Anthropogenic CO2 from 1994 to 2007
Jens Daniel Müller
11 August, 2020
Last updated: 2020-08-11
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Knit directory: Cant_eMLR/
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library(tidyverse)
library(lubridate)
library(tidync)
library(stars)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
inv_dcant <- tidync(here::here("data/Gruber_2019",
"inv_dcant_emlr_cstar_gruber_94-07_vs1.nc"))
dcant <- tidync(here::here("data/Gruber_2019",
"dcant_emlr_cstar_gruber_94-07_vs1.nc"))
dcant <- dcant %>% activate(DCANT_01)
dcant_tibble <- dcant %>% hyper_tibble()
inv_dcant <- inv_dcant %>% activate(DCANT_INV01)
inv_dcant_tibble <- inv_dcant %>% hyper_tibble()3 Cant plots
depth_surface_selection <- c(0)
Atl_lon <- 335.5
Pac_lon <- 190.5Below, following subsets of the climatologies are plotted for all relevant parameters:
- Horizontal planes at 0m
- Meridional sections at longitudes:
- Atlantic: 335.5
- Pacific: 190.5
Section locations are indicated as white lines in maps.
Please note that longitudes in the climatologies range from -179.5 - 179.5, which is different from GLODAP mapped climatologies.
3.1 Surface Inventory map
inv_dcant_tibble %>%
ggplot(aes(LONGITUDE, LATITUDE, fill = DCANT_INV01)) +
geom_raster() +
geom_vline(xintercept = c(Atl_lon, Pac_lon), col = "white") +
coord_quickmap(expand = 0) +
scale_fill_viridis_c() +
theme(legend.position = "top")
3.2 Sections
3.2.1 Atlantic
dcant_tibble %>%
filter(LONGITUDE == Atl_lon) %>%
ggplot(aes(LATITUDE, DEPTH, z = DCANT_01)) +
geom_contour_filled() +
scale_fill_viridis_d(name = "Cant") +
guides(fill = guide_colorsteps(barheight = unit(7, "cm"))) +
scale_y_reverse() +
coord_cartesian(expand = 0)
3.2.2 Pacific
dcant_tibble %>%
filter(LONGITUDE == Pac_lon) %>%
ggplot(aes(LATITUDE, DEPTH, z = DCANT_01)) +
geom_contour_filled() +
scale_fill_viridis_d(name = "Cant") +
guides(fill = guide_colorsteps(barheight = unit(7, "cm"))) +
scale_y_reverse() +
coord_cartesian(expand = 0)
3.3 Write files
dcant_tibble %>%
write_csv(here::here("data/Gruber_2019/_summarized_files",
"dcant.csv"))
inv_dcant_tibble %>%
write_csv(here::here("data/Gruber_2019/_summarized_files",
"inv_dcant.csv"))4 Open tasks
5 Questions
- Why are there no negative Cant estimates?
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] stars_0.4-3 sf_0.9-5 abind_1.4-5 tidync_0.2.4
[5] lubridate_1.7.9 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[9] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[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_1.7.0 viridisLite_0.3.0 here_0.1
[5] modelr_0.1.8 assertthat_0.2.1 blob_1.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.4.6 backports_1.1.8 glue_1.4.1
[13] digest_0.6.25 promises_1.1.1 rvest_0.3.6 colorspace_1.4-1
[17] htmltools_0.5.0 httpuv_1.5.4 pkgconfig_2.0.3 broom_0.7.0
[21] haven_2.3.1 scales_1.1.1 whisker_0.4 later_1.1.0.1
[25] git2r_0.27.1 generics_0.0.2 farver_2.0.3 ellipsis_0.3.1
[29] withr_2.2.0 cli_2.0.2 magrittr_1.5 crayon_1.3.4
[33] readxl_1.3.1 evaluate_0.14 fs_1.4.2 ncdf4_1.17
[37] fansi_0.4.1 xml2_1.3.2 lwgeom_0.2-5 class_7.3-17
[41] tools_4.0.2 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[45] reprex_0.3.0 isoband_0.2.2 compiler_4.0.2 e1071_1.7-3
[49] RNetCDF_2.3-1 rlang_0.4.7 classInt_0.4-3 units_0.6-7
[53] grid_4.0.2 rstudioapi_0.11 labeling_0.3 rmarkdown_2.3
[57] gtable_0.3.0 DBI_1.1.0 R6_2.4.1 ncmeta_0.2.5
[61] knitr_1.29 rprojroot_1.3-2 KernSmooth_2.23-17 stringi_1.4.6
[65] parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.2 dbplyr_1.4.4
[69] tidyselect_1.1.0 xfun_0.16