MLD
Jens Daniel Müller
31 March, 2020
Last updated: 2020-03-31
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Knit directory: BloomSail/
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library(tidyverse)
library(seacarb)
library(oce)
library(marelac)
library(patchwork)1 Approach
In order to test how (and how well) the depth-integrated NCP estimates can be reproduced if only surface CO2 data were available, the BloomSail observations were restricted to those made in surface water and two reconstruction approaches were tested:
- MLD: Integration of surface observation across the MLD, assuming homogenious vertical patterns
- Temperature: Vertical reconstruction of incremental CT changes based on profiles of incremental changes in temperature
2 Sensor data
1m gridded, downcast profiles were used.
ts_profiles_ID <-
read_csv(here::here("Data/_merged_data_files", "ts_profiles_ID_long_cum_MLD.csv"))
ts_profiles_ID <- ts_profiles_ID %>%
select(ID, date_time_ID, dep, CT = value, CT_diff = value_diff, CT_cum = value_cum, sign, rho_lim, MLD)
ts_profiles_ID <- ts_profiles_ID %>%
mutate(CT = if_else(dep == 3.5, CT, NaN),
rho_lim = as.factor(rho_lim))3 MLD approach
MLD calculation was previously described in the CT dynamics chapter.
3.1 Timeseries
For comparison to MLD, the effective penetration depth of NCP, zeff, was calculated as the ratio of the inremental, depth-integrated change of CT, divided by the change in surface CT, for all cases where the change in surface CT was negative.
ts_profiles_ID_surface <- ts_profiles_ID %>%
filter(sign=="neg",
rho_lim == "0.5",
dep == 3.5) %>%
group_by(ID, date_time_ID) %>%
summarise(CT_diff_surf = mean(CT_diff, na.rm = TRUE)) %>%
ungroup()
ts_profiles_ID_i <- ts_profiles_ID %>%
filter(sign=="neg",
rho_lim == "0.5") %>%
group_by(ID, date_time_ID) %>%
summarise(CT_diff_i = sum(CT_diff, na.rm = TRUE))
zeff <- inner_join(ts_profiles_ID_surface, ts_profiles_ID_i)
rm(ts_profiles_ID_surface, ts_profiles_ID_i)
zeff <- zeff %>%
mutate(zeff = CT_diff_i / CT_diff_surf)ts_profiles_ID %>%
ggplot()+
geom_hline(yintercept = 0)+
geom_line(data = zeff, aes(date_time_ID, zeff, linetype="zeff"))+
geom_line(aes(date_time_ID, MLD, col=rho_lim, linetype="MLD"))+
scale_y_reverse()+
scale_color_viridis_d(name="Rho limit")+
scale_linetype(name="Estimate")+
labs(y="Depth (m)")+
theme(axis.title.x = element_blank())
rm(zeff)3.2 iCT calculation
Integrated CT depletion was calculated as the product of observed incremental CT changes in surface waters and the respective mixed layer depth.
ts_profiles_ID_surface <- ts_profiles_ID %>%
filter(dep==3.5) %>%
group_by(rho_lim) %>%
arrange(date_time_ID) %>%
mutate(iCT_diff = CT_diff * MLD / 1000,
iCT_cum = cumsum(replace_na(iCT_diff, 0))) %>%
ungroup()3.3 Incremental and cumulative timeseries
Total incremental and cumulative CT changes inbetween cruise dates were calculated.
p_iCT <- ts_profiles_ID_surface %>%
ggplot(aes(date_time_ID, iCT_diff, fill= rho_lim))+
geom_hline(yintercept = 0)+
geom_col(col="black", position = "dodge")+
scale_y_continuous(breaks = seq(-100, 100, 0.2))+
scale_fill_viridis_d()+
labs(y="integrated CT changes [mol/m2]")+
theme(axis.title.x = element_blank())
p_iCT_cum <- ts_profiles_ID_surface %>%
ggplot(aes(date_time_ID, iCT_cum,
col=rho_lim))+
geom_line()+
geom_hline(yintercept = 0)+
scale_color_viridis_d()+
scale_y_continuous(breaks = seq(-100, 100, 0.2))+
theme(strip.background = element_blank(),
strip.text = element_blank())+
labs(y="integrated, cumulative CT changes [mol/m2]", x="date")
(p_iCT / p_iCT_cum)+
plot_layout(guides = 'collect')
rm(p_iCT, p_iCT_cum)
rm(ts_profiles_ID, ts_profiles_ID_surface)4 Temperature approach
ts_profiles_ID <-
read_csv(here::here("Data/_merged_data_files", "ts_profiles_ID.csv"))
ts_profiles_ID <- ts_profiles_ID %>%
mutate(CT = if_else(dep == 3.5, CT, NaN),
ID = as.factor(ID))4.1 CT vs temperature
As primary production (negative changes in CT) and increase in seawater temperature have a common driver (light), the relation between both changes was investigated.
ts_profiles_ID_diff <- ts_profiles_ID %>%
drop_na() %>%
arrange(date_time_ID) %>%
mutate(CT_diff = CT - lag(CT, default = first(CT)),
tem_diff = tem - lag(tem, default = first(tem)),
factor = CT_diff / tem_diff,
factor = if_else(is.na(factor), 0, factor))
ts_profiles_ID_diff %>%
ggplot(aes(tem_diff, CT_diff))+
geom_hline(yintercept = 0)+
geom_vline(xintercept = 0)+
geom_path()+
geom_point()
4.2 Reconstruction of CT dynamics
The ratio of the incremental change of CT with temperature at the seasurface was applied to calculate the CT in other water depth based on the known change in temperature.
ts_profiles_ID_diff <- ts_profiles_ID_diff %>%
select(ID, factor)
ts_profiles_ID <- full_join(ts_profiles_ID, ts_profiles_ID_diff)
rm(ts_profiles_ID_diff)
ts_profiles_ID <- ts_profiles_ID %>%
arrange(date_time_ID) %>%
group_by(dep) %>%
mutate(tem_diff = tem - lag(tem, default = first(tem))) %>%
ungroup()
ts_profiles_ID <- ts_profiles_ID %>%
mutate(CT_diff = tem_diff * factor) %>%
select(-factor)The reconstructed incremental changes are added up to derive cummulative CT changes throughout the water column.
ts_profiles_ID_long <- ts_profiles_ID %>%
select(ID, date_time_ID, dep, tem = tem_diff, CT = CT_diff) %>%
pivot_longer(4:5, names_to = "var", values_to = "value_diff") %>%
group_by(dep, var) %>%
arrange(date_time_ID) %>%
mutate(diff_time = as.numeric(date_time_ID - lag(date_time_ID)),
value_diff_daily = value_diff / diff_time,
value_cum = cumsum(value_diff)) %>%
ungroup()4.3 Profiles of incremental changes
Changes of seawater parameters at each depth were reconstructed from one cruise day to the next and divided by the number of days inbetween.
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_diff, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")+
labs(x="Change of value inbetween cruises per day")
4.4 Profiles of cumulative changes
Cumulative changes of seawater parameters were calculated at each depth relative to the first cruise day on July 5.
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
labs(x="Cumulative change of value")+
facet_wrap(~var, scales = "free_x")
4.5 Timeseries
Total incremental and cumulative CT changes inbetween cruise dates were calculated for the upper 10 m of the water body.
iCT_10 <- ts_profiles_ID_long %>%
filter(dep < 10,
var == "CT") %>%
select(ID, date_time_ID, CT_diff=value_diff, CT_cum=value_cum) %>%
group_by(ID, date_time_ID) %>%
summarise(CT_i_diff = sum(CT_diff)/1000,
CT_i_cum = sum(CT_cum)/1000) %>%
ungroup()
iCT_10 %>%
ggplot()+
#geom_point(data = cruise_dates, aes(date_time_ID, 0), shape=21)+
geom_col(aes(date_time_ID, CT_i_diff),
position = "dodge", alpha=0.3)+
geom_line(aes(date_time_ID, CT_i_cum))+
scale_color_viridis_d(name="Depth limit (m)")+
scale_fill_viridis_d(name="Depth limit (m)")+
labs(y="iCT [mol/m2]", x="")+
theme_bw()
4.6 Hovmoeller plots
4.6.1 Daily changes
bin_CT <- 2.5
df %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=diff_value_daily),
breaks = MakeBreaks(bin_CT),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_divergent(breaks = MakeBreaks(bin_CT),
guide = "colorstrip",
name="CT (µmol/kg)")+
scale_y_reverse()+
theme_bw()+
labs(y="Depth (m)")+
coord_cartesian(expand = 0)+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
sessionInfo()R version 3.5.0 (2018-04-23)
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] patchwork_1.0.0 marelac_2.1.9 shape_1.4.4 seacarb_3.2.12
[5] oce_1.2-0 gsw_1.0-5 testthat_2.3.1 forcats_0.4.0
[9] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
[13] tidyr_1.0.0 tibble_2.1.3 ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 lubridate_1.7.4 here_0.1 lattice_0.20-35
[5] assertthat_0.2.1 zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.22
[9] R6_2.4.0 cellranger_1.1.0 backports_1.1.5 reprex_0.3.0
[13] evaluate_0.14 httr_1.4.1 pillar_1.4.2 rlang_0.4.5
[17] readxl_1.3.1 rstudioapi_0.10 rmarkdown_2.0 labeling_0.3
[21] munsell_0.5.0 broom_0.5.3 compiler_3.5.0 httpuv_1.5.2
[25] modelr_0.1.5 xfun_0.10 pkgconfig_2.0.3 htmltools_0.4.0
[29] tidyselect_0.2.5 workflowr_1.6.0 viridisLite_0.3.0 crayon_1.3.4
[33] dbplyr_1.4.2 withr_2.1.2 later_1.0.0 grid_3.5.0
[37] nlme_3.1-137 jsonlite_1.6 gtable_0.3.0 lifecycle_0.1.0
[41] DBI_1.0.0 git2r_0.26.1 magrittr_1.5 scales_1.0.0
[45] cli_1.1.0 stringi_1.4.3 fs_1.3.1 promises_1.1.0
[49] xml2_1.2.2 ellipsis_0.3.0 generics_0.0.2 vctrs_0.2.0
[53] tools_3.5.0 glue_1.3.1 hms_0.5.2 yaml_2.2.0
[57] colorspace_1.4-1 rvest_0.3.5 knitr_1.26 haven_2.2.0