Last updated: 2021-01-27

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File	Version	Author	Date	Message
html	342402d	Donghe-Zhu	2021-01-27	Build site.
html	5bad5c2	Donghe-Zhu	2021-01-27	Build site.
html	61efb56	Donghe-Zhu	2021-01-25	Build site.
html	48f638e	Donghe-Zhu	2021-01-25	Build site.
Rmd	17a7d1a	Donghe-Zhu	2021-01-25	MLR = SO
html	c1cec47	Donghe-Zhu	2021-01-25	Build site.
html	05ffb0c	Donghe-Zhu	2021-01-25	Build site.
html	8b97165	Donghe-Zhu	2021-01-25	Build site.
html	c569946	Donghe-Zhu	2021-01-24	Build site.
html	a2f0d56	Donghe-Zhu	2021-01-23	Build site.
html	28509fc	Donghe-Zhu	2021-01-23	Build site.
html	4c28e4a	Donghe-Zhu	2021-01-22	Build site.
html	24cc264	jens-daniel-mueller	2021-01-22	cleaned /docs before creating copies
html	88eb28f	Donghe-Zhu	2021-01-21	Build site.
Rmd	72f8e94	Donghe-Zhu	2021-01-21	DIC plots
html	2679490	Donghe-Zhu	2021-01-21	Build site.
Rmd	962521f	Donghe-Zhu	2021-01-21	Test with nitrate replace oxygen as predictor, atmospheric equilibrium and plot set to n
html	7891955	Donghe-Zhu	2021-01-21	Build site.
Rmd	13c3031	Donghe-Zhu	2021-01-21	Test with nitrate replace oxygen as predictor, atmospheric equilibrium and plot set to n
html	d4cf1cb	Donghe-Zhu	2021-01-21	Build site.
Rmd	167eeec	Donghe-Zhu	2021-01-21	surface DIC calculation with atmospheric equilibrium option
html	1f3e5b6	jens-daniel-mueller	2021-01-20	Build site.
Rmd	0e7bdf1	jens-daniel-mueller	2021-01-15	cleaning template repository
html	0e7bdf1	jens-daniel-mueller	2021-01-15	cleaning template repository
html	73cbef3	jens-daniel-mueller	2021-01-15	Build site.
html	4571843	jens-daniel-mueller	2021-01-14	revision and html deleted for template copying
html	23151cd	jens-daniel-mueller	2021-01-14	Build site.
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html	8d032c3	jens-daniel-mueller	2021-01-14	Build site.
html	022871c	Donghe-Zhu	2021-01-13	Build site.
Rmd	d44f36f	Donghe-Zhu	2021-01-13	reorder analysis final
html	17dee1d	jens-daniel-mueller	2021-01-13	Build site.
html	a076226	Donghe-Zhu	2021-01-11	Build site.
html	7cdea0c	jens-daniel-mueller	2021-01-06	Build site.
Rmd	b5934dd	jens-daniel-mueller	2021-01-06	local rebuild after revision
html	fa85b93	jens-daniel-mueller	2021-01-06	Build site.
html	e5cb81a	Donghe-Zhu	2021-01-05	Build site.
html	a499f10	Donghe-Zhu	2021-01-05	Build site.
Rmd	715bdb4	Donghe-Zhu	2021-01-02	model modification
html	fb8a752	Donghe-Zhu	2020-12-23	Build site.
Rmd	82e3c9c	Donghe-Zhu	2020-12-23	first build after creating model template
html	8fae0b2	Donghe-Zhu	2020-12-21	Build site.
Rmd	00a1322	Donghe-Zhu	2020-12-21	first build after creating model template
html	c8b76b3	jens-daniel-mueller	2020-12-19	Build site.
Rmd	b5fedce	jens-daniel-mueller	2020-12-19	first build after creating model template
Rmd	8e8abf5	Jens Müller	2020-12-18	Initial commit

1 Required data

1.1 Predictor fields

Currently, we use following cmorized climatology predictor fields in 2007:

predictors <-
  read_csv(paste(path_version_data,
                 "predictors_M2007.csv",
                 sep = ""))

predictors_surface <-
  read_csv(paste(path_version_data,
                 "predictors_surface_M2007.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 <- full_join(predictors, lm_best_cant)

if (params_local$MLR_basins == "SO") {
  cant <- cant %>%
    drop_na()
}

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/model/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.

The second option for the surface Cant calculation is based on atmospheric equilibrium assumption. That is calculate dCant from reference year DIC, which are obtained using seacarb with TAlk climatology and reference year pCO2.

3.2.1 pCO₂ climatology

Plots below show the calculated climatological pCO₂ values.

if (params_local$surface_DIC_calculation == "revelle factor") {
  # calculate pCO2 from talk and tco2 climatology
  predictors_surface <- predictors_surface %>%
    mutate(
      pCO2 = carb(
        flag = 15,
        var1 = TAlk * 1e-6,
        var2 = TCO2 * 1e-6,
        S = sal,
        T = temp,
        P = depth / 10,
        Pt = phosphate * 1e-6,
        Sit = silicate * 1e-6,
        k1k2 = "l"
      )$pCO2
    )
}

if (params_local$surface_DIC_calculation == "revelle factor") {
  p_map_climatology(df = predictors_surface,
                    var = "pCO2")
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

if (params_local$surface_DIC_calculation == "revelle factor") {
  p_section_climatology_regular(df = predictors_surface,
                                var = "pCO2",
                                surface = "y")
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

3.2.2 Revelle factor

Plots below show the calculated climatological Revelle factor values.

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

if (params_local$surface_DIC_calculation == "revelle factor") {
  p_map_climatology(df = predictors_surface,
                    var = "rev_fac")
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

if (params_local$surface_DIC_calculation == "revelle factor") {
  p_section_climatology_regular(df = predictors_surface,
                                var = "rev_fac",
                                surface = "y")
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

3.2.3 Cant calculation

# surface cant calculation with revelle factor option
if (params_local$surface_DIC_calculation == "revelle factor") {
  # 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)
}

# surface cant calculation with atmospheric equilibrium option
if (params_local$surface_DIC_calculation == "atmospheric equilibrium") {
  predictors_surface <-
    full_join(predictors_surface,
              co2_atm_tref %>% select(pCO2_tref),
              by = character())
  
  # calculate DIC from TAlk climatology and pCO2 in reference years
  predictors_surface <- predictors_surface %>%
    mutate(
      DIC = carb(
        flag = 24,
        var1 = pCO2_tref,
        var2 = TAlk * 1e-6,
        S = sal,
        T = temp,
        P = depth / 10,
        Pt = phosphate * 1e-6,
        Sit = silicate * 1e-6,
        k1k2 = "l"
      )$DIC * 1e6
    )
  
  # 
  p_map_climatology(df = predictors_surface,
                    var = "DIC")
  
  #
  p_section_climatology_regular(df = predictors_surface,
                                var = "DIC",
                                surface = "y")
  
  predictors_surface <- predictors_surface %>%
    group_by(lon,
             lat,
             depth,
             basin_AIP,
             basin,
             gamma,
             sal,
             temp,
             TCO2,
             TAlk,
             phosphate,
             silicate) %>%
    # hard coded for 3 eras, need to change later
    mutate(DIC_era1 = DIC[2] - DIC[1], DIC_era2 = DIC[3] - DIC[2]) %>%
    ungroup() %>%
    select(-pCO2_tref,-DIC) %>%
    unique()
  
  predictors_surface_era1 <- predictors_surface %>%
    select(-DIC_era2) %>%
    rename(cant = DIC_era1) %>%
    mutate(eras = paste(co2_atm_tref$era[1], co2_atm_tref$era[2], sep = " --> "))
  
  predictors_surface_era2 <- predictors_surface %>%
    select(-DIC_era1) %>%
    rename(cant = DIC_era2) %>%
    mutate(eras = paste(co2_atm_tref$era[2], co2_atm_tref$era[3], sep = " --> "))
  
  cant_surface <-
    full_join(predictors_surface_era1, predictors_surface_era2)
}

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

3.2.4 Control plots

# i_eras <- unique(cant_average$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_map_climatology(df = cant_surface %>% filter(eras == i_eras),
                      var = "cant")  +
      labs(subtitle = paste("era:", i_eras))
  )
  
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
2679490	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
2679490	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

# i_eras <- unique(cant_surface$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_section_climatology_regular(df = cant_surface %>% filter(eras == i_eras),
                                  var = "cant",
                                  surface = "y") +
      labs(subtitle = paste("era:", i_eras))
  )
  
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
2679490	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
2679490	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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(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,
            cant, cant_pos, cant_sd, cant_pos_sd,
            gamma, gamma_sd, gamma_slab)

# i_eras <- unique(cant_average$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_map_climatology(
      df = cant_average %>% filter(eras == i_eras),
      var = "cant_pos",
      subtitle_text = paste("era:", i_eras)
    )
  )
  
}

Version	Author	Date
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
61efb56	Donghe-Zhu	2021-01-25
48f638e	Donghe-Zhu	2021-01-25
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
a2f0d56	Donghe-Zhu	2021-01-23
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
17dee1d	jens-daniel-mueller	2021-01-13
7cdea0c	jens-daniel-mueller	2021-01-06
fa85b93	jens-daniel-mueller	2021-01-06
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
61efb56	Donghe-Zhu	2021-01-25
48f638e	Donghe-Zhu	2021-01-25
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
a2f0d56	Donghe-Zhu	2021-01-23
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
17dee1d	jens-daniel-mueller	2021-01-13
7cdea0c	jens-daniel-mueller	2021-01-06
fa85b93	jens-daniel-mueller	2021-01-06
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

# i_eras <- unique(cant_surface$eras)[2]

for (i_eras in unique(cant_surface$eras)) {
  print(
    p_section_climatology_regular(
      df = cant_average %>% filter(eras == i_eras),
      var = "cant_pos",
      subtitle_text = paste("era:", i_eras)
    )
  )
}

Version	Author	Date
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
61efb56	Donghe-Zhu	2021-01-25
48f638e	Donghe-Zhu	2021-01-25
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
a2f0d56	Donghe-Zhu	2021-01-23
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
17dee1d	jens-daniel-mueller	2021-01-13
fa85b93	jens-daniel-mueller	2021-01-06
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
61efb56	Donghe-Zhu	2021-01-25
48f638e	Donghe-Zhu	2021-01-25
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
a2f0d56	Donghe-Zhu	2021-01-23
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
17dee1d	jens-daniel-mueller	2021-01-13
7cdea0c	jens-daniel-mueller	2021-01-06
fa85b93	jens-daniel-mueller	2021-01-06
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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

cant_average_zonal %>% 
  filter(basin_AIP == "Atlantic",
         eras == "1982-1999 --> 2000-2012") %>% 
  ggplot(aes(lat, depth, z = cant_pos_mean)) +
  geom_contour_fill() +
  scale_y_reverse() +
  scale_fill_viridis_c()

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(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 <- m_cant_inv(cant_average)

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

Version	Author	Date
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
61efb56	Donghe-Zhu	2021-01-25
48f638e	Donghe-Zhu	2021-01-25
c1cec47	Donghe-Zhu	2021-01-25
05ffb0c	Donghe-Zhu	2021-01-25
8b97165	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
a2f0d56	Donghe-Zhu	2021-01-23
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
88eb28f	Donghe-Zhu	2021-01-21
2679490	Donghe-Zhu	2021-01-21
7891955	Donghe-Zhu	2021-01-21
d4cf1cb	Donghe-Zhu	2021-01-21
1f3e5b6	jens-daniel-mueller	2021-01-20
0e7bdf1	jens-daniel-mueller	2021-01-15
4571843	jens-daniel-mueller	2021-01-14
b3564aa	jens-daniel-mueller	2021-01-14
8d032c3	jens-daniel-mueller	2021-01-14
17dee1d	jens-daniel-mueller	2021-01-13
7cdea0c	jens-daniel-mueller	2021-01-06
fa85b93	jens-daniel-mueller	2021-01-06
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
fb8a752	Donghe-Zhu	2020-12-23
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

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_3.2.15  oce_1.2-0       gsw_1.0-5       testthat_3.0.1 
 [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_1.0.2     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               jsonlite_1.7.2           viridisLite_0.3.0       
 [4] here_1.0.1               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_0.3.3.9.1      digest_0.6.27           
[16] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_2.0-0         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.3             
[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_0.1.0           ellipsis_0.3.1           withr_2.3.0             
[34] cli_2.2.0                magrittr_2.0.1           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_0.10.1.2.2
[43] tools_4.0.3              data.table_1.13.6        hms_0.5.3               
[46] lifecycle_0.2.0          munsell_0.5.0            reprex_0.3.0            
[49] isoband_0.2.3            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_0.3.6              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.20

Mapping - Cant

Jens Daniel Müller and Donghe Zhu

27 January, 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 and Donghe Zhu

27 January, 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