Last updated: 2020-03-20

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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=ID), shape=21)+
  scale_fill_viridis_d()

3 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.

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)

The reconstructed incremental changes are added up to derive cummulative CT changes throughout the water column.

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))

3.1 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.

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")

3.2 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")

Reconstructed 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")

4 Timeseries

4.1 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)

5 Hovmoeller plots

5.1 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 C~T~ 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.

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