Last updated: 2020-03-20

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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)

MLD_sum <- MLD %>% 
  select(-station) %>% 
  group_by(ID, date_time_ID, rho_lim)%>%
  summarise_all(list(~mean(.),~sd(.),~min(.), ~max(.))) %>% 
  ungroup()

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

Overview density profiles at stations (P01-P14) and cruise dates (ID). Horizontal lines indicate determined MLD

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.

3.1 CT and NCP changes

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 == 3.5,
         parameter == "CT",
         sign == "neg") %>% 
  group_by(date_time_ID) %>% 
  summarise(dCT = mean(diff_value)) %>% 
  ungroup()

NCP_integ <- CT_ts %>% 
  filter(sign == "neg") %>% 
  group_by(ID, date_time_ID) %>% 
  summarise(dNCPi = sum(dCT)) %>% 
  ungroup()

CT <- full_join(CT_conc_surf, NCP_integ)

rm(CT_conc_surf, NCP_integ, CT_ts, CT_profiles)

CT <- CT %>% 
  mutate(zeff = (dNCPi/dCT)*1e3)

CT_long <- CT %>% 
  pivot_longer(c(dCT, dNCPi), names_to = "parameter", values_to = "values")

CT_long %>% 
  ggplot(aes(date_time_ID, values))+
  geom_path()+
  geom_point()+
  facet_grid(parameter~., scales = "free_y")

3.2 Time series MLD and zeff

df <- full_join(MLD_sum, CT)

date_grid <- df %>% 
  select(ID, date_time_ID) %>% 
  unique() %>% 
  arrange(date_time_ID) %>% 
  mutate(diff_date = difftime(date_time_ID, lag(date_time_ID))/2,
         mean_date = date_time_ID - diff_date) %>% 
  select(ID, mean_date)

df <- full_join(df, date_grid)
rm(date_grid)

df %>% 
  ggplot()+
  geom_ribbon(aes(date_time_ID, ymin = min, ymax = max, fill="MLD"), alpha = 0.2)+
  geom_path(aes(date_time_ID, mean, col="MLD"))+
  geom_errorbar(aes(date_time_ID, ymin = mean-sd, ymax = mean+sd, col="MLD"))+
  geom_point(aes(date_time_ID, mean, col="MLD"))+
  geom_point(aes(mean_date, zeff, col="zeff"))+
  geom_line(aes(mean_date, zeff, col="zeff"))+
  labs(x="Mean transect date", y="Depth [m]")+
  scale_y_reverse()+
  scale_color_brewer(palette = "Set1", direction = -1, name="")+
  scale_fill_brewer(palette = "Set1", direction = -1, guide=FALSE)+
  facet_grid(rho_lim~., labeller = label_both)


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        RColorBrewer_1.1-2
[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