CT changes
Jens Daniel Müller
12 March, 2020
Last updated: 2020-03-12
Checks: 7 0
Knit directory: BloomSail/
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library(tidyverse)
library(patchwork)
library(seacarb)
library(metR)
library(scico)
# library(broom)
# library(lubridate)
# library(tibbletime)1 Sensor profile data
Profiles data are prepared by:
- ignoring those made on June 16 (pCO2 sensor not in operation)
- removing Flush and Zero periods
- selecting only continous downcast periods
- grid profiles to 1m depth intervals
- select profiles with no more than 2 observation missing from depth intervals within upper 20 m (except shallow coastal station at Ostergarnsholm “P14”)
Remove all incomplete profiles (2 or more missing observation in 1m depth intervals), except at P14.
df <-
read_csv(here::here("data/_merged_data_files",
"BloomSail_CTD_HydroC_track_RT.csv"),
col_types = cols(ID = col_character(),
pCO2_analog = col_double(),
pCO2 = col_double(),
Zero = col_character(),
Flush = col_character(),
mixing = col_character(),
Zero_ID = col_integer(),
deployment = col_integer(),
lon = col_double(),
lat = col_double(),
pCO2_RT = col_double()))
# Filter relevant rows and columns
df <- df %>%
filter(type == "P",
Flush == "0",
Zero == "0",
ID != "180616",
!(station %in% c("PX1", "PX2"))) %>%
select(date_time, ID, type, station, lat, lon, dep, sal, tem, pCO2_raw = pCO2, pCO2 = pCO2_RT_mean, duration)
# Assign meta information
df <- df %>%
group_by(ID, station) %>%
mutate(duration = as.numeric(date_time - min(date_time))) %>%
arrange(date_time) %>%
ungroup()
meta <- read_csv(here::here("Data/_summarized_data_files",
"Tina_V_Sensor_meta.csv"),
col_types = cols(ID = col_character()))
meta <- meta %>%
filter(ID != "180616",
!(station %in% c("PX1", "PX2")))
df <- full_join(df, meta)
rm(meta)
# creating descriptive variables
df <- df %>%
mutate(phase = "standby",
phase = if_else(duration >= start & duration < down & !is.na(down) & !is.na(start), "down", phase),
phase = if_else(duration >= down & duration < lift & !is.na(lift) & !is.na(down ), "low", phase),
phase = if_else(duration >= lift & duration < up & !is.na(up ) & !is.na(lift ), "mid", phase),
phase = if_else(duration >= up & duration < end & !is.na(end ) & !is.na(up ), "up", phase))
df <- df %>%
select(-c(start, down, lift, up, end, comment, p_type, duration, type))
# select downcasst only
df <- df %>%
filter(phase == "down") %>%
select(-phase)
# grid observation to 1m depth intervals
df <- df %>%
mutate(dep_int = as.numeric(as.character( cut(dep, seq(0,40,1), seq(0.5,39.5,1))))) %>%
group_by(ID, station, dep_int) %>%
summarise_all("mean", na.rm = TRUE) %>%
ungroup() %>%
select(-dep, dep=dep_int)
# subset complete profiles
profiles_in <- df %>%
filter(dep < 20) %>%
group_by(ID, station) %>%
summarise(nr = n()) %>%
mutate(select = if_else(nr > 18 | station == "P14", "in", "out")) %>%
select(-nr) %>%
ungroup()
df <- full_join(df, profiles_in)
rm(profiles_in)
df %>%
filter(dep < 30) %>%
arrange(date_time) %>%
ggplot(aes(pCO2, dep, col=select))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_brewer(palette = "Set1", direction = -1)+
coord_cartesian(xlim = c(0,400))+
facet_grid(station~ID)
df <- df %>%
filter(select == "in") %>%
select(-select)1.1 Station map
map <- read_csv(here::here("data/Maps","Bathymetry_Gotland_east_small.csv"))
df %>%
ggplot()+
geom_raster(data=map, aes(lon, lat, fill=elev))+
scale_fill_scico(palette = "grayC", na.value = "grey", name="Depth [m]", direction = -1)+
geom_point(aes(lon, lat, col=station))+
coord_quickmap(expand = 0, xlim = c(18.7, 19.9), ylim = c(57.25,57.6))+
theme_bw()
Location of stations sampled between the east coast of Gotland and Gotland deep.
rm(map)1.2 Data coverage
cover <- df %>%
group_by(ID, station) %>%
summarise(date = mean(date_time)) %>%
ungroup()
cover %>%
ggplot(aes(date, station, fill=ID))+
geom_point(shape=21)+
scale_fill_viridis_d()
Spatio-temporal data coverage, indicated as station visits over time. ID (color) refers to the starting date of the cruise, except for P14, which was visited twice during each cruise.
rm(cover)2 Bottle CT and AT
At stations P07 and P10 discrete samples for lab measurments of CT and AT were collected. Please not that - in contrast to the pCO2 profiles - samples were taken on June 16.
CO2 <-
read_csv(here::here("Data/_summarized_data_files", "Tina_V_Bottle_CO2_lab.csv"),
col_types = cols(ID = col_character()))
CO2 <- CO2 %>%
filter(station %in% c("P07", "P10")) %>%
select(-pH_Mosley) %>%
mutate(CT_AT_ratio = CT/AT)2.1 Vertical profiles
CO2_long <- CO2 %>%
pivot_longer(4:7, names_to = "parameter", values_to = "value")
CO2_long %>%
ggplot(aes(value, dep))+
geom_path(aes(col=ID))+
geom_point(aes(fill=ID), shape=21)+
scale_y_reverse()+
scale_fill_viridis_d()+
scale_color_viridis_d()+
facet_grid(station~parameter, scales = "free_x")2.2 Surface time series
CO2_ts <- CO2_long %>%
filter(dep<10) %>%
group_by(ID, parameter, station) %>%
summarise(value = mean(value, na.rm = TRUE)) %>%
ungroup()
rm(CO2_long)
CO2_ts %>%
ggplot(aes(lubridate::ymd(ID), value, col=station))+
geom_point()+
geom_path()+
scale_fill_viridis_d()+
scale_color_brewer(palette = "Set1")+
facet_grid(parameter~., scales = "free_y")+
labs(x="Transect starting date (from ID)")2.3 Mean alkalinity
In order to derive CT from measured pCO2 profiles, the mean alkalinity in the upper 20 m and both stations was calculated as:
AT_mean <- CO2 %>%
filter(dep <= 20) %>%
summarise(AT = mean(AT, na.rm = TRUE)) %>%
pull()
AT_mean[1] 1716.205sal_mean <- CO2 %>%
filter(dep <= 20) %>%
summarise(sal = mean(sal, na.rm = TRUE)) %>%
pull()
sal_mean[1] 6.9203573 CT profiles
3.1 Calculation from pCO2
CT profiles were calculated from sensor pCO2 and T profiles, and constant salinity and alkalinity values. Note that the impact of fixed vs. measured salinity has only a negligible impact on CT profiles.
df <- df %>%
drop_na()
df <- df %>%
filter(pCO2 > 0)
df <- df %>%
mutate(CT = carb(24, var1=pCO2, var2=1720*1e-6,
S=sal_mean, T=tem, P=dep/10, k1k2="m10", kf="dg", ks="d",
gas="insitu")[,16]*1e6)
rm(sal_mean, AT_mean)
df %>%
write_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT.csv"))3.2 Mean profiles
Mean vertical profiles are displayed for each cruise day (ID). Note that:
- ID represents the start date of the cruise, not the mean sampling date
- Coastal station P14 was excluded so far from the analysis
# df %>%
# filter(dep < 20) %>%
# arrange(date_time) %>%
# ggplot(aes(CT, dep))+
# geom_point()+
# geom_path()+
# scale_y_reverse()+
# coord_cartesian(ylim = c(30,0))+
# facet_grid(station~ID)
mean_profiles <- df %>%
filter(station != "P14", dep < 25) %>%
select(-c(station,lat, lon, pCO2_raw)) %>%
group_by(ID, dep) %>%
summarise_all(list(mean), na.rm=TRUE) %>%
ungroup()
mean_profiles_long <- mean_profiles %>%
pivot_longer(4:7, names_to = "parameter", values_to = "value")
mean_profiles_long %>%
ggplot(aes(value, dep, col=ID))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~parameter, scales = "free_x")
Mean vertical profiles per cruise day across all stations, except P14.
3.3 Profiles of incremental changes
mean_profiles_long <- mean_profiles_long %>%
group_by(parameter, dep) %>%
arrange(date_time) %>%
mutate(diff_value = value - lag(value, default = first(value)),
diff_time = as.numeric(date_time - lag(date_time)),
diff_value_daily = diff_value / diff_time,
cum_value = cumsum(diff_value)) %>%
ungroup()
mean_profiles_long %>%
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")
3.4 Profiles of cumulative changes
mean_profiles_long %>%
arrange(dep) %>%
ggplot(aes(cum_value, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~parameter, scales = "free_x")
mean_profiles_long <- mean_profiles_long %>%
mutate(sign = if_else(diff_value < 0, "neg", "pos")) %>%
group_by(parameter, dep, sign) %>%
arrange(date_time) %>%
mutate(cum_value_sign = cumsum(diff_value)) %>%
ungroup()
mean_profiles_long %>%
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_grid(sign~parameter, scales = "free_x")
4 Timeseries
timeseries <- mean_profiles_long %>%
mutate(dep = cut(dep, seq(0,30,5))) %>%
group_by(ID, dep, parameter) %>%
summarise_all(list(mean), na.rm=TRUE)
timeseries %>%
ggplot(aes(date_time, value, col=as.factor(dep)))+
geom_path()+
geom_point()+
scale_color_viridis_d(name="Depth [m]")+
facet_wrap(~parameter, scales = "free_y", ncol=1)
NCP <- mean_profiles_long %>%
filter(parameter == "CT") %>%
group_by(ID, sign) %>%
summarise(date_time = mean(date_time),
dCT = sum(cum_value_sign)/1000) %>%
ungroup()
NCP <- NCP %>%
group_by(sign) %>%
arrange(date_time) %>%
mutate(dCT_cum = cumsum(dCT)) %>%
ungroup()
NCP %>%
ggplot(aes(date_time, dCT_cum, col=sign))+
geom_hline(yintercept = 0)+
geom_point()+
geom_path()+
scale_color_brewer(palette = "Set1")+
labs(y="integrated, cumulative, directional CT changes [mol/m2]", x="date")
mean_profiles %>%
ggplot(aes(date_time, dep, col=CT))+
geom_point()+
scale_color_viridis_c(direction = -1)+
scale_y_reverse()
mean_profiles_long %>%
filter(parameter == "CT") %>%
ggplot(aes(date_time, dep, col=diff))+
geom_point()+
scale_color_divergent()+
scale_y_reverse()
mean_profiles_long %>%
filter(parameter == "tem") %>%
ggplot(aes(date_time, dep, col=diff))+
geom_point()+
scale_color_divergent()+
scale_y_reverse()pdf(file=here::here("output/Plots/sensor_data",
"profiles_check.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(df$ID)){
for(i_station in unique(df$station)){
if (nrow(df %>% filter(ID == i_ID, station == i_station)) > 0){
# i_ID <- unique(df$ID)[1]
# i_station <- unique(df$station)[1]
p_pCO2 <-
df %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(pCO2, dep))+
geom_point(aes(pCO2_raw, dep), col="lightgrey")+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_brewer(palette = "Set1")+
labs(y="Depth [m]", x="pCO2 [µatm]", title = str_c(i_ID," | ",i_station))+
coord_cartesian(xlim = c(0,200), ylim = c(30,0))+
theme_bw()+
theme(legend.position = "left")
p_tem <-
df %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(tem, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="Tem [°C]")+
coord_cartesian(xlim = c(14,26), ylim = c(30,0))+
theme_bw()
p_sal <-
df %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(sal, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="Tem [°C]")+
coord_cartesian(xlim = c(6.5,7.5), ylim = c(30,0))+
theme_bw()
p_CT <-
df %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(CT, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="CT* [µmol/kg]")+
coord_cartesian(xlim = c(1400,1700), ylim = c(30,0))+
theme_bw()
print(
p_pCO2 + p_tem + p_sal + p_CT
)
rm(p_pCO2, p_sal, p_tem, p_CT)
}
}
}
dev.off()
rm(i_ID, i_station)5 Open tasks / questions
- Significance of changes in AT for calculated CT changes
- Harmonize selection of profiles for tau optimization and BGC interpretation (how many missing discrete depth intervals are allowed?)
- Vergleich der Stationen
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] scico_1.1.0 metR_0.5.0 seacarb_3.2.12 oce_1.2-0
[5] gsw_1.0-5 testthat_2.3.1 patchwork_1.0.0 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] nlme_3.1-137 bitops_1.0-6 fs_1.3.1
[4] lubridate_1.7.4 RColorBrewer_1.1-2 httr_1.4.1
[7] rprojroot_1.3-2 tools_3.5.0 backports_1.1.5
[10] R6_2.4.0 DBI_1.0.0 colorspace_1.4-1
[13] withr_2.1.2 sp_1.3-2 tidyselect_0.2.5
[16] gridExtra_2.3 compiler_3.5.0 git2r_0.26.1
[19] cli_1.1.0 rvest_0.3.5 xml2_1.2.2
[22] labeling_0.3 scales_1.0.0 checkmate_1.9.4
[25] digest_0.6.22 foreign_0.8-70 rmarkdown_2.0
[28] pkgconfig_2.0.3 htmltools_0.4.0 dbplyr_1.4.2
[31] highr_0.8 maps_3.3.0 rlang_0.4.5
[34] readxl_1.3.1 rstudioapi_0.10 generics_0.0.2
[37] jsonlite_1.6 RCurl_1.95-4.12 magrittr_1.5
[40] Formula_1.2-3 dotCall64_1.0-0 Matrix_1.2-14
[43] Rcpp_1.0.2 munsell_0.5.0 lifecycle_0.1.0
[46] stringi_1.4.3 yaml_2.2.0 plyr_1.8.4
[49] grid_3.5.0 maptools_0.9-8 formula.tools_1.7.1
[52] promises_1.1.0 crayon_1.3.4 lattice_0.20-35
[55] haven_2.2.0 hms_0.5.2 zeallot_0.1.0
[58] knitr_1.26 pillar_1.4.2 reprex_0.3.0
[61] glue_1.3.1 evaluate_0.14 data.table_1.12.6
[64] modelr_0.1.5 operator.tools_1.6.3 vctrs_0.2.0
[67] spam_2.3-0.2 httpuv_1.5.2 cellranger_1.1.0
[70] gtable_0.3.0 assertthat_0.2.1 xfun_0.10
[73] broom_0.5.3 later_1.0.0 viridisLite_0.3.0
[76] memoise_1.1.0 fields_9.9 workflowr_1.6.0
[79] ellipsis_0.3.0 here_0.1