Cant variable climate AD
Jens Daniel Müller
02 November, 2022
Last updated: 2022-11-02
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d0575c0 | jens-daniel-mueller | 2022-11-02 | included calculation of cstar variants |
| html | 297418e | jens-daniel-mueller | 2022-10-24 | Build site. |
| html | 5dd2e34 | jens-daniel-mueller | 2022-09-27 | Build site. |
| Rmd | fd57f09 | jens-daniel-mueller | 2022-09-27 | extract Cnat from CESM |
| html | e829a11 | jens-daniel-mueller | 2022-07-05 | Build site. |
| Rmd | 923e07e | jens-daniel-mueller | 2022-07-04 | rerun for all models |
| html | 9f6bdf3 | jens-daniel-mueller | 2022-06-30 | Build site. |
| Rmd | b4251cb | jens-daniel-mueller | 2022-06-30 | rerun with writing dissic fields |
| html | 89cbb74 | jens-daniel-mueller | 2022-05-26 | Build site. |
| html | 42bbb89 | jens-daniel-mueller | 2022-05-24 | Build site. |
| Rmd | e82a302 | jens-daniel-mueller | 2022-05-23 | bug fixes for 5 models |
| html | 40863a2 | jens-daniel-mueller | 2022-05-22 | Build site. |
| Rmd | d9d4d68 | jens-daniel-mueller | 2022-05-21 | rebuild with revised output structure and 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)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"
)
)]
# depth levels not available
models <-
models[!str_detect(models, pattern = "CNRM")]
# files chunked into decades
models <-
models[!str_detect(models, pattern = "ORCA025-GEOMAR")]
# no run D
models <-
models[!str_detect(models, pattern = "MOM6-Princeton")]1 Calculate annual Cant field
# for loop across variables
# 3d variables
variables <-
c("so", "thetao", "dissic", "talk", "po4", "no3")
# models <- models[7]
for (i_model in models) {
# i_model <- models[4]
print(i_model)
for (i_model_ID in c("C", "B")) {
# i_model_ID <- c("C", "B")[1]
print(i_model_ID)
variables_available <-
list.files(
path = paste0(path_cmorized, i_model),
pattern = paste0("_", i_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)
print(variables_available)
for (i_variable in variables_available) {
# i_variable <- variables_available[2]
# read list of all files
file <-
list.files(
path = paste0(path_cmorized, i_model),
pattern = paste0(i_variable, "_")
)
if (i_model == "MRI-ESM2-1_3D_ALL_v20220502" &
i_model_ID == "B" &
i_variable %in% c("so", "thetao", "talk", "po4", "no3")) {
file <-
file[str_detect(file, pattern = paste0("_", "C", "_"))]
} else {
file <-
file[str_detect(file, pattern = paste0("_", i_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 {
variable_data <-
read_ncdf(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
make_units = FALSE)
}
# 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)))
# sort(unique(variable_data_tibble$lev))
# sort(unique(variable_data_tibble$lon))
if (i_model == "CESM-ETHZ_3D_ALL_v20211122") {
variable_data_tibble <- variable_data_tibble %>%
rename(time = time_ann) %>%
mutate(depth = as.numeric(depth))
}
if (i_model == "CNRM-ESM2-1_3D_ALL_v20211208") {
variable_data_tibble <- variable_data_tibble %>%
rename(depth = lev) %>%
group_by(time) %>%
mutate(year = as.character(1979 + cur_group_id())) %>%
ungroup()
variable_data_tibble <- variable_data_tibble %>%
mutate(time = as.Date(year, format = "%Y")) %>%
select(-year)
}
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'),
depth = as.numeric(depth))
}
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
) %>%
mutate(depth = as.numeric(depth))
}
if (i_model == "planktom12_3d_all_v20220404" &
i_variable == "po4") {
variable_data_tibble <-
variable_data_tibble %>%
mutate(po4 = po4 / 100)
}
variable_data_tibble <- variable_data_tibble %>%
mutate(time = year(time))
if (exists("annual")) {
annual <- left_join(annual, variable_data_tibble)
}
if (!exists("annual")) {
annual <- variable_data_tibble
}
rm(variable_data_tibble)
}
if (i_model == "FESOM_REcoM_LR_3D_all_v20211119") {
annual <- annual %>%
mutate(po4 = no3 / 16)
}
if (i_model == "planktom12_3d_all_v20220404") {
annual <- annual %>%
mutate(no3 = po4 * 16)
}
if (i_model == "MRI-ESM2-1_3D_ALL_v20220502" &
i_model_ID == "B") {
annual <- right_join(annual,
CB_annual %>%
select(-dissic))
}
annual <- annual %>%
mutate(model_ID = i_model_ID)
if (exists("CB_annual")) {
CB_annual <- bind_rows(CB_annual, annual)
}
if (!exists("CB_annual")) {
CB_annual <- annual
}
rm(annual)
}
# harmonize column names and coordinates
CB_annual <- CB_annual %>%
select(
year = time,
lon,
lat,
depth,
sal = so,
theta = thetao,
dissic,
talk,
phosphate = po4,
nitrate = no3,
model_ID
) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# calculate model temperature
CB_annual <- CB_annual %>%
mutate(temp = gsw_pt_from_t(
SA = sal,
t = theta,
p = 10.1325,
p_ref = depth
))
# unit transfer from mol/m3 to µmol/kg
CB_annual <- CB_annual %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = sal,
t = temp,
p = depth,
p_ref = 10.1325
),
dissic = dissic * (1e+6 / rho),
talk = talk * (1e+6 / rho),
phosphate = phosphate * (1e+6 / rho),
nitrate = nitrate * (1e+6 / rho)
)
CB_annual <- CB_annual %>%
select(-c(sal, temp, theta, rho))
# sort(unique(CB_annual$depth))
# sort(unique(CB_annual$lon))
CB_annual <- CB_annual %>%
mutate(depth = round(depth))
## Apply basin mask
CB_annual <- inner_join(CB_annual, basinmask)
# calculate cstar field
CB_annual_cstar <- CB_annual
params_local <- lst(rCP = 117,
rNP = 16)
CB_annual_cstar <- CB_annual_cstar %>%
mutate(
cstar_nitrate_talk = b_cstar_nitrate_talk(
tco2 = dissic,
nitrate = nitrate,
talk = talk
),
cstar_phosphate_talk = b_cstar_phosphate_talk(
tco2 = dissic,
phosphate = phosphate,
talk = talk
),
cstar_nitrate = b_cstar_nitrate(
tco2 = dissic,
nitrate = nitrate
),
cstar_phosphate = b_cstar_phosphate(
tco2 = dissic,
phosphate = phosphate
),
cstar_talk = b_cstar_talk(
tco2 = dissic,
talk = talk
)
)
rm(params_local)
# Calculate Cant fields
CB_annual <- CB_annual %>%
select(year, lon, lat, depth, dissic, model_ID, basin_AIP)
CB_annual <- CB_annual %>% pivot_wider(names_from = model_ID,
values_from = dissic)
CB_annual_dissic <- CB_annual
CB_annual <- CB_annual %>%
mutate(tcant = C - B) %>%
select(-c(C, B))
# Remove 3D_all from model name
i_model <- str_remove(i_model, "3D_ALL_|3D_all_|3d_all_")
# write annual Cant files
for (i_year in unique(CB_annual$year)) {
# i_year = unique(CB_annual$year)[1]
CB_annual %>%
filter(year == i_year) %>%
write_csv(
paste0(
path_preprocessing,
"Cant_CB_annual_all_models/",
i_year,
"_",
i_model,
".csv"
)
)
CB_annual_dissic %>%
filter(year == i_year) %>%
write_csv(
paste0(
path_preprocessing,
"Cant_CB_annual_all_models/dissic/",
i_year,
"_",
i_model,
".csv"
)
)
CB_annual_cstar %>%
filter(year == i_year,
model_ID == "C") %>%
write_csv(
paste0(
path_preprocessing,
"Cant_CB_annual_all_models/Cstar_C_annual_all_models/",
i_year,
"_",
i_model,
".csv"
)
)
CB_annual_cstar %>%
filter(year == i_year,
model_ID == "B") %>%
write_csv(
paste0(
path_preprocessing,
"Cant_CB_annual_all_models/Cstar_B_annual_all_models/",
i_year,
"_",
i_model,
".csv"
)
)
}
rm(CB_annual)
}[1] "CESM-ETHZ_3D_ALL_v20211122"
[1] "C"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_CESM-ETHZ_C_1_gr_1980-2018_v20211122.nc"
[1] "no3_CESM-ETHZ_C_1_gr_1980-2018_v20211122.nc"
[1] "po4_CESM-ETHZ_C_1_gr_1980-2018_v20211122.nc"
[1] "so_CESM-ETHZ_C_1_gr_1980-2018_v20211122.nc"
[1] "talk_CESM-ETHZ_C_1_gr_1980-2018_v20211122.nc"
[1] "thetao_CESM-ETHZ_C_1_gr_1980-2018_v20211122.nc"
[1] "B"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_CESM-ETHZ_B_1_gr_1980-2018_v20211122.nc"
[1] "no3_CESM-ETHZ_B_1_gr_1980-2018_v20211122.nc"
[1] "po4_CESM-ETHZ_B_1_gr_1980-2018_v20211122.nc"
[1] "so_CESM-ETHZ_B_1_gr_1980-2018_v20211122.nc"
[1] "talk_CESM-ETHZ_B_1_gr_1980-2018_v20211122.nc"
[1] "thetao_CESM-ETHZ_B_1_gr_1980-2018_v20211122.nc"
[1] "EC-Earth3_3D_ALL_v20220323"
[1] "C"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_EC-Earth3_C_1_gr_1980-2018_v20220323.nc"
[1] "no3_EC-Earth3_C_1_gr_1980-2018_v20220323.nc"
[1] "po4_EC-Earth3_C_1_gr_1980-2018_v20220323.nc"
[1] "so_EC-Earth3_C_1_gr_1980-2018_v20220323.nc"
[1] "talk_EC-Earth3_C_1_gr_1980-2018_v20220323.nc"
[1] "thetao_EC-Earth3_C_1_gr_1980-2018_v20220323.nc"
[1] "B"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_EC-Earth3_B_1_gr_1980-2018_v20220323.nc"
[1] "no3_EC-Earth3_B_1_gr_1980-2018_v20220323.nc"
[1] "po4_EC-Earth3_B_1_gr_1980-2018_v20220323.nc"
[1] "so_EC-Earth3_B_1_gr_1980-2018_v20220323.nc"
[1] "talk_EC-Earth3_B_1_gr_1980-2018_v20220323.nc"
[1] "thetao_EC-Earth3_B_1_gr_1980-2018_v20220323.nc"
[1] "FESOM_REcoM_LR_3D_all_v20211119"
[1] "C"
[1] "dissic" "no3" "so" "talk" "thetao"
[1] "dissic_FESOM_REcoM_LR_C_1_gr_1980-2018_v20211119.nc"
[1] "no3_FESOM_REcoM_LR_C_1_gr_1980-2018_v20211119.nc"
[1] "so_FESOM_REcoM_LR_C_1_gr_1980-2018_v20211119.nc"
[1] "talk_FESOM_REcoM_LR_C_1_gr_1980-2018_v20211119.nc"
[1] "thetao_FESOM_REcoM_LR_C_1_gr_1980-2018_v20211119.nc"
[1] "B"
[1] "dissic" "no3" "so" "talk" "thetao"
[1] "dissic_FESOM_REcoM_LR_B_1_gr_1980-2018_v20211119.nc"
[1] "no3_FESOM_REcoM_LR_B_1_gr_1980-2018_v20211119.nc"
[1] "so_FESOM_REcoM_LR_B_1_gr_1980-2018_v20211119.nc"
[1] "talk_FESOM_REcoM_LR_B_1_gr_1980-2018_v20211119.nc"
[1] "thetao_FESOM_REcoM_LR_B_1_gr_1980-2018_v20211119.nc"
[1] "MRI-ESM2-1_3D_ALL_v20220502"
[1] "C"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_MRI-ESM2-1_C_1_gr_1980-2018_v20220502.nc"
[1] "no3_MRI-ESM2-1_C_1_gr_1980-2018_v20220502.nc"
[1] "po4_MRI-ESM2-1_C_1_gr_1980-2018_v20220502.nc"
[1] "so_MRI-ESM2-1_C_1_gr_1980-2018_v20220502.nc"
[1] "talk_MRI-ESM2-1_C_1_gr_1980-2018_v20220502.nc"
[1] "thetao_MRI-ESM2-1_C_1_gr_1980-2018_v20220502.nc"
[1] "B"
[1] "dissic"
[1] "dissic_MRI-ESM2-1_B_1_gr_1980-2018_v20220502.nc"
[1] "NorESM-OC1.2_3D_ALL_v20211125"
[1] "C"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_NorESM-OC1.2_C_1_gr_1980-2018_v20211125.nc"
[1] "no3_NorESM-OC1.2_C_1_gr_1980-2018_v20211125.nc"
[1] "po4_NorESM-OC1.2_C_1_gr_1980-2018_v20211125.nc"
[1] "so_NorESM-OC1.2_C_1_gr_1980-2018_v20211125.nc"
[1] "talk_NorESM-OC1.2_C_1_gr_1980-2018_v20211125.nc"
[1] "thetao_NorESM-OC1.2_C_1_gr_1980-2018_v20211125.nc"
[1] "B"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_NorESM-OC1.2_B_1_gr_1980-2018_v20211125.nc"
[1] "no3_NorESM-OC1.2_B_1_gr_1980-2018_v20211125.nc"
[1] "po4_NorESM-OC1.2_B_1_gr_1980-2018_v20211125.nc"
[1] "so_NorESM-OC1.2_B_1_gr_1980-2018_v20211125.nc"
[1] "talk_NorESM-OC1.2_B_1_gr_1980-2018_v20211125.nc"
[1] "thetao_NorESM-OC1.2_B_1_gr_1980-2018_v20211125.nc"
[1] "ORCA1-LIM3-PISCES_3D_ALL_v20211215"
[1] "C"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_ORCA1-LIM3-PISCES_C_1_gr_1980-2018_v20211215.nc"
[1] "no3_ORCA1-LIM3-PISCES_C_1_gr_1980-2018_v20211215.nc"
[1] "po4_ORCA1-LIM3-PISCES_C_1_gr_1980-2018_v20211215.nc"
[1] "so_ORCA1-LIM3-PISCES_C_1_gr_1980-2018_v20211215.nc"
[1] "talk_ORCA1-LIM3-PISCES_C_1_gr_1980-2018_v20211215.nc"
[1] "thetao_ORCA1-LIM3-PISCES_C_1_gr_1980-2018_v20211215.nc"
[1] "B"
[1] "dissic" "no3" "po4" "so" "talk" "thetao"
[1] "dissic_ORCA1-LIM3-PISCES_B_1_gr_1980-2018_v20211215.nc"
[1] "no3_ORCA1-LIM3-PISCES_B_1_gr_1980-2018_v20211215.nc"
[1] "po4_ORCA1-LIM3-PISCES_B_1_gr_1980-2018_v20211215.nc"
[1] "so_ORCA1-LIM3-PISCES_B_1_gr_1980-2018_v20211215.nc"
[1] "talk_ORCA1-LIM3-PISCES_B_1_gr_1980-2018_v20211215.nc"
[1] "thetao_ORCA1-LIM3-PISCES_B_1_gr_1980-2018_v20211215.nc"
[1] "planktom12_3d_all_v20220404"
[1] "C"
[1] "dissic" "po4" "so" "talk" "thetao"
[1] "dissic_PlankTOM12_C_1_gr_1980-2018_v20220404.nc"
[1] "po4_PlankTOM12_C_1_gr_1980-2018_v20220404.nc"
[1] "so_PlankTOM12_C_1_gr_1980-2018_v20220404.nc"
[1] "talk_PlankTOM12_C_1_gr_1980-2018_v20220404.nc"
[1] "thetao_PlankTOM12_C_1_gr_1980-2018_v20220404.nc"
[1] "B"
[1] "dissic" "po4" "so" "talk" "thetao"
[1] "dissic_PlankTOM12_B_1_gr_1980-2018_v20220404.nc"
[1] "po4_PlankTOM12_B_1_gr_1980-2018_v20220404.nc"
[1] "so_PlankTOM12_B_1_gr_1980-2018_v20220404.nc"
[1] "talk_PlankTOM12_B_1_gr_1980-2018_v20220404.nc"
[1] "thetao_PlankTOM12_B_1_gr_1980-2018_v20220404.nc"2 Calculate change in Cant 1994 - 2004 - 2014
for (i_model in models) {
# i_model <- models[1]
print(i_model)
for (i_year in c("1994", "2004", "2014")) {
# i_year = "1994"
cant_year <-
read_csv(paste0(path_preprocessing,
"Cant_CB_annual_all_models/",
i_year,
"_",
str_remove(i_model, "3D_ALL_|3D_all_|3d_all_"),
".csv"))
cant_year <- cant_year %>%
mutate(model = i_model)
if (exists("tcant")) {
tcant <- bind_rows(tcant, cant_year)
}
if (!exists("tcant")) {
tcant <- cant_year
}
}
}[1] "CESM-ETHZ_3D_ALL_v20211122"
[1] "EC-Earth3_3D_ALL_v20220323"
[1] "FESOM_REcoM_LR_3D_all_v20211119"
[1] "MRI-ESM2-1_3D_ALL_v20220502"
[1] "NorESM-OC1.2_3D_ALL_v20211125"
[1] "ORCA1-LIM3-PISCES_3D_ALL_v20211215"
[1] "planktom12_3d_all_v20220404"tcant <- tcant %>%
# head(10) %>%
arrange(year) %>%
group_by(model, lon, lat, depth, basin_AIP) %>%
mutate(dcant = tcant - lag(tcant),
eras = paste(year, "-" , lag(year))) %>%
ungroup()3 Zonal mean sections
tcant <- tcant %>%
rename(data_source = model)
tcant_zonal <- tcant %>%
select(-year) %>%
filter(!is.na(dcant)) %>%
group_by(data_source, eras) %>%
nest() %>%
mutate(zonal = map(.x = data, ~m_zonal_mean_sd(.x))) %>%
select(-data) %>%
unnest(zonal)3.1 delta Cant
tcant_zonal %>%
filter(depth <= params_global$inventory_depth_standard) %>%
group_by(basin_AIP) %>%
group_split() %>%
head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_mean",
plot_slabs = "n",
subtitle_text = .x$basin_AIP
) +
facet_grid(data_source ~ eras)
)[[1]]
3.2 total Cant
tcant_zonal %>%
filter(depth <= params_global$inventory_depth_standard) %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "tcant_mean",
plot_slabs = "n",
subtitle_text = .x$basin_AIP,
breaks = c(-Inf,seq(0,80,10), Inf)
) +
facet_grid(data_source ~ eras)
)[[1]]
[[2]]
[[3]]
4 Column inventory
4.1 delta Cant
dcant_inv <- tcant %>%
select(-year) %>%
filter(!is.na(dcant)) %>%
mutate(dcant_pos = 0) %>%
group_by(data_source, eras) %>%
nest() %>%
mutate(inv = map(.x = data, ~ m_dcant_inv(.x))) %>%
select(-data) %>%
unnest(inv)
dcant_inv %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
p_map_cant_inv(var = "dcant",
subtitle_text = "for predefined integration depths") +
facet_grid(data_source ~ eras)
4.2 Cant total
# this is just a work around, because the function is designed to calculate cant inventories, but not cant_total inventories
tcant_total_inv <- m_cant_inv(
tcant %>%
select(-cant_pos) %>%
rename(cant_pos = cant_1994))p_map_cant_inv(tcant_total_inv %>% filter(inv_depth == 3000),
breaks = seq(0,100,10),
subtitle_text = "Cant total in 1994")
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] lubridate_1.8.0 gsw_1.0-6 stars_0.5-5 sf_1.0-5
[5] abind_1.4-5 tidync_0.2.4 geomtextpath_0.1.0 colorspace_2.0-2
[9] marelac_2.1.10 shape_1.4.6 ggforce_0.3.3 metR_0.11.0
[13] scico_1.3.0 patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1
[17] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[21] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[25] 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] compiler_4.1.2 httr_1.4.2 backports_1.4.1 assertthat_0.2.1
[21] fastmap_1.1.0 cli_3.1.1 later_1.3.0 tweenr_1.0.2
[25] htmltools_0.5.2 tools_4.1.2 gtable_0.3.0 glue_1.6.0
[29] Rcpp_1.0.8 cellranger_1.1.0 jquerylib_0.1.4 RNetCDF_2.5-2
[33] vctrs_0.3.8 lwgeom_0.2-8 xfun_0.29 ps_1.6.0
[37] rvest_1.0.2 lifecycle_1.0.1 ncmeta_0.3.0 oce_1.5-0
[41] getPass_0.2-2 MASS_7.3-55 scales_1.1.1 vroom_1.5.7
[45] hms_1.1.1 promises_1.2.0.1 parallel_4.1.2 yaml_2.2.1
[49] sass_0.4.0 stringi_1.7.6 highr_0.9 e1071_1.7-9
[53] checkmate_2.0.0 rlang_1.0.2 pkgconfig_2.0.3 systemfonts_1.0.3
[57] evaluate_0.14 SolveSAPHE_2.1.0 labeling_0.4.2 bit_4.0.4
[61] processx_3.5.2 tidyselect_1.1.1 seacarb_3.3.0 magrittr_2.0.1
[65] R6_2.5.1 generics_0.1.1 DBI_1.1.2 pillar_1.6.4
[69] haven_2.4.3 whisker_0.4 withr_2.4.3 units_0.7-2
[73] modelr_0.1.8 crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2
[77] tzdb_0.2.0 rmarkdown_2.11 grid_4.1.2 readxl_1.3.1
[81] isoband_0.2.5 data.table_1.14.2 callr_3.7.0 git2r_0.29.0
[85] reprex_2.0.1 digest_0.6.29 classInt_0.4-3 httpuv_1.6.5
[89] textshaping_0.3.6 munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1