Reconstruction of NCP based on surface pCO2 and CTD data
Jens Daniel Müller
20 March, 2020
Last updated: 2020-03-20
Checks: 7 0
Knit directory: BloomSail/
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library(tidyverse)
library(seacarb)
library(patchwork)
library(metR)1 Sensor data
1m gridded, downcast profiles were used. CO2-data at depths other than 3-4m were discarded to simulate a situation were only surface CO2 observations are available.
df <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT_cumulative_profiles.csv"))
df <- df %>%
select(1:5) %>%
pivot_wider(names_from = "parameter", values_from = "value") %>%
mutate(ID = as.factor(ID),
CT = if_else(dep==3.5, CT, NaN))2 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.
df_CT <- df %>%
drop_na() %>%
arrange(date_time_ID) %>%
mutate(dCT = CT - lag(CT, default = first(CT)))
df <- df %>%
group_by(dep) %>%
arrange(date_time_ID) %>%
mutate(dtem = tem - lag(tem, default = first(tem))) %>%
ungroup()
df <- full_join(df, df_CT)
rm(df_CT)
df %>%
filter(!is.na(dCT), dCT != 0) %>%
ggplot(aes(dtem, dCT))+
geom_hline(yintercept = 0)+
geom_vline(xintercept = 0)+
geom_path()+
geom_point(aes(fill=as.factor(ID)), shape=21)+
scale_fill_viridis_d()
df_factor <- df %>%
drop_na() %>%
mutate(factor = dCT/dtem,
factor = if_else(is.na(factor), 0, factor)) %>%
select(ID, factor)
df <- full_join(df, df_factor)
rm(df_factor)
df <- df %>%
group_by(ID) %>%
mutate(diff_value = dtem * factor) %>%
ungroup() %>%
select(-factor)df <- df %>%
group_by(dep) %>%
arrange(date_time_ID) %>%
mutate(diff_time = as.numeric(date_time_ID - lag(date_time_ID)),
diff_value_daily = diff_value / diff_time,
cum_value = cumsum(diff_value))2.1 Profiles of incremental changes
Changes of seawater parameters at each depth are calculated from one cruise day to the next and divided by the number of days inbetween.
df %>%
arrange(dep) %>%
ggplot(aes(diff_value_daily, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
#facet_wrap(~parameter, scales = "free_x")+
labs(x="Change of value inbetween cruises per day")
2.2 Profiles of cumulative changes
Cumulative changes of seawater parameters were calculated at each depth relative to the first cruise day on July 5.
df %>%
arrange(dep) %>%
ggplot(aes(cum_value, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
labs(x="Cumulative change of value")
Cumulative positive and negative changes of seawater parameters were calculated separately at each depth relative to the first cruise day on July 5.
df <- df %>%
mutate(sign = if_else(diff_value < 0, "neg", "pos")) %>%
group_by(dep, sign) %>%
arrange(date_time_ID) %>%
mutate(cum_value_sign = cumsum(diff_value)) %>%
ungroup()
df %>%
arrange(dep) %>%
ggplot(aes(cum_value_sign, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
scale_fill_viridis_d()+
facet_wrap(~sign, scales = "free_x", ncol=4)+
labs(x="Cumulative directional change of value")
3 Timeseries
3.1 Incremental and cumulative
Total incremental and cumulative CT changes inbetween cruise dates were calculated for 5m depth intervals.
NCP <- df %>%
mutate(dep = cut(dep, seq(0,30,5))) %>%
group_by(ID, date_time_ID, dep, sign) %>%
summarise(dCT = sum(diff_value)/1000) %>%
ungroup()
NCP <- NCP %>%
group_by(sign, dep) %>%
arrange(date_time_ID) %>%
mutate(dCT_cum = cumsum(dCT)) %>%
ungroup()
NCP_grid <- expand_grid(
unique(NCP$date_time_ID),
unique(NCP$dep),
unique(NCP$sign)
)
NCP_grid <- NCP_grid %>%
set_names(c("date_time_ID","dep", "sign"))
NCP <- full_join(NCP, NCP_grid)
rm(NCP_grid)
NCP <- NCP %>%
arrange(sign, dep, date_time_ID) %>%
group_by(sign, dep) %>%
fill(dCT_cum) %>%
ungroup() %>%
mutate(dCT_cum = if_else(is.na(dCT_cum), 0, dCT_cum))p_iNCP <- NCP %>%
ggplot(aes(date_time_ID, dCT, fill=dep))+
geom_hline(yintercept = 0)+
geom_bar(stat="identity", col="black")+
scale_fill_viridis_d()+
scale_y_continuous(breaks = seq(-100, 100, 0.2))+
facet_grid(rev(sign)~., scales = "free_y", space = "free_y")+
theme(strip.background = element_blank(),
strip.text = element_blank())+
labs(y="integrated, directional CT changes [mol/m2]", x="date")
p_iNCPcum <- NCP %>%
ggplot(aes(date_time_ID, dCT_cum, fill=dep))+
geom_hline(yintercept = 0)+
geom_area(col="black")+
scale_fill_viridis_d()+
scale_y_continuous(breaks = seq(-100, 100, 0.2))+
facet_grid(rev(sign)~., scales = "free_y", space = "free_y")+
theme(strip.background = element_blank(),
strip.text = element_blank())+
labs(y="integrated, cumulative, directional CT changes [mol/m2]", x="date")
(p_iNCP / p_iNCPcum)+
plot_layout(guides = 'collect')
rm(p_iNCP, p_iNCPcum)4 Hovmoeller plots
4.1 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())
Hovmoeller plots of daily changes in CT and temperature. Note: Daily changes are currently plotted against the day when they were observed compared to the previous transect, although plotting against the mean date would be more plausible.
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_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] metR_0.5.0 patchwork_1.0.0 seacarb_3.2.12 oce_1.2-0
[5] gsw_1.0-5 testthat_2.3.1 forcats_0.4.0 stringr_1.4.0
[9] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0
[13] tibble_2.1.3 ggplot2_3.3.0 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] nlme_3.1-137 bitops_1.0-6 fs_1.3.1
[4] lubridate_1.7.4 httr_1.4.1 rprojroot_1.3-2
[7] tools_3.5.0 backports_1.1.5 R6_2.4.0
[10] DBI_1.0.0 colorspace_1.4-1 withr_2.1.2
[13] sp_1.3-2 tidyselect_0.2.5 gridExtra_2.3
[16] compiler_3.5.0 git2r_0.26.1 cli_1.1.0
[19] rvest_0.3.5 xml2_1.2.2 labeling_0.3
[22] scales_1.0.0 checkmate_1.9.4 digest_0.6.22
[25] foreign_0.8-70 rmarkdown_2.0 pkgconfig_2.0.3
[28] htmltools_0.4.0 dbplyr_1.4.2 highr_0.8
[31] maps_3.3.0 rlang_0.4.5 readxl_1.3.1
[34] rstudioapi_0.10 generics_0.0.2 jsonlite_1.6
[37] RCurl_1.95-4.12 magrittr_1.5 Formula_1.2-3
[40] dotCall64_1.0-0 Matrix_1.2-14 Rcpp_1.0.2
[43] munsell_0.5.0 lifecycle_0.1.0 stringi_1.4.3
[46] yaml_2.2.0 plyr_1.8.4 grid_3.5.0
[49] maptools_0.9-8 formula.tools_1.7.1 promises_1.1.0
[52] crayon_1.3.4 lattice_0.20-35 haven_2.2.0
[55] hms_0.5.2 zeallot_0.1.0 knitr_1.26
[58] pillar_1.4.2 reprex_0.3.0 glue_1.3.1
[61] evaluate_0.14 data.table_1.12.6 modelr_0.1.5
[64] operator.tools_1.6.3 vctrs_0.2.0 spam_2.3-0.2
[67] httpuv_1.5.2 cellranger_1.1.0 gtable_0.3.0
[70] assertthat_0.2.1 xfun_0.10 broom_0.5.3
[73] later_1.0.0 viridisLite_0.3.0 memoise_1.1.0
[76] fields_9.9 workflowr_1.6.0 ellipsis_0.3.0
[79] here_0.1