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Rmd	7a4b015	jens-daniel-mueller	2020-11-30	first rebuild on ETH server

1 Read files

Main data source for this project is the 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.

GLODAP <-
  read_csv(paste(path_preprocessing,
                 "GLODAPv2.2020_preprocessed.csv",
                 sep = ""))

2 Data preparation

2.1 Reference eras

Samples were assigned to following eras:

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

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

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

levels(GLODAP$era)

[1] "1982-1999" "2000-2012" "2013-2019"

rm(labels)

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 observations were made 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)

2.2.2 Depth

Observations collected shallower than:

minimum sampling depth: 150m

were excluded from the analysis to avoid seasonal bias.

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

2.2.3 Bottomdepth

Observations collected 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)

2.2.4 Neutral density threshold

Observations collected in an area with:

neutral density below: 0m

were excluded from the analysis due to expected high seasonality, except when:

minimum sampling depth: 150m

was fulfilled

GLODAP <- GLODAP %>% 
  filter(gamma > params_local$gamma_min | depth >= params_local$depth_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, 0
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 observations were already removed in the preprocessing. The map below shows the coverage of preprocessed GLODAP 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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
984697e	jens-daniel-mueller	2020-12-12
3ebff89	jens-daniel-mueller	2020-12-12
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	jens-daniel-mueller	2020-12-07
37e9dac	jens-daniel-mueller	2020-12-02
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
24a632f	jens-daniel-mueller	2020-12-07
6a8004b	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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

##

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

# ###

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

###

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

###

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

###

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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
7d82772	jens-daniel-mueller	2020-12-11
c8acfcb	jens-daniel-mueller	2020-12-11
70bf1a5	jens-daniel-mueller	2020-12-07
7555355	jens-daniel-mueller	2020-12-07
143d6fa	jens-daniel-mueller	2020-12-07
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_cruises)

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 observation remains available after cleaning.

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

2.6 Write summary file

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

3 Overview plots

3.1 Cleaning stats

Number of observations 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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
5d452fe	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_stats_long)

3.2 Assign coarse spatial grid

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

5° x 5°

GLODAP <- m_grid_horizontal_coarse(GLODAP)

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
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
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	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

rm(GLODAP_histogram_lat)

3.4 Median years (tref)

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

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

era_median_year

# A tibble: 3 x 2
  era       t_ref
  <fct>     <dbl>
1 1982-1999  1994
2 2000-2012  2007
3 2013-2019  2015

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,
    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
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
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
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
0ff728b	jens-daniel-mueller	2020-12-01
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_histogram_year,
   era_median_year)

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
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
c8acfcb	jens-daniel-mueller	2020-12-11
2fd0a2a	jens-daniel-mueller	2020-12-11
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
b02b7a4	jens-daniel-mueller	2020-12-01
196be51	jens-daniel-mueller	2020-11-30

rm(GLODAP_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
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
d5c5378	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

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 observations",
       subtitle = paste("Version:", params_local$Version_ID)) +
  theme(axis.title = element_blank())

Version	Author	Date
2886da0	jens-daniel-mueller	2020-12-19
02f0ee9	jens-daniel-mueller	2020-12-18
158fe26	jens-daniel-mueller	2020-12-15
3ebff89	jens-daniel-mueller	2020-12-12
2fd0a2a	jens-daniel-mueller	2020-12-11
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
7c25f7a	jens-daniel-mueller	2020-12-02
d5c5378	jens-daniel-mueller	2020-12-02
083b3b0	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

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 15.1

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_1.1.0
 [5] collapse_1.4.2  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_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               jsonlite_1.7.1           viridisLite_0.3.0       
 [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_2.0-0         htmltools_0.5.0         
[22] httpuv_1.5.4             Matrix_1.2-18            pkgconfig_2.0.3         
[25] broom_0.7.2              haven_2.3.1              scales_1.1.1            
[28] whisker_0.4              later_1.1.0.1            git2r_0.27.1            
[31] generics_0.0.2           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_0.10.1.2.0 tools_4.0.3              data.table_1.13.2       
[46] hms_0.5.3                lifecycle_0.2.0          munsell_0.5.0           
[49] reprex_0.3.0             compiler_4.0.3           rlang_0.4.9             
[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_0.3.5              dbplyr_1.4.4            
[67] tidyselect_1.1.0         xfun_0.18

GLODAPv2_2020 subsetting

Jens Daniel Müller

23 December, 2020