Anthropogenic CO2 from 1994 to 2007
Jens Daniel Müller
23 October, 2022
Last updated: 2022-10-23
Checks: 7 0
Knit directory: emlr_obs_preprocessing/
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path_sabine_2004 <- "/nfs/kryo/work/updata/glodapv1_1/GLODAP_gridded.data/"
path_preprocessing <- paste(path_root, "/observations/preprocessing/", sep = "")library(marelac)1 Data source
- Anthropogenic CO2 estimates (1800-1994) by Sabine et al. (2004) downloaded in August 2020 from NOAA/NCEI Ocean Carbon Data System (OCADS)
2 Read nc files
# read text files
AnthCO2_data <-
read_csv(
paste(path_sabine_2004,
"AnthCO2.data/AnthCO2.data.txt",
sep = ""),
col_names = FALSE,
na = "-999",
col_types = list(.default = "d")
)
# read respective depth layers and convert to vector
Depth_centers <-
read_file(paste(path_sabine_2004,
"Depth.centers.txt",
sep = ""))
Depth_centers <- Depth_centers %>%
str_split(",") %>%
as_vector()
# read respective latitudes and convert to vector
Lat_centers <-
read_file(paste(path_sabine_2004, "Lat.centers.txt",
sep = ""))
Lat_centers <- Lat_centers %>%
str_split(",") %>%
as_vector()
# read respective longitudes and convert to vector
Long_centers <-
read_file(paste(path_sabine_2004, "Long.centers.txt",
sep = ""))
Long_centers <- Long_centers %>%
str_split(",") %>%
as_vector()
# match lon, lat and depth vectors with Cant value file
names(AnthCO2_data) <- Lat_centers
Long_Depth <-
expand_grid(depth = Depth_centers, lon = Long_centers) %>%
mutate(lon = as.numeric(lon),
depth = as.numeric(depth))
tcant_3d <- bind_cols(AnthCO2_data, Long_Depth)
# adjust file dimensions
tcant_3d <- tcant_3d %>%
pivot_longer(1:180, names_to = "lat", values_to = "tcant") %>%
mutate(lat = as.numeric(lat))
tcant_3d <- tcant_3d %>%
drop_na()
# harmonize coordinates
tcant_3d <- tcant_3d %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
rm(AnthCO2_data,
Long_Depth,
Depth_centers,
Lat_centers,
Long_centers)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)
tcant_3d_unmasked <- tcant_3d
tcant_3d <- inner_join(tcant_3d, basinmask)
ggplot() +
geom_tile(data = tcant_3d_unmasked %>%
distinct(lon, lat),
aes(lon, lat, fill = "basin mask not applied")) +
geom_tile(data = tcant_3d %>%
distinct(lon, lat),
aes(lon, lat, fill = "basin mask applied")) +
coord_quickmap()
| Version | Author | Date |
|---|---|---|
| b6bf005 | jens-daniel-mueller | 2022-04-26 |
rm(tcant_3d_unmasked)4 Calculation
4.1 Column inventory
tcant_3d <- tcant_3d %>%
mutate(tcant_pos = if_else(tcant <= 0, 0, tcant))
tcant_inv_layers <- m_tcant_inv(tcant_3d)
tcant_inv <- tcant_inv_layers %>%
filter(inv_depth == params_global$inventory_depth_standard)4.2 Zonal mean section
tcant_zonal <- m_zonal_mean_sd(tcant_3d)m_dcant_budget(
tcant_inv_layers %>%
rename(dcant = tcant,
dcant_pos = tcant_pos) %>%
mutate(method = "total",
data_source = "obs")) %>%
select(-c(data_source, method)) %>%
group_by(estimate) %>%
mutate(ratio = round(value / lag(value),3)) %>%
ungroup() %>%
arrange(estimate, inv_depth)# A tibble: 10 × 4
inv_depth estimate value ratio
<dbl> <chr> <dbl> <dbl>
1 100 dcant 16.8 NA
2 500 dcant 63.0 3.75
3 1000 dcant 87.9 1.39
4 3000 dcant 102. 1.16
5 10000 dcant 97.7 0.963
6 100 dcant_pos 16.8 NA
7 500 dcant_pos 63.0 3.75
8 1000 dcant_pos 88.2 1.40
9 3000 dcant_pos 104. 1.18
10 10000 dcant_pos 106. 1.02 5 Plots
5.1 Inventory map
p_map_cant_inv(
df = tcant_inv,
var = "tcant_pos",
breaks = seq(0,max(tcant_inv$tcant_pos),5))
p_map_cant_inv(
df = tcant_inv,
var = "tcant",
breaks = seq(0,max(tcant_inv$tcant_pos),5))
5.2 Horizontal plane maps
p_map_climatology(
df = tcant_3d,
var = "tcant",
col = "divergent")
5.3 Global section
p_section_global(
df = tcant_3d,
var = "tcant",
col = "divergent")
5.4 Sections at regular longitudes
p_section_climatology_regular(
df = tcant_3d,
var = "tcant",
col = "divergent")
5.5 Write files
tcant_3d %>%
write_csv(paste(path_preprocessing,
"S04_tcant_3d.csv", sep = ""))
tcant_inv %>%
write_csv(paste(path_preprocessing,
"S04_tcant_inv.csv", sep = ""))
tcant_zonal %>%
write_csv(paste(path_preprocessing,
"S04_tcant_zonal.csv", sep = ""))6 Anomalous changes
tcant_inv_S04 <- tcant_inv
dcant_inv_G19 <- read_csv(paste(path_preprocessing,
"G19_dcant_inv.csv", sep = ""))7 Comparison of previous estimates
Cant inventory estimates of S04 (Sabine et al, 2004) and G19 (Gruber et al, 2019) were compared.
7.1 Merge data sets
cant_inv <- full_join(dcant_inv_G19 %>%
mutate(estimate = "G19") %>%
rename(cant_pos = dcant_pos) %>%
select(-dcant),
tcant_inv_S04 %>%
mutate(estimate = "S04") %>%
rename(cant_pos = tcant_pos) %>%
select(-tcant))
rm(dcant_inv_G19, tcant_inv_S04)7.2 Inventory maps
Spanning different time periods, the Cant inventories differ in magnitude. Please note, that we refer to cant_pos here, but strictly speaking we compare dcant and tcant.
map +
geom_raster(data = cant_inv,
aes(lon, lat, fill = cant_pos)) +
scale_fill_viridis_c() +
facet_wrap( ~ estimate, ncol = 1) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
7.3 Cant budgets
Global Cant inventories were estimated in Pg-C. Please note that here we only added positive Cant values in the upper m and do not apply additional corrections for areas not covered.
cant_inv <- cant_inv %>%
mutate(surface_area = earth_surf(lat, lon),
cant_pos_grid = cant_pos*surface_area)
cant_inv_budget <- cant_inv %>%
group_by(estimate, basin_AIP) %>%
summarise(cant_pos_total = sum(cant_pos_grid)*12*1e-15,
cant_pos_total = round(cant_pos_total,1)) %>%
ungroup() %>%
pivot_wider(values_from = cant_pos_total, names_from = basin_AIP) %>%
mutate(total = Atlantic + Indian + Pacific)
cant_inv_budget# A tibble: 2 × 5
estimate Atlantic Indian Pacific total
<chr> <dbl> <dbl> <dbl> <dbl>
1 G19 11 7.1 13.4 31.5
2 S04 39.6 23.4 41.4 104. 7.4 Relative inventories
cant_inv_wide <- cant_inv %>%
pivot_wider(values_from = c(cant_pos, cant_pos_grid),
names_from = estimate)
cant_inv_wide <- cant_inv_wide %>%
drop_na() %>%
mutate(G19_rel = cant_pos_grid_G19 / sum(cant_pos_grid_G19),
S04_rel = cant_pos_grid_S04 / sum(cant_pos_grid_S04),
cant_ratio_rel = G19_rel / S04_rel)
cant_inv_rel <- cant_inv_wide %>%
pivot_longer(
cols = c(G19_rel, S04_rel),
names_to = "estimate",
values_to = "cant_pos_rel"
)map +
geom_raster(data = cant_inv_rel,
aes(lon, lat, fill = cant_pos_rel*100)) +
scale_fill_viridis_c() +
facet_wrap( ~ estimate, ncol = 1) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
7.5 Relative inventory ratios
map +
geom_contour_filled(data = cant_inv_wide %>%
filter(cant_ratio_rel < 10,
cant_ratio_rel > 0.1),
aes(lon, lat, z = log10(cant_ratio_rel))) +
coord_quickmap(expand = 0) +
scale_fill_brewer(palette = "RdBu", direction = -1) +
labs(title = "Cant inventory distribution | 1994-2007 vs preind-1994",
subtitle = "Log ratio of relative contributions to total inventory") +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.title = element_blank()
)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] geomtextpath_0.1.0 colorspace_2.0-2 marelac_2.1.10 shape_1.4.6
[5] ggforce_0.3.3 metR_0.11.0 scico_1.3.0 patchwork_1.1.1
[9] collapse_1.7.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[13] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6
[17] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0 gsw_1.0-6
[5] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2 tools_4.1.2
[9] backports_1.4.1 bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.2 withr_2.4.3 tidyselect_1.1.1 processx_3.5.2
[17] bit_4.0.4 compiler_4.1.2 git2r_0.29.0 textshaping_0.3.6
[21] cli_3.1.1 rvest_1.0.2 xml2_1.3.3 isoband_0.2.5
[25] labeling_0.4.2 sass_0.4.0 scales_1.1.1 checkmate_2.0.0
[29] SolveSAPHE_2.1.0 callr_3.7.0 systemfonts_1.0.3 digest_0.6.29
[33] rmarkdown_2.11 oce_1.5-0 pkgconfig_2.0.3 htmltools_0.5.2
[37] highr_0.9 dbplyr_2.1.1 fastmap_1.1.0 rlang_1.0.2
[41] readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.1
[45] farver_2.1.0 jsonlite_1.7.3 vroom_1.5.7 magrittr_2.0.1
[49] Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2 lifecycle_1.0.1
[53] stringi_1.7.6 whisker_0.4 yaml_2.2.1 MASS_7.3-55
[57] grid_4.1.2 parallel_4.1.2 promises_1.2.0.1 crayon_1.4.2
[61] haven_2.4.3 hms_1.1.1 seacarb_3.3.0 knitr_1.37
[65] ps_1.6.0 pillar_1.6.4 reprex_2.0.1 glue_1.6.0
[69] evaluate_0.14 getPass_0.2-2 data.table_1.14.2 modelr_0.1.8
[73] vctrs_0.3.8 tzdb_0.2.0 tweenr_1.0.2 httpuv_1.6.5
[77] cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0 assertthat_0.2.1
[81] xfun_0.29 broom_0.7.11 later_1.3.0 viridisLite_0.4.0
[85] ellipsis_0.3.2