Anthropogenic CO2 from 1994 to 2007
Jens Daniel Müller
07 July, 2021
Last updated: 2021-07-07
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Knit directory: emlr_obs_preprocessing/
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| html | 6312bd4 | jens-daniel-mueller | 2021-07-07 | Build site. |
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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)Error in get(genname, envir = envir) : object 'testthat_print' not found1 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 <- inner_join(tcant_3d, basinmask)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$inventory_depth_standard)4.2 Zonal mean section
tcant_zonal <- m_zonal_mean_sd(tcant_3d)5 Plots
5.1 Inventory map
p_map_cant_inv(
df = tcant_inv,
var = "tcant_pos",
breaks = seq(0,max(tcant_inv$tcant_pos),5))
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
5.2 Horizontal plane maps
p_map_climatology(
df = tcant_3d,
var = "tcant",
col = "divergent")
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
5.3 Global section
p_section_global(
df = tcant_3d,
var = "tcant",
col = "divergent")
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
5.4 Sections at regular longitudes
p_section_climatology_regular(
df = tcant_3d,
var = "tcant",
col = "divergent")
| Version | Author | Date |
|---|---|---|
| 58bc706 | jens-daniel-mueller | 2021-07-06 |
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()
)
| Version | Author | Date |
|---|---|---|
| 6312bd4 | jens-daniel-mueller | 2021-07-07 |
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 x 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()
)
| Version | Author | Date |
|---|---|---|
| 6312bd4 | jens-daniel-mueller | 2021-07-07 |
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()
)
| Version | Author | Date |
|---|---|---|
| 6312bd4 | jens-daniel-mueller | 2021-07-07 |
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] marelac_2.1.10 shape_1.4.5 ggforce_0.3.3 metR_0.9.0
[5] scico_1.2.0 patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 gsw_1.0-5
[4] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.0.3 backports_1.1.10 utf8_1.1.4
[10] R6_2.5.0 DBI_1.1.0 colorspace_1.4-1
[13] withr_2.3.0 tidyselect_1.1.0 compiler_4.0.3
[16] git2r_0.27.1 cli_2.1.0 rvest_0.3.6
[19] xml2_1.3.2 isoband_0.2.2 labeling_0.4.2
[22] scales_1.1.1 checkmate_2.0.0 digest_0.6.27
[25] rmarkdown_2.5 oce_1.2-0 pkgconfig_2.0.3
[28] htmltools_0.5.0 dbplyr_1.4.4 rlang_0.4.10
[31] readxl_1.3.1 rstudioapi_0.11 farver_2.0.3
[34] generics_0.0.2 jsonlite_1.7.1 magrittr_1.5
[37] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
[40] fansi_0.4.1 lifecycle_1.0.0 stringi_1.5.3
[43] whisker_0.4 yaml_2.2.1 MASS_7.3-53
[46] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[49] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[52] haven_2.3.1 hms_0.5.3 seacarb_3.2.14
[55] knitr_1.30 pillar_1.4.7 reprex_0.3.0
[58] glue_1.4.2 evaluate_0.14 RcppArmadillo_0.10.1.2.0
[61] data.table_1.13.2 modelr_0.1.8 vctrs_0.3.5
[64] tweenr_1.0.2 httpuv_1.5.4 testthat_2.3.2
[67] cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0
[70] assertthat_0.2.1 xfun_0.18 broom_0.7.5
[73] RcppEigen_0.3.3.7.0 later_1.1.0.1 viridisLite_0.3.0
[76] ellipsis_0.3.1