eMLR - data preparation
Jens Daniel Müller
27 November, 2020
Last updated: 2020-11-27
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(marelac)
library(lubridate)2 Required data
Required are:
- GLODAPv2.2020
- cleaned data file
- horizontal grid of sampling coordinates
- Cant from GLODAPv2_2016b_MappedClimatologies
- annual mean atmospheric pCO2
GLODAP <-
read_csv(
here::here(
"data/interim",
"GLODAPv2.2020_clean.csv"
)
)
GLODAP_obs_grid <-
read_csv(
here::here(
"data/interim",
"GLODAPv2.2020_clean_obs_grid.csv"
)
)
S04_cant_3d <-
read_csv(
here::here(
"data/interim",
"S04_cant_3d.csv"
)
)
# G19_cant_3d <-
# read_csv(
# here::here(
# "data/interim",
# "G19_cant_3d.csv"
# )
# )
co2_atm <-
read_csv(here::here(
"data/interim",
"co2_atm.csv"
))3 C*
C* serves as a conservative tracer of anthropogenic CO2 uptake. It is derived from measured DIC by removing the impact of
- organic matter formation and respiration
- calcification and calcium carbonate dissolution
Contributions of those processes are estimated from phosphate and alkalinity concentrations.
3.1 Stoichiometric ratios
The stoichiometric nutrient ratios for the production and mineralization of organic matter were set to:
- C/P: 117
- N/P: 16
3.2 Calculation
C* is calculated as:
print(b_cstar)function (tco2, phosphate, talk)
{
cstar = tco2 - (parameters$rCP * phosphate) - 0.5 * (talk -
(parameters$rNP * phosphate))
return(cstar)
}GLODAP <- GLODAP %>%
mutate(rCP_phosphate = -parameters$rCP * phosphate,
talk_05 = -0.5 * talk,
rNP_phosphate_05 = -0.5 * parameters$rNP * phosphate,
cstar = b_cstar(tco2, phosphate, talk))4 PO4* calculation
The predictor PO4* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an errornous equations for PO4* calculation is given in the supplement of Gruber et al (2019), based on nitrate.
GLODAP <- GLODAP %>%
mutate(phosphate_star = b_phosphate_star(phosphate, oxygen))5 Reference year adjustment
The reference year adjustment relies on an apriori estimate of Cant at a given location and depth, which is used as a scaling factor for the concurrent change in atmospheric CO2. The underlying assumption is a transient steady state for the oceanic Cant uptake. Here, Cant from the GLODAP mapped Climatology was used.
Note that eq. 6 in Clement and Gruber (2018) misses pCO2 pre-industrial in the denominator. Here we use the equation published in Gruber et al. (2019).
5.1 Merge data sets
5.1.1 GLODAP + Cant
S04_cant_3d_obs <- left_join(
GLODAP_obs_grid,
S04_cant_3d %>% select(-c(cant, eras))
)
# calculate number of cant data points per grid cell
S04_cant_3d_obs <- S04_cant_3d_obs %>%
group_by(lon, lat) %>%
mutate(n = n()) %>%
ungroup()
# S04_cant_3d_obs %>%
# filter(n <= 1) %>%
# ggplot(aes(lon,lat)) +
# geom_point(data = GLODAP_obs_grid, aes(lon, lat)) +
# geom_point(col = "red")
rm(S04_cant_3d, GLODAP_obs_grid)
GLODAP_cant_obs <- full_join(GLODAP, S04_cant_3d_obs)
rm(GLODAP, S04_cant_3d_obs)
# fill number of cant data points per grid cell to all observations
GLODAP_cant_obs <- GLODAP_cant_obs %>%
group_by(lon, lat) %>%
fill(n, .direction = "updown") %>%
ungroup()The mapped Cant product was merged with GLODAP observation by:
- using an identical 1x1° horizontal grid
- linear interpolation of Cant from standard to sampling depth
# interpolate cant to observation depth
GLODAP_cant_obs_int <- GLODAP_cant_obs %>%
filter(n > 1) %>%
group_by(lat, lon) %>%
arrange(depth) %>%
mutate(cant_pos_int = approxfun(depth, cant_pos, rule = 2)(depth)) %>%
ungroup()
# set cant for observation depth if only one cant available
GLODAP_cant_obs_set <- GLODAP_cant_obs %>%
filter(n == 1) %>%
group_by(lat, lon) %>%
mutate(cant_pos_int = mean(cant_pos, na.rm = TRUE)) %>%
ungroup()
# bin data sets with interpolated and set cant
GLODAP_cant_obs <- bind_rows(GLODAP_cant_obs_int, GLODAP_cant_obs_set)
rm(GLODAP_cant_obs_int, GLODAP_cant_obs_set)
ggplot() +
geom_path(
data = GLODAP_cant_obs %>%
filter(lat == 48.5, lon == 165.5,!is.na(cant_pos)) %>%
arrange(depth),
aes(cant_pos, depth, col = "mapped")
) +
geom_point(
data = GLODAP_cant_obs %>%
filter(lat == 48.5, lon == 165.5,!is.na(cant_pos)) %>%
arrange(depth),
aes(cant_pos, depth, col = "mapped")
) +
geom_point(
data = GLODAP_cant_obs %>%
filter(lat == 48.5, lon == 165.5, date == ymd("2018-06-27")),
aes(cant_pos_int, depth, col = "interpolated")
) +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", name = "") +
labs(title = "Cant interpolation to sampling depth - example profile")
# remove cant data at grid cells without observations
GLODAP <- GLODAP_cant_obs %>%
filter(!is.na(cstar)) %>%
mutate(cant_pos = cant_pos_int) %>%
select(-cant_pos_int, n)
rm(GLODAP_cant_obs)5.1.2 GLODAP + atm. pCO2
GLODAP observations were merged with mean annual atmospheric pCO2 levels by year.
GLODAP <- left_join(GLODAP, co2_atm)5.2 Calculate adjustment
Cant for tref = 2007 was calculated by applying alpha = 0.28 to the estimate of Sabine et al. (2004).
For tref = 2015 we scaled alpha as 0.28/13 * dt.
# assign reference year
GLODAP <- GLODAP %>%
group_by(era) %>%
mutate(tref = median(year)) %>%
ungroup()
# calculate reference year
tref <- GLODAP %>%
group_by(era) %>%
summarise(year = median(year)) %>%
ungroup()
# extract atm pCO2 at reference year
co2_atm_tref <- right_join(co2_atm, tref) %>%
select(-year) %>%
rename(pCO2_tref = pCO2)
# merge atm pCO2 at tref with GLODAP
GLODAP <- full_join(GLODAP, co2_atm_tref)
rm(co2_atm, tref)
# scale cant to reference year
GLODAP <- GLODAP %>%
mutate(alpha = (tref - min(tref)) * (0.28 / 13),
cant_pos = cant_pos * (1 + alpha))
# calculate cstar for reference year
GLODAP <- GLODAP %>%
mutate(
cstar_tref_delta =
((pCO2 - pCO2_tref) / (pCO2_tref - parameters$preind_atm_pCO2)) * cant_pos,
cstar_tref = cstar - cstar_tref_delta)5.3 Control plots
5.3.1 Histogram
GLODAP %>%
ggplot(aes(cstar_tref_delta)) +
geom_histogram()
5.3.2 Time series
GLODAP %>%
sample_n(1e4) %>%
ggplot(aes(year, cstar_tref_delta, col = cant_pos)) +
geom_point() +
scale_color_viridis_c() +
labs(title = "random subsample 1e4")
GLODAP %>%
ggplot(aes(year, cstar_tref_delta)) +
geom_bin2d() +
scale_fill_viridis_c()
6 Selected section plots
A selected section is plotted to demonstrate the magnitude of various parameters and corrections relevant to C*.
GLODAP_cruise <- GLODAP %>%
filter(cruise %in% parameters$cruises_meridional)map +
geom_path(data = GLODAP_cruise %>%
arrange(date),
aes(lon, lat)) +
geom_point(data = GLODAP_cruise %>%
arrange(date),
aes(lon, lat, col = date)) +
scale_color_viridis_c(trans = "date") +
labs(title = paste("Cruise year:", mean(GLODAP_cruise$year)))
lat_section <-
GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
scale_color_viridis_c() +
theme(axis.title.x = element_blank())
lat_section +
geom_point(aes(col = tco2))
lat_section +
geom_point(aes(col = rCP_phosphate))
lat_section +
geom_point(aes(col = talk_05))
lat_section +
geom_point(aes(col = rNP_phosphate_05))
lat_section +
geom_point(aes(col = cstar))
lat_section +
geom_point(aes(col = cstar_tref))
rm(lat_section, GLODAP_cruise)
# remove columns not required for further analysis
GLODAP <- GLODAP %>%
select(-c(rCP_phosphate:cstar, cant_pos:cstar_tref_delta))7 Isoneutral slabs
The following boundaries for isoneutral slabs were defined:
- Atlantic: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1, 28.15, 28.2,
- Indo-Pacific: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1,
Continuous neutral densities (gamma) values from GLODAP are grouped into isoneutral slabs.
GLODAP <- m_cut_gamma(GLODAP, "gamma")GLODAP_cruise <- GLODAP %>%
filter(cruise %in% parameters$cruises_meridional)
lat_section <-
GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
theme(legend.position = "bottom")
lat_section +
geom_point(aes(col = gamma_slab)) +
scale_color_viridis_d()
rm(lat_section, GLODAP_cruise)GLODAP_cruise <- GLODAP %>%
filter(cruise %in% parameters$cruises_meridional)
library(oce)
library(gsw)
# calculate pressure from depth
GLODAP_cruise <- GLODAP_cruise %>%
mutate(CTDPRS = gsw_p_from_z(-depth,
lat))
GLODAP_cruise <- GLODAP_cruise %>%
mutate(THETA = swTheta(salinity = sal,
temperature = tem,
pressure = CTDPRS,
referencePressure = 0,
longitude = lon-180,
latitude = lat))
GLODAP_cruise <- GLODAP_cruise %>%
rename(LATITUDE = lat,
LONGITUDE = lon,
SALNTY = sal,
gamma_provided = gamma)
library(reticulate)
source_python(here::here("code/python_scripts",
"Gamma_GLODAP_python.py"))
GLODAP_cruise <- calculate_gamma(GLODAP_cruise)
GLODAP_cruise <- GLODAP_cruise %>%
mutate(gamma_delta = gamma_provided - GAMMA)
lat_section <-
GLODAP_cruise %>%
ggplot(aes(LATITUDE, CTDPRS)) +
scale_y_reverse() +
theme(legend.position = "bottom")
lat_section +
geom_point(aes(col = gamma_delta)) +
scale_color_viridis_c()
GLODAP_cruise %>%
ggplot(aes(gamma_delta))+
geom_histogram()
rm(lat_section, GLODAP_cruise, cruises_meridional)8 Observations coverage
GLODAP <- GLODAP %>%
mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era"))) %>%
mutate(gamma_slab = factor(gamma_slab),
gamma_slab = factor(gamma_slab, levels = rev(levels(gamma_slab))))
GLODAP %>%
filter(basin == "Atlantic") %>%
ggplot(aes(lat, gamma_slab)) +
geom_bin2d(binwidth = 5) +
scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
scale_x_continuous(breaks = seq(-100,100,20)) +
facet_grid(era~basin)
GLODAP %>%
filter(basin == "Indo-Pacific") %>%
ggplot(aes(lat, gamma_slab)) +
geom_bin2d(binwidth = 5) +
scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
scale_x_continuous(breaks = seq(-100,100,20)) +
facet_grid(era~basin)
9 Individual cruise sections
Zonal and meridional section plots are produce for each cruise individually and can be downloaded here.
cruises <- GLODAP %>%
group_by(cruise) %>%
summarise(date_mean = mean(date, na.rm = TRUE),
n = n()) %>%
ungroup() %>%
arrange(date_mean)
GLODAP <- full_join(GLODAP, cruises)
n <- 0
for (i_cruise in unique(cruises$cruise)) {
# i_cruise <- unique(cruises$cruise)[1]
n <- n + 1
print(n)
GLODAP_cruise <- GLODAP %>%
filter(cruise == i_cruise) %>%
arrange(date)
cruises_cruise <- cruises %>%
filter(cruise == i_cruise)
map_plot <-
map +
geom_point(data = GLODAP_cruise,
aes(lon, lat, col = date)) +
scale_color_viridis_c(trans = "date") +
labs(title = paste("Mean date:", cruises_cruise$date_mean,
"| cruise:", cruises_cruise$cruise,
"| n(samples):", cruises_cruise$n))
lon_section <- GLODAP_cruise %>%
ggplot(aes(lon, depth)) +
scale_y_reverse() +
scale_color_viridis_c()
lon_tco2 <- lon_section+
geom_point(aes(col=tco2))
lon_talk <- lon_section+
geom_point(aes(col=talk))
lon_phosphate <- lon_section+
geom_point(aes(col=phosphate))
lon_oxygen <- lon_section+
geom_point(aes(col=oxygen))
lon_aou <- lon_section+
geom_point(aes(col=aou))
lon_phosphate_star <- lon_section+
geom_point(aes(col=phosphate_star))
lon_nitrate <- lon_section+
geom_point(aes(col=nitrate))
lon_cstar <- lon_section+
geom_point(aes(col=cstar_tref))
lat_section <- GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
scale_color_viridis_c()
lat_tco2 <- lat_section+
geom_point(aes(col=tco2))
lat_talk <- lat_section+
geom_point(aes(col=talk))
lat_phosphate <- lat_section+
geom_point(aes(col=phosphate))
lat_oxygen <- lat_section+
geom_point(aes(col=oxygen))
lat_aou <- lat_section+
geom_point(aes(col=aou))
lat_phosphate_star <- lat_section+
geom_point(aes(col=phosphate_star))
lat_nitrate <- lat_section+
geom_point(aes(col=nitrate))
lat_cstar <- lat_section+
geom_point(aes(col=cstar_tref))
hist_tco2 <- GLODAP_cruise %>%
ggplot(aes(tco2)) +
geom_histogram()
hist_talk <- GLODAP_cruise %>%
ggplot(aes(talk)) +
geom_histogram()
hist_phosphate <- GLODAP_cruise %>%
ggplot(aes(phosphate)) +
geom_histogram()
hist_oxygen <- GLODAP_cruise %>%
ggplot(aes(oxygen)) +
geom_histogram()
hist_aou <- GLODAP_cruise %>%
ggplot(aes(aou)) +
geom_histogram()
hist_phosphate_star <- GLODAP_cruise %>%
ggplot(aes(phosphate_star)) +
geom_histogram()
hist_nitrate <- GLODAP_cruise %>%
ggplot(aes(nitrate)) +
geom_histogram()
hist_cstar <- GLODAP_cruise %>%
ggplot(aes(cstar_tref)) +
geom_histogram()
(map_plot /
((hist_tco2 / hist_talk / hist_phosphate / hist_cstar) |
(hist_oxygen / hist_phosphate_star / hist_nitrate / hist_aou)
)) |
((lat_tco2 / lat_talk / lat_phosphate / lat_oxygen / lat_aou / lat_phosphate_star / lat_nitrate / lat_cstar) |
(lon_tco2 / lon_talk / lon_phosphate / lon_oxygen / lon_aou /lon_phosphate_star / lon_nitrate / lon_cstar))
ggsave(here::here("output/figure/eMLR/data_preparation/all_cruises_clean",
paste("GLODAP_cruise_date",
cruises_cruise$date_mean,
"n",
cruises_cruise$n,
"cruise",
cruises_cruise$cruise,
".png",
sep = "_")),
width = 20, height = 12)
rm(map_plot,
lon_section, lat_section,
lat_tco2, lat_talk, lat_phosphate, lon_tco2, lon_talk, lon_phosphate,
GLODAP_cruise, cruises_cruise)
}10 Write files
GLODAP <- GLODAP %>%
rename(cstar = cstar_tref)
GLODAP %>% write_csv(here::here("data/interim",
"GLODAPv2.2020_MLR_fitting_ready.csv"))
co2_atm_tref %>%
write_csv(here::here(
"data/interim",
"co2_atm_tref.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] lubridate_1.7.9 marelac_2.1.10 shape_1.4.4 metR_0.7.0
[5] scico_1.2.0 patchwork_1.0.1 collapse_1.3.2 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[13] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[17] 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 RColorBrewer_1.1-2
[17] promises_1.1.1 checkmate_2.0.0 rvest_0.3.6 colorspace_1.4-1
[21] sandwich_2.5-1 htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18
[25] pkgconfig_2.0.3 broom_0.7.0 seacarb_3.2.13 haven_2.3.1
[29] xtable_1.8-4 scales_1.1.1 whisker_0.4 later_1.1.0.1
[33] git2r_0.27.1 farver_2.0.3 generics_0.0.2 ellipsis_0.3.1
[37] withr_2.2.0 cli_2.0.2 magrittr_1.5 crayon_1.3.4
[41] readxl_1.3.1 evaluate_0.14 fs_1.4.2 fansi_0.4.1
[45] xml2_1.3.2 oce_1.2-0 tools_4.0.2 data.table_1.13.0
[49] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[53] gsw_1.0-5 compiler_4.0.2 lfe_2.8-5.1 rlang_0.4.7
[57] grid_4.0.2 rstudioapi_0.11 labeling_0.3 rmarkdown_2.3
[61] testthat_2.3.2 gtable_0.3.0 DBI_1.1.0 R6_2.4.1
[65] zoo_1.8-8 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