Climatology
Jens Daniel Müller
23 December, 2020
Last updated: 2020-12-23
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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1 Read nc file
Here we used annual output of cmorized (1x1) model with variable forcing (RECCAP2 RunA) in year 2007 as the predictor climatology. Predictors include:
- Salinity (sal)
- Potential temperature (theta, not predictor, used for temperature calculation)
- In-situ temperature (temp, calculated)
- DIC (tco2)
- ALK (talk)
- oxygen
- AOU (calculated)
- nitrate
- phosphate
- silicate
# read in RECCAP2 RunA file
climatology_temp <- tidync(paste(
path_cmorized,
"RECCAP2_RunA.nc",
sep = ""
))
# read-in 2007 as tibble
climatology <- climatology_temp %>%
hyper_filter(time_ann = time_ann == 10036) %>%
hyper_tibble()
# convert data variable to year
climatology <- climatology %>%
mutate(year = format(as.Date(climatology$time_ann, origin = '1980-01-01'), "%Y"),
year = as.numeric(year)) %>%
select(-time_ann)
# select annual cmorized 2007 as climatology
climatology <- climatology %>%
select(-epc) %>%
rename(
sal = so,
THETA = thetao,
tco2 = dissic,
talk = talk,
oxygen = o2,
nitrate = no3,
phosphate = po4,
silicate = si
) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(depth = round(depth))
rm(climatology_temp)2 Apply basin mask
# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)
# restrict predictor fields to basin mask grid
climatology <- inner_join(climatology, basinmask)3 In-situ temperature
In-situ temperature was calculated as input paramter for AOU calculation.
climatology <- climatology %>%
mutate(temp = gsw_pt_from_t(
SA = sal,
t = THETA,
p = 10.1325,
p_ref = depth
))Example profile from North Atlantic Ocean.
climatology %>%
filter(lat == params_global$lat_Atl_profile,
lon == params_global$lon_Atl_section) %>%
ggplot() +
geom_line(aes(temp, depth, col = "insitu")) +
geom_point(aes(temp, depth, col = "insitu")) +
geom_line(aes(THETA, depth, col = "theta")) +
geom_point(aes(THETA, depth, col = "theta")) +
scale_y_reverse() +
scale_color_brewer(palette = "Dark2", name = "Scale")
| Version | Author | Date |
|---|---|---|
| f6c7b00 | jens-daniel-mueller | 2020-12-21 |
4 Unit conversion
Model results are given in [mol m-3], whereas GLODAP data are in [µmol kg-1].
For comparison, model results were converted from [mol m-3] to [µmol kg-1]
# unit conversion from mol/m3 to µmol/kg
climatology <- climatology %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = sal,
t = temp,
p = depth,
p_ref = 10.1325
),
tco2 = tco2 * (1e+06 / rho),
talk = talk * (1e+06 / rho),
oxygen = oxygen * (1e+06 / rho),
nitrate = nitrate * (1e+06 / rho),
phosphate = phosphate * (1e+06 / rho),
silicate = silicate * (1e+06 / rho)
)5 AOU calculation
climatology <- climatology %>%
mutate(
oxygen_sat_m3 = gas_satconc(
S = sal,
t = temp,
P = 1.013253,
species = "O2"
),
oxygen_sat_kg = oxygen_sat_m3 * (1e+3 / rho),
AOU = oxygen_sat_kg - oxygen
) %>%
select(-oxygen_sat_kg,-oxygen_sat_m3)6 Neutral density calculation
Neutral density gamma was calculated with a Python script provided by Serazin et al (2011), which performs a polynomial approximation of the original gamma calculation.
# calculate pressure from depth
climatology <- climatology %>%
mutate(CTDPRS = gsw_p_from_z(-depth,
lat))
# rename variables according to python script
climatology_gamma_prep <- climatology %>%
rename(LATITUDE = lat,
LONGITUDE = lon,
SALNTY = sal)
# load python scripts
source_python(paste(
path_functions,
"python_scripts/Gamma_GLODAP_python.py",
sep = ""
))
# calculate gamma
climatology_gamma_calc <-
calculate_gamma(climatology_gamma_prep)
# reverse variable naming
climatology <- climatology_gamma_calc %>%
select(-c(CTDPRS, THETA)) %>%
rename(
lat = LATITUDE,
lon = LONGITUDE,
sal = SALNTY,
gamma = GAMMA
)
climatology <- as_tibble(climatology)
rm(climatology_gamma_calc, climatology_gamma_prep)7 Write file
# select relevant columns
climatology <- climatology %>%
select(-c(year, rho))
# write csv file
climatology %>%
write_csv(paste(path_preprocessing,
"climatology_runA_2007.csv",
sep = ""))8 Plots
Below, following subsets of the climatology are plotted for all relevant predictors:
- Horizontal planes at 4 depth levels
- Global section along track indicated by white lines in maps.
# define plotting variables
vars <- c(
"tco2",
"talk",
"sal",
"nitrate",
"phosphate",
"silicate",
"oxygen",
"temp",
"AOU",
"gamma"
)
# i_var <- vars[1]
for (i_var in vars) {
# plot maps
print(p_map_climatology(df = climatology,
var = i_var))
# plot sections
print(p_section_global(df = climatology,
var = i_var))
}
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sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] marelac_2.1.10 shape_1.4.5 geosphere_1.5-10 oce_1.2-0
[5] gsw_1.0-5 testthat_3.0.1 reticulate_1.18 tidync_0.2.4
[9] metR_0.8.0 scico_1.2.0 patchwork_1.1.0 collapse_1.4.2
[13] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[17] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[21] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 RColorBrewer_1.1-2
[4] httr_1.4.2 rprojroot_2.0.2 tools_4.0.3
[7] backports_1.2.1 R6_2.5.0 DBI_1.1.0
[10] colorspace_2.0-0 withr_2.3.0 sp_1.4-4
[13] tidyselect_1.1.0 compiler_4.0.3 git2r_0.27.1
[16] cli_2.2.0 rvest_0.3.6 RNetCDF_2.4-2
[19] xml2_1.3.2 isoband_0.2.3 labeling_0.4.2
[22] scales_1.1.1 checkmate_2.0.0 rappdirs_0.3.1
[25] digest_0.6.27 rmarkdown_2.5 pkgconfig_2.0.3
[28] htmltools_0.5.0 dbplyr_1.4.4 rlang_0.4.9
[31] readxl_1.3.1 rstudioapi_0.13 farver_2.0.3
[34] generics_0.1.0 jsonlite_1.7.2 magrittr_2.0.1
[37] ncmeta_0.3.0 Matrix_1.2-18 Rcpp_1.0.5
[40] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0
[43] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[46] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[49] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[52] haven_2.3.1 hms_0.5.3 seacarb_3.2.14
[55] knitr_1.30 pillar_1.4.7 reprex_0.3.0
[58] glue_1.4.2 evaluate_0.14 RcppArmadillo_0.10.1.2.0
[61] data.table_1.13.4 modelr_0.1.8 vctrs_0.3.6
[64] httpuv_1.5.4 cellranger_1.1.0 gtable_0.3.0
[67] assertthat_0.2.1 xfun_0.19 broom_0.7.3
[70] RcppEigen_0.3.3.9.1 later_1.1.0.1 viridisLite_0.3.0
[73] ncdf4_1.17 ellipsis_0.3.1