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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.
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html	1f3e5b6	jens-daniel-mueller	2021-01-20	Build site.
Rmd	d76b436	Donghe-Zhu	2021-01-19	model quality check
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Rmd	ac37e34	Donghe-Zhu	2021-01-11	add climate and subsetting condition
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Rmd	8e8abf5	Jens Müller	2020-12-18	Initial commit

1 Required data

Required are:

Synthetic cmorized model subsetting data based on preprocessed GLODAP data
- cleaned data file
Cmorized annual cant field for three reference year
Cmorized annual mean atmospheric pCO₂

if (params_local$subsetting == "GLODAP") {
  
GLODAP <-
  read_csv(paste(
    path_version_data,
    "GLODAPv2.2020_clean_GLODAP.csv",
    sep = ""
  ))

}

if (params_local$subsetting == "random") {
  
GLODAP <-
  read_csv(paste(
    path_version_data,
    "GLODAPv2.2020_clean_random.csv",
    sep = ""
  ))

}

2 Calculation of tref

Calculate the reference year for each era and store it as csv file for further selection of corresponding Cant fields.

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

# write file
tref  %>%  write_csv(paste(path_version_data,
                           "tref.csv",
                           sep = ""))

cant_tref_1 <-
  read_csv(paste(
    path_preprocessing,
    "cant_annual_field_", params_local$model_runs, "/cant_",
    unique(tref$year[1]),
    ".csv",
    sep = ""
  ))

cant_tref_2 <-
  read_csv(paste(
    path_preprocessing,
    "cant_annual_field_", params_local$model_runs, "/cant_",
    unique(tref$year[2]),
    ".csv",
    sep = ""
  ))

cant_tref_3 <-
  read_csv(paste(
    path_preprocessing,
    "cant_annual_field_", params_local$model_runs, "/cant_",
    unique(tref$year[3]),
    ".csv",
    sep = ""
  ))

co2_atm <-
  read_csv(paste(path_preprocessing,
                 "co2_atm.csv",
                 sep = ""))

3 PO₄*

3.1 Calculation

The predictor PO₄* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an erroneous equations for PO4* calculation is given in the supplement of Gruber et al (2019), based on nitrate.

Here we use following equation:

print(b_phosphate_star)

function (phosphate, oxygen) 
{
    phosphate_star = phosphate + (oxygen/params_local$rPO) - 
        params_local$rPO_offset
    return(phosphate_star)
}

if ("phosphate_star" %in% params_local$MLR_predictors) {
GLODAP <- GLODAP %>% 
  mutate(phosphate_star = b_phosphate_star(phosphate, oxygen))
}

4 C*

C* serves as a conservative tracer of anthropogenic CO₂ uptake. It is derived from synthetic subsetted DIC by removing the impact of

organic matter formation and respiration
calcification and calcium carbonate dissolution

Contributions of those processes are estimated from phosphate and alkalinity concentrations.

4.1 Stoichiometric ratios

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

C/P: 117
N/P: 16

4.2 Calculation

C* was calculated as:

print(b_cstar)

function (tco2, phosphate, talk) 
{
    cstar = tco2 - (params_local$rCP * phosphate) - 0.5 * (talk - 
        (params_local$rNP * phosphate))
    return(cstar)
}

GLODAP <- GLODAP %>% 
  mutate(rCP_phosphate = -params_local$rCP * phosphate,
         talk_05 = -0.5 * talk,
         rNP_phosphate_05 = -0.5 * params_local$rNP * phosphate,
         cstar = b_cstar(tco2, phosphate, talk))

4.3 Reference year adjustment

To adjust C* values to the reference year of each observation period, we assume a transient steady state change of cant between the time of model subsetting and the reference year. The adjustment requires an approximation of the cant concentration at the reference year. We here use the model-estimated annual cant field for each reference year.

4.3.1 Cant at tref

Read in Cant field for each reference year.

# print reference year table
kable(tref) %>%
  add_header_above() %>%
  kable_styling()

era	year
1982-1999	1991
2000-2012	2006
2013-2019	2016

# join cant with tref
cant_3d <- bind_rows(cant_tref_1, cant_tref_2, cant_tref_3)

cant_3d <- left_join(cant_3d, tref) %>%
  arrange(lon, lat, depth) %>% 
  select(lon, lat, depth, era, cant_total)

rm(cant_tref_1, cant_tref_2, cant_tref_3)

map +
  geom_raster(data = cant_3d %>% filter(depth == 5),
              aes(lon, lat, fill = cant_total)) +
  facet_wrap(~ era, ncol = 1) +
  scale_fill_viridis_c() +
  labs(title = "Surface total Cant concentration")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
fa85b93	jens-daniel-mueller	2021-01-06

4.3.2 Combine GLODAP + Cant

# observations grid per era
GLODAP_obs_grid_era <- GLODAP %>% 
  distinct(lat, lon, era)

# cant data at observations grid
cant_3d_obs <- left_join(
  GLODAP_obs_grid_era,
  cant_3d)

# calculate number of cant data points per grid cell
cant_3d_obs <- cant_3d_obs %>%
  group_by(lon, lat, era) %>% 
  mutate(n = n()) %>% 
  ungroup()

# GLODAP-based model subset with only one Cant value
map +
  geom_bin2d(data = cant_3d_obs,
             aes(lon, lat),
             binwidth = 1) +
  scale_fill_viridis_c() +
  facet_wrap(~ era, ncol = 1) +
  labs(title = "Number of Cant depth levels",
       subtitle = "available per latxlon grid cell")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
fa85b93	jens-daniel-mueller	2021-01-06
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

rm(cant_3d, GLODAP_obs_grid_era)

GLODAP_cant_obs <- full_join(GLODAP, cant_3d_obs)

rm(GLODAP, cant_3d_obs)

# fill number of cant data points per grid cell to all model subsetting
GLODAP_cant_obs <- GLODAP_cant_obs %>%
  group_by(lon, lat, era) %>% 
  fill(n, .direction = "updown") %>% 
  ungroup()

The model-estimated annual cant fields were merged with GLODAP-based synthetic cmorized model subsetting by:

using an identical 1x1° horizontal grid
linear interpolation of C_ant from standard to subsetting depth

# define positive cant values
GLODAP_cant_obs <- GLODAP_cant_obs %>%
  mutate(cant_total_pos = if_else(cant_total < 0, 0, cant_total))

# interpolate cant to subsetting depth
GLODAP_cant_obs_int <- GLODAP_cant_obs %>%
  filter(n > 1) %>% 
  group_by(lat, lon, era) %>%
  arrange(depth) %>%
  mutate(cant_int = approxfun(depth, cant_total_pos, rule = 2)(depth)) %>%
  ungroup()

# set cant for subsetting depth if only one cant available
#GLODAP_cant_obs_set <- GLODAP_cant_obs %>%
#  filter(n == 1) %>%
#  group_by(lat, lon, era) %>%
#  mutate(cant_int = mean(cant_total, na.rm = TRUE)) %>%
#  ungroup()

### bin data sets with interpolated and set cant
GLODAP_cant_obs <- GLODAP_cant_obs_int
rm(GLODAP_cant_obs_int)

if (params_local$subsetting == "GLODAP") {
  ggplot() +
    geom_path(
      data = GLODAP_cant_obs %>%
        filter(lat == 48.5, lon == 165.5, !is.na(cant_total)) %>%
        arrange(depth),
      aes(cant_total, depth, col = "mapped")
    ) +
    geom_point(
      data = GLODAP_cant_obs %>%
        filter(lat == 48.5, lon == 165.5, !is.na(cant_total)) %>%
        arrange(depth),
      aes(cant_total, depth, col = "mapped")
    ) +
    geom_point(
      data = GLODAP_cant_obs %>%
        filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
      aes(cant_int, depth, col = "interpolated")
    ) +
    scale_y_reverse() +
    facet_wrap( ~ era) +
    scale_color_brewer(palette = "Dark2", name = "") +
    labs(title = "Cant interpolation to subsetting depth - example profile")
  
}

if (params_local$subsetting == "random") {
  ggplot() +
    geom_path(
      data = GLODAP_cant_obs %>%
        filter(lat == 48.5, lon == 165.5, !is.na(cant_total)) %>%
        arrange(depth),
      aes(cant_total, depth, col = "mapped")
    ) +
    geom_point(
      data = GLODAP_cant_obs %>%
        filter(lat == 48.5, lon == 165.5, !is.na(cant_total)) %>%
        arrange(depth),
      aes(cant_total, depth, col = "mapped")
    ) +
    geom_point(
      data = GLODAP_cant_obs %>%
        filter(lat == 48.5, lon == 165.5, month == 6),
      aes(cant_int, depth, col = "interpolated")
    ) +
    scale_y_reverse() +
    facet_wrap( ~ era) +
    scale_color_brewer(palette = "Dark2", name = "") +
    labs(title = "Cant interpolation to subsetting depth - example profile")
  
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
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

# remove cant data at grid cells without observations
GLODAP <- GLODAP_cant_obs %>%
  filter(!is.na(cstar)) %>%
  mutate(cant_total_pos = cant_int) %>%
  select(-c(cant_int, cant_total, n))

rm(GLODAP_cant_obs)

4.3.3 Merge GLODAP + atm. pCO₂

GLODAP-based subsetting were merged with mean annual atmospheric pCO₂ levels by year.

GLODAP <- left_join(GLODAP, co2_atm)

4.3.4 Calculation

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

# extract atm pCO2 at reference year
co2_atm_tref <- right_join(co2_atm, tref) %>% 
  select(-year) %>% 
  rename(pCO2_tref = pCO2)

# merge atm pCO2 at tref with GLODAP
GLODAP <- full_join(GLODAP, co2_atm_tref)
rm(co2_atm, tref)

# calculate cstar for reference year
GLODAP <- GLODAP %>%
  mutate(
    cstar_tref_delta =
      ((pCO2 - pCO2_tref) / (pCO2_tref - params_local$preind_atm_pCO2)) * cant_total_pos,
    cstar_tref = cstar - cstar_tref_delta)

4.4 Control plots

GLODAP %>% 
  ggplot(aes(cstar_tref_delta)) +
  geom_histogram(binwidth = 1) +
  labs(title = "Histogramm with binwidth = 1")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
7cdea0c	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

GLODAP %>% 
  sample_n(1e4) %>% 
  ggplot(aes(year, cstar_tref_delta, col = cant_total_pos)) +
  geom_point() +
  scale_color_viridis_c() +
  labs(title = "Time series of random subsample 1e4")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
a076226	Donghe-Zhu	2021-01-11
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

GLODAP %>% 
  ggplot(aes(year, cstar_tref_delta)) +
  geom_bin2d(binwidth = 1) +
  scale_fill_viridis_c(trans = "log10") +
  labs(title = "Heatmap with binwidth = 1")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
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

5 Selected section plots

A selected section is plotted to demonstrate the magnitude of various parameters and corrections relevant to C*.

if (params_local$subsetting == "GLODAP") {

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

}

if (params_local$subsetting == "random") {
  
  GLODAP_cruise <- GLODAP %>%
    filter(lon %in% params_global$lon_Atl_section)
  
}

if (params_local$subsetting == "GLODAP") {
  map +
    geom_path(data = GLODAP_cruise %>%
                arrange(date),
              aes(lon, lat)) +
    geom_point(data = GLODAP_cruise %>%
                 arrange(date),
               aes(lon, lat, col = date)) +
    scale_color_viridis_c(trans = "date") +
    labs(title = paste("Cruise year:", mean(GLODAP_cruise$year)))
}

if (params_local$subsetting == "random") {
  map +
    geom_path(data = GLODAP_cruise,
              aes(lon, lat)) +
    geom_point(data = GLODAP_cruise,
               aes(lon, lat)) +
    scale_color_viridis_c(trans = "date") +
    labs(title = paste("Cruise year:", mean(GLODAP_cruise$year)))
  
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
c8b76b3	jens-daniel-mueller	2020-12-19

lat_section <-
  GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  scale_fill_viridis_c() +
  theme(axis.title.x = element_blank())

for (i_var in c("tco2",
                "rCP_phosphate",
                "talk_05",
                "rNP_phosphate_05",
                "cstar",
                "cstar_tref")) {
  print(lat_section +
          stat_summary_2d(aes(z = !!sym(i_var))) +
          scale_fill_viridis_c(name = i_var)
        )
  
}

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
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
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
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
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
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
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
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
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
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
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
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

rm(lat_section, GLODAP_cruise)

6 Isoneutral slabs

The following boundaries for isoneutral slabs were defined:

Atlantic: -, 26, 26.75, 27.25, 27.75, 27.95, 28.1, 28.2,
Indo-Pacific: -, 26, 26.75, 27.25, 27.75, 27.95, 28.1,

Continuous neutral densities (gamma) values from model subsetting are grouped into isoneutral slabs.

GLODAP <- m_cut_gamma(GLODAP, "gamma")

if (params_local$subsetting == "GLODAP") {

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

}

if (params_local$subsetting == "random") {
  
  GLODAP_cruise <- GLODAP %>% 
  filter(lon %in% params_global$lon_Atl_section)
  
}

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  theme(legend.position = "bottom")

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

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
e5cb81a	Donghe-Zhu	2021-01-05
c8b76b3	jens-daniel-mueller	2020-12-19

rm(lat_section, GLODAP_cruise)

# this section was only used to calculate gamma locally, and compare it to the value provided in GLODAP data set
if (params_local$subsetting == "GLODAP") {

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

}

if (params_local$subsetting == "random") {
  
  GLODAP_cruise <- GLODAP %>% 
  filter(lon %in% params_global$lon_Atl_section)
  
}

library(oce)
library(gsw)
# calculate pressure from depth

GLODAP_cruise <- GLODAP_cruise %>% 
  mutate(CTDPRS = gsw_p_from_z(-depth,
                               lat))

GLODAP_cruise <- GLODAP_cruise %>% 
  mutate(THETA = swTheta(salinity = sal,
                         temperature = temp,
                         pressure = CTDPRS,
                         referencePressure = 0,
                         longitude = lon-180,
                         latitude = lat))

GLODAP_cruise <- GLODAP_cruise %>% 
  rename(LATITUDE = lat,
         LONGITUDE = lon,
         SALNTY = sal,
         gamma_provided = gamma)

library(reticulate)
source_python(
  paste(
    path_root,
    "/utilities/functions/python_scripts/",
    "Gamma_GLODAP_python.py",
    sep = ""
  )
)

GLODAP_cruise <- calculate_gamma(GLODAP_cruise)

GLODAP_cruise <- GLODAP_cruise %>% 
  mutate(gamma_delta = gamma_provided - GAMMA)

lat_section <- 
GLODAP_cruise %>%
  ggplot(aes(LATITUDE, CTDPRS)) +
  scale_y_reverse() +
  theme(legend.position = "bottom")

lat_section +
  stat_summary_2d(aes(z = gamma_delta)) +
  scale_color_viridis_c()

GLODAP_cruise %>% 
  ggplot(aes(gamma_delta))+
  geom_histogram()

rm(lat_section, GLODAP_cruise, cruises_meridional)

7 Synthetic data coverage

GLODAP <- GLODAP %>% 
  mutate(gamma_slab = factor(gamma_slab), 
         gamma_slab = factor(gamma_slab, levels = rev(levels(gamma_slab))))

for (i_basin in unique(GLODAP$basin)) {
  # i_basin <- unique(GLODAP$basin)[3]
  
  print(
    GLODAP %>%
      filter(basin == i_basin) %>%
      ggplot(aes(lat, gamma_slab)) +
      geom_bin2d(binwidth = 5) +
      scale_fill_viridis_c(
        option = "magma",
        direction = -1,
        trans = "log10"
      ) +
      scale_x_continuous(breaks = seq(-100, 100, 20),
                         limits = c(params_global$lat_min,
                                    params_global$lat_max)) +
      facet_grid(era ~ .) +
      labs(title = paste("MLR region: ", i_basin))
  )
  
}

Version	Author	Date
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

Version	Author	Date
28509fc	Donghe-Zhu	2021-01-23
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
e5cb81a	Donghe-Zhu	2021-01-05
a499f10	Donghe-Zhu	2021-01-05
8fae0b2	Donghe-Zhu	2020-12-21
c8b76b3	jens-daniel-mueller	2020-12-19

7.1 Histograms

GLODAP_vars <- GLODAP %>% 
  select(params_local$MLR_target,
         params_local$MLR_predictors)

GLODAP_vars_long <- GLODAP_vars %>% 
  pivot_longer(
    cols = c(params_local$MLR_target,
             params_local$MLR_predictors),
    names_to = "variable",
    values_to = "value"
  )

GLODAP_vars_long %>% 
  ggplot(aes(value)) +
  geom_histogram() +
  facet_wrap(~ variable,
             ncol = 2,
             scales = "free")

Version	Author	Date
4c28e4a	Donghe-Zhu	2021-01-22
24cc264	jens-daniel-mueller	2021-01-22
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
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

rm(GLODAP_vars, GLODAP_vars_long)

8 Individual cruise sections

Zonal and meridional section plots are produce for each cruise individually and are available under:

/nfs/kryo/work/jenmueller/emlr_cant/model/v_XXX/figures/Cruise_sections_histograms/

if (params_local$subsetting == "GLODAP") {
  if (params_local$plot_all_figures == "y") {
    cruises <- GLODAP %>%
      group_by(cruise) %>%
      summarise(date_mean = mean(date, na.rm = TRUE),
                n = n()) %>%
      ungroup() %>%
      arrange(date_mean)
    
    GLODAP <- full_join(GLODAP, cruises)
    
    n <- 0
    for (i_cruise in unique(cruises$cruise)) {
      # i_cruise <- unique(cruises$cruise)[1]
      # n <- n + 1
      # print(n)
      
      GLODAP_cruise <- GLODAP %>%
        filter(cruise == i_cruise) %>%
        arrange(date)
      
      cruises_cruise <- cruises %>%
        filter(cruise == i_cruise)
      
      map_plot <-
        map +
        geom_point(data = GLODAP_cruise,
                   aes(lon, lat, col = date)) +
        scale_color_viridis_c(trans = "date") +
        labs(
          title = paste(
            "Mean date:",
            cruises_cruise$date_mean,
            "| cruise:",
            cruises_cruise$cruise,
            "| n(samples):",
            cruises_cruise$n
          )
        )
      
      
      lon_section <- GLODAP_cruise %>%
        ggplot(aes(lon, depth)) +
        scale_y_reverse() +
        scale_fill_viridis_c()
      
      lon_tco2 <- lon_section +
        stat_summary_2d(aes(z = tco2))
      
      lon_talk <- lon_section +
        stat_summary_2d(aes(z = talk))
      
      lon_phosphate <- lon_section +
        stat_summary_2d(aes(z = phosphate))
      
      lon_oxygen <- lon_section +
        stat_summary_2d(aes(z = oxygen))
      
      lon_aou <- lon_section +
        stat_summary_2d(aes(z = aou))
      
      lon_phosphate_star <- lon_section +
        stat_summary_2d(aes(z = phosphate_star))
      
      lon_nitrate <- lon_section +
        stat_summary_2d(aes(z = nitrate))
      
      lon_cstar <- lon_section +
        stat_summary_2d(aes(z = cstar_tref))
      
      
      lat_section <- GLODAP_cruise %>%
        ggplot(aes(lat, depth)) +
        scale_y_reverse() +
        scale_fill_viridis_c()
      
      lat_tco2 <- lat_section +
        stat_summary_2d(aes(z = tco2))
      
      lat_talk <- lat_section +
        stat_summary_2d(aes(z = talk))
      
      lat_phosphate <- lat_section +
        stat_summary_2d(aes(z = phosphate))
      
      lat_oxygen <- lat_section +
        stat_summary_2d(aes(z = oxygen))
      
      lat_aou <- lat_section +
        stat_summary_2d(aes(z = aou))
      
      lat_phosphate_star <- lat_section +
        stat_summary_2d(aes(z = phosphate_star))
      
      lat_nitrate <- lat_section +
        stat_summary_2d(aes(z = nitrate))
      
      lat_cstar <- lat_section +
        stat_summary_2d(aes(z = cstar_tref))
      
      
      hist_tco2 <- GLODAP_cruise %>%
        ggplot(aes(tco2)) +
        geom_histogram()
      
      hist_talk <- GLODAP_cruise %>%
        ggplot(aes(talk)) +
        geom_histogram()
      
      hist_phosphate <- GLODAP_cruise %>%
        ggplot(aes(phosphate)) +
        geom_histogram()
      
      hist_oxygen <- GLODAP_cruise %>%
        ggplot(aes(oxygen)) +
        geom_histogram()
      
      hist_aou <- GLODAP_cruise %>%
        ggplot(aes(aou)) +
        geom_histogram()
      
      hist_phosphate_star <- GLODAP_cruise %>%
        ggplot(aes(phosphate_star)) +
        geom_histogram()
      
      hist_nitrate <- GLODAP_cruise %>%
        ggplot(aes(nitrate)) +
        geom_histogram()
      
      hist_cstar <- GLODAP_cruise %>%
        ggplot(aes(cstar_tref)) +
        geom_histogram()
      
      (map_plot /
          ((hist_tco2 / hist_talk / hist_phosphate / hist_cstar) |
             (
               hist_oxygen / hist_phosphate_star / hist_nitrate / hist_aou
             )
          )) |
        ((
          lat_tco2 / lat_talk / lat_phosphate / lat_oxygen / lat_aou / lat_phosphate_star / lat_nitrate / lat_cstar
        ) |
          (
            lon_tco2 / lon_talk / lon_phosphate / lon_oxygen /  lon_aou / lon_phosphate_star / lon_nitrate / lon_cstar
          )
        )
      
      ggsave(
        path = paste(
          path_version_figures,
          "Cruise_sections_histograms/",
          sep = ""
        ),
        filename = paste(
          "Cruise_date",
          cruises_cruise$date_mean,
          "count",
          cruises_cruise$n,
          "cruiseID",
          cruises_cruise$cruise,
          ".png",
          sep = "_"
        ),
        width = 20,
        height = 12
      )
      
      rm(
        map_plot,
        lon_section,
        lat_section,
        lat_tco2,
        lat_talk,
        lat_phosphate,
        lon_tco2,
        lon_talk,
        lon_phosphate,
        GLODAP_cruise,
        cruises_cruise
      )
      
    }
    
  }
  
}

9 Write files

if (params_local$subsetting == "GLODAP") {
  # select relevant columns
  GLODAP <- GLODAP %>%
    select(
      year,
      date,
      era,
      basin,
      basin_AIP,
      lat,
      lon,
      depth,
      gamma,
      gamma_slab,
      params_local$MLR_predictors,
      params_local$MLR_target
    )
  
  GLODAP %>% write_csv(paste(
    path_version_data,
    "GLODAPv2.2020_MLR_fitting_ready.csv",
    sep = ""
  ))
  
}

if (params_local$subsetting == "random") {
  # select relevant columns
  GLODAP <- GLODAP %>%
    select(
      year,
      month,
      era,
      basin,
      basin_AIP,
      lat,
      lon,
      depth,
      gamma,
      gamma_slab,
      params_local$MLR_predictors,
      params_local$MLR_target
    )
  
  GLODAP %>% write_csv(paste(
    path_version_data,
    "GLODAPv2.2020_MLR_fitting_ready.csv",
    sep = ""
  ))
}

co2_atm_tref %>%  write_csv(paste(path_version_data,
                                  "co2_atm_tref.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] kableExtra_1.3.1 knitr_1.30       lubridate_1.7.9  marelac_2.1.10  
 [5] shape_1.4.5      metR_0.9.0       scico_1.2.0      patchwork_1.1.1 
 [9] collapse_1.5.0   forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2     
[13] purrr_0.3.4      readr_1.4.0      tidyr_1.1.2      tibble_3.0.4    
[17] ggplot2_3.3.3    tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 gsw_1.0-5                webshot_0.5.2           
 [4] RColorBrewer_1.1-2       httr_1.4.2               rprojroot_2.0.2         
 [7] tools_4.0.3              backports_1.1.10         R6_2.5.0                
[10] DBI_1.1.0                colorspace_2.0-0         withr_2.3.0             
[13] tidyselect_1.1.0         compiler_4.0.3           git2r_0.27.1            
[16] cli_2.2.0                rvest_0.3.6              xml2_1.3.2              
[19] labeling_0.4.2           scales_1.1.1             checkmate_2.0.0         
[22] digest_0.6.27            rmarkdown_2.5            oce_1.2-0               
[25] pkgconfig_2.0.3          htmltools_0.5.0          dbplyr_1.4.4            
[28] highr_0.8                rlang_0.4.10             readxl_1.3.1            
[31] rstudioapi_0.13          generics_0.1.0           farver_2.0.3            
[34] jsonlite_1.7.2           magrittr_2.0.1           Matrix_1.2-18           
[37] Rcpp_1.0.5               munsell_0.5.0            fansi_0.4.1             
[40] lifecycle_0.2.0          stringi_1.5.3            whisker_0.4             
[43] yaml_2.2.1               grid_4.0.3               blob_1.2.1              
[46] parallel_4.0.3           promises_1.1.1           crayon_1.3.4            
[49] lattice_0.20-41          haven_2.3.1              hms_0.5.3               
[52] seacarb_3.2.15           pillar_1.4.7             reprex_0.3.0            
[55] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.2
[58] data.table_1.13.6        modelr_0.1.8             vctrs_0.3.6             
[61] httpuv_1.5.4             testthat_3.0.1           cellranger_1.1.0        
[64] gtable_0.3.0             assertthat_0.2.1         xfun_0.20               
[67] broom_0.7.3              RcppEigen_0.3.3.9.1      later_1.1.0.1           
[70] viridisLite_0.3.0        ellipsis_0.3.1           here_1.0.1

eMLR - data preparation

Jens Daniel Müller and Donghe Zhu

23 January, 2021

1 Required data

2 Calculation of tref

3 PO₄*

3.1 Calculation

4 C*

4.1 Stoichiometric ratios

4.2 Calculation

4.3 Reference year adjustment

4.3.1 Cant at tref

4.3.2 Combine GLODAP + Cant

4.3.3 Merge GLODAP + atm. pCO₂

4.3.4 Calculation

4.4 Control plots

5 Selected section plots

6 Isoneutral slabs

7 Synthetic data coverage

7.1 Histograms

8 Individual cruise sections

9 Write files

eMLR - data preparation

Jens Daniel Müller and Donghe Zhu

23 January, 2021

1 Required data

2 Calculation of tref

3 PO4*

3.1 Calculation

4 C*

4.1 Stoichiometric ratios

4.2 Calculation

4.3 Reference year adjustment

4.3.1 Cant at tref

4.3.2 Combine GLODAP + Cant

4.3.3 Merge GLODAP + atm. pCO2

4.3.4 Calculation

4.4 Control plots

5 Selected section plots

6 Isoneutral slabs

7 Synthetic data coverage

7.1 Histograms

8 Individual cruise sections

9 Write files

3 PO₄*

4.3.3 Merge GLODAP + atm. pCO₂