_per_emission_sd
2004	82.66365	29.06819	2.990649	376.95	147.0682	21.99065	35.16442	3.617852
2014	101.81399	27.12704	4.174423	397.34	174.1952	26.16507	26.64373	4.100049

11.0.0.1 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] BSDA_1.2.1        lattice_0.20-45   kableExtra_1.3.4  ggdist_3.1.1     
 [5] ggalluvial_0.12.3 colorspace_2.0-2  marelac_2.1.10    shape_1.4.6      
 [9] ggforce_0.3.3     metR_0.11.0       scico_1.3.0       patchwork_1.1.1  
[13] collapse_1.7.0    forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7      
[17] purrr_0.3.4       readr_2.1.1       tidyr_1.1.4       tibble_3.1.6     
[21] ggplot2_3.3.5     tidyverse_1.3.1   workflowr_1.7.0  

loaded via a namespace (and not attached):
  [1] ellipsis_0.3.2       class_7.3-20         rprojroot_2.0.2     
  [4] htmlTable_2.4.0      base64enc_0.1-3      fs_1.5.2            
  [7] rstudioapi_0.13      proxy_0.4-26         farver_2.1.0        
 [10] ggrepel_0.9.1        bit64_4.0.5          fansi_1.0.2         
 [13] lubridate_1.8.0      xml2_1.3.3           splines_4.1.2       
 [16] knitr_1.37           polyclip_1.10-0      Formula_1.2-4       
 [19] jsonlite_1.7.3       gsw_1.0-6            broom_0.7.11        
 [22] cluster_2.1.2        dbplyr_2.1.1         png_0.1-7           
 [25] compiler_4.1.2       httr_1.4.2           backports_1.4.1     
 [28] Matrix_1.4-0         assertthat_0.2.1     fastmap_1.1.0       
 [31] cli_3.1.1            later_1.3.0          tweenr_1.0.2        
 [34] htmltools_0.5.2      tools_4.1.2          gtable_0.3.0        
 [37] glue_1.6.0           Rcpp_1.0.8           cellranger_1.1.0    
 [40] jquerylib_0.1.4      vctrs_0.3.8          svglite_2.0.0       
 [43] xfun_0.29            ps_1.6.0             rvest_1.0.2         
 [46] lifecycle_1.0.1      oce_1.5-0            getPass_0.2-2       
 [49] MASS_7.3-55          zoo_1.8-9            scales_1.1.1        
 [52] vroom_1.5.7          hms_1.1.1            promises_1.2.0.1    
 [55] parallel_4.1.2       RColorBrewer_1.1-2   yaml_2.2.1          
 [58] gridExtra_2.3        sass_0.4.0           rpart_4.1-15        
 [61] latticeExtra_0.6-29  stringi_1.7.6        highr_0.9           
 [64] e1071_1.7-9          checkmate_2.0.0      rlang_1.0.2         
 [67] pkgconfig_2.0.3      systemfonts_1.0.3    distributional_0.3.0
 [70] evaluate_0.14        SolveSAPHE_2.1.0     htmlwidgets_1.5.4   
 [73] labeling_0.4.2       bit_4.0.4            processx_3.5.2      
 [76] tidyselect_1.1.1     here_1.0.1           seacarb_3.3.0       
 [79] magrittr_2.0.1       R6_2.5.1             generics_0.1.1      
 [82] Hmisc_4.6-0          DBI_1.1.2            foreign_0.8-82      
 [85] pillar_1.6.4         haven_2.4.3          whisker_0.4         
 [88] withr_2.4.3          nnet_7.3-17          survival_3.2-13     
 [91] modelr_0.1.8         crayon_1.4.2         utf8_1.2.2          
 [94] tzdb_0.2.0           rmarkdown_2.11       jpeg_0.1-9          
 [97] grid_4.1.2           readxl_1.3.1         isoband_0.2.5       
[100] data.table_1.14.2    callr_3.7.0          git2r_0.29.0        
[103] reprex_2.0.1         digest_0.6.29        webshot_0.5.2       
[106] httpuv_1.6.5         munsell_0.5.0        viridisLite_0.4.0   
[109] bslib_0.3.1