Results publication

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Last updated: 2022-02-02

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Knit directory: emlr_obs_analysis/analysis/

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File	Version	Author	Date	Message
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_b <- 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_IDs_ensemble <- c(Version_IDs_b, Version_IDs_o)

2.2 Parameters

for (i_Version_IDs in Version_IDs) {
  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
  )
  
  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, 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
  )
  
  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, tref1, tref2)

2.3 Budgets

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

for (i_Version_IDs in Version_IDs) {
  # 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_1103"
[1] "v_2103"
[1] "v_3103"

rm(
  dcant_budget_basin_MLR,
  dcant_budget_basin_MLR_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_ensemble <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_MLR.csv",
                   sep = ""))
  
  dcant_budget_basin_MLR_mod_truth_ensemble <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_MLR_mod_truth.csv",
      sep = ""
    ))
  
    
  dcant_budget_basin_MLR_ensemble <- bind_rows(dcant_budget_basin_MLR_ensemble,
                                      dcant_budget_basin_MLR_mod_truth_ensemble)
  
  dcant_budget_basin_MLR_ensemble <- dcant_budget_basin_MLR_ensemble %>%
    mutate(Version_ID = i_Version_IDs)
  

  if (exists("dcant_budget_basin_MLR_all_ensemble")) {
    dcant_budget_basin_MLR_all_ensemble <-
      bind_rows(dcant_budget_basin_MLR_all_ensemble, dcant_budget_basin_MLR_ensemble)
  }
  
  if (!exists("dcant_budget_basin_MLR_all_ensemble")) {
    dcant_budget_basin_MLR_all_ensemble <- dcant_budget_basin_MLR_ensemble
  }

}

[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"

rm(
  dcant_budget_basin_MLR_ensemble,
  dcant_budget_basin_MLR_mod_truth_ensemble
)

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

# dcant_budget_global_all_depth <- dcant_budget_global_all

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

# dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
#   filter(estimate == "dcant") %>%
#   select(-c(estimate))

# dcant_budget_global_bias_all_depth <- dcant_budget_global_bias_all
# 
# dcant_budget_global_bias_all <- dcant_budget_global_bias_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)

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

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

2.4 Inventories

for (i_Version_IDs in Version_IDs) {
  # i_Version_IDs <- Version_IDs[1]
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  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 Zonal sections

for (i_Version_IDs in Version_IDs) {

  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)

2.6 Atm CO2

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

2.7 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 = 1750,
         tref2 = 1994)

3 Define labels

dcant_pgc_label <- expression(Delta * C["ant"]~(PgC))
dcant_pgc_scaled_label <- expression(Delta * C["ant"]~(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)")

4 Inventory maps

4.1 Absoulte values

4.1.1 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,
         dcant_scaled = dcant / delta_pCO2,
         dcant_pos_scaled = dcant_pos / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

dcant_inv_all %>%
  p_map_cant_inv(var = "dcant") +
  facet_grid(period ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of invetory_maps_absolute-1.png

Version	Author	Date
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 = "dcant_scaled",
                 breaks = c(-Inf, seq(0, 1, 0.1), Inf),) +
  facet_grid(period ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of invetory_maps_absolute-2.png

Version	Author	Date
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 = "dcant_pos_scaled",
                 breaks = c(-Inf, seq(0, 1, 0.1), Inf),) +
  facet_grid(period ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of invetory_maps_absolute-3.png

Version	Author	Date
32e9682	jens-daniel-mueller	2022-02-02

4.1.1.1 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"
)


for (i_palette in scico_continous_palettes) {
  p_reg <- map +
    geom_tile(data = dcant_inv_all_color_test,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_scico_d(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    # guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
    labs(title = i_palette)
  
  p_rev <- map +
    geom_tile(data = dcant_inv_all_color_test,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_scico_d(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      direction = -1,
      guide = "none"
    ) +
    # guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
    labs(title = paste(i_palette, "rev"))
  
  print(p_reg | p_rev)
  
}


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


for (i_palette in viridis_continous_palettes) {
  p_reg <- map +
    geom_tile(data = dcant_inv_all_color_test,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_viridis_d(
      option = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    # guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
    labs(title = i_palette)

  p_rev <- map +
    geom_tile(data = dcant_inv_all_color_test,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_viridis_d(
      option = i_palette,
      drop = FALSE,
      name = legend_title,
      direction = -1,
      guide = "none"
    ) +
    # guides(fill = guide_colorsteps(barheight = unit(3, "cm"))) +
    labs(title = paste(i_palette, "rev"))

  print(p_reg | p_rev)

}

4.1.2 dcant - absolute delta

dcant_inv_all %>%
  filter(data_source %in% c("mod", "obs"),
         period != "1994 - 2014") %>%
  select(data_source, lon, lat, 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_map_cant_inv(df = .x,
                     var = "delta_dcant",
                     subtitle_text = paste("data_source:",
                                           unique(.x$data_source)),
                     col = "divergent") +
      # facet_grid(period ~ .) +
      theme(axis.text = element_blank(),
            axis.ticks = element_blank())
  )

[[1]]

Past versions of invetory_maps_absolute_delta-1.png

Version	Author	Date
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

4.1.3 dcant - absolute delta scaled

dcant_budget_scaling <- dcant_budget_global_all %>%
  filter(#data_source %in% c("mod", "obs"),
         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,
          dcant_budget_scaling) %>%
  filter(#data_source %in% c("mod", "obs"),
         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",
                     subtitle_text = paste("data_source:",
                                           unique(.x$data_source)),
                     col = "divergent") +
      # 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
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

[[2]]

Past versions of invetory_maps_absolute_delta_scaled-2.png

Version	Author	Date
32e9682	jens-daniel-mueller	2022-02-02
4fe7150	jens-daniel-mueller	2022-01-21

4.1.4 dcant pos - absolute

dcant_inv_all %>%
  filter(data_source %in% c("mod", "obs"),
         period != "1994 - 2014") %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_cant_inv(df = .x,
                     var = "dcant_pos",
                     subtitle_text = paste("data_source:",
                                           unique(.x$data_source))) +
      facet_grid(period ~ .) +
      theme(axis.text = element_blank(),
            axis.ticks = element_blank())
  )

[[1]]

Past versions of cases_absolute_dcant_pos-1.png

Version	Author	Date
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
cc31f4b	jens-daniel-mueller	2022-01-20

5 Zonal sections

5.1 dcant - absolut

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) +
  facet_grid(basin_AIP ~ period)

Past versions of zonal_sections-1.png

Version	Author	Date
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

5.2 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",
      title_text = NULL,
      col = "bias"
    ) +
      facet_grid(basin_AIP ~ .)
  )

[[1]]

Past versions of inventory_maps_absolute_delta-1.png

Version	Author	Date
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
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

6 Concentration profiles

6.1 dcant - absolute

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-1.png

Version	Author	Date
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
d6b399a	jens-daniel-mueller	2022-01-21

[[2]]

Past versions of concentration_profiles-2.png

Version	Author	Date
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
c6fe495	jens-daniel-mueller	2022-01-26
d6b399a	jens-daniel-mueller	2022-01-21

[[3]]

Past versions of concentration_profiles-3.png

Version	Author	Date
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
d6b399a	jens-daniel-mueller	2022-01-21

dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         MLR_basins == "5", 
         depth <= params_global$inventory_depth_standard) %>%
    mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  ) %>% 
  group_split(data_source) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(dcant,
                 depth)) +
      geom_hline(yintercept = params_global$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
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
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

6.2 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
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
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
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",
         MLR_basins == "5") %>%
  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",
         MLR_basins == "5") %>%
  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 %>%
  mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  ) %>%
  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
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
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

7 Budgets

7.1 Ensemble

dcant_budget_basin_MLR_all_ensemble %>%
  mutate(class = str_sub(Version_ID, 4, 4)) %>% 
  ggplot(aes(period, dcant, col = class)) +
  geom_point() +
  facet_grid(basin ~ data_source)

Past versions of dcant_budget_basin_ensemble-1.png

Version	Author	Date
fe11bfd	jens-daniel-mueller	2022-02-02

dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs") %>% 
  mutate(class = str_sub(Version_ID, 4, 4)) %>% 
  ggplot(aes(dcant)) +
  geom_histogram() +
  geom_density() +
  facet_grid(basin ~ period, scales = "free")

Past versions of dcant_budget_basin_ensemble-2.png

Version	Author	Date
c7b4984	jens-daniel-mueller	2022-02-02

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")

7.2 Basin & hemisphere

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

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


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

tcant_budget_basin_MLR <-
  inner_join(tcant_inv, basinmask %>% filter(MLR_basins == "5")) %>%
  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 <- tcant_budget_basin_MLR %>% 
  mutate(pCO21 = 280,
         tref1 = 1750,
         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)

dcant_budget_basin_MLR_all_plot <- dcant_budget_basin_MLR_all_plot %>% 
  mutate(
    basin = str_replace(basin, "_", ". "),
        basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  )

7.2.1 Absolute

g1 <- dcant_budget_basin_MLR_all_plot %>%
  filter(period != "1750 - 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
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 != "1750 - 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
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

7.2.2 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,
         dcant_scaled = 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,
         dcant_scaled = dcant_mean / delta_pCO2,
         dcant_sd_scaled = dcant_sd / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

g2 <- dcant_budget_basin_MLR_all_plot %>%
  filter(period != "1750 - 1994") %>% 
  ggplot(aes(
    y = dcant_scaled,
    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
ed903f7	jens-daniel-mueller	2022-02-02

g2 <- dcant_budget_basin_MLR_all_plot %>%
  ggplot(aes(
    y = dcant_scaled,
    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
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 = dcant_scaled,
    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, 1.8), 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"),
         x = ) %>% 
  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),
)

uncertainty_layer_basin <- uncertainty_layer_basin %>% 
  mutate(dcant_sd_scaled = as.character(round(dcant_sd_scaled,2)),
         dcant_sd_scaled = paste0("±",dcant_sd_scaled))

# construct change labels

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, "%"))

# 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_change = as.character(round(dcant_change,2)),
         dcant_change = paste0(dcant_change, "\n±", dcant_sd_scaled))

g2 +
  geom_text(data = budget_layer_basin,
            aes(
              x = x,
              y = y + 0.03,
              label = dcant
            ),
            inherit.aes = FALSE) +
  geom_text(data = uncertainty_layer_basin,
            aes(
              x = x,
              y = y - 0.03,
              label = dcant_sd_scaled
            ),
            inherit.aes = FALSE) +
  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 + 0.13,
              label = dcant_change
            ),
            inherit.aes = FALSE)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled_incl_sabine_uncertainty-1.png

Version	Author	Date
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

7.3 Depth layer

7.3.1 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
913e42f	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27

7.3.2 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(tref2, Version_ID)) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease")) %>%
  mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  )

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  ) %>% 
  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 = 0.3
  ) +
  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 = "Decade") +
  scale_fill_brewer(palette = "Set1", direction = -1, name = "Decadal change") +
  facet_grid(basin ~ .) +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)",
       title = "Layer inventory change")

Past versions of depth_layer_budget_basin_MLR-1.png

Version	Author	Date
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
d2ae5fe	jens-daniel-mueller	2022-01-31
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27

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")) %>%
  mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  )

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() %>%
  mutate(
    basin = str_replace(basin, "_", ". "),
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian"
    )
  ) %>% 
  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_grid(basin ~ .) +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)",
       title = "Cumulative layer inventory change")

Past versions of depth_layer_budget_basin_MLR_cumulative-1.png

Version	Author	Date
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

8 Time series

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 <- 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 = 102, 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 = 1750, 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))


tcant_ts %>% 
  ggplot(aes(pCO2, tcant)) +
  geom_ribbon(aes(ymin = tcant - tcant_sd, ymax = tcant + tcant_sd, fill = "cummulative\nuncertainty"),
              alpha = 0.2) +
  geom_linerange(aes(ymin = tcant - dcant_sd, ymax = tcant + dcant_sd, col = "interval\nuncertainty")) +
  geom_line() +
  scale_x_continuous(breaks = seq(280, 400, 30)
                     # sec.axis = dup_axis(labels =  c(1750, 1940, 1980, 2000, 2015),
                     #                     name = "Year")
                     ) +
  scale_fill_grey() +
  scale_color_manual(values = "black") +
  geom_text(aes(label = year), nudge_x = -8, nudge_y = 5) +
  labs(x = expression(Atmospheric~pCO[2]~(µatm)),
       y = expression(Total~oceanic~C[ant]~(PgC))) +
  theme(legend.title = element_blank(),
        legend.background = element_rect(fill = "transparent"),
        legend.position=c(0.85, 0.2))

Past versions of mean_tcant_over_atm_co2-1.png

Version	Author	Date
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 = "output/publication",
#        filename = "Fig_global_dcant_budget_vs_atm_pCO2.png",
#        height = 4,
#        width = 7)

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] marelac_2.1.10    shape_1.4.6       ggalluvial_0.12.3 ggforce_0.3.3    
 [5] metR_0.11.0       scico_1.3.0       patchwork_1.1.1   collapse_1.7.0   
 [9] forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7       purrr_0.3.4      
[13] readr_2.1.1       tidyr_1.1.4       tibble_3.1.6      ggplot2_3.3.5    
[17] tidyverse_1.3.1   workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] fs_1.5.2           gsw_1.0-6          lubridate_1.8.0    bit64_4.0.5       
 [5] RColorBrewer_1.1-2 httr_1.4.2         rprojroot_2.0.2    tools_4.1.2       
 [9] backports_1.4.1    bslib_0.3.1        utf8_1.2.2         R6_2.5.1          
[13] DBI_1.1.2          colorspace_2.0-2   withr_2.4.3        tidyselect_1.1.1  
[17] processx_3.5.2     bit_4.0.4          compiler_4.1.2     git2r_0.29.0      
[21] cli_3.1.1          rvest_1.0.2        xml2_1.3.3         isoband_0.2.5     
[25] labeling_0.4.2     sass_0.4.0         scales_1.1.1       checkmate_2.0.0   
[29] SolveSAPHE_2.1.0   callr_3.7.0        digest_0.6.29      oce_1.5-0         
[33] rmarkdown_2.11     pkgconfig_2.0.3    htmltools_0.5.2    highr_0.9         
[37] dbplyr_2.1.1       fastmap_1.1.0      rlang_0.4.12       readxl_1.3.1      
[41] rstudioapi_0.13    jquerylib_0.1.4    generics_0.1.1     farver_2.1.0      
[45] jsonlite_1.7.3     vroom_1.5.7        magrittr_2.0.1     Rcpp_1.0.8        
[49] munsell_0.5.0      fansi_1.0.2        lifecycle_1.0.1    stringi_1.7.6     
[53] whisker_0.4        yaml_2.2.1         MASS_7.3-55        grid_4.1.2        
[57] ggrepel_0.9.1      parallel_4.1.2     promises_1.2.0.1   crayon_1.4.2      
[61] haven_2.4.3        seacarb_3.3.0      hms_1.1.1          knitr_1.37        
[65] ps_1.6.0           pillar_1.6.4       reprex_2.0.1       glue_1.6.0        
[69] evaluate_0.14      getPass_0.2-2      data.table_1.14.2  modelr_0.1.8      
[73] vctrs_0.3.8        tzdb_0.2.0         tweenr_1.0.2       httpuv_1.6.5      
[77] cellranger_1.1.0   gtable_0.3.0       polyclip_1.10-0    assertthat_0.2.1  
[81] xfun_0.29          broom_0.7.11       later_1.3.0        viridisLite_0.4.0 
[85] ellipsis_0.3.2