Budgets

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-07-15

Checks: 7 0

Knit directory: emlr_obs_analysis/analysis/

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

---

R Markdown file: up-to-date

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

Environment: empty

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

Seed: set.seed(20210412)

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

Session information: recorded

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

Cache: none

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

File paths: relative

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

Repository version: afb27ad

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 afb27ad. 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/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.

---

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

File	Version	Author	Date	Message
html	1aabfea	jens-daniel-mueller	2022-07-12	Build site.
html	36462a6	jens-daniel-mueller	2022-07-12	Build site.
Rmd	62c22e8	jens-daniel-mueller	2022-07-12	revised bias decomposition
html	d2b50eb	jens-daniel-mueller	2022-07-07	Build site.
Rmd	bbb470e	jens-daniel-mueller	2022-07-07	generalized plots to config parameter
html	b44c72a	jens-daniel-mueller	2022-07-03	Build site.
html	157af41	jens-daniel-mueller	2022-07-03	Build site.
Rmd	ac45ab7	jens-daniel-mueller	2022-07-03	rebuild without bias contribution separation
html	a13a7cf	jens-daniel-mueller	2022-06-28	Build site.
html	b52b159	jens-daniel-mueller	2022-06-27	Build site.
html	1da59de	jens-daniel-mueller	2022-06-25	Build site.
html	f5f6e64	jens-daniel-mueller	2022-06-20	Build site.
html	09b0780	jens-daniel-mueller	2022-05-24	Build site.
html	25da2fb	jens-daniel-mueller	2022-05-24	Build site.
html	e09320d	jens-daniel-mueller	2022-04-12	Build site.
html	8dca96a	jens-daniel-mueller	2022-04-12	Build site.
Rmd	e5e9288	jens-daniel-mueller	2022-04-12	3 data adjustment procedures implemented

---

version_id_pattern <- "1"
config <- "MLR_basins"

1 Read files

print(version_id_pattern)

[1] "1"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", "1"))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", "1"))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", "1"))

Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

# print(Version_IDs)

1.1 Global

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_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)

  params_local <-
    read_rds(paste(path_version_data,
                   "params_local.rds",
                   sep = ""))
  
  params_local <- bind_cols(
    Version_ID = i_Version_IDs,
    MLR_basins := str_c(params_local$MLR_basins, collapse = "|"),
    tref1 = params_local$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("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,
  params_local,
  tref
)

1.2 Basins

# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]

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_AIP <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP.csv",
                   sep = ""))
  
  dcant_budget_basin_AIP_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_AIP_mod_truth.csv",
      sep = ""
    ))
  
    
  dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
                                      dcant_budget_basin_AIP_mod_truth)
  
  dcant_budget_basin_AIP_bias <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP_bias.csv",
                   sep = ""))
  
  dcant_slab_budget_bias <-
    read_csv(paste0(path_version_data,
                    "dcant_slab_budget_bias.csv"))

  dcant_slab_budget <-
    read_csv(paste0(path_version_data,
                    "dcant_slab_budget.csv"))

  dcant_budget_basin_AIP <- dcant_budget_basin_AIP %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP_bias %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_slab_budget <- dcant_slab_budget %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_slab_budget_bias <- dcant_slab_budget_bias %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("dcant_budget_basin_AIP_all")) {
    dcant_budget_basin_AIP_all <-
      bind_rows(dcant_budget_basin_AIP_all, dcant_budget_basin_AIP)
  }
  
  if (!exists("dcant_budget_basin_AIP_all")) {
    dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP
  }
  
  if (exists("dcant_budget_basin_AIP_bias_all")) {
    dcant_budget_basin_AIP_bias_all <-
      bind_rows(dcant_budget_basin_AIP_bias_all,
                dcant_budget_basin_AIP_bias)
  }
  
  if (!exists("dcant_budget_basin_AIP_bias_all")) {
    dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias
  }
  
  if (exists("dcant_slab_budget_all")) {
    dcant_slab_budget_all <-
      bind_rows(dcant_slab_budget_all, dcant_slab_budget)
  }
  
  if (!exists("dcant_slab_budget_all")) {
    dcant_slab_budget_all <- dcant_slab_budget
  }
  
  if (exists("dcant_slab_budget_bias_all")) {
    dcant_slab_budget_bias_all <-
      bind_rows(dcant_slab_budget_bias_all,
                dcant_slab_budget_bias)
  }
  
  if (!exists("dcant_slab_budget_bias_all")) {
    dcant_slab_budget_bias_all <- dcant_slab_budget_bias
  }
  
}

rm(
  dcant_budget_basin_AIP,
  dcant_budget_basin_AIP_bias,
  dcant_budget_basin_AIP_mod_truth,
  dcant_slab_budget,
  dcant_slab_budget_bias
)

1.3 Basins hemisphere

# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]

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
  }

  
}

rm(
  dcant_budget_basin_MLR,
  dcant_budget_basin_MLR_mod_truth
)

1.4 Steady state

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_obs_budget <-
    read_csv(paste0(path_version_data,
                    "anom_dcant_obs_budget.csv"))
  
  dcant_obs_budget <- dcant_obs_budget %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("dcant_obs_budget_all")) {
    dcant_obs_budget_all <-
      bind_rows(dcant_obs_budget_all, dcant_obs_budget)
  }
  
  if (!exists("dcant_obs_budget_all")) {
    dcant_obs_budget_all <- dcant_obs_budget
  }
  
}


rm(dcant_obs_budget)

1.5 Atm CO2

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

all_predictors <- c("saltempaouoxygenphosphatenitratesilicate")

params_local_all <- params_local_all %>%
  mutate(MLR_predictors = str_remove_all(all_predictors,
                                         MLR_predictors))

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

dcant_budget_basin_AIP_all_depth <- dcant_budget_basin_AIP_all

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

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

dcant_budget_basin_AIP_bias_all_depth <- dcant_budget_basin_AIP_bias_all

dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_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_depth <- dcant_budget_basin_MLR_all

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

# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias_all %>%
#   filter(estimate == "dcant") %>% 
#   select(-c(estimate))
# 
# dcant_budget_basin_MLR_bias_all_depth <- dcant_budget_basin_MLR_bias_all
# 
# dcant_budget_basin_MLR_bias_all <- dcant_budget_basin_MLR_bias_all %>%
#   filter(inv_depth == params_global$inventory_depth_standard)

2 Bias thresholds

global_bias_rel_max <- 10
global_bias_rel_max

[1] 10

regional_bias_rel_max <- 20
regional_bias_rel_max

[1] 20

3 Individual cases

3.1 Global

3.1.1 Absoulte values

legend_title = expression(atop(Delta * C[ant],
                               (mu * mol ~ kg ^ {
                                 -1
                               })))

dcant_budget_global_all %>%
  ggplot(aes(period, dcant, col = MLR_basins)) +
  geom_jitter(width = 0.05, height = 0) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ data_source) +
  ylim(0,NA) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Past versions of cases_absolute_global-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.1.2 Biases

dcant_budget_global_bias_all %>%
  ggplot(aes(period, dcant_bias, col=MLR_basins)) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Dark2") +
  labs(y = expression(atop(Delta * C[ant] ~ bias,
                               (PgC)))) +
  geom_point() +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank())

Past versions of cases_bias_global-1.png

Version	Author	Date
d2b50eb	jens-daniel-mueller	2022-07-07
b44c72a	jens-daniel-mueller	2022-07-03
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

p_global_bias <-
  dcant_budget_global_bias_all %>%
  ggplot() +
  geom_hline(yintercept = global_bias_rel_max * c(-1, 1),
             linetype = 2) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Dark2") +
  labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
       title = "Model-based assesment") +
  theme(axis.title.x = element_blank()) +
  geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
  alpha = 0.7) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank())

p_global_bias

Past versions of cases_bias_global-2.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_budget_global_bias_all %>%
  group_by(period) %>%
  summarise(
    dcant_bias_sd = sd(dcant_bias),
    dcant_bias = mean(dcant_bias),
    dcant_bias_rel_sd = sd(dcant_bias_rel),
    dcant_bias_rel = mean(dcant_bias_rel)
  ) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

period	dcant_bias_sd	dcant_bias	dcant_bias_rel_sd	dcant_bias_rel
1994 - 2004	1.485683	1.392000	8.422240	7.891157
1994 - 2014	1.691531	2.772667	4.397242	7.207722
2004 - 2014	0.884303	1.518833	4.245741	7.292267

3.2 Basins

3.2.1 Absoulte values

dcant_budget_basin_AIP_all %>%
  ggplot(aes(period, dcant, col = MLR_basins)) +
  geom_jitter(width = 0.05, height = 0) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin_AIP ~ data_source) +
  ylim(0,NA) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Past versions of cases_absolute-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.2.2 Biases

dcant_budget_basin_AIP_bias_all %>%
  ggplot(aes(period, dcant_bias, col=MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_point() +
  facet_grid(basin_AIP ~ .)

Past versions of cases_bias-1.png

Version	Author	Date
d2b50eb	jens-daniel-mueller	2022-07-07
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_budget_basin_AIP_bias_all %>%
  ggplot() +
  geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
                x = "2004 - 2014", width = Inf,
                fill = "bias\nthreshold"), alpha = 0.5) +
  geom_hline(yintercept = 0) +
  scale_fill_manual(values = "grey70", name = "") +
  scale_color_brewer(palette = "Dark2") +
  labs(y = expression(Delta ~ C[ant] ~ bias)) +
  theme(axis.title.x = element_blank()) +
  geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
              width = 0.05, height = 0) +
  facet_grid(. ~ basin_AIP)

Past versions of cases_bias-2.png

Version	Author	Date
d2b50eb	jens-daniel-mueller	2022-07-07
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

p_regional_bias <- 
  dcant_budget_basin_AIP_bias_all %>%
  ggplot() +
  geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
             linetype = 2) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Dark2") +
  labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
       title = "Model-based assesment") +
  theme(axis.title.x = element_blank()) +
  geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
             alpha = 0.7) +
    theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank()) +
  facet_grid(. ~ basin_AIP) +
  theme(
  strip.background = element_blank(),
  strip.text.x = element_blank()
)

p_regional_bias

Past versions of cases_bias-3.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.3 Slab budgets

3.3.1 Absolute values

dcant_slab_budget_all %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>% 
  ggplot(aes(MLR_basins, dcant, fill = gamma_slab)) +
  geom_hline(yintercept = 0, col = "red") +
  geom_col() +
  scale_fill_scico_d(direction = -1) +
  facet_grid(basin_AIP ~ period)

Past versions of slab_budgets-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_slab_budget_all %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  map(
    ~ ggplot(data = .x,
             aes(MLR_basins, dcant, fill = gamma_slab)) +
      geom_hline(yintercept = 0) +
      geom_col() +
      scale_fill_scico_d(direction = -1) +
      labs(title = paste("data_source:", unique(.x$basin_AIP))) +
      facet_grid(gamma_slab ~ period)
  )

[[1]]

Past versions of slab_budgets_individual-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Past versions of slab_budgets_individual-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[3]]

Past versions of slab_budgets_individual-3.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.3.2 Bias

dcant_slab_budget_bias_all %>%
  filter(period != "1994 - 2014") %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>% 
  map(
    ~ ggplot(data = .x,
             aes(gamma_slab, dcant_bias, fill = gamma_slab)) +
      geom_col() +
      coord_flip() +
      scale_x_discrete(limits = rev) +
      scale_fill_scico_d(direction = -1) +
      facet_grid(period ~ MLR_basins) +
      labs(title = paste("data_source:", unique(.x$basin_AIP)))
    )

[[1]]

Warning: Removed 48 rows containing missing values (position_stack).

Past versions of slab_budgets_bias-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Warning: Removed 24 rows containing missing values (position_stack).

Past versions of slab_budgets_bias-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[3]]

Warning: Removed 108 rows containing missing values (position_stack).

Past versions of slab_budgets_bias-3.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.3.3 Spread

dcant_slab_budget_all %>%
  filter(period != "1994 - 2014",
         data_source != "mod_truth") %>%
  group_by(data_source, basin_AIP, gamma_slab, period) %>%
  summarise(dcant_range = max(dcant) - min(dcant)) %>%
  ungroup() %>%
  group_split(basin_AIP) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(gamma_slab, dcant_range, fill = gamma_slab)) +
      geom_col() +
      coord_flip() +
      scale_x_discrete(limits = rev) +
      scale_fill_scico_d(direction = -1) +
      facet_grid(period ~ data_source) +
      labs(title = paste("data_source:", unique(.x$basin_AIP)))
  )

`summarise()` has grouped output by 'data_source', 'basin_AIP', 'gamma_slab'.
You can override using the `.groups` argument.

[[1]]

Past versions of slab_budgets_spread-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Past versions of slab_budgets_spread-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[3]]

Past versions of slab_budgets_spread-3.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.4 Basins hemisphere

3.4.1 Absoulte values

dcant_budget_basin_MLR_all %>%
  ggplot(aes(period, dcant, col = MLR_basins)) +
  geom_jitter(width = 0.05, height = 0) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ data_source) +
  ylim(0,NA) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Past versions of cases_absolute_basins_hemisphere-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

3.4.2 Biases

dcant_budget_basin_MLR_bias_all <-
  dcant_budget_basin_MLR_all %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  pivot_wider(names_from = data_source,
              values_from = dcant) %>%
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = 100*(mod - mod_truth)/mod_truth)
  
dcant_budget_basin_MLR_bias_all %>%   
  ggplot(aes(period, dcant_bias, col=MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_point() +
  facet_grid(basin ~ .)

Past versions of cases_bias_basins_hemisphere-1.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03

dcant_budget_basin_MLR_bias_all %>%
  ggplot() +
  geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
                x = "2004 - 2014", width = Inf,
                fill = "bias\nthreshold"), alpha = 0.5) +
  geom_hline(yintercept = 0) +
  scale_fill_manual(values = "grey70", name = "") +
  scale_color_brewer(palette = "Dark2") +
  labs(y = expression(Delta ~ C[ant] ~ bias)) +
  theme(axis.title.x = element_blank()) +
  geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
              width = 0.05, height = 0) +
  facet_grid(. ~ basin)

Past versions of cases_bias_basins_hemisphere-2.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03

p_regional_bias <- 
  dcant_budget_basin_MLR_bias_all %>%
  ggplot() +
  geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
             linetype = 2) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Dark2") +
  labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
       title = "Model-based assesment") +
  theme(axis.title.x = element_blank()) +
  geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
             alpha = 0.7) +
    theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank()) +
  facet_grid(. ~ basin) +
  theme(
  strip.background = element_blank(),
  strip.text.x = element_blank()
)

p_regional_bias

Past versions of cases_bias_basins_hemisphere-3.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03

dcant_budget_basin_MLR_bias_all %>%
  group_by(period, basin) %>%
  summarise(
    dcant_bias_sd = sd(dcant_bias),
    dcant_bias = mean(dcant_bias),
    dcant_bias_rel_sd = sd(dcant_bias_rel),
    dcant_bias_rel = mean(dcant_bias_rel)
  ) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

`summarise()` has grouped output by 'period'. You can override using the
`.groups` argument.

period	basin	dcant_bias_sd	dcant_bias	dcant_bias_rel_sd	dcant_bias_rel
1994 - 2004	Indian	1.0142292	0.2160000	21.037734	4.4803983
1994 - 2004	N_Atlantic	0.2337224	0.1918333	11.979621	9.8325645
1994 - 2004	N_Pacific	0.1737463	0.4411667	6.397138	16.2432499
1994 - 2004	S_Atlantic	0.2893176	0.1415000	11.572706	5.6600000
1994 - 2004	S_Pacific	0.8272838	0.4011667	14.637011	7.0977825
1994 - 2014	Indian	1.2990934	0.7751667	12.433896	7.4192828
1994 - 2014	N_Atlantic	0.3484375	-0.0196667	8.110742	-0.4577902
1994 - 2014	N_Pacific	0.2743412	0.5986667	4.699232	10.2546534
1994 - 2014	S_Atlantic	0.3555303	0.5111667	6.722070	9.6647129
1994 - 2014	S_Pacific	1.2939301	0.9075000	10.271732	7.2040962
2004 - 2014	Indian	0.5387555	0.5173333	9.576173	9.1954023
2004 - 2014	N_Atlantic	0.1777421	-0.1003333	7.579622	-4.2786070
2004 - 2014	N_Pacific	0.2202066	0.1951667	7.053384	6.2513346
2004 - 2014	S_Atlantic	0.2128893	0.4436667	7.633178	15.9077328
2004 - 2014	S_Pacific	0.5041044	0.4640000	7.258522	6.6810655

4 Ensemble

4.1 Global

dcant_budget_global_all_in <- dcant_budget_global_all %>% 
  filter(data_source %in% c("mod", "obs"))

dcant_budget_global_ensemble <- dcant_budget_global_all_in %>% 
  group_by(data_source, period, tref2) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant),
            dcant_range = max(dcant)- min(dcant)) %>% 
  ungroup()

`summarise()` has grouped output by 'data_source', 'period'. You can override
using the `.groups` argument.

4.1.1 Mean

legend_title = expression(Delta * C[ant]~(PgC))

ggplot() +
  geom_col(data = dcant_budget_global_ensemble,
           aes(x = period,
               y = dcant_mean),
           fill = "darkgrey") +
  geom_errorbar(
    data = dcant_budget_global_ensemble,
    aes(
      x = period,
      y = dcant_mean,
      ymax = dcant_mean + dcant_sd,
      ymin = dcant_mean - dcant_sd
    ),
    width = 0.1
  ) +
  geom_point(
    data = dcant_budget_global_all,
    aes(period, dcant, col = MLR_basins),
    alpha = 0.7,
    position = position_jitter(width = 0.2, height = 0)
  ) +
  scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ data_source) +
  labs(y = legend_title) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Past versions of ensemble_mean_global-1.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

p_global_dcant <- ggplot() +
  geom_col(data = dcant_budget_global_ensemble %>% 
             filter(data_source == "obs"),
           aes(x = period,
               y = dcant_mean),
           fill = "darkgrey") +
    geom_point(
    data = dcant_budget_global_all %>% 
             filter(data_source == "obs"),
    aes(period, dcant, col = MLR_basins),
    alpha = 0.7,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_errorbar(
    data = dcant_budget_global_ensemble %>% 
             filter(data_source == "obs"),
    aes(
      x = period,
      y = dcant_mean,
      ymax = dcant_mean + dcant_sd,
      ymin = dcant_mean - dcant_sd
    ),
    width = 0.1
  ) +
  scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
  scale_color_brewer(palette = "Dark2") +
  labs(y = legend_title,
       title = "Observation-based results") +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank())

p_global_dcant_bias <-
p_global_dcant / p_global_bias +
  plot_layout(guides = 'collect',
              heights = c(2,1))

p_global_dcant_bias

Past versions of ensemble_mean_global-2.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

# ggsave(plot = p_global_dcant_bias,
#        path = here::here("output/publication"),
#        filename = "Fig_global_dcant_budget.png",
#        height = 5,
#        width = 5)

rm(p_global_bias, p_global_dcant, p_global_dcant_bias)

4.1.2 Mean vs atm CO2

dcant_ensemble <- dcant_budget_global_ensemble %>% 
  filter(data_source == "obs",
         period != "1994 - 2014") %>% 
  select(year = tref2, dcant_mean, dcant_sd)

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

tcant_ensemble <- full_join(dcant_ensemble, tcant_S04)

Joining, by = c("year", "dcant_mean", "dcant_sd")

tcant_ensemble <- left_join(tcant_ensemble, co2_atm)

Joining, by = "year"

co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1750, dcant_sd = 0)

tcant_ensemble <- full_join(tcant_ensemble, co2_atm_pi)

Joining, by = c("year", "dcant_mean", "dcant_sd", "pCO2")

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

tcant_ensemble %>% 
  ggplot(aes(pCO2, tcant, ymin = tcant - tcant_sd, ymax = tcant + tcant_sd)) +
  geom_ribbon(fill = "grey80") +
  geom_point() +
  geom_line() +
  scale_x_continuous(breaks = seq(280, 400, 30),
                     sec.axis = dup_axis(labels =  c(1750, 1940, 1980, 2000, 2015),
                                         name = "Year")) +
  geom_text(aes(label = year), nudge_x = -5, nudge_y = 5) +
  labs(x = expression(Atmospheric~pCO[2]~(µatm)),
       y = expression(Total~oceanic~C[ant]~(PgC)))

Past versions of mean_tcant_over_atm_co2-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

# ggsave(path = "output/publication",
#        filename = "Fig_global_dcant_budget_vs_atm_pCO2.png",
#        height = 4,
#        width = 7)

4.1.3 Sum decades

dcant_budget_global_all_in_sum <-
  dcant_budget_global_all_in %>%
  filter(period != "1994 - 2014") %>%
  arrange(tref1) %>%
  group_by(data_source, MLR_basins) %>%
  mutate(dcant = dcant + lag(dcant)) %>% 
  ungroup() %>%
  drop_na() %>% 
  mutate(estimate = "sum")

dcant_budget_global_all_in_sum <-
  bind_rows(
    dcant_budget_global_all_in_sum,
    dcant_budget_global_all_in %>%
      filter(period == "1994 - 2014") %>%
      mutate(estimate = "direct")
  )

ggplot() +
  geom_point(
    data = dcant_budget_global_all_in_sum,
    aes(estimate, dcant, col = MLR_basins),
    alpha = 0.7,
    position = position_jitter(width = 0, height = 0)
  ) +
  scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ data_source) +
  theme(axis.text.x = element_text(angle = 45, hjust=1),
        axis.title.x = element_blank())

Past versions of summed_decades-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

4.1.4 Mean bias

dcant_budget_global_ensemble_bias <- full_join(
  dcant_budget_global_ensemble %>%
    filter(data_source == "mod") %>% 
    select(period, dcant_mean, dcant_sd),
  dcant_budget_global_all %>%
    filter(data_source == "mod_truth",
           MLR_basins == unique(dcant_budget_global_all$MLR_basins)[1]) %>% 
    select(period, dcant)
)

Joining, by = "period"

dcant_budget_global_ensemble_bias <- dcant_budget_global_ensemble_bias %>% 
  mutate(dcant_mean_bias = dcant_mean - dcant,
         dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)

dcant_budget_global_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias_global-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_budget_global_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias_rel)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias_global-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

4.1.5 Vertical patterns

4.1.5.1 Absoulte values

dcant_budget_global_all_depth %>%
  filter(data_source != "mod_truth") %>% 
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~  ggplot(data = .x,
              aes(dcant, MLR_basins, fill=period)) +
      geom_vline(xintercept = 0) +
      geom_col(position = "dodge") +
      scale_fill_brewer(palette = "Dark2") +
      facet_grid(inv_depth ~ .) +
      labs(title = paste("data_source:", unique(.x$data_source)))
  )

[[1]]

Past versions of cases_absolute_depth_global-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Past versions of cases_absolute_depth_global-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

4.1.5.2 Biases

dcant_budget_global_bias_all_depth %>%
  ggplot(aes(dcant_bias, MLR_basins, fill = period)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Past versions of cases_bias_depth_global-1.png

Version	Author	Date
d2b50eb	jens-daniel-mueller	2022-07-07
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_budget_global_bias_all_depth %>%
  ggplot(aes(dcant_bias_rel, MLR_basins, fill = period)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ .)

Past versions of cases_bias_rel_depth_global-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

rm(dcant_budget_global_all,
   dcant_budget_global_all_depth,
   dcant_budget_global_bias_all,
   dcant_budget_global_bias_all_depth,
   dcant_budget_global_ensemble,
   dcant_budget_global_ensemble_bias)

4.2 Basins

dcant_budget_basin_AIP_ensemble <- dcant_budget_basin_AIP_all %>% 
  filter(data_source %in% c("mod", "obs")) %>% 
  group_by(basin_AIP, data_source, period) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant),
            dcant_range = max(dcant)- min(dcant)) %>% 
  ungroup()

`summarise()` has grouped output by 'basin_AIP', 'data_source'. You can override
using the `.groups` argument.

4.2.1 Mean

dcant_budget_basin_AIP_ensemble %>%
  ggplot(aes(period, dcant_mean, col=basin_AIP)) +
  geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
                      ymin = dcant_mean - dcant_sd),
                  shape = 21) +
  facet_grid(. ~ data_source)

Past versions of ensemble_mean-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

p_regional_dcant <- ggplot() +
  geom_col(
    data = dcant_budget_basin_AIP_ensemble %>%
      filter(data_source == "obs"),
    aes(x = period,
        y = dcant_mean),
    fill = "darkgrey"
  ) +
  geom_point(
    data = dcant_budget_basin_AIP_all %>%
      filter(data_source == "obs"),
    aes(period, dcant, col = MLR_basins),
    position = position_jitter(width = 0.1, height = 0),
    alpha = 0.7
  ) +
  geom_errorbar(
    data = dcant_budget_basin_AIP_ensemble %>%
      filter(data_source == "obs"),
    aes(
      x = period,
      y = dcant_mean,
      ymax = dcant_mean + dcant_sd,
      ymin = dcant_mean - dcant_sd
    ),
    width = 0.1
  ) +
  scale_y_continuous(limits = c(0, 35), expand = c(0, 0)) +
  scale_color_brewer(palette = "Dark2") +
  labs(y = legend_title,
       title = "Observation-based results") +
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank()) +
  facet_grid(. ~ basin_AIP)

p_regional_dcant_bias <-
p_regional_dcant / p_regional_bias +
  plot_layout(guides = 'collect',
              heights = c(2,1))

p_regional_dcant_bias

Past versions of ensemble_mean-2.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

# ggsave(plot = p_regional_dcant_bias,
#        path = "output/publication",
#        filename = "Fig_regional_dcant_budget.png",
#        height = 5,
#        width = 10)

rm(p_regional_bias, p_regional_dcant, p_regional_dcant_bias)

4.2.2 Mean bias

dcant_budget_basin_AIP_ensemble_bias <- full_join(
  dcant_budget_basin_AIP_ensemble %>%
    filter(data_source == "mod") %>% 
    select(basin_AIP, period, dcant_mean, dcant_sd),
  dcant_budget_basin_AIP_all %>%
    filter(data_source == "mod_truth",
           MLR_basins == unique(dcant_budget_basin_AIP_all$MLR_basins)[1]) %>% 
    select(basin_AIP, period, dcant)
)

Joining, by = c("basin_AIP", "period")

dcant_budget_basin_AIP_ensemble_bias <- dcant_budget_basin_AIP_ensemble_bias %>% 
  mutate(dcant_mean_bias = dcant_mean - dcant,
         dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)


dcant_budget_basin_AIP_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias, col = basin_AIP)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_budget_basin_AIP_ensemble_bias %>%
  ggplot(aes(period, dcant_mean_bias_rel, col = basin_AIP)) +
  geom_hline(yintercept = 0) +
  geom_point()

Past versions of ensemble_mean_bias-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

4.2.3 Vertical patterns

4.2.3.1 Absoulte values

dcant_budget_basin_AIP_all_depth %>%
  filter(data_source != "mod_truth") %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~  ggplot(data = .x,
              aes(dcant, MLR_basins, fill = basin_AIP)) +
      geom_vline(xintercept = 0) +
      geom_col() +
      scale_fill_brewer(palette = "Dark2") +
      facet_grid(inv_depth ~ period) +
      labs(title = paste("data_source:", unique(.x$data_source)))
  )

[[1]]

Past versions of cases_absolute_depth-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Past versions of cases_absolute_depth-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

4.2.3.2 Biases

dcant_budget_basin_AIP_bias_all_depth %>%
  ggplot(aes(dcant_bias, MLR_basins, fill = basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of cases_bias_depth-1.png

Version	Author	Date
d2b50eb	jens-daniel-mueller	2022-07-07
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

dcant_budget_basin_AIP_bias_all_depth %>%
  ggplot(aes(dcant_bias_rel, MLR_basins, fill = basin_AIP)) +
  geom_vline(xintercept = 0) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of cases_bias_rel_depth-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

5 Steady state

dcant_obs_budget_all %>%
  group_by(inv_depth) %>%
  group_split() %>%
  # head(1) %>% 
  map(
    ~ ggplot(data = .x,
             aes(estimate, dcant_pos, fill = basin_AIP)) +
      scale_fill_brewer(palette = "Dark2") +
      geom_col() +
      facet_grid(MLR_basins ~ period) +
      labs(title = paste("inventory depth:",unique(.x$inv_depth)))
  )

[[1]]

Past versions of steady_state_comparison-1.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Past versions of steady_state_comparison-2.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[3]]

Past versions of steady_state_comparison-3.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[4]]

Past versions of steady_state_comparison-4.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[5]]

Past versions of steady_state_comparison-5.png

Version	Author	Date
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

6 Predictor analysis

lm_best_predictor_counts_all <-
  full_join(lm_best_predictor_counts_all,
            params_local_all)

Joining, by = "Version_ID"

lm_best_predictor_counts_all <- lm_best_predictor_counts_all %>% 
  mutate(n_predictors_total = rowSums(across(aou:temp), na.rm = TRUE)/10)

lm_best_predictor_counts_all %>%
  ggplot(aes(x = MLR_basins, y = n_predictors_total)) +
  # ggdist::stat_halfeye(
  #   adjust = .5,
  #   width = .6,
  #   .width = 0,
  #   justification = -.2,
  #   point_colour = NA
  # ) +
  geom_boxplot(width = 0.5,
               outlier.shape = NA) +
  gghalves::geom_half_point(
    side = "l",
    range_scale = .4,
    alpha = .5,
    aes(col = gamma_slab)
  ) +
  scale_color_viridis_d() +
  facet_grid(basin ~ data_source)

Past versions of predictor_analysis-1.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

lm_best_predictor_counts_all %>%
  pivot_longer(aou:temp,
               names_to = "predictor",
               values_to = "count") %>%
  group_split(predictor) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(MLR_basins, count, color = gamma_slab)) +
      geom_jitter(alpha = 0.5) +
      scale_color_viridis_d() +
      labs(title = paste0("predictor:", unique(.x$predictor))) +
      coord_cartesian(ylim = c(0, 10)) +
      facet_grid(basin ~ data_source)
  )

[[1]]

Warning: Removed 2 rows containing missing values (geom_point).

Past versions of predictor_analysis-2.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[2]]

Warning: Removed 40 rows containing missing values (geom_point).

Past versions of predictor_analysis-3.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[3]]

Warning: Removed 2 rows containing missing values (geom_point).

Past versions of predictor_analysis-4.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[4]]

Warning: Removed 6 rows containing missing values (geom_point).

Past versions of predictor_analysis-5.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[5]]

Past versions of predictor_analysis-6.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[6]]

Warning: Removed 3 rows containing missing values (geom_point).

Past versions of predictor_analysis-7.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

[[7]]

Warning: Removed 3 rows containing missing values (geom_point).

Past versions of predictor_analysis-8.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
1da59de	jens-daniel-mueller	2022-06-25
f5f6e64	jens-daniel-mueller	2022-06-20
8dca96a	jens-daniel-mueller	2022-04-12

lm_best_dcant_all <-
  full_join(lm_best_dcant_all,
            params_local_all)

Joining, by = "Version_ID"

lm_best_dcant_all %>%
  count(basin, data_source, gamma_slab, MLR_basins, period) %>%
  ggplot(aes(MLR_basins, n)) +
  geom_jitter(height = 0, alpha = 0.3) +
  facet_grid(basin ~ data_source)

Past versions of predictor_analysis-9.png

Version	Author	Date
157af41	jens-daniel-mueller	2022-07-03
8dca96a	jens-daniel-mueller	2022-04-12

7 Drift and bias

dcant_budget_global_all_dissic %>%
  filter(estimate == "dcant") %>%
  ggplot(aes(inv_depth, value, col = !!sym(config))) +
  geom_hline(yintercept = 0) +
  scale_color_brewer(palette = "Dark2") +
  geom_point() +
  geom_path() +
  labs(y = "DIC change (PgC)") +
  facet_grid(data_source ~ period, scales = "free_y")

Past versions of drift_bias_plots-1.png

Version	Author	Date
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition <-
  dcant_budget_global_bias_all_decomposition %>%
  filter(estimate == "dcant") %>%
  select(inv_depth, dcant_bias, contribution, !!sym(config), period) %>%
  pivot_wider(names_from = contribution,
              values_from = dcant_bias)

dcant_budget_global_bias_all_decomposition <-
  full_join(
    dcant_budget_global_bias_all_decomposition,
    dcant_budget_global_bias_all_depth %>%
      select(inv_depth, !!sym(config), period, mod_truth)
  )

Joining, by = c("inv_depth", "MLR_basins", "period")

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(`dcant offset`, `delta C* - mod_truth`, col = !!sym(config))) +
  geom_vline(xintercept = 0, col = "grey50") +
  geom_hline(yintercept = 0, col = "grey50") +
  geom_abline(intercept = 0, slope = 1) +
  geom_point() +
  coord_fixed() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of drift_bias_plots-2.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(`dcant offset`, `C* prediction error`, col = !!sym(config))) +
  geom_vline(xintercept = 0, col = "grey50") +
  geom_hline(yintercept = 0, col = "grey50") +
  geom_abline(intercept = 0, slope = 1) +
  geom_point() +
  coord_fixed() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of drift_bias_plots-3.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(
    `dcant offset`,
    `C* prediction error` + `delta C* - mod_truth`,
    col = !!sym(config)
  )) +
  geom_vline(xintercept = 0, col = "grey50") +
  geom_hline(yintercept = 0, col = "grey50") +
  geom_abline(intercept = 0, slope = 1) +
  geom_point() +
  coord_fixed() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of drift_bias_plots-4.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(`dcant offset`, `C* drift`, col = !!sym(config))) +
  geom_vline(xintercept = 0, col = "grey50") +
  geom_hline(yintercept = 0, col = "grey50") +
  geom_abline(intercept = 0, slope = 1) +
  geom_point() +
  coord_fixed() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of drift_bias_plots-5.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(
    `dcant offset` - `C* drift`,
    `C* prediction error`,
    col = !!sym(config)
  )) +
  geom_vline(xintercept = 0, col = "grey50") +
  geom_hline(yintercept = 0, col = "grey50") +
  geom_abline(intercept = 0, slope = 1) +
  geom_point() +
  coord_fixed() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(inv_depth ~ period)

Past versions of drift_bias_plots-6.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(
    x = period,
    fill = !!sym(config),
    col = !!sym(config)
  )) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(y = `dcant offset`, shape = "dcant offset"),
    position = position_nudge(x = -0.05),
    alpha = 0.5
  ) +
  geom_point(
    aes(y = `dcant offset` - `C* drift`, shape = "dcant offset - C* drift"),
    position = position_nudge(x = 0.05),
    alpha = 0.5
  ) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = c(21,23)) +
  facet_grid(inv_depth ~ .)

Past versions of drift_bias_plots-7.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12
d2b50eb	jens-daniel-mueller	2022-07-07

dcant_budget_global_bias_all_decomposition <-
  dcant_budget_global_bias_all_decomposition %>%
  mutate(
    `dcant offset rel` = 100 * `dcant offset` / mod_truth,
    `dcant offset rel corr` = 100 * (`dcant offset` - `C* drift`) / mod_truth,
    `C* prediction error rel` = 100 * (`C* prediction error`) / mod_truth
  )

dcant_budget_global_bias_all_decomposition %>%
  ggplot(aes(
    x = period,
    fill = !!sym(config),
    col = !!sym(config)
  )) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(y = `dcant offset rel`, shape = "dcant offset"),
    position = position_nudge(x = -0.05),
    alpha = 0.5
  ) +
  geom_point(
    aes(y = `dcant offset rel corr`, shape = "dcant offset - C* drift"),
    position = position_nudge(x = 0.05),
    alpha = 0.5
  ) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = c(21,23)) +
  facet_grid(inv_depth ~ .)

Past versions of drift_bias_plots-8.png

Version	Author	Date
36462a6	jens-daniel-mueller	2022-07-12

dcant_budget_global_bias_all_decomposition <-
  dcant_budget_global_bias_all_decomposition %>%
  pivot_longer(-c(inv_depth:period),
               names_to = "estimate",
               values_to = "value")


dcant_budget_global_bias_all_decomposition %>%
  group_by(inv_depth, estimate) %>%
  summarise(mean = mean(value),
            sd = sd(value)) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

`summarise()` has grouped output by 'inv_depth'. You can override using the
`.groups` argument.

inv_depth	estimate	mean	sd
100	C* drift	0.2033333	0.0740032
100	C* prediction error	-0.2376667	0.3392998
100	C* prediction error rel	-4.4050129	8.0805241
100	dcant offset	0.3195000	0.1243857
100	dcant offset rel	6.9134300	2.2204617
100	dcant offset rel corr	2.6213633	2.0169199
100	delta C* - mod_truth	-0.3946667	0.2164184
100	mod_truth	4.7563333	1.7425592
500	C* drift	1.3446667	0.4893907
500	C* prediction error	-1.2810000	0.9592100
500	C* prediction error rel	-7.3058332	4.9515702
500	dcant offset	0.4991111	0.6732145
500	dcant offset rel	3.1758190	4.4405305
500	dcant offset rel corr	-4.7905465	4.0558360
500	delta C* - mod_truth	0.3773333	0.1374833
500	mod_truth	16.9600000	6.2242462
1000	C* drift	3.1486667	1.1458415
1000	C* prediction error	-2.9315000	1.6039245
1000	C* prediction error rel	-12.7689603	5.1737624
1000	dcant offset	0.5580000	1.0492995
1000	dcant offset rel	2.5861247	4.9419568
1000	dcant offset rel corr	-11.3229449	4.6725333
1000	delta C* - mod_truth	2.0866667	0.7598902
1000	mod_truth	22.7516667	8.3557301
3000	C* drift	3.9900000	1.4521475
3000	C* prediction error	-2.1265000	1.4870885
3000	C* prediction error rel	-8.2695572	5.2662253
3000	dcant offset	1.8945000	1.4600860
3000	dcant offset rel	7.4637152	5.6524161
3000	dcant offset rel corr	-8.1804293	5.7423845
3000	delta C* - mod_truth	2.6026667	0.9469936
3000	mod_truth	25.6453333	9.4254881
10000	C* drift	3.3760000	1.2289476
10000	C* prediction error	-0.9889444	1.8321683
10000	C* prediction error rel	-3.7394457	7.4364161
10000	dcant offset	2.1837778	2.0352785
10000	dcant offset rel	8.4840875	7.9962442
10000	dcant offset rel corr	-4.5547886	8.0361144
10000	delta C* - mod_truth	1.7546667	0.6384472
10000	mod_truth	26.0420000	9.5725748

dcant_budget_global_bias_all_decomposition %>%
  group_by(inv_depth, estimate, period) %>%
  summarise(mean = mean(value),
            sd = sd(value)) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

`summarise()` has grouped output by 'inv_depth', 'estimate'. You can override
using the `.groups` argument.

inv_depth	estimate	period	mean	sd
100	C* drift	1994 - 2004	0.1550000	0.0000000
100	C* drift	1994 - 2014	0.3050000	0.0000000
100	C* drift	2004 - 2014	0.1500000	0.0000000
100	C* prediction error	1994 - 2004	0.1193333	0.1890563
100	C* prediction error	1994 - 2014	-0.4103333	0.2441128
100	C* prediction error	2004 - 2014	-0.4220000	0.2580690
100	C* prediction error rel	1994 - 2004	3.5954605	5.6961812
100	C* prediction error rel	1994 - 2014	-5.7517989	3.4218225
100	C* prediction error rel	2004 - 2014	-11.0587002	6.7628140
100	dcant offset	1994 - 2004	0.2986667	0.0574967
100	dcant offset	1994 - 2014	0.4553333	0.0652431
100	dcant offset	2004 - 2014	0.2045000	0.0806840
100	dcant offset rel	1994 - 2004	8.9986944	1.7323491
100	dcant offset rel	1994 - 2014	6.3825811	0.9145379
100	dcant offset rel	2004 - 2014	5.3590147	2.1143593
100	dcant offset rel corr	1994 - 2004	4.3286130	1.7323491
100	dcant offset rel corr	1994 - 2014	2.1072797	0.9145379
100	dcant offset rel corr	2004 - 2014	1.4281971	2.1143593
100	delta C* - mod_truth	1994 - 2004	-0.4900000	0.0000000
100	delta C* - mod_truth	1994 - 2014	-0.5910000	0.0000000
100	delta C* - mod_truth	2004 - 2014	-0.1030000	0.0000000
100	mod_truth	1994 - 2004	3.3190000	0.0000000
100	mod_truth	1994 - 2014	7.1340000	0.0000000
100	mod_truth	2004 - 2014	3.8160000	0.0000000
500	C* drift	1994 - 2004	1.0250000	0.0000000
500	C* drift	1994 - 2014	2.0170000	0.0000000
500	C* drift	2004 - 2014	0.9920000	0.0000000
500	C* prediction error	1994 - 2004	-0.4736667	0.6039251
500	C* prediction error	1994 - 2014	-1.9928333	1.0011336
500	C* prediction error	2004 - 2014	-1.3765000	0.6001282
500	C* prediction error rel	1994 - 2004	-4.0329218	5.1419757
500	C* prediction error rel	1994 - 2014	-7.8334644	3.9352737
500	C* prediction error rel	2004 - 2014	-10.0511135	4.3820974
500	dcant offset	1994 - 2004	0.8048333	0.4926546
500	dcant offset	1994 - 2014	0.7065000	0.7878116
500	dcant offset	2004 - 2014	-0.0140000	0.4448299
500	dcant offset rel	1994 - 2004	6.8525614	4.1945901
500	dcant offset rel	1994 - 2014	2.7771226	3.0967437
500	dcant offset rel	2004 - 2014	-0.1022271	3.2481187
500	dcant offset rel corr	1994 - 2004	-1.8745565	4.1945901
500	dcant offset rel corr	1994 - 2014	-5.1513365	3.0967437
500	dcant offset rel corr	2004 - 2014	-7.3457466	3.2481187
500	delta C* - mod_truth	1994 - 2004	0.2920000	0.0000000
500	delta C* - mod_truth	1994 - 2014	0.5660000	0.0000000
500	delta C* - mod_truth	2004 - 2014	0.2740000	0.0000000
500	mod_truth	1994 - 2004	11.7450000	0.0000000
500	mod_truth	1994 - 2014	25.4400000	0.0000000
500	mod_truth	2004 - 2014	13.6950000	0.0000000
1000	C* drift	1994 - 2004	2.3950000	0.0000000
1000	C* drift	1994 - 2014	4.7230000	0.0000000
1000	C* drift	2004 - 2014	2.3280000	0.0000000
1000	C* prediction error	1994 - 2004	-1.8000000	1.1841083
1000	C* prediction error	1994 - 2014	-4.4521667	1.5975944
1000	C* prediction error	2004 - 2014	-2.5423333	0.5471075
1000	C* prediction error rel	1994 - 2004	-11.4649682	7.5420909
1000	C* prediction error rel	1994 - 2014	-13.0458777	4.6813209
1000	C* prediction error rel	2004 - 2014	-13.7960350	2.9688927
1000	dcant offset	1994 - 2004	0.7438333	1.1288531
1000	dcant offset	1994 - 2014	0.8120000	1.4050153
1000	dcant offset	2004 - 2014	0.1181667	0.3796295
1000	dcant offset rel	1994 - 2004	4.7377919	7.1901473
1000	dcant offset rel	1994 - 2014	2.3793477	4.1170197
1000	dcant offset rel	2004 - 2014	0.6412344	2.0600690
1000	dcant offset rel corr	1994 - 2004	-10.5169851	7.1901473
1000	dcant offset rel corr	1994 - 2014	-11.4601342	4.1170197
1000	dcant offset rel corr	2004 - 2014	-11.9917155	2.0600690
1000	delta C* - mod_truth	1994 - 2004	1.5570000	0.0000000
1000	delta C* - mod_truth	1994 - 2014	3.1310000	0.0000000
1000	delta C* - mod_truth	2004 - 2014	1.5720000	0.0000000
1000	mod_truth	1994 - 2004	15.7000000	0.0000000
1000	mod_truth	1994 - 2014	34.1270000	0.0000000
1000	mod_truth	2004 - 2014	18.4280000	0.0000000
3000	C* drift	1994 - 2004	3.0410000	0.0000000
3000	C* drift	1994 - 2014	5.9850000	0.0000000
3000	C* drift	2004 - 2014	2.9440000	0.0000000
3000	C* prediction error	1994 - 2004	-1.5745000	1.3468354
3000	C* prediction error	1994 - 2014	-3.2643333	1.6294280
3000	C* prediction error	2004 - 2014	-1.5406667	0.8478122
3000	C* prediction error rel	1994 - 2004	-8.9257370	7.6351211
3000	C* prediction error rel	1994 - 2014	-8.4858410	4.2358011
3000	C* prediction error rel	2004 - 2014	-7.3970937	4.0705404
3000	dcant offset	1994 - 2004	1.3920000	1.4856831
3000	dcant offset	1994 - 2014	2.7726667	1.6915312
3000	dcant offset	2004 - 2014	1.5188333	0.8843030
3000	dcant offset rel	1994 - 2004	7.8911565	8.4222400
3000	dcant offset rel	1994 - 2014	7.2077224	4.3972424
3000	dcant offset rel	2004 - 2014	7.2922668	4.2457412
3000	dcant offset rel corr	1994 - 2004	-9.3480726	8.4222400
3000	dcant offset rel corr	1994 - 2014	-8.3506638	4.3972424
3000	dcant offset rel corr	2004 - 2014	-6.8425517	4.2457412
3000	delta C* - mod_truth	1994 - 2004	1.9330000	0.0000000
3000	delta C* - mod_truth	1994 - 2014	3.9040000	0.0000000
3000	delta C* - mod_truth	2004 - 2014	1.9710000	0.0000000
3000	mod_truth	1994 - 2004	17.6400000	0.0000000
3000	mod_truth	1994 - 2014	38.4680000	0.0000000
3000	mod_truth	2004 - 2014	20.8280000	0.0000000
10000	C* drift	1994 - 2004	2.5830000	0.0000000
10000	C* drift	1994 - 2014	5.0640000	0.0000000
10000	C* drift	2004 - 2014	2.4810000	0.0000000
10000	C* prediction error	1994 - 2004	-0.7285000	2.0086752
10000	C* prediction error	1994 - 2014	-1.5831667	2.3767461
10000	C* prediction error	2004 - 2014	-0.6551667	1.0443914
10000	C* prediction error rel	1994 - 2004	-4.0693777	11.2203953
10000	C* prediction error rel	1994 - 2014	-4.0528548	6.0843922
10000	C* prediction error rel	2004 - 2014	-3.0961045	4.9354538
10000	dcant offset	1994 - 2004	1.6080000	2.1818824
10000	dcant offset	1994 - 2014	3.1816667	2.5109502
10000	dcant offset	2004 - 2014	1.7616667	1.1008912
10000	dcant offset rel	1994 - 2004	8.9822366	12.1879254
10000	dcant offset rel	1994 - 2014	8.1449624	6.4279503
10000	dcant offset rel	2004 - 2014	8.3250634	5.2024536
10000	dcant offset rel corr	1994 - 2004	-5.4463188	12.1879254
10000	dcant offset rel corr	1994 - 2014	-4.8187117	6.4279503
10000	dcant offset rel corr	2004 - 2014	-3.3993353	5.2024536
10000	delta C* - mod_truth	1994 - 2004	1.3030000	0.0000000
10000	delta C* - mod_truth	1994 - 2014	2.6320000	0.0000000
10000	delta C* - mod_truth	2004 - 2014	1.3290000	0.0000000
10000	mod_truth	1994 - 2004	17.9020000	0.0000000
10000	mod_truth	1994 - 2014	39.0630000	0.0000000
10000	mod_truth	2004 - 2014	21.1610000	0.0000000

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

loaded via a namespace (and not attached):
 [1] fs_1.5.2           gghalves_0.1.1     bit64_4.0.5        lubridate_1.8.0   
 [5] gsw_1.0-6          RColorBrewer_1.1-2 webshot_0.5.2      httr_1.4.2        
 [9] rprojroot_2.0.2    tools_4.1.2        backports_1.4.1    bslib_0.3.1       
[13] utf8_1.2.2         R6_2.5.1           DBI_1.1.2          withr_2.4.3       
[17] tidyselect_1.1.1   processx_3.5.2     bit_4.0.4          compiler_4.1.2    
[21] git2r_0.29.0       cli_3.1.1          rvest_1.0.2        xml2_1.3.3        
[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        systemfonts_1.0.3  digest_0.6.29     
[33] svglite_2.0.0      rmarkdown_2.11     oce_1.5-0          pkgconfig_2.0.3   
[37] htmltools_0.5.2    highr_0.9          dbplyr_2.1.1       fastmap_1.1.0     
[41] rlang_1.0.2        readxl_1.3.1       rstudioapi_0.13    jquerylib_0.1.4   
[45] generics_0.1.1     farver_2.1.0       jsonlite_1.7.3     vroom_1.5.7       
[49] magrittr_2.0.1     Rcpp_1.0.8         munsell_0.5.0      fansi_1.0.2       
[53] lifecycle_1.0.1    stringi_1.7.6      whisker_0.4        yaml_2.2.1        
[57] MASS_7.3-55        grid_4.1.2         parallel_4.1.2     promises_1.2.0.1  
[61] crayon_1.4.2       haven_2.4.3        hms_1.1.1          seacarb_3.3.0     
[65] knitr_1.37         ps_1.6.0           pillar_1.6.4       reprex_2.0.1      
[69] glue_1.6.0         evaluate_0.14      getPass_0.2-2      data.table_1.14.2 
[73] modelr_0.1.8       vctrs_0.3.8        tzdb_0.2.0         tweenr_1.0.2      
[77] httpuv_1.6.5       cellranger_1.1.0   gtable_0.3.0       polyclip_1.10-0   
[81] assertthat_0.2.1   xfun_0.29          broom_0.7.11       later_1.3.0       
[85] viridisLite_0.4.0  ellipsis_0.3.2     here_1.0.1