GLODAPv2_2020
Jens Daniel Müller
21 July, 2020
Last updated: 2020-07-21
Checks: 7 0
Knit directory: Cant_eMLR/
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| Rmd | d2ed0f8 | jens-daniel-mueller | 2020-07-21 | harmonied lat lon labeling |
| html | b47adc2 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 366d7d5 | jens-daniel-mueller | 2020-07-20 | update plots |
| html | 25812b9 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | e45a882 | jens-daniel-mueller | 2020-07-20 | basins included, higher grid resolution for maps |
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| Rmd | 50be8de | jens-daniel-mueller | 2020-07-20 | formating |
| html | b9769e8 | jens-daniel-mueller | 2020-07-20 | Build site. |
| Rmd | 82120fe | jens-daniel-mueller | 2020-07-20 | nitrate, gamma included, 65N removed |
| html | fe55909 | jens-daniel-mueller | 2020-07-19 | Build site. |
| Rmd | 66540a9 | jens-daniel-mueller | 2020-07-19 | formating and color scale |
| html | 61b3390 | jens-daniel-mueller | 2020-07-18 | Build site. |
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| html | 3964fd7 | jens-daniel-mueller | 2020-07-17 | Build site. |
| Rmd | 4685ad7 | jens-daniel-mueller | 2020-07-17 | 150m depth limit implemented |
| html | 56c3ed9 | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | aed89af | jens-daniel-mueller | 2020-07-14 | added Rmd for MappedClimatologies |
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| html | 1c511ce | jens-daniel-mueller | 2020-07-14 | Build site. |
| Rmd | e03016e | jens-daniel-mueller | 2020-07-14 | split read in per data set |
library(tidyverse)
library(lubridate)
library(patchwork)1 Read master file
- Data source:
GLODAPv2.2020_Merged_Master_File.csvfrom glodap.info
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 <- 1Only samples with all relevant parameters determined were selected. The following flagging criteria were defined:
- 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
max_lat <- 65Only observations with:
- minimum depth: 150m
- maximum latitude: 65°N
were taken into consideration.
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_stats_temp <- GLODAP %>%
summarise(eMLR_variables_150m_65N = 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)rm(min_depth, max_lat)2.3 Reference eras
start <- 1981
JGOFS <- 1999
GOSHIP <- 2012Samples 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))# rm(start, JGOFS, GOSHIP)2.4 Basin mask
basinmask <- read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"))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))GLODAP <- inner_join(GLODAP, basinmask)
rm(basinmask)2.5 Write summary file
GLODAP %>% write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))
rm(GLODAP)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 5°x5° intervals.
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 lats
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)
rm(GLODAP_histogram_lat)3.4 Histogram year
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)+
coord_cartesian(expand = 0)
rm(GLODAP_histogram_year,
era_median_year)3.5 Hovmoeller (lat vs year)
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)
rm(GLODAP_hovmoeller_year)3.6 Maps by era
mapWorld <- borders("world", colour="gray60", fill="gray60")
GLODAP_map_era <- GLODAP %>%
group_by(era, lat, lon) %>%
tally() %>%
ungroup()
GLODAP_map_era <- GLODAP_map_era %>%
mutate(era = factor(era, c("JGOFS/WOCE", "GO-SHIP", "new_era")))
GLODAP_map_era %>%
ggplot(aes(lon, lat, fill=log10(n)))+
mapWorld+
geom_tile()+
scale_fill_viridis_c(option = "magma", direction = -1)+
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)
rm(GLODAP_map_era,
mapWorld)4 Individual cruise sections
Zonal and meridional section plots are produce for each cruise individually and stored locally.
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))+
geom_path()+
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$n))
lon_section <- GLODAP_cruise %>%
ggplot(aes(lon, depth))+
scale_y_reverse()+
scale_color_viridis_c()
lon_tco2 <- lon_section+
geom_point(aes(col=tco2))
lon_talk <- lon_section+
geom_point(aes(col=talk))
lon_phosphate <- lon_section+
geom_point(aes(col=phosphate))
lat_section <- GLODAP_cruise %>%
ggplot(aes(lat, depth))+
scale_y_reverse()+
scale_color_viridis_c()
lat_tco2 <- lat_section+
geom_point(aes(col=tco2))
lat_talk <- lat_section+
geom_point(aes(col=talk))
lat_phosphate <- lat_section+
geom_point(aes(col=phosphate))
map /
((lat_tco2 / lat_talk / lat_phosphate) |
(lon_tco2 / lon_talk / lon_phosphate))
ggsave(here::here("output/plots/data/all_cruises_clean",
paste("GLODAP_cruise_date",
cruises_cruise$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
- remove marginal seas
- move A16 cruises from new_era to GO-SHIP era
6 Questions
- exclude coastal area in general?
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-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] RColorBrewer_1.1-2 jsonlite_1.7.0 rstudioapi_0.11 generics_0.0.2
[5] magrittr_1.5 farver_2.0.3 gtable_0.3.0 rmarkdown_2.3
[9] vctrs_0.3.1 fs_1.4.2 hms_0.5.3 xml2_1.3.2
[13] pillar_1.4.6 htmltools_0.5.0 haven_2.3.1 later_1.1.0.1
[17] broom_0.7.0 cellranger_1.1.0 tidyselect_1.1.0 knitr_1.29
[21] git2r_0.27.1 whisker_0.4 lifecycle_0.2.0 pkgconfig_2.0.3
[25] R6_2.4.1 digest_0.6.25 xfun_0.15 colorspace_1.4-1
[29] rprojroot_1.3-2 stringi_1.4.6 yaml_2.2.1 evaluate_0.14
[33] labeling_0.3 fansi_0.4.1 httr_1.4.1 compiler_3.6.3
[37] here_0.1 cli_2.0.2 withr_2.2.0 backports_1.1.5
[41] munsell_0.5.0 DBI_1.1.0 modelr_0.1.8 Rcpp_1.0.5
[45] readxl_1.3.1 maps_3.3.0 dbplyr_1.4.4 ellipsis_0.3.1
[49] assertthat_0.2.1 blob_1.2.1 tools_3.6.3 reprex_0.3.0
[53] viridisLite_0.3.0 httpuv_1.5.4 scales_1.1.1 crayon_1.3.4
[57] glue_1.4.1 rlang_0.4.7 rvest_0.3.5 promises_1.1.1
[61] grid_3.6.3