Data base
Jens Daniel Müller
14 July, 2020
Last updated: 2020-07-14
Checks: 7 0
Knit directory: Cant_eMLR/
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| File | Version | Author | Date | Message |
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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 |
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| html | b1ece68 | jens-daniel-mueller | 2020-07-13 | Build site. |
| Rmd | 8eb1b22 | jens-daniel-mueller | 2020-07-13 | cleaned data base file |
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| html | 79312b2 | jens-daniel-mueller | 2020-07-13 | Build site. |
| Rmd | ffbc2a1 | jens-daniel-mueller | 2020-07-13 | added Cstar calculation |
| html | 090cfeb | jens-daniel-mueller | 2020-07-13 | Build site. |
| Rmd | e6a2ade | jens-daniel-mueller | 2020-07-13 | added Cstar calculation |
library(tidyverse)
library(lubridate)
library(patchwork)1 Open clean data set
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAPv2.2020_clean.csv"))2 C* calculation
rCP <- 117
rNP <- 16GLODAP <- GLODAP %>%
mutate(C_star = tco2 - (rCP * phosphate) - 0.5 * (talk + rNP * phosphate))2.1 Selected section plots
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 %>%
ggplot(aes(longitude, latitude))+
mapWorld+
geom_point(aes(col=date))+
geom_path()+
coord_quickmap(expand = FALSE)+
scale_color_viridis_c(trans = "date")
lat_section <- GLODAP_cruise %>%
ggplot(aes(latitude, 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_C_star <- lat_section+
geom_point(aes(col=C_star))
map / lat_tco2 / lat_talk / lat_phosphate / lat_C_star
rm(map, lat_tco2, lat_talk, lat_phosphate, lat_C_star)3 Reference year adjustment
The scaling factor Cant_apriori at a given location and depth was estimated for each reference year from XXX. Note that eq. 6 in Clement and Gruber (2018) misses pCO2 pre-industrial in the denominator.
GLODAP <- GLODAP %>%
mutate(C_star_ref = C_star -
( (pCO2_atm(year) - pCO2_atm(year_ref)) /
(pCO2_atm(year_ref) - pCO2_atm(year_pi) ) * Cant_ref)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] jsonlite_1.7.0 rstudioapi_0.11 generics_0.0.2 magrittr_1.5
[5] farver_2.0.3 gtable_0.3.0 rmarkdown_2.3 vctrs_0.3.1
[9] fs_1.4.2 hms_0.5.3 xml2_1.3.2 pillar_1.4.6
[13] htmltools_0.5.0 haven_2.3.1 later_1.1.0.1 broom_0.7.0
[17] cellranger_1.1.0 tidyselect_1.1.0 knitr_1.29 git2r_0.27.1
[21] whisker_0.4 lifecycle_0.2.0 pkgconfig_2.0.3 R6_2.4.1
[25] digest_0.6.25 xfun_0.15 colorspace_1.4-1 rprojroot_1.3-2
[29] stringi_1.4.6 yaml_2.2.1 evaluate_0.14 labeling_0.3
[33] fansi_0.4.1 httr_1.4.1 compiler_3.6.3 here_0.1
[37] cli_2.0.2 withr_2.2.0 backports_1.1.5 munsell_0.5.0
[41] DBI_1.1.0 modelr_0.1.8 Rcpp_1.0.5 readxl_1.3.1
[45] dbplyr_1.4.4 maps_3.3.0 ellipsis_0.3.1 assertthat_0.2.1
[49] blob_1.2.1 tools_3.6.3 reprex_0.3.0 viridisLite_0.3.0
[53] httpuv_1.5.4 scales_1.1.1 crayon_1.3.4 glue_1.4.1
[57] rlang_0.4.7 rvest_0.3.5 promises_1.1.1 grid_3.6.3