Anthropogenic CO2 from 1994 to 2007
Jens Daniel Müller
28 August, 2020
Last updated: 2020-08-28
Checks: 7 0
Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)1 Data source
- Anthropogenic CO2 estimates (1994-2007) by Gruber et al. (2019) downloaded in August 2020 from NOAA/NCEI Ocean Carbon Data System (OCADS)
2 Read ncdfs
AnthCO2_data <- read_csv("data/GLODAPv1_1/GLODAP_gridded.data/AnthCO2.data/AnthCO2.data.txt",
col_names = FALSE,
na = "-999",
col_types = list(.default = "d"))
Depth_centers <- read_file("data/GLODAPv1_1/GLODAP_gridded.data/Depth.centers.txt")
Depth_centers <- Depth_centers %>%
str_split(",") %>%
as_vector()
Lat_centers <- read_file("data/GLODAPv1_1/GLODAP_gridded.data/Lat.centers.txt")
Lat_centers <- Lat_centers %>%
str_split(",") %>%
as_vector()
Long_centers <- read_file("data/GLODAPv1_1/GLODAP_gridded.data/Long.centers.txt")
Long_centers <- Long_centers %>%
str_split(",") %>%
as_vector()
names(AnthCO2_data) <- Lat_centers
Long_Depth <- expand_grid(depth = Depth_centers, lon = Long_centers) %>%
mutate(lon = as.numeric(lon),
depth = as.numeric(depth))
Cant <- bind_cols(AnthCO2_data, Long_Depth)
Cant <- Cant %>%
pivot_longer(1:180, names_to = "lat", values_to = "cant") %>%
mutate(lat = as.numeric(lat))
Cant <- Cant %>%
drop_na()
Cant <- Cant %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
rm(AnthCO2_data, Long_Depth, Depth_centers, Lat_centers, Long_centers)3 Apply basin mask
Cant <- inner_join(Cant, basinmask)4 Inventory calculation
source(here::here("code", "inventory_calculation.R"))
Cant <- layer_inventory(Cant, "cant")
Cant_inv <- Cant %>%
filter(depth <= parameters$inventory_depth) %>%
group_by(lon, lat, basin, basin_AIP) %>%
summarise(cant_inv = sum(layer_inv_pos, na.rm = TRUE) / 1000,
cant_inv_incl_neg = sum(layer_inv, na.rm = TRUE) / 1000) %>%
ungroup()5 Cant plots
Below, following subsets of the climatologies are plotted for all relevant parameters:
- Horizontal planes at 150, 500, 1000, 2000m
- Meridional sections at longitudes: 335.5, 190.5, 70.5
Section locations are indicated as white lines in maps.
5.1 Horizontal plane maps
map_climatology(Cant, "cant")
5.2 Sections
section_climatology(Cant, "cant")
5.3 Sections shallow
section_climatology_shallow(Cant, "cant")
5.4 Inventory maps
map_climatology_inv(Cant_inv, "cant_inv")
5.5 Write files
Cant %>%
write_csv(here::here("data/GLODAPv1_1/_summarized_files",
"Cant_94.csv"))
Cant_inv %>%
write_csv(here::here("data/GLODAPv1_1/_summarized_files",
"Cant_94_inv.csv"))6 Open tasks
7 Questions
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] lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.16 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.3.2 generics_0.0.2 viridisLite_0.3.0 htmltools_0.5.0
[9] yaml_2.2.1 blob_1.2.1 rlang_0.4.7 isoband_0.2.2
[13] later_1.1.0.1 pillar_1.4.6 withr_2.2.0 glue_1.4.1
[17] DBI_1.1.0 dbplyr_1.4.4 modelr_0.1.8 readxl_1.3.1
[21] lifecycle_0.2.0 munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0
[25] rvest_0.3.6 evaluate_0.14 labeling_0.3 knitr_1.29
[29] httpuv_1.5.4 fansi_0.4.1 broom_0.7.0 Rcpp_1.0.5
[33] promises_1.1.1 backports_1.1.8 scales_1.1.1 jsonlite_1.7.0
[37] farver_2.0.3 fs_1.4.2 hms_0.5.3 digest_0.6.25
[41] stringi_1.4.6 rprojroot_1.3-2 grid_4.0.2 here_0.1
[45] cli_2.0.2 tools_4.0.2 magrittr_1.5 crayon_1.3.4
[49] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2
[53] reprex_0.3.0 assertthat_0.2.1 rmarkdown_2.3 httr_1.4.2
[57] rstudioapi_0.11 R6_2.4.1 git2r_0.27.1 compiler_4.0.2