Mapping - Cant

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Last updated: 2021-03-03

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Knit directory: emlr_mod_v_XXX/

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Rmd	8f5fa79	Donghe-Zhu	2021-02-08	local rebuild
html	2f095d7	Donghe-Zhu	2021-02-07	Build site.
html	2305044	Donghe-Zhu	2021-02-07	Build site.
html	1fad5f1	Donghe-Zhu	2021-02-07	Build site.
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html	e2ffc14	Donghe-Zhu	2021-02-05	Build site.
html	cd7c52c	Donghe-Zhu	2021-02-04	Build site.
Rmd	c85e2ef	jens-daniel-mueller	2021-02-04	“atmospheric pCO2 increase” approach for surface cant calculation
Rmd	42b8495	Donghe-Zhu	2021-02-03	plots
html	bcf84f4	Donghe-Zhu	2021-02-02	Build site.
html	a518739	Donghe-Zhu	2021-02-01	Build site.
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html	b50fe52	Donghe-Zhu	2021-01-31	Build site.
Rmd	a543fb2	Donghe-Zhu	2021-01-31	local rebuild
html	ac99ae5	jens-daniel-mueller	2021-01-29	code review
html	b5bdcaf	Donghe-Zhu	2021-01-29	Build site.
Rmd	b234505	Donghe-Zhu	2021-01-29	MLR approach across all depth
html	442010d	Donghe-Zhu	2021-01-29	Build site.
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Rmd	17a7d1a	Donghe-Zhu	2021-01-25	MLR = SO
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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
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Rmd	d44f36f	Donghe-Zhu	2021-01-13	reorder analysis final
html	17dee1d	jens-daniel-mueller	2021-01-13	Build site.
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html	7cdea0c	jens-daniel-mueller	2021-01-06	Build site.
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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$MLR_basins == "SO" || params_local$MLR_basins == "5") {
  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") {

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

cant_M <- cant_M %>%
  rename(cant_M = cant, cant_pos_M = cant_pos)

cant_join <- left_join(cant, cant_M) %>%
  mutate(cant_offset = cant - cant_M,
         cant_pos_offset = cant_pos - cant_pos_M)

  for (i_eras in unique(cant_join$eras)) {
    #i_eras <- unique(cant_join$eras)[1]
    cant_eras <- cant_join %>%
      filter(eras == i_eras)
    
    for (i_lon in params_global$longitude_sections_regular) {
      #i_lon <- params_global$longitude_sections_regular[1]
      cant_eras_lon <- cant_eras %>%
        filter(lon == i_lon)
      
      # limits = max(abs(cant_eras_lon$cant_offset)) * c(-1, 1)
      
      cant_eras_lon %>%
        ggplot(aes(lat, depth, z = cant_offset)) +
        stat_summary_2d(
          fun = "mean",
          na.rm = TRUE,
          bins = 20,
          col = "grey"
        ) +
        scale_fill_divergent(name = "Cant offset") +
        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
      )
      
    }
  }

  rm(cant_join, cant_M, cant_eras, cant_eras_lon, i_eras, i_lon)

}

3.2 Surface water

As outlined in Gruber et al. (2019), a 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.

The first approach to calculate equilibrium Cant changes applies eq 10.2.16 from OBD:

\(\Delta\)_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$depth_min != 0) {
  if (params_local$surface_DIC_calculation == "revelle factor" |
      params_local$surface_DIC_calculation == "atmospheric pCO2 increase") {
    # calculate pCO2 from talk and tco2 climatology
    predictors_surface <- predictors_surface %>%
      mutate(
        pCO2 = carb(
          flag = 15,
          var1 = TAlk * 1e-6,
          var2 = TCO2 * 1e-6,
          S = sal,
          T = temp,
          P = depth / 10,
          Pt = phosphate * 1e-6,
          Sit = silicate * 1e-6,
          k1k2 = "l"
        )$pCO2
      )
  }
}

if (params_local$depth_min != 0) {
  if (params_local$surface_DIC_calculation == "revelle factor" |
      params_local$surface_DIC_calculation == "atmospheric pCO2 increase") {
    p_map_climatology(df = predictors_surface,
                      var = "pCO2")
  }
}  

Past versions of pCO2_climatology_map-1.png

Version	Author	Date
287123c	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
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$depth_min != 0) {
  if (params_local$surface_DIC_calculation == "revelle factor") {
    p_section_climatology_regular(df = predictors_surface,
                                  var = "pCO2",
                                  surface = "y")
  }
}

3.2.2 Revelle factor

Plots below show the calculated climatological Revelle factor values.

if (params_local$depth_min != 0) {
  if (params_local$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$depth_min != 0) {
  if (params_local$surface_DIC_calculation == "revelle factor") {
    p_map_climatology(df = predictors_surface,
                      var = "rev_fac")
  }
}

if (params_local$depth_min != 0) {
  if (params_local$surface_DIC_calculation == "revelle factor") {
    p_section_climatology_regular(df = predictors_surface,
                                  var = "rev_fac",
                                  surface = "y")
  }
}

3.2.3 Cant calculation

if (params_local$depth_min != 0) {
  
  # 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)
  }
  
  # surface cant calculation with atmospheric pCO2 increase
  if (params_local$surface_DIC_calculation == "atmospheric pCO2 increase") {
    
    # 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())
    
    cant_surface <- cant_surface %>% 
      mutate(pCO2_final = pCO2 + d_pCO2_tref)

    
    # calculate DIC from TAlk climatology and pCO2 in reference years
    cant_surface <- cant_surface %>% 
      mutate(
        DIC_final = carb(
          flag = 24,
          var1 = pCO2_final,
          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 = cant_surface,
                      var = "DIC_final")
    
    #
    p_section_climatology_regular(df = cant_surface,
                                  var = "DIC_final",
                                  surface = "y")
    
    cant_surface <- cant_surface %>% 
      mutate(cant = DIC_final - TCO2)
    
  }
  
  # calculate positive cant
  cant_surface <- cant_surface %>%
    mutate(cant_pos = if_else(cant < 0, 0, cant))
}

3.2.4 Control plots

if (params_local$depth_min != 0) {
  for (i_eras in unique(cant_surface$eras)) {
    print(
      p_map_climatology(df = cant_surface %>% filter(eras == i_eras),
                        var = "cant")  +
        labs(subtitle = paste("era:", i_eras))
    )
  }
}

Past versions of cant_surface_climatology_map-1.png

Version	Author	Date
287123c	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
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

Past versions of cant_surface_climatology_map-2.png

Version	Author	Date
287123c	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
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

if (params_local$depth_min != 0) {
  for (i_eras in unique(cant_surface$eras)) {
    print(
      p_section_climatology_regular(
        df = cant_surface %>% filter(eras == i_eras),
        var = "cant",
        surface = "y"
      ) +
        labs(subtitle = paste("era:", i_eras))
    )
  }
}

Past versions of cant_surface_climatology_sections-1.png

Version	Author	Date
287123c	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
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

Past versions of cant_surface_climatology_sections-2.png

Version	Author	Date
287123c	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
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 5 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)

if (params_local$depth_min != 0) {
  for (i_eras in unique(cant_surface$eras)) {
    print(
      p_map_climatology(
        df = cant_average %>% filter(eras == i_eras),
        var = "cant_pos",
        subtitle_text = paste("era:", i_eras)
      )
    )
  }
}

Past versions of cant_deep_climatology_map-1.png

Version	Author	Date
89c3e58	Donghe-Zhu	2021-03-03
c407a50	Donghe-Zhu	2021-03-03
c911669	Donghe-Zhu	2021-03-03
b71c719	Donghe-Zhu	2021-03-01
13666ca	Donghe-Zhu	2021-03-01
c6e60fe	Donghe-Zhu	2021-03-01
7a388f7	Donghe-Zhu	2021-03-01
799e913	Donghe-Zhu	2021-03-01
66ff99f	Donghe-Zhu	2021-03-01
ac9bb7a	Donghe-Zhu	2021-02-28
efdc047	Donghe-Zhu	2021-02-28
e9a7418	Donghe-Zhu	2021-02-28
e152917	Donghe-Zhu	2021-02-28
287123c	Donghe-Zhu	2021-02-27
54d5b5b	Donghe-Zhu	2021-02-27
330f064	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
5937141	Donghe-Zhu	2021-02-27
4414bbf	Donghe-Zhu	2021-02-27
a265efb	Donghe-Zhu	2021-02-27
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
354c224	Donghe-Zhu	2021-02-24
1a0a88a	Donghe-Zhu	2021-02-24
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
401eab3	Donghe-Zhu	2021-02-15
e3bba84	Donghe-Zhu	2021-02-15
5dce4b1	Donghe-Zhu	2021-02-15
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
1fad5f1	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
e2ffc14	Donghe-Zhu	2021-02-05
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
a518739	Donghe-Zhu	2021-02-01
61666de	Donghe-Zhu	2021-01-31
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
59e006e	Donghe-Zhu	2021-01-31
a1c8f87	Donghe-Zhu	2021-01-31
ae5c18f	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
af8788e	Donghe-Zhu	2021-01-29
21c91c9	Donghe-Zhu	2021-01-29
eded038	Donghe-Zhu	2021-01-29
541d4dd	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
ceed31b	Donghe-Zhu	2021-01-27
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

Past versions of cant_deep_climatology_map-2.png

Version	Author	Date
89c3e58	Donghe-Zhu	2021-03-03
c407a50	Donghe-Zhu	2021-03-03
c911669	Donghe-Zhu	2021-03-03
b71c719	Donghe-Zhu	2021-03-01
13666ca	Donghe-Zhu	2021-03-01
c6e60fe	Donghe-Zhu	2021-03-01
7a388f7	Donghe-Zhu	2021-03-01
799e913	Donghe-Zhu	2021-03-01
66ff99f	Donghe-Zhu	2021-03-01
ac9bb7a	Donghe-Zhu	2021-02-28
efdc047	Donghe-Zhu	2021-02-28
e9a7418	Donghe-Zhu	2021-02-28
e152917	Donghe-Zhu	2021-02-28
287123c	Donghe-Zhu	2021-02-27
54d5b5b	Donghe-Zhu	2021-02-27
330f064	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
5937141	Donghe-Zhu	2021-02-27
4414bbf	Donghe-Zhu	2021-02-27
a265efb	Donghe-Zhu	2021-02-27
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
354c224	Donghe-Zhu	2021-02-24
1a0a88a	Donghe-Zhu	2021-02-24
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
401eab3	Donghe-Zhu	2021-02-15
e3bba84	Donghe-Zhu	2021-02-15
5dce4b1	Donghe-Zhu	2021-02-15
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
1fad5f1	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
e2ffc14	Donghe-Zhu	2021-02-05
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
a518739	Donghe-Zhu	2021-02-01
61666de	Donghe-Zhu	2021-01-31
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
59e006e	Donghe-Zhu	2021-01-31
a1c8f87	Donghe-Zhu	2021-01-31
ae5c18f	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
af8788e	Donghe-Zhu	2021-01-29
21c91c9	Donghe-Zhu	2021-01-29
eded038	Donghe-Zhu	2021-01-29
541d4dd	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
ceed31b	Donghe-Zhu	2021-01-27
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

if (params_local$depth_min != 0) {
  for (i_eras in unique(cant_surface$eras)) {
    print(
      p_section_climatology_regular(
        df = cant_average %>% filter(eras == i_eras),
        var = "cant_pos",
        subtitle_text = paste("era:", i_eras)
      )
    )
  }
}

Past versions of cant_deep_climatology_sections-1.png

Version	Author	Date
89c3e58	Donghe-Zhu	2021-03-03
c407a50	Donghe-Zhu	2021-03-03
c911669	Donghe-Zhu	2021-03-03
b71c719	Donghe-Zhu	2021-03-01
13666ca	Donghe-Zhu	2021-03-01
c6e60fe	Donghe-Zhu	2021-03-01
7a388f7	Donghe-Zhu	2021-03-01
799e913	Donghe-Zhu	2021-03-01
66ff99f	Donghe-Zhu	2021-03-01
ac9bb7a	Donghe-Zhu	2021-02-28
efdc047	Donghe-Zhu	2021-02-28
e9a7418	Donghe-Zhu	2021-02-28
e152917	Donghe-Zhu	2021-02-28
287123c	Donghe-Zhu	2021-02-27
54d5b5b	Donghe-Zhu	2021-02-27
330f064	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
5937141	Donghe-Zhu	2021-02-27
4414bbf	Donghe-Zhu	2021-02-27
a265efb	Donghe-Zhu	2021-02-27
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
354c224	Donghe-Zhu	2021-02-24
1a0a88a	Donghe-Zhu	2021-02-24
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
401eab3	Donghe-Zhu	2021-02-15
e3bba84	Donghe-Zhu	2021-02-15
5dce4b1	Donghe-Zhu	2021-02-15
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
1fad5f1	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
e2ffc14	Donghe-Zhu	2021-02-05
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
a518739	Donghe-Zhu	2021-02-01
61666de	Donghe-Zhu	2021-01-31
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
59e006e	Donghe-Zhu	2021-01-31
a1c8f87	Donghe-Zhu	2021-01-31
ae5c18f	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
af8788e	Donghe-Zhu	2021-01-29
21c91c9	Donghe-Zhu	2021-01-29
eded038	Donghe-Zhu	2021-01-29
541d4dd	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
ceed31b	Donghe-Zhu	2021-01-27
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

Past versions of cant_deep_climatology_sections-2.png

Version	Author	Date
89c3e58	Donghe-Zhu	2021-03-03
c407a50	Donghe-Zhu	2021-03-03
c911669	Donghe-Zhu	2021-03-03
b71c719	Donghe-Zhu	2021-03-01
13666ca	Donghe-Zhu	2021-03-01
c6e60fe	Donghe-Zhu	2021-03-01
7a388f7	Donghe-Zhu	2021-03-01
799e913	Donghe-Zhu	2021-03-01
66ff99f	Donghe-Zhu	2021-03-01
ac9bb7a	Donghe-Zhu	2021-02-28
efdc047	Donghe-Zhu	2021-02-28
e9a7418	Donghe-Zhu	2021-02-28
e152917	Donghe-Zhu	2021-02-28
287123c	Donghe-Zhu	2021-02-27
54d5b5b	Donghe-Zhu	2021-02-27
330f064	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
5937141	Donghe-Zhu	2021-02-27
4414bbf	Donghe-Zhu	2021-02-27
a265efb	Donghe-Zhu	2021-02-27
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
354c224	Donghe-Zhu	2021-02-24
1a0a88a	Donghe-Zhu	2021-02-24
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
e3bba84	Donghe-Zhu	2021-02-15
5dce4b1	Donghe-Zhu	2021-02-15
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
1fad5f1	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
e2ffc14	Donghe-Zhu	2021-02-05
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
a518739	Donghe-Zhu	2021-02-01
61666de	Donghe-Zhu	2021-01-31
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
59e006e	Donghe-Zhu	2021-01-31
a1c8f87	Donghe-Zhu	2021-01-31
ae5c18f	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
af8788e	Donghe-Zhu	2021-01-29
21c91c9	Donghe-Zhu	2021-01-29
eded038	Donghe-Zhu	2021-01-29
541d4dd	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
ceed31b	Donghe-Zhu	2021-01-27
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.

if (params_local$depth_min != 0) {
  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

if (params_local$depth_min != 0) {
  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:

1. Convert Cant concentrations to volumetric units
2. Multiply layer thickness with volumetric Cant concentration to get a layer inventory
3. 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)

Past versions of cant_inventories-1.png

Version	Author	Date
89c3e58	Donghe-Zhu	2021-03-03
c407a50	Donghe-Zhu	2021-03-03
c911669	Donghe-Zhu	2021-03-03
b71c719	Donghe-Zhu	2021-03-01
13666ca	Donghe-Zhu	2021-03-01
c6e60fe	Donghe-Zhu	2021-03-01
7a388f7	Donghe-Zhu	2021-03-01
799e913	Donghe-Zhu	2021-03-01
66ff99f	Donghe-Zhu	2021-03-01
ac9bb7a	Donghe-Zhu	2021-02-28
efdc047	Donghe-Zhu	2021-02-28
e9a7418	Donghe-Zhu	2021-02-28
e152917	Donghe-Zhu	2021-02-28
287123c	Donghe-Zhu	2021-02-27
54d5b5b	Donghe-Zhu	2021-02-27
330f064	Donghe-Zhu	2021-02-27
adbc9bc	Donghe-Zhu	2021-02-27
5937141	Donghe-Zhu	2021-02-27
4414bbf	Donghe-Zhu	2021-02-27
a265efb	Donghe-Zhu	2021-02-27
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
354c224	Donghe-Zhu	2021-02-24
1a0a88a	Donghe-Zhu	2021-02-24
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
401eab3	Donghe-Zhu	2021-02-15
e3bba84	Donghe-Zhu	2021-02-15
5dce4b1	Donghe-Zhu	2021-02-15
4469a0c	Donghe-Zhu	2021-02-13
5ae6a69	Donghe-Zhu	2021-02-10
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
c011832	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
2f095d7	Donghe-Zhu	2021-02-07
2305044	Donghe-Zhu	2021-02-07
1fad5f1	Donghe-Zhu	2021-02-07
ca03c39	Donghe-Zhu	2021-02-07
e2ffc14	Donghe-Zhu	2021-02-05
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
a518739	Donghe-Zhu	2021-02-01
61666de	Donghe-Zhu	2021-01-31
865b582	Donghe-Zhu	2021-01-31
3e68089	Donghe-Zhu	2021-01-31
ecf335c	Donghe-Zhu	2021-01-31
a618965	Donghe-Zhu	2021-01-31
59e006e	Donghe-Zhu	2021-01-31
a1c8f87	Donghe-Zhu	2021-01-31
ae5c18f	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
442010d	Donghe-Zhu	2021-01-29
372adf5	Donghe-Zhu	2021-01-29
af8788e	Donghe-Zhu	2021-01-29
21c91c9	Donghe-Zhu	2021-01-29
eded038	Donghe-Zhu	2021-01-29
541d4dd	Donghe-Zhu	2021-01-29
6a75576	Donghe-Zhu	2021-01-28
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
ceed31b	Donghe-Zhu	2021-01-27
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 = ""))

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] 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_0.7.5             
[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_0.1.0           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