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library(tidyverse)
library(lubridate)
library(patchwork)

1 Read master file

Main data source for this project is GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.

GLODAP <- read_csv(here::here("data/GLODAPv2_2020/Merged_data_product",
                              "GLODAPv2.2020_Merged_Master_File.csv"),
                   na = "-9999",
                   col_types = cols(.default = col_double()))

# relevant columns
GLODAP <- GLODAP %>% 
  select(cruise:talkqc)

GLODAP <- GLODAP %>% 
  mutate(date = ymd(paste(year, month, day))) %>% 
         #decade = as.factor(floor(year / 10) * 10)) %>% 
  relocate(date)

GLODAP <- GLODAP %>% 
  select(-c(month:minute, 
            maxsampdepth, pressure,
            theta, sigma0:sigma4,
            nitrite:nitritef))

2 Data preparation

2.1 Flags and missing data

flag_f <- 2
flag_qc <- 1

Only rows (samples) for which all relevant parameters are available were selected. In additions, following flagging criteria were applied:

flag f: 2
flag qc: 1

Summary statistics were calculated during cleaning process.

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

##

GLODAP <- GLODAP %>% 
  filter(!is.na(tco2))

GLODAP <- GLODAP %>% 
  filter(tco2f == flag_f) %>% 
  select(-tco2f)

GLODAP <- GLODAP %>% 
  filter(tco2qc == flag_qc) %>% 
  select(-tco2qc)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(tco2 = n())

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

##

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

GLODAP <- GLODAP %>% 
  filter(talkf == flag_f) %>% 
  select(-talkf)

GLODAP <- GLODAP %>% 
  filter(talkqc == flag_qc) %>% 
  select(-talkqc)

##

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

GLODAP <- GLODAP %>% 
  filter(phosphatef == flag_f) %>% 
  select(-phosphatef)

GLODAP <- GLODAP %>% 
  filter(phosphateqc == flag_qc) %>% 
  select(-phosphateqc)

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

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

##

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

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

GLODAP <- GLODAP %>% 
  filter(salinityf == flag_f) %>% 
  select(-salinityf)

GLODAP <- GLODAP %>% 
  filter(salinityqc == flag_qc) %>% 
  select(-salinityqc)

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

GLODAP <- GLODAP %>% 
  filter(silicatef == flag_f) %>% 
  select(-silicatef)

GLODAP <- GLODAP %>% 
  filter(silicateqc == flag_qc) %>% 
  select(-silicateqc)

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

GLODAP <- GLODAP %>% 
  filter(oxygenf == flag_f) %>% 
  select(-oxygenf)

GLODAP <- GLODAP %>% 
  filter(oxygenqc == flag_qc) %>% 
  select(-oxygenqc)

##

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

GLODAP <- GLODAP %>% 
  filter(aouf == flag_f) %>% 
  select(-aouf)

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

GLODAP <- GLODAP %>% 
  filter(nitratef == flag_f) %>% 
  select(-nitratef)

GLODAP <- GLODAP %>% 
  filter(nitrateqc == flag_qc) %>% 
  select(-nitrateqc)

##

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)
rm(flag_f, flag_qc)

2.2 Spatial boundaries

min_depth <- 150
min_bottomdepth <- 500
max_lat <- 65

Only observations were taken into consideration with:

minimum sampling depth: 150m
minimum bottom depth: 500m
maximum latitude: 65°N

GLODAP <- GLODAP %>% 
  filter(depth >= min_depth)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables_150m = n())

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

##

GLODAP <- GLODAP %>% 
  filter(latitude <= max_lat)

GLODAP <- GLODAP %>% 
  filter(bottomdepth >= min_bottomdepth)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables_150m_65N_500m = n())

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


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

GLODAP_stats_long  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                             "GLODAPv2.2020_stats.csv"))

rm(GLODAP_stats_long, GLODAP_stats)
rm(min_depth, max_lat, min_bottomdepth)

2.3 Reference eras

start <- 1981
JGOFS <- 1999
GOSHIP <- 2012

Samples were assigned to following eras:

JGOFS-WOCE: 1981 - 1999
GO-SHIP: 2000 - 2012
new-era: > 2013

GLODAP <- GLODAP %>%
  filter(year >= start) %>% 
  mutate(era = "JGOFS_WOCE",
         era = if_else(year > JGOFS, "GO_SHIP", era),
         era = if_else(year > GOSHIP, "new_era", era))

2.4 Horizontal gridding

For merging with other data sets, all observations were grouped into latitude intervals of:

1° x 1°

GLODAP <- GLODAP %>% 
  mutate(lat = cut(latitude, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
         lat = as.numeric(as.character(lat)),
         lon = cut(longitude, seq(-180, 180, 1), seq(-179.5, 179.5, 1)),
         lon = as.numeric(as.character(lon))) %>% 
  arrange(date) %>% 
  select(-c(latitude,longitude))

2.5 Basin mask

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

basinmask <- read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                                 "basin_mask_WOA18.csv"))

GLODAP_obs <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

mapWorld <- borders("world", col = "gray60", fill = "gray60") 

ggplot() +
  mapWorld +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

rm(mapWorld, GLODAP_obs)

Please note that some GLODAP observations were made outside the WOA18 basin mask and will be removed for further analysis.

GLODAP <- inner_join(GLODAP, basinmask)
rm(basinmask)

2.6 Observations grid

GLODAP_obs_grid <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

2.7 Write summary filea

GLODAP_obs_grid  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                           "GLODAPv2.2020_clean_obs_grid.csv"))

GLODAP  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                  "GLODAPv2.2020_clean.csv"))

rm(GLODAP, GLODAP_obs_grid)

3 Overview plots

3.1 Cleaning stats

GLODAP_stats_long <-  read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                             "GLODAPv2.2020_stats.csv"))

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

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

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 <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                              "GLODAPv2.2020_clean.csv"))

GLODAP <- GLODAP %>% 
  mutate(lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
         lat_grid = as.numeric(as.character(lat_grid)),
         lon_grid = cut(lon, seq(-180, 180, 5), seq(-177.5, 177.5, 5)),
         lon_grid = as.numeric(as.character(lon_grid))) %>% 
  arrange(date)

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_x_continuous(breaks = seq(-87.5,90,10)) +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap(~basin) +
  coord_flip(expand = 0) +
  theme(legend.position = "top",
        legend.title = element_blank())

rm(GLODAP_histogram_lat)

3.4 Histogram temporal coverage

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

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

GLODAP_histogram_year %>% 
  ggplot() +
  geom_vline(xintercept = c(JGOFS + 0.5, GOSHIP + 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 = "") +
  coord_cartesian(expand = 0) +
  theme(legend.position = "top",
        legend.direction = "vertical",
        legend.title = element_blank())

rm(GLODAP_histogram_year,
   era_median_year)

3.5 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 = log10(n))) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma", direction = -1) +
  facet_wrap(~basin, ncol = 1) +
  theme(legend.position = "top")

rm(GLODAP_hovmoeller_year)

3.6 Coverage maps by era

mapWorld <- borders("world", colour = "gray60", fill = "gray60")

GLODAP <- GLODAP %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))

GLODAP %>%
  ggplot(aes(lon, lat)) +
  mapWorld +
  geom_bin2d(binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10",
                       name = "log10(n)") +
  scale_x_continuous(breaks = seq(-180, 180, 30)) +
  scale_y_continuous(breaks = seq(-90, 90, 30)) +
  coord_quickmap(expand = FALSE) +
  facet_wrap(~era, ncol = 1) +
  theme(legend.position = "top")

rm(mapWorld)

4 Individual cruise sections

Zonal and meridional section plots are produce for each cruise individually and can be downloaded here.

cruises <- GLODAP %>% 
  group_by(cruise) %>% 
  summarise(date_mean = mean(date, na.rm = TRUE),
            n = n()) %>% 
  ungroup() %>% 
  arrange(date_mean)

GLODAP <- full_join(GLODAP, cruises)

mapWorld <- borders("world", colour="gray60", fill="gray60")

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 <- GLODAP_cruise %>%
  ggplot(aes(lon, lat)) +
  mapWorld+
  geom_point(aes(col=date)) +
  coord_quickmap(expand = FALSE) +
  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_color_viridis_c()

lon_tco2 <- lon_section+
  geom_point(aes(col=tco2))

lon_talk <- lon_section+
  geom_point(aes(col=talk))

lon_phosphate <- lon_section+
  geom_point(aes(col=phosphate))


lat_section <- GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  scale_color_viridis_c()

lat_tco2 <- lat_section+
  geom_point(aes(col=tco2))

lat_talk <- lat_section+
  geom_point(aes(col=talk))

lat_phosphate <- lat_section+
  geom_point(aes(col=phosphate))

map /
((lat_tco2 / lat_talk / lat_phosphate) |
(lon_tco2 / lon_talk / lon_phosphate))

ggsave(here::here("output/figure/data/all_cruises_clean",
                  paste("GLODAP_cruise_date",
                        cruises_cruise$date_mean,
                        "n",
                        cruises_cruise$n,
                        "cruise",
                        cruises_cruise$cruise,
                        ".png",
                        sep = "_")),
                  width = 9, height = 9)

rm(map,
   lon_section, lat_section,
   lat_tco2, lat_talk, lat_phosphate, lon_tco2, lon_talk, lon_phosphate,
   GLODAP_cruise, cruises_cruise)

}

# library("rnaturalearth")
# library("rnaturalearthdata")
# library("sf")
# 
# world <- ne_countries(scale = "small", returnclass = "sf")
# class(world)
# 
# GLODAP_map <- GLODAP %>% 
#   group_by(lat_grid, lon_grid) %>% 
#   tally() %>% 
#   ungroup()
# 
# ggplot() +
#   geom_raster(data = GLODAP_map, aes(lon_grid, lat_grid)) +
#   geom_sf(data = world) +
#   coord_sf(crs = "+proj=robin +lat_0=0 +lon_0=0 +x0=0 +y0=0")


# https://gist.github.com/clauswilke/783e1a8ee3233775c9c3b8bfe531e28a
# https://twitter.com/clauswilke/status/1066024436208406529
# https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html

5 Open tasks

move A16 cruises in 2013 from new_era to GO_SHIP era

6 Questions

Apply coastal filter?
Keep horizontal gridding to 1°x1° (thereby discarding some horizontal information)?
Quality of WOA18 basin mask
- excluded areas (e.g. Bay of Bengal, South Norwegian Sea, Sea of Japan)
- Pacific-Atlantic boundary

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.0.1 lubridate_1.7.9 forcats_0.5.0   stringr_1.4.0  
 [5] dplyr_1.0.0     purrr_0.3.4     readr_1.3.1     tidyr_1.1.0    
 [9] tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         here_0.1           assertthat_0.2.1   rprojroot_1.3-2   
 [5] digest_0.6.25      R6_2.4.1           cellranger_1.1.0   backports_1.1.8   
 [9] reprex_0.3.0       evaluate_0.14      httr_1.4.2         pillar_1.4.6      
[13] rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11    whisker_0.4       
[17] blob_1.2.1         rmarkdown_2.3      labeling_0.3       munsell_0.5.0     
[21] broom_0.7.0        compiler_4.0.2     httpuv_1.5.4       modelr_0.1.8      
[25] xfun_0.16          pkgconfig_2.0.3    htmltools_0.5.0    tidyselect_1.1.0  
[29] viridisLite_0.3.0  fansi_0.4.1        crayon_1.3.4       dbplyr_1.4.4      
[33] withr_2.2.0        later_1.1.0.1      grid_4.0.2         jsonlite_1.7.0    
[37] gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0          git2r_0.27.1      
[41] magrittr_1.5       scales_1.1.1       cli_2.0.2          stringi_1.4.6     
[45] farver_2.0.3       fs_1.4.2           promises_1.1.1     xml2_1.3.2        
[49] ellipsis_0.3.1     generics_0.0.2     vctrs_0.3.2        RColorBrewer_1.1-2
[53] tools_4.0.2        glue_1.4.1         maps_3.3.0         hms_0.5.3         
[57] yaml_2.2.1         colorspace_1.4-1   rvest_0.3.6        knitr_1.29        
[61] haven_2.3.1

GLODAPv2_2020

Jens Daniel Müller

10 August, 2020