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1 Read files

Main data source for this project is the synthetic cmorized model subset based on preprocessed version of the GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.

CAVEAT: This file still contains neutral densities gamma calculated with a preliminary method. However, this is consistent with the way gamma is currently calculated in this script and should therefore be maintained until changed on all levels.

if (params_local$model_runs == "AD") {
  GLODAP <-
    read_csv(
      paste(
        path_preprocessing,
        "GLODAPv2.2020_preprocessed_model_runA_final.csv",
        sep = ""
      )
    )
  
  if (params_local$random == "grid") {
    random <- read_csv(
      paste(
        path_preprocessing,
        "GLODAPv2.2020_preprocessed_model_runA_random_subset_grid.csv",
        sep = ""
      )
    )
  }
  
  if (params_local$random == "lat") {
    random <- read_csv(
      paste(
        path_preprocessing,
        "GLODAPv2.2020_preprocessed_model_runA_random_subset_lat.csv",
        sep = ""
      )
    )
  }
  
}

if (params_local$model_runs == "CB") {
  GLODAP <-
    read_csv(
      paste(
        path_preprocessing,
        "GLODAPv2.2020_preprocessed_model_runC_final.csv",
        sep = ""
      )
    )
  if (params_local$random == "grid") {
    random <- read_csv(
      paste(
        path_preprocessing,
        "GLODAPv2.2020_preprocessed_model_runC_random_subset_grid.csv",
        sep = ""
      )
    )
  }
  
  if (params_local$random == "lat") {
    random <- read_csv(
      paste(
        path_preprocessing,
        "GLODAPv2.2020_preprocessed_model_runC_random_subset_lat.csv",
        sep = ""
      )
    )
  }
  
}

2 Data preparation

2.1 Reference eras

Samples were assigned to following eras:

# create labels for era
labels_GLODAP <- bind_cols(
  start = params_local$era_breaks_GLODAP+1,
  end = lead(params_local$era_breaks_GLODAP))

labels_GLODAP <- labels_GLODAP %>% 
  filter(!is.na(end)) %>% 
  mutate(end = if_else(end == Inf, max(GLODAP$year), end),
         label = paste(start, end, sep = "-")) %>% 
  select(label) %>% 
  pull()

labels_random <- bind_cols(
  start = params_local$era_breaks_random+1,
  end = lead(params_local$era_breaks_random))

labels_random <- labels_random %>% 
  filter(!is.na(end)) %>% 
  mutate(end = if_else(end == Inf, max(random$year), end),
         label = paste(start, end, sep = "-")) %>% 
  select(label) %>% 
  pull()

# cut observation years into era applying the labels
GLODAP <- GLODAP %>%
  filter(year > params_local$era_breaks_GLODAP[1]) %>% 
  mutate(era = cut(year,
                   params_local$era_breaks_GLODAP,
                   labels = labels_GLODAP))

random <- random %>%
  filter(year > params_local$era_breaks_random[1]) %>% 
  mutate(era = cut(year,
                   params_local$era_breaks_random,
                   labels = labels_random))

levels(GLODAP$era)
[1] "1982-1999" "2000-2012" "2013-2019"
levels(random$era)
[1] "1982-1999" "2000-2012" "2013-2019"
rm(labels_GLODAP, labels_random)

2.2 Spatial boundaries

2.2.1 Basin mask

The basin mask from the World Ocean Atlas was used. For details consult the data base subsection for WOA18 data.

Please note that some GLODAP-based subsetting model data were outside the WOA18 basin mask (i.e. in marginal seas) and will be removed for further analysis.

# use only data inside basinmask
GLODAP <- inner_join(GLODAP, basinmask)
random <- inner_join(random, basinmask)

2.2.2 Depth

GLODAP-based subsetting model data with depth shallower than:

  • minimum sampling depth: 150m

were excluded from the analysis to avoid seasonal bias.

GLODAP <- GLODAP %>% 
  filter(depth >= params_local$depth_min)

random <- random %>% 
  filter(depth >= params_local$depth_min)

2.2.3 Bottomdepth

GLODAP-based subsetting model data in an area with a:

  • minimum bottom depth: 0m

were excluded from the analysis to avoid coastal impacts. Please note that minimum bottom depth criterion of 0m means that no filtering was applied here.

GLODAP <- GLODAP %>% 
  filter(bottomdepth >= params_local$bottomdepth_min)

2.3 Flags and missing data

Only rows (samples) for which all relevant parameters are available were selected, ie NA’s were removed.

According to Olsen et al (2020), flags within the merged master file identify:

  • f:

    • 2: Acceptable
    • 0: Interpolated (nutrients/oxygen) or calculated (CO[2] variables)
    • 9: Data not used (so, only NA data should have this flag)

  • qc:

    • 1: Adjusted or unadjusted data
    • 0: Data appear of good quality but have not been subjected to full secondary QC
    • data with poor or uncertain quality are excluded.

Following flagging criteria were taken into account:

  • flag_f: 2
  • flag_qc: 1

The cleaning process was performed successively and the maps below represent the data coverage at various cleaning levels.

Summary statistics were calculated during cleaning process.

2.3.1 tco2

2.3.1.1 NA

Rows with missing tco2 in GLODAP-based subsetting model data were already removed in the preprocessing. The map below shows the coverage of preprocessed GLODAP-based subsetting model data.

GLODAP_stats <- GLODAP %>% 
  summarise(tco2_values = n())

GLODAP_obs_grid <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "tco2_values")
GLODAP_obs <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

map +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
a145fa7 Donghe-Zhu 2021-02-09
1fad5f1 Donghe-Zhu 2021-02-07
3e68089 Donghe-Zhu 2021-01-31
ecf335c Donghe-Zhu 2021-01-31
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0e7bdf1 jens-daniel-mueller 2021-01-15
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e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23 
rm(GLODAP_obs)

2.3.1.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, tco2f)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ tco2f) +
  theme(legend.position = "top")

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
8fae0b2 Donghe-Zhu 2020-12-21
c8b76b3 jens-daniel-mueller 2020-12-19 
rm(GLODAP_obs_grid_temp)

GLODAP <- GLODAP %>%
  filter(tco2f %in% params_local$flag_f)

2.3.1.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, tco2qc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ tco2qc) +
  theme(legend.position = "top")

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 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 
##

GLODAP <- GLODAP %>%
  filter(tco2qc %in% params_local$flag_qc)

GLODAP_stats_temp <- GLODAP %>%
  summarise(tco2_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "tco2_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.2 talk

2.3.2.1 NA

GLODAP <- GLODAP %>% 
  mutate(talkna = if_else(is.na(talk), "NA", "Value"))

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkna)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkna) +
  theme(legend.position = "top")

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
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 
GLODAP <- GLODAP %>% 
  select(-talkna) %>% 
  filter(!is.na(talk))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(talk_values = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "talk_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.2.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkf)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkf) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 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 
# ###

GLODAP <- GLODAP %>%
  filter(talkf %in% params_local$flag_f)

2.3.2.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, talkqc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ talkqc) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 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 
###

GLODAP <- GLODAP %>%
  filter(talkqc %in% params_local$flag_qc)

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(talk_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "talk_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.3 Phosphate

2.3.3.1 NA

GLODAP <- GLODAP %>% 
  mutate(phosphatena = if_else(is.na(phosphate), "NA", "Value"))

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphatena)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era ~ phosphatena) +
  theme(legend.position = "top")

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
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 
GLODAP <- GLODAP %>% 
  select(-phosphatena) %>% 
  filter(!is.na(phosphate))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(phosphate_values = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>% 
  count(lat, lon, era) %>% 
  mutate(cleaning_level = "phosphate_values")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.3.2 f flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphatef)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
    scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era~phosphatef) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 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 
###

GLODAP <- GLODAP %>%
  filter(phosphatef %in% params_local$flag_f)

2.3.3.3 qc flag

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era, phosphateqc)

map +
  geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
    scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_grid(era~phosphateqc) +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 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 
###

GLODAP <- GLODAP %>%
  filter(phosphateqc %in% params_local$flag_qc)

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(phosphate_flag = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "phosphate_flag")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)

2.3.4 eMLR variables

Variables required as predictors for the MLR fits, are subsetted for NAs and flags.

if ("temp" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(temp))
}

##

if ("sal" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(sal))
  
  GLODAP <- GLODAP %>%
    filter(salinityf %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(salinityqc %in% params_local$flag_qc)
}

##

if ("silicate" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(silicate))
  
  GLODAP <- GLODAP %>%
    filter(silicatef %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(silicateqc %in% params_local$flag_qc)
}

##

if ("oxygen" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(oxygen))
  
  GLODAP <- GLODAP %>%
    filter(oxygenf %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(oxygenqc %in% params_local$flag_qc)
}

##

if ("aou" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(aou))
  
  GLODAP <- GLODAP %>%
    filter(aouf %in% params_local$flag_f)
}

##

if ("nitrate" %in% params_local$MLR_predictors) {
  GLODAP <- GLODAP %>%
    filter(!is.na(nitrate))
  
  GLODAP <- GLODAP %>%
    filter(nitratef %in% params_local$flag_f)
  
  GLODAP <- GLODAP %>%
    filter(nitrateqc %in% params_local$flag_qc)
}

##

GLODAP <- GLODAP %>%
  filter(!is.na(depth))

GLODAP <- GLODAP %>%
  filter(!is.na(gamma))

##

GLODAP_stats_temp <- GLODAP %>%
  summarise(eMLR_variables = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)

rm(GLODAP_stats_temp)

##

GLODAP_obs_grid_temp <- GLODAP %>%
  count(lat, lon, era) %>%
  mutate(cleaning_level = "eMLR_variables")

GLODAP_obs_grid <-
  bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)

rm(GLODAP_obs_grid_temp)
GLODAP <- GLODAP %>% 
  select(-ends_with(c("f", "qc")))

2.4 Manual adjustment A16 cruise

For harmonization with Gruber et al. (2019), cruises 1041 (A16N) and 1042 (A16S) were grouped into the 2000-2012 era despite taking place in 2013/14.

GLODAP_cruises <- GLODAP %>% 
  filter(basin_AIP == "Atlantic",
         year %in% c(2013, 2014)) %>% 
  count(lat, lon, cruise)

map +
  geom_raster(data = GLODAP_cruises, aes(lon, lat, fill = as.factor(cruise))) +
  scale_fill_brewer(palette = "Dark2") +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
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
21c91c9 Donghe-Zhu 2021-01-29
eded038 Donghe-Zhu 2021-01-29
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
e5cb81a Donghe-Zhu 2021-01-05
c8b76b3 jens-daniel-mueller 2020-12-19 
rm(GLODAP_cruises)
if (params_local$A16_GO_SHIP == "y") {

GLODAP <- GLODAP %>%
   mutate(era = if_else(cruise %in% c(1041, 1042),
                        sort(unique(GLODAP$era))[2], era))

}

2.5 Create clean observations grid

Grid containing all grid cells where at least one synthetic subsetting remains available after cleaning.

GLODAP_obs_grid_clean <- GLODAP %>% 
  count(lat, lon) %>% 
  select(-n)

random_obs_grid_clean <- random %>% 
  count(lat, lon) %>% 
  select(-n)

2.6 Write summary file

GLODAP_obs_grid_clean  %>%  write_csv(paste(
  path_version_data,
  "GLODAPv2.2020_clean_GLODAP_obs_grid.csv",
  sep = ""
))

# select relevant columns for further analysis
GLODAP <- GLODAP %>%
  select(
    year,
    date,
    era,
    basin,
    basin_AIP,
    lat,
    lon,
    cruise,
    bottomdepth,
    depth,
    temp,
    sal,
    gamma,
    tco2,
    talk,
    phosphate,
    oxygen,
    aou,
    nitrate,
    silicate
  )

GLODAP  %>%  write_csv(paste(path_version_data,
                             "GLODAPv2.2020_clean_GLODAP.csv",
                             sep = ""))

random_obs_grid_clean  %>%  write_csv(paste(
  path_version_data,
  "GLODAPv2.2020_clean_random_obs_grid.csv",
  sep = ""
))

random  %>%  write_csv(paste(path_version_data,
                             "GLODAPv2.2020_clean_random.csv",
                             sep = ""))

3 Overview plots

3.1 Cleaning stats

Number of GLODAP-based subsetting model data at various steps of data cleaning.

GLODAP_stats_long <- GLODAP_stats %>%
  pivot_longer(1:length(GLODAP_stats),
               names_to = "parameter",
               values_to = "n")

GLODAP_stats_long <- GLODAP_stats_long %>%
  mutate(parameter = fct_reorder(parameter, n))

GLODAP_stats_long %>% 
  ggplot(aes(parameter, n/1000)) +
  geom_col() +
  coord_flip() +
  theme(axis.title.y = element_blank())

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
19edd1e Donghe-Zhu 2021-02-27
354c224 Donghe-Zhu 2021-02-24
5dce4b1 Donghe-Zhu 2021-02-15
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
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
e5cb81a Donghe-Zhu 2021-01-05
a499f10 Donghe-Zhu 2021-01-05
fb8a752 Donghe-Zhu 2020-12-23 
rm(GLODAP_stats_long)

3.2 Assign coarse spatial grid

For the following plots, the cleaned data set was re-opened and GLODAP-based subsetting data were gridded spatially to intervals of:

  • 5° x 5°
GLODAP <- m_grid_horizontal_coarse(GLODAP)
random <- m_grid_horizontal_coarse(random)

3.3 Histogram Zonal coverage

GLODAP_histogram_lat <- GLODAP %>%
  group_by(era, lat_grid, basin) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_lat %>%
  ggplot(aes(lat_grid, n, fill = era)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap( ~ basin) +
  coord_flip() +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
a145fa7 Donghe-Zhu 2021-02-09
1fad5f1 Donghe-Zhu 2021-02-07
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
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
ceed31b Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 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
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(GLODAP_histogram_lat)

random_histogram_lat <- random %>%
  group_by(era, lat_grid, basin) %>%
  tally() %>%
  ungroup()

random_histogram_lat %>%
  ggplot(aes(lat_grid, n, fill = era)) +
  geom_col() +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap( ~ basin) +
  coord_flip() +
  theme(legend.position = "top",
        legend.title = element_blank())

Version Author Date
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
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
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
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 
rm(random_histogram_lat)

3.4 Median years (tref)

Median years of each era (tref) were determined as:

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

era_median_year_GLODAP
# A tibble: 3 x 2
  era       t_ref
  <fct>     <dbl>
1 1982-1999  1995
2 2000-2012  2007
3 2013-2019  2016
era_median_year_random <- random %>%
  group_by(era) %>%
  summarise(t_ref = median(year)) %>%
  ungroup()

era_median_year_random
# A tibble: 3 x 2
  era       t_ref
  <fct>     <dbl>
1 1982-1999  1991
2 2000-2012  2006
3 2013-2019  2016

3.5 Histogram temporal coverage

GLODAP_histogram_year <- GLODAP %>%
  group_by(year, basin) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_year %>%
  ggplot() +
  geom_vline(xintercept = c(
    params_local$era_breaks + 0.5
  )) +
  geom_col(aes(year, n, fill = basin)) +
  geom_point(
    data = era_median_year_GLODAP,
    aes(t_ref, 0, shape = "Median year"),
    size = 2,
    fill = "white"
  ) +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = 24, name = "") +
  scale_y_continuous() +
  coord_cartesian() +
  theme(
    legend.position = "top",
    legend.direction = "vertical",
    legend.title = element_blank(),
    axis.title.x = element_blank()
  )

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
a145fa7 Donghe-Zhu 2021-02-09
c344e42 Donghe-Zhu 2021-02-08
1fad5f1 Donghe-Zhu 2021-02-07
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
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
ceed31b Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 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
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(GLODAP_histogram_year,
   era_median_year_GLODAP)

random_histogram_year <- random %>%
  group_by(year, basin) %>%
  tally() %>%
  ungroup()

random_histogram_year %>%
  ggplot() +
  geom_vline(xintercept = c(
    params_local$era_breaks + 0.5
  )) +
  geom_col(aes(year, n, fill = basin)) +
  geom_point(
    data = era_median_year_random,
    aes(t_ref, 0, shape = "Median year"),
    size = 2,
    fill = "white"
  ) +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = 24, name = "") +
  scale_y_continuous() +
  coord_cartesian() +
  theme(
    legend.position = "top",
    legend.direction = "vertical",
    legend.title = element_blank(),
    axis.title.x = element_blank()
  )

Version Author Date
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
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
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
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 
rm(random_histogram_year,
   era_median_year_random)

3.6 Zonal temporal coverage (Hovmoeller)

GLODAP_hovmoeller_year <- GLODAP %>%
  group_by(year, lat_grid, basin) %>%
  tally() %>%
  ungroup()

GLODAP_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = n)) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_wrap( ~ basin, ncol = 1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
354c224 Donghe-Zhu 2021-02-24
57f701e Donghe-Zhu 2021-02-24
06f3149 Donghe-Zhu 2021-02-16
5dce4b1 Donghe-Zhu 2021-02-15
4469a0c Donghe-Zhu 2021-02-13
5ae6a69 Donghe-Zhu 2021-02-10
a145fa7 Donghe-Zhu 2021-02-09
1fad5f1 Donghe-Zhu 2021-02-07
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
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
ceed31b Donghe-Zhu 2021-01-27
61efb56 Donghe-Zhu 2021-01-25
48f638e Donghe-Zhu 2021-01-25
c1cec47 Donghe-Zhu 2021-01-25
05ffb0c Donghe-Zhu 2021-01-25
8b97165 Donghe-Zhu 2021-01-25
c569946 Donghe-Zhu 2021-01-24
a2f0d56 Donghe-Zhu 2021-01-23
28509fc Donghe-Zhu 2021-01-23
4c28e4a Donghe-Zhu 2021-01-22
24cc264 jens-daniel-mueller 2021-01-22
7891955 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
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(GLODAP_hovmoeller_year)

random_hovmoeller_year <- random %>%
  group_by(year, lat_grid, basin) %>%
  tally() %>%
  ungroup()

random_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = n)) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10") +
  facet_wrap( ~ basin, ncol = 1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

Version Author Date
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
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
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
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
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 
rm(random_hovmoeller_year)

3.7 Coverage maps by era

3.7.1 Subsetting process

The following plots show the remaining data after individual cleaning steps, separately for each era.

GLODAP_obs_grid <- GLODAP_obs_grid %>%
  mutate(cleaning_level = factor(
           cleaning_level,
           unique(GLODAP_obs_grid$cleaning_level)
         ))

map +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level == "tco2_values") %>%
                select(-cleaning_level),
              aes(lon, lat, fill = "tco2_values")) +
  geom_raster(data = GLODAP_obs_grid %>%
                filter(cleaning_level != "tco2_values"),
              aes(lon, lat, fill = "subset")) +
  scale_fill_brewer(palette = "Set1", name = "") +
  facet_grid(cleaning_level ~ era) +
  theme(legend.position = "top",
        axis.title = element_blank())

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
5dce4b1 Donghe-Zhu 2021-02-15
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
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
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

3.7.2 Final input data

The following plots show the remaining data density in each grid cell after all cleaning steps, separately for each era.

GLODAP_tco2_grid <- GLODAP %>%
  count(lat, lon)

map +
  # geom_raster(data = GLODAP_tco2_grid, aes(lon, lat), fill = "grey80") +
  geom_bin2d(data = GLODAP,
             aes(lon, lat),
             binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
  facet_wrap(~era, ncol = 1) +
  labs(title = "Cleaned GLODAP-based model subsetting",
       subtitle = paste("Version:", params_local$Version_ID)) +
  theme(axis.title = element_blank())

Version Author Date
66ff99f Donghe-Zhu 2021-03-01
ac9bb7a Donghe-Zhu 2021-02-28
efdc047 Donghe-Zhu 2021-02-28
19edd1e Donghe-Zhu 2021-02-27
f20483f Donghe-Zhu 2021-02-26
6a2c7b3 Donghe-Zhu 2021-02-25
354c224 Donghe-Zhu 2021-02-24
5dce4b1 Donghe-Zhu 2021-02-15
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
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
1f3e5b6 jens-daniel-mueller 2021-01-20
0e7bdf1 jens-daniel-mueller 2021-01-15
4571843 jens-daniel-mueller 2021-01-14
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 
ggsave(path = path_version_figures,
       filename = "data_distribution_era.png",
       height = 8,
       width = 5)

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] lubridate_1.7.9 metR_0.9.0      scico_1.2.0     patchwork_1.1.1
 [5] collapse_1.5.0  forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2    
 [9] purrr_0.3.4     readr_1.4.0     tidyr_1.1.2     tibble_3.0.4   
[13] ggplot2_3.3.3   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               jsonlite_1.7.2           viridisLite_0.3.0       
 [4] here_1.0.1               modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.9.1      digest_0.6.27           
[16] RColorBrewer_1.1-2       promises_1.1.1           checkmate_2.0.0         
[19] rvest_0.3.6              colorspace_2.0-0         htmltools_0.5.0         
[22] httpuv_1.5.4             Matrix_1.2-18            pkgconfig_2.0.3         
[25] broom_0.7.5              haven_2.3.1              scales_1.1.1            
[28] whisker_0.4              later_1.1.0.1            git2r_0.27.1            
[31] generics_0.1.0           farver_2.0.3             ellipsis_0.3.1          
[34] withr_2.3.0              cli_2.2.0                magrittr_2.0.1          
[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_0.10.1.2.2 tools_4.0.3              data.table_1.13.6       
[46] hms_0.5.3                lifecycle_0.2.0          munsell_0.5.0           
[49] reprex_0.3.0             compiler_4.0.3           rlang_0.4.10            
[52] grid_4.0.3               rstudioapi_0.13          labeling_0.4.2          
[55] rmarkdown_2.5            gtable_0.3.0             DBI_1.1.0               
[58] R6_2.5.0                 knitr_1.30               utf8_1.1.4              
[61] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[64] Rcpp_1.0.5               vctrs_0.3.6              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.20