Gas exchange
Jens Daniel Müller
16 March, 2020
Last updated: 2020-03-16
Checks: 7 0
Knit directory: BloomSail/
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library(tidyverse)
#library(patchwork)
library(seacarb)
library(zoo)
#library(metR)
#library(scico)
# library(broom)
library(lubridate)
# library(tibbletime)
library(marelac)
library(seacarb)1 Sensor data
water <-
read_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_CT.csv"))
water <- water %>%
filter(dep < 3) %>%
select(date_time, ID, tem, pCO2_water = pCO2)
water_ID <- water %>%
group_by(ID) %>%
summarise_all(mean) %>%
ungroup() %>%
select(-ID)
water_ID %>%
ggplot(aes(date_time, pCO2_water))+
geom_point(data=water, aes(date_time, pCO2_water), col="grey")+
geom_path()+
geom_point()
start <- min(water$date_time)
end <- max(water$date_time)2 Wind data
Metrological data were recorded on the flux tower located on Ostergarnsholm island.
air <- read_delim(here::here("Data/Ostergarnsholm/Tower", "Oes_Jens_atm_water_June_to_August_2018.csv"),
delim = ";" )
air <- air %>%
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)
air <- air %>%
mutate(freq = "30 min") %>%
select(date_time, freq, pCO2_air = "CO2 12m [ppm]", wind = "WS 12m [m/s]")df <- full_join(air, water_ID) %>%
arrange(date_time)
df <- df %>%
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_air))
df %>%
ggplot(aes(date_time, pCO2_water))+
geom_point(data=water, aes(date_time, pCO2_water, col="measured"))+
geom_path()+
geom_point()+
geom_point(data = df, aes(date_time, pCO2_water, col="interpol"))
df_daily <- df %>%
mutate(day = yday(date_time)) %>%
group_by(day) %>%
summarise_all(mean, na.rm = TRUE) %>%
ungroup() %>%
select(-day) %>%
mutate(freq = "daily")
df <- bind_rows(df, df_daily)
rm(air, water_ID, water, df_daily)2.1 Time series plot
df_long <- df %>%
gather("parameter", "value", 3:6)
df_long %>%
ggplot(aes(date_time, value, col=freq))+
geom_line()+
facet_grid(parameter~., scales = "free_y")+
scale_color_brewer(palette = "Set1", direction = -1)+
theme_bw()
3 Gas exchange
F = k * K0 * dpCO2 = k * dCO2
Units used here are:
- pCO2: µatm
- K0: mol atm-1 kg-1
CO2: µmol kg-1
wind speed U: m s-1
- gas transfer velocities k: cm hr-1 (= 6060100 m s-1)
Air sea CO2 flux F: mol m–2 yr–1 (= 365 * mol m–2 d–1)
Coefficient for k calculation (eg 0.251 in W14): cm hr-1 (m s-1)-2
W14_simplyfied: F = 7.7 * 10–4 * U2 * dpCO2
Sc_W14 <- function(tem) {
2116.8 - 136.25 * tem + 4.7353 * tem^2 - 0.092307 * tem^3 + 0.0007555 * tem^4
}
Sc_W14(20)[1] 668.344df <- df %>%
mutate(dpCO2 = pCO2_water - pCO2_air,
dCO2 = dpCO2 * K0(S=7, T=tem),
k_W92 = gas_transfer(t = tem, u10 = wind, species = "CO2", method = "Wanninkhof1")* 60^2 * 100,
k_W14 = 0.251 * wind^2 * (Sc_W14(tem)/660)^(-0.5),
#F_W14_simple = 7.7 * 10^(–4) wind^2,
k_SM18 = 0.24 * wind^2 * ((1943-119.6*tem + 3.488*tem^2 - 0.0417*tem^3) / 660)^(-0.5)) %>%
pivot_longer(9:11, names_to = "k_para", values_to = "k_value")
# calculate flux F [mol m–2 yr–1]
df <- df %>%
mutate(flux = k_value*dCO2)
df %>%
ggplot(aes(date_time, flux, col=k_para))+
geom_line()+
labs(y="F (mol m-2 yr-1)")+
facet_wrap(~freq)
# scale flux to time interval
df <- df %>%
mutate(scale = if_else(freq == "daily", 365, 365*24*2)) %>%
mutate(flux = flux / scale) %>%
group_by(freq, k_para) %>%
arrange(date_time) %>%
mutate(flux_cum = cumsum(flux)) %>%
ungroup()
df %>%
ggplot(aes(date_time, flux_cum, col=k_para))+
geom_line()+
labs(y="F (mol m-2)")+
facet_wrap(~freq)
4 Open tasks / questions
- Calculate mean wind direction correctly for northerly winds
- Conversion of CO2 concentration from mol kg-1 to mol m-3 required?
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 lubridate_1.7.4 zoo_1.8-6
[5] seacarb_3.2.12 oce_1.2-0 gsw_1.0-5 testthat_2.3.1
[9] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[13] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.3.0
[17] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 here_0.1 lattice_0.20-35 assertthat_0.2.1
[5] zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.22 R6_2.4.0
[9] cellranger_1.1.0 backports_1.1.5 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 pillar_1.4.2 rlang_0.4.5 readxl_1.3.1
[17] rstudioapi_0.10 rmarkdown_2.0 labeling_0.3 munsell_0.5.0
[21] broom_0.5.3 compiler_3.5.0 httpuv_1.5.2 modelr_0.1.5
[25] xfun_0.10 pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_0.2.5
[29] workflowr_1.6.0 crayon_1.3.4 dbplyr_1.4.2 withr_2.1.2
[33] later_1.0.0 grid_3.5.0 nlme_3.1-137 jsonlite_1.6
[37] gtable_0.3.0 lifecycle_0.1.0 DBI_1.0.0 git2r_0.26.1
[41] magrittr_1.5 scales_1.0.0 cli_1.1.0 stringi_1.4.3
[45] fs_1.3.1 promises_1.1.0 xml2_1.2.2 ellipsis_0.3.0
[49] generics_0.0.2 vctrs_0.2.0 RColorBrewer_1.1-2 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