Last updated: 2020-07-23

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Rmd	1ce10e7	jens-daniel-mueller	2020-07-23	read full GLODAP Cant data set rather than joining again
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Rmd	ffbc2a1	jens-daniel-mueller	2020-07-13	added Cstar calculation
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Rmd	e6a2ade	jens-daniel-mueller	2020-07-13	added Cstar calculation

library(tidyverse)
library(lubridate)
library(patchwork)
library(broom)
library(GGally)

1 Required data

Required are:

clean version of GLODAPGLODAPv2.2020
C_ant from GLODAPv2_2016b_MappedClimatologies
annual mean atmospheric pCO₂

GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                              "GLODAPv2.2020_clean.csv"))

Cant_clim <- read_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
                                 "Cant.csv"))

co2_atm <- read_csv(here::here("data/pCO2_atmosphere/_summarized_data_files",
                               "co2_atm.csv"))

2 C*

2.1 Stoichiometric ratios

rCP <- 117
rNP <- 16

The stoichiometric nutrient ratios for the production and mineralization of organic matter were set to:

C/P: 117
N/P: 16

2.2 Calculation

GLODAP <- GLODAP %>% 
  mutate(rCP_phosphate = - rCP * phosphate,
         talk_05 = - 0.5 * talk,
         rNP_phosphate_05 = - 0.5 * rNP * phosphate,
         Cstar = tco2 + rCP_phosphate + talk_05 + rNP_phosphate_05)

3 Reference year adjustment

The scaling factor for the reference year adjustment is an apriori estiamte of Cant at a given location and depth. Here, Cant from the GLODAP mapped Climatology was used.

Note that eq. 6 in Clement and Gruber (2018) misses pCO2 pre-industrial in the denominator. Here we use the equation published in Gruber et al. (2019).

3.1 Merge GLODAP data set with…

3.1.1 … Cant

Cant_clim <- Cant_clim %>% 
  drop_na()

# GLODAP_Cant_full <- full_join(GLODAP, Cant_clim)
# 
# GLODAP_Cant_full %>% write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
#                                           "GLODAP_Cant_full.csv"))

GLODAP_Cant_full <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                          "GLODAP_Cant_full.csv"))

The mapped Cant product was merged with GLODAP observation by:

using an identical 1x1° horizontal grid
linear interpolation of Cant from standard to sampling depth

GLODAP_Cant_observations_available <- GLODAP_Cant_full %>% 
  group_by(lat, lon) %>% 
  mutate(n_GLODAP = sum(!is.na(Cstar))) %>% 
  ungroup() %>% 
  filter(n_GLODAP > 0) %>% 
  select(-n_GLODAP)

rm(GLODAP_Cant_full)

GLODAP_Cant_observations_available <- GLODAP_Cant_observations_available %>% 
  group_by(lat, lon) %>% 
  arrange(depth) %>% 
  mutate(Cant_int = approxfun(depth, Cant, rule = 2)(depth)) %>% 
  ungroup()

ggplot()+
    geom_path(data = GLODAP_Cant_observations_available %>% 
              filter(lat == 48.5, lon == 165.5, !is.na(Cant)) %>% 
              arrange(depth),
            aes(Cant, depth, col="mapped"))+
  geom_point(data = GLODAP_Cant_observations_available %>% 
              filter(lat == 48.5, lon == 165.5, !is.na(Cant)) %>% 
              arrange(depth),
            aes(Cant, depth, col="mapped"))+
  geom_point(data = GLODAP_Cant_observations_available %>% 
              filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
            aes(Cant_int, depth, col="interpolated"))+
  scale_y_reverse()+
  scale_color_brewer(palette = "Dark2", name="")+
  labs(title = "Cant interpolation to sampling depth - example profile")

GLODAP <- GLODAP_Cant_observations_available %>% 
  filter(!is.na(Cstar)) %>% 
  mutate(Cant = Cant_int) %>% 
  select(-Cant_int)

rm(GLODAP_Cant_observations_available, Cant_clim)

3.1.2 … Atmospheric pCO2

GLODAP <- left_join(GLODAP, co2_atm)

3.2 Calculate adjustment

GLODAP <- GLODAP %>% 
  group_by(era) %>% 
  mutate(tref = median(year)) %>% 
  ungroup()

tref <- GLODAP %>% 
  group_by(era) %>% 
  summarise(year = median(year)) %>% 
  ungroup()

co2_atm_tref <- right_join(co2_atm, tref) %>% 
  select(-year) %>% 
  rename(pCO2_tref = pCO2)

GLODAP <- full_join(GLODAP, co2_atm_tref)

rm(co2_atm, co2_atm_tref, tref)

GLODAP <- GLODAP %>% 
  mutate(Cstar_tref_delta = 
           ( (pCO2 - pCO2_tref) / (pCO2_tref - 280) ) * Cant,
         Cstar_tref = Cstar - Cstar_tref_delta)

3.3 Control plots

GLODAP %>% 
  ggplot(aes(Cstar_tref_delta))+
  geom_histogram()

GLODAP %>% 
  sample_n(10000) %>% 
  ggplot(aes(year - tref, Cstar_tref_delta, col=Cant))+
  geom_point()+
  scale_color_viridis_c()+
  labs(title = "random subsample 1e4")

4 Selected section plots

Selected sections are plotted to demonstrate the magnitude of various parameters and corrections relevant to C*.

cruises_meridional <- c("1041")

# cruises_meridional <- c("1041","1042", "260",
#                         "2011", "393", "1031", "394", "395",
#                         "1088", "983")

# cruises_zonal <- c()

GLODAP_cruise <- GLODAP %>% 
  filter(cruise %in% cruises_meridional)

mapWorld <- borders("world", colour="gray60", fill="gray60")

#map <- 
GLODAP_cruise %>%
  arrange(date) %>% 
  ggplot(aes(lon, lat))+
  mapWorld+
  geom_path()+
  geom_point(aes(col=date))+
  coord_quickmap(expand = FALSE)+
  scale_color_viridis_c(trans = "date")+
  labs(title = paste("Cruise year:", mean(GLODAP_cruise$year)))

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(lat, depth))+
  scale_y_reverse()+
  scale_color_viridis_c()

#lat_tco2 <- 
lat_section+
  geom_point(aes(col=tco2))

#lat_talk <- 
lat_section+
  geom_point(aes(col=talk))

#lat_phosphate <- 
lat_section+
  geom_point(aes(col=phosphate))

#lat_rCP_phosphate <- 
lat_section+
  geom_point(aes(col=rCP_phosphate))

#lat_talk_05 <- 
lat_section+
  geom_point(aes(col=talk_05))

#lat_rNP_phosphate_05 <- 
lat_section+
  geom_point(aes(col=rNP_phosphate_05))

#lat_Cstar <- 
lat_section+
  geom_point(aes(col=Cstar))

lat_section+
  geom_point(aes(col=Cant))

#lat_Cstar_tref <- 
lat_section+
  geom_point(aes(col=-Cstar_tref_delta))

# map / lat_tco2 / lat_talk / lat_phosphate / lat_rCP_phosphate /lat_talk_05 /lat_rNP_phosphate_05 / lat_Cstar / lat_Cstar_tref
# 
rm(mapWorld, lat_section, GLODAP_cruise)

5 MLR fitting

5.1 Isoneutral slabs

slabs_Atl <- c(
-Inf,
26.00,
26.50,
26.75,
27.00,
27.25,
27.50,
27.75,
27.85,
27.95,
28.05,
28.10,
28.15,
28.20,
Inf)

slabs_Ind_Pac <- c(
-Inf,
26.00,
26.50,
26.75,
27.00,
27.25,
27.50,
27.75,
27.85,
27.95,
28.05,
28.10,
Inf)

The following boundaries for isoneutral slabs were defined:

Atlantic: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1, 28.15, 28.2,
Indo-Pacific: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1,

GLODAP_Atl <- GLODAP %>% 
  filter(basin == "Atlantic") %>% 
  mutate(gamma_slab = cut(gamma, slabs_Atl))

GLODAP_Ind_Pac <- GLODAP %>% 
  filter(basin == "Indo-Pacific") %>% 
  mutate(gamma_slab = cut(gamma, slabs_Ind_Pac))

GLODAP <- bind_rows(GLODAP_Atl, GLODAP_Ind_Pac)
rm(GLODAP_Atl, GLODAP_Ind_Pac)

GLODAP_cruise <- GLODAP %>% 
  filter(cruise %in% cruises_meridional)

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(lat, depth))+
  scale_y_reverse()

lat_section+
  geom_point(aes(col=gamma))+
  scale_color_viridis_c()

lat_section+
  geom_point(aes(col=gamma_slab))+
  scale_color_viridis_d()

# GLODAP_cruise %>%
#   ggplot(aes(lat, depth, z=tco2))+
#   geom_contour_fill(na.fill = TRUE)+
#   scale_y_reverse()

5.2 PO₄* calculation

GLODAP <- GLODAP %>% 
  mutate(phosphate_star = phosphate - 16*nitrate + 29)

5.3 Predictor correlation

GLODAP %>% 
  sample_n(1e3) %>% 
  ggpairs(columns = c("Cstar",
                      "salinity",
                      "temperature",
                      "aou",
                      "oxygen",
                      "silicate",
                      "phosphate",
                      "phosphate_star"),
          ggplot2::aes(col = gamma_slab, fill = gamma_slab, alpha = 0.01))+
      scale_fill_viridis_d()+
      scale_color_viridis_d()+
      labs(title = paste("Basin: all | era: all | subsample size: 10^3"))

for (i_basin in unique(GLODAP$basin)) {
  for (i_era in unique(GLODAP$era)) {

# i_basin <- unique(GLODAP$basin)[1]
# i_era   <- unique(GLODAP$era)[1]
print(i_basin)
print(i_era)

p <- GLODAP %>% 
  filter(basin == i_basin, era == i_era) %>% 
  sample_n(1e3) %>% 
  ggpairs(columns = c("salinity","temperature", "aou", "oxygen", "silicate", "phosphate", "phosphate_star"),
          ggplot2::aes(col = gamma_slab, fill = gamma_slab, alpha = 0.01))+
      scale_fill_viridis_d()+
      scale_color_viridis_d()+
      labs(title = paste("Basin:", i_basin, "| era:", i_era, "| subsample size: 10^3"))+
      theme(text = element_text(size=20))
  
png(here::here("output/figure/eMLR/predictor_correlation",
               paste("predictor_correlation", i_basin, i_era, ".png", sep = "_")),
    width = 20, height = 20, units = "in", res = 300)

print(p)

dev.off()

  }
}

MLRs <- GLODAP %>%
  nest(data = -c(basin, era, gamma_slab)) %>% 
  mutate(
    fit = map(data, ~ lm(Cstar ~ salinity + temperature + aou + oxygen + silicate + phosphate + phosphate_star,
                         data = .x)),
    tidied = map(fit, tidy),
    glanced = map(fit, glance),
    augmented = map(fit, augment)
  )

MLRs_tidied <- MLRs %>% 
  unnest(tidied)

MLRs_tidied

# A tibble: 624 x 12
   era   basin gamma_slab data  fit   term  estimate std.error statistic
   <chr> <chr> <fct>      <lis> <lis> <chr>    <dbl>     <dbl>     <dbl>
 1 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  (Int~  1.05e+3  269.         3.91 
 2 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  sali~  1.17e+1    2.87       4.08 
 3 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  temp~ -1.52e+1    3.66      -4.16 
 4 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  aou   -6.83e-1    0.729     -0.938
 5 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  oxyg~ -1.40e+0    0.718     -1.96 
 6 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  sili~ -2.27e+0    0.285     -7.95 
 7 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  phos~ -1.05e+2    5.49     -19.2  
 8 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  phos~  3.07e-3    0.0245     0.125
 9 JGOF~ Atla~ (27.25,27~ <tib~ <lm>  (Int~  1.66e+3  103.        16.1  
10 JGOF~ Atla~ (27.25,27~ <tib~ <lm>  sali~  9.62e+0    1.74       5.52 
# ... with 614 more rows, and 3 more variables: p.value <dbl>, glanced <list>,
#   augmented <list>

MLRs_tidied <- MLRs_tidied %>% 
  select(era, basin, gamma_slab, term, estimate, p.value)

MLRs_tidied_wide <- MLRs_tidied %>% 
  select(-p.value) %>% 
  pivot_wider(names_from = era, values_from = estimate, names_prefix = "coeff_")


MLRs_tidied %>% 
  ggplot(aes(p.value, term, col=gamma_slab))+
  geom_point()+
  facet_grid(basin~era)

MLRs_tidied %>% 
  filter(p.value < 0.05) %>% 
  ggplot(aes(p.value, term, col=gamma_slab))+
  geom_point()+
  facet_grid(basin~era)

MLRs_tidied %>% 
  ggplot(aes(p.value, term))+
  geom_boxplot()+
  facet_grid(basin~era)

MLRs %>% 
  unnest(glanced)

# A tibble: 78 x 19
   era   basin gamma_slab data  fit   tidied r.squared adj.r.squared sigma
   <chr> <chr> <fct>      <lis> <lis> <list>     <dbl>         <dbl> <dbl>
 1 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~     0.924         0.924  5.17
 2 JGOF~ Atla~ (27.25,27~ <tib~ <lm>  <tibb~     0.986         0.986  4.67
 3 JGOF~ Atla~ (27.5,27.~ <tib~ <lm>  <tibb~     0.988         0.988  4.17
 4 JGOF~ Atla~ (26.75,27] <tib~ <lm>  <tibb~     0.950         0.950  4.69
 5 JGOF~ Atla~ (26,26.5]  <tib~ <lm>  <tibb~     0.896         0.894  5.38
 6 JGOF~ Atla~ (-Inf,26]  <tib~ <lm>  <tibb~     0.768         0.743  7.46
 7 JGOF~ Atla~ (28.1,28.~ <tib~ <lm>  <tibb~     0.963         0.963  3.34
 8 JGOF~ Atla~ (27,27.25] <tib~ <lm>  <tibb~     0.975         0.975  4.91
 9 GO-S~ Atla~ (27.75,27~ <tib~ <lm>  <tibb~     0.984         0.983  4.34
10 GO-S~ Atla~ (27,27.25] <tib~ <lm>  <tibb~     0.977         0.977  4.91
# ... with 68 more rows, and 10 more variables: statistic <dbl>, p.value <dbl>,
#   df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#   df.residual <int>, nobs <int>, augmented <list>

MLRs %>% 
  unnest(augmented)

# A tibble: 177,773 x 21
   era   basin gamma_slab data  fit   tidied glanced Cstar salinity temperature
   <chr> <chr> <fct>      <lis> <lis> <list> <list>  <dbl>    <dbl>       <dbl>
 1 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  859.     36.6        18.1
 2 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  844.     35.5        14.6
 3 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  834.     35.4        13.9
 4 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  843.     35.3        13.0
 5 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  838.     35.3        13.1
 6 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  877.     36.1        16.5
 7 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  868.     36.1        16.4
 8 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  868.     36.1        15.7
 9 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  864.     36.0        15.5
10 JGOF~ Atla~ (26.5,26.~ <tib~ <lm>  <tibb~ <tibbl~  872.     36.1        16.2
# ... with 177,763 more rows, and 11 more variables: aou <dbl>, oxygen <dbl>,
#   silicate <dbl>, phosphate <dbl>, phosphate_star <dbl>, .fitted <dbl>,
#   .resid <dbl>, .std.resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>

temperature
salinity
phosphate
silicate
phosphate_star = phosphate + (oxygen / 170) - 1.95
oxygen
aou

basins <- c("Atlantic", "Indo_Pacific")
slabs <- c("")

for (i_basin in basins) {
  for (i_slab in slabs) {
    
    
  }
  
}

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] GGally_2.0.0    broom_0.7.0     patchwork_1.0.1 lubridate_1.7.9
 [5] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.0     purrr_0.3.4    
 [9] readr_1.3.1     tidyr_1.1.0     tibble_3.0.3    ggplot2_3.3.2  
[13] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] RColorBrewer_1.1-2 jsonlite_1.7.0     rstudioapi_0.11    generics_0.0.2    
 [5] magrittr_1.5       farver_2.0.3       gtable_0.3.0       rmarkdown_2.3     
 [9] vctrs_0.3.1        fs_1.4.2           hms_0.5.3          utf8_1.1.4        
[13] xml2_1.3.2         pillar_1.4.6       htmltools_0.5.0    haven_2.3.1       
[17] later_1.1.0.1      cellranger_1.1.0   tidyselect_1.1.0   plyr_1.8.6        
[21] knitr_1.29         git2r_0.27.1       whisker_0.4        lifecycle_0.2.0   
[25] pkgconfig_2.0.3    R6_2.4.1           digest_0.6.25      reshape_0.8.8     
[29] xfun_0.15          colorspace_1.4-1   rprojroot_1.3-2    stringi_1.4.6     
[33] yaml_2.2.1         evaluate_0.14      labeling_0.3       fansi_0.4.1       
[37] httr_1.4.1         compiler_3.6.3     here_0.1           cli_2.0.2         
[41] withr_2.2.0        backports_1.1.5    munsell_0.5.0      DBI_1.1.0         
[45] modelr_0.1.8       Rcpp_1.0.5         readxl_1.3.1       maps_3.3.0        
[49] dbplyr_1.4.4       ellipsis_0.3.1     assertthat_0.2.1   blob_1.2.1        
[53] tools_3.6.3        reprex_0.3.0       viridisLite_0.3.0  httpuv_1.5.4      
[57] scales_1.1.1       crayon_1.3.4       glue_1.4.1         rlang_0.4.7       
[61] rvest_0.3.5        promises_1.1.1     grid_3.6.3

Data base

Jens Daniel Müller

23 July, 2020

1 Required data

2 C*

2.1 Stoichiometric ratios

2.2 Calculation

3 Reference year adjustment

3.1 Merge GLODAP data set with…

3.1.1 … Cant

3.1.2 … Atmospheric pCO2

3.2 Calculate adjustment

3.3 Control plots

4 Selected section plots

5 MLR fitting

5.1 Isoneutral slabs

5.2 PO₄* calculation

5.3 Predictor correlation

Data base

Jens Daniel Müller

23 July, 2020

1 Required data

2 C*

2.1 Stoichiometric ratios

2.2 Calculation

3 Reference year adjustment

3.1 Merge GLODAP data set with…

3.1.1 … Cant

3.1.2 … Atmospheric pCO2

3.2 Calculate adjustment

3.3 Control plots

4 Selected section plots

5 MLR fitting

5.1 Isoneutral slabs

5.2 PO4* calculation

5.3 Predictor correlation

5.2 PO₄* calculation