Anthropogenic CO2 from 1994 to 2007
Jens Daniel Müller
25 November, 2020
Last updated: 2020-11-25
Checks: 7 0
Knit directory: Cant_eMLR/
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1 Libraries
Loading libraries specific to the the analysis performed in this section.
library(tidync)2 Data source
- Anthropogenic CO2 estimates (1994-2007) by Gruber et al. (2019) downloaded in August 2020 from NOAA/NCEI Ocean Carbon Data System (OCADS)
3 Read ncdfs
3.1 Public data sets
The publicly available data sets contain only positive Cant estimates.
dcant <- tidync(here::here("data/input/Gruber_2019",
"dcant_emlr_cstar_gruber_94-07_vs1.nc"))
dcant <- dcant %>% activate(DCANT_01)
dcant <- dcant %>% hyper_tibble()
# harmonize column names and coordinates
dcant <- dcant %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
depth = DEPTH,
cant_pos = DCANT_01) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))dcant_inv <- tidync(here::here("data/input/Gruber_2019",
"inv_dcant_emlr_cstar_gruber_94-07_vs1.nc"))
dcant_inv <- dcant_inv %>% activate(DCANT_INV01)
dcant_inv <- dcant_inv %>% hyper_tibble()
# harmonize column names and coordinates
dcant_inv <- dcant_inv %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
cant_pos_inv = DCANT_INV01) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(eras = "JGOFS_GO")3.2 Raw data
Internally available data sets also contain negative Cant estimates, as they are contained in the “raw” output of the eMLR mapping step.
V101 <- tidync(here::here("data/input/Gruber_2019",
"Cant_V101new.nc"))
V101 <- V101 %>% activate(Cant)
V101 <- V101 %>% hyper_tibble()
# harmonize column names and coordinates
V101 <- V101 %>%
rename(lon = longitude,
lat = latitude,
cant = Cant) %>%
filter(cant != -999) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))4 Apply basin mask
# basinmask <- basinmask %>%
# select(-basin)
dcant <- inner_join(dcant, basinmask)
dcant_inv <- inner_join(dcant_inv, basinmask)
V101 <- inner_join(V101, basinmask)5 Join pos and all Cant
cant_3d <- inner_join(dcant, V101)
cant_3d <- cant_3d %>%
mutate(eras = "JGOFS_GO")
rm(dcant, V101)6 Zonal mean section
cant_zonal <- m_zonal_mean_section(cant_3d %>% select(-basin))7 Column inventory
7.1 From 3d fields
cant_inv <- m_cant_inv(cant_3d)p_map_cant_inv(cant_inv)
p_map_cant_pos_inv(cant_inv)
7.2 From pubished inventory data
p_map_cant_pos_inv(dcant_inv)
7.3 Published - 3d
cant_offset <- inner_join(
cant_inv %>% rename(cant_re = cant_pos_inv),
dcant_inv %>% rename(cant_pub = cant_pos_inv)
)
cant_offset <- cant_offset %>%
mutate(delta_cant = cant_re - cant_pub)
p_map_cant_inv_offset(df = cant_offset,
var = "delta_cant",
breaks = seq(-3,3,1))
rm(cant_offset, dcant_inv)8 Cant plots
Below, following subsets of the climatologies are plotted for all relevant parameters:
- Horizontal planes at 0, 150, 500, 2000m
- Meridional sections at longitudes: 335.5, 190.5, 70.5
Section locations are indicated as white lines in maps.
8.1 Horizontal plane maps
8.1.1 All values
p_map_climatology_divergent(cant_3d, "cant")
8.1.2 Positive values
p_map_climatology_continous(cant_3d, "cant_pos")
8.2 Sections basin
8.2.1 All values
p_section_global_divergent(cant_3d, "cant")
8.2.2 Positive values
p_section_global_continous(cant_3d, "cant_pos")
8.3 Sections at regular longitudes
8.3.1 All values
p_section_climatology_regular_divergent(cant_3d, "cant")
8.3.2 Positive values
p_section_climatology_regular_continous(cant_3d, "cant_pos")
9 Write files
cant_3d %>%
write_csv(here::here("data/interim",
"G19_cant_3d.csv"))
cant_inv %>%
write_csv(here::here("data/interim",
"G19_cant_inv.csv"))
cant_zonal %>%
write_csv(here::here("data/interim",
"G19_cant_zonal.csv"))
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] tidync_0.2.4 metR_0.7.0 scico_1.2.0 patchwork_1.0.1
[5] collapse_1.3.2 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 viridisLite_0.3.0 jsonlite_1.7.0 here_0.1
[5] modelr_0.1.8 Formula_1.2-3 assertthat_0.2.1 blob_1.2.1
[9] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.6 backports_1.1.8
[13] lattice_0.20-41 glue_1.4.1 digest_0.6.25 promises_1.1.1
[17] checkmate_2.0.0 rvest_0.3.6 colorspace_1.4-1 sandwich_2.5-1
[21] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18 pkgconfig_2.0.3
[25] broom_0.7.0 haven_2.3.1 xtable_1.8-4 scales_1.1.1
[29] whisker_0.4 later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[33] generics_0.0.2 ellipsis_0.3.1 withr_2.2.0 cli_2.0.2
[37] magrittr_1.5 crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[41] ncdf4_1.17 fs_1.4.2 fansi_0.4.1 xml2_1.3.2
[45] tools_4.0.2 data.table_1.13.0 hms_0.5.3 lifecycle_0.2.0
[49] munsell_0.5.0 reprex_0.3.0 isoband_0.2.2 compiler_4.0.2
[53] lfe_2.8-5.1 RNetCDF_2.3-1 rlang_0.4.7 grid_4.0.2
[57] rstudioapi_0.11 labeling_0.3 rmarkdown_2.3 gtable_0.3.0
[61] DBI_1.1.0 R6_2.4.1 ncmeta_0.2.5 zoo_1.8-8
[65] lubridate_1.7.9 knitr_1.30 rprojroot_1.3-2 stringi_1.4.6
[69] parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.2 dbplyr_1.4.4
[73] tidyselect_1.1.0 xfun_0.16