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Knit directory: emlr_mod_preprocessing/
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path_GLODAP_preprocessing <-
paste(path_root, "/observations/preprocessing/", sep = "")
path_cmorized <-
"/nfs/kryo/work/updata/reccap2/Models/3D_ALL/"
path_preprocessing <-
paste0(path_root, "/model/preprocessing/GLODAP_subset_A_all_models/")# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)GLODAP <-
read_csv(paste(path_GLODAP_preprocessing,
"GLODAPv2.2021_preprocessed.csv",
sep = ""))
GLODAP <- GLODAP %>%
mutate(month = month(date))
GLODAP <- GLODAP %>%
filter(year <= 2019)Here we subset cmorized (1x1) data from the model with variable forcing, according to the presence of GLODAP observations in a previously cleaned file.
Besides, Model results are given in [mol m-3], whereas GLODAP data are in [µmol kg-1]. This refers to the variables:
For comparison, model results were converted from [mol m-3] to [µmol kg-1]
# set name of model to be subsetted
model_ID <- "A"
# for loop across variables
variables <-
c("so", "thetao", "dissic", "talk", "o2", "no3", "po4", "si")
models <- list.files(path_cmorized)
models <-
models[!str_detect(models, pattern = "\\.t|\\.z")]
models <-
models[str_detect(
models,
pattern = c(
"CESM|CNRM|EC-Earth3|FESOM_REcoM_LR|MOM6-Princeton|MRI-ESM2-1|NorESM-OC1.2|ORCA025-GEOMAR|ORCA1-LIM3-PISCES|planktom12"
)
)]# models <- models[c(2,8)]
for (i_model in models) {
# i_model <- models[8]
print(i_model)
variables_available <-
list.files(path = paste0(path_cmorized, i_model),
pattern = paste0("_", model_ID, "_"))
variables_available <-
str_split(variables_available,
pattern = "_",
simplify = TRUE)[, 1]
variables_available <-
variables_available[variables_available %in% variables]
variables_available <- unique(variables_available)
GLODAP_joined <- GLODAP
for (i_variable in variables_available) {
# i_variable <- variables_available[1]
# read list of all files
file <-
list.files(path = paste0(path_cmorized, i_model),
pattern = paste0(i_variable, "_"))
file <-
file[str_detect(file, pattern = paste0("_", model_ID, "_"))]
print(file)
# read in data
if (i_model == "NorESM-OC1.2_3D_ALL_v20211125") {
variable_data <-
tidync::tidync(paste(paste0(path_cmorized, i_model),
file,
sep = "/")) %>%
tidync::hyper_tibble() %>%
mutate(time = as.Date(time, origin = "1980-01-01"))
} else if (i_model %in% c("EC-Earth3_3D_ALL_v20220323",
"CNRM-ESM2-1_3D_ALL_v20211208")) {
variable_data <-
read_ncdf(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
make_units = FALSE,
make_time = FALSE)
} else if (i_model %in% c("ORCA025-GEOMAR_3D_ALL_v20210804")) {
for (i_file in file) {
# i_file <- file[1]
temp <-
read_ncdf(paste(paste0(path_cmorized, i_model),
i_file,
sep = "/"),
make_units = FALSE)
temp_dates <-
read_stars(paste(paste0(path_cmorized, i_model),
i_file,
sep = "/"),
make_units = FALSE)
temp_dates <- st_get_dimension_values(temp_dates, "time")
temp <-
st_set_dimensions(temp,
"time",
values = temp_dates)
if (exists("variable_data")) {
variable_data <- c(variable_data, temp)
}
if (!exists("variable_data")) {
variable_data <- temp
}
rm(temp, temp_dates)
}
} else {
variable_data <-
read_ncdf(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
make_units = FALSE)
}
if (i_model == "CNRM-ESM2-1_3D_ALL_v20211208") {
CNRM_depth <-
read_ncdf(
paste(
paste0(path_cmorized, i_model),
"depth_CNRM-ESM2-1_1_gr_1980-2018_v20211208.nc",
sep = "/"
),
make_units = FALSE,
make_time = FALSE
) %>%
as_tibble()
CNRM_depth <- CNRM_depth %>%
distinct(lev) %>%
arrange(lev) %>%
rename(depth = lev) %>%
pull()
variable_data <-
st_set_dimensions(variable_data,
"lev",
values = CNRM_depth,
names = "depth")
variable_data <-
st_set_dimensions(variable_data,
"time",
values = ymd(1980:2018, truncated = 2L))
# CNRM_depth %>%
# slice_sample(n = 1e4) %>%
# ggplot(aes(lev, depth)) +
# geom_point(alpha = 0.3) +
# labs(title = "depth_CNRM-ESM2-1_1_gr_1980-2018_v20211208.nc",
# subtitle = "random sample of 1000 data points")
#
# CNRM_depth %>%
# filter(lev == unique(CNRM_depth$lev)[70]) %>%
# ggplot(aes(lon, lat, fill = depth)) +
# geom_tile() +
# scale_fill_viridis_c(direction = -1) +
# coord_quickmap(expand = 0) +
# labs(title = "depth_CNRM-ESM2-1_1_gr_1980-2018_v20211208.nc",
# subtitle = "lev == 4787.702 (position 70)")
}
# convert to tibble
variable_data_tibble <- variable_data %>%
as_tibble()
# remove open link to nc file
rm(variable_data)
# remove na values
variable_data_tibble <-
variable_data_tibble %>%
filter(!is.na(!!sym(i_variable)))
if (i_model == "CESM-ETHZ_3D_ALL_v20211122") {
variable_data_tibble <- variable_data_tibble %>%
rename(time = time_ann)
}
if (i_model == "EC-Earth3_3D_ALL_v20220323") {
variable_data_tibble <- variable_data_tibble %>%
mutate(time = as.Date(time, origin = "1850-06-01"))
}
if (i_model == "FESOM_REcoM_LR_3D_all_v20211119") {
variable_data_tibble <- variable_data_tibble %>%
rename(
lat = Lat,
lon = Lon,
depth = Depth,
time = Time
) %>%
mutate(time = as.Date(time, origin = '1980-01-01'))
}
if (i_model == "MOM6-Princeton_3D_ALL_v20220125") {
variable_data_tibble <- variable_data_tibble %>%
rename(depth = z_l)
}
if (i_model == "MRI-ESM2-1_3D_ALL_v20220502") {
variable_data_tibble <- variable_data_tibble %>%
rename(depth = lev)
}
if (i_model == "planktom12_3d_all_v20220404") {
variable_data_tibble <- variable_data_tibble %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
depth = DEPTH,
time = TIME)
}
# harmonize longitudes
variable_data_tibble <- variable_data_tibble %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# only consider model grids within basinmask
variable_data_tibble <-
inner_join(variable_data_tibble, basinmask) %>%
select(-basin_AIP)
# mutate variables
variable_data_tibble <- variable_data_tibble %>%
mutate(year = year(time)) %>%
select(-time)
if (i_model == "MRI-ESM2-1_3D_ALL_v20220502" &
i_variable == "si") {
variable_data_tibble <-
crossing(variable_data_tibble %>%
select(-year),
year = seq(1980, 2018, 1))
}
if (i_model == "planktom12_3d_all_v20220404" &
i_variable == "po4") {
variable_data_tibble <-
variable_data_tibble %>%
mutate(po4 = po4 / 100)
}
# for loop across years
years <- unique(variable_data_tibble$year)
years <- years[years %in% (unique(GLODAP$year))]
for (i_year in years) {
# i_year <- years[1]
print(i_year)
# select GLODAP data for that year
GLODAP_year <- GLODAP %>%
filter(year == i_year)
# create lat x lon grid of observations
Glodap_year_grid_horizontal <- GLODAP_year %>%
distinct(lat, lon)
# create lat x lon x depth grid of observations
Glodap_year_grid_depth <- GLODAP_year %>%
distinct(lat, lon, depth)
# select GLODAP data for that year
variable_data_tibble_year <- variable_data_tibble %>%
filter(year == i_year)
# calculate model summary stats
stats <- variable_data_tibble_year %>%
pull(!!sym(i_variable)) %>%
summary()
stats <- c(year = i_year, variable = i_variable, stats)
if (exists("stats_summary")) {
stats_summary <- bind_rows(stats_summary, stats)
}
if (!exists("stats_summary")) {
stats_summary <- stats
}
rm(stats)
# subset model at lat x lon grid of observations
model_grid_horizontal <-
inner_join(Glodap_year_grid_horizontal,
variable_data_tibble_year)
# join model and lat x lon x depth grid of observations
model_obs <-
full_join(model_grid_horizontal, Glodap_year_grid_depth)
# calculate nr of observations per lat x lon grid
model_obs <- model_obs %>%
group_by(lat, lon) %>%
mutate(n = sum(!is.na(!!sym(i_variable)))) %>%
ungroup()
# set variable value from model for observation depth, if only one model depth available
model_obs_set <- model_obs %>%
filter(n == 1) %>%
group_by(lon, lat) %>%
mutate(!!sym(i_variable) := mean(!!sym(i_variable), na.rm = TRUE)) %>%
ungroup()
# interpolate variable value from model to observation depth
model_obs_interpo <- model_obs %>%
filter(n > 1) %>%
group_by(lon, lat) %>%
arrange(depth) %>%
mutate(!!sym(i_variable) := approxfun(depth, !!sym(i_variable), rule = 2)(depth)) %>%
ungroup()
# join interpolated and set model values
model_obs_interpo <-
full_join(model_obs_interpo, model_obs_set)
rm(model_obs_set)
# subsetted interpolated values at observation depth
model_obs_interpo <-
inner_join(Glodap_year_grid_depth, model_obs_interpo) %>%
select(-n) %>%
mutate(year = as.numeric(i_year))
# select observation grids without corresponding model subset
na_model <-
full_join(Glodap_year_grid_depth, model_obs_interpo) %>%
filter(is.na(!!sym(i_variable))) %>%
select(year, lat, lon) %>%
unique()
# rename interpolated model variable to indicate as model output
model_obs_interpo <- model_obs_interpo %>%
rename(!!sym(paste(i_variable, "model", sep = "_")) := !!sym(i_variable))
# add model subset to GLODAP
GLODAP_joined <-
natural_join(
GLODAP_joined,
model_obs_interpo,
by = c("year", "lat", "lon", "depth"),
jointype = "FULL"
)
# select surface annual average variable
variable_data_tibble_year_surface <-
variable_data_tibble_year %>%
filter(depth == min(depth))
# surface map of variable
p_map <- map +
geom_raster(data = variable_data_tibble_year_surface,
aes(lon, lat, fill = !!sym(i_variable))) +
scale_fill_viridis_c(name = i_variable) +
geom_tile(data = model_obs_interpo,
aes(lon, lat, width = 1, height = 1), fill = "black") +
labs(
title = "GLODAP-based cmorized subset distribution",
subtitle = paste(
"Model depth: 5m | Annual average of year",
i_year,
"| red = subsetting failed"
),
x = "Longitude",
y = "Latitude"
)
if (nrow(na_model) > 0){
p_map <- p_map +
geom_tile(data = na_model,
aes(lon, lat, width = 1, height = 1), fill = "red")
}
p_map
ggsave(
paste(
path_preprocessing,
"regular_subset_distribution_runA_2021/",
str_remove(i_model, "3D_ALL_|3D_all_|3d_all_"),
"_",
i_variable,
"_",
i_year,
".png",
sep = ""
),
width = 5,
height = 3
)
}
}
if (i_model == "FESOM_REcoM_LR_3D_all_v20211119") {
GLODAP_joined <- GLODAP_joined %>%
mutate(po4_model = no3_model/16)
}
if (i_model == "planktom12_3d_all_v20220404") {
GLODAP_joined <- GLODAP_joined %>%
mutate(no3_model = po4_model*16)
}
# Remove 3D_all from model name
i_model <- str_remove(i_model, "3D_ALL_|3D_all_|3d_all_")
# write raw data file for GLODAP-based subsetting model variables
GLODAP_joined %>%
write_csv(paste(
path_preprocessing,
paste(
i_model,
"GLODAPv2.2021_preprocessed_model_runA_raw_subset_ann.csv",
sep = "_"
),
sep = ""
))
rm(GLODAP_joined)
# write file for model summary statistics (original cmorized unit)
stats_summary %>%
write_csv(
paste(
path_preprocessing,
"regular_subset_distribution_runA_2021/",
i_model,
"_",
model_ID,
"_summary_stats.csv",
sep = ""
)
)
}[1] "CESM-ETHZ_3D_ALL_v20211122"
[1] "dissic_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "CNRM-ESM2-1_3D_ALL_v20211208"
[1] "dissic_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_CNRM-ESM2-1_A_1_gr_1980-2018_v20211208.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "EC-Earth3_3D_ALL_v20220323"
[1] "dissic_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_EC-Earth3_A_1_gr_1980-2018_v20220323.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "FESOM_REcoM_LR_3D_all_v20211119"
[1] "dissic_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_FESOM_REcoM_LR_A_1_gr_1980-2018_v20211119.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "MOM6-Princeton_3D_ALL_v20220125"
[1] "dissic_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] "talk_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "MRI-ESM2-1_3D_ALL_v20220502"
[1] "dissic_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_MRI-ESM2-1_A_1_gr_1980-1980_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_MRI-ESM2-1_A_1_gr_1980-2018_v20220502.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "NorESM-OC1.2_3D_ALL_v20211125"
[1] "dissic_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_NorESM-OC1.2_A_1_gr_1980-2018_v20211125.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "ORCA025-GEOMAR_3D_ALL_v20210804"
[1] "dissic_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "dissic_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "dissic_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "dissic_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "no3_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "no3_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "no3_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "o2_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "o2_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "o2_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "po4_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "po4_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "po4_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "so_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "so_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "so_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "talk_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "talk_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "talk_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_ORCA025-GEOMAR_A_1_gr_1980-1989_v20210804.nc"
[2] "thetao_ORCA025-GEOMAR_A_1_gr_1990-1999_v20210804.nc"
[3] "thetao_ORCA025-GEOMAR_A_1_gr_2000-2009_v20210804.nc"
[4] "thetao_ORCA025-GEOMAR_A_1_gr_2010-2018_v20210804.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "ORCA1-LIM3-PISCES_3D_ALL_v20211215"
[1] "dissic_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "no3_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_ORCA1-LIM3-PISCES_A_1_gr_1980-2018_v20211215.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "planktom12_3d_all_v20220404"
[1] "dissic_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "o2_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "po4_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "si_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "so_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "talk_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018[1] "thetao_PlankTOM12_A_1_gr_1980-2018_v20220404.nc"
[1] 1981[1] 1982[1] 1983[1] 1984[1] 1985[1] 1986[1] 1987[1] 1988[1] 1989[1] 1990[1] 1991[1] 1992[1] 1993[1] 1994[1] 1995[1] 1996[1] 1997[1] 1998[1] 1999[1] 2000[1] 2001[1] 2002[1] 2003[1] 2004[1] 2005[1] 2006[1] 2007[1] 2008[1] 2009[1] 2010[1] 2011[1] 2012[1] 2013[1] 2014[1] 2015[1] 2016[1] 2017[1] 2018rm(GLODAP, GLODAP_joined)
for (i_model in models) {
# i_model <- models[3]
# Remove 3D_all from model name
i_model <- str_remove(i_model, "3D_ALL_|3D_all_|3d_all_")
# read file for GLODAP-based subsetting model variables
GLODAP_single <-
read_csv(
paste0(
path_preprocessing,
i_model,
"_GLODAPv2.2021_preprocessed_model_runA_raw_subset_ann.csv"
),
col_types = cols(
.default = col_double(),
basin_AIP = col_character(),
date = col_date(format = ""),
dissic_model = col_double(),
no3_model = col_double(),
o2_model = col_double(),
po4_model = col_double(),
si_model = col_double(),
so_model = col_double(),
talk_model = col_double(),
thetao_model = col_double()
)
)
GLODAP_single <- GLODAP_single %>%
mutate(model = i_model)
if (exists("GLODAP")) {
GLODAP <- bind_rows(GLODAP, GLODAP_single)
}
if (!exists("GLODAP")) {
GLODAP <- GLODAP_single
}
}
rm(GLODAP_single)
# GLODAP <- GLODAP %>%
# sample_n(10000)
# filter out GLODAP grids without model subset
GLODAP <- GLODAP %>%
filter(!is.na(so_model))
# calculate model temperature
GLODAP <- GLODAP %>%
mutate(temp_model = gsw_pt_from_t(
SA = so_model,
t = thetao_model,
p = 10.1325,
p_ref = depth
))
# unit conversion from mol/m3 to µmol/kg
GLODAP <- GLODAP %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = so_model,
t = temp_model,
p = depth,
p_ref = 10.1325
),
dissic_model = dissic_model * (1e+6 / rho),
talk_model = talk_model * (1e+6 / rho),
o2_model = o2_model * (1e+6 / rho),
no3_model = no3_model * (1e+6 / rho),
po4_model = po4_model * (1e+6 / rho),
si_model = si_model * (1e+6 / rho)
)
# calculate AOU
GLODAP <- GLODAP %>%
mutate(
oxygen_sat_m3 = gas_satconc(
S = so_model,
t = temp_model,
P = 1.013253,
species = "O2"
),
oxygen_sat_kg = oxygen_sat_m3 * (1e+3 / rho),
aou_model = oxygen_sat_kg - o2_model
) %>%
select(-oxygen_sat_kg, -oxygen_sat_m3)
# calculate gamma model in temporary data frame
GLODAP_gamma <- GLODAP %>%
select(depth, lat,
so_model, thetao_model, lon)
GLODAP_gamma <- GLODAP_gamma %>%
mutate(CTDPRS = gsw_p_from_z(-depth,
lat))
GLODAP_gamma <- GLODAP_gamma %>%
rename(
LATITUDE = lat,
LONGITUDE = lon,
SALNTY = so_model,
THETA = thetao_model
)
source_python(
paste(
path_root,
"/utilities/functions/python_scripts/",
"Gamma_GLODAP_python.py",
sep = ""
)
)
GLODAP_gamma <- calculate_gamma(GLODAP_gamma)
# join gamma column with original data frame
GLODAP <- GLODAP %>%
mutate(gamma_model = GLODAP_gamma$GAMMA)
GLODAP_NA_filled <- GLODAP
rm(GLODAP_gamma)
# Replace model value with NA, if observations are NA
GLODAP$so_model <-
ifelse(is.na(GLODAP$sal), NA, GLODAP$so_model)
GLODAP$thetao_model <-
ifelse(is.na(GLODAP$theta), NA, GLODAP$thetao_model)
GLODAP$temp_model <-
ifelse(is.na(GLODAP$temp), NA, GLODAP$temp_model)
GLODAP$dissic_model <-
ifelse(is.na(GLODAP$tco2), NA, GLODAP$dissic_model)
GLODAP$talk_model <-
ifelse(is.na(GLODAP$talk), NA, GLODAP$talk_model)
GLODAP$o2_model <-
ifelse(is.na(GLODAP$oxygen), NA, GLODAP$o2_model)
GLODAP$no3_model <-
ifelse(is.na(GLODAP$nitrate), NA, GLODAP$no3_model)
GLODAP$po4_model <-
ifelse(is.na(GLODAP$phosphate), NA, GLODAP$po4_model)
GLODAP$si_model <-
ifelse(is.na(GLODAP$silicate), NA, GLODAP$si_model)
GLODAP$aou_model <-
ifelse(is.na(GLODAP$aou), NA, GLODAP$aou_model)
# write file for GLODAP observations + subsetted model variables
GLODAP %>%
group_by(model) %>%
group_walk(~ write_csv(
.x,
paste(
path_preprocessing,
.y$model,
"_GLODAPv2.2021_preprocessed_model_runA_both_ann.csv",
sep = ""
)
))
GLODAP_NA_filled %>%
group_by(model) %>%
group_walk(~ write_csv(
.x,
paste(
path_preprocessing,
.y$model,
"_GLODAPv2.2021_preprocessed_model_runA_both_NA_filled_ann.csv",
sep = ""
)
))
# remove GLODAP observations and rename model subset
GLODAP <- GLODAP %>%
select(-c(
sal,
theta,
temp,
tco2,
talk,
oxygen,
nitrate,
phosphate,
silicate,
aou,
gamma
)) %>%
rename(
sal = so_model,
theta = thetao_model,
temp = temp_model,
tco2 = dissic_model,
talk = talk_model,
oxygen = o2_model,
nitrate = no3_model,
phosphate = po4_model,
silicate = si_model,
aou = aou_model,
gamma = gamma_model
)
GLODAP_NA_filled <- GLODAP_NA_filled %>%
select(-c(
sal,
theta,
temp,
tco2,
talk,
oxygen,
nitrate,
phosphate,
silicate,
aou,
gamma
)) %>%
rename(
sal = so_model,
theta = thetao_model,
temp = temp_model,
tco2 = dissic_model,
talk = talk_model,
oxygen = o2_model,
nitrate = no3_model,
phosphate = po4_model,
silicate = si_model,
aou = aou_model,
gamma = gamma_model
)# write final file for GLODAP-based subsetting model variables
GLODAP %>%
select(-row_number) %>%
group_by(model) %>%
group_walk( ~ write_csv(
.x,
paste(
path_preprocessing,
.y$model,
"_GLODAPv2.2021_preprocessed_model_runA_final_ann.csv",
sep = ""
)
))
GLODAP_NA_filled %>%
select(-row_number) %>%
group_by(model) %>%
group_walk(~ write_csv(
.x,
paste(
path_preprocessing,
.y$model,
"_GLODAPv2.2021_preprocessed_model_runA_final_NA_filled_ann.csv",
sep = ""
)
))GLODAP <-
read_csv(paste(path_GLODAP_preprocessing,
"GLODAPv2.2021_preprocessed.csv",
sep = ""))
GLODAP <- GLODAP %>%
mutate(month = month(date))
# select GLODAP data for 2019
GLODAP_2019 <- GLODAP %>%
filter(year == 2018)
# create month x lat x lon grid of observations
Glodap_year_grid_horizontal <- GLODAP_2019 %>%
select(month, lat, lon) %>%
unique()
# create month x lat x lon x depth grid of observations
Glodap_year_grid_depth <- GLODAP_2019 %>%
select(month, lat, lon, depth) %>%
unique()
# read in cmorized tco2 output in year 2019
variable_data_tibble <-
read_ncdf(paste(path_cmorized,
"dissic_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc",
sep = ""
)) %>%
as_tibble()
# remove na values
variable_data_tibble <-
variable_data_tibble %>%
filter(!is.na(dissic))
# harmonize longitudes
variable_data_tibble <- variable_data_tibble %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# only consider model grids within basinmask
variable_data_tibble <-
inner_join(variable_data_tibble, basinmask) %>%
select(-basin_AIP)
# mutate variables
variable_data_tibble <- variable_data_tibble %>%
mutate(year = year(time_ann) + 1, dissic = as.numeric(dissic)) %>%
filter(year == 2018) %>%
select(-time_ann)
# subset model at month x lat x lon grid of observations
model_grid_horizontal <-
inner_join(Glodap_year_grid_horizontal, variable_data_tibble)
# join model and month x lat x lon x depth grid of observations
model_obs <-
full_join(model_grid_horizontal, Glodap_year_grid_depth)
# calculate nr of observations per month x lat x lon grid
model_obs <- model_obs %>%
group_by(month, lat, lon) %>%
mutate(n = sum(!is.na(dissic))) %>%
ungroup()
# interpolate variable value from model to observation depth
model_obs_interpo <- model_obs %>%
filter(n > 1) %>%
group_by(lon, lat, month) %>%
arrange(month, lat, lon, depth) %>%
mutate(dissic_interpolate = approxfun(depth, dissic, rule = 2)(depth)) %>%
ungroup()
ggplot() +
geom_path(
data = model_obs_interpo %>%
filter(lat == -65.5, lon == 139.5, !is.na(dissic), month == 1) %>%
arrange(depth),
aes(dissic, depth, col = "model")
) +
geom_point(
data = model_obs_interpo %>%
filter(lat == -65.5, lon == 139.5, !is.na(dissic), month == 1) %>%
arrange(depth),
aes(dissic, depth, col = "model")
) +
geom_point(
data = model_obs_interpo %>%
filter(lat == -65.5, lon == 139.5, is.na(dissic), month == 1),
aes(dissic_interpolate, depth, col = "interpolated")
) +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", name = "") +
labs(title = "Interpolation to sampling depth - tco2 of year 2019")GLODAP_max_depth <- GLODAP %>%
select(lon, lat, depth) %>%
group_by(lon, lat) %>%
summarise(max_depth_GLODAP = max(depth))
model_max_depth <- variable_data_tibble %>%
select(lon, lat, depth) %>%
group_by(lon, lat) %>%
summarise(max_depth_model = max(depth))
GLODAP_model_depth_offset <-
left_join(GLODAP_max_depth, model_max_depth) %>%
mutate(offset = max_depth_GLODAP - max_depth_model)
map +
geom_raster(
data = GLODAP_model_depth_offset %>%
filter(offset > 500),
aes(lon, lat, fill = offset)
) +
scale_fill_viridis_c(direction = -1) +
labs(
title = "GLODAP-model max sampling depth offset",
subtitle = "Year 1971 - 2019",
x = "Longitude",
y = "Latitude"
)files <- fs::dir_ls(path_preprocessing)
files <- files[str_detect(files, pattern = "preprocessed_model_runA_both_ann.csv")]
GLODAP_cmorized <- files %>%
map_dfr(read_csv, .id = "model")
GLODAP_cmorized <- GLODAP_cmorized %>%
mutate(
model = str_remove(model, path_preprocessing),
model = str_remove(
model,
"_GLODAPv2.2021_preprocessed_model_runA_both_ann.csv"
)
)GLODAP_cmorized_beta <- GLODAP_cmorized %>%
filter(!is.na(talk),
!is.na(tco2),
!is.na(temp),
!is.na(sal),
!is.na(pressure),
!is.na(talk_model),
!is.na(dissic_model),
!is.na(temp_model),
!is.na(so_model)) %>%
mutate(
BetaD = buffer(
flag = 15,
var1 = talk* 1e-6,
var2 = tco2* 1e-6,
S = sal,
T = temp,
k1k2 = "l",
P = pressure/10,
Pt = phosphate* 1e-6,
Sit = silicate* 1e-6,
warn = "n"
)$BetaD,
BetaD_model = buffer(
flag = 15,
var1 = talk_model* 1e-6,
var2 = dissic_model* 1e-6,
S = so_model,
T = temp_model,
k1k2 = "l",
P = pressure/10,
Pt = po4_model* 1e-6,
Sit = si_model* 1e-6,
warn = "n"
)$BetaD
)GLODAP_cmorized_beta %>%
group_by(model) %>%
group_walk(~ write_csv(
.x,
paste(
path_preprocessing,
.y$model,
"_GLODAPv2.2021_preprocessed_model_runA_both_ann_incl_betaD.csv",
sep = ""
)
))files <- fs::dir_ls(path_preprocessing)
files <- files[str_detect(files, pattern = "preprocessed_model_runA_both_ann_incl_betaD.csv")]
GLODAP_cmorized_beta <- files %>%
map_dfr(read_csv, .id = "model")
GLODAP_cmorized_beta <- GLODAP_cmorized_beta %>%
mutate(
model = str_remove(model, path_preprocessing),
model = str_remove(
model,
"_GLODAPv2.2021_preprocessed_model_runA_both_ann_incl_betaD.csv"
)
)
GLODAP_cmorized_beta <- GLODAP_cmorized_beta %>%
mutate(BetaD_tco2 = tco2 / BetaD / 100,
BetaD_dissic_model = dissic_model / BetaD_model / 100)GLODAP_cmorized <- GLODAP_cmorized %>%
mutate(
model = str_split(model, "_v", simplify = TRUE)[,1]
)
GLODAP_cmorized_beta <- GLODAP_cmorized_beta %>%
mutate(
model = str_split(model, "_v", simplify = TRUE)[,1]
)
# for loop across variables
obs_var <-
c(
"tco2",
"talk",
"oxygen",
"aou",
"nitrate",
"phosphate",
"silicate",
"sal",
"temp",
"theta",
"gamma",
"BetaD",
"BetaD_tco2"
)
model_var <-
c(
"dissic_model",
"talk_model",
"o2_model",
"aou_model",
"no3_model",
"po4_model",
"si_model",
"so_model",
"temp_model",
"thetao_model",
"gamma_model",
"BetaD_model",
"BetaD_dissic_model"
)
for (i in 1:length(obs_var)) {
# i <- 1
pdf(
file = paste0(
path_preprocessing,
"models_vs_GLODAP/",
str_remove(model_var[i], "_model"),
"_RECCAP2_models_vs_GLODAPv2-2021.pdf"
),
width = 14,
height = 8
)
print(obs_var[i])
# select correlated observation and model variable
if (obs_var[i] %in% c("BetaD", "BetaD_tco2")) {
GLODAP_cmorized_var <- GLODAP_cmorized_beta %>%
select(model,
year,
month,
lat,
lon,
depth,
basin_AIP,
!!sym(obs_var[i]),
!!sym(model_var[i])) %>%
drop_na() %>%
mutate(
season = case_when(
month %in% c(3, 4, 5) ~ "Spring",
month %in% c(6, 7, 8) ~ "Summer",
month %in% c(9, 10, 11) ~ "Autumn",
month %in% c(12, 1, 2) ~ "Winter"
)
)
} else{
GLODAP_cmorized_var <- GLODAP_cmorized %>%
select(model,
year,
month,
lat,
lon,
depth,
basin_AIP,
!!sym(obs_var[i]),
!!sym(model_var[i])) %>%
drop_na() %>%
mutate(
season = case_when(
month %in% c(3, 4, 5) ~ "Spring",
month %in% c(6, 7, 8) ~ "Summer",
month %in% c(9, 10, 11) ~ "Autumn",
month %in% c(12, 1, 2) ~ "Winter"
)
)
}
# calculate equal axis limits and binwidth
axis_lims <- GLODAP_cmorized_var %>%
summarise(max_value = max(c(max(!!sym(
obs_var[i]
)),
max(
!!sym(model_var[i])
))),
min_value = min(c(min(!!sym(
obs_var[i]
)),
min(
!!sym(model_var[i])
))))
binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 40
axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
# obs-model plot (season)
print(
ggplot(GLODAP_cmorized_var, aes(
x = !!sym(obs_var[i]),
y = !!sym(model_var[i])
)) +
geom_bin2d(binwidth = binwidth_value) +
labs(title = "Observation (x) vs Model (y)",
subtitle = "Seasonal comparison") +
scale_fill_viridis_c(trans = "log10") +
geom_abline(slope = 1,
col = 'red') +
coord_equal(xlim = axis_lims,
ylim = axis_lims) +
facet_grid(season ~ model)
)
# obs-model plot (year)
print(
ggplot(GLODAP_cmorized_var, aes(
x = !!sym(obs_var[i]),
y = !!sym(model_var[i])
)) +
geom_bin2d(binwidth = binwidth_value) +
labs(title = "Observation (x) vs Model (y)",
subtitle = "All years") +
scale_fill_viridis_c(trans = "log10") +
geom_abline(slope = 1, col = 'red') +
coord_equal(xlim = axis_lims,
ylim = axis_lims) +
facet_wrap( ~ model, ncol = 5)
)
# Calculate variable offset
GLODAP_cmorized_var <- GLODAP_cmorized_var %>%
mutate(offset = !!sym(model_var[i])-!!sym(obs_var[i]))
# Calculate annual mean offset
GLODAP_cmorized_var_year <- GLODAP_cmorized_var %>%
group_by(year, model) %>%
summarise(offset = mean(offset)) %>%
ungroup()
muted <- c(colour("muted", names = FALSE)(9),"#999999")
# plot annual mean offset
print(
GLODAP_cmorized_var_year %>%
ggplot(aes(year, offset, col = model)) +
geom_point() +
geom_line() +
scale_color_manual(values = muted) +
labs(title = "Annual mean offset",
subtitle = paste(sym(model_var[i]), "-", sym(obs_var[i]))) +
theme(legend.position = "bottom",
legend.title = element_blank())
)
# spatial distribution of the model-observations offset for 4 depth intervals
intervals <- c(0, 150, 500, 2000, 8000)
for (j in 1:4) {
print(intervals[j])
# j <- 1
GLODAP_cmorized_var_interval <- GLODAP_cmorized_var %>%
filter(depth >= intervals[j],
depth < intervals[j + 1])
# Calculate annual mean offset
GLODAP_cmorized_var_year <- GLODAP_cmorized_var_interval %>%
group_by(year, model) %>%
summarise(offset = mean(offset)) %>%
ungroup()
# plot annual mean offset
print(
GLODAP_cmorized_var_year %>%
ggplot(aes(year, offset, col = model)) +
geom_point() +
geom_line() +
labs(
title = paste(
"Annual mean offset | Depth interval:",
intervals[j],
"-",
intervals[j + 1],
"m"
),
subtitle = paste(sym(model_var[i]), "-", sym(obs_var[i]))
) +
scale_color_manual(values = muted) +
theme(legend.position = "bottom",
legend.title = element_blank())
)
GLODAP_cmorized_var_grid <- GLODAP_cmorized_var_interval %>%
group_by(lat, lon, model) %>%
summarise(offset = mean(offset)) %>%
ungroup()
# plot mean spatial distribution (model - obs)
limit <-
quantile(abs(GLODAP_cmorized_var_grid$offset), 0.98) * c(-1, 1)
print(
map +
geom_raster(data = GLODAP_cmorized_var_grid, aes(lon, lat, fill = offset)) +
scale_fill_continuous_divergingx(
palette = "Spectral",
name = "offset",
rev = TRUE,
limit = limit,
na.value = "black"
) +
labs(
title = paste(model_var[i],
"-",
obs_var[i],
" | mean offset per grid cell"),
subtitle = paste(
"Depth interval:",
intervals[j],
"-",
intervals[j + 1],
"m | black: outside 98th percentile"
),
x = "Longitude",
y = "Latitude"
) +
facet_wrap( ~ model, ncol = 4)
)
print(
GLODAP_cmorized_var_grid %>%
ggplot(aes(offset)) +
geom_vline(xintercept = limit) +
geom_histogram() +
labs(
title = paste(
model_var[i],
" - ",
obs_var[i],
"mean offset per grid cell | histogram + abs. 98th Percentile"
),
subtitle = paste("Depth interval:",
intervals[j], "-", intervals[j + 1], "m")
) +
coord_cartesian(expand = FALSE) +
facet_wrap(~ model, ncol = 3)
)
}
# zonal mean section of the model-observations offset for each basin
for (i_basin_AIP in unique(GLODAP_cmorized_var$basin_AIP)) {
# i_basin_AIP = "Atlantic"
print(i_basin_AIP)
GLODAP_cmorized_var_zonal <- GLODAP_cmorized_var %>%
filter(basin_AIP == i_basin_AIP) %>%
group_by(lat, depth, model) %>%
summarise(offset = mean(offset, na.rm = TRUE)) %>%
ungroup()
# plot zonal mean section (model - obs)
lat_max <- params_global$lat_max
lat_min <- params_global$lat_min
limit <-
quantile(abs(GLODAP_cmorized_var_zonal$offset), 0.98) * c(-1, 1)
print(
GLODAP_cmorized_var_zonal %>%
ggplot(aes(lat, depth, z = offset)) +
stat_summary_2d(binwidth = c(1, 100)) +
scale_fill_continuous_divergingx(
palette = "Spectral",
name = "offset",
rev = TRUE,
limit = limit,
na.value = "black"
) +
coord_fixed(expand = 0,
xlim = c(lat_min, lat_max),
ratio = 0.01) +
scale_y_reverse(limits = c(6000, 0),
breaks = seq(0,6000,2000)) +
labs(
title = paste(model_var[i],
"-",
obs_var[i],
" | zonal mean offset"),
subtitle = paste("Basin:",
i_basin_AIP,
" | black: outside 98th percentile"),
x = "Latitude (°N)",
y = "Depth (m)"
) +
facet_wrap(~ model, ncol = 4)
)
}
dev.off()
}[1] "tco2"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "talk"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "oxygen"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "aou"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "nitrate"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "phosphate"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "silicate"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "sal"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "temp"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "theta"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "gamma"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "BetaD"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"[1] "BetaD_tco2"[1] 0[1] 150[1] 500[1] 2000[1] "Atlantic"[1] "Indian"[1] "Pacific"
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] khroma_1.9.0 seacarb_3.3.0 SolveSAPHE_2.1.0 reticulate_1.23
[5] oce_1.5-0 gsw_1.0-6 rqdatatable_1.3.0 rquery_1.4.7
[9] wrapr_2.0.8 lubridate_1.8.0 stars_0.5-5 sf_1.0-5
[13] abind_1.4-5 geomtextpath_0.1.0 colorspace_2.0-2 marelac_2.1.10
[17] shape_1.4.6 ggforce_0.3.3 metR_0.11.0 scico_1.3.0
[21] patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1 stringr_1.4.0
[25] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[29] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ellipsis_0.3.2 class_7.3-20 rprojroot_2.0.2 fs_1.5.2
[5] rstudioapi_0.13 proxy_0.4-26 farver_2.1.0 bit64_4.0.5
[9] fansi_1.0.2 xml2_1.3.3 ncdf4_1.19 knitr_1.37
[13] polyclip_1.10-0 jsonlite_1.7.3 broom_0.7.11 dbplyr_2.1.1
[17] png_0.1-7 compiler_4.1.2 httr_1.4.2 backports_1.4.1
[21] assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0 cli_3.1.1
[25] later_1.3.0 tweenr_1.0.2 htmltools_0.5.2 tools_4.1.2
[29] gtable_0.3.0 glue_1.6.0 rappdirs_0.3.3 Rcpp_1.0.8
[33] RNetCDF_2.5-2 cellranger_1.1.0 jquerylib_0.1.4 vctrs_0.3.8
[37] lwgeom_0.2-8 xfun_0.29 ps_1.6.0 rvest_1.0.2
[41] ncmeta_0.3.0 lifecycle_1.0.1 tidync_0.2.4 getPass_0.2-2
[45] MASS_7.3-55 scales_1.1.1 vroom_1.5.7 hms_1.1.1
[49] promises_1.2.0.1 parallel_4.1.2 yaml_2.2.1 sass_0.4.0
[53] stringi_1.7.6 e1071_1.7-9 checkmate_2.0.0 rlang_1.0.2
[57] pkgconfig_2.0.3 systemfonts_1.0.3 evaluate_0.14 lattice_0.20-45
[61] labeling_0.4.2 bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[65] here_1.0.1 magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[69] DBI_1.1.2 pillar_1.6.4 haven_2.4.3 whisker_0.4
[73] withr_2.4.3 units_0.7-2 modelr_0.1.8 crayon_1.4.2
[77] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[81] grid_4.1.2 readxl_1.3.1 data.table_1.14.2 callr_3.7.0
[85] git2r_0.29.0 reprex_2.0.1 digest_0.6.29 classInt_0.4-3
[89] httpuv_1.6.5 textshaping_0.3.6 munsell_0.5.0 viridisLite_0.4.0
[93] bslib_0.3.1