GLODAPv2_2020
Jens Daniel Müller
01 December, 2020
Last updated: 2020-12-01
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Knit directory: emlr_obs_v_XXX/
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path_functions <- "/nfs/kryo/work/updata/emlr_cant/utilities/functions/"
path_files <- "/nfs/kryo/work/updata/emlr_cant/utilities/files/"path_preprocessing <-
"/nfs/kryo/work/updata/emlr_cant/observations/preprocessing/"
path_version_data <-
paste(
"/nfs/kryo/work/updata/emlr_cant/observations/",
params_local$Version_ID,
"/data/",
sep = ""
)
path_version_figures <-
paste(
"/nfs/kryo/work/updata/emlr_cant/observations/",
params_local$Version_ID,
"/figures/",
sep = ""
)1 Libraries
Loading libraries specific to the the analysis performed in this section.
library(lubridate)2 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.
GLODAP <-
read_csv(paste(path_preprocessing,
"GLODAPv2.2020_preprocessed.csv",
sep = ""))3 Data preparation
3.1 Reference eras
Samples were assigned to following eras:
- JGOFS_WOCE:
- GO_SHIP:
- new_era: >
GLODAP <- GLODAP %>%
filter(year >= params_local$year_JGOFS_start) %>%
mutate(era = "JGOFS_WOCE",
era = if_else(year > params_local$year_JGOFS_end, "GO_SHIP", era),
era = if_else(year > params_local$year_GOSHIP_end, "new_era", era))
GLODAP <- GLODAP %>%
mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))GLODAP <- GLODAP %>%
filter(year > params_local$era_breaks[1]) %>%
mutate(era = cut(year, params_local$era_breaks, dig.lab = 5))
# GLODAP %>%
# filter(year == 1999) %>%
# select(era)3.2 Spatial boundaries
3.2.1 Depth
Observations collected shallower than:
- minimum sampling depth: 150m
were excluded from the analysis to avoid seasonal bias.
GLODAP <- GLODAP %>%
filter(depth >= params_local$depth_min)3.2.2 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$bottomdepth_min)3.3 Flags and missing data
Only rows (samples) for which all relevant params_local are available were selected, ie NA’s were removed.
According to Olsen et al (2020), flags within the merged master file identify:
f:
- 2: Acceptable
- 0: Interpolated (nutrients/oxygen) or calculated (CO[2] params_local) value
- 9: Data not used (so, only NA data should have this flag)
qc:
- 1: Adjusted or unadjusted data
- 0: Data appear of good quality but have not been subjected to full secondary QC
- data with poor or uncertain quality are excluded.
Following flagging criteria were taken into account:
- flag_f: 2, 0
- flag_qc: 1
Summary statistics were calculated during cleaning process.
3.3.1 tco2
Rows with missing tco2 observations were already removed in the preprocessing
GLODAP_stats <- GLODAP %>%
summarise(tco2_values = n())
GLODAP_obs_grid <- GLODAP %>%
count(lat, lon, era) %>%
mutate(cleaning_level = "tco2_values")GLODAP_obs <- GLODAP %>%
group_by(lat, lon) %>%
summarise(n = n()) %>%
ungroup()
map +
geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
geom_raster(data = GLODAP_obs, aes(lon, lat)) +
scale_fill_brewer(palette = "Dark2") +
theme(legend.position = "top",
legend.title = element_blank())
rm(GLODAP_obs)3.3.1.1 f flag
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era, tco2f)
map +
geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(era ~ tco2f) +
theme(legend.position = "top")
rm(GLODAP_obs_grid_temp)
GLODAP <- GLODAP %>%
filter(tco2f %in% params_local$flag_f)3.3.1.2 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")
##
GLODAP <- GLODAP %>%
filter(tco2qc %in% params_local$flag_qc)
GLODAP_stats_temp <- GLODAP %>%
summarise(tco2_flag = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era) %>%
mutate(cleaning_level = "tco2_flag")
GLODAP_obs_grid <-
bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
rm(GLODAP_obs_grid_temp)3.3.2 talk
3.3.2.1 NA
Quite a few tco2 observations are discarded, due to missing talk data, in particular in the JGOFS/WOCE era. However, there seems to be a high number of observations remaining from the affected cruises, so that the coverage does not seem to be reduced drastically.
GLODAP <- GLODAP %>%
mutate(talkna = if_else(is.na(talk), "NA", "Value"))
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era, talkna)
map +
geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(era ~ talkna) +
theme(legend.position = "top")
GLODAP <- GLODAP %>%
select(-talkna) %>%
filter(!is.na(talk))
##
GLODAP_stats_temp <- GLODAP %>%
summarise(talk_values = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era) %>%
mutate(cleaning_level = "talk_values")
GLODAP_obs_grid <-
bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
rm(GLODAP_obs_grid_temp)3.3.2.2 f flag
Restricting the f flag to 2 would results in data gaps in the south-east Pacific. Interpolated or calculated data (f flag 0) are therefore included.
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era, talkf)
map +
geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(era ~ talkf) +
theme(legend.position = "top",
legend.title = element_blank())
# ###
GLODAP <- GLODAP %>%
filter(talkf %in% params_local$flag_f)3.3.2.3 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())
###
GLODAP <- GLODAP %>%
filter(talkqc %in% params_local$flag_qc)
##
GLODAP_stats_temp <- GLODAP %>%
summarise(talk_flag = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era) %>%
mutate(cleaning_level = "talk_flag")
GLODAP_obs_grid <-
bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
rm(GLODAP_obs_grid_temp)3.3.3 Phosphate
3.3.3.1 NA
Quite a few tco2/talk observations are discarded, due to missing phosphate data. However, there seems to be a high number of observations remaining from the affected cruises, so that the coverage does not seem to be reduced drastically.
GLODAP <- GLODAP %>%
mutate(phosphatena = if_else(is.na(phosphate), "NA", "Value"))
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era, phosphatena)
map +
geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(era ~ phosphatena) +
theme(legend.position = "top")
GLODAP <- GLODAP %>%
select(-phosphatena) %>%
filter(!is.na(phosphate))
##
GLODAP_stats_temp <- GLODAP %>%
summarise(phosphate_values = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era) %>%
mutate(cleaning_level = "phosphate_values")
GLODAP_obs_grid <-
bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
rm(GLODAP_obs_grid_temp)3.3.3.2 f flag
Restricting the f flag to 2 would not drastically reduce available data. Interpolated or calculated data (f flag 0) are therefore included.
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era, phosphatef)
map +
geom_raster(data = GLODAP_obs_grid_temp, aes(lon, lat, fill = n)) +
scale_fill_viridis_c(option = "magma",
direction = -1,
trans = "log10") +
facet_grid(era~phosphatef) +
theme(legend.position = "top",
legend.title = element_blank())
###
GLODAP <- GLODAP %>%
filter(phosphatef %in% params_local$flag_f)3.3.3.3 qc flag
Phosphate data for which secondary quality was not applied (qc flag 0) were removed.
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())
###
GLODAP <- GLODAP %>%
filter(phosphateqc %in% params_local$flag_qc)
##
GLODAP_stats_temp <- GLODAP %>%
summarise(phosphate_flag = n())
GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)
##
GLODAP_obs_grid_temp <- GLODAP %>%
count(lat, lon, era) %>%
mutate(cleaning_level = "phosphate_flag")
GLODAP_obs_grid <-
bind_rows(GLODAP_obs_grid, GLODAP_obs_grid_temp)
rm(GLODAP_obs_grid_temp)3.3.4 eMLR variables
Rows with missing eMLR variables were removed, only the qc flag was considered.
GLODAP <- GLODAP %>%
filter(!is.na(tem))
##
GLODAP <- GLODAP %>%
filter(!is.na(sal))
GLODAP <- GLODAP %>%
filter(salinityf %in% params_local$flag_f)
GLODAP <- GLODAP %>%
filter(salinityqc %in% params_local$flag_qc)
##
GLODAP <- GLODAP %>%
filter(!is.na(silicate))
GLODAP <- GLODAP %>%
filter(silicatef %in% params_local$flag_f)
GLODAP <- GLODAP %>%
filter(silicateqc %in% params_local$flag_qc)
##
GLODAP <- GLODAP %>%
filter(!is.na(oxygen))
GLODAP <- GLODAP %>%
filter(oxygenf %in% params_local$flag_f)
GLODAP <- GLODAP %>%
filter(oxygenqc %in% params_local$flag_qc)
##
GLODAP <- GLODAP %>%
filter(!is.na(aou))
GLODAP <- GLODAP %>%
filter(aouf %in% params_local$flag_f)
##
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")))3.3.5 Manual adjustment A16 cruise
For harmonization with Gruber et al. (2019), cruises 1041 (A16N) and 1042 (A16S) were grouped into the GO_SHIP area despite taking place in 2013/14.
GLODAP_cruises <- GLODAP %>%
filter(basin == "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 |
|---|---|---|
| 196be51 | jens-daniel-mueller | 2020-11-30 |
rm(GLODAP_cruises)GLODAP <- GLODAP %>%
mutate(era = if_else(cruise %in% c(1041, 1042),
sort(unique(GLODAP$era))[2], era))3.4 Create clean observations grid
GLODAP_obs_grid_clean <- GLODAP %>%
count(lat, lon) %>%
select(-n)3.5 Write summary file
GLODAP_stats_long <- GLODAP_stats %>%
pivot_longer(1:length(GLODAP_stats),
names_to = "parameter",
values_to = "n")
##
GLODAP_obs_grid_clean %>% write_csv(paste(path_version_data,
"GLODAPv2.2020_clean_obs_grid.csv",
sep = ""))
##
GLODAP <- GLODAP %>%
select(year, date, era, basin, lat, lon, cruise,
bottomdepth, depth, tem, sal, gamma,
tco2, talk, phosphate,
oxygen, aou, nitrate, silicate)
GLODAP %>% write_csv(paste(path_version_data,
"GLODAPv2.2020_clean.csv",
sep = ""))4 Overview plots
4.1 Cleaning stats
Number of observations at various steps of data cleaning.
GLODAP_stats_long <- GLODAP_stats_long %>%
mutate(parameter = fct_reorder(parameter, n))
GLODAP_stats_long %>%
ggplot(aes(parameter, n/1000)) +
geom_col() +
coord_flip() +
labs(y = "n (1000)") +
theme(axis.title.y = element_blank())
rm(GLODAP_stats_long)4.2 Assign coarse spatial grid
For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:
- 5° x 5°
GLODAP <- m_grid_horizontal_coarse(GLODAP)4.3 Histogram Zonal coverage
GLODAP_histogram_lat <- GLODAP %>%
group_by(era, lat_grid, basin) %>%
tally() %>%
ungroup()
GLODAP_histogram_lat %>%
ggplot(aes(lat_grid, n, fill = era)) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_wrap( ~ basin) +
coord_flip(expand = 0) +
theme(legend.position = "top",
legend.title = element_blank())
rm(GLODAP_histogram_lat)4.4 Histogram temporal coverage
GLODAP_histogram_year <- GLODAP %>%
group_by(year, basin) %>%
tally() %>%
ungroup()
era_median_year <- GLODAP %>%
group_by(era) %>%
summarise(t_ref = median(year)) %>%
ungroup()
era_median_year# A tibble: 3 x 2
era t_ref
<fct> <dbl>
1 (1981,1999] 1995
2 (1999,2012] 2008
3 (2012,Inf] 2016GLODAP_histogram_year %>%
ggplot() +
geom_vline(xintercept = c(
params_local$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(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)4.5 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)4.6 Coverage maps by era
4.6.1 Subsetting process
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 = "") +
theme(legend.position = "top",
axis.title = element_blank())
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())
4.6.2 eMLR variables
Grey pixels refer to sampling locations filtered for availability of tco2 data only.
GLODAP_tco2_grid <- GLODAP %>%
count(lat, lon, era)
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",
name = "log10(eMLR_variables)") +
facet_wrap(~era, ncol = 1) +
theme(legend.position = "top",
axis.title = element_blank())
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