GLODAPv2_2020
Jens Daniel Müller
18 August, 2020
Last updated: 2020-08-18
Checks: 7 0
Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
library(patchwork)
library(cutr)1 Read files
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())
)
# select relevant columns
GLODAP <- GLODAP %>%
select(cruise:talkqc)
# create date column
GLODAP <- GLODAP %>%
mutate(date = ymd(paste(year, month, day))) %>%
#decade = as.factor(floor(year / 10) * 10)) %>%
relocate(date)
# harmonize column names
GLODAP <- GLODAP %>%
rename(sal = salinity,
tem = temperature)
# harmonize coordinates
GLODAP <- GLODAP %>%
rename(lon = longitude,
lat = latitude) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# remove irrelevant columns
GLODAP <- GLODAP %>%
select(-c(month:minute,
maxsampdepth, sigma0:sigma4,
nitrite:nitritef))2 Data preparation
2.1 Flags and missing data
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 == parameters$flag_f) %>%
select(-tco2f)
GLODAP <- GLODAP %>%
filter(tco2qc == parameters$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 == parameters$flag_f) %>%
select(-talkf)
GLODAP <- GLODAP %>%
filter(talkqc == parameters$flag_qc) %>%
select(-talkqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(phosphate))
GLODAP <- GLODAP %>%
filter(phosphatef == parameters$flag_f) %>%
select(-phosphatef)
GLODAP <- GLODAP %>%
filter(phosphateqc == parameters$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(tem))
##
GLODAP <- GLODAP %>%
filter(!is.na(sal))
GLODAP <- GLODAP %>%
filter(salinityf == parameters$flag_f) %>%
select(-salinityf)
GLODAP <- GLODAP %>%
filter(salinityqc == parameters$flag_qc) %>%
select(-salinityqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(silicate))
GLODAP <- GLODAP %>%
filter(silicatef == parameters$flag_f) %>%
select(-silicatef)
GLODAP <- GLODAP %>%
filter(silicateqc == parameters$flag_qc) %>%
select(-silicateqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(oxygen))
GLODAP <- GLODAP %>%
filter(oxygenf == parameters$flag_f) %>%
select(-oxygenf)
GLODAP <- GLODAP %>%
filter(oxygenqc == parameters$flag_qc) %>%
select(-oxygenqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(aou))
GLODAP <- GLODAP %>%
filter(aouf == parameters$flag_f) %>%
select(-aouf)
##
GLODAP <- GLODAP %>%
filter(!is.na(nitrate))
GLODAP <- GLODAP %>%
filter(nitratef == parameters$flag_f) %>%
select(-nitratef)
GLODAP <- GLODAP %>%
filter(nitrateqc == parameters$flag_qc) %>%
select(-nitrateqc)
##
GLODAP <- GLODAP %>%
filter(!is.na(depth))
GLODAP <- GLODAP %>%
filter(!is.na(gamma))
##
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)2.2 Spatial boundaries
Only observations were taken into consideration with:
- minimum sampling depth: 150m
- minimum bottom depth: 500m
- maximum latitude: 65°N
GLODAP <- GLODAP %>%
filter(depth >= parameters$depth_min)
GLODAP_stats_temp <- GLODAP %>%
summarise(eMLR_variables_150m = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP <- GLODAP %>%
filter(lat <= parameters$lat_max)
GLODAP <- GLODAP %>%
filter(bottomdepth >= parameters$bottomdepth_min)
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)2.3 Reference eras
Samples were assigned to following eras:
JGOFS_WOCE: 1981 - 1999
GO_SHIP: 2000 - 2012
new_era: > 2013
GLODAP <- GLODAP %>%
filter(year >= parameters$year_JGOFS_start) %>%
mutate(era = "JGOFS_WOCE",
era = if_else(year > parameters$year_JGOFS_end, "GO_SHIP", era),
era = if_else(year > parameters$year_GOSHIP_end, "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(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
lon = as.numeric(as.character(lon))) %>%
arrange(date)2.5 Basin mask
The basin mask from the World Ocean Atlas was used. For details consult the data base subsection for WOA18 data.
GLODAP_obs <- GLODAP %>%
group_by(lat, lon) %>%
summarise(n = n()) %>%
ungroup()
ggplot() +
geom_raster(data = landmask %>% filter(region == "land"),
aes(lon, lat), fill = "grey80") +
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(GLODAP_obs)Please note that some GLODAP observations were made outside the WOA18 basin mask and will be removed for further analysis (eg North Sea, Sea of Japan).
GLODAP <- inner_join(GLODAP, basinmask)
rm(basinmask)2.6 Observations grid
A 1x1 grid containing all coordinates with at least one GLODAP observation was created.
GLODAP_obs_grid <- GLODAP %>%
group_by(lat, lon) %>%
summarise(n = n()) %>%
ungroup()2.7 Write summary file
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
Number of observations at various steps of data cleaning.
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 = smart_cut(
lat,
seq(-90, 90, parameters$coarse_grid_resolution),
labels = ~ mean(.y)
),
lat_grid = as.numeric(as.character(lat_grid)),
lon_grid = smart_cut(
lon,
seq(-1000, 1000, parameters$coarse_grid_resolution),
labels = ~ mean(.y)
),
lon_grid = as.numeric(as.character(lon_grid))
) %>%
arrange(date)GLODAP <- GLODAP %>%
mutate(lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
lat_grid = as.numeric(as.character(lat_grid)),
lon_grid = cut(lon, seq(20, 380, 5), seq(22.5, 377.5, 5)),
lon_grid = as.numeric(as.character(lon_grid))) %>%
arrange(date)3.3 Histogram Zonal coverage
GLODAP_histogram_lat <- GLODAP %>%
group_by(era, lat_grid, basin) %>%
tally() %>%
ungroup()
GLODAP_histogram_lat %>%
ggplot(aes(lat_grid, n, fill = era)) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_wrap( ~ basin) +
coord_flip(expand = 0) +
theme(legend.position = "top",
legend.title = element_blank())
rm(GLODAP_histogram_lat)3.4 Histogram temporal coverage
GLODAP_histogram_year <- GLODAP %>%
group_by(year, basin) %>%
tally() %>%
ungroup()
era_median_year <- GLODAP %>%
group_by(era) %>%
summarise(t_ref = median(year)) %>%
ungroup()
GLODAP_histogram_year %>%
ggplot() +
geom_vline(xintercept = c(
parameters$year_JGOFS_end + 0.5,
parameters$year_GOSHIP_end + 0.5
)) +
geom_col(aes(year, n, fill = basin)) +
geom_point(
data = era_median_year,
aes(t_ref, 0, shape = "Median year"),
size = 2,
fill = "white"
) +
scale_fill_brewer(palette = "Dark2") +
scale_shape_manual(values = 24, name = "") +
scale_y_continuous() +
coord_cartesian(expand = 0) +
theme(
legend.position = "top",
legend.direction = "vertical",
legend.title = element_blank(),
axis.title.x = element_blank()
)
rm(GLODAP_histogram_year,
era_median_year)3.5 Zonal temporal coverage (Hovmoeller)
GLODAP_hovmoeller_year <- GLODAP %>%
group_by(year, lat_grid, basin) %>%
tally() %>%
ungroup()
GLODAP_hovmoeller_year %>%
ggplot(aes(year, lat_grid, fill = log10(n))) +
geom_tile() +
geom_vline(xintercept = c(1999.5, 2012.5)) +
scale_fill_viridis_c(option = "magma", direction = -1) +
facet_wrap( ~ basin, ncol = 1) +
theme(legend.position = "top",
axis.title.x = element_blank())
rm(GLODAP_hovmoeller_year)3.6 Coverage maps by era
GLODAP <- GLODAP %>%
mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))
GLODAP %>%
ggplot(aes(lon, lat)) +
geom_raster(data = landmask %>% filter(region == "land"),
aes(lon, lat), fill = "grey80") +
geom_bin2d(binwidth = c(1,1)) +
scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10",
name = "log10(n)") +
coord_quickmap(expand = FALSE) +
facet_wrap(~era, ncol = 1) +
theme(legend.position = "top",
axis.title = element_blank())
4 Individual cruise sections
Zonal and meridional section plots are produce for each cruise individually and can be downloaded here.
cruises <- GLODAP %>%
group_by(cruise) %>%
summarise(date_mean = mean(date, na.rm = TRUE),
n = n()) %>%
ungroup() %>%
arrange(date_mean)
GLODAP <- full_join(GLODAP, cruises)
n <- 0
for (i_cruise in unique(cruises$cruise)) {
# i_cruise <- unique(cruises$cruise)[1]
n <- n+1
print(n)
GLODAP_cruise <- GLODAP %>%
filter(cruise == i_cruise) %>%
arrange(date)
cruises_cruise <- cruises %>%
filter(cruise == i_cruise)
map <- GLODAP_cruise %>%
ggplot(aes(lon, lat)) +
geom_raster(data = landmask %>% filter(region == "land"),
aes(lon, lat), fill = "grey80") +
geom_point(aes(col=date)) +
coord_quickmap(expand = FALSE) +
scale_color_viridis_c(trans = "date") +
labs(title = paste("Mean date:", cruises_cruise$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.html5 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] cutr_0.0.0.9000 patchwork_1.0.1 lubridate_1.7.9 forcats_0.5.0
[5] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[9] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[13] 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 hms_0.5.3 yaml_2.2.1
[57] colorspace_1.4-1 rvest_0.3.6 knitr_1.29 haven_2.3.1