Last updated: 2022-07-29

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Knit directory: emlr_obs_analysis/analysis/

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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())

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())

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

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 1.4553836 1.313667 8.250474 7.447090
1994 - 2014 1.7007077 2.724167 4.421097 7.081644
2004 - 2014 0.8704288 1.500667 4.179128 7.205044

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())

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 ~ .)

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)

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

Version Author Date
157af41 jens-daniel-mueller 2022-07-03
1da59de jens-daniel-mueller 2022-06-25
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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)

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

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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.
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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())

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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 ~ .)

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)

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

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 0.9899218 0.2035000 20.533536 4.2211160
1994 - 2004 N_Atlantic 0.2350280 0.1871667 12.046541 9.5933709
1994 - 2004 N_Pacific 0.1796446 0.4528333 6.614306 16.6728031
1994 - 2004 S_Atlantic 0.2890401 0.0811667 11.561603 3.2466667
1994 - 2004 S_Pacific 0.7957880 0.3888333 14.079760 6.8795707
1994 - 2014 Indian 1.2917064 0.7668333 12.363193 7.3395227
1994 - 2014 N_Atlantic 0.3486700 -0.0270000 8.116156 -0.6284916
1994 - 2014 N_Pacific 0.2483106 0.6121667 4.253351 10.4858970
1994 - 2014 S_Atlantic 0.3482876 0.4736667 6.585132 8.9556942
1994 - 2014 S_Pacific 1.2834369 0.8988333 10.188433 7.1352968
2004 - 2014 Indian 0.5363243 0.5108333 9.532960 9.0798673
2004 - 2014 N_Atlantic 0.1782074 -0.1016667 7.599461 -4.3354655
2004 - 2014 N_Pacific 0.2114710 0.1910000 6.773576 6.1178732
2004 - 2014 S_Atlantic 0.2098051 0.4408333 7.522591 15.8061432
2004 - 2014 S_Pacific 0.5003441 0.4606667 7.204379 6.6330694

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())

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

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)))

Version Author Date
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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())

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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()

Version Author Date
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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()

Version Author Date
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f5f6e64 jens-daniel-mueller 2022-06-20
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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]]

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[[2]]

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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 ~ .)

Version Author Date
d2b50eb jens-daniel-mueller 2022-07-07
1da59de jens-daniel-mueller 2022-06-25
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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 ~ .)

Version Author Date
1da59de jens-daniel-mueller 2022-06-25
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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)

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

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()

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

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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]]

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[[2]]

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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)

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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)

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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]]

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[[2]]

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[[3]]

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[[4]]

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[[5]]

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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)

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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).

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[[2]]
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[[3]]
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[[5]]

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[[7]]
Warning: Removed 3 rows containing missing values (geom_point).

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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)

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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")

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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)

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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)

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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)

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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)

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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)

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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 ~ .)

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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 ~ .)

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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 -0.2300000 0.3363849
100 C* prediction error rel -4.2737707 8.2280632
100 dcant offset 0.3212222 0.1255054
100 dcant offset rel 6.9442037 2.2298199
100 dcant offset rel corr 2.6521369 2.0175847
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 -1.2855556 0.9459816
500 C* prediction error rel -7.3522861 4.9299652
500 dcant offset 0.4878333 0.6694865
500 dcant offset rel 3.0966768 4.4057615
500 dcant offset rel corr -4.8696887 4.0276182
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 -2.9558333 1.5916053
1000 C* prediction error rel -12.9011178 5.1547888
1000 dcant offset 0.5272778 1.0447148
1000 dcant offset rel 2.4313390 4.9103820
1000 dcant offset rel corr -11.4777306 4.6698313
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 -2.1685000 1.4773328
3000 C* prediction error rel -8.4686896 5.1995633
3000 dcant offset 1.8461667 1.4528640
3000 dcant offset rel 7.2445927 5.5615402
3000 dcant offset rel corr -8.3995519 5.6881750
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 -1.0246667 1.8136274
10000 C* prediction error rel -3.9159433 7.2255701
10000 dcant offset 2.1417778 2.0201160
10000 dcant offset rel 8.2878354 7.7836248
10000 dcant offset rel corr -4.7510406 7.8391879
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 0.1323333 0.1946296
100 C* prediction error 1994 - 2014 -0.3890000 0.2168041
100 C* prediction error 2004 - 2014 -0.4333333 0.2496971
100 C* prediction error rel 1994 - 2004 3.9871447 5.8641025
100 C* prediction error rel 1994 - 2014 -5.4527614 3.0390252
100 C* prediction error rel 2004 - 2014 -11.3556953 6.5434264
100 dcant offset 1994 - 2004 0.3016667 0.0558056
100 dcant offset 1994 - 2014 0.4598333 0.0606611
100 dcant offset 2004 - 2014 0.2021667 0.0789795
100 dcant offset rel 1994 - 2004 9.0890831 1.6813985
100 dcant offset rel 1994 - 2014 6.4456593 0.8503095
100 dcant offset rel 2004 - 2014 5.2978686 2.0696943
100 dcant offset rel corr 1994 - 2004 4.4190017 1.6813985
100 dcant offset rel corr 1994 - 2014 2.1703579 0.8503095
100 dcant offset rel corr 2004 - 2014 1.3670510 2.0696943
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 -0.4826667 0.6174313
500 C* prediction error 1994 - 2014 -1.9843333 0.9744058
500 C* prediction error 2004 - 2014 -1.3896667 0.5825769
500 C* prediction error rel 1994 - 2004 -4.1095502 5.2569719
500 C* prediction error rel 1994 - 2014 -7.8000524 3.8302115
500 C* prediction error rel 2004 - 2014 -10.1472557 4.2539388
500 dcant offset 1994 - 2004 0.7845000 0.4981999
500 dcant offset 1994 - 2014 0.6963333 0.7844327
500 dcant offset 2004 - 2014 -0.0173333 0.4431382
500 dcant offset rel 1994 - 2004 6.6794381 4.2418038
500 dcant offset rel 1994 - 2014 2.7371593 3.0834619
500 dcant offset rel 2004 - 2014 -0.1265669 3.2357664
500 dcant offset rel corr 1994 - 2004 -2.0476799 4.2418038
500 dcant offset rel corr 1994 - 2014 -5.1912998 3.0834619
500 dcant offset rel corr 2004 - 2014 -7.3700864 3.2357664
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 -1.8418333 1.1946326
1000 C* prediction error 1994 - 2014 -4.4678333 1.5751166
1000 C* prediction error 2004 - 2014 -2.5578333 0.5311431
1000 C* prediction error rel 1994 - 2004 -11.7314225 7.6091246
1000 C* prediction error rel 1994 - 2014 -13.0917846 4.6154557
1000 C* prediction error rel 2004 - 2014 -13.8801462 2.8822611
1000 dcant offset 1994 - 2004 0.6913333 1.1341520
1000 dcant offset 1994 - 2014 0.7778333 1.4023736
1000 dcant offset 2004 - 2014 0.1126667 0.3794637
1000 dcant offset rel 1994 - 2004 4.4033970 7.2238978
1000 dcant offset rel 1994 - 2014 2.2792315 4.1092789
1000 dcant offset rel 2004 - 2014 0.6113885 2.0591690
1000 dcant offset rel corr 1994 - 2004 -10.8513800 7.2238978
1000 dcant offset rel corr 1994 - 2014 -11.5602504 4.1092789
1000 dcant offset rel corr 2004 - 2014 -12.0215614 2.0591690
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 -1.6423333 1.3107675
3000 C* prediction error 1994 - 2014 -3.2945000 1.6428471
3000 C* prediction error 2004 - 2014 -1.5686667 0.8473787
3000 C* prediction error rel 1994 - 2004 -9.3102797 7.4306548
3000 C* prediction error rel 1994 - 2014 -8.5642612 4.2706851
3000 C* prediction error rel 2004 - 2014 -7.5315281 4.0684593
3000 dcant offset 1994 - 2004 1.3136667 1.4553836
3000 dcant offset 1994 - 2014 2.7241667 1.7007077
3000 dcant offset 2004 - 2014 1.5006667 0.8704288
3000 dcant offset rel 1994 - 2004 7.4470899 8.2504740
3000 dcant offset rel 1994 - 2014 7.0816436 4.4210972
3000 dcant offset rel 2004 - 2014 7.2050445 4.1791280
3000 dcant offset rel corr 1994 - 2004 -9.7921391 8.2504740
3000 dcant offset rel corr 1994 - 2014 -8.4767426 4.4210972
3000 dcant offset rel corr 2004 - 2014 -6.9297740 4.1791280
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 -0.7838333 1.9104399
10000 C* prediction error 1994 - 2014 -1.5945000 2.4176019
10000 C* prediction error 2004 - 2014 -0.6956667 1.0486701
10000 C* prediction error rel 1994 - 2004 -4.3784680 10.6716562
10000 C* prediction error rel 1994 - 2014 -4.0818678 6.1889817
10000 C* prediction error rel 2004 - 2014 -3.2874943 4.9556738
10000 dcant offset 1994 - 2004 1.5415000 2.0958221
10000 dcant offset 1994 - 2014 3.1521667 2.5363748
10000 dcant offset 2004 - 2014 1.7316667 1.0908682
10000 dcant offset rel 1994 - 2004 8.6107697 11.7071953
10000 dcant offset rel 1994 - 2014 8.0694434 6.4930365
10000 dcant offset rel 2004 - 2014 8.1832932 5.1550882
10000 dcant offset rel corr 1994 - 2004 -5.8177857 11.7071953
10000 dcant offset rel corr 1994 - 2014 -4.8942307 6.4930365
10000 dcant offset rel corr 2004 - 2014 -3.5411055 5.1550882
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

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   geomtextpath_0.1.0 colorspace_2.0-2   marelac_2.1.10    
 [5] shape_1.4.6        ggforce_0.3.3      metR_0.11.0        scico_1.3.0       
 [9] patchwork_1.1.1    collapse_1.7.0     forcats_0.5.1      stringr_1.4.0     
[13] dplyr_1.0.7        purrr_0.3.4        readr_2.1.1        tidyr_1.1.4       
[17] tibble_3.1.6       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       textshaping_0.3.6  cli_3.1.1          rvest_1.0.2       
[25] xml2_1.3.3         labeling_0.4.2     sass_0.4.0         scales_1.1.1      
[29] checkmate_2.0.0    SolveSAPHE_2.1.0   callr_3.7.0        systemfonts_1.0.3 
[33] digest_0.6.29      svglite_2.0.0      rmarkdown_2.11     oce_1.5-0         
[37] pkgconfig_2.0.3    htmltools_0.5.2    highr_0.9          dbplyr_2.1.1      
[41] fastmap_1.1.0      rlang_1.0.2        readxl_1.3.1       rstudioapi_0.13   
[45] jquerylib_0.1.4    generics_0.1.1     farver_2.1.0       jsonlite_1.7.3    
[49] vroom_1.5.7        magrittr_2.0.1     Rcpp_1.0.8         munsell_0.5.0     
[53] fansi_1.0.2        lifecycle_1.0.1    stringi_1.7.6      whisker_0.4       
[57] yaml_2.2.1         MASS_7.3-55        grid_4.1.2         parallel_4.1.2    
[61] promises_1.2.0.1   crayon_1.4.2       haven_2.4.3        hms_1.1.1         
[65] seacarb_3.3.0      knitr_1.37         ps_1.6.0           pillar_1.6.4      
[69] reprex_2.0.1       glue_1.6.0         evaluate_0.14      getPass_0.2-2     
[73] data.table_1.14.2  modelr_0.1.8       vctrs_0.3.8        tzdb_0.2.0        
[77] tweenr_1.0.2       httpuv_1.6.5       cellranger_1.1.0   gtable_0.3.0      
[81] polyclip_1.10-0    assertthat_0.2.1   xfun_0.29          broom_0.7.11      
[85] later_1.3.0        viridisLite_0.4.0  ellipsis_0.3.2     here_1.0.1