Mapping target variable

workflowr

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Last updated: 2022-10-18

Checks: 7 0

Knit directory: emlr_obs_v_XXX/

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Rmd	8b0b203	jens-daniel-mueller	2022-06-23	profile decomposition
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Rmd	4db7076	jens-daniel-mueller	2022-06-23	added target profile calculation
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Rmd	b555f89	jens-daniel-mueller	2022-06-22	included gamma slab analysis
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Rmd	5d282dc	jens-daniel-mueller	2022-06-17	included budget calculation
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Rmd	c6b299a	jens-daniel-mueller	2022-06-16	distuiguished data source
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Rmd	370ce72	jens-daniel-mueller	2022-06-15	testrun with target variable mapping
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Rmd	fe13638	jens-daniel-mueller	2021-01-07	removed GLODAP gamma filter, target variable mapped by eras+era
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Rmd	e7e5ff1	jens-daniel-mueller	2020-12-15	rebuild with eMLR target variable selection

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1 Scope

Map 3d fields of the predicted target variable (either tco2, cstar or cstar_tref) based on fitted MLRs and climatological fields of predictor variables. In contrast to the direct Cant calculation based on coefficient differences, we apply here the fitted coefficients directly. Cant could also be estimates as the differences between the target variable fields mapped here.

2 Required data

2.1 Cstar mod_truth

dcant_3d_cstar <-
  read_csv(paste(path_version_data,
                  "dcant_3d_cstar_A.csv", sep = ""))

2.2 Predictor fields

Currently, we use following combined predictor fields:

• WOA18: S, T, and derived variables
• GLODAP16: Oxygen, PO4, NO3, Silicate, and derived variables

predictors <-
  read_csv(paste(path_version_data,
                 "W18_st_G16_opsn.csv",
                 sep = ""))

2.3 MLR models

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

2.4 Spatial MLR residuals

spatial_residual <-
  read_csv(paste(path_version_data,
                 "lm_spatial_residual.csv",
                 sep = ""))

spatial_residual_offset <-
  read_csv(paste(path_version_data,
                 "lm_spatial_residual_offset.csv",
                 sep = ""))

2.5 Others

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

3 Join MLR + climatologies

# remove predictor variable from model
lm_best_target <- lm_best_target %>%
  mutate(model = str_remove(model, paste(params_local$MLR_target, "~ ")))

# join predictors and MLR
target <- full_join(predictors, lm_best_target)

rm(predictors, lm_best_target)

4 Map target variable

4.1 Apply MLRs to predictor

target <- b_target_model(target)

target <- target %>%
  select(data_source,
         lon,
         lat,
         depth,
         era,
         basin,
         basin_AIP,
         gamma,
         params_local$MLR_target)

4.2 Average model target variable

target_3d <- m_target_model_average(target)
target_3d <- m_cut_gamma(target_3d, "gamma")

rm(target)

4.3 Zonal mean sections

For each basin and era combination, the zonal mean of the target variable is calculated. Likewise, sd is calculated for the averaging of the mean basin fields.

target_zonal <- target_3d %>%
  group_by(data_source, era) %>%
  nest() %>%
  mutate(zonal = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
  select(-data) %>%
  unnest(zonal)

target_zonal <- m_cut_gamma(target_zonal, "gamma_mean")

target_zonal %>%
  ggplot(aes_string(paste0(params_local$MLR_target, "_mean"), col = "era")) +
  geom_density() +
  facet_grid(. ~ data_source)

Past versions of Calculate_target_mean_sections-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
a28c3f7	jens-daniel-mueller	2022-06-16
469a640	jens-daniel-mueller	2022-06-16
2d729bf	jens-daniel-mueller	2022-06-16
0c2ca93	jens-daniel-mueller	2022-06-15

target_zonal %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = paste0(params_local$MLR_target, "_mean"),
      breaks = seq(740, 940, 20),
      plot_slabs = "n",
      subtitle_text = paste("basin:",
                            unique(.x$basin_AIP))
    ) +
      facet_grid(era ~ data_source)
  )

[[1]]

Past versions of Calculate_target_mean_sections-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
62ee1cd	jens-daniel-mueller	2022-06-22
a28c3f7	jens-daniel-mueller	2022-06-16
469a640	jens-daniel-mueller	2022-06-16
b7af400	jens-daniel-mueller	2022-06-16
2d729bf	jens-daniel-mueller	2022-06-16
0c2ca93	jens-daniel-mueller	2022-06-15

[[2]]

Past versions of Calculate_target_mean_sections-3.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
62ee1cd	jens-daniel-mueller	2022-06-22
a28c3f7	jens-daniel-mueller	2022-06-16
469a640	jens-daniel-mueller	2022-06-16
b7af400	jens-daniel-mueller	2022-06-16
bb9a531	jens-daniel-mueller	2022-06-16
2d729bf	jens-daniel-mueller	2022-06-16
0c2ca93	jens-daniel-mueller	2022-06-15

[[3]]

Past versions of Calculate_target_mean_sections-4.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
62ee1cd	jens-daniel-mueller	2022-06-22
a28c3f7	jens-daniel-mueller	2022-06-16
469a640	jens-daniel-mueller	2022-06-16
b7af400	jens-daniel-mueller	2022-06-16
bb9a531	jens-daniel-mueller	2022-06-16
2d729bf	jens-daniel-mueller	2022-06-16
0c2ca93	jens-daniel-mueller	2022-06-15

4.4 Profiles

For each basin, the mean (+ min and max) dcant profile is calculated, again for all vs positive only values. Likewise, sd is calculated for the averaging of the mean basin fields.

var <- sym(params_local$MLR_target)

target_profile <- target_3d %>%
  mutate(area = earth_surf(lat, lon),
         !!paste0(var, "_scaled") :=
           !!var * area) %>%
  group_by(data_source, basin_AIP, depth) %>%
  summarise(!!paste0(var, "_sd") := sqrt(Hmisc::wtd.var(!!var, area, na.rm = TRUE)),
            !!var := sum(!!sym(paste0(var, "_scaled"))) / sum(area)) %>%
  ungroup()

target_profile_basin_MLR <-
  full_join(
    target_3d %>% select(data_source, lon, lat, depth, !!var),
    basinmask %>% select(MLR_basins, basin, lon, lat)
  ) %>%
  mutate(area = earth_surf(lat, lon),
         !!paste0(var, "_scaled") :=
           !!var * area) %>%
  drop_na() %>%
  group_by(data_source, basin, MLR_basins, depth) %>%
  summarise(!!paste0(var, "_sd") := sqrt(Hmisc::wtd.var(!!var, area, na.rm = TRUE)),
            !!var := sum(!!sym(paste0(var, "_scaled"))) / sum(area)) %>%
  ungroup()

5 Join true C*

dcant_3d_cstar <- dcant_3d_cstar %>% 
  mutate(data_source = if_else(data_source == "po4",
                               "phosphate",
                               "nitrate")) %>% 
  filter(data_source == params_local$cstar_nutrient)

dcant_3d_cstar <- dcant_3d_cstar %>% 
  select(lon:cstar_tref_2, -c(data_source, method)) %>% 
  pivot_longer(starts_with("cstar_tref_"),
               values_to = "cstar_tref_mod_truth",
               names_to = "era",
               names_prefix = "cstar_tref_") %>% 
  mutate(era = if_else(era == "1",
                       unique(tref$era[1]),
                       unique(tref$era[2])))

target_3d <- inner_join(
  target_3d %>% 
    filter(data_source == "mod") %>% 
    select(-data_source),
  dcant_3d_cstar
)

target_3d <- target_3d %>% 
  mutate(dcant = cstar_tref - cstar_tref_mod_truth) %>% 
  select(lat, lon, depth, basin_AIP, era, gamma, dcant)

target_3d <- target_3d %>%
  mutate(dcant_pos = dcant)

target_3d <- m_cut_gamma(target_3d %>% rename(gamma_mean = gamma), "gamma_mean")

target_3d <-
  target_3d %>% rename(gamma = gamma_mean)

6 Zonal mean sections

For each basin and era combination, the zonal mean of the target variable is calculated. Likewise, sd is calculated for the averaging of the mean basin fields.

6.1 C* bias

target_zonal <- target_3d %>%
  group_by(era) %>%
  nest() %>%
  mutate(zonal = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
  select(-data) %>%
  unnest(zonal)

target_zonal <- m_cut_gamma(target_zonal, "gamma_mean")

target_zonal %>%
  ggplot(aes(dcant_mean, col = era)) +
  geom_density()

Past versions of calculate_target_3d_mean_sections-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
200d6b9	jens-daniel-mueller	2022-06-17

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

[[1]]

Past versions of calculate_target_3d_mean_sections-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
7761704	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
8380e17	jens-daniel-mueller	2022-06-22
200d6b9	jens-daniel-mueller	2022-06-17

[[2]]

Past versions of calculate_target_3d_mean_sections-3.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
7761704	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
8380e17	jens-daniel-mueller	2022-06-22
200d6b9	jens-daniel-mueller	2022-06-17

[[3]]

Past versions of calculate_target_3d_mean_sections-4.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
7761704	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
8380e17	jens-daniel-mueller	2022-06-22
200d6b9	jens-daniel-mueller	2022-06-17

6.2 C* bias offset

target_zonal <- target_zonal %>% 
  arrange(basin_AIP, lat, depth, era) %>% 
  group_by(basin_AIP, lat, depth) %>% 
  mutate(dcant_mean_bias = dcant_mean - lag(dcant_mean)) %>% 
  ungroup()

target_zonal %>%
  ggplot() +
  geom_density(aes(dcant_mean, col = era)) +
  geom_density(aes(dcant_mean_bias, col = "bias"))

Past versions of calculate_target_3d_mean_sections_offset-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
62ee1cd	jens-daniel-mueller	2022-06-22
200d6b9	jens-daniel-mueller	2022-06-17

target_zonal %>%
  filter(!is.na(dcant_mean_bias)) %>%
  p_section_zonal_continous_depth(var = "dcant_mean_bias",
                                  col = "divergent",
                                  plot_slabs = "n") +
  facet_grid(basin_AIP ~ .)

Past versions of calculate_target_3d_mean_sections_offset-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
62ee1cd	jens-daniel-mueller	2022-06-22
200d6b9	jens-daniel-mueller	2022-06-17

7 Profiles

For each basin and era combination, the mean profile of the target variable is calculated. Likewise, sd is calculated for the averaging of the mean basin fields.

7.1 C* bias

target_profile <- target_3d %>%
  mutate(area = earth_surf(lat, lon),
         dcant_scaled = dcant * area) %>% 
  group_by(era, basin_AIP, depth) %>% 
  summarise(dcant_sd = sqrt(
              Hmisc::wtd.var(dcant, area, na.rm = TRUE)),
            dcant = sum(dcant_scaled) / sum(area)) %>% 
  ungroup()

target_profile %>%
  ggplot(aes(dcant, depth,
             col = era, fill=era)) +
  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() +
  facet_grid( ~ basin_AIP)

Past versions of calc_dcant_profiles-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
5a5c592	jens-daniel-mueller	2022-06-23

target_profile_basin_MLR <-
  full_join(
    target_3d %>% select(era, lon, lat, depth, dcant),
    basinmask %>% select(MLR_basins, basin, lon, lat)
  ) %>%
  mutate(area = earth_surf(lat, lon),
         dcant_scaled = dcant * area) %>% 
  group_by(era, basin, MLR_basins, depth) %>% 
  summarise(dcant_sd = sqrt(
              Hmisc::wtd.var(dcant, area, na.rm = TRUE)),
            dcant = sum(dcant_scaled) / sum(area)) %>% 
  ungroup()

target_profile_basin_MLR %>%
  filter(MLR_basins == "5") %>%
  ggplot(aes(dcant, depth,
             col = era, fill = era)) +
  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() +
  facet_grid(~ basin)

Past versions of calc_dcant_profiles_MLR_regions-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
5a5c592	jens-daniel-mueller	2022-06-23

7.2 C* bias offset

target_profile <- target_profile %>% 
  arrange(basin_AIP, depth, era) %>% 
  group_by(basin_AIP, depth) %>% 
  mutate(dcant_bias = dcant - lag(dcant)) %>% 
  ungroup()

target_profile %>%
  filter(!is.na(dcant_bias)) %>%
  ggplot(aes(dcant_bias, depth)) +
  geom_vline(xintercept = 0) +
  geom_path() +
  scale_y_reverse() +
  facet_grid( ~ basin_AIP)

Past versions of calculate_target_3d_profile_offset-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
a61e1f2	jens-daniel-mueller	2022-06-23
5a5c592	jens-daniel-mueller	2022-06-23

target_profile_basin_MLR <- target_profile_basin_MLR %>% 
  arrange(basin, MLR_basins, depth, era) %>% 
  group_by(basin, MLR_basins, depth) %>% 
  mutate(dcant_bias = dcant - lag(dcant)) %>% 
  ungroup()

target_profile_basin_MLR %>%
  filter(MLR_basins == "5",
         !is.na(dcant_bias)) %>%
  ggplot(aes(dcant_bias, depth)) +
  geom_vline(xintercept = 0) +
  geom_path() +
  scale_y_reverse() +
  facet_grid(~ basin)

Past versions of calculate_target_3d_profile_offset_MLR_regions-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
a61e1f2	jens-daniel-mueller	2022-06-23
5a5c592	jens-daniel-mueller	2022-06-23

8 Inventory calculation

To calculate dcant column inventories, we:

1. Convert dcant concentrations to volumetric units
2. Multiply layer thickness with volumetric dcant concentration to get a layer inventory
3. For each horizontal grid cell and era, sum dcant layer inventories for different inventory depths (100, 500, 1000, 3000, 10^{4} m)

Step 2 is performed separately for all dcant and positive dcant values only.

8.1 C* bias

target_inv <- target_3d %>%
  group_by(era) %>%
  nest() %>%
  mutate(inv = map(.x = data, ~m_dcant_inv(.x))) %>%
  select(-data) %>%
  unnest(inv)

p_map_cant_inv(df = target_inv,
               var = "dcant",
               col = "bias",
               subtitle_text = "for predefined integration depths") +
  facet_grid(inv_depth ~ era)

Past versions of calculate_target_inventory-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
7761704	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
200d6b9	jens-daniel-mueller	2022-06-17

p_map_cant_inv(df = target_inv %>%   filter(inv_depth == params_global$inventory_depth_standard),
               var = "dcant",
               col = "bias",
               subtitle_text = "for predefined integration depths") +
  facet_grid(. ~ era)

Past versions of calculate_target_inventory-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
7761704	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
929ef23	jens-daniel-mueller	2022-06-29

8.2 C* bias offset

target_inv <- target_inv %>% 
  arrange(era) %>% 
  group_by(basin_AIP, lat, lon, inv_depth) %>% 
  mutate(dcant_mean_bias = dcant - lag(dcant)) %>% 
  ungroup()

target_inv %>%
  ggplot() +
  geom_density(aes(dcant, col = era)) +
  geom_density(aes(dcant_mean_bias, col = "bias"))

Past versions of calculate_target_inventory_offset-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
200d6b9	jens-daniel-mueller	2022-06-17

p_map_cant_inv(df = target_inv %>% filter(!is.na(dcant_mean_bias)),
               var = "dcant_mean_bias",
               col = "bias",
               subtitle_text = "for predefined integration depths") +
  facet_grid(inv_depth ~ .)

Past versions of calculate_target_inventory_offset-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
7761704	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
200d6b9	jens-daniel-mueller	2022-06-17

9 Slap concentrations

target_slab <- target_3d %>%
  mutate(dcant_sd = 0,
         dcant_pos_sd = 0) %>% 
  group_by(era) %>%
  nest() %>%
  mutate(slab = map(.x = data, ~ m_dcant_slab_concentration(.x))) %>%
  select(-data) %>%
  unnest(slab)

9.1 C* bias

target_slab %>%
  filter(gamma_slab %in% params_local$plot_slabs) %>%
  group_by(gamma_slab) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "dcant",
      col = "bias",
      subtitle_text = paste("Neutral density:",
                            unique(.x$gamma_slab))
    ) +
      facet_grid(era ~ .)
  )

[[1]]

Past versions of dcant_all_gamma_slab_maps-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24
4274a87	jens-daniel-mueller	2022-06-22

[[2]]

Past versions of dcant_all_gamma_slab_maps-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24
4274a87	jens-daniel-mueller	2022-06-22

[[3]]

Past versions of dcant_all_gamma_slab_maps-3.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24
4274a87	jens-daniel-mueller	2022-06-22

spatial_residual %>%
  filter(gamma_slab %in% params_local$plot_slabs) %>%
  group_by(gamma_slab) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = ".resid_mean",
      col = "bias",
      subtitle_text = paste("Neutral density:",
                            unique(.x$gamma_slab))
    ) +
      facet_grid(data_source ~ era) +
      theme_dark()
  )

[[1]]

Past versions of dcant_all_gamma_slab_maps-4.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

[[2]]

Past versions of dcant_all_gamma_slab_maps-5.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

[[3]]

Past versions of dcant_all_gamma_slab_maps-6.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

target_slab_residual <-
  full_join(
    target_slab %>%
      select(era, lat, lon, gamma_slab, cstar_predition_bias = dcant),
    spatial_residual %>%
      filter(data_source == "mod") %>%
      select(era, lat, lon, gamma_slab, cstar_MLR_residual = .resid_mean)
  )


target_slab_residual %>%
  ggplot(aes(cstar_MLR_residual, cstar_predition_bias)) +
  geom_bin_2d(binwidth = 1) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  coord_equal() +
  scale_fill_viridis_c(trans = "log10") +
  facet_grid(gamma_slab ~ era)

Past versions of dcant_all_gamma_slab_maps-7.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

target_slab_residual %>%
  mutate(resid_pred_offset = cstar_predition_bias + cstar_MLR_residual) %>%
  filter(gamma_slab %in% params_local$plot_slabs) %>%
  group_by(gamma_slab) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "resid_pred_offset",
      col = "bias",
      subtitle_text = paste("Neutral density:",
                            unique(.x$gamma_slab),
                            "| cstar_predition_bias + cstar_MLR_residual")
    ) +
      facet_grid(. ~ era) +
      theme_dark()
  )

[[1]]

Past versions of dcant_all_gamma_slab_maps-8.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

[[2]]

Past versions of dcant_all_gamma_slab_maps-9.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

[[3]]

Past versions of dcant_all_gamma_slab_maps-10.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24

9.2 C* bias offset

target_slab <- target_slab %>% 
  arrange(era) %>% 
  group_by(lat, lon, gamma_slab) %>% 
  mutate(dcant_mean_bias = dcant - lag(dcant)) %>% 
  ungroup()

target_slab %>%
  filter(gamma_slab %in% params_local$plot_slabs) %>%
  group_split(gamma_slab) %>%
  # head(1) %>%
  map(~ p_map_dcant_slab(
    df = .x,
    var = "dcant_mean_bias",
    col = "divergent",
    subtitle_text = paste("Neutral density:", unique(.x$gamma_slab))
  ))

[[1]]

Past versions of dcant_gamma_slab_maps_bias-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24
4274a87	jens-daniel-mueller	2022-06-22

[[2]]

Past versions of dcant_gamma_slab_maps_bias-2.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24
4274a87	jens-daniel-mueller	2022-06-22

[[3]]

Past versions of dcant_gamma_slab_maps_bias-3.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24
4274a87	jens-daniel-mueller	2022-06-22

spatial_residual_offset %>%
  filter(gamma_slab %in% params_local$plot_slabs) %>%
  group_by(gamma_slab) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = ".resid_mean_offset",
      col = "bias",
      subtitle_text = paste("Neutral density:",
                            unique(.x$gamma_slab))
    ) +
      facet_grid(data_source ~ era) +
      theme_dark()
  )

[[1]]

Past versions of dcant_gamma_slab_maps_bias-4.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

[[2]]

Past versions of dcant_gamma_slab_maps_bias-5.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

[[3]]

Past versions of dcant_gamma_slab_maps_bias-6.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
12cb319	jens-daniel-mueller	2022-09-26
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

target_slab_residual <-
  full_join(
    target_slab %>%
      filter(!is.na(dcant_mean_bias)) %>% 
      select(era, lat, lon, gamma_slab, cstar_predition_bias = dcant_mean_bias),
    spatial_residual_offset %>%
      filter(data_source == "mod") %>%
      select(era, lat, lon, gamma_slab, cstar_MLR_residual = .resid_mean_offset)
  )


target_slab_residual %>%
  ggplot(aes(cstar_MLR_residual, cstar_predition_bias)) +
  geom_bin_2d(binwidth = 1) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  coord_equal() +
  scale_fill_viridis_c(trans = "log10") +
  facet_wrap(~ gamma_slab)

Past versions of dcant_gamma_slab_maps_bias-7.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

target_slab_residual %>%
  mutate(resid_pred_offset = cstar_predition_bias + cstar_MLR_residual) %>%
  filter(gamma_slab %in% params_local$plot_slabs) %>%
  group_by(gamma_slab) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "resid_pred_offset",
      col = "bias",
      subtitle_text = paste("Neutral density:",
                            unique(.x$gamma_slab),
                            "| cstar_predition_bias + cstar_MLR_residual")
    ) +
      facet_grid(. ~ era) +
      theme_dark()
  )

[[1]]

Past versions of dcant_gamma_slab_maps_bias-8.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

[[2]]

Past versions of dcant_gamma_slab_maps_bias-9.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

[[3]]

Past versions of dcant_gamma_slab_maps_bias-10.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
093016f	jens-daniel-mueller	2022-06-24
790cec6	jens-daniel-mueller	2022-06-24

10 Budgets

Global dcant budgets were estimated in units of Pg C. Please note that here we added dcant (all vs postitive only) values and do not apply additional corrections for areas not covered.

target_inv <- target_inv %>%
  filter(!is.na(dcant_mean_bias)) %>%
  select(era, lon, lat, basin_AIP, inv_depth, dcant = dcant_mean_bias) %>%
  mutate(data_source = "mod",
         method = "total",
         dcant_pos = dcant)

target_budget_global <- m_dcant_budget(target_inv)

target_budget_global <- target_budget_global %>%
  mutate(inv_depth = as.factor(inv_depth))

target_budget_global %>%
  filter(estimate == "dcant") %>% 
  ggplot() +
  scale_fill_viridis_d() +
  geom_col(aes(value, inv_depth)) +
  scale_y_discrete(limits=rev)

Past versions of budget_global-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
376182f	jens-daniel-mueller	2022-06-24
8380e17	jens-daniel-mueller	2022-06-22
bdef06a	jens-daniel-mueller	2022-06-17

target_budget_basin_AIP <- target_inv %>% 
  group_by(basin_AIP) %>%
  nest() %>% 
  mutate(budget = map(.x = data, ~m_dcant_budget(.x))) %>% 
  select(-data) %>%
  unnest(budget) %>% 
  ungroup()

target_budget_basin_AIP <- target_budget_basin_AIP %>% 
  mutate(inv_depth = as.factor(inv_depth))

target_budget_basin_AIP %>%
  filter(estimate == "dcant") %>% 
  ggplot() +
  geom_vline(xintercept = 0) +
  scale_fill_viridis_d() +
  geom_col(aes(value, inv_depth)) +
  scale_y_discrete(limits=rev) +
  facet_grid(.~basin_AIP)

Past versions of budget_basin_AIP-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
376182f	jens-daniel-mueller	2022-06-24
8380e17	jens-daniel-mueller	2022-06-22

target_budget_basin_MLR <-
  full_join(target_inv, basinmask) %>%
  filter(!is.na(dcant)) %>% 
  group_by(basin, MLR_basins) %>%
  nest() %>%
  mutate(budget = map(.x = data, ~ m_dcant_budget(.x))) %>%
  select(-data) %>%
  unnest(budget)

target_budget_basin_MLR <- target_budget_basin_MLR %>% 
  mutate(inv_depth = as.factor(inv_depth))

target_budget_basin_MLR %>%
  filter(estimate == "dcant",
         MLR_basins == "5") %>% 
  ggplot() +
  geom_vline(xintercept = 0) +
  scale_fill_viridis_d() +
  geom_col(aes(value, inv_depth)) +
  scale_y_discrete(limits=rev) +
  facet_grid(basin~MLR_basins)

Past versions of budget_MLR_regions-1.png

Version	Author	Date
a4c6d61	jens-daniel-mueller	2022-10-18
5ed1b6b	jens-daniel-mueller	2022-07-19
233fa96	jens-daniel-mueller	2022-07-19
b92af2e	jens-daniel-mueller	2022-07-13
5599f87	jens-daniel-mueller	2022-07-13
b9847f2	jens-daniel-mueller	2022-07-07
840dfb7	jens-daniel-mueller	2022-07-07
78dd2f2	jens-daniel-mueller	2022-07-05
2807f99	jens-daniel-mueller	2022-07-05
82cbd17	jens-daniel-mueller	2022-07-05
b661d5a	jens-daniel-mueller	2022-07-05
f96b063	jens-daniel-mueller	2022-07-04
767609e	jens-daniel-mueller	2022-07-04
f1f428f	jens-daniel-mueller	2022-06-30
e743d50	jens-daniel-mueller	2022-06-30
98ba5c6	jens-daniel-mueller	2022-06-30
21dbfca	jens-daniel-mueller	2022-06-29
7e8d2ef	jens-daniel-mueller	2022-06-29
ccd9f87	jens-daniel-mueller	2022-06-24
376182f	jens-daniel-mueller	2022-06-24
8380e17	jens-daniel-mueller	2022-06-22

11 Write csv

target_3d %>%
  write_csv(paste(path_version_data,
                  "target_3d.csv",
                  sep = ""))

target_zonal %>%
  write_csv(paste(path_version_data,
                  "target_zonal.csv",
                  sep = ""))

target_profile %>%
  write_csv(paste(path_version_data,
                  "target_profile.csv", sep = ""))

target_profile_basin_MLR %>%
  write_csv(paste(path_version_data,
                  "target_profile_basin_MLR.csv", sep = ""))

target_inv %>%
  write_csv(paste(path_version_data,
                  "target_inv.csv", sep = ""))

target_slab %>%
  write_csv(paste(path_version_data,
                  "target_slab.csv", sep = ""))

target_budget_basin_MLR %>%
  write_csv(paste(path_version_data,
                  "target_budget_basin_MLR.csv", sep = ""))

target_budget_basin_AIP %>%
  write_csv(paste(path_version_data,
                  "target_budget_basin_AIP.csv", sep = ""))

target_budget_global %>%
  write_csv(paste(path_version_data,
                  "target_budget_global.csv", sep = ""))

Session information

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] geomtextpath_0.1.0 colorspace_2.0-2   marelac_2.1.10     shape_1.4.6       
 [5] ggforce_0.3.3      metR_0.11.0        scico_1.3.0        patchwork_1.1.1   
 [9] collapse_1.7.0     forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7       
[13] purrr_0.3.4        readr_2.1.1        tidyr_1.1.4        tibble_3.1.6      
[17] ggplot2_3.3.5      tidyverse_1.3.1    workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] ellipsis_0.3.2      rprojroot_2.0.2     htmlTable_2.4.0    
  [4] base64enc_0.1-3     fs_1.5.2            rstudioapi_0.13    
  [7] farver_2.1.0        bit64_4.0.5         fansi_1.0.2        
 [10] lubridate_1.8.0     xml2_1.3.3          splines_4.1.2      
 [13] knitr_1.37          polyclip_1.10-0     Formula_1.2-4      
 [16] jsonlite_1.7.3      gsw_1.0-6           broom_0.7.11       
 [19] cluster_2.1.2       dbplyr_2.1.1        png_0.1-7          
 [22] compiler_4.1.2      httr_1.4.2          backports_1.4.1    
 [25] assertthat_0.2.1    Matrix_1.4-0        fastmap_1.1.0      
 [28] cli_3.1.1           later_1.3.0         tweenr_1.0.2       
 [31] htmltools_0.5.2     tools_4.1.2         gtable_0.3.0       
 [34] glue_1.6.0          Rcpp_1.0.8          cellranger_1.1.0   
 [37] jquerylib_0.1.4     vctrs_0.3.8         xfun_0.29          
 [40] ps_1.6.0            rvest_1.0.2         lifecycle_1.0.1    
 [43] oce_1.5-0           getPass_0.2-2       MASS_7.3-55        
 [46] scales_1.1.1        vroom_1.5.7         hms_1.1.1          
 [49] promises_1.2.0.1    parallel_4.1.2      RColorBrewer_1.1-2 
 [52] yaml_2.2.1          gridExtra_2.3       sass_0.4.0         
 [55] rpart_4.1-15        latticeExtra_0.6-29 stringi_1.7.6      
 [58] highr_0.9           checkmate_2.0.0     rlang_1.0.2        
 [61] pkgconfig_2.0.3     systemfonts_1.0.3   evaluate_0.14      
 [64] lattice_0.20-45     SolveSAPHE_2.1.0    htmlwidgets_1.5.4  
 [67] labeling_0.4.2      bit_4.0.4           processx_3.5.2     
 [70] tidyselect_1.1.1    here_1.0.1          seacarb_3.3.0      
 [73] magrittr_2.0.1      R6_2.5.1            generics_0.1.1     
 [76] Hmisc_4.6-0         DBI_1.1.2           foreign_0.8-82     
 [79] pillar_1.6.4        haven_2.4.3         whisker_0.4        
 [82] withr_2.4.3         nnet_7.3-17         survival_3.2-13    
 [85] modelr_0.1.8        crayon_1.4.2        utf8_1.2.2         
 [88] tzdb_0.2.0          rmarkdown_2.11      jpeg_0.1-9         
 [91] grid_4.1.2          readxl_1.3.1        isoband_0.2.5      
 [94] data.table_1.14.2   callr_3.7.0         git2r_0.29.0       
 [97] reprex_2.0.1        digest_0.6.29       httpuv_1.6.5       
[100] textshaping_0.3.6   munsell_0.5.0       viridisLite_0.4.0  
[103] bslib_0.3.1