Last updated: 2024-05-16

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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/"

CESM

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()

CESM_temp <- CESM_temp %>%
  mutate(time = ymd_hms("1980-01-01 00:00:00") + days(time))
gc()

CESM_temp$year <- year(CESM_temp$time)
CESM_temp$month <- month(CESM_temp$time)
gc()

#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()
#Write to file
write_rds(CESM_natlantic_east_2023,
          file = paste0(path_argo_core_preprocessed,"/", "CESM_temp2023_NorthAtlantic_east.rds"))

# rm(CESM_temp) 
CESM_natlantic_east_2023<- read_rds(file = paste0(path_argo_core_preprocessed,"/", "CESM_temp2023_NorthAtlantic_east.rds"))

# 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 calculation

We calculate the temperature climatology of CESM over the period 2004-2019 (to match with argo climatology) this operation is done only on the North Atlantic east, for computation efficiency

#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 %>% 
  fgroup_by(lat, lon, depth, month) %>% 
  fsummarize(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 %>%
    fmutate(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(CESM_2004_2019_natlantic_east)
gc()
# Read data
CESM_anomaly_natlantic_east_2023<-read_rds(file = 
                                             paste0(path_argo_core_preprocessed,"/", "CESM_temp_anomaly2023_NorthAtlantic_east_clim2004-2019.rds"))
# 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)

Argo

Read data

Float location

CESM_anomaly_natlantic_east_2023$month<- factor(format(CESM_anomaly_natlantic_east_2023$time, "%m"))

CESM_with_float <- CESM_anomaly_natlantic_east_2023 %>% 
  right_join(core_anomaly_east_2023%>% distinct(lat, lon, month, platform_number, cycle_number), 
            by = c("lat", "lon", "month"))

# Float coverage -- east north atlantic over 2023
platform_counts <- aggregate(platform_number ~ month, data = CESM_with_float, FUN = function(x) length(unique(x)))
cycle_count_per_platform_month <- CESM_with_float %>%
  group_by(month, platform_number) %>%
  summarise(cycle_count = n_distinct(cycle_number))

1 Float

unique_platform<-CESM_with_float %>% 
  filter(platform_number==1902396)

ggplot(unique_platform, aes(x = temp_anomaly , y = depth, color = factor(cycle_number))) +
  geom_path() +
  geom_vline(xintercept = 0) +
  scale_y_reverse() +
  coord_cartesian(xlim = c(-6, 6), ylim = c(200, 0)) +
  labs(title = 'Anomaly profiles by month',
       subtitle= paste0('Platform ', unique(unique_platform$platform_number)),
       x = 'Temperature (°C)', y = 'Depth (m)', color = 'Cycle Number') +
  scale_color_viridis(discrete = TRUE) + 
  facet_wrap(~month, scales = "free", ncol = 3)

Lat/long

#Calculating monthly mean anomaly + std for each lat/lon pair of the area
anomaly_lat_lon <- CESM_with_float %>%
  group_by(lat, lon, depth, month, platform_number, cycle_number) %>%
  summarise(
    temp_count = n(),
    temp_anomaly_mean = mean(temp_anomaly, na.rm = TRUE)
    )

# Longitude - gradient north-south
ggplot(anomaly_lat_lon, aes(x = temp_anomaly_mean, y = depth, group = interaction(platform_number, cycle_number), color = as.numeric(lon))) +
  geom_path() +
  geom_vline(xintercept = 0) +
  scale_y_reverse(limits = c(200, 0)) +
  coord_cartesian(xlim = c(-6, 6)) +
  facet_wrap(~ month, ncol = 3) +
  labs(title = "Anomaly Profiles for all longitudes", 
       subtitle = "All floats and cycles included",
       x = "Temperature (°C)", y = "Depth (m)", color = "Longitude") + 
  scale_color_viridis_c()

# Latitude - gradient west-east
ggplot(anomaly_lat_lon, aes(x = temp_anomaly_mean, y = depth, group = interaction(platform_number, cycle_number), color = as.numeric(lat))) +
  geom_path() +
  geom_vline(xintercept = 0) +
  scale_y_reverse(limits = c(200, 0)) +
  coord_cartesian(xlim = c(-6, 6)) +
  facet_wrap(~ month, ncol = 3) +
  labs(title = "Anomaly Profile for all latitudes", 
       subtitle = "All floats and cycles included",
       x = "Temperature (°C)", y = "Depth (m)", color = "Latitude") +  
  scale_color_viridis_c()

Monthy average - entire extent

# Calculating monthly mean anomaly over the east area by averaging the anomaly of each float present
anomaly_mean <- CESM_with_float %>% 
  group_by(depth, month) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(temp_anomaly , na.rm = TRUE),
            temp_anomaly_sd = sd(temp_anomaly , na.rm = TRUE))

# Vertical anomaly profile for the North atlatinc east region - monthly - CESM
anomaly_plot_monthly <- ggplot(anomaly_mean, aes(x = temp_anomaly_mean, y = depth, color = factor(month))) +
  geom_path() +
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth), alpha = 0.2) +
  geom_vline(xintercept = 0) +
  scale_y_reverse() +
  coord_cartesian(xlim = c(-4, 4), ylim = c(200, 0)) +
  labs(title = paste('CESM temperature output - monthly anomaly profile'), 
       subtitle = paste0("Extent: East", "\nfrom surface to 200m"),
       x = 'Temperature (°C)', y = 'Depth (m)') +
  guides(color = FALSE)+
  facet_wrap(~month)

print(anomaly_plot_monthly)


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] labeling_0.4.2      sass_0.4.4          scales_1.2.1       
[25] classInt_0.4-8      SolveSAPHE_2.1.0    callr_3.7.3        
[28] proxy_0.4-27        digest_0.6.30       oce_1.7-10         
[31] rmarkdown_2.18      pkgconfig_2.0.3     htmltools_0.5.8.1  
[34] highr_0.9           dbplyr_2.2.1        fastmap_1.1.0      
[37] rlang_1.1.1         readxl_1.4.1        rstudioapi_0.15.0  
[40] farver_2.1.1        jquerylib_0.1.4     generics_0.1.3     
[43] jsonlite_1.8.3      googlesheets4_1.0.1 magrittr_2.0.3     
[46] ncmeta_0.3.5        Rcpp_1.0.10         munsell_0.5.0      
[49] fansi_1.0.3         lifecycle_1.0.3     stringi_1.7.8      
[52] whisker_0.4         yaml_2.3.6          grid_4.2.2         
[55] parallel_4.2.2      promises_1.2.0.1    crayon_1.5.2       
[58] haven_2.5.1         seacarb_3.3.1       hms_1.1.2          
[61] knitr_1.41          ps_1.7.2            pillar_1.9.0       
[64] reprex_2.0.2        glue_1.6.2          evaluate_0.18      
[67] getPass_0.2-2       modelr_0.1.10       vctrs_0.6.4        
[70] tzdb_0.3.0          httpuv_1.6.6        cellranger_1.1.0   
[73] gtable_0.3.1        rematch2_2.1.2      assertthat_0.2.1   
[76] cachem_1.0.6        xfun_0.35           lwgeom_0.2-10      
[79] broom_1.0.5         e1071_1.7-12        later_1.3.0        
[82] class_7.3-20        googledrive_2.0.0   gargle_1.2.1       
[85] units_0.8-0         ellipsis_0.3.2