Last updated: 2021-05-12

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Rmd	7a953b4	jens-daniel-mueller	2021-03-11	test with weak filtering
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html	85a5ed2	jens-daniel-mueller	2021-03-10	Build site.
Rmd	749b5db	jens-daniel-mueller	2021-03-10	filter based on comparison to CANYON-B
html	081a12a	jens-daniel-mueller	2021-03-06	Build site.
Rmd	dafd78e	jens-daniel-mueller	2021-03-06	Canyon-B comparison for singla and all years
html	dd2b3a1	jens-daniel-mueller	2021-03-06	Build site.
Rmd	bc38fd4	jens-daniel-mueller	2021-03-06	Canyon-B comparison for singla and all years
html	1dc27ab	jens-daniel-mueller	2021-03-06	Build site.
Rmd	8abeada	jens-daniel-mueller	2021-03-06	Canyon-B comparison for singla and all years
html	e59c7c3	jens-daniel-mueller	2021-03-06	Build site.
Rmd	c3e762b	jens-daniel-mueller	2021-03-06	Canyon-B comparison for all years together
html	98fb407	jens-daniel-mueller	2021-03-06	Build site.
Rmd	bd24d69	jens-daniel-mueller	2021-03-06	Canyon-B comparison for all years
html	c71fdae	jens-daniel-mueller	2021-03-05	Build site.
Rmd	8a40d10	jens-daniel-mueller	2021-03-05	Canyon-B comparison for all years
html	00688a1	jens-daniel-mueller	2021-03-05	Build site.
Rmd	a805084	jens-daniel-mueller	2021-03-05	comparison to CANYON-B started
html	6c0bec6	jens-daniel-mueller	2021-03-05	Build site.
Rmd	559dd52	jens-daniel-mueller	2021-03-05	rebuild with cruise rmse < 10 filter
html	3c2ec33	jens-daniel-mueller	2021-03-05	Build site.
Rmd	006c875	jens-daniel-mueller	2021-03-05	rebuild with NA in Cant replaced by 0
html	af70b94	jens-daniel-mueller	2021-03-04	Build site.
Rmd	c9cf1fd	jens-daniel-mueller	2021-03-04	rebuild with NA in Cant replaced by 0
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html	3d3b4cc	jens-daniel-mueller	2021-02-23	Build site.
html	7b672f7	jens-daniel-mueller	2021-01-11	Build site.
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html	318609d	jens-daniel-mueller	2020-12-23	adapted more variable predictor selection
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Rmd	a4531df	jens-daniel-mueller	2020-12-23	test 106
html	0aa2b50	jens-daniel-mueller	2020-12-23	remove html before duplication
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Rmd	bef9220	jens-daniel-mueller	2020-12-23	rebuild before sensitivity test
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html	02f0ee9	jens-daniel-mueller	2020-12-18	cleaned up for copying template
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Rmd	ca65bf5	jens-daniel-mueller	2020-12-18	rebuild after final cleaning
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html	22b07fb	jens-daniel-mueller	2020-12-17	Build site.
html	f3a708f	jens-daniel-mueller	2020-12-17	Build site.
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Rmd	449195a	jens-daniel-mueller	2020-12-15	rebuild without subsetting nitrate
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html	4d612dd	jens-daniel-mueller	2020-12-15	Build site.
Rmd	e7e5ff1	jens-daniel-mueller	2020-12-15	rebuild with eMLR target variable selection
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html	42daf5c	jens-daniel-mueller	2020-12-14	Build site.
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Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server
Rmd	bc61ce3	Jens Müller	2020-11-30	Initial commit
html	bc61ce3	Jens Müller	2020-11-30	Initial commit

1 Required data

Required are:

GLODAPv2.2020
- cleaned data file
Cant from Sabine 2004 (S04)
Cant from Gruber 2019 (G19)
annual mean atmospheric pCO₂

GLODAP <-
  read_csv(paste(path_version_data,
                 "GLODAPv2.2020_clean.csv",
                 sep = ""))

S04_cant_3d <-
  read_csv(paste(path_preprocessing,
                 "S04_cant_3d.csv",
                 sep = ""))

G19_cant_3d <-
  read_csv(paste(path_preprocessing,
                 "G19_cant_3d.csv",
                 sep = ""))

m94_cant_3d <-
  read_csv(paste(
    path_preprocessing_model,
    "cant_annual_field_AD/cant_1994.csv",
    sep = ""
  ))

m07_cant_3d <-
  read_csv(paste(
    path_preprocessing_model,
    "cant_annual_field_AD/cant_2007.csv",
    sep = ""
  ))

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

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

1.1 Convert to long format

GLODAP <- GLODAP %>% 
  rename_with(~ gsub("_model", "_mod", .x)) %>% 
  rename_with(.cols = c(temp, sal, gamma, tco2, talk, phosphate,
                        oxygen, aou, nitrate, silicate),
              ~ paste(.x, "obs", sep = "_"))

GLODAP <- GLODAP %>% 
  pivot_longer(
    -c(year:depth), 
    names_to = c(".value", "data_source"), 
    names_sep = "_"
  )

2 PO₄*

2.1 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))
}

3 C*

C* serves as a conservative tracer of anthropogenic CO₂ uptake. It is derived from measured 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.

3.1 Stoichiometric ratios

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

C/P: 117
N/P: 16

3.2 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))

3.3 Reference year adjustment

To adjust observation-based C* values to the reference year of each observation period, we assume a transient steady state change of cant between the time of sampling the reference year. The adjustment requires an approximation of the cant concentration at the reference year. We approximate this concentration by adding the delta cant signal estimated by Gruber et al (2019) to the “base line” total cant concentration determined for 1994 by Sabine et al (2004):

Cant(tref) = S04 + (tref-1994)/13 * G19

This way, we use exactly S04+G19 for tref=2007. For all other tref we scale Cant with the observed anomalous change over the 1994-2007 period, rather than assuming a transient steady state. However, one assumes a linear behaviour of the anomalous change over time, which might be wrong in particular for the years past 2007.

For the model data, we perform the adjustment based on the total Cant estimate for 1994, and the delta Cant estimate for 1994 - 2007.

3.3.1 Join Cant fields

Join Cant fields of G19 and S04

G19_cant_3d <- G19_cant_3d %>% 
  select(lon, lat, depth, cant_pos_G19 = cant_pos)

S04_cant_3d <- S04_cant_3d %>% 
  select(lon, lat, depth, cant_pos_S04 = cant_pos)

cant_3d_coverage <- full_join(
  S04_cant_3d %>% distinct(lat, lon),
  G19_cant_3d %>% distinct(lat, lon)
)

cant_3d_coverage <- full_join(
  cant_3d_coverage,
  G19_cant_3d %>% distinct(lat, lon) %>% mutate(G19 = "y")
)

cant_3d_coverage <- full_join(
  cant_3d_coverage,
  S04_cant_3d %>% distinct(lat, lon) %>% mutate(S04 = "y")
)

cant_3d_coverage <- cant_3d_coverage %>% 
  mutate(coverage = case_when(
    G19 == "y" & S04 == "y" ~ "both",
    is.na(G19) & S04 == "y" ~ "S04",
    G19 == "y" & is.na(S04) ~ "G19"))

map +
  geom_raster(data = cant_3d_coverage,
              aes(lon, lat, fill = coverage)) +
  geom_raster(data = GLODAP %>% distinct(lat, lon),
              aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2")

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04

rm(cant_3d_coverage)

cant_3d <- full_join(S04_cant_3d, G19_cant_3d)

cant_3d <- cant_3d %>%
  mutate(cant_pos_S04 = replace_na(cant_pos_S04, 0),
         cant_pos_G19 = replace_na(cant_pos_G19, 0))

# cant_3d %>%
#   filter_all(any_vars(is.na(.)))

Join model Cant fields of 1994 and 2007

m94_cant_3d <- m94_cant_3d %>% 
  mutate(cant_pos = if_else(cant_total <= 0, 0, cant_total)) %>% 
  select(lon, lat, depth, cant_pos_S04 = cant_pos)

m07_cant_3d <- m07_cant_3d %>% 
  mutate(cant_pos = if_else(cant_total <= 0, 0, cant_total)) %>% 
  select(lon, lat, depth, cant_pos_m07 = cant_pos)

mod_cant_3d <- full_join(
  m94_cant_3d,
  m07_cant_3d
)

mod_cant_3d <- mod_cant_3d %>% 
  mutate(cant_pos_G19 = cant_pos_m07 - cant_pos_S04,
         cant_pos_G19 = if_else(cant_pos_G19 <= 0, 0, cant_pos_G19)) %>% 
  select(-cant_pos_m07)

rm(m07_cant_3d,
   m94_cant_3d)

Join mod and obs Cant

cant_3d <- bind_rows(
  mod_cant_3d %>% mutate(data_source = "mod"),
  cant_3d %>% mutate(data_source = "obs")
)

3.3.2 Cant at tref

Calculate Cant at tref by adding G19, scaled for the time since 1994.

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

# join cant with tref
cant_3d <- expand_grid(cant_3d, tref)

# calculate cant fields for all tref
cant_3d <- cant_3d %>%
  mutate(cant_pos =
           cant_pos_S04 +
           ((median_year - 1994) / 13 * cant_pos_G19))

# remove columns
cant_3d <- cant_3d %>% 
  select(data_source, lon, lat, depth, era, cant_pos)

3.3.3 Combine GLODAP + Cant

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

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

cant_3d_obs <- cant_3d_obs %>%
  mutate(cant_pos = replace_na(cant_pos, 0),
         depth = replace_na(depth, 0))

# calculate number of cant data points per grid cell
cant_3d_obs <- cant_3d_obs %>%
  group_by(lon, lat, era, data_source) %>% 
  mutate(n = n(),
         n_group = if_else(n > 1, "n > 1", "n <= 1")) %>% 
  ungroup()

# GLODAP observations with only one Cant value
map +
  geom_raster(data = cant_3d_obs,
             aes(lon, lat, fill = n_group)) +
  scale_fill_brewer(palette = "Set1", name="n") +
  facet_grid(data_source ~ era) +
  labs(title = "Number of Cant depth levels",
       subtitle = "available per latxlon grid cell")

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
39113c3	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18

cant_3d_obs <- cant_3d_obs %>% 
  select(-n_group)

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 observations
GLODAP_cant_obs <- GLODAP_cant_obs %>%
  group_by(lon, lat, era, data_source) %>% 
  fill(n, .direction = "updown") %>% 
  ungroup()

The mapped Cant product was merged with GLODAP observation by:

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

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

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

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


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

rm(GLODAP_cant_obs)

3.3.4 Merge GLODAP + atm. pCO₂

GLODAP observations were merged with mean annual atmospheric pCO₂ levels by year.

GLODAP <- left_join(GLODAP, co2_atm)

3.3.5 Calculation

# assign reference year
GLODAP <- full_join(GLODAP, tref)

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

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

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

3.4 Control plots

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

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
be095c6	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

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

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
be095c6	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

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

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
be095c6	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
3c2ec33	jens-daniel-mueller	2021-03-05
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
090e4d5	jens-daniel-mueller	2020-12-02
37e9dac	jens-daniel-mueller	2020-12-02
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30
bc61ce3	Jens Müller	2020-11-30

4 Selected section plots

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

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

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))) +
  facet_wrap(~ data_source)

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

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)
        )
  
}

rm(lat_section, GLODAP_cruise)

5 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, 28.2, 28.3, 28.4,

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

GLODAP <- m_cut_gamma(GLODAP, "gamma")

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

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

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

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

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

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)
data_source_python(here::here("code/python_scripts",
                         "Gamma_GLODAP_python.py"))

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)

6 Observations 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)[1]
  
  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 ~ data_source) +
      labs(title = paste("MLR region: ", i_basin))
  )
  
}

Version	Author	Date
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
f155edd	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11

6.1 Histograms

GLODAP_vars <- GLODAP %>% 
  select(data_source,
         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_grid(data_source ~ variable,
             scales = "free_x")

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
be095c6	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
85a5ed2	jens-daniel-mueller	2021-03-10
af70b94	jens-daniel-mueller	2021-03-04
7b672f7	jens-daniel-mueller	2021-01-11
33ba23c	jens-daniel-mueller	2021-01-07
318609d	jens-daniel-mueller	2020-12-23
9d0b2d0	jens-daniel-mueller	2020-12-23
0aa2b50	jens-daniel-mueller	2020-12-23
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
7bcb4eb	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
7d82772	jens-daniel-mueller	2020-12-11
7788175	jens-daniel-mueller	2020-12-09

rm(GLODAP_vars, GLODAP_vars_long)

7 Cruise quality check

7.1 MLR models

The the following full MLR model was fitted to all GLODAP, irrespective of the sampling era:

#define full model
model <- paste("cstar",
               paste(params_local$MLR_predictors, collapse = " + "),
               sep = " ~ ")

model

[1] "cstar ~ sal + temp + aou + nitrate + silicate + phosphate + phosphate_star"

# prepare nested data frame
GLODAP_nested <- GLODAP %>%
  group_by(gamma_slab, basin, data_source) %>%
  nest()

# expand with model definitions
GLODAP_nested_lm <- expand_grid(GLODAP_nested,
                                model)

# fit models and extract tidy model output
GLODAP_nested_lm_fit <- GLODAP_nested_lm %>%
  mutate(
    fit = map2(.x = data, .y = model,
               ~ lm(as.formula(.y), data = .x)),
    tidied = map(fit, tidy),
    glanced = map(fit, glance),
    augmented = map(fit, augment)
  )

# extract augmented model output (fitted values and residuals)
GLODAP_augmented <- GLODAP_nested_lm_fit %>%
  select(-c(data, fit, tidied, glanced)) %>%
  unnest(augmented)

# extract input data
GLODAP_data <- GLODAP_nested_lm_fit %>%
  select(-c(fit, tidied, glanced, augmented)) %>%
  unnest(data)

# append input data with augmented data
GLODAP_augmented <- bind_cols(GLODAP_data,
                              GLODAP_augmented %>% select(.fitted, .resid))

rm(GLODAP_data)

7.1.1 Residuals

Below, the residuals of C* from the mean C* and from C* predicted with the global model are shown.

GLODAP_augmented %>%
  group_by(data_source) %>% 
  mutate(cstar_minus_mean = cstar_tref - mean(cstar_tref)) %>% 
  ungroup() %>% 
  ggplot(aes(year, cstar_minus_mean)) +
  geom_hline(yintercept = 0) +
  geom_bin2d(binwidth = c(1, 1)) +
  scale_fill_viridis_c() +
  facet_grid(. ~ data_source)

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
a00ec94	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
29edae5	jens-daniel-mueller	2021-04-14
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
be095c6	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11

GLODAP_augmented %>%
  ggplot(aes(year, .resid)) +
  geom_hline(yintercept = 0) +
  geom_bin2d(binwidth = c(1, 1)) +
  scale_fill_viridis_c() +
  facet_grid(. ~ data_source)

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11

7.1.2 Ranked RMSE

# calculate RMSE from augmented output per cruise
cruise_all <- GLODAP_augmented %>%
  group_by(cruise, data_source) %>%
  summarise(rmse = sqrt(c(crossprod(.resid)) / length(.resid))) %>%
  ungroup()

# rank RMSE
cruise_all <- cruise_all %>% 
  mutate(cruise = as.factor(cruise)) %>% 
  group_by(data_source) %>% 
  mutate(rank_rmse = rank(rmse)) %>% 
  ungroup()

cruise_out <- cruise_all %>% 
  filter(data_source == "obs",
         rmse > params_local$c_star_rmse_max)

GLODAP_out <- GLODAP_augmented %>% 
  filter(cruise %in% cruise_out$cruise)

ggplot() +
  geom_hline(yintercept = params_local$c_star_rmse_max) +
  geom_point(data = cruise_all,
             aes(rank_rmse, rmse)) +
  geom_point(data = cruise_out,
             aes(rank_rmse, rmse, col = cruise)) +
  facet_grid(. ~ data_source)

Version	Author	Date
969e631	jens-daniel-mueller	2021-05-12
d2a83bc	jens-daniel-mueller	2021-04-16
c0a47df	jens-daniel-mueller	2021-04-16
50290e8	jens-daniel-mueller	2021-04-16
b6fe355	jens-daniel-mueller	2021-04-16
81b7c6d	jens-daniel-mueller	2021-04-16
ddec5b7	jens-daniel-mueller	2021-04-15
099d566	jens-daniel-mueller	2021-04-14
bb44686	jens-daniel-mueller	2021-04-14
bf40480	jens-daniel-mueller	2021-04-13
9f31fe3	jens-daniel-mueller	2021-04-13
338dd3c	jens-daniel-mueller	2021-04-09
a79ca2c	jens-daniel-mueller	2021-04-09
eb827c9	jens-daniel-mueller	2021-04-07
857bad3	jens-daniel-mueller	2021-03-24
03b6009	jens-daniel-mueller	2021-03-23
555750f	jens-daniel-mueller	2021-03-23
83a13de	jens-daniel-mueller	2021-03-20
cf98c6d	jens-daniel-mueller	2021-03-16
a1d52ff	jens-daniel-mueller	2021-03-15
0bade3b	jens-daniel-mueller	2021-03-15
27c1f4b	jens-daniel-mueller	2021-03-14
af75ebf	jens-daniel-mueller	2021-03-14
5017709	jens-daniel-mueller	2021-03-11
585b07f	jens-daniel-mueller	2021-03-11

7.1.3 Removed cruises

Following fraction (%) of cruises was removed:

nrow(GLODAP_out)/nrow(GLODAP_augmented)*100

[1] 0

if (nrow(GLODAP_out) > 0) {
  map +
  geom_raster(data = GLODAP_out %>% distinct(lat, lon, era),
              aes(lon, lat)) +
  facet_wrap( ~ era, ncol = 1) +
  labs(title = "Maps of removed cruises")

} else {
  print("no cruises removed")
  
}

[1] "no cruises removed"

7.2 Individual cruise sections

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

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

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)

}

}

8 Write files

# select relevant columns
GLODAP <- GLODAP %>%
  filter(!(cruise %in% cruise_out$cruise)) %>%
  select(
    year,
    date,
    era,
    basin,
    basin_AIP,
    lat,
    lon,
    depth,
    data_source,
    gamma,
    gamma_slab,
    params_local$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 = ""))

9 Spatial autocorrelation

GLODAP_sp <- GLODAP %>% 
  filter(depth == 150)

map +
  geom_raster(data = GLODAP_sp,
              aes(lon, lat, fill = temp)) +
  scale_fill_viridis_c()

class(GLODAP_sp)

GLODAP_sp <- GLODAP_sp %>% 
  mutate(lon = if_else(lon > 180, lon - 360, lon))

ggplot() +
  geom_raster(data = GLODAP_sp,
              aes(lon, lat, fill = temp)) +
  scale_fill_viridis_c() +
  coord_quickmap()

GLODAP_sp <- as.data.frame(GLODAP_sp)

library(sp)
coordinates(GLODAP_sp) = ~lon+lat
class(GLODAP_sp)

summary(GLODAP_sp)

is.projected(GLODAP_sp)
proj4string(GLODAP_sp) <-
  CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")


GLODAP_sp_grid <- GLODAP_sp

gridded(GLODAP_sp_grid) <- TRUE


spplot(GLODAP_sp,
       zcol = "temp")

spplot(GLODAP_sp_grid,
       zcol = "temp")

library(sf)
library(stars)
GLODAP_sf <- st_as_sf(GLODAP_sp_grid)
GLODAP_stars <- st_as_stars(GLODAP_sp_grid)
class(GLODAP_stars)

plot(GLODAP_stars)

ggplot() +
  geom_stars(data = GLODAP_stars,
             aes(x, y, fill = temp)) +
  scale_fill_viridis_c(na.value = "transparent") +
  coord_quickmap(expand = 0)

library(rnaturalearth)
coastlines <- ne_coastline(scale = "small", returnclass = "sf")

ggplot() +
  geom_sf(data = GLODAP_sf,
             aes(col = temp)) +
  scale_fill_viridis_c(na.value = "transparent") +
  geom_sf(data = st_wrap_dateline(coastlines),
          colour = "black") +
  coord_sf(crs = st_crs('ESRI:54030')) +
  theme_bw()

summary(GLODAP_sp)

library(gstat)
vg_temp <- variogram(temp~1,
                     data = GLODAP_sp_grid,
                     cutoff = 1e4)
fit_temp <- fit.variogram(vg_temp, vgm("Sph"))

plot(vg_temp, fit_temp)

vg_temp <- variogram(temp~1,
                     data = GLODAP_sp_grid,
                     alpha = c(0,90),
                     cutoff = 1e4)
plot(vg_temp)


vg <- gstat(id = params_local$MLR_target,
            formula = as.formula(paste(sym(
              params_local$MLR_target
            ), "~ 1")),
            data = GLODAP_sp_grid)

for (i_var in params_local$MLR_predictors) {
  #i_var <- params_local$MLR_predictors[1]
  vg <- gstat(vg,
            id = i_var,
            formula = as.formula(paste(sym(
              i_var
            ), "~ 1")),
            data = GLODAP_sp_grid)
}



plot(variogram(vg, cutoff = 1e4))

# ### kriging
# 
# lzn.kr1 = krige(formula = temp~1,
#                 GLODAP_sp,
#                 GLODAP_sp_grid,
#                 model = fit_temp)
# #> [using universal kriging]
# plot(lzn.kr1[1])

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] broom_0.7.5     lubridate_1.7.9 marelac_2.1.10  shape_1.4.5    
 [5] metR_0.9.0      scico_1.2.0     patchwork_1.1.1 collapse_1.5.0 
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4    
[13] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3  
[17] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               viridisLite_0.3.0        jsonlite_1.7.1          
 [4] here_0.1                 modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] RColorBrewer_1.1-2       promises_1.1.1           checkmate_2.0.0         
[19] rvest_0.3.6              colorspace_1.4-1         htmltools_0.5.0         
[22] httpuv_1.5.4             Matrix_1.2-18            pkgconfig_2.0.3         
[25] seacarb_3.2.14           haven_2.3.1              scales_1.1.1            
[28] whisker_0.4              later_1.1.0.1            git2r_0.27.1            
[31] farver_2.0.3             generics_0.0.2           ellipsis_0.3.1          
[34] withr_2.3.0              cli_2.1.0                magrittr_1.5            
[37] crayon_1.3.4             readxl_1.3.1             evaluate_0.14           
[40] fs_1.5.0                 fansi_0.4.1              xml2_1.3.2              
[43] RcppArmadillo_0.10.1.2.0 oce_1.2-0                tools_4.0.3             
[46] data.table_1.13.2        hms_0.5.3                lifecycle_1.0.0         
[49] munsell_0.5.0            reprex_0.3.0             gsw_1.0-5               
[52] compiler_4.0.3           rlang_0.4.10             grid_4.0.3              
[55] rstudioapi_0.13          labeling_0.4.2           rmarkdown_2.5           
[58] testthat_2.3.2           gtable_0.3.0             DBI_1.1.0               
[61] R6_2.5.0                 knitr_1.30               rprojroot_2.0.2         
[64] stringi_1.5.3            parallel_4.0.3           Rcpp_1.0.5              
[67] vctrs_0.3.5              dbplyr_1.4.4             tidyselect_1.1.0        
[70] xfun_0.18

eMLR data preparation

Jens Daniel Müller

12 May, 2021

1 Required data

1.1 Convert to long format

2 PO₄*

2.1 Calculation

3 C*

3.1 Stoichiometric ratios

3.2 Calculation

3.3 Reference year adjustment

3.3.1 Join Cant fields

3.3.2 Cant at tref

3.3.3 Combine GLODAP + Cant

3.3.4 Merge GLODAP + atm. pCO₂

3.3.5 Calculation

3.4 Control plots

4 Selected section plots

5 Isoneutral slabs

6 Observations coverage

6.1 Histograms

7 Cruise quality check

7.1 MLR models

7.1.1 Residuals

7.1.2 Ranked RMSE

7.1.3 Removed cruises

7.2 Individual cruise sections

8 Write files

9 Spatial autocorrelation

eMLR data preparation

Jens Daniel Müller

12 May, 2021

1 Required data

1.1 Convert to long format

2 PO4*

2.1 Calculation

3 C*

3.1 Stoichiometric ratios

3.2 Calculation

3.3 Reference year adjustment

3.3.1 Join Cant fields

3.3.2 Cant at tref

3.3.3 Combine GLODAP + Cant

3.3.4 Merge GLODAP + atm. pCO2

3.3.5 Calculation

3.4 Control plots

4 Selected section plots

5 Isoneutral slabs

6 Observations coverage

6.1 Histograms

7 Cruise quality check

7.1 MLR models

7.1.1 Residuals

7.1.2 Ranked RMSE

7.1.3 Removed cruises

7.2 Individual cruise sections

8 Write files

9 Spatial autocorrelation

2 PO₄*

3.3.4 Merge GLODAP + atm. pCO₂