MLD
Jens Daniel Müller
18 March, 2020
Last updated: 2020-03-18
Checks: 7 0
Knit directory: BloomSail/
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library(tidyverse)
library(seacarb)
library(oce)
library(marelac)1 Sensor data
1m gridded, downcast profiles were used.
df <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT.csv"))2 Mixed layer depth
2.1 Calculation
Seawater density Rho was determined from S, T, and p according to TEOS-10.
df <- df %>%
mutate(rho = swSigma(salinity = sal, temperature = tem, pressure = dep/10))
# seacarb = rho(S = sal, T = tem, P = pres),
# marelac = sw_dens(S = sal, t = tem, p = pres + 1, method = "Gibbs")Mixed layer depth (MLD) was determined based on the difference between density at the surface and at depth, for a range of density criteria
# density criterion
df <- expand_grid(df, rho_lim = c(0.1,0.2,0.5))
df <- df %>%
group_by(ID, date_time_ID, station, rho_lim) %>%
arrange(dep) %>%
mutate(d_rho = rho - first(rho)) %>%
mutate(layer = if_else(d_rho > rho_lim, "deep", "surface")) %>%
ungroup()
MLD <- df %>%
group_by(ID, date_time_ID, station, rho_lim) %>%
filter(d_rho > rho_lim) %>%
summarise(MLD = min(dep)) %>%
ungroup()
df <- full_join(df, MLD)2.2 Density profiles
df %>%
filter(dep < 30) %>%
arrange(dep) %>%
ggplot(aes(rho, dep))+
geom_hline(aes(yintercept = MLD, col=as.factor(rho_lim)))+
geom_path()+
scale_y_reverse()+
scale_color_discrete(name="Rho lim")+
facet_grid(ID~station)
Overview density profiles at stations (P01-P14) and cruise dates (ID). Horizontal lines indicate determined MLD
2.3 Timeseries plot
MLD_sum <- MLD %>%
select(-station) %>%
group_by(ID, date_time_ID, rho_lim)%>%
summarise_all(list(~mean(.),~sd(.),~min(.), ~max(.)))
MLD_sum %>%
ggplot()+
geom_ribbon(aes(date_time_ID, ymin = min, ymax = max), alpha = 0.5)+
geom_path(aes(date_time_ID, mean))+
geom_errorbar(aes(date_time_ID, ymin = mean-sd, ymax = mean+sd))+
geom_point(aes(date_time_ID, mean))+
labs(x="Mean transect date", y="Mixed layer depth [m]")+
ylim(0,30)+
facet_grid(rho_lim~., labeller = label_both)
Rho, S, and T profiles were plotted individually pdf here.
df_long <- df %>%
pivot_longer(cols = c("sal", "tem", "rho"), names_to = "parameter", values_to = "value")
pdf(file=here::here("output/Plots/MLD",
"profiles_MLD_individual.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(df_long$ID)){
for(i_station in unique(df_long$station)){
# i_ID <- unique(df_long$ID)[1]
# i_station <- unique(df_long$station)[1]
if (nrow(df_long %>% filter(ID == i_ID, station == i_station)) > 0){
print(
df_long %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(value,dep))+
geom_hline(aes(yintercept = MLD, col=as.factor(rho_lim)))+
geom_path()+
scale_y_reverse(limits = c(30,0))+
scale_color_discrete(name="Rho lim")+
labs(y="Depth [m]", title = str_c(i_ID," | ",i_station))+
facet_wrap(~parameter, scales = "free_x")+
theme_bw()
)
}
}
}
dev.off()
rm(i_ID, i_station)3 NCP penetration depth
The effective NCP penetration depth, zeff, was calculated as the ratio of the observed inremental change of the depth integrated NCP, divided by the change in surface CT.
CT_ts <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT_cumulative_timeseries.csv"))
CT_profiles <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT_cumulative_profiles.csv"))
CT_conc_surf <-
CT_profiles %>%
filter(dep < 3,
parameter == "CT",
sign == "neg") %>%
group_by(ID, date_time_ID) %>%
summarise(dCT = mean(diff_value))
NCP_integ <- CT_ts %>%
filter(sign == "neg") %>%
group_by(ID, date_time_ID) %>%
summarise(dNCPi = sum(dCT))
CT <- full_join(CT_conc_surf, NCP_integ)
CT <- CT %>%
mutate(zeff = (dNCPi/dCT)*1e3)
CT_long <- CT %>%
pivot_longer(3:4, names_to = "parameter", values_to = "values")
CT_long %>%
ggplot(aes(date_time_ID, values))+
geom_path()+
geom_point()+
facet_grid(parameter~., scales = "free_y")
CT %>%
ggplot(aes(date_time_ID, zeff))+
geom_path()+
geom_point()+
scale_y_reverse()
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] marelac_2.1.9 shape_1.4.4 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] 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 highr_0.8 pillar_1.4.2
[17] rlang_0.4.5 readxl_1.3.1 rstudioapi_0.10 rmarkdown_2.0
[21] labeling_0.3 munsell_0.5.0 broom_0.5.3 compiler_3.5.0
[25] httpuv_1.5.2 modelr_0.1.5 xfun_0.10 pkgconfig_2.0.3
[29] htmltools_0.4.0 tidyselect_0.2.5 workflowr_1.6.0 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 generics_0.0.2 vctrs_0.2.0 tools_3.5.0
[53] glue_1.3.1 hms_0.5.2 yaml_2.2.0 colorspace_1.4-1
[57] rvest_0.3.5 knitr_1.26 haven_2.2.0