Zonal sections

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Last updated: 2021-10-26

Checks: 7 0

Knit directory: emlr_obs_analysis/

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File	Version	Author	Date	Message
Rmd	5e671d5	jens-daniel-mueller	2021-10-26	revised composite plots
html	5d80e21	jens-daniel-mueller	2021-10-26	Build site.
Rmd	c4ac357	jens-daniel-mueller	2021-10-26	revised composite plots
html	8b4e334	jens-daniel-mueller	2021-10-26	Build site.
Rmd	09c80f9	jens-daniel-mueller	2021-10-26	revised composite plots
html	93f903d	jens-daniel-mueller	2021-10-25	Build site.
Rmd	ea4285f	jens-daniel-mueller	2021-10-25	layer budgets added
html	d444839	jens-daniel-mueller	2021-10-25	Build site.
Rmd	88c4411	jens-daniel-mueller	2021-10-25	layer budgets added
html	8c9fa17	jens-daniel-mueller	2021-10-22	Build site.
html	6c49790	jens-daniel-mueller	2021-10-18	Build site.
Rmd	f352581	jens-daniel-mueller	2021-10-18	profiles revised
html	968fe94	jens-daniel-mueller	2021-10-18	Build site.
Rmd	88117a8	jens-daniel-mueller	2021-10-18	profile analysis added
html	581baa0	jens-daniel-mueller	2021-10-07	Build site.
html	a7af62f	jens-daniel-mueller	2021-10-06	Build site.
html	f9b4f93	jens-daniel-mueller	2021-10-05	Build site.
html	29faa21	jens-daniel-mueller	2021-09-29	Build site.
Rmd	e42ccd7	jens-daniel-mueller	2021-09-29	plots for presentation
html	960d158	jens-daniel-mueller	2021-09-29	Build site.
html	0573621	jens-daniel-mueller	2021-09-29	Build site.
html	f51dcb9	jens-daniel-mueller	2021-09-28	Build site.
Rmd	056c790	jens-daniel-mueller	2021-09-28	new MLR_basin added
html	55d2059	jens-daniel-mueller	2021-09-28	Build site.
html	d0945da	jens-daniel-mueller	2021-09-28	Build site.
Rmd	7780618	jens-daniel-mueller	2021-09-28	update zonal section plots
html	09002a7	jens-daniel-mueller	2021-09-26	Build site.
html	0c6be7e	jens-daniel-mueller	2021-09-24	Build site.
Rmd	6a79fd0	jens-daniel-mueller	2021-09-24	consider ensemble selection across all analysis
html	f54d5db	jens-daniel-mueller	2021-09-24	Build site.
Rmd	76c942b	jens-daniel-mueller	2021-09-24	1990 as start year 1 for classic runs
html	31c33cb	jens-daniel-mueller	2021-09-23	Build site.
Rmd	9b1df4d	jens-daniel-mueller	2021-09-23	1994 vs 2014 added
html	ec4f702	jens-daniel-mueller	2021-09-22	Build site.
html	6525680	jens-daniel-mueller	2021-09-21	Build site.
html	6bf38c9	jens-daniel-mueller	2021-09-21	Build site.
html	63911d0	jens-daniel-mueller	2021-09-21	Build site.
html	499c9d5	jens-daniel-mueller	2021-09-21	Build site.
html	271a3ac	jens-daniel-mueller	2021-09-21	Build site.
html	35ad8b5	jens-daniel-mueller	2021-09-20	Build site.
Rmd	919f74b	jens-daniel-mueller	2021-09-20	rebuildt with canyon b cleaning analysis

---

1 Read files

ensemble_members <- 
  read_rds("data/ensemble_members.rds")

MLR_basins_in <- 
  read_rds("data/MLR_basins_in.rds")

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = "v_10")

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = "v_20")

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = "v_30")

# Version_IDs <- c(Version_IDs_1, Version_IDs_2)
Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
# rm(Version_IDs_1, Version_IDs_2)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

# 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_zonal <-
    read_csv(paste(path_version_data,
                   "dcant_zonal.csv",
                   sep = ""))
  
  dcant_zonal_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_mod_truth.csv",
                   sep = ""))
  
  dcant_zonal <- bind_rows(dcant_zonal,
                         dcant_zonal_mod_truth)
  
  dcant_profile <-
    read_csv(paste(path_version_data,
                   "dcant_profile.csv",
                   sep = ""))
  
  dcant_profile_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_profile_mod_truth.csv",
                   sep = ""))
  
  dcant_profile <- bind_rows(dcant_profile,
                             dcant_profile_mod_truth)
  
  dcant_budget_basin_AIP_layer <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP_layer.csv",
                   sep = ""))
  
  dcant_zonal_bias <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_bias.csv",
                   sep = ""))
  

  dcant_zonal <- dcant_zonal %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_profile <- dcant_profile %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_AIP_layer <- dcant_budget_basin_AIP_layer %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_zonal_bias <- dcant_zonal_bias %>% 
    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 = params_local$MLR_basins,
    tref1 = params_local$tref1,
    tref2 = params_local$tref2,
    gap_filling = params_local$gap_filling,
    rarefication = params_local$rarefication,
    rarefication_threshold = params_local$rarefication_threshold,
    MLR_predictors = str_c(params_local$MLR_predictors, collapse = "+"),
    vif_max = params_local$vif_max
  )
  
  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_zonal_all")) {
    dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
  }
  
  if (!exists("dcant_zonal_all")) {
    dcant_zonal_all <- dcant_zonal
  }

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

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

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

  if (exists("params_local_all")) {
    params_local_all <- bind_rows(params_local_all, params_local)
  }
  
  if (!exists("params_local_all")) {
    params_local_all <- params_local
  }
  
  
}

[1] "v_1001"
[1] "v_1002"
[1] "v_1003"
[1] "v_1004"
[1] "v_1005"
[1] "v_1006"
[1] "v_2001"
[1] "v_2002"
[1] "v_2003"
[1] "v_2004"
[1] "v_2005"
[1] "v_2006"
[1] "v_3001"
[1] "v_3002"
[1] "v_3003"
[1] "v_3004"
[1] "v_3005"
[1] "v_3006"

rm(dcant_zonal, dcant_zonal_bias, dcant_zonal_mod_truth,
   dcant_budget_basin_AIP_layer,
   tref)

dcant_zonal_all <- full_join(dcant_zonal_all,
                           params_local_all)

dcant_profile_all <- full_join(dcant_profile_all,
                           params_local_all)

dcant_budget_basin_AIP_layer_all <-
  full_join(dcant_budget_basin_AIP_layer_all,
            params_local_all)

dcant_zonal_bias_all <- full_join(dcant_zonal_bias_all,
                                params_local_all)

dcant_zonal_all <- dcant_zonal_all %>% 
  filter(MLR_basins %in% MLR_basins_in)

dcant_profile_all <- dcant_profile_all %>% 
  filter(MLR_basins %in% MLR_basins_in)

dcant_zonal_bias_all <- dcant_zonal_bias_all %>% 
  filter(MLR_basins %in% MLR_basins_in)

2 Individual cases

2.1 Absoulte values

dcant_zonal_all %>%
  filter(data_source %in% c("mod", "obs")) %>%
  group_by(basin_AIP, data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant",
      plot_slabs = "n",
      subtitle_text = paste(
        "data_source: ",
        unique(.x$data_source),
        "| basin:",
        unique(.x$basin_AIP)
      )
    ) +
      facet_grid(MLR_basins ~ period)
  )

[[1]]

Past versions of cases_absolute-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[2]]

Past versions of cases_absolute-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[3]]

Past versions of cases_absolute-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[4]]

Past versions of cases_absolute-4.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[5]]

Past versions of cases_absolute-5.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[6]]

Past versions of cases_absolute-6.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

p_dcant_Indian_1994_2004 <-
  dcant_zonal_all %>%
  filter(data_source %in% c("obs"),
         period == "1994 - 2004",
         basin_AIP == "Indian") %>%
  p_section_zonal_continous_depth(var = "dcant",
                                  plot_slabs = "n",
                                  subtitle_text = "Indian Ocean") +
  facet_grid(MLR_basins ~ period)

ggsave(plot = p_dcant_Indian_1994_2004,
       path = "output/publication",
       filename = "zonal_indian_1994_2004.png",
       height = 8,
       width = 5)

p_dcant_Indian_2004_2014 <-
  dcant_zonal_all %>%
  filter(data_source %in% c("obs"),
         period == "2004 - 2014",
         basin_AIP == "Pacific") %>%
  p_section_zonal_continous_depth(var = "dcant",
                                  plot_slabs = "n",
                                  subtitle_text = "Pacific Ocean") +
  facet_grid(MLR_basins ~ period)

ggsave(plot = p_dcant_Indian_2004_2014,
       path = "output/publication",
       filename = "zonal_Pacific_2004_2014.png",
       height = 8,
       width = 5)

2.2 Biases

dcant_zonal_bias_all %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant_bias",
      col = "divergent",
      plot_slabs = "n",
      subtitle_text = paste("basin:",
        unique(.x$basin_AIP)
      )
    ) +
      facet_grid(MLR_basins ~ period)
  )

[[1]]

Past versions of cases_bias-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[2]]

Past versions of cases_bias-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[3]]

Past versions of cases_bias-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

2.3 Bias correlation

dcant_zonal_bias_all_corr <- dcant_zonal_bias_all %>%
  select(lat, depth, basin_AIP, dcant_bias, MLR_basins, period) %>% 
  pivot_wider(names_from = period,
              values_from = dcant_bias, 
              names_prefix = "dcant_bias ")

dcant_zonal_bias_all_corr %>% 
  ggplot(aes(`dcant_bias 1994 - 2004`, `dcant_bias 2004 - 2014`)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  geom_bin2d() +
  facet_grid(basin_AIP ~ MLR_basins) +
  scale_fill_viridis_c()

Past versions of cases_bias_correlation-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
f54d5db	jens-daniel-mueller	2021-09-24
35ad8b5	jens-daniel-mueller	2021-09-20

2.4 Profiles

dcant_profile_all %>%
  group_split(period) %>%
  map(
    ~ ggplot(data = .x,
             aes(
               dcant, depth,
               col = data_source, fill = data_source
             )) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(xmin = dcant - dcant_sd,
            xmax = dcant + dcant_sd),
        alpha = 0.2,
        col = "transparent"
      ) +
      geom_path() +
      scale_y_reverse() +
      labs(title = paste("period", unique(.x$period))) +
      facet_grid(MLR_basins ~ basin_AIP)
  )

[[1]]

Past versions of profiles_per_data_source-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

[[2]]

Past versions of profiles_per_data_source-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

[[3]]

Past versions of profiles_per_data_source-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

dcant_profile_all %>%
  group_split(period) %>%
  map(
    ~ ggplot(data = .x,
             aes(
               dcant, depth,
               col = MLR_basins, fill = MLR_basins
             )) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path() +
      scale_y_reverse() +
      labs(title = paste("period", unique(.x$period))) +
      facet_grid(data_source ~ basin_AIP)
  )

[[1]]

Past versions of profiles_per_MLR_basins-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

[[2]]

Past versions of profiles_per_MLR_basins-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

[[3]]

Past versions of profiles_per_MLR_basins-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

dcant_profile_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  group_split(data_source) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(
        dcant,
        depth,
        col = period,
        group = interaction(MLR_basins, period)
      )
    ) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path() +
      scale_y_reverse() +
      labs(title = paste("data_source", unique(.x$data_source))) +
      facet_grid(. ~ basin_AIP)
  )

[[1]]

Past versions of profiles_per_period-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

[[2]]

Past versions of profiles_per_period-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

[[3]]

Past versions of profiles_per_period-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
6c49790	jens-daniel-mueller	2021-10-18
968fe94	jens-daniel-mueller	2021-10-18

2.5 Layer budgets

dcant_budget_basin_AIP_layer_all %>%
  filter(estimate == "dcant") %>% 
  mutate(dcant = value,
         inv_depth = fct_inorder(as.factor(inv_depth))) %>% 
  group_split(period) %>%
  # head(1) %>% 
  map(
    ~ ggplot(data = .x,
             aes(dcant, inv_depth,
                 fill = MLR_basins)) +
      geom_vline(xintercept = 0) +
      geom_col(position = "dodge") +
      scale_y_discrete(limits = rev) +
      scale_fill_brewer(palette = "Dark2") +
      labs(title = paste("period", unique(.x$period))) +
      facet_grid(data_source ~ basin_AIP)
  )

[[1]]

Past versions of layer_budget_per_data_source-1.png

Version	Author	Date
d444839	jens-daniel-mueller	2021-10-25

[[2]]

Past versions of layer_budget_per_data_source-2.png

Version	Author	Date
93f903d	jens-daniel-mueller	2021-10-25

[[3]]

Past versions of layer_budget_per_data_source-3.png

Version	Author	Date
93f903d	jens-daniel-mueller	2021-10-25

3 Ensemble

dcant_zonal_ensemble <- dcant_zonal_all %>% 
  filter(data_source %in% c("mod", "obs"),
         MLR_basins %in% ensemble_members) %>% 
  group_by(lat, depth, basin_AIP, data_source, period) %>% 
  summarise(dcant_ensemble_mean = mean(dcant),
            dcant_sd = sd(dcant),
            dcant_range = max(dcant)- min(dcant)) %>% 
  ungroup()


dcant_budget_basin_AIP_layer_ensemble <- dcant_budget_basin_AIP_layer_all %>% 
  mutate(inv_depth = fct_inorder(as.factor(inv_depth))) %>%
  filter(data_source %in% c("mod", "obs"),
         MLR_basins %in% ensemble_members,
         estimate == "dcant") %>%
  rename(dcant = value) %>% 
  group_by(inv_depth, data_source, period, basin_AIP) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant),
            dcant_max = max(dcant),
            dcant_min = min(dcant)) %>% 
  ungroup()

3.1 Mean

dcant_zonal_ensemble %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant_ensemble_mean",
      plot_slabs = "n",
      subtitle_text = paste("basin:",
                            unique(.x$basin_AIP))
    ) +
      facet_grid(data_source ~ period)
  )

[[1]]

Past versions of ensemble_mean-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[2]]

Past versions of ensemble_mean-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[3]]

Past versions of ensemble_mean-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

3.2 Mean bias

dcant_zonal_ensemble_bias <- full_join(
  dcant_zonal_ensemble %>%
    filter(data_source == "mod") %>% 
    select(lat, depth, basin_AIP, period, dcant_ensemble_mean, dcant_sd),
  dcant_zonal_all %>%
    filter(data_source == "mod_truth",
           MLR_basins == "2") %>% 
    select(lat, depth, basin_AIP, period, dcant_mod_truth = dcant)
)

dcant_zonal_ensemble_bias <- dcant_zonal_ensemble_bias %>% 
  mutate(dcant_bias = dcant_ensemble_mean - dcant_mod_truth)

dcant_zonal_ensemble_bias %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant_bias",
      col = "divergent",
      plot_slabs = "n",
      subtitle_text = paste("basin:",
        unique(.x$basin_AIP)
      )
    ) +
      facet_grid(. ~ period)
  )

[[1]]

Past versions of ensemble_mean_bias-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[2]]

Past versions of ensemble_mean_bias-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[3]]

Past versions of ensemble_mean_bias-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

3.3 Mean depth layer budgets

dcant_lat_grid_ensemble %>%
  ggplot(aes(lat_grid, dcant_mean)) +
  geom_hline(yintercept = 0) +
  geom_col(position = "dodge",
           fill = "grey80",
           col = "grey20") +
  geom_errorbar(aes(
    ymin = dcant_min,
    ymax = dcant_max
  ),
  col = "grey20",
  width = 0) +
  scale_color_brewer(palette = "Set1") +
  coord_flip() +
  scale_fill_brewer(palette = "Dark2") +
  facet_grid(data_source ~ period)

3.4 Standard deviation

dcant_zonal_ensemble %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant_sd",
      breaks = c(seq(0,4,0.4), Inf),
      plot_slabs = "n",
      subtitle_text = paste("basin:",
                            unique(.x$basin_AIP))
    ) +
      facet_grid(data_source ~ period)
  )

[[1]]

Past versions of ensemble_sd-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[2]]

Past versions of ensemble_sd-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[3]]

Past versions of ensemble_sd-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

3.5 SD as uncertainty

uncertainty_grid <- dcant_zonal_ensemble %>%
  filter(dcant_sd > 1.5) %>% 
  distinct(depth, lat, data_source, period, basin_AIP)

uncertainty_grid <- uncertainty_grid %>%
  mutate(
    lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
    lat_grid = as.numeric(as.character(lat_grid)),
    depth_grid = cut(depth, seq(0, 1e4, 500), seq(250, 1e4, 500)),
    depth_grid = as.numeric(as.character(depth_grid))
  ) %>%
  distinct(depth_grid, lat_grid, data_source, period, basin_AIP)

uncertainty_grid %>%
  filter(data_source == "obs") %>%
  ggplot() +
  geom_point(aes(lat_grid, depth_grid),
             shape = 3) +
  facet_grid(basin_AIP ~ period) +
  scale_y_reverse()

Past versions of ensemble_sd_uncertainty-1.png

Version	Author	Date
5d80e21	jens-daniel-mueller	2021-10-26
8b4e334	jens-daniel-mueller	2021-10-26

3.6 SD vs bias

dcant_zonal_ensemble_bias %>% 
  ggplot(aes(dcant_bias, dcant_sd)) +
  geom_bin2d() +
  scale_fill_viridis_c() +
  facet_grid(basin_AIP ~ period)

Past versions of ensemble_sd_vs_bias-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

dcant_zonal_ensemble_bias %>% 
  select(dcant_ensemble_mean, dcant_bias, period) %>% 
  pivot_longer(dcant_ensemble_mean:dcant_bias,
               names_to = "estimate",
               values_to = "value") %>% 
  ggplot(aes(value, col=estimate, linetype = period)) +
  scale_color_brewer(palette = "Set1") +
  geom_density()

Past versions of ensemble_sd_vs_bias-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

dcant_zonal_ensemble %>% 
  ggplot(aes(dcant_sd)) +
  geom_histogram() +
  facet_grid(data_source ~ period) +
  coord_cartesian(ylim = c(0,50))

Past versions of ensemble_sd_vs_bias-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

3.7 Composed figure

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

p_zonal_ensemble <- dcant_zonal_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  p_section_zonal_continous_depth(var = "dcant_ensemble_mean",
                                  plot_slabs = "n",
                                  title_text = NULL) +
  geom_point(data = uncertainty_grid,
             aes(lat_grid, depth_grid),
             shape = 3,
             col = "red") +
  facet_grid(basin_AIP ~ period,
             switch = "y") +
  theme(legend.position = "left")

p_profiles <-
  dcant_profile_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>%
  ggplot(aes(
           dcant,
           depth,
           col = period,
           fill = "grey80",
           group = interaction(MLR_basins, period)
         )) +
  geom_hline(yintercept = params_global$inventory_depth_standard) +
  geom_vline(xintercept = 0) +
  geom_path() +
  scale_y_reverse(name = "Depth (m)") +
  scale_color_brewer(palette = "Set1") +
  facet_grid(basin_AIP ~.) +
  theme(legend.position = "top",
        legend.direction = "vertical",
        legend.title = element_blank(),
        strip.background = element_blank(),
        strip.text = element_blank())


p_layer_budget <- dcant_budget_basin_AIP_layer_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(dcant_mean, inv_depth, col = period)) +
  geom_col(position = "dodge",
           orientation = "y",
           fill = "grey80") +
  geom_errorbar(
    aes(xmin = dcant_min,
        xmax = dcant_max),
    width = 0,
    position = position_dodge(width = 0.9)
  ) +
  scale_color_brewer(palette = "Set1", guide = "none") +
  scale_x_continuous(
    limits = c(0, NA),
    expand = c(0, 0),
    name = expression(Delta * C[ant] ~ (PgC))
  ) +
  scale_y_discrete(name = "Depth intervals (m)",
                   limits = rev) +
  facet_grid(basin_AIP ~ .) +
  theme(legend.position = "top",
        legend.title = element_blank())


p_zonal_ensemble + p_profiles + p_layer_budget +
  plot_layout(widths = c(4,1,1))

Past versions of composed_figure-1.png

Version	Author	Date
5d80e21	jens-daniel-mueller	2021-10-26
8b4e334	jens-daniel-mueller	2021-10-26

ggsave("output/publication/Fig_zonal_mean.png",
       width=15.25,
       height=9.27)

4 Cases vs ensemble

4.1 Offset from mean

dcant_zonal_all <- full_join(dcant_zonal_all %>% select(-dcant_sd),
                           dcant_zonal_ensemble)

dcant_zonal_all <- dcant_zonal_all %>% 
  mutate(dcant_offset = dcant - dcant_ensemble_mean)


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

dcant_zonal_all %>%
  filter(data_source %in% c("mod", "obs")) %>%
  group_by(basin_AIP, data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant_offset",
      col = "divergent",
      plot_slabs = "n",
      subtitle_text = paste("basin:",
        unique(.x$basin_AIP)
      )
    ) +
      facet_grid(MLR_basins ~ period)
  )

[[1]]

Past versions of ensemble_deviation_from_mean-1.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[2]]

Past versions of ensemble_deviation_from_mean-2.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[3]]

Past versions of ensemble_deviation_from_mean-3.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[4]]

Past versions of ensemble_deviation_from_mean-4.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[5]]

Past versions of ensemble_deviation_from_mean-5.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

[[6]]

Past versions of ensemble_deviation_from_mean-6.png

Version	Author	Date
8c9fa17	jens-daniel-mueller	2021-10-22
968fe94	jens-daniel-mueller	2021-10-18
f51dcb9	jens-daniel-mueller	2021-09-28
55d2059	jens-daniel-mueller	2021-09-28
d0945da	jens-daniel-mueller	2021-09-28
09002a7	jens-daniel-mueller	2021-09-26
0c6be7e	jens-daniel-mueller	2021-09-24
f54d5db	jens-daniel-mueller	2021-09-24
31c33cb	jens-daniel-mueller	2021-09-23
35ad8b5	jens-daniel-mueller	2021-09-20

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] ggforce_0.3.3   metR_0.9.0      scico_1.2.0     patchwork_1.1.1
 [5] collapse_1.5.0  forcats_0.5.0   stringr_1.4.0   dplyr_1.0.5    
 [9] purrr_0.3.4     readr_1.4.0     tidyr_1.1.3     tibble_3.1.3   
[13] ggplot2_3.3.5   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 lubridate_1.7.9          RColorBrewer_1.1-2      
 [4] httr_1.4.2               rprojroot_2.0.2          tools_4.0.3             
 [7] backports_1.1.10         bslib_0.2.5.1            utf8_1.1.4              
[10] R6_2.5.0                 DBI_1.1.0                colorspace_2.0-2        
[13] withr_2.3.0              tidyselect_1.1.0         compiler_4.0.3          
[16] git2r_0.27.1             cli_3.0.1                rvest_0.3.6             
[19] xml2_1.3.2               isoband_0.2.2            labeling_0.4.2          
[22] sass_0.4.0               scales_1.1.1             checkmate_2.0.0         
[25] digest_0.6.27            rmarkdown_2.10           pkgconfig_2.0.3         
[28] htmltools_0.5.1.1        dbplyr_1.4.4             highr_0.8               
[31] rlang_0.4.11             readxl_1.3.1             rstudioapi_0.13         
[34] jquerylib_0.1.4          generics_0.1.0           farver_2.0.3            
[37] jsonlite_1.7.1           magrittr_1.5             Matrix_1.2-18           
[40] Rcpp_1.0.5               munsell_0.5.0            fansi_0.4.1             
[43] lifecycle_1.0.0          stringi_1.5.3            whisker_0.4             
[46] yaml_2.2.1               MASS_7.3-53              grid_4.0.3              
[49] blob_1.2.1               parallel_4.0.3           promises_1.1.1          
[52] crayon_1.3.4             lattice_0.20-41          haven_2.3.1             
[55] hms_0.5.3                knitr_1.33               pillar_1.6.2            
[58] reprex_0.3.0             glue_1.4.2               evaluate_0.14           
[61] RcppArmadillo_0.10.1.2.0 data.table_1.14.0        modelr_0.1.8            
[64] vctrs_0.3.8              tweenr_1.0.2             httpuv_1.5.4            
[67] cellranger_1.1.0         gtable_0.3.0             polyclip_1.10-0         
[70] assertthat_0.2.1         xfun_0.25                broom_0.7.9             
[73] RcppEigen_0.3.3.7.0      later_1.2.0              viridisLite_0.3.0       
[76] ellipsis_0.3.2