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1 Version ID

The results displayed on this site correspond to the Version_ID: v_XXX

2 Data sources

dcant estimates from this sensitivity case:

  • Mean and SD per grid cell (lat, lon, depth)
  • Zonal mean and SD (basin, lat, depth)
  • Inventories (lat, lon)
dcant_3d <-
  read_csv(paste(path_version_data,
                 "dcant_3d.csv",
                 sep = ""))

dcant_3d_mod_truth <-
  read_csv(paste(path_version_data,
                 "dcant_3d_mod_truth.csv",
                 sep = ""))

dcant_3d <- bind_rows(dcant_3d,
                     dcant_3d_mod_truth)

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

Cleaned GLODAPv2_2020 file as used in this sensitivity case

GLODAP <-
  read_csv(paste(
    path_version_data,
    "GLODAPv2.2020_MLR_fitting_ready.csv",
    sep = ""
  ))

GLODAP_grid <- GLODAP %>% 
  distinct(lon, lat, gamma_slab, era)

GLODAP_grid <- GLODAP_grid %>% 
  group_by(lat, lon, gamma_slab) %>% 
  mutate(n = n()) %>% 
  ungroup() %>% 
  mutate(era_label = if_else(n==1, era, "both"))
dcant_slab <- dcant_3d %>%
  group_by(data_source) %>%
  nest() %>%
  mutate(slab = map(.x = data, ~ m_dcant_slab_concentration(.x))) %>%
  select(-data) %>%
  unnest(slab)

dcant_slab <- dcant_slab %>% 
  arrange(gamma_slab, data_source)

3 dcant - positive

In a first series of plots we explore the distribution of dcant, taking only positive estimates into account (positive here refers to the mean dcant estimate across MLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.

Mean of positive dcant within each horizontal grid cell (lon x lat) per isoneutral slab.

Please note that:

  • density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
  • gaps in the maps represent areas where (thin) density layers fit between discrete depth levels used for mapping

3.1 Absolute

dcant_slab %>%
  group_by(gamma_slab, data_source) %>%
  group_split() %>% 
  # head(4) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      subtitle_text = paste("data_source:", unique(.x$data_source),
                            "| Neutral density:", unique(.x$gamma_slab)),
      var = "n_layer"
      ) +
      geom_raster(
        data = GLODAP_grid %>% filter(gamma_slab == unique(.x$gamma_slab),
                                      era_label == "both"),
        aes(lon, lat), fill = "red") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[1]
        ),
        aes(lon, lat), fill = "grey50") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[2]
        ),
        aes(lon, lat),fill = "black")
  )
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3.2 Bias

dcant_slab_bias <- dcant_slab %>% 
  select(-c(layer_thickness, layer_volume, n_layer,
            dcant_pos_sd, dcant_sd)) %>% 
  pivot_wider(names_from = data_source,
              values_from = c("dcant", "dcant_pos")) %>% 
  mutate(dcant_bias = dcant_mod - dcant_mod_truth,
         dcant_pos_bias = dcant_pos_mod - dcant_pos_mod_truth) %>% 
  select(c(lat, lon, gamma_slab, ends_with("bias")))

dcant_slab_bias %>%
  group_split(gamma_slab) %>%
  # head(2) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "dcant_pos_bias",
      col = "divergent",
      subtitle_text = paste("Neutral density:", unique(.x$gamma_slab))
    )  +
      geom_raster(
        data = GLODAP_grid %>% filter(gamma_slab == unique(.x$gamma_slab),
                                      era_label == "both"),
        aes(lon, lat), fill = "red") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[1]
        ),
        aes(lon, lat), fill = "grey50") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[2]
        ),
        aes(lon, lat),fill = "black")
  )
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4 dcant - all

In a second series of plots we explore the distribution of all dcant, taking positive and negative estimates into account.

Mean of all dcant within each horizontal grid cell (lon x lat) per isoneutral slab.

Please note that:

  • density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
  • gaps in the maps represent areas where (thin) density layers fit between discrete depth levels used for mapping

4.1 Absolute

dcant_slab %>%
  group_by(gamma_slab, data_source) %>%
  group_split() %>% 
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "dcant",
      col = "continuous",
      subtitle_text = paste(
        "data_source:",
        unique(.x$data_source),
        "| Neutral density:",
        unique(.x$gamma_slab)
      )
    )  +
      geom_raster(
        data = GLODAP_grid %>% filter(gamma_slab == unique(.x$gamma_slab),
                                      era_label == "both"),
        aes(lon, lat), fill = "red") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[1]
        ),
        aes(lon, lat), fill = "grey50") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[2]
        ),
        aes(lon, lat),fill = "black")
  )
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4.2 Bias

dcant_slab_bias %>%
  group_split(gamma_slab) %>%
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "dcant_bias",
      col = "divergent",
      subtitle_text = paste("Neutral density:", unique(.x$gamma_slab))
    )  +
      geom_raster(
        data = GLODAP_grid %>% filter(gamma_slab == unique(.x$gamma_slab),
                                      era_label == "both"),
        aes(lon, lat), fill = "red") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[1]
        ),
        aes(lon, lat), fill = "grey50") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[2]
        ),
        aes(lon, lat),fill = "black")
  )
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4.3 SD across models

dcant_slab %>%
  filter(data_source %in% c("mod", "obs")) %>% 
  group_by(gamma_slab, data_source) %>%
  group_split() %>% 
  # head(1) %>%
  map(
    ~ p_map_dcant_slab(
      df = .x,
      var = "dcant_sd",
      col = "continuous",
      subtitle_text = paste(
        "data_source:",
        unique(.x$data_source),
        "| Neutral density:",
        unique(.x$gamma_slab)
      ),
      breaks = c(seq(0,3,0.3), Inf)
    )  +
      geom_raster(
        data = GLODAP_grid %>% filter(gamma_slab == unique(.x$gamma_slab),
                                      era_label == "both"),
        aes(lon, lat), fill = "red") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[1]
        ),
        aes(lon, lat), fill = "grey50") +
      geom_raster(
        data = GLODAP_grid %>% filter(
          gamma_slab == unique(.x$gamma_slab),
          era_label == unique(tref$era)[2]
        ),
        aes(lon, lat),fill = "black")
  )
[[1]]

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5 Slab Budgets

dcant_slab_budget <- dcant_3d %>%
  group_by(data_source) %>%
  nest() %>%
  mutate(slab_budget = map(.x = data, ~ m_dcant_slab_budget(.x))) %>%
  select(-data) %>%
  unnest(slab_budget)

dcant_slab_budget %>% 
  ggplot(aes(data_source, dcant_pos, fill = gamma_slab)) +
  geom_col() +
  scale_fill_viridis_d(direction = -1) +
  facet_wrap(~ basin_AIP)

Version Author Date
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dcant_slab_budget %>% 
  ggplot(aes(data_source, dcant, fill = gamma_slab)) +
  geom_hline(yintercept = 0, col = "red") +
  geom_col() +
  scale_fill_viridis_d(direction = -1) +
  facet_wrap(~ basin_AIP)

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4fcd066 jens-daniel-mueller 2021-06-24
aedd245 jens-daniel-mueller 2021-06-23
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c6b3da6 jens-daniel-mueller 2021-06-14
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db7df0e jens-daniel-mueller 2021-06-04
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4b7a5ee jens-daniel-mueller 2021-05-28
25bd183 jens-daniel-mueller 2021-05-26
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dcant_slab_budget %>% 
  ggplot(aes(data_source, dcant, fill = basin_AIP)) +
  geom_hline(yintercept = 0, col = "red") +
  geom_col() +
  scale_fill_viridis_d(direction = -1)

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dcant_slab_budget_bias <- dcant_slab_budget %>%
  filter(data_source %in% c("mod", "mod_truth")) %>%
  pivot_wider(names_from = data_source,
              values_from = c("dcant", "dcant_pos")) %>%
  mutate(dcant_bias = dcant_mod - dcant_mod_truth,
         dcant_pos_bias = dcant_pos_mod - dcant_pos_mod_truth) %>% 
  select(c(basin_AIP, gamma_slab, ends_with("bias")))


dcant_slab_budget_bias %>% 
  ggplot(aes(gamma_slab, dcant_pos_bias)) +
  geom_col() +
  facet_grid(basin_AIP ~ ., scales = "free_y") +
  coord_flip()

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dcant_slab_budget_bias %>% 
  ggplot(aes(gamma_slab, dcant_bias)) +
  geom_col() +
  facet_grid(basin_AIP ~ ., scales = "free_y") +
  coord_flip()

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6 Write files

dcant_slab_budget_bias %>%
  write_csv(paste0(path_version_data,
                   "dcant_slab_budget_bias.csv"))

dcant_slab_budget %>%
  write_csv(paste0(path_version_data,
                   "dcant_slab_budget.csv"))

dcant_slab_bias %>%
  write_csv(paste0(path_version_data,
                   "dcant_slab_bias.csv"))

dcant_slab %>%
  write_csv(paste0(path_version_data,
                   "dcant_slab.csv"))

7 Slab boundary depth

dcant_slab %>%
  group_by(gamma_slab, data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(~ map  +
        geom_raster(data = .x,
                    aes(lon, lat, fill = layer_thickness)) +
        scale_fill_viridis_c() +
        labs(title = paste("Slab:", .x$gamma_slab,
                           "| Data source:", .x$data_source))
  )
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# gamma field in Atl
predictors_gamma_Atl <- dcant_3d %>%
  select(data_source, basin_AIP, lat, lon, depth, gamma) %>% 
  filter(basin_AIP == "Atlantic") %>% 
  distinct()

# filter grid cells with at least two depth layers
predictors_gamma_Atl <- predictors_gamma_Atl %>%
  group_by(data_source, lat, lon) %>%
  mutate(n = n()) %>%
  ungroup() %>% 
  filter(n > 1) %>% 
  select(-n)

# 3D grid with Atl slab boundaries
slabs_Atl <- expand_grid(predictors_gamma_Atl %>% distinct(data_source, lat, lon),
                         gamma = params_local$slabs_Atl) %>% 
  mutate(basin_AIP = "flag")

# join slab boundary grid with gamma field
slabs_Atl_int <- full_join(slabs_Atl,
                           predictors_gamma_Atl) %>%
  arrange(lat, lon, gamma)

# interpolate depth to slab boundary
slabs_Atl_int <- slabs_Atl_int %>% 
  group_by(data_source, lat, lon) %>% 
  mutate(depth = approxfun(gamma, depth, rule = 1)(gamma)) %>% 
  ungroup()

# filter interpolated depths
slabs_Atl_int <- slabs_Atl_int %>% 
  filter(basin_AIP == "flag",
         !is.na(depth))

#### Indo-Pacific

# gamma field in Pac
predictors_gamma_Ind_Pac <- dcant_3d %>%
  select(data_source, basin_AIP, lat, lon, depth, gamma) %>% 
  filter(basin_AIP %in% c("Indian", "Pacific")) %>% 
  distinct()

# filter grid cells with at least two depth layers
predictors_gamma_Ind_Pac <- predictors_gamma_Ind_Pac %>%
  group_by(data_source, lat, lon) %>%
  mutate(n = n()) %>%
  ungroup() %>% 
  filter(n > 1) %>% 
  select(-n)

# 3D grid with Pac slab boundaries
slabs_Ind_Pac <- expand_grid(predictors_gamma_Ind_Pac %>% distinct(data_source, lat, lon),
                         gamma = params_local$slabs_Ind_Pac) %>% 
  mutate(basin_AIP = "flag")

# join slab boundary grid with gamma field
slabs_Ind_Pac_int <- full_join(slabs_Ind_Pac,
                           predictors_gamma_Ind_Pac) %>%
  arrange(lat, lon, gamma)

# interpolate depth to slab boundary
slabs_Ind_Pac_int <- slabs_Ind_Pac_int %>% 
  group_by(data_source, lat, lon) %>% 
  mutate(depth = approxfun(gamma, depth, rule = 1)(gamma)) %>% 
  ungroup()

# filter interpolated depths
slabs_Ind_Pac_int <- slabs_Ind_Pac_int %>% 
  filter(basin_AIP == "flag",
         !is.na(depth))


# join interpolated gamma depth
slabs <- bind_rows(slabs_Atl_int,
                   slabs_Ind_Pac_int) %>%
  select(-basin_AIP)

slabs <- inner_join(
  basinmask %>% select(lat, lon, basin_AIP) %>% distinct(),
  slabs)


map + 
  geom_raster(data = slabs,
              aes(lon, lat, fill = depth)) +
  facet_grid(gamma ~ data_source) +
  scale_fill_viridis_c(direction = -1)

rm(slabs_Atl, slabs_Atl_int, slabs_Ind_Pac, slabs_Ind_Pac_int)

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] gt_0.2.2         kableExtra_1.3.1 marelac_2.1.10   shape_1.4.5     
 [5] scales_1.1.1     ggforce_0.3.3    metR_0.9.0       scico_1.2.0     
 [9] patchwork_1.1.1  collapse_1.5.0   forcats_0.5.0    stringr_1.4.0   
[13] dplyr_1.0.5      purrr_0.3.4      readr_1.4.0      tidyr_1.1.2     
[17] tibble_3.0.4     ggplot2_3.3.3    tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 lubridate_1.7.9          gsw_1.0-5               
 [4] webshot_0.5.2            httr_1.4.2               rprojroot_2.0.2         
 [7] tools_4.0.3              backports_1.1.10         R6_2.5.0                
[10] DBI_1.1.0                colorspace_1.4-1         withr_2.3.0             
[13] tidyselect_1.1.0         compiler_4.0.3           git2r_0.27.1            
[16] cli_2.1.0                rvest_0.3.6              xml2_1.3.2              
[19] labeling_0.4.2           checkmate_2.0.0          digest_0.6.27           
[22] rmarkdown_2.5            oce_1.2-0                pkgconfig_2.0.3         
[25] htmltools_0.5.0          dbplyr_1.4.4             rlang_0.4.10            
[28] readxl_1.3.1             rstudioapi_0.11          farver_2.0.3            
[31] generics_0.0.2           jsonlite_1.7.1           magrittr_1.5            
[34] Matrix_1.2-18            Rcpp_1.0.5               munsell_0.5.0           
[37] fansi_0.4.1              lifecycle_1.0.0          stringi_1.5.3           
[40] whisker_0.4              yaml_2.2.1               MASS_7.3-53             
[43] grid_4.0.3               blob_1.2.1               parallel_4.0.3          
[46] promises_1.1.1           crayon_1.3.4             lattice_0.20-41         
[49] haven_2.3.1              hms_0.5.3                seacarb_3.2.14          
[52] knitr_1.30               pillar_1.4.7             reprex_0.3.0            
[55] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.0
[58] data.table_1.13.2        modelr_0.1.8             vctrs_0.3.5             
[61] tweenr_1.0.2             httpuv_1.5.4             testthat_2.3.2          
[64] cellranger_1.1.0         gtable_0.3.0             polyclip_1.10-0         
[67] assertthat_0.2.1         xfun_0.18                broom_0.7.5             
[70] RcppEigen_0.3.3.7.0      later_1.1.0.1            viridisLite_0.3.0       
[73] ellipsis_0.3.1           here_0.1