GLODAPv2_2016: Mapped Climatologies
Jens Daniel Müller
06 October, 2021
Last updated: 2021-10-06
Checks: 7 0
Knit directory: emlr_obs_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5ed301b | jens-daniel-mueller | 2021-10-06 | OceanSODA read-in updated |
| html | 3a99e0b | jens-daniel-mueller | 2021-10-05 | Build site. |
| Rmd | f36c707 | jens-daniel-mueller | 2021-10-05 | OceanSODA read-in updated |
| html | 5db29f4 | jens-daniel-mueller | 2021-10-05 | Build site. |
| Rmd | caf8c9a | jens-daniel-mueller | 2021-10-05 | OceanSODA read-in updated |
| html | e49875a | jens-daniel-mueller | 2021-07-07 | Build site. |
| html | 6312bd4 | jens-daniel-mueller | 2021-07-07 | Build site. |
| html | 58bc706 | jens-daniel-mueller | 2021-07-06 | Build site. |
| html | f600971 | jens-daniel-mueller | 2021-07-02 | Build site. |
| html | ad81465 | jens-daniel-mueller | 2021-06-17 | Build site. |
| Rmd | 3bc04be | jens-daniel-mueller | 2021-06-17 | derive air sea disequilibrium |
| html | 240c1f4 | jens-daniel-mueller | 2021-06-07 | Build site. |
| Rmd | 2730067 | jens-daniel-mueller | 2021-06-07 | write preprocessed file |
| html | 4df00b5 | jens-daniel-mueller | 2021-06-07 | Build site. |
| Rmd | f16f718 | jens-daniel-mueller | 2021-06-07 | added multi parameter analysis global trends |
| html | 8186273 | jens-daniel-mueller | 2021-06-07 | Build site. |
| Rmd | 663eb4d | jens-daniel-mueller | 2021-06-07 | added multi parameter analysis |
| html | efc80ab | jens-daniel-mueller | 2021-06-07 | Build site. |
| Rmd | 0f42222 | jens-daniel-mueller | 2021-06-07 | improved revelle factor analysis |
| html | 265c4ef | jens-daniel-mueller | 2021-06-04 | Build site. |
| Rmd | 00065c8 | jens-daniel-mueller | 2021-06-04 | included OceanSODA |
1 Read source files
# 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)OceanSODA <-
tidync(paste(
path_updata,
"pco2_oceansoda-ethz/OS-ETHZ-GRaCER-v2021a_1982-2020.nc",
sep = ""
))
OceanSODA <- OceanSODA %>%
hyper_tibble()
OceanSODA <- OceanSODA %>%
mutate(date = as.Date(time, origin = '1985-01-01'),
year = year(date))
OceanSODA <- OceanSODA %>%
select(year, date, lat, lon,
sal = salinity, temp = temperature,
tco2 = dic, talk,
rev_fac = revelle_factor,
pCO2 = spco2)
OceanSODA <- OceanSODA %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
OceanSODA <- inner_join(OceanSODA, basinmask)path_SeaFlux <-
paste0(path_updata,"pco2_seaflux/")
icefrac <-
tidync(paste0(path_SeaFlux,
"SeaFluxV2021.01_icefrac_1988-2018.nc")) %>%
hyper_tibble()
icefrac <- icefrac %>%
mutate(date = as.Date(time, origin = '1988-01-01'),
year = year(date))
kw <-
tidync(paste0(path_SeaFlux,
"SeaFluxV2021.01_kwScaled16.5cmhr_1988-2018.nc"))
kw <- kw %>%
hyper_filter(wind = wind == "ERA5") %>%
hyper_tibble() %>%
select(-wind)
pCO2atm <-
tidync(paste0(path_SeaFlux,
"SeaFluxV2021.01_pCO2atm_NOAAmbl_ERA5mslp_1988-2018.nc")) %>%
hyper_tibble()
sol <-
tidync(paste0(path_SeaFlux,
"SeaFluxV2021.01_solWeis74.nc")) %>%
hyper_tibble()
all_variables <- full_join(icefrac, kw)
all_variables <- full_join(all_variables, pCO2atm)
all_variables <- full_join(all_variables, sol)
all_variables <- all_variables %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
OceanSODA <- inner_join(all_variables %>% select(-c(time)),
OceanSODA)all_variables <- OceanSODA %>%
select(
time_mon = date,
lon,
lat,
spco2 = pCO2,
pco2atm = pCO2atm,
fice = ice,
alpha = sol_Weiss74,
Kw = kw_scaled
) %>%
drop_na()
all_variables <- all_variables %>%
mutate(area = earth_surf(lat = lat))
mol_to_g <- 12
P <- 1e-15
cm_to_m <- 100
hr_to_yr <- 24 * 365
unit_conversion_to_PgCyr <- mol_to_g * P * hr_to_yr / cm_to_m
all_variables <- all_variables %>%
mutate(
delta_pco2 = spco2 - pco2atm,
scale = area * Kw * alpha * (1 - fice),
fgco2 = delta_pco2 * scale
)
delta_pco2_monthly <- all_variables %>%
group_by(time_mon) %>%
summarise(
scaling_glob = sum(scale),
fgco2_glob = sum(fgco2),
delta_pco2_glob = fgco2_glob / scaling_glob
) %>%
ungroup() %>%
mutate(fgco2_glob = fgco2_glob * unit_conversion_to_PgCyr)
delta_pco2_annual <- delta_pco2_monthly %>%
mutate(year = year(time_mon)) %>%
group_by(year) %>%
summarise(
scaling_glob = mean(scaling_glob),
fgco2_glob = mean(fgco2_glob),
delta_pco2_glob = mean(delta_pco2_glob)
) %>%
ungroup()
print(
ggplot() +
geom_path(data = delta_pco2_monthly,
aes(
decimal_date(time_mon), delta_pco2_glob, col = "monthly"
)) +
geom_path(data = delta_pco2_annual,
aes(year, delta_pco2_glob, col = "annual")) +
scale_color_brewer(palette = "Set1", name = "Average") +
labs(x = "year")
)
print(
ggplot() +
geom_path(data = delta_pco2_monthly,
aes(decimal_date(time_mon), fgco2_glob, col = "monthly")) +
geom_path(data = delta_pco2_annual,
aes(year, fgco2_glob, col = "annual")) +
scale_color_brewer(palette = "Set1", name = "Average") +
labs(x = "year")
)
print(ggplot() +
geom_path(data = delta_pco2_annual,
aes(
year,
scaling_glob * unit_conversion_to_PgCyr
)))
print(
ggplot() +
geom_path(
data = delta_pco2_annual,
aes(
year,
scaling_glob * delta_pco2_glob * unit_conversion_to_PgCyr,
col = "scaled"
)
) +
geom_path(data = delta_pco2_annual,
aes(year, fgco2_glob, col = "integrated")) +
scale_color_brewer(palette = "Set1", name = "Estimate") +
scale_y_continuous(name = "Air-sea flux [PgC yr-1]") +
labs(x = "year")
)
# write raw data file for GLODAP-based subsetting model variables
delta_pco2_annual %>%
select(-c(scaling_glob, fgco2_glob)) %>%
write_csv(file = paste(
path_preprocessing,
"OceanSODA_disequilibrium_annual.csv",
sep = ""
))
rm(delta_pco2_annual,
delta_pco2_monthly)# calculate annual averaged fields
OceanSODA_annual_all <- OceanSODA %>%
mutate(tco2_over_pCO2 = tco2 / pCO2) %>%
group_by(year, lat, lon) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
ungroup() %>%
mutate(grid_area = earth_surf(lat = lat))
# grid data in space and time, remove data outside grid
OceanSODA_annual <- OceanSODA_annual_all %>%
mutate(
grid_area = earth_surf(lat = lat),
lat_bands = cut(lat, seq(-80, 80, 20)),
decade = cut(year,
seq(1990, 2020, 10),
right = FALSE,
labels = c("1990-1999", "2000-2009", "2010-2019"))
) %>%
drop_na()
# calculate climatological fields
OceanSODA_clim <- OceanSODA_annual %>%
select(-c(grid_area)) %>%
group_by(lat, lon) %>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
ungroup()
# calculate area-weighted annual mean within latitude band
OceanSODA_annual_lat <- OceanSODA_annual %>%
pivot_longer(sal:tco2_over_pCO2,
names_to = "parameter",
values_to = "value") %>%
mutate(value_area = value * grid_area) %>%
group_by(year, lat_bands, decade, parameter) %>%
summarise(
area_total = sum(grid_area),
value_area_total = sum(value_area),
value_area_ave = value_area_total / area_total
) %>%
ungroup() %>%
select(-c(area_total,value_area_total))
# fit decadel linear trends per latitude band
OceanSODA_annual_lat_trend <- OceanSODA_annual_lat %>%
nest(data = -c(decade, lat_bands, parameter)) %>%
mutate(tidy = map(data,
~tidy(lm(value_area_ave ~ year, data = .x)))) %>%
select(-data) %>%
unnest(tidy)
# calculate area-weighted annual mean globally
OceanSODA_annual_glob <- OceanSODA_annual %>%
pivot_longer(sal:tco2_over_pCO2,
names_to = "parameter",
values_to = "value") %>%
mutate(value_area = value * grid_area) %>%
group_by(year, decade, parameter) %>%
summarise(
area_total = sum(grid_area),
value_area_total = sum(value_area),
value_area_ave = value_area_total / area_total
) %>%
ungroup() %>%
select(-c(area_total,value_area_total))
# fit decadel linear trends globally
OceanSODA_annual_glob_trend <- OceanSODA_annual_glob %>%
nest(data = -c(decade, parameter)) %>%
mutate(tidy = map(data,
~tidy(lm(value_area_ave ~ year, data = .x)))) %>%
select(-data) %>%
unnest(tidy)1.1 Write file
OceanSODA_annual_all %>%
write_csv(paste0(path_preprocessing,
"OceanSODA.csv"))1.2 Control plots
map +
geom_raster(data = OceanSODA_annual %>%
filter(year == 2010), aes(lon, lat, fill = lat_bands)) +
scale_fill_brewer(palette = "Spectral") +
labs(title = "Year: 2010")
map +
geom_raster(data = OceanSODA_annual %>%
filter(year == 2010), aes(lon, lat, fill = grid_area)) +
scale_fill_viridis_c() +
labs(title = "Year: 2010")
2 Climatology map
unique(OceanSODA_clim$year) [1] 2004.000 2013.000 2010.000 2003.792 2001.444 2004.321 2004.464 2004.731
[9] 2004.179 2004.036 2003.962 2004.370 2003.778 2003.750 2003.926 2003.630
[17] 2003.680 2003.607 2003.333 2004.815OceanSODA_clim %>%
pivot_longer(sal:tco2_over_pCO2,
names_to = "parameter",
values_to = "value") %>%
group_split(parameter) %>%
# head(1) %>%
map( ~ map +
geom_raster(data = .x,
aes(lon, lat, fill = value)) +
scale_fill_viridis_c(name = .x$parameter))[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
3 Time series
OceanSODA_annual_lat %>%
group_split(parameter) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, value_area_ave, col = lat_bands)) +
scale_color_brewer(palette = "Spectral") +
geom_path() +
geom_point() +
labs(y = .x$parameter)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
4 Decadel trends
4.1 Global
OceanSODA_annual_glob %>%
group_split(parameter) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, value_area_ave, col = decade)) +
scale_color_brewer(palette = "Set1") +
geom_point() +
labs(y = .x$parameter) +
geom_smooth(method = "lm", se = FALSE)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
OceanSODA_annual_glob_trend %>%
filter(term == "year") %>%
group_split(parameter) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decade, estimate)) +
scale_fill_brewer(palette = "Spectral") +
geom_point(shape = 21) +
geom_path() +
labs(y = paste(.x$parameter, "annual change"))
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
4.2 Latitude bands
OceanSODA_annual_lat %>%
group_split(parameter) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(year, value_area_ave, col = decade)) +
scale_color_brewer(palette = "Set1") +
geom_point() +
labs(y = .x$parameter) +
geom_smooth(method = "lm", se = FALSE) +
facet_wrap( ~ lat_bands)
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
OceanSODA_annual_lat_trend %>%
filter(term == "year") %>%
group_split(parameter) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(decade, estimate, fill = lat_bands)) +
scale_fill_brewer(palette = "Spectral") +
geom_point(shape = 21) +
geom_path() +
labs(y = paste(.x$parameter, "annual change"))
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
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] broom_0.7.9 marelac_2.1.10 shape_1.4.5 lubridate_1.7.9
[5] tidync_0.2.4 ggforce_0.3.3 metR_0.9.0 scico_1.2.0
[9] patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0 stringr_1.4.0
[13] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[17] tibble_3.1.3 ggplot2_3.3.5 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-149 fs_1.5.0 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 bslib_0.2.5.1
[10] utf8_1.1.4 R6_2.5.0 mgcv_1.8-33
[13] DBI_1.1.0 colorspace_2.0-2 withr_2.3.0
[16] tidyselect_1.1.0 compiler_4.0.3 git2r_0.27.1
[19] cli_3.0.1 rvest_0.3.6 RNetCDF_2.4-2
[22] xml2_1.3.2 labeling_0.4.2 sass_0.4.0
[25] scales_1.1.1 checkmate_2.0.0 digest_0.6.27
[28] oce_1.2-0 rmarkdown_2.10 pkgconfig_2.0.3
[31] htmltools_0.5.1.1 highr_0.8 dbplyr_1.4.4
[34] rlang_0.4.11 readxl_1.3.1 rstudioapi_0.13
[37] jquerylib_0.1.4 generics_0.1.0 farver_2.0.3
[40] jsonlite_1.7.1 magrittr_1.5 ncmeta_0.3.0
[43] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
[46] fansi_0.4.1 lifecycle_1.0.0 stringi_1.5.3
[49] whisker_0.4 yaml_2.2.1 MASS_7.3-53
[52] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[55] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[58] splines_4.0.3 haven_2.3.1 seacarb_3.2.14
[61] hms_0.5.3 knitr_1.33 pillar_1.6.2
[64] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[67] RcppArmadillo_0.10.1.2.0 data.table_1.14.0 modelr_0.1.8
[70] vctrs_0.3.8 tweenr_1.0.2 httpuv_1.5.4
[73] testthat_2.3.2 cellranger_1.1.0 gtable_0.3.0
[76] polyclip_1.10-0 assertthat_0.2.1 xfun_0.25
[79] RcppEigen_0.3.3.7.0 later_1.2.0 viridisLite_0.3.0
[82] ncdf4_1.17 ellipsis_0.3.2