Last updated: 2020-04-28
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Knit directory: BloomSail/
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Rmd | 058c709 | jens-daniel-mueller | 2020-04-28 | Moved nomenlacture to seperate Rmd |
html | d2036b0 | jens-daniel-mueller | 2020-04-24 | Build site. |
Rmd | c28b943 | jens-daniel-mueller | 2020-04-24 | discrete data in CT timeseries plot |
html | b004af3 | jens-daniel-mueller | 2020-04-24 | Build site. |
Rmd | e07781a | jens-daniel-mueller | 2020-04-24 | discrete surface CT in timeseries |
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Rmd | 72f9a86 | jens-daniel-mueller | 2020-04-24 | Refined depth for discrete surface time series |
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Rmd | f8fcf50 | jens-daniel-mueller | 2020-04-19 | created pub figures for time series |
html | f8fcf50 | jens-daniel-mueller | 2020-04-19 | created pub figures for time series |
html | 6810175 | jens-daniel-mueller | 2020-04-17 | Build site. |
Rmd | 864596a | jens-daniel-mueller | 2020-04-17 | plotted all profiles |
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Rmd | acc1379 | jens-daniel-mueller | 2020-04-17 | calculate AT sd |
html | 729b4c6 | jens-daniel-mueller | 2020-04-17 | Build site. |
Rmd | 2edd18d | jens-daniel-mueller | 2020-04-17 | included bottle CT AT from 180723 |
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Rmd | d0eb264 | jens-daniel-mueller | 2020-04-17 | all stations on map |
html | cc2baf3 | jens-daniel-mueller | 2020-04-16 | Build site. |
Rmd | 13436a3 | jens-daniel-mueller | 2020-04-16 | worked on map |
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Rmd | 86b0833 | jens-daniel-mueller | 2020-04-16 | New fixed integration depth 12m |
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Rmd | 95380d4 | jens-daniel-mueller | 2020-04-16 | Cumulative temperature distribution on July 23 |
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Rmd | 4e9464f | jens-daniel-mueller | 2020-04-09 | corrected na approx function |
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Rmd | c199200 | jens-daniel-mueller | 2020-04-01 | included BloomSail data to Finnmaid analysis |
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Rmd | b1613b7 | jens-daniel-mueller | 2020-04-01 | re-calculated MLD, renamed objects and structured site |
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Rmd | 50ab313 | jens-daniel-mueller | 2020-03-31 | implemented temperature reconstruction |
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Rmd | d8120b3 | jens-daniel-mueller | 2020-03-30 | reconstruction BloomSail surface started, merging MLD and DT approach |
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Rmd | e69d1f0 | jens-daniel-mueller | 2020-03-30 | cleaned object names |
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Rmd | 9edf20d | jens-daniel-mueller | 2020-03-30 | flux and mixing correction revised |
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Rmd | 265e568 | jens-daniel-mueller | 2020-03-30 | NCP calculation finished |
html | 2ade511 | jens-daniel-mueller | 2020-03-27 | Build site. |
Rmd | 858e01f | jens-daniel-mueller | 2020-03-27 | iCT flux correction applied |
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Rmd | 9118b70 | jens-daniel-mueller | 2020-03-27 | iCT flux correction applied |
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Rmd | d17a2b0 | jens-daniel-mueller | 2020-03-27 | Added air sea CO2 fluxes |
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Rmd | 6afdea9 | jens-daniel-mueller | 2020-03-26 | selected iCT time series for NCP |
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Rmd | 4d734a1 | jens-daniel-mueller | 2020-03-26 | Started NCP estimation |
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Rmd | 275b061 | jens-daniel-mueller | 2020-03-26 | renamed NCP correctly als iCT |
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Rmd | 0405651 | jens-daniel-mueller | 2020-03-26 | Restructure MLD iCT chapter |
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Rmd | baa81d6 | jens-daniel-mueller | 2020-03-26 | heigth surface timeseries reduced |
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Rmd | 1b8a11e | jens-daniel-mueller | 2020-03-26 | restructured NCP chapter, and renamed as iCT |
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Rmd | 6ec4005 | jens-daniel-mueller | 2020-03-26 | added interpretation notes |
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Rmd | 069600c | jens-daniel-mueller | 2020-03-26 | theme_bw |
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Rmd | 69ec53e | jens-daniel-mueller | 2020-03-26 | Comparison iCT estimates |
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Rmd | 07690b6 | jens-daniel-mueller | 2020-03-25 | NCP MLD approach implmented |
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Rmd | 93800e0 | jens-daniel-mueller | 2020-03-25 | NCP MLD approach implmented |
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Rmd | a13c901 | jens-daniel-mueller | 2020-03-25 | NCP fixed depth, new variable names, ref dates introduced |
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Rmd | 90979bb | jens-daniel-mueller | 2020-03-24 | nameing convention and NCP approaches list |
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Rmd | 1e2508a | jens-daniel-mueller | 2020-03-24 | harmonized starting dates |
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html | 473ab25 | jens-daniel-mueller | 2020-03-19 | Build site. |
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Rmd | ff79dbe | jens-daniel-mueller | 2020-03-19 | remoced errorbars in ts plot |
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Rmd | 0d90486 | jens-daniel-mueller | 2020-03-19 | Hovmoeller daily changes |
html | 592f3b5 | jens-daniel-mueller | 2020-03-19 | Build site. |
Rmd | 4103279 | jens-daniel-mueller | 2020-03-19 | CT: removed coastal, added errorbars and hovmoeller |
html | 81f022e | jens-daniel-mueller | 2020-03-18 | Build site. |
html | 18a74d1 | jens-daniel-mueller | 2020-03-18 | Build site. |
Rmd | b839b18 | jens-daniel-mueller | 2020-03-18 | CT vs tem changes implemented |
html | 1e39d85 | jens-daniel-mueller | 2020-03-18 | Build site. |
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html | 4858097 | jens-daniel-mueller | 2020-03-18 | Build site. |
Rmd | f0233c2 | jens-daniel-mueller | 2020-03-18 | MLD and NCP penetration depth |
html | 05b9bdc | jens-daniel-mueller | 2020-03-17 | Build site. |
html | 943cd6b | jens-daniel-mueller | 2020-03-17 | Build site. |
Rmd | 859c4a4 | jens-daniel-mueller | 2020-03-17 | corrected gas exchange calculation |
html | 26bc407 | jens-daniel-mueller | 2020-03-17 | Build site. |
Rmd | 7be14e4 | jens-daniel-mueller | 2020-03-17 | corrected CT cum timeseries, used exact mean dates |
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Rmd | 7c10336 | jens-daniel-mueller | 2020-03-17 | corrected CT cum timeseries, used exact mean dates |
html | 0202742 | jens-daniel-mueller | 2020-03-16 | Build site. |
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Rmd | 53ee423 | jens-daniel-mueller | 2020-03-16 | gas exchange calculation completed |
html | 9f0c30b | jens-daniel-mueller | 2020-03-16 | Build site. |
Rmd | 1c60add | jens-daniel-mueller | 2020-03-16 | incremental CT changes timeseries + raw pCO2 profiles plotted |
html | 4150817 | jens-daniel-mueller | 2020-03-13 | Build site. |
Rmd | 94e12d8 | jens-daniel-mueller | 2020-03-13 | final cleaning |
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Rmd | 39b841d | jens-daniel-mueller | 2020-03-13 | all profiles pdfs included |
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Rmd | f49ce78 | jens-daniel-mueller | 2020-03-13 | cumulative changes per depth |
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Rmd | e9725fe | jens-daniel-mueller | 2020-03-12 | Clean CT dynamics |
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Rmd | 3c17c46 | jens-daniel-mueller | 2020-03-12 | update CT cynamics |
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Rmd | 97355fa | jens-daniel-mueller | 2020-03-12 | CT calculations and plots |
library(tidyverse)
library(patchwork)
library(seacarb)
library(marelac)
library(metR)
library(scico)
library(lubridate)
library(zoo)
Profile data are prepared by:
Please note that:
ts <-
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
ts_profiles <- ts %>%
filter(type == "P",
Flush == "0",
Zero == "0",
!ID %in% c("180616","180820"),
!(station %in% c("PX1", "PX2"))) %>%
select(date_time, ID, station, lat, lon, dep, sal, tem, pCO2_raw = pCO2, pCO2 = pCO2_RT_mean, duration)
stations <- ts_profiles %>%
group_by(station) %>%
summarise(lat = mean(lat),
lon = mean(lon)) %>%
ungroup()
ts_profiles <- ts_profiles %>%
filter(!(station %in% c("P14", "P13", "P01")))
# Assign meta information
ts_profiles <- ts_profiles %>%
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 %in% c("180616","180820"),
!(station %in% c("PX1", "PX2", "P14", "P13", "P01")))
ts_profiles <- full_join(ts_profiles, meta)
rm(meta)
# creating descriptive variables
ts_profiles <- ts_profiles %>%
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))
ts_profiles <- ts_profiles %>%
select(-c(start, down, lift, up, end, comment, p_type, duration))
# select downcasst only
ts_profiles <- ts_profiles %>%
filter(phase == "down") %>%
select(-phase)
# ts_profiles_highres <- ts_profiles
# grid observation to 1m depth intervals
ts_profiles <- ts_profiles %>%
mutate(dep_grid = as.numeric(as.character( cut(dep, seq(0,40,1), seq(0.5,39.5,1))))) %>%
group_by(ID, station, dep_grid) %>%
summarise_all("mean", na.rm = TRUE) %>%
ungroup() %>%
select(-dep, dep=dep_grid)
# subset complete profiles
profiles_in <- ts_profiles %>%
filter(dep < 20) %>%
group_by(ID, station) %>%
summarise(nr = n()) %>%
mutate(select = if_else(nr > 18 | station == "P14", "in", "out")) %>%
select(-nr) %>%
ungroup()
ts_profiles <- full_join(ts_profiles, profiles_in)
rm(profiles_in)
ts_profiles %>%
arrange(date_time) %>%
ggplot(aes(pCO2, dep, col=select))+
geom_hline(yintercept = 25)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_x_continuous(breaks = c(0, 600), labels = c(0, 600))+
scale_color_brewer(palette = "Set1", direction = -1)+
coord_cartesian(xlim = c(0,600))+
facet_grid(ID~station)
ts_profiles <- ts_profiles %>%
filter(select == "in") %>%
select(-select) %>%
filter(dep < 25)
# assign mean date_time stamp
cruise_dates <- ts_profiles %>%
group_by(ID) %>%
summarise(date_time_ID = mean(date_time),
date_ID = format(as.Date(date_time_ID), "%b %d")) %>%
ungroup()
# inner_join remove P14 observations lacking date_time_ID
ts_profiles <- inner_join(cruise_dates, ts_profiles)
lat_lo <- 57.25
lat_hi <- 57.6
lon_lo <- 18.6
lon_hi <- 19.7
map <- read_csv(here::here("data/Maps","Bathymetry_Gotland_east_small.csv")) %>%
filter(lat < lat_hi, lat > lat_lo,
lon < lon_hi, lon > lon_lo)
map_low_res <- map %>%
mutate(lat = cut(lat,
breaks = seq(57,58,0.01),
labels = seq(57.005,57.995,0.01)),
lon = cut(lon,
breaks = seq(18,22,0.01),
labels = seq(18.005,21.995,0.01))) %>%
group_by(lat,lon) %>%
summarise_all(mean, na.rm=TRUE) %>%
ungroup() %>%
mutate(lat = as.numeric(as.character(lat)),
lon = as.numeric(as.character(lon)))
fm_bs <-
read_csv(here::here("Data/_summarized_data_files",
"Finnmaid.csv")) %>%
filter(Lat <= lat_hi, Lat >= lat_lo, Lon >= lon_lo)
ggplot()+
geom_contour_fill(data = map_low_res,
aes(x=lon, y=lat, z=-elev),
na.fill = TRUE,
breaks = seq(0,300,20))+
geom_raster(data=map %>% filter(is.na(elev)),
aes(lon, lat), fill="black")+
geom_line(data = fm_bs, aes(Lon, Lat, group=ID), col="grey")+
geom_line(data = fm_bs %>% filter(Area == "BS"), aes(Lon, Lat, group=ID), col="red")+
geom_label(data = stations %>% filter(!(station %in% c("P14", "P13", "P01"))),
aes(lon, lat, label=station), col="red")+
geom_label(data = stations %>% filter(station %in% c("P14", "P13", "P01")),
aes(lon, lat, label=station), col="grey")+
coord_quickmap(expand = 0, ylim = c(lat_lo+0.01, lat_hi-0.01))+
labs(x="Longitude (°E)", y="Latitude (°N)")+
scale_fill_scico(palette = "oslo", na.value = "grey",
name="Depth [m]", direction = -1,
breaks = seq(0,160,20),
guide = "colorstrip")
ggsave(here::here("output/Plots/Figures_publication/article", "station_map.pdf"),
width = 180, height = 150, dpi = 300, units = "mm")
rm(map, map_low_res, lat_hi, lat_lo, lon_hi, lon_lo, fm_bs)
cover <- ts_profiles %>%
group_by(ID, station) %>%
summarise(date = mean(date_time),
date_time_ID = mean(date_time_ID)) %>%
ungroup()
cover %>%
ggplot(aes(date, station, fill=ID))+
geom_vline(aes(xintercept = date_time_ID, col=ID))+
geom_point(shape=21)+
scale_fill_viridis_d(labels = cruise_dates$date_ID,
name = "Mean cruise date")+
scale_color_viridis_d(labels = cruise_dates$date_ID,
name = "Mean cruise date")+
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d")+
theme(axis.title.x = element_blank())
ggsave(here::here("output/Plots/Figures_publication/article", "data_coverage.pdf"),
width = 130, height = 65, dpi = 300, units = "mm")
rm(cover)
At stations P07 and P10 discrete samples for lab measurments of CT and AT were collected. Please note that - in contrast to the pCO2 profiles - samples were taken on June 16, but removed here for harmonization of results.
tb <-
read_csv(here::here("Data/_summarized_data_files", "Tina_V_Bottle_CO2_lab.csv"),
col_types = cols(ID = col_character()))
tb <- tb %>%
filter(station %in% c("P07", "P10")) %>%
select(-pH_Mosley) %>%
mutate(CT_AT_ratio = CT/AT,
ID = if_else(ID == "180722", "180723", ID))
tb <- inner_join(tb, cruise_dates)
tb_long <- tb %>%
pivot_longer(4:7, names_to = "var", values_to = "value")
tb_long %>%
ggplot(aes(value, dep))+
geom_path(aes(col=ID))+
geom_point(aes(fill=ID), shape=21)+
scale_y_reverse()+
scale_fill_viridis_d(labels = cruise_dates$date_ID)+
scale_color_viridis_d(labels = cruise_dates$date_ID)+
facet_grid(station~var, scales = "free_x")+
theme(legend.position = "bottom",
legend.title = element_blank())
Important notes: - Spatio-temporal variation of AT is small, which jusitfies conversion of pCO2 to CT based on a fixed mean AT - On July 31 we see a drop in surface salinity, associated with a rise in AT, clearly pointing at exchange of water masses, presumably later
tb_surface <- tb_long %>%
filter(dep<9) %>%
group_by(ID, date_time_ID, var, station) %>%
summarise(value = mean(value, na.rm = TRUE)) %>%
ungroup()
tb_surface_station_mean <- tb_long %>%
filter(dep<9) %>%
group_by(ID, date_time_ID, var) %>%
summarise(value = mean(value, na.rm = TRUE)) %>%
ungroup()
tb_long %>%
filter(dep<11) %>%
ggplot()+
geom_line(data = tb_surface, aes(date_time_ID, value, col="Individual"))+
geom_line(data = tb_surface_station_mean, aes(date_time_ID, value, col="Both (mean)"))+
geom_point(aes(date_time_ID, value, fill=dep), shape=21)+
scale_fill_scico(palette = "oslo", direction = -1, name="Depth (m)")+
scale_color_brewer(palette = "Set1", name="Station")+
facet_grid(var~station, scales = "free_y")+
labs(x="Mean transect date")
rm(tb_long, tb_surface_station_mean)
AT_mean <- tb %>%
filter(dep <= 20) %>%
summarise(AT = mean(AT, na.rm = TRUE)) %>%
pull()
tb_surface %>%
filter(var == "CT_AT_ratio") %>%
ggplot(aes(date_time_ID, value*AT_mean, col=station))+
geom_point()+
geom_path()+
scale_fill_viridis_d()+
scale_color_brewer(palette = "Set1")+
labs(x="Mean transect date", y="CT-AT-ratio * mean AT")
Important notes: - CT drop and temporal patterns observed in the CT/AT time series agrees well with those found in the CT time series derived from pCO2 measurements
In order to derive CT from measured pCO2 profiles, the alkalinity mean + sd in the upper 20 m and both stations was calculated as:
AT_mean
[1] 1718.872
AT_sd <- tb %>%
filter(dep <= 20) %>%
summarise(AT = sd(AT, na.rm = TRUE)) %>%
pull()
AT_sd
[1] 26.23092
rm(AT_sd)
Likewise, the mean salinity amounts to:
sal_mean <- tb %>%
filter(dep <= 20) %>%
summarise(sal = mean(sal, na.rm = TRUE)) %>%
pull()
sal_mean
[1] 6.907083
bind_cols(start = min(ts_profiles$date_time),
end = max(ts_profiles$date_time),
AT = AT_mean,
sal = sal_mean) %>%
write_csv(here::here("Data/_summarized_data_files", "tb_fix.csv"))
rm(tb)
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.
ts_profiles <- ts_profiles %>%
drop_na()
ts_profiles <- ts_profiles %>%
filter(pCO2 > 0)
ts_profiles <- ts_profiles %>%
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)
ts_profiles %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles.csv"))
ts_profiles <- ts_profiles %>%
arrange(date_time_ID)
p_tem <-
ts_profiles %>%
ggplot(aes(tem, dep, col=ID, group = interaction(station, ID)))+
geom_path()+
scale_y_reverse(expand = c(0,0))+
labs(x = "Temperature (\u00B0C)",
y = "Depth (m)")+
scale_color_viridis_d(guide = FALSE)
p_pCO2 <-
ts_profiles %>%
ggplot(aes(pCO2, dep, col=ID, group = interaction(station, ID)))+
geom_path()+
scale_y_reverse(expand = c(0,0))+
labs(x = expression(pCO[2]~(µatm)))+
scale_color_viridis_d(guide = FALSE)+
theme(axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank())
p_CT <-
ts_profiles %>%
ggplot(aes(CT, dep, col=ID, group = interaction(station, ID)))+
geom_path()+
scale_y_reverse(expand = c(0,0))+
labs(x = expression(nC[T]~(µmol~kg^{-1})))+
scale_color_viridis_d(labels = cruise_dates$date_ID)+
theme(legend.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank())
p_tem + p_pCO2 + p_CT
ggsave(here::here("output/Plots/Figures_publication/article", "profiles_all.pdf"),
width = 180, height = 80, dpi = 300, units = "mm")
rm( p_tem, p_pCO2, p_CT)
Mean vertical profiles were calculated for each cruise day (ID).
ts_profiles_ID_mean <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, date_time)) %>%
group_by(ID, date_time_ID, dep) %>%
summarise_all(list(mean), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, date_time)) %>%
group_by(ID, date_time_ID, dep) %>%
summarise_all(list(sd), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd_long <- ts_profiles_ID_sd %>%
pivot_longer(sal:CT, names_to = "var", values_to = "sd")
ts_profiles_ID_mean_long <- ts_profiles_ID_mean %>%
pivot_longer(sal:CT, names_to = "var", values_to = "value")
ts_profiles_ID_long <- inner_join(ts_profiles_ID_mean_long, ts_profiles_ID_sd_long)
ts_profiles_ID_mean %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles_ID.csv"))
rm(ts_profiles_ID_sd_long, ts_profiles_ID_sd, ts_profiles_ID_mean_long, ts_profiles_ID_mean)
ts_profiles_ID_long %>%
ggplot(aes(value, dep, col=ID))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")
all <- ts_profiles_ID_long %>%
filter(var %in% c("CT", "tem")) %>%
rename(group = ID)
ts_profiles_ID_long %>%
filter(var %in% c("CT", "tem")) %>%
ggplot()+
geom_path(data=all, aes(value, dep, group=group))+
geom_ribbon(aes(xmin = value-sd, xmax=value+sd, y=dep, fill=ID), alpha=0.5)+
geom_path(aes(value, dep, col=ID))+
scale_y_reverse()+
scale_color_viridis_d()+
scale_fill_viridis_d()+
facet_grid(ID~var, scales = "free_x")
rm(all)
Important notes:
CT, pCO2, S, and T profiles were plotted individually pdf here and grouped by ID pdf here. The later gives an idea of the differences between stations at one point in time.
pdf(file=here::here("output/Plots/CT_dynamics",
"ts_profiles_pCO2_tem_sal_CT.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(ts_profiles$ID)){
for(i_station in unique(ts_profiles$station)){
if (nrow(ts_profiles %>% filter(ID == i_ID, station == i_station)) > 0){
# i_ID <- unique(ts_profiles$ID)[1]
# i_station <- unique(ts_profiles$station)[1]
p_pCO2 <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(pCO2, dep, col="grid_RT"))+
geom_point(data = ts_profiles_highres %>% arrange(date_time) %>% filter(ID == i_ID, station == i_station),
aes(pCO2_raw, dep, col="raw"))+
geom_point(data = ts_profiles_highres %>% arrange(date_time) %>% filter(ID == i_ID, station == i_station),
aes(pCO2, dep, col="raw_RT"))+
geom_point(aes(pCO2_raw, dep, col="grid"))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_brewer(palette = "Set1")+
labs(y="Depth [m]", x="pCO2 [µatm]", title = str_c(i_ID," | ",i_station))+
coord_cartesian(xlim = c(0,200), ylim = c(30,0))+
theme_bw()+
theme(legend.position = "left")
p_tem <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(tem, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="Tem [°C]")+
coord_cartesian(xlim = c(14,26), ylim = c(30,0))+
theme_bw()
p_sal <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(sal, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="Tem [°C]")+
coord_cartesian(xlim = c(6.5,7.5), ylim = c(30,0))+
theme_bw()
p_CT <-
ts_profiles %>%
arrange(date_time) %>%
filter(ID == i_ID,
station == i_station) %>%
ggplot(aes(CT, dep))+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(y="Depth [m]", x="CT* [µmol/kg]")+
coord_cartesian(xlim = c(1400,1700), ylim = c(30,0))+
theme_bw()
print(
p_pCO2 + p_tem + p_sal + p_CT
)
rm(p_pCO2, p_sal, p_tem, p_CT)
}
}
}
dev.off()
rm(i_ID, i_station, ts_profiles_highres)
ts_profiles_long <- ts_profiles %>%
select(-c(lat, lon, pCO2_raw)) %>%
pivot_longer(sal:CT, values_to = "value", names_to = "var")
pdf(file=here::here("output/Plots/CT_dynamics",
"ts_profiles_ID_pCO2_tem_sal_CT.pdf"), onefile = TRUE, width = 9, height = 5)
for(i_ID in unique(ts_profiles$ID)){
#i_ID <- unique(ts_profiles$ID)[1]
sub_ts_profiles_long <- ts_profiles_long %>%
arrange(date_time) %>%
filter(ID == i_ID)
print(
sub_ts_profiles_long %>%
ggplot()+
geom_path(data = ts_profiles_long, aes(value, dep, group=interaction(station, ID)), col="grey")+
geom_path(aes(value, dep, col=station))+
scale_y_reverse()+
labs(y="Depth [m]", title = str_c("ID: ", i_ID))+
theme_bw()+
facet_wrap(~var, scales = "free_x")
)
rm(sub_ts_profiles_long)
}
dev.off()
rm(i_ID, ts_profiles_long)
Changes of seawater vars at each depth are calculated from one cruise day to the next and divided by the number of days inbetween.
ts_profiles_ID_long <- ts_profiles_ID_long %>%
group_by(var, dep) %>%
arrange(date_time_ID) %>%
mutate(date_time_ID_diff = as.numeric(date_time_ID - lag(date_time_ID)),
date_time_ID_ref = date_time_ID - (date_time_ID - lag(date_time_ID))/2,
value_diff = value - lag(value, default = first(value)),
value_diff_daily = value_diff / date_time_ID_diff,
value_cum = cumsum(value_diff)) %>%
ungroup()
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_diff_daily, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")+
labs(x="Change of value inbetween cruises per day")
Cumulative changes of seawater vars were calculated at each depth relative to the first cruise day on July 5.
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep, col=ID))+
geom_vline(xintercept = 0)+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")+
labs(x="Cumulative change of value")
Important notes:
Cumulative positive and negative changes of seawater vars were calculated separately at each depth relative to the first cruise day on July 5.
ts_profiles_ID_long <- ts_profiles_ID_long %>%
mutate(sign = if_else(value_diff < 0, "neg", "pos")) %>%
group_by(var, dep, sign) %>%
arrange(date_time_ID) %>%
mutate(value_cum_sign = cumsum(value_diff)) %>%
ungroup()
ts_profiles_ID_long %>%
arrange(dep) %>%
ggplot(aes(value_cum_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(~interaction(sign, var), scales = "free_x", ncol=4)+
labs(x="Cumulative directional change of value")
ts_profiles_ID_long %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles_ID_long_cum.csv"))
Mean seawater parameters were calculated for 5m depth intervals.
ts_profiles_ID_long_grid <- ts_profiles_ID_long %>%
mutate(dep = cut(dep, seq(0,30,5))) %>%
group_by(ID, date_time_ID, dep, var) %>%
summarise_all(list(mean), na.rm=TRUE)
ts_profiles_ID_long_grid %>%
ggplot(aes(date_time_ID, value, col=as.factor(dep)))+
geom_path()+
#geom_errorbar(aes(date_time_ID, ymax=value+sd, ymin=value-sd, col=as.factor(dep)))+
geom_point()+
scale_color_viridis_d(name="Depth [m]")+
facet_wrap(~var, scales = "free_y", ncol=1)
rm(ts_profiles_ID_long_grid)
bin_CT <- 30
p_CT_hov <- ts_profiles_ID_long %>%
filter(var == "CT") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value),
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_scico(breaks = MakeBreaks(bin_CT),
guide = "colorstrip",
name="CT (µmol/kg)",
palette = "davos", direction = -1)+
scale_y_reverse()+
theme_bw()+
labs(y="Depth (m)")+
coord_cartesian(expand = 0)+
theme(axis.title.x = element_blank(),
legend.position = "left")
bin_Tem <- 2
p_tem_hov <- ts_profiles_ID_long %>%
filter(var == "tem") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value),
breaks = MakeBreaks(bin_Tem),
col="black")+
geom_point(aes(x=date_time_ID, y=c(24.5)), size=3, shape=24, fill="white")+
scale_fill_viridis_c(breaks = MakeBreaks(bin_Tem),
guide = "colorstrip",
name="Tem (°C)",
option = "inferno")+
scale_y_reverse()+
labs(y="Depth (m)")+
coord_cartesian(expand = 0)+
theme(axis.title.x = element_blank(),
legend.position = "left")
p_CT_hov / p_tem_hov
rm(p_CT_hov, bin_CT, p_tem_hov, bin_Tem)
bin_CT <- 2.5
CT_hov <- ts_profiles_ID_long %>%
filter(var == "CT") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID_ref, y=dep, z=value_diff_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())
bin_Tem <- 0.1
Tem_hov <- ts_profiles_ID_long %>%
filter(var == "tem") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID_ref, y=dep, z=value_diff_daily),
breaks = MakeBreaks(bin_Tem),
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_Tem),
guide = "colorstrip",
name="Tem (°C)")+
scale_y_reverse()+
theme_bw()+
labs(x="",y="Depth (m)")+
coord_cartesian(expand = 0)
CT_hov / Tem_hov
rm(CT_hov, bin_CT, Tem_hov, bin_Tem)
bin_CT <- 20
CT_hov <- ts_profiles_ID_long %>%
filter(var == "CT") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value_cum),
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())
bin_Tem <- 2
Tem_hov <- ts_profiles_ID_long %>%
filter(var == "tem") %>%
ggplot()+
geom_contour_fill(aes(x=date_time_ID, y=dep, z=value_cum),
breaks = MakeBreaks(bin_Tem),
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_Tem),
guide = "colorstrip",
name="Tem (°C)")+
scale_y_reverse()+
theme_bw()+
labs(x="",y="Depth (m)")+
coord_cartesian(expand = 0)
CT_hov / Tem_hov
rm(CT_hov, bin_CT, Tem_hov, bin_Tem)
A critical first step for the determination of net community production (NCP) is the integration of observed changes in CT over depth to derive iCT. Two approaches were tested:
Both aproaches deliver depth-integrated, incremental changes of CT inbetween cruise dates. Those were summed up to derive a trajectory of cummulative iCT changes.
Incremental and cumulative CT changes inbetween cruise dates were integrated across the water colums down to predefined depth limits. This was done separately for observed positive/negative changes in CT, as well as for the total observed changes.
iCT_grid_sign <- ts_profiles_ID_long %>%
select(ID, date_time_ID, date_time_ID_ref) %>%
unique() %>%
expand_grid(sign = c("pos", "neg"))
iCT_grid_total <- ts_profiles_ID_long %>%
select(ID, date_time_ID, date_time_ID_ref) %>%
unique() %>%
expand_grid(sign = c("total"))
# dep_i <- 10
#rm(iCT, dep_i)
for (dep_i in seq(9,13,1)) {
iCT_sign_temp <- ts_profiles_ID_long %>%
filter(var == "CT", dep < dep_i) %>%
mutate(sign = if_else(ID == "180705" & dep == 0.5, "neg", sign)) %>%
group_by(ID, date_time_ID, date_time_ID_ref, sign) %>%
summarise(CT_i_diff = sum(value_diff)/1000) %>%
ungroup()
iCT_sign_temp <- iCT_sign_temp %>%
group_by(sign) %>%
arrange(date_time_ID) %>%
mutate(CT_i_cum = cumsum(CT_i_diff)) %>%
ungroup()
iCT_sign_temp <- full_join(iCT_sign_temp, iCT_grid_sign) %>%
arrange(sign, date_time_ID) %>%
fill(CT_i_cum)
iCT_total_temp <- ts_profiles_ID_long %>%
filter(var == "CT", dep < dep_i) %>%
group_by(ID, date_time_ID, date_time_ID_ref) %>%
summarise(CT_i_diff = sum(value_diff)/1000) %>%
ungroup()
iCT_total_temp <- iCT_total_temp %>%
arrange(date_time_ID) %>%
mutate(CT_i_cum = cumsum(CT_i_diff)) %>%
ungroup() %>%
mutate(sign = "total")
iCT_total_temp <- full_join(iCT_total_temp, iCT_grid_total) %>%
arrange(sign, date_time_ID) %>%
fill(CT_i_cum)
iCT_temp <- bind_rows(iCT_sign_temp, iCT_total_temp) %>%
mutate(dep_i = dep_i)
if (exists("iCT")) {
iCT <- bind_rows(iCT, iCT_temp)
} else {iCT <- iCT_temp}
rm(iCT_temp, iCT_sign_temp, iCT_total_temp)
}
rm(iCT_grid_sign, iCT_grid_total)
iCT <- iCT %>%
mutate(dep_i = as.factor(dep_i))
iCT %>%
ggplot()+
geom_point(data = cruise_dates, aes(date_time_ID, 0), shape=21)+
geom_col(aes(date_time_ID_ref, CT_i_diff, fill=dep_i),
position = "dodge", alpha=0.3)+
geom_line(aes(date_time_ID, CT_i_cum, col=dep_i))+
scale_color_viridis_d(name="Depth limit (m)")+
scale_fill_viridis_d(name="Depth limit (m)")+
labs(y="iCT [mol/m2]", x="")+
facet_grid(sign~., scales = "free_y", space = "free_y")+
theme_bw()
iCT_fixed_dep <- iCT
rm(iCT, dep_i)
# iCT %>%
# write_csv(here::here("Data/_merged_data_files", "iCT_dep_limits.csv"))
As an alternative to fixed depth levels, vertical integration as low as the mixed layer depth was tested.
Seawater density Rho was determined from S, T, and p according to TEOS-10.
ts_profiles <- ts_profiles %>%
mutate(rho = swSigma(salinity = sal, temperature = tem, pressure = dep/10))
ts_profiles_ID_mean_hydro <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, pCO2, CT, date_time)) %>%
group_by(ID, date_time_ID, date_ID, dep) %>%
summarise_all(list(mean), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd_hydro <- ts_profiles %>%
select(-c(station,lat, lon, pCO2_raw, pCO2, CT, date_time)) %>%
group_by(ID, date_time_ID, date_ID, dep) %>%
summarise_all(list(sd), na.rm=TRUE) %>%
ungroup()
ts_profiles_ID_sd_hydro_long <- ts_profiles_ID_sd_hydro %>%
pivot_longer(sal:rho, names_to = "var", values_to = "sd")
ts_profiles_ID_mean_hydro_long <- ts_profiles_ID_mean_hydro %>%
pivot_longer(sal:rho, names_to = "var", values_to = "value")
ts_profiles_ID_hydro_long <- inner_join(ts_profiles_ID_mean_hydro_long, ts_profiles_ID_sd_hydro_long)
ts_profiles_ID_hydro <- ts_profiles_ID_mean_hydro
rm(ts_profiles_ID_mean_hydro_long,
ts_profiles_ID_mean_hydro,
ts_profiles_ID_sd_hydro_long,
ts_profiles_ID_sd_hydro)
ts_profiles_ID_hydro_long %>%
ggplot(aes(value, dep, col=ID))+
geom_point()+
geom_path()+
scale_y_reverse()+
scale_color_viridis_d()+
facet_wrap(~var, scales = "free_x")
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
ts_profiles_ID_hydro <- expand_grid(ts_profiles_ID_hydro, rho_lim = c(0.1,0.2,0.5))
MLD <- ts_profiles_ID_hydro %>%
arrange(dep) %>%
group_by(ID, date_time_ID, rho_lim) %>%
mutate(d_rho = rho - first(rho)) %>%
filter(d_rho > rho_lim) %>%
summarise(MLD = min(dep)) %>%
ungroup()
ts_profiles_ID_hydro <-
full_join(ts_profiles_ID_hydro, MLD)
ts_profiles_ID_hydro %>%
arrange(dep) %>%
ggplot(aes(rho, dep))+
geom_hline(aes(yintercept = MLD, col=as.factor(rho_lim)))+
geom_path()+
scale_y_reverse()+
scale_color_brewer(palette = "Set1", name= "Rho limit")+
facet_wrap(~ID)+
theme_bw()
MLD %>%
ggplot(aes(date_time_ID, MLD, col=as.factor(rho_lim)))+
geom_hline(yintercept = 0)+
geom_point()+
geom_path()+
scale_color_brewer(palette = "Set1", name= "Rho limit")+
scale_y_reverse()+
labs(x="")
# iCT_grid_sign <- ts_profiles_ID_long %>%
# select(ID, date_time_ID, date_time_ID_ref) %>%
# unique() %>%
# expand_grid(sign = c("pos", "neg"))
#
# iCT_grid_total <- ts_profiles_ID_long %>%
# select(ID, date_time_ID, date_time_ID_ref) %>%
# unique() %>%
# expand_grid(sign = c("total"))
iCT <- ts_profiles_ID_long %>%
filter(var == "CT")
iCT <- full_join(iCT, MLD)
iCT %>%
write_csv(here::here("Data/_merged_data_files", "ts_profiles_ID_long_cum_MLD.csv"))
iCT <- iCT %>%
filter(dep <= MLD)
iCT <- iCT %>%
group_by(ID, date_time_ID, date_time_ID_ref, rho_lim) %>%
summarise(CT_i_diff = sum(value_diff)/1000) %>%
ungroup()
iCT <- iCT %>%
group_by(rho_lim) %>%
arrange(date_time_ID) %>%
mutate(CT_i_cum = cumsum(CT_i_diff)) %>%
ungroup()
# iCT_total_temp <- full_join(iCT_total_temp, iCT_grid_total) %>%
# arrange(sign, date_time_ID) %>%
# fill(CT_i_cum)
iCT <- iCT %>%
mutate(rho_lim = as.factor(rho_lim))
iCT %>%
ggplot()+
geom_point(data = cruise_dates, aes(date_time_ID, 0), shape=21)+
geom_col(aes(date_time_ID_ref, CT_i_diff, fill=rho_lim),
position = "dodge", alpha=0.3)+
geom_line(aes(date_time_ID, CT_i_cum, col=rho_lim))+
scale_color_viridis_d(name="Rho limit")+
scale_fill_viridis_d(name="Rho limit")+
labs(y="iCT [mol/m2]", x="")+
#facet_grid(sign~., scales = "free_y", space = "free_y")+
theme_bw()
iCT_MLD <- iCT
rm(iCT, MLD)
# iCT %>%
# write_csv(here::here("Data/_merged_data_files", "iCT_dep_limits.csv"))
In the following, all cummulative iCT trajectories are displayed. Highlighted are those obtained for the fixed depth approach with 10 m limit, and the MLD approach with a high density threshold of 0.5 kg/m3.
iCT <- full_join(iCT_fixed_dep, iCT_MLD)
iCT <- iCT %>%
mutate(group = paste(as.character(sign), as.character(dep_i), as.character(rho_lim)))
iCT %>%
arrange(date_time_ID) %>%
ggplot()+
geom_hline(yintercept = 0)+
geom_point(data = cruise_dates, aes(date_time_ID, 0), shape=21)+
geom_line(aes(date_time_ID, CT_i_cum,
group=group), col="grey")+
geom_line(data = iCT_fixed_dep %>% filter(dep_i==12, sign=="total"),
aes(date_time_ID, CT_i_cum, col="12m - total"))+
geom_line(data = iCT_MLD %>% filter(rho_lim == 0.1),
aes(date_time_ID, CT_i_cum, col="MLD - 0.1"))+
scale_color_brewer(palette = "Set1", name="")+
labs(y="iCT [mol/m2]", x="")
rm(iCT, iCT_MLD)
In order to derive an estimate of the net community production NCP (which is equivalent to the formed organic matter that can be exported from the investigated surface layer), two steps are required:
The cummulative iCT trajectory determined by integration of CT to a fixed water depth of 10 m was used for NCP calculation for the following reasons:
ts_profiles_ID_long_180723 <- ts_profiles_ID_long %>%
filter(ID == 180723,
var == "CT")
p_ts_profiles_ID_long <- ts_profiles_ID_long_180723 %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep))+
geom_vline(xintercept = 0)+
geom_hline(yintercept = 12, col="red")+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(x="Cumulative change of CT on July 23 (180723)")+
theme(legend.position = "left")
ts_profiles_ID_long_180723_dep <- ts_profiles_ID_long_180723 %>%
select(dep, value_cum) %>%
filter(value_cum < 0) %>%
arrange(dep) %>%
mutate(value_cum_i = sum(value_cum),
value_cum_dep = cumsum(value_cum),
value_cum_i_rel = value_cum_dep/value_cum_i*100)
p_ts_profiles_ID_long_rel <- ts_profiles_ID_long_180723_dep %>%
ggplot(aes(value_cum_i_rel, dep))+
geom_hline(yintercept = 12, col="red")+
geom_vline(xintercept = 90)+
geom_point()+
geom_line()+
scale_y_reverse(limits = c(25,0))+
scale_x_continuous(breaks = seq(0,100,10))+
labs(y = "Depth (m)", x = "Relative contribution on July 23")+
theme_bw()
p_ts_profiles_ID_long + p_ts_profiles_ID_long_rel
rm(ts_profiles_ID_long_180723,
ts_profiles_ID_long_180723_dep,
p_ts_profiles_ID_long,
p_ts_profiles_ID_long_rel)
ts_profiles_ID_long_180723 <- ts_profiles_ID_long %>%
filter(ID == 180723,
var == "tem")
p_ts_profiles_ID_long <- ts_profiles_ID_long_180723 %>%
arrange(dep) %>%
ggplot(aes(value_cum, dep))+
geom_vline(xintercept = 0)+
geom_hline(yintercept = 12, col="red")+
geom_point()+
geom_path()+
scale_y_reverse()+
labs(x="Cumulative change of Temp on July 23")+
theme(legend.position = "left")
ts_profiles_ID_long_180723_dep <- ts_profiles_ID_long_180723 %>%
select(dep, value_cum) %>%
filter(value_cum > 0) %>%
arrange(dep) %>%
mutate(value_cum_i = sum(value_cum),
value_cum_dep = cumsum(value_cum),
value_cum_i_rel = value_cum_dep/value_cum_i*100)
p_ts_profiles_ID_long_rel <- ts_profiles_ID_long_180723_dep %>%
ggplot(aes(value_cum_i_rel, dep))+
geom_hline(yintercept = 12, col="red")+
geom_vline(xintercept = 90)+
geom_point()+
geom_line()+
scale_y_reverse(limits = c(25,0))+
scale_x_continuous(breaks = seq(0,100,10))+
labs(y = "Depth (m)", x = "Relative contribution on July 23")+
theme_bw()
p_ts_profiles_ID_long + p_ts_profiles_ID_long_rel
rm(ts_profiles_ID_long_180723,
ts_profiles_ID_long_180723_dep,
p_ts_profiles_ID_long,
p_ts_profiles_ID_long_rel)
The cruise mean pCO2 recorded in profiling-mode (stations only) and depths < 6m was used for gas exchange calcualtions.
ts_profiles_surface_long <- ts_profiles %>%
filter(dep < 6) %>%
select(date_time = date_time_ID, ID, tem, pCO2_water = pCO2, CT) %>%
pivot_longer(tem:CT, values_to = "value", names_to = "var")
ts_profiles_surface_long_ID <- ts_profiles_surface_long %>%
group_by(ID, date_time, var) %>%
summarise_all(list(~mean(.), ~sd(.), ~min(.), ~max(.))) %>%
ungroup()
p_pCO2_surf <- ts_profiles_surface_long_ID %>%
filter(var == "pCO2_water") %>%
ggplot(aes(x=date_time))+
geom_ribbon(aes(ymin=mean-sd, ymax=mean+sd), alpha=0.2)+
geom_path(aes(y=mean))+
geom_point(aes(y=mean))+
scale_fill_discrete(guide=FALSE)+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = expression(atop(pCO[2],
(mu*atm))))+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
p_tem_surf <- ts_profiles_surface_long_ID %>%
filter(var == "tem") %>%
ggplot(aes(x=date_time))+
geom_ribbon(aes(ymin=mean-sd, ymax=mean+sd), alpha=0.2)+
geom_path(aes(y=mean))+
geom_point(aes(y=mean))+
scale_fill_discrete(guide=FALSE)+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = "temperature \n (\u00B0C)")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
p_CT_surf <-
ts_profiles_surface_long_ID %>%
filter(var == "CT") %>%
ggplot()+
geom_ribbon(aes(x=date_time, ymin=mean-sd, ymax=mean+sd), alpha=0.2)+
geom_path(aes(x=date_time, y=mean))+
geom_point(aes(x=date_time, y=mean))+
geom_point(data = tb_surface %>% filter(var == "CT_AT_ratio"),
aes(date_time_ID, value*AT_mean, col=station)) +
scale_color_brewer(palette = "Set1")+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = expression(atop(nC[T],
(mu*mol~kg^{-1}))))+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = c(0.35,0.8),
legend.title = element_blank(),
legend.direction = "horizontal",
legend.background = element_rect(fill = "transparent"),
legend.key = element_rect(fill = "transparent"))
p_pCO2_surf + p_tem_surf + p_CT_surf +
plot_layout(ncol = 1)
#rm(ts_profiles_surface)
start <- min(ts_profiles_surface_long_ID$date_time)
end <- max(ts_profiles_surface_long_ID$date_time)
Metrological data were recorded on the flux tower located on Ostergarnsholm island.
og <- read_delim(here::here("Data/Ostergarnsholm/Tower", "Oes_Jens_atm_water_June_to_August_2018.csv"),
delim = ";" )
og <- og %>%
mutate(date_time = ymd_hms( paste(paste(year, month, day, sep = "/"),
paste(hour, min, sec, sep = ":")))) %>%
select("date_time",
"CO2 12m [ppm]",
"w_c [ppm m/s]",
"WS 12m [m/s]",
"WD 12m [degrees]",
"T 12m [degrees C]",
"RIS [W/m^2]"
) %>%
filter(date_time > start,
date_time < end)
rm(end, start)
og <- og %>%
select(date_time, pCO2_atm = "CO2 12m [ppm]", wind = "WS 12m [m/s]")
Data sets for atmospheric and seawater observations were merged and interpolated to a common time stamp.
ts_profiles_surface_ID <- ts_profiles_surface_long_ID %>%
filter(var %in% c("pCO2_water", "tem")) %>%
select(date_time:mean) %>%
pivot_wider(names_from = "var", values_from = "mean")
ts_og <- full_join(og, ts_profiles_surface_ID) %>%
arrange(date_time)
ts_og <- ts_og %>%
mutate(pCO2_water = na.approx(pCO2_water, rule = 2),
tem = na.approx(tem, rule = 2),
wind = na.approx(wind, rule = 2)) %>%
filter(!is.na(pCO2_atm))
rm(ts_profiles_surface_ID, og)
ts_og_long <- ts_og %>%
pivot_longer("pCO2_atm":"tem",
names_to = "var",
values_to = "value")
p_pCO2_atm <- ts_og_long %>%
filter(var == "pCO2_atm") %>%
ggplot(aes(x=date_time))+
geom_path(aes(y=value))+
scale_fill_discrete(guide=FALSE)+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = expression(atop(pCO["2,atm"],
(mu*atm))))+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
p_wind <- ts_og_long %>%
filter(var == "wind") %>%
ggplot(aes(x=date_time))+
geom_path(aes(y=value))+
scale_fill_discrete(guide=FALSE)+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = expression(atop(windspeed,
(m~s^{-1}))))+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
p_pCO2_atm + p_wind +
plot_layout(ncol = 1)
F = k * dCO2
with
dCO2 = K0 * dpCO2 and
k = coeff * U^2 * (660/Sc)^0.5
Units used here are:
dpCO2: µatm
K0: mol atm-1 kg-1
dCO2: µmol kg-1
wind speed U: m s-1
coeff for k calculation (eg 0.251 in W14): cm hr-1 (m s-1)-2
gas transfer velocities k: cm hr-1 (= 6060100 m s-1)
air sea CO2 flux F: mol m–2 d–1
conversion between the unit of k * dCO2 and F requires a factor of 10-5 * 24
Sc_W14 <- function(tem) {
2116.8 - 136.25 * tem + 4.7353 * tem^2 - 0.092307 * tem^3 + 0.0007555 * tem^4
}
# Sc_W14(20)
ts_og <- ts_og %>%
mutate(dpCO2 = pCO2_water - pCO2_atm,
dCO2 = dpCO2 * K0(S=6.92, T=tem),
# W92 = gas_transfer(t = tem, u10 = wind, species = "CO2",
# method = "Wanninkhof1")* 60^2 * 100,
#k_SM18 = 0.24 * wind^2 * ((1943-119.6*tem + 3.488*tem^2 - 0.0417*tem^3) / 660)^(-0.5),
k = 0.251 * wind^2 * (Sc_W14(tem)/660)^(-0.5))
# pivot_longer(9:10, names_to = "k_para", values_to = "k_value")
# calculate flux F [mol m–2 d–1]
ts_og <- ts_og %>%
mutate(flux_daily = k*dCO2*1e-5*24)
rm(Sc_W14)
p_flux_daily <- ts_og %>%
ggplot(aes(x=date_time))+
geom_path(aes(y=flux_daily))+
scale_fill_discrete(guide=FALSE)+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = expression(atop(flux[daily],
(mol~m^{-2}~d^{-1}))))+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
# scale flux to time interval
ts_og <- ts_og %>%
mutate(scale = 24*2) %>%
mutate(flux_scale = flux_daily / scale) %>%
#group_by(freq, k_para) %>%
arrange(date_time) %>%
mutate(flux_cum = cumsum(flux_scale)) %>%
ungroup()
p_flux_cum <- ts_og %>%
ggplot(aes(x=date_time))+
geom_path(aes(y=flux_cum))+
scale_fill_discrete(guide=FALSE)+
scale_x_datetime(date_breaks = "week",
sec.axis = dup_axis())+
labs(y = expression(atop(flux[cum],
(mol~m^{-2}))))+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank())
The cumulative iCT time series obtained through integration across the upper 10m of the water column was used for further calculations of NCP.
Correction of iCT for air-sea CO2 fluxes will be based on estimates derived from observation with 30min measurement interval and calculation according to Wanninkhof (2014).
To derive an integrated NCP estimated, the observed change in iCT must be corrected for the air-sea flux of CO2. iCT was determined for the upper 10m of the water column. The MLD was always shallower 10m, except for the last cruise day. Therefore:
During the last cruise, deeper mixing up to 17m water depth was observed, resulting in increased CT values at 0-10 m and a decrease of CT in 10-17m. The loss of CT in 10-17m can be assumed to be entirely cause by mixing with low-CT surface water. Some CT loss is balanced through CT input attributable to the air-sea flux. Therefore, the observed loss, corrected for 7m/MLD-share of the air-sea-flux, was added to the integrated CT changes in 0-10m.
# extract CT data for fixed depth approach, depth limit 10m
iCT_10 <- iCT_fixed_dep %>%
filter(dep_i == 12, sign=="total") %>%
select(-c(sign, dep_i))
rm(iCT_fixed_dep)
iCT_10 <- iCT_10 %>%
select(ID, date_time = date_time_ID, date_time_ID_ref, CT_i_diff, CT_i_cum)
# date of the second last cruise
date_180806 <- unique(iCT_10$date_time)[7]
# calculate cumulative air-sea fluxes affecting 0-10m
ts_og_flux <- ts_og %>%
mutate(flux_scale = if_else(date_time > date_180806,
12/17 * flux_scale,
flux_scale)) %>%
arrange(date_time) %>%
mutate(flux_cum = cumsum(flux_scale)) %>%
select(date_time, flux_cum)
# calculate cumulative air-sea fluxes affecting 10-17m
ts_og_flux_dep <- ts_og %>%
filter(date_time > date_180806) %>%
mutate(flux_scale = 5/17 * flux_scale) %>%
arrange(date_time) %>%
mutate(flux_cum = cumsum(flux_scale)) %>%
select(date_time, flux_cum)
iCT_10_flux <- full_join(iCT_10, ts_og_flux) %>%
arrange(date_time)
rm(ts_og_flux, iCT_10, ts_og_long, ts_og)
# linear interpolation of cumulative changes to frequency of the flux estimates estimates
iCT_10_flux <- iCT_10_flux %>%
mutate(CT_i_cum = na.approx(CT_i_cum, rule = 2),
flux_cum = na.approx(flux_cum, rule = 2),
CT_i_flux_cum = CT_i_cum + flux_cum)
# calculate cumulative fluxes inbetween cruises
iCT_10_flux_diff <- iCT_10_flux %>%
filter(!is.na(date_time_ID_ref)) %>%
mutate(flux_diff = flux_cum - lag(flux_cum, default = 0)) %>%
select(date_time_ID_ref, observed=CT_i_diff, flux=flux_diff) %>%
pivot_longer(cols = 2:3, names_to = "var", values_to = "value_diff")
# calculate mixing with deep waters, corrected for air sea fluxes
iCT_10_mix <- ts_profiles_ID_long %>%
filter(ID == "180815",
var == "CT",
dep < 17,
dep > 12) %>%
group_by(ID, date_time_ID,
date_time_ID_ref) %>%
summarise(value_diff = sum(value_diff)/1000 + min(ts_og_flux_dep$flux_cum)) %>%
ungroup()
rm(ts_og_flux_dep)
iCT_10_mix_diff <- iCT_10_mix %>%
select(date_time_ID_ref, value_diff) %>%
mutate(var="mixing")
iCT_10_flux_mix_diff <-
full_join(iCT_10_flux_diff, iCT_10_mix_diff)
iCT_10_flux_mix <-
full_join(iCT_10_flux,
iCT_10_mix %>% rename(mix_cum = value_diff))
rm(iCT_10_mix, iCT_10_mix_diff, iCT_10_flux, iCT_10_flux_diff, date_180806)
iCT_10_flux_mix <- iCT_10_flux_mix %>%
arrange(date_time) %>%
fill(ID) %>%
mutate(mix_cum = if_else(ID %in% c("180806", 180815), mix_cum, 0),
mix_cum = na.approx(mix_cum),
CT_i_flux_mix_cum = CT_i_flux_cum + mix_cum)
p_iCT <- iCT_10_flux_mix %>%
arrange(date_time) %>%
ggplot()+
geom_col(data = iCT_10_flux_mix_diff,
aes(date_time_ID_ref, value_diff, fill=var),
position = position_dodge2(preserve = "single"),
alpha=0.5)+
geom_hline(yintercept = 0)+
geom_point(data = cruise_dates, aes(date_time_ID, 0), shape=21)+
geom_line(aes(date_time, CT_i_cum, col="observed"))+
geom_line(aes(date_time, CT_i_flux_mix_cum, col="mixing + flux corrected"))+
geom_line(aes(date_time, CT_i_flux_cum, col="flux corrected"))+
scale_x_datetime(date_breaks = "week",
date_labels = "%b %d",
sec.axis = dup_axis())+
scale_fill_brewer(palette = "Set1", name="incremental changes")+
scale_color_brewer(palette = "Set1", name="cumulative changes")+
labs(y=expression(atop(integrated~nC[T],
(mol~m^{-2}))))+
guides(guide_colourbar(order = 1))+
theme(axis.title.x = element_blank(),
axis.text.x.top = element_blank(),
legend.position = "bottom",
legend.direction = "vertical")
p_iCT
iCT_10_flux_mix %>%
write_csv(here::here("Data/_merged_data_files", "ts_NCP_cum.csv"))
iCT_10_flux_mix_diff %>%
write_csv(here::here("Data/_merged_data_files", "ts_NCP_inc.csv"))
p_pCO2_surf + p_tem_surf + p_CT_surf +
p_pCO2_atm + p_wind + p_flux_daily + p_flux_cum +
p_iCT +
plot_layout(ncol = 1,
heights = c(rep(1,7), 3))
ggsave(here::here("output/Plots/Figures_publication/article", "atm_water_timeseries.pdf"),
width = 120, height = 270, dpi = 300, units = "mm")
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] zoo_1.8-7 lubridate_1.7.4 scico_1.1.0 metR_0.6.0
[5] marelac_2.1.10 shape_1.4.4 seacarb_3.2.13 oce_1.2-0
[9] gsw_1.0-5 testthat_2.3.2 patchwork_1.0.0 forcats_0.5.0
[13] stringr_1.4.0 dplyr_0.8.5 purrr_0.3.3 readr_1.3.1
[17] tidyr_1.0.2 tibble_3.0.0 ggplot2_3.3.0 tidyverse_1.3.0
[21] workflowr_1.6.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4 whisker_0.4 knitr_1.28 xml2_1.3.0
[5] magrittr_1.5 hms_0.5.3 rvest_0.3.5 tidyselect_1.0.0
[9] viridisLite_0.3.0 here_0.1 colorspace_1.4-1 lattice_0.20-41
[13] R6_2.4.1 rlang_0.4.5 fansi_0.4.1 broom_0.5.5
[17] xfun_0.12 dbplyr_1.4.2 modelr_0.1.6 withr_2.1.2
[21] git2r_0.26.1 ellipsis_0.3.0 htmltools_0.4.0 assertthat_0.2.1
[25] rprojroot_1.3-2 digest_0.6.25 lifecycle_0.2.0 haven_2.2.0
[29] rmarkdown_2.1 sp_1.4-1 compiler_3.6.3 cellranger_1.1.0
[33] pillar_1.4.3 scales_1.1.0 backports_1.1.5 generics_0.0.2
[37] jsonlite_1.6.1 httpuv_1.5.2 pkgconfig_2.0.3 rstudioapi_0.11
[41] munsell_0.5.0 plyr_1.8.6 highr_0.8 httr_1.4.1
[45] tools_3.6.3 grid_3.6.3 nlme_3.1-145 data.table_1.12.8
[49] gtable_0.3.0 checkmate_2.0.0 DBI_1.1.0 cli_2.0.2
[53] readxl_1.3.1 yaml_2.2.1 crayon_1.3.4 farver_2.0.3
[57] RColorBrewer_1.1-2 later_1.0.0 promises_1.1.0 fs_1.4.0
[61] vctrs_0.2.4 memoise_1.1.0 glue_1.3.2 evaluate_0.14
[65] labeling_0.3 reprex_0.3.0 stringi_1.4.6