Anthropogenic CO2 from 1994 to 2007
Jens Daniel Müller
30 March, 2021
Last updated: 2021-03-30
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Knit directory: emlr_obs_preprocessing/
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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 nc files
Here, we use the standard case V101 for public and raw data sets.
2.1 Public data sets
The publicly available data sets contain only positive Cant estimates.
2.1.1 3d fields
# open file
dcant <- tidync(paste(
path_gruber_2019,
"dcant_emlr_cstar_gruber_94-07_vs1.nc",
sep = ""
))
# read gamma field as tibble
dcant <- dcant %>% activate(GAMMA_DENS)
dcant_gamma <- dcant %>% hyper_tibble()
# read delta cant field
dcant <- dcant %>% activate(DCANT_01)
dcant <- dcant %>% hyper_tibble()
# join cant and gamma fields
dcant <- left_join(dcant, dcant_gamma)
# harmonize column names and coordinates
dcant <- dcant %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
depth = DEPTH,
gamma = GAMMA_DENS,
cant_pos = DCANT_01) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
rm(dcant_gamma)2.1.2 Column inventories
dcant_inv <- tidync(paste(
path_gruber_2019,
"inv_dcant_emlr_cstar_gruber_94-07_vs1.nc",
sep = ""
))
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")2.2 Raw data
Internally available data sets also contain negative Cant estimates, as they are generated in the “raw” output of the eMLR mapping step.
# open v 101 file
V101 <- tidync(paste(path_gruber_2019,
"Cant_V101new.nc",
sep = ""))
# create tibble
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))3 Apply basin mask
# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)
dcant <- inner_join(dcant, basinmask)
dcant_inv <- inner_join(dcant_inv, basinmask)
V101 <- inner_join(V101, basinmask)4 Join pos and all Cant
# join files
cant_3d <- inner_join(dcant, V101)
# assign era label
cant_3d <- cant_3d %>%
mutate(eras = "JGOFS_GO")
rm(dcant, V101)5 Zonal mean section
cant_zonal <- m_zonal_mean_section(cant_3d)6 Column inventory
6.1 Calculation
cant_inv_layers <- m_cant_inv(cant_3d)
cant_inv <- cant_inv_layers %>%
filter(inv_depth == params_global$inventory_depth_standard)6.2 Plots
6.2.1 All Cant
p_map_cant_inv(
df = cant_inv,
var = "cant_inv",
col = "divergent")
6.2.2 Pos Cant
p_map_cant_inv(
df = cant_inv,
var = "cant_pos_inv")
6.2.3 Published inventories
p_map_cant_inv(
df = dcant_inv,
var = "cant_pos_inv")
6.2.4 Published vs calculated
# join published and calculated data sets
cant_offset <- inner_join(
cant_inv %>% rename(cant_re = cant_pos_inv),
dcant_inv %>% rename(cant_pub = cant_pos_inv)
)
# calculate offset
cant_offset <- cant_offset %>%
mutate(delta_cant = cant_re - cant_pub)
# plot map
p_map_cant_inv_offset(df = cant_offset,
var = "delta_cant",
breaks = seq(-3,3,0.25))
rm(cant_offset, dcant_inv)7 Horizontal plane maps
7.1 All Cant
p_map_climatology(
df = cant_3d,
var = "cant",
col = "divergent")
7.2 Positive Cant
p_map_climatology(
df = cant_3d,
var = "cant_pos")
8 Zonal mean section plot
8.1 Positive Cant
for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
print(
p_section_zonal(
df = cant_zonal %>% filter(basin_AIP == i_basin_AIP),
var = "cant_pos_mean",
plot_slabs = "n",
subtitle_text = paste("Basin:", i_basin_AIP)
)
)
}
9 Global sections plot
9.1 All Cant
p_section_global(
df = cant_3d,
var = "cant",
col = "divergent")
9.2 Positive Cant
p_section_global(
df = cant_3d,
var = "cant_pos")
10 Sections at regular longitudes
10.1 All Cant
p_section_climatology_regular(
df = cant_3d,
var = "cant",
col = "divergent")
10.2 Positive Cant
p_section_climatology_regular(
df = cant_3d,
var = "cant_pos")
11 Write files
cant_3d %>%
write_csv(paste(path_preprocessing,
"G19_cant_3d.csv",
sep = ""))
cant_inv %>%
write_csv(paste(path_preprocessing,
"G19_cant_inv.csv",
sep = ""))
cant_zonal %>%
write_csv(paste(path_preprocessing,
"G19_cant_zonal.csv",
sep = ""))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidync_0.2.4 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[5] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[13] ggplot2_3.3.3 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_1.7.1
[4] modelr_0.1.8 assertthat_0.2.1 blob_1.2.1
[7] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.7
[10] backports_1.1.10 lattice_0.20-41 glue_1.4.2
[13] RcppEigen_0.3.3.7.0 digest_0.6.27 promises_1.1.1
[16] checkmate_2.0.0 rvest_0.3.6 colorspace_1.4-1
[19] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18
[22] pkgconfig_2.0.3 broom_0.7.5 haven_2.3.1
[25] scales_1.1.1 whisker_0.4 later_1.1.0.1
[28] git2r_0.27.1 farver_2.0.3 generics_0.0.2
[31] ellipsis_0.3.1 withr_2.3.0 cli_2.1.0
[34] magrittr_1.5 crayon_1.3.4 readxl_1.3.1
[37] evaluate_0.14 fs_1.5.0 ncdf4_1.17
[40] fansi_0.4.1 xml2_1.3.2 RcppArmadillo_0.10.1.2.0
[43] tools_4.0.3 data.table_1.13.2 hms_0.5.3
[46] lifecycle_1.0.0 munsell_0.5.0 reprex_0.3.0
[49] isoband_0.2.2 compiler_4.0.3 RNetCDF_2.4-2
[52] rlang_0.4.10 grid_4.0.3 rstudioapi_0.13
[55] labeling_0.4.2 rmarkdown_2.5 gtable_0.3.0
[58] DBI_1.1.0 R6_2.5.0 ncmeta_0.3.0
[61] lubridate_1.7.9 knitr_1.30 rprojroot_2.0.2
[64] stringi_1.5.3 parallel_4.0.3 Rcpp_1.0.5
[67] vctrs_0.3.5 dbplyr_1.4.4 tidyselect_1.1.0
[70] xfun_0.18