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html	c0a47df	jens-daniel-mueller	2021-04-16	Build site.
html	50290e8	jens-daniel-mueller	2021-04-16	overlapping eras
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Rmd	bca0564	jens-daniel-mueller	2021-04-16	map only when model available
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html	338dd3c	jens-daniel-mueller	2021-04-09	Build site.
html	a79ca2c	jens-daniel-mueller	2021-04-09	included model data
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Rmd	7397a49	jens-daniel-mueller	2021-04-09	included model data
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Rmd	84b33f7	jens-daniel-mueller	2021-04-07	included model data
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html	6482ed7	jens-daniel-mueller	2021-03-11	Build site.
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html	e5c81bf	jens-daniel-mueller	2021-01-07	Build site.
Rmd	fe13638	jens-daniel-mueller	2021-01-07	removed GLODAP gamma filter, target variable mapped by eras+era
html	318609d	jens-daniel-mueller	2020-12-23	adapted more variable predictor selection
html	84ddb8b	jens-daniel-mueller	2020-12-23	Build site.
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html	02f0ee9	jens-daniel-mueller	2020-12-18	cleaned up for copying template
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Rmd	e7e5ff1	jens-daniel-mueller	2020-12-15	rebuild with eMLR target variable selection

1 Required data

1.1 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 = ""))

1.2 Atm. pCO2

Required only to estimate the change of Cant 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 = ""))

1.3 MLR models

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

2 Join MLRs + climatologies

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

# join predictors and MLR
cant <- left_join(lm_best_cant, predictors)

rm(predictors, lm_best_cant)

3 Map Cant

3.1 Deep water

3.1.1 Apply MLRs to predictor

cant <- b_cant(cant)

3.1.2 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
      )
      
    }
  }
}

3.2 Surface water

As outlined in Gruber et al. (2019), a transient equilibrium approach was applied to estimate Cant 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.

3.2.1 pCO₂ climatology

Plots below show the calculated climatolofical pCO₂ values.

# calculate pCO2 from talk and tco2 climatology
predictors_surface <- predictors_surface %>%
  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 %>%
  group_split(data_source) %>%
  map( ~
         p_map_climatology(
           df = .x,
           var = "pCO2",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08

predictors_surface %>%
  group_split(data_source) %>%
  map( ~
         p_section_climatology_regular(
           df = .x,
           var = "pCO2",
           surface = "y",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
2792743	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08

3.2.2 Revelle factor

Plots below show the calculated climatolofical 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) %>%
  map( ~
         p_map_climatology(
           df = .x,
           var = "rev_fac",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08

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

[[1]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
2792743	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08

3.2.3 Cant 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),
         eras = paste(lag(era), era, sep = " --> ")) %>%
  drop_na() %>%
  select(eras, d_pCO2_tref)

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

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

# calculate positive cant
cant_surface <- cant_surface %>%
  mutate(cant_pos = if_else(cant < 0, 0, cant))

3.2.4 Control plots

cant_surface %>%
  group_split(data_source) %>%
  map( ~
         p_map_climatology(
           df = .x,
           var = "cant",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
31bcf6d	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
4d612dd	jens-daniel-mueller	2020-12-15

cant_surface %>%
  group_split(data_source) %>%
  map( ~
         p_section_climatology_regular(
           df = .x,
           var = "cant",
           surface = "y",
           subtitle_text = paste("Data source: ", unique(.x$data_source))
         ))

[[1]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
31bcf6d	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
2792743	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
2792743	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
4d612dd	jens-daniel-mueller	2020-12-15

3.3 Average model Cant

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.

3.3.1 Deep water averaging

cant_average <- m_cant_model_average_data_source(cant)
cant_average <- m_cut_gamma(cant_average, "gamma")

# split data set for individual predictor contributions and total cant
cant_predictor_average <- cant_average %>% 
  select(-c("cant", "cant_pos", ends_with("_sd")))

cant_average <- cant_average %>% 
  select(lon, lat, depth, eras, basin, basin_AIP, data_source,
            cant, cant_pos, cant_sd, cant_pos_sd,
            gamma, gamma_sd, gamma_slab)

cant_average %>%
  group_split(data_source) %>%
  map(~ p_map_climatology(
    df = .x,
    var = "cant_pos",
    subtitle_text = paste("data_source:", unique(.x$data_source))
  ))

[[1]]

Version	Author	Date
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
0a6099c	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
31bcf6d	jens-daniel-mueller	2021-03-23
380d215	jens-daniel-mueller	2021-03-21
33b385b	jens-daniel-mueller	2021-03-20
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
d397028	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
0a6099c	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
f155edd	jens-daniel-mueller	2021-03-23
380d215	jens-daniel-mueller	2021-03-21
33b385b	jens-daniel-mueller	2021-03-20
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
d397028	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15

cant_average %>%
  group_split(data_source) %>%
  map(~ p_section_climatology_regular(
    df = .x,
    var = "cant_pos",
    subtitle_text = paste("data_source:", unique(.x$data_source))
  ))

[[1]]

Version	Author	Date
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
0a6099c	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
31bcf6d	jens-daniel-mueller	2021-03-23
380d215	jens-daniel-mueller	2021-03-21
33b385b	jens-daniel-mueller	2021-03-20
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
d397028	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15


[[2]]

Version	Author	Date
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
0a6099c	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
f155edd	jens-daniel-mueller	2021-03-23
380d215	jens-daniel-mueller	2021-03-21
33b385b	jens-daniel-mueller	2021-03-20
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
d397028	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15

3.3.2 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.

cant_surface_average <-
  m_cant_model_average_data_source(cant_surface)

cant_surface_average <- m_cut_gamma(cant_surface_average, "gamma")
rm(cant_surface)

3.3.3 Join surface and deep water

cant_average <- full_join(cant_average, cant_surface_average)
rm(cant_surface_average)

3.4 Zonal mean sections

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

cant_average_zonal <- m_cant_zonal_mean_data_source(cant_average)
cant_average_zonal <- m_cut_gamma(cant_average_zonal, "gamma_mean")

3.5 Mean cant sections by coefficient

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

cant_predictor_average_zonal <-
  m_cant_predictor_zonal_mean_data_source(cant_predictor_average)

cant_predictor_average_zonal <-
  m_cut_gamma(cant_predictor_average_zonal, "gamma")

3.6 Inventory calculation

To calculate Cant column inventories, we:

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

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

cant_inv_mod <- m_cant_inv(cant_average %>% filter(data_source == "mod")) %>% 
  mutate(data_source = "mod")
cant_inv_obs <- m_cant_inv(cant_average %>% filter(data_source == "obs")) %>% 
  mutate(data_source = "obs")

cant_inv <- bind_rows(
  cant_inv_mod,
  cant_inv_obs
)

rm(cant_inv_mod, cant_inv_obs)

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

Version	Author	Date
52e7583	jens-daniel-mueller	2021-05-12
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
0a6099c	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
7831fd3	jens-daniel-mueller	2021-04-09
05a66fd	jens-daniel-mueller	2021-04-08
eb827c9	jens-daniel-mueller	2021-04-07
03b6009	jens-daniel-mueller	2021-03-23
31bcf6d	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
380d215	jens-daniel-mueller	2021-03-21
33b385b	jens-daniel-mueller	2021-03-20
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
6c0bec6	jens-daniel-mueller	2021-03-05
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
e5c81bf	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
84ddb8b	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
d397028	jens-daniel-mueller	2020-12-18
e4ca289	jens-daniel-mueller	2020-12-16
158fe26	jens-daniel-mueller	2020-12-15
7a9a4cb	jens-daniel-mueller	2020-12-15
61b263c	jens-daniel-mueller	2020-12-15
4d612dd	jens-daniel-mueller	2020-12-15

4 Write csv

cant_average %>%
  write_csv(paste(path_version_data,
                  "cant_3d.csv", sep = ""))

cant_predictor_average %>%
  write_csv(paste(path_version_data,
                  "cant_predictor_3d.csv", sep = ""))

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

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

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

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] seacarb_3.2.14  oce_1.2-0       gsw_1.0-5       testthat_2.3.2 
 [5] metR_0.9.0      scico_1.2.0     patchwork_1.1.1 collapse_1.5.0 
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4    
[13] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3  
[17] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               viridisLite_0.3.0        jsonlite_1.7.1          
 [4] here_0.1                 modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_1.4-1         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.5             
[25] haven_2.3.1              scales_1.1.1             whisker_0.4             
[28] later_1.1.0.1            git2r_0.27.1             farver_2.0.3            
[31] generics_0.0.2           ellipsis_0.3.1           withr_2.3.0             
[34] cli_2.1.0                magrittr_1.5             crayon_1.3.4            
[37] readxl_1.3.1             evaluate_0.14            fs_1.5.0                
[40] fansi_0.4.1              xml2_1.3.2               RcppArmadillo_0.10.1.2.0
[43] tools_4.0.3              data.table_1.13.2        hms_0.5.3               
[46] lifecycle_1.0.0          munsell_0.5.0            reprex_0.3.0            
[49] isoband_0.2.2            compiler_4.0.3           rlang_0.4.10            
[52] grid_4.0.3               rstudioapi_0.13          labeling_0.4.2          
[55] rmarkdown_2.5            gtable_0.3.0             DBI_1.1.0               
[58] R6_2.5.0                 lubridate_1.7.9          knitr_1.30              
[61] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[64] Rcpp_1.0.5               vctrs_0.3.5              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.18

Mapping cant

Jens Daniel Müller

13 May, 2021

1 Required data

1.1 Predictor fields

1.2 Atm. pCO2

1.3 MLR models

2 Join MLRs + climatologies

3 Map Cant

3.1 Deep water

3.1.1 Apply MLRs to predictor

3.1.2 Sections by model

3.2 Surface water

3.2.1 pCO₂ climatology

3.2.2 Revelle factor

3.2.3 Cant calculation

3.2.4 Control plots

3.3 Average model Cant

3.3.1 Deep water averaging

3.3.2 Surface water averaging

3.3.3 Join surface and deep water

3.4 Zonal mean sections

3.5 Mean cant sections by coefficient

3.6 Inventory calculation

4 Write csv

Mapping cant

Jens Daniel Müller

13 May, 2021

1 Required data

1.1 Predictor fields

1.2 Atm. pCO2

1.3 MLR models

2 Join MLRs + climatologies

3 Map Cant

3.1 Deep water

3.1.1 Apply MLRs to predictor

3.1.2 Sections by model

3.2 Surface water

3.2.1 pCO2 climatology

3.2.2 Revelle factor

3.2.3 Cant calculation

3.2.4 Control plots

3.3 Average model Cant

3.3.1 Deep water averaging

3.3.2 Surface water averaging

3.3.3 Join surface and deep water

3.4 Zonal mean sections

3.5 Mean cant sections by coefficient

3.6 Inventory calculation

4 Write csv

3.2.1 pCO₂ climatology