Last updated: 2024-05-15

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Task

Dependencies

Outputs

path CESM 2023 data: “/nfs/kryo/work/loher/GlobalMarineHeatwaves/ETHZ_BEC/Heatwaves_RunA.nc” variable for temperature: thetao

path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_basin_mask <- "/nfs/kryo/work/datasets/gridded/ocean/interior/reccap2/supplementary/"

path_argo_core <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/core_argo_r_argodata_2024-03-13'
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")

path_CESM<-"/nfs/kryo/work/loher/GlobalMarineHeatwaves/ETHZ_BEC/"

Read data

# Read NetCDF file containing CESM outputs (35 variables - 4dim: time, lat, lon, depth)
CESM_temp <- tidync(paste0(path_CESM, "Heatwaves_RunA.nc"))
CESM_temp <- CESM_temp %>%
  hyper_tibble(select_var = "thetao", # thetao: seawater potential temperature [°C]
                       force = TRUE)

CESM_temp <- CESM_temp %>%
  filter(thetao < 1e36) %>% # thetao ~ e36 because??
  rename(temp = thetao)

gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    1995040   106.6     4035317    215.6     3470729    185.4
Vcells 5267718845 40189.6 21682694924 165425.9 26690135370 203629.6
CESM_temp <- CESM_temp %>%
  mutate(time = ymd_hms("1980-01-01 00:00:00") + days(time))

gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    2004397   107.1     4035317    215.6     3470729    185.4
Vcells 5267740084 40189.7 24264805791 185125.8 28959495069 220943.5
CESM_temp$year <- year(CESM_temp$time)
CESM_temp$month <- month(CESM_temp$time)

gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    2004486   107.1     4035317    215.6     3470729    185.4
Vcells 7373672463 56256.7 24264805791 185125.8 28959495069 220943.5
#Area of interest: North Atlantic east - lat:(0,40), lon:(-30,0)
CESM_temp <- CESM_temp %>% 
  mutate(lon = ifelse(lon > 180, lon - 360, lon))

CESM_natlantic_east <- CESM_temp %>% 
  filter(lat>0, lat<40, -30<lon, lon<0)


#Select 2023
CESM_natlantic_east_2023 <- CESM_natlantic_east %>% 
  filter(year==2023) 

gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    2004591   107.1     4035317    215.6     3470729    185.4
Vcells 7518163058 57359.1 24264805791 185125.8 28959495069 220943.5
# rm(CESM_temp)
# Visualization
CESM_natlantic_east_2023 %>%
  filter(depth == 5) %>%
  ggplot(aes(lon, lat, fill = temp)) +
  geom_raster() +
  scale_fill_viridis_c(option = "magma") +
  labs(title = "Monthly visualisation of CESM seawater potential temperature",
       subtitle = paste0("depth=5m -- Period: 2023"))+
  coord_quickmap(expand = 0)+
  facet_wrap(~month, nrow = 3)

CESM_natlantic_east_2023 %>%
  filter(lat == 30.5) %>%
  ggplot(aes(lon, depth, z = temp)) +
  geom_contour_filled(breaks = seq(-10,40,2)) +
  scale_y_reverse(limits = c(3000, 0)) +
  coord_cartesian(expand = 0) +
  labs(title = "Visualisation of CESM seawater potential temperature",
       subtitle = paste0( "transect section -- lat: 30.5, Period: 2023"))+
  scale_fill_viridis_d(option = "magma")+
  facet_wrap(~month, nrow = 3)

#Climatology of CESM temp output over the period 2004-2019 (to match with argo climatology) -- only on the North Atlantic east
CESM_2004_2019_natlantic_east<- CESM_natlantic_east %>% 
  filter(year>=2004, year<=2019)

CESM_2004_2019_natlantic_east<-CESM_2004_2019_natlantic_east %>% 
  group_by(lat, lon, depth, month) %>% 
  summarize(mean_temp=mean(temp, na.rm=TRUE))

# Visualization
CESM_2004_2019_natlantic_east %>%
  filter(depth == 5) %>%
  ggplot(aes(lon, lat, fill = mean_temp)) +
  geom_raster() +
  scale_fill_viridis_c(option = "magma") +
  labs(title = "Mean CESM seawater potential temperature",
       subtitle = paste0("depth=5m, Period: 2004-2019"))+
  coord_quickmap(expand = 0)+
  facet_wrap(~month, nrow = 3)

CESM_2004_2019_natlantic_east %>%
  filter(lat == 30.5) %>%
  ggplot(aes(lon, depth, z = mean_temp)) +
  geom_contour_filled(breaks = seq(-10,40,2)) +
  scale_y_reverse(limits = c(3000, 0)) +
  coord_cartesian(expand = 0) +
  labs(title = "Mean CESM seawater potential temperature",
       subtitle = paste0( "transect section -- lat: 30.5, Period: 2004-2019"))+
  scale_fill_viridis_d(option = "magma")+
  facet_wrap(~month, nrow = 3)

# Temperature anomaly
CESM_anomaly_natlantic_east_2023 <- inner_join(CESM_natlantic_east_2023, CESM_2004_2019_natlantic_east, by = c("month", "lat", "lon", "depth")) 


# Calculate temperature anomaly
CESM_anomaly_natlantic_east_2023 <- CESM_anomaly_natlantic_east_2023 %>%
    mutate(temp_anomaly = temp - mean_temp)

#Write 
write_rds(CESM_anomaly_natlantic_east_2023,
          file = paste0(path_argo_core_preprocessed,"/", "CESM_temp_anomaly2023_NorthAtlantic_east_clim2004-2019.rds"))

# rm(merged_data, CESM_2004_2019_natlantic_east)
gc()
             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    2428685   129.8     6548621    349.8     6548621    349.8
Vcells 7526086630 57419.5 24264805791 185125.8 28959495069 220943.5
# Visualization
CESM_anomaly_natlantic_east_2023 %>%
  filter(depth == 5) %>%
  ggplot(aes(lon, lat, fill = temp_anomaly)) +
  geom_raster() +
  scale_fill_viridis_c(option = "magma") +
  labs(title = "CESM temperature anomaly - 2023",
       subtitle = paste0("depth=5m, clim: 2004-2019"))+
  coord_quickmap(expand = 0)+
  facet_wrap(~month, nrow = 3)

CESM_anomaly_natlantic_east_2023 %>%
  filter(lat == 30.5) %>%
  ggplot(aes(lon, depth, z = temp_anomaly)) +
  geom_contour_filled(breaks = seq(-10,40,0.5)) +
  scale_y_reverse(limits = c(3000, 0)) +
  coord_cartesian(expand = 0) +
  labs(title = "CESM temperature anomaly - 2023",
       subtitle = paste0( "transect section -- lat: 30.5, clim: 2004-2019"))+
  scale_fill_viridis_d(option = "magma")+
  facet_wrap(~month, nrow = 3)


sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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] collapse_2.0.13    ncdf4_1.22         tidync_0.3.0       marelac_2.1.10    
 [5] shape_1.4.6        RColorBrewer_1.1-3 stars_0.6-0        sf_1.0-9          
 [9] abind_1.4-5        paletteer_1.6.0    cluster_2.1.6      gridExtra_2.3     
[13] viridis_0.6.2      viridisLite_0.4.1  lubridate_1.9.0    timechange_0.1.1  
[17] forcats_0.5.2      stringr_1.5.0      dplyr_1.1.3        purrr_1.0.2       
[21] readr_2.1.3        tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.4     
[25] tidyverse_1.3.2    workflowr_1.7.0   

loaded via a namespace (and not attached):
 [1] fs_1.5.2            gsw_1.1-1           httr_1.4.4         
 [4] rprojroot_2.0.3     tools_4.2.2         backports_1.4.1    
 [7] bslib_0.4.1         utf8_1.2.2          R6_2.5.1           
[10] KernSmooth_2.23-20  DBI_1.2.2           colorspace_2.0-3   
[13] withr_2.5.0         tidyselect_1.2.0    processx_3.8.0     
[16] compiler_4.2.2      git2r_0.30.1        cli_3.6.1          
[19] rvest_1.0.3         RNetCDF_2.6-1       xml2_1.3.3         
[22] isoband_0.2.6       labeling_0.4.2      sass_0.4.4         
[25] scales_1.2.1        classInt_0.4-8      SolveSAPHE_2.1.0   
[28] callr_3.7.3         proxy_0.4-27        digest_0.6.30      
[31] oce_1.7-10          rmarkdown_2.18      pkgconfig_2.0.3    
[34] htmltools_0.5.8.1   highr_0.9           dbplyr_2.2.1       
[37] fastmap_1.1.0       rlang_1.1.1         readxl_1.4.1       
[40] rstudioapi_0.15.0   farver_2.1.1        jquerylib_0.1.4    
[43] generics_0.1.3      jsonlite_1.8.3      googlesheets4_1.0.1
[46] magrittr_2.0.3      ncmeta_0.3.5        Rcpp_1.0.10        
[49] munsell_0.5.0       fansi_1.0.3         lifecycle_1.0.3    
[52] stringi_1.7.8       whisker_0.4         yaml_2.3.6         
[55] grid_4.2.2          parallel_4.2.2      promises_1.2.0.1   
[58] crayon_1.5.2        haven_2.5.1         seacarb_3.3.1      
[61] hms_1.1.2           knitr_1.41          ps_1.7.2           
[64] pillar_1.9.0        reprex_2.0.2        glue_1.6.2         
[67] evaluate_0.18       getPass_0.2-2       modelr_0.1.10      
[70] vctrs_0.6.4         tzdb_0.3.0          httpuv_1.6.6       
[73] cellranger_1.1.0    gtable_0.3.1        rematch2_2.1.2     
[76] assertthat_0.2.1    cachem_1.0.6        xfun_0.35          
[79] lwgeom_0.2-10       broom_1.0.5         e1071_1.7-12       
[82] later_1.3.0         class_7.3-20        googledrive_2.0.0  
[85] gargle_1.2.1        units_0.8-0         ellipsis_0.3.2