Mapping dcant eMLR

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Last updated: 2021-08-03

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Knit directory: emlr_obs_v_XXX/

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File	Version	Author	Date	Message
html	a53656d	jens-daniel-mueller	2021-08-03	Build site.
html	88f7356	jens-daniel-mueller	2021-08-02	Build site.
html	d759279	jens-daniel-mueller	2021-08-02	Build site.
html	127b801	jens-daniel-mueller	2021-07-24	Build site.
html	1f65ef1	jens-daniel-mueller	2021-07-23	Build site.
html	912d90e	jens-daniel-mueller	2021-07-23	Build site.
html	2477316	jens-daniel-mueller	2021-07-23	rebuild: surface dcant mapping seperate
html	c9ccc00	jens-daniel-mueller	2021-07-22	Build site.
Rmd	6a7f226	jens-daniel-mueller	2021-07-22	surface dcant mapping seperate
html	f3c0d7a	jens-daniel-mueller	2021-07-22	Build site.
Rmd	203223f	jens-daniel-mueller	2021-07-22	surface dcant mapping seperate
html	c75b2a0	jens-daniel-mueller	2021-07-22	Build site.
Rmd	7d6951d	jens-daniel-mueller	2021-07-22	surface dcant mapping seperate
html	768ae83	jens-daniel-mueller	2021-07-22	Build site.
Rmd	17db872	jens-daniel-mueller	2021-07-22	surface dcant mapping with surface data only
html	426b2df	jens-daniel-mueller	2021-07-21	Build site.
html	78fe930	jens-daniel-mueller	2021-07-21	Build site.
Rmd	ee0e941	jens-daniel-mueller	2021-07-21	surface dcant mapping with preformed DIC
html	971ce87	jens-daniel-mueller	2021-07-13	Build site.
Rmd	89d9fcb	jens-daniel-mueller	2021-07-13	complete revision
html	bef1b71	jens-daniel-mueller	2021-07-09	Build site.
Rmd	f682536	jens-daniel-mueller	2021-07-09	complete revision

---

1 Version ID

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

2 Required data

2.1 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 = ""))

predictors_surface <-
  read_csv(paste(path_version_data,
                 "W18_st_G16_opsn_surface.csv",
                 sep = ""))

2.2 Atm. pCO2

Required only to estimate the change of dcant in surface water and assuming that the ocean pCO2 trend follows the atmospheric forcing.

co2_atm_tref <-
  read_csv(paste(path_version_data,
                 "co2_atm_tref.csv",
                 sep = ""))

2.3 MLR models

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

3 Join MLRs + climatologies

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

# join predictors and MLR
dcant <- left_join(lm_best_dcant, predictors)

rm(predictors, lm_best_dcant)

4 Map dcant

4.1 Deep water

4.1.1 Apply MLRs to predictor

dcant <- b_dcant(dcant)

4.1.2 Sections by model

Zonal section plots are produced for every 20° longitude, each era and for all models individually. Plots can be accessed here:

• /nfs/kryo/work/jenmueller/emlr_cant/observations/v_XXX/figures/Cant_model_sections/

if (params_local$plot_all_figures == "y") {
  for (i_eras in unique(cant$eras)) {
    # i_eras <- unique(cant$eras)[2]
    cant_eras <- cant %>%
      filter(eras == i_eras)
    
    for (i_lon in params_global$longitude_sections_regular) {
      # i_lon <- params_global$longitude_sections_regular[7]
      cant_eras_lon <- cant_eras %>%
        filter(lon == i_lon)
      
      limits = max(abs(cant_eras_lon$cant)) * c(-1, 1)
      
      cant_eras_lon %>%
        ggplot(aes(lat, depth, z = cant)) +
        stat_summary_2d(
          fun = "mean",
          na.rm = TRUE,
          bins = 20,
          col = "grey"
        ) +
        scale_fill_scico(name = "Cant",
                         palette = "vik",
                         limit = limits) +
        scale_y_reverse(limits = c(params_global$plotting_depth, NA)) +
        scale_x_continuous(limits = c(-85, 85)) +
        labs(title = paste(
          "eras:",
          i_eras,
          "| lon:",
          i_lon,
          "|",
          params_local$Version_ID
        )) +
        facet_wrap(~ model, ncol = 5)
      
      ggsave(
        paste(
          path_version_figures,
          "Cant_model_sections/",
          paste("Cant_model",
                i_eras,
                "lon",
                i_lon,
                "section.png",
                sep = "_"),
          sep = ""
        ),
        width = 17,
        height = 9
      )
      
    }
  }
}

4.2 Surface water

As outlined in Gruber et al. (2019), a transient equilibrium approach was applied to estimate dcant in surface waters, assuming that the CO₂ system in these waters has followed the increase in atmospheric CO₂ closely.

Using eq 10.2.16 from OBD, the change in anthropogenic CO₂ in the upper ocean was computed as:

\(\Delta\)_tC_ant,eq(t2 − t1) = 1∕\(\gamma\) ⋅ DIC/pCO₂ ⋅ (pCO_2,atm (t2)− pCO_2,atm(t1))

, where DIC and pCO₂ are the in situ values, where \(\gamma\) is the buffer (Revelle) factor and where we evaluated the right-hand side using seacarb employing the Luecker constants using the climatological values for temperature, salinity, DIC and Alk.

4.2.1 Layer depth

surface_layer <- predictors_surface %>% 
  group_by(lat, lon, data_source) %>% 
  summarise(depth_max = max(depth),
            n_layer = n()) %>% 
  ungroup()


map +
  geom_raster(data = surface_layer,
              aes(lon, lat, fill=depth_max)) +
  scale_fill_scico(palette = "nuuk", direction = -1) +
  facet_grid(data_source ~ .)

Past versions of surface_layer_depth-1.png

Version	Author	Date
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

map +
  geom_raster(data = surface_layer,
              aes(lon, lat, fill=n_layer)) +
  scale_fill_scico(palette = "tokyo") +
  facet_grid(data_source ~ .)

Past versions of surface_layer_depth-2.png

Version	Author	Date
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

4.2.2 Surface pCO2

predictors_surface_all_depth <- predictors_surface

predictors_surface <- predictors_surface %>%
  filter(depth %in% c(0, 5)) %>%
  mutate(
    pCO2 = carb(
      flag = 15,
      var1 = TAlk * 1e-6,
      var2 = TCO2 * 1e-6,
      S = sal,
      T = temp,
      P = depth / 10,
      Pt = phosphate * 1e-6,
      Sit = silicate * 1e-6,
      k1k2 = "l"
    )$pCO2
  )

predictors_surface %>%
  mutate(depth = 0) %>% 
  group_split(data_source) %>%
  # head(1) %>%
  map( ~
         p_map_climatology(
           df = .x,
           var = "pCO2",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Past versions of pCO2_climatology_map-1.png

Version	Author	Date
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of pCO2_climatology_map-2.png

Version	Author	Date
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

4.2.3 Revelle factor

Plots below show the calculated climatological Revelle factor values.

predictors_surface <- predictors_surface %>%
  mutate(
    rev_fac = buffer(
      flag = 15,
      var1 = TAlk * 1e-6,
      var2 = TCO2 * 1e-6,
      S = sal,
      T = temp,
      P = depth / 10,
      Pt = phosphate * 1e-6,
      Sit = silicate * 1e-6,
      k1k2 = "l"
    )$BetaD
  )

predictors_surface %>%
  group_split(data_source) %>%
  # head(1) %>%
  map( ~
         p_map_climatology(
           df = .x,
           var = "rev_fac",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Past versions of revelle_factor_climatology_map-1.png

Version	Author	Date
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of revelle_factor_climatology_map-2.png

Version	Author	Date
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

predictors_surface %>%
  group_split(data_source) %>%
  # head(1) %>%
  map( ~
         p_section_climatology_regular(
           df = .x,
           var = "rev_fac",
           surface = "y",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

4.2.4 dcant calculation

# calculate increase in atm pCO2 between eras
co2_atm_tref <- co2_atm_tref %>%
  arrange(pCO2_tref) %>%
  mutate(d_pCO2_tref = pCO2_tref - lag(pCO2_tref)) %>%
  drop_na() %>%
  select(d_pCO2_tref)

dcant_surface <- full_join(predictors_surface, co2_atm_tref,
                           by = character())

# calculate cant
dcant_surface <- dcant_surface %>%
  mutate(dcant = (1 / rev_fac) *
           (TCO2 / pCO2) * d_pCO2_tref)

# calculate positive cant
dcant_surface <- dcant_surface %>%
  mutate(dcant_pos = if_else(dcant < 0, 0, dcant)) %>% 
  select(lon, lat, data_source, dcant, dcant_pos)


dcant_surface <- full_join(
  dcant_surface,
  predictors_surface_all_depth
)

4.2.5 Control plots

dcant_surface %>%
  group_split(data_source) %>%
  # head(1) %>%
  map( ~
         p_map_climatology(
           df = .x,
           var = "dcant",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Past versions of dcant_surface_climatology_map-1.png

Version	Author	Date
88f7356	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of dcant_surface_climatology_map-2.png

Version	Author	Date
88f7356	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
bef1b71	jens-daniel-mueller	2021-07-09

dcant_surface %>%
  group_split(data_source) %>%
  # head(1) %>%
  map( ~
         p_section_climatology_regular(
           df = .x,
           var = "dcant",
           surface = "y",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Past versions of dcant_surface_climatology_sections-1.png

Version	Author	Date
88f7356	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of dcant_surface_climatology_sections-2.png

Version	Author	Date
88f7356	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

4.3 Average model dcant

Mean and sd are calculated across 10 models for Cant in each grid cell (XYZ), basin and era combination. Calculations are performed for all cant values vs positive values only.

4.3.1 Deep water averaging

dcant_average <- m_dcant_3d_average(dcant)
dcant_average <- m_cut_gamma(dcant_average, "gamma")

# split data set for individual predictor contributions and total cant
dcant_predictor_3d <- dcant_average %>% 
  select(-c("dcant", "dcant_pos", ends_with("_sd")))

dcant_average <- dcant_average %>%
  select(
    lon,
    lat,
    depth,
    basin_AIP,
    data_source,
    dcant,
    dcant_pos,
    dcant_sd,
    dcant_pos_sd,
    gamma,
    gamma_sd,
    gamma_slab
  )

dcant_average %>%
  group_split(data_source) %>%
  # head(1) %>% 
  map(~ p_map_climatology(
    df = .x,
    var = "dcant_pos",
    subtitle_text = paste("data_source:", unique(.x$data_source))
  ))

[[1]]

Past versions of cant_deep_climatology_map-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of cant_deep_climatology_map-2.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
bef1b71	jens-daniel-mueller	2021-07-09

dcant_average %>%
  group_split(data_source) %>%
  # head(1) %>% 
  map(~ p_section_climatology_regular(
    df = .x,
    surface = "n",
    var = "dcant_pos",
    subtitle_text = paste("data_source:", unique(.x$data_source))
  ))

[[1]]

Past versions of cant_deep_climatology_sections-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of cant_deep_climatology_sections-2.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
bef1b71	jens-daniel-mueller	2021-07-09

4.3.2 Surface water averaging

The averaging function is also applied to the surface data, although only one value per grid cell was mapped, to ensure consistency with the deep water values.

dcant_surface_average <-
  m_dcant_3d_average(dcant_surface)

dcant_surface_average <- m_cut_gamma(dcant_surface_average, "gamma")
rm(dcant_surface)

4.4 Join surface and deep water

dcant_3d <-
  full_join(
    dcant_average %>% mutate(method = "eMLR"),
    dcant_surface_average %>%  mutate(method = "surface")
  )

rm(dcant_surface_average, dcant_average)

4.5 Zonal sections

For each basin and era combination, the zonal mean dcant is calculated, again for all vs positive only values. Likewise, sd is calculated for the averaging of the mean basin fields.

dcant_zonal <- dcant_3d %>%
  group_by(data_source) %>%
  nest() %>%
  mutate(zonal = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
  select(-data) %>%
  unnest(zonal)

dcant_zonal <- m_cut_gamma(dcant_zonal,
                           "gamma_mean")

dcant_zonal_method <- dcant_3d %>%
  group_by(data_source, method) %>%
  nest() %>%
  mutate(zonal = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
  select(-data) %>%
  unnest(zonal)

dcant_zonal_method <- m_cut_gamma(dcant_zonal_method,
                                   "gamma_mean")

4.6 Mean dcant sections by coefficient

For each basin and era combination, the zonal mean is calculated for the term of each predictor.

dcant_predictor_zonal <- dcant_predictor_3d %>%
  group_by(data_source) %>%
  nest() %>%
  mutate(zonal = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
  select(-data) %>%
  unnest(zonal)

dcant_predictor_zonal <-
  m_cut_gamma(dcant_predictor_zonal, "gamma_mean")

4.7 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.

4.7.1 Full water column

dcant_inv <- dcant_3d %>%
  group_by(data_source) %>%
  nest() %>%
  mutate(inv = map(.x = data, ~m_dcant_inv(.x))) %>%
  select(-data) %>%
  unnest(inv)

p_map_cant_inv(df = dcant_inv,
               var = "dcant_pos",
               subtitle_text = "for predefined integration depths") +
  facet_grid(inv_depth ~ data_source)

Past versions of cant_inventories-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
971ce87	jens-daniel-mueller	2021-07-13
bef1b71	jens-daniel-mueller	2021-07-09

4.7.2 Surface layer

dcant_inv_surface <- dcant_3d %>%
  filter(method == "surface") %>% 
  group_by(data_source) %>%
  nest() %>%
  mutate(inv = map(.x = data, ~m_dcant_inv(.x))) %>%
  select(-data) %>%
  unnest(inv)

p_map_cant_inv(df = dcant_inv_surface %>% 
                 filter(inv_depth < 1000),
               var = "dcant_pos",
               subtitle_text = "for predefined integration depths",
               breaks = c(-Inf,seq(0,4,0.5), Inf)) +
  facet_grid(inv_depth ~ data_source)

Past versions of cant_inventories_surface-1.png

Version	Author	Date
88f7356	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

4.7.3 eMLR only

dcant_inv <- full_join(
  dcant_inv %>% rename(dcant_total = dcant,
                       dcant_pos_total = dcant_pos),
  dcant_inv_surface %>% rename(dcant_surface = dcant,
                               dcant_pos_surface = dcant_pos)
)

dcant_inv <- dcant_inv %>%
  mutate(dcant_eMLR = dcant_total -
           replace(dcant_surface, is.na(dcant_surface), 0),
         dcant_pos_eMLR = dcant_pos_total -
           replace(dcant_pos_surface, is.na(dcant_pos_surface), 0))

dcant_inv_all <- dcant_inv %>% 
  select(-starts_with("dcant_pos")) %>% 
  pivot_longer(starts_with("dcant_"),
               names_to = "method",
               names_prefix = "dcant_",
               values_to = "dcant")

dcant_inv_pos <- dcant_inv %>% 
  select(data_source, lon, lat, basin_AIP, inv_depth,
         starts_with("dcant_pos_")) %>% 
  pivot_longer(starts_with("dcant_pos_"),
               names_to = "method",
               names_prefix = "dcant_pos_",
               values_to = "dcant_pos")

dcant_inv <- full_join(
  dcant_inv_all,
  dcant_inv_pos
)

rm(dcant_inv_all, dcant_inv_pos, dcant_inv_surface)

dcant_inv %>%
  group_by(inv_depth) %>%
  group_split() %>% 
  # tail(1) %>%
  map(
    ~ p_map_cant_inv(df = .x,
                     var = "dcant",
                     subtitle_text = paste("Integration depth",
                                           unique(.x$inv_depth))) +
      facet_grid(method ~ data_source)
  )

[[1]]

Past versions of combine_dcant_inventories-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

[[2]]

Past versions of combine_dcant_inventories-2.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
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127b801	jens-daniel-mueller	2021-07-24
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2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

[[3]]

Past versions of combine_dcant_inventories-3.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
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c75b2a0	jens-daniel-mueller	2021-07-22

[[4]]

Past versions of combine_dcant_inventories-4.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
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d759279	jens-daniel-mueller	2021-08-02
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2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

[[5]]

Past versions of combine_dcant_inventories-5.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
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c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

4.8 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.

molC_to_PgC <- 12*1e-15

dcant_budget <- dcant_inv %>% 
  mutate(surface_area = earth_surf(lat, lon),
         dcant_grid = dcant*surface_area*molC_to_PgC,
         dcant_pos_grid = dcant_pos*surface_area*molC_to_PgC)

dcant_budget_global <- dcant_budget %>%
  group_by(data_source, inv_depth, method) %>% 
  summarise(dcant = sum(dcant_grid, na.rm = TRUE),
            dcant = round(dcant,3),
            dcant_pos = sum(dcant_pos_grid, na.rm = TRUE),
            dcant_pos = round(dcant_pos,3)) %>% 
  ungroup() %>% 
  pivot_longer(cols = dcant:dcant_pos,
               names_to = "estimate",
               values_to = "value")

dcant_budget_global %>%
  filter(inv_depth == params_global$inventory_depth_standard,
         method == "total") %>%
  ggplot(aes(estimate, value)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(~data_source)

Past versions of budget_global-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

dcant_budget_global %>%
  filter(inv_depth == params_global$inventory_depth_standard,
         method %in% c("surface", "eMLR")) %>%
  ggplot(aes(estimate, value, fill=method)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(.~data_source)

Past versions of budget_global_surface-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
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2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

dcant_budget_basin_AIP <- dcant_budget %>% 
  group_by(basin_AIP, data_source, method, inv_depth) %>% 
  summarise(dcant = sum(dcant_grid, na.rm = TRUE),
            dcant = round(dcant,3),
            dcant_pos = sum(dcant_pos_grid, na.rm = TRUE),
            dcant_pos = round(dcant_pos,3)) %>% 
  ungroup() %>% 
  pivot_longer(cols = dcant:dcant_pos,
               names_to = "estimate",
               values_to = "value")

dcant_budget_basin_AIP %>%
  filter(inv_depth == params_global$inventory_depth_standard,
         method %in% c("surface", "eMLR")) %>%
  ggplot(aes(basin_AIP, value, fill=method)) +
  scale_fill_brewer(palette = "Dark2") +
  geom_col() +
  facet_grid(estimate~data_source)

Past versions of budget_basin_AIP-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

dcant_budget_basin_MLR <-
  full_join(dcant_budget, basinmask) %>% 
  group_by(basin, MLR_basins, data_source, method, inv_depth) %>% 
  summarise(dcant = sum(dcant_grid, na.rm = TRUE),
            dcant = round(dcant,3),
            dcant_pos = sum(dcant_pos_grid, na.rm = TRUE),
            dcant_pos = round(dcant_pos,3)) %>% 
  ungroup() %>% 
  pivot_longer(cols = dcant:dcant_pos,
               names_to = "estimate",
               values_to = "value")

dcant_budget_basin_MLR %>%
  filter(inv_depth == params_global$inventory_depth_standard,
         method %in% c("surface", "eMLR")) %>%
  group_by(MLR_basins) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(basin, value, fill = method)) +
      scale_fill_brewer(palette = "Dark2") +
      geom_col() +
      facet_grid(estimate ~ data_source) +
      labs(title = paste("MLR_basins:", unique(.x$MLR_basins)))
  )

[[1]]

Past versions of budget_MLR_regions-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
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2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of budget_MLR_regions-2.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
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c75b2a0	jens-daniel-mueller	2021-07-22

[[3]]

Past versions of budget_MLR_regions-3.png

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c75b2a0	jens-daniel-mueller	2021-07-22

[[4]]

Past versions of budget_MLR_regions-4.png

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a53656d	jens-daniel-mueller	2021-08-03
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c75b2a0	jens-daniel-mueller	2021-07-22

[[5]]

Past versions of budget_MLR_regions-5.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
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d759279	jens-daniel-mueller	2021-08-02
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c75b2a0	jens-daniel-mueller	2021-07-22

[[6]]

Past versions of budget_MLR_regions-6.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
912d90e	jens-daniel-mueller	2021-07-23
2477316	jens-daniel-mueller	2021-07-23
c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22

dcant_budget_lat_grid <- 
  dcant_budget %>% 
  m_grid_horizontal_coarse() %>%
  group_by(lat_grid, basin_AIP, data_source, method, inv_depth) %>% 
  summarise(dcant = sum(dcant_grid, na.rm = TRUE),
            dcant = round(dcant,3),
            dcant_pos = sum(dcant_pos_grid, na.rm = TRUE),
            dcant_pos = round(dcant_pos,3)) %>% 
  ungroup() %>% 
  pivot_longer(cols = dcant:dcant_pos,
               names_to = "estimate",
               values_to = "value")

dcant_budget_lat_grid %>%
  filter(inv_depth == params_global$inventory_depth_standard,
         method %in% c("surface", "eMLR")) %>%
  group_by(basin_AIP) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(lat_grid, value, fill = method)) +
      scale_fill_brewer(palette = "Dark2") +
      geom_col() +
      coord_flip() +
      facet_grid(estimate ~ data_source) +
      labs(title = paste("MLR_basins:", unique(.x$basin_AIP)))
  )

[[1]]

Past versions of budget_lat_grid-1.png

Version	Author	Date
a53656d	jens-daniel-mueller	2021-08-03
88f7356	jens-daniel-mueller	2021-08-02
d759279	jens-daniel-mueller	2021-08-02
127b801	jens-daniel-mueller	2021-07-24
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c9ccc00	jens-daniel-mueller	2021-07-22
c75b2a0	jens-daniel-mueller	2021-07-22
768ae83	jens-daniel-mueller	2021-07-22
78fe930	jens-daniel-mueller	2021-07-21
bef1b71	jens-daniel-mueller	2021-07-09

[[2]]

Past versions of budget_lat_grid-2.png

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[[3]]

Past versions of budget_lat_grid-3.png

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a53656d	jens-daniel-mueller	2021-08-03
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f3c0d7a	jens-daniel-mueller	2021-07-22

5 Write csv

dcant_3d %>%
  write_csv(paste(path_version_data,
                  "dcant_3d.csv", sep = ""))

dcant_predictor_3d %>%
  write_csv(paste(path_version_data,
                  "dcant_predictor_3d.csv", sep = ""))

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

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

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

dcant_inv %>%
  filter(method == "total") %>% 
  select(-method) %>% 
  write_csv(paste(path_version_data,
                  "dcant_inv.csv", sep = ""))

dcant_inv %>%
  filter(method != "total") %>% 
  write_csv(paste(path_version_data,
                  "dcant_inv_method.csv", sep = ""))

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

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

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

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

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] marelac_2.1.10  shape_1.4.5     seacarb_3.2.14  oce_1.2-0      
 [5] gsw_1.0-5       testthat_2.3.2  ggforce_0.3.3   metR_0.9.0     
 [9] scico_1.2.0     patchwork_1.1.1 collapse_1.5.0  forcats_0.5.0  
[13] stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4     readr_1.4.0    
[17] tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3   tidyverse_1.3.0
[21] 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         R6_2.5.0                 DBI_1.1.0               
[10] colorspace_1.4-1         withr_2.3.0              tidyselect_1.1.0        
[13] compiler_4.0.3           git2r_0.27.1             cli_2.1.0               
[16] rvest_0.3.6              xml2_1.3.2               isoband_0.2.2           
[19] labeling_0.4.2           scales_1.1.1             checkmate_2.0.0         
[22] digest_0.6.27            rmarkdown_2.5            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                knitr_1.30              
[52] pillar_1.4.7             reprex_0.3.0             glue_1.4.2              
[55] evaluate_0.14            RcppArmadillo_0.10.1.2.0 data.table_1.13.2       
[58] modelr_0.1.8             vctrs_0.3.5              tweenr_1.0.2            
[61] httpuv_1.5.4             cellranger_1.1.0         gtable_0.3.0            
[64] polyclip_1.10-0          assertthat_0.2.1         xfun_0.18               
[67] broom_0.7.5              RcppEigen_0.3.3.7.0      later_1.1.0.1           
[70] viridisLite_0.3.0        ellipsis_0.3.1           here_0.1