eMLR - data preparation

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Last updated: 2021-03-09

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Knit directory: emlr_mod_v_XXX/

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---

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 = round(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 SiO₃*

4.1 Calculation

The predictor SiO₃* was be calculated according to Sarmiento et al. (2004), ie based on silicate and nitrate.

Here we use following equation:

print(b_silicate_star)

function (silicate, nitrate) 
{
    silicate_star = silicate - nitrate
    return(silicate_star)
}

if ("silicate_star" %in% params_local$MLR_predictors) {
GLODAP <- GLODAP %>% 
  mutate(silicate_star = b_silicate_star(silicate, nitrate))
}

5 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.

5.1 Stoichiometric ratios

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

• C/P: 117
• N/P: 16

5.2 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))

5.3 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.

5.3.1 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")

Past versions of cant_surface_maps-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
ca03c39	Donghe-Zhu	2021-02-07
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
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

5.3.2 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")

Past versions of merge_cstar_cant-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
ca03c39	Donghe-Zhu	2021-02-07
cd7c52c	Donghe-Zhu	2021-02-04
bcf84f4	Donghe-Zhu	2021-02-02
865b582	Donghe-Zhu	2021-01-31
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a618965	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
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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")
  
}

Past versions of cant_interpolation_to_observations-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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)

5.3.3 Merge GLODAP + atm. pCO₂

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

GLODAP <- left_join(GLODAP, co2_atm)

5.3.4 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)

5.4 Control plots

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

Past versions of cstar_reference_year_adjustment_histogram-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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")

Past versions of cstar_reference_year_adjustment_vs_time_cant-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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")

Past versions of cstar_reference_year_adjustment_vs_time_counts-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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

6 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)))
  
}

Past versions of meridional_sections-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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)
        )
  
}

Past versions of meridional_sections-2.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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

Past versions of meridional_sections-3.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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

Past versions of meridional_sections-4.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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

Past versions of meridional_sections-5.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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

Past versions of meridional_sections-6.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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

Past versions of meridional_sections-7.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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)

7 Isoneutral slabs

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,

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()

Past versions of meridional_sections_isoneutral_slabs-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
1843412	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
ffda45a	Donghe-Zhu	2021-03-03
691ba81	Donghe-Zhu	2021-03-03
c5e45a2	Donghe-Zhu	2021-03-03
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
16fba40	Donghe-Zhu	2021-01-28
12bc567	Donghe-Zhu	2021-01-27
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
61efb56	Donghe-Zhu	2021-01-25
c569946	Donghe-Zhu	2021-01-24
a2f0d56	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
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)

8 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))
  )
  
}

Past versions of obs_gamma_slab-1.png

Version	Author	Date
1691156	Donghe-Zhu	2021-03-08
c0ceaf8	Donghe-Zhu	2021-03-08
112dea0	Donghe-Zhu	2021-03-08
1843412	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
627c8fb	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
ffda45a	Donghe-Zhu	2021-03-03
691ba81	Donghe-Zhu	2021-03-03
c5e45a2	Donghe-Zhu	2021-03-03
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
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
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
1fad5f1	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
59e006e	Donghe-Zhu	2021-01-31
a1c8f87	Donghe-Zhu	2021-01-31
b50fe52	Donghe-Zhu	2021-01-31
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	Donghe-Zhu	2021-01-29
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
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
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

Past versions of obs_gamma_slab-2.png

Version	Author	Date
1691156	Donghe-Zhu	2021-03-08
c0ceaf8	Donghe-Zhu	2021-03-08
058e0a1	Donghe-Zhu	2021-03-08
112dea0	Donghe-Zhu	2021-03-08
1843412	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
627c8fb	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
691ba81	Donghe-Zhu	2021-03-03
c5e45a2	Donghe-Zhu	2021-03-03
19edd1e	Donghe-Zhu	2021-02-27
f20483f	Donghe-Zhu	2021-02-26
6a2c7b3	Donghe-Zhu	2021-02-25
57f701e	Donghe-Zhu	2021-02-24
06f3149	Donghe-Zhu	2021-02-16
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
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
1fad5f1	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
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
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
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
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

8.1 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")

Past versions of histogram_predictor-1.png

Version	Author	Date
112dea0	Donghe-Zhu	2021-03-08
3fbbfa4	Donghe-Zhu	2021-03-07
8c1e978	Donghe-Zhu	2021-03-05
865f68c	Donghe-Zhu	2021-03-05
ee69bc1	Donghe-Zhu	2021-03-05
a79291f	Donghe-Zhu	2021-03-05
e8c6f30	Donghe-Zhu	2021-03-04
59288fe	Donghe-Zhu	2021-03-04
731abc8	Donghe-Zhu	2021-03-04
e2a5a33	Donghe-Zhu	2021-03-04
c7892c1	Donghe-Zhu	2021-03-04
924430b	Donghe-Zhu	2021-03-03
0d0bca1	Donghe-Zhu	2021-03-03
cb63c16	Donghe-Zhu	2021-03-03
89c3e58	Donghe-Zhu	2021-03-03
c911669	Donghe-Zhu	2021-03-03
13666ca	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
54d5b5b	Donghe-Zhu	2021-02-27
330f064	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
5dce4b1	Donghe-Zhu	2021-02-15
4469a0c	Donghe-Zhu	2021-02-13
05385dc	Donghe-Zhu	2021-02-10
f791ae4	Donghe-Zhu	2021-02-09
f71ae34	Donghe-Zhu	2021-02-09
a145fa7	Donghe-Zhu	2021-02-09
c344e42	Donghe-Zhu	2021-02-08
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
ac99ae5	jens-daniel-mueller	2021-01-29
b5bdcaf	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
342402d	Donghe-Zhu	2021-01-27
5bad5c2	Donghe-Zhu	2021-01-27
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
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)

9 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
      )
      
    }
    
  }
  
}

10 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 = ""))

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] 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_0.7.5              RcppEigen_0.3.3.9.1      later_1.1.0.1           
[70] viridisLite_0.3.0        ellipsis_0.3.1           here_1.0.1