Data base
Jens Daniel Müller
22 July, 2020
Last updated: 2020-07-22
Checks: 7 0
Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
library(patchwork)1 Required data
Required are:
- clean version of GLODAPGLODAPv2.2020
- C_ant from GLODAPv2_2016b_MappedClimatologies
- annual mean atmospheric pCO2
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))
Cant_clim <- read_csv(here::here("data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
"Cant.csv"))
co2_atm <- read_csv(here::here("data/pCO2_atmosphere/_summarized_data_files",
"co2_atm.csv"))2 C*
2.1 Stoichiometric ratios
rCP <- 117
rNP <- 16The stoichiometric nutrient ratios for the production and mineralization of organic matter were set to:
- C/P: 117
- N/P: 16
2.2 Calculation
GLODAP <- GLODAP %>%
mutate(rCP_phosphate = - rCP * phosphate,
talk_05 = - 0.5 * talk,
rNP_phosphate_05 = - 0.5 * rNP * phosphate,
Cstar = tco2 + rCP_phosphate + talk_05 + rNP_phosphate_05)3 Reference year adjustment
The scaling factor for the reference year adjustment is an apriori estiamte of Cant at a given location and depth. Here, Cant from the GLODAP mapped Climatology was used.
Note that eq. 6 in Clement and Gruber (2018) misses pCO2 pre-industrial in the denominator. Here we use the equation published in Gruber et al. (2019).
3.1 Merge GLODAP data set with…
3.1.1 … Cant
Cant_clim <- Cant_clim %>%
drop_na()
GLODAP_Cant_full <- full_join(GLODAP, Cant_clim)The mapped Cant product was merged with GLODAP observation by:
- using an identical 1x1° horizontal grid
- linear interpolation of Cant from standard to sampling depth
GLODAP_Cant_observations_available <- GLODAP_Cant_full %>%
group_by(lat, lon) %>%
mutate(n_GLODAP = sum(!is.na(Cstar))) %>%
ungroup() %>%
filter(n_GLODAP > 0) %>%
select(-n_GLODAP)
rm(GLODAP_Cant_full)
GLODAP_Cant_observations_available <- GLODAP_Cant_observations_available %>%
group_by(lat, lon) %>%
arrange(depth) %>%
mutate(Cant_int = approxfun(depth, Cant, rule = 2)(depth)) %>%
ungroup()
ggplot()+
geom_path(data = GLODAP_Cant_observations_available %>%
filter(lat == 48.5, lon == 165.5, !is.na(Cant)) %>%
arrange(depth),
aes(Cant, depth, col="mapped"))+
geom_point(data = GLODAP_Cant_observations_available %>%
filter(lat == 48.5, lon == 165.5, !is.na(Cant)) %>%
arrange(depth),
aes(Cant, depth, col="mapped"))+
geom_point(data = GLODAP_Cant_observations_available %>%
filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
aes(Cant_int, depth, col="interpolated"))+
scale_y_reverse()+
scale_color_brewer(palette = "Dark2", name="")+
labs(title = "Cant interpolation to sampling depth - example profile")
GLODAP <- GLODAP_Cant_observations_available %>%
filter(!is.na(Cstar)) %>%
mutate(Cant = Cant_int) %>%
select(-Cant_int)
rm(GLODAP_Cant_observations_available, Cant_clim)3.1.2 … Atmospheric pCO2
GLODAP <- left_join(GLODAP, co2_atm)3.2 Calculate adjustment
GLODAP <- GLODAP %>%
group_by(era) %>%
mutate(tref = median(year)) %>%
ungroup()
tref <- GLODAP %>%
group_by(era) %>%
summarise(year = median(year)) %>%
ungroup()
co2_atm_tref <- right_join(co2_atm, tref) %>%
select(-year) %>%
rename(pCO2_tref = pCO2)
GLODAP <- full_join(GLODAP, co2_atm_tref)
rm(co2_atm, co2_atm_tref, tref)
GLODAP <- GLODAP %>%
mutate(Cstar_tref_delta =
( (pCO2 - pCO2_tref) / (pCO2_tref - 280) ) * Cant,
Cstar_tref = Cstar - Cstar_tref_delta)3.3 Control plots
GLODAP %>%
ggplot(aes(Cstar_tref_delta))+
geom_histogram()
GLODAP %>%
sample_n(10000) %>%
ggplot(aes(year - tref, Cstar_tref_delta, col=Cant))+
geom_point()+
scale_color_viridis_c()+
labs(title = "random subsample 1e4")
3.4 Selected section plots
Selected sections are plotted to demonstrate the magnitude of various parameters and corrections relevant to C*.
cruises_meridional <- c("1041")
# cruises_meridional <- c("1041","1042", "260",
# "2011", "393", "1031", "394", "395",
# "1088", "983")
# cruises_zonal <- c()
GLODAP_cruise <- GLODAP %>%
filter(cruise %in% cruises_meridional)mapWorld <- borders("world", colour="gray60", fill="gray60")
#map <-
GLODAP_cruise %>%
arrange(date) %>%
ggplot(aes(lon, lat))+
mapWorld+
geom_path()+
geom_point(aes(col=date))+
coord_quickmap(expand = FALSE)+
scale_color_viridis_c(trans = "date")+
labs(title = mean(date))
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))
#lat_rCP_phosphate <-
lat_section+
geom_point(aes(col=rCP_phosphate))
#lat_talk_05 <-
lat_section+
geom_point(aes(col=talk_05))
#lat_rNP_phosphate_05 <-
lat_section+
geom_point(aes(col=rNP_phosphate_05))
#lat_Cstar <-
lat_section+
geom_point(aes(col=Cstar))
lat_section+
geom_point(aes(col=Cant))
#lat_Cstar_tref <-
lat_section+
geom_point(aes(col=Cstar_tref_delta))
# map / lat_tco2 / lat_talk / lat_phosphate / lat_rCP_phosphate /lat_talk_05 /lat_rNP_phosphate_05 / lat_Cstar / lat_Cstar_tref
#
# rm(map, lat_tco2, lat_talk, lat_phosphate, lat_Cstar, lat_Cstar_tref)4 MLR fitting
temperature
salinity
phosphate
silicate
phosphate_star = phosphate + (oxygen / 170) - 1.95
oxygen
aoubasins <- c("Atlantic", "Indo_Pacific")
slabs <- c("")
for (i_basin in basins) {
for (i_slab in slabs) {
}
}
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