Last updated: 2024-05-24

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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()

#Transformations
#--time
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()

#--longitude
CESM_temp <- CESM_temp %>% 
  mutate(lon = ifelse(lon > 180, lon - 360, lon))

#Select 2023
CESM_temp_2023 <- CESM_temp %>%
  filter(year==2023)
gc()
#Write CESM outputs for 2023 to file
write_rds(CESM_temp_2023,
          file = paste0(path_argo_core_preprocessed,"/", "CESM_temp2023.rds"))
CESM_temp_2023<- read_rds(file = paste0(path_argo_core_preprocessed,"/", "CESM_temp2023.rds"))

# Visualization
CESM_temp_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)

Version Author Date
7f729d4 mlarriere 2024-05-16
0a71d56 mlarriere 2024-05-15 
# CESM_temp_2023 %>%
#   filter(lat == -50.5) %>%
#   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)
CESM_temp_2004_2019<- CESM_temp %>% 
  filter(year>=2004, year<=2019)

CESM_temp_2004_2019<-CESM_temp_2004_2019 %>% 
  fgroup_by(lat, lon, depth, month) %>% 
  fsummarize(mean_temp=mean(temp, na.rm=TRUE))

# Visualization
CESM_temp_2004_2019 %>%
  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)


# Temperature anomaly
CESM_anomaly_2023 <- inner_join(CESM_temp_2023, CESM_temp_2004_2019, by = c("month", "lat", "lon", "depth")) 

# Calculate temperature anomaly
CESM_anomaly_2023 <- CESM_anomaly_2023 %>%
    fmutate(temp_anomaly = temp - mean_temp)

#Write 
write_rds(CESM_anomaly_2023,
          file = paste0(path_argo_core_preprocessed,"/", "CESM_temp_anomaly2023_clim2004-2019.rds"))

# rm(CESM_2004_2019_natlantic_east)
gc()
# Read data
CESM_anomaly_2023<-read_rds(file =paste0(path_argo_core_preprocessed,"/", "CESM_temp_anomaly2023_clim2004-2019.rds"))

# Visualization
CESM_anomaly_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)

Version Author Date
b21e0e6 mlarriere 2024-05-20
7f729d4 mlarriere 2024-05-16 
#Area of interest: North Atlantic north west - lat:(60,30), lon:(-70,-30), North Atlantic east - lat:(0,40), lon:(-30,0)
chosen_extent <- list(
  lat_min = 0, #30
  lat_max = 40, #60
  lon_min = -30, #-70
  lon_max = 0 #-30
)  
name_extent<- "East" #Northwest

CESM_natlantic_2023_subset <- CESM_anomaly_2023 %>%
  filter(lat > chosen_extent$lat_min, lat < chosen_extent$lat_max, 
         lon > chosen_extent$lon_min, lon < chosen_extent$lon_max)
CESM_natlantic_2023_subset$month<- factor(format(CESM_natlantic_2023_subset$time, "%m"))

# Visualization
#--Base map
world_coordinates <- map_data("world") 
ggplot() +
  geom_raster(data= CESM_natlantic_2023_subset %>% filter(depth == 5),  aes(lon, lat, fill = temp_anomaly)) +
  geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") +
  # geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), color = "black", fill = NA) +
  lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) + 
  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)

Version Author Date
b21e0e6 mlarriere 2024-05-20

Argo

Read data

Float location

CESM_with_float <- CESM_natlantic_2023_subset %>% 
  right_join(core_anomaly_2023_natlantic_subset %>% distinct(lat, lon, month, platform_number, cycle_number), 
            by = c("lat", "lon", "month")) 

# Float coverage -- subset 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))

Float distribution

# topofile='ETOPO15_mean_bath.nc'
# topopath='/nfs/kryo/work/updata/bathymetry/ETOPO1/'
# bathymetry<-tidync(paste0(topopath,topofile)) 
# bathymetry<-bathymetry %>% 
#   hyper_tibble(select_var = c("latitude","longitude", "mean_bath"), 
#                        force = TRUE)
# 
# east_extent<-bathymetry %>% 
#   filter(latitude>0, latitude<40, longitude>-30, longitude<0, mean_bath>0)

CESM_SSTanomaly_mean2023<- CESM_natlantic_2023_subset %>%
  filter(depth == 5) %>%
  group_by(lat, lon) %>% 
  summarise(yearly_SSTanomaly= mean(temp_anomaly, na.rm = TRUE))

float_monthly_count <- CESM_with_float %>%
  group_by(lon, lat) %>%
  summarise(months_present = n_distinct(month)) %>%
  ungroup()


#--Plots
SST_2023_plot <- ggplot()+
  geom_raster(data=CESM_SSTanomaly_mean2023, aes(lon, lat, fill = yearly_SSTanomaly)) +
  geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") +
  # geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), color = "black", fill = NA) +
  lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) + 
  scale_fill_viridis_c(option = "magma") +
  labs(title = "SST anomaly, \nannual average 2023",
       subtitle = "Resolution: 1°x1°",
       fill = "SST \nanomalies [°C]")+
  coord_quickmap(expand = 0)+
   theme(plot.title = element_text(size = 18), 
        plot.subtitle = element_text(size = 12),
        legend.text = element_text(size = 12),  
    legend.title = element_text(size = 14) ,
          legend.key.width = unit(0.3, "cm"),
                legend.key.height = unit(1, "cm")
  )
library(scico)

float_distrib <- ggplot() +
  geom_point(data = float_monthly_count, aes(x = lon, y = lat, color = months_present)) +
  geom_map(data = world_coordinates, map = world_coordinates, aes(long, lat, map_id = region), fill = "grey") +
  lims(x = c(chosen_extent$lon_min, chosen_extent$lon_max), y=c(chosen_extent$lat_min, chosen_extent$lat_max)) + 
  labs(title = "Platform Locations",
       subtitle = "Resolution: 1°x1°",
       color = "Months \nwith float") +
    scale_color_scico(palette = "oslo", breaks = seq(1, 12, by = 1), limits = c(1, 12), direction=-1) +

  # scale_color_gradient(low = "blue", high = "red", breaks = seq(1, 12, by = 1), limits = c(1, 12)) +
  coord_quickmap(expand = 0) +
  theme(plot.title = element_text(size = 18), 
        plot.subtitle = element_text(size = 12),
        legend.text = element_text(size = 12),  
    legend.title = element_text(size = 14),
        legend.key.width = unit(0.3, "cm"),
                legend.key.height = unit(1, "cm"))


combined_plot <- SST_2023_plot + float_distrib + plot_layout(ncol = 2)
combined_plot

Version Author Date
b21e0e6 mlarriere 2024-05-20

1 Float

unique_platform_CEM<-CESM_with_float %>%
  filter(platform_number==1902323  )

unique_platform_ARGO<-core_anomaly_2023_natlantic_subset %>%
filter(platform_number==1902323  )

# ggplot(unique_platform, aes(x = 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)
# 
CEM_singlfloat<- ggplot() +
  geom_path(data=unique_platform_CEM, aes(x = temp_anomaly , y = depth, color = factor(month), group = cycle_number)) +
  geom_vline(xintercept = 0) +
  scale_y_reverse() +
  coord_cartesian(xlim = c(-6, 6), ylim = c(200, 0)) +
    scale_color_manual(values = colorRampPalette(c("#2796A5", "#F3712B", "#880D1E"))(12)) +
    labs(
         # subtitle = "CESM ocean model", 
       x = 'Temperature (°C)', y = 'Depth (m)', color = 'Months') +
    theme(plot.title = element_text(size = 18), 
        plot.subtitle = element_text(size = 14),
        legend.text = element_text(size = 12),  
        legend.title = element_text(size = 14, face = "bold"),
        legend.background = element_rect(fill = "transparent", color='transparent')
        # legend.position=c(.91,.28),
    )


ARGO_singlfloat<- ggplot() +
  geom_path(data=unique_platform_ARGO, aes(x = anomaly , y = depth, color = factor(month), group = cycle_number)) +
  geom_vline(xintercept = 0) +
  scale_y_reverse() +
  coord_cartesian(xlim = c(-6, 6), ylim = c(200, 0)) +
    scale_color_manual(values = colorRampPalette(c("#2796A5", "#F3712B", "#880D1E"))(12)) +
    labs(
         # subtitle = "Argo floats", 
       x = 'Temperature (°C)', y = 'Depth (m)', color = 'Months') +
   theme(plot.title = element_text(size = 18), 
        plot.subtitle = element_text(size = 14),
            legend.position = "none"

        # legend.text = element_text(size = 12),
        # legend.title = element_text(size = 14, face = "bold"),
        # legend.background = element_rect(fill = "transparent", color='transparent'),
        # legend.position=c(.91,.28),
    )




combined_plot <- ARGO_singlfloat + CEM_singlfloat + 
  plot_layout(ncol = 2) +
  plot_annotation(
    title = 'SST anomalies propagation of a single float',
    subtitle = 'Location: Canaries Islands (Eddies corridor)',
    theme = theme(
      plot.title = element_text(size = 18),
      plot.subtitle = element_text(size = 14)
    )
  )
combined_plot

Version Author Date
b21e0e6 mlarriere 2024-05-20
7f729d4 mlarriere 2024-05-16

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
longitude<- 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
latitude<- 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()
combined_plot <- latitude + longitude + plot_layout(ncol = 2)
combined_plot

Version Author Date
b21e0e6 mlarriere 2024-05-20
7f729d4 mlarriere 2024-05-16

Monthy average - ARGO exent

# 
# ggplot(anomaly_CESM, 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 Monthly anomaly profile'), 
#        subtitle = paste0("Extent: East and ARGO float locations"),
#        x = 'Temperature (°C)', y = 'Depth (m)') +
#   guides(color = FALSE)+
#   facet_wrap(~month)

Monthy average - entire extent

# Calculating monthly mean anomaly over the east area by averaging the anomaly of each float present
anomaly_CESM_entire_extent <- CESM_natlantic_2023_subset %>% 
  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))


anomaly_ARGO <- core_anomaly_2023_natlantic_subset %>% #ARGO
  group_by(depth, month) %>%
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly , na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly , na.rm = TRUE))

# Defining period of 2months and calculating monthly mean anomaly over the east area 
anomaly_CESM<-CESM_with_float %>% #CESM
  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))

# difference ARGO vs full extent
difference_extent<- merge(anomaly_CESM_entire_extent, anomaly_CESM, 
                          by = c("depth","month"), suffixes = c("_float", "_entire_extent")) %>%
  as_tibble()
difference_extent$diff_temp_anomaly_mean <- difference_extent$temp_anomaly_mean_entire_extent    - difference_extent$temp_anomaly_mean_float

Distributions of the anomaly

#ARGO
mean_anomaly_argo <- mean(core_anomaly_2023_natlantic_subset$anomaly, na.rm = TRUE)
sd_anomaly_argo <- sd(core_anomaly_2023_natlantic_subset$anomaly, na.rm = TRUE)
argo_hist <-   ggplot(core_anomaly_2023_natlantic_subset, aes(x=anomaly)) +
  geom_histogram(aes(y=..density.., fill="Values"), bins=30, alpha=0.5) +
  stat_function(fun=dnorm, args=list(mean=mean_anomaly_argo, sd=sd_anomaly_argo), aes(color="Gaussian"), size=1) +
  scale_fill_manual(values = "blue", name = NULL) +  # Legend for histogram bars
  scale_color_manual(values = "red", name = NULL) +  # Legend for Gaussian curve
  labs(subtitle="Argo floats",
       x="Temperature Anomaly",
       y="Density")


#CESM
mean_anomaly_cesm_full <- mean(CESM_natlantic_2023_subset$temp_anomaly, na.rm = TRUE)
sd_anomaly_cesm_full <- sd(CESM_natlantic_2023_subset$temp_anomaly, na.rm = TRUE)

cesm_hist_full<- ggplot(CESM_natlantic_2023_subset, aes(x=temp_anomaly)) +
  geom_histogram(aes(y=..density..), bins=30, fill="blue", alpha=0.5) +
  stat_function(fun=dnorm, args=list(mean=mean_anomaly_cesm_full, sd=sd_anomaly_cesm_full), color="red", size=1) +
  labs(subtitle='CESM - full extent', 
    x="Temperature Anomaly",
       y="Density") 


mean_anomaly_cesm <- mean(CESM_with_float$temp_anomaly, na.rm = TRUE)
sd_anomaly_cesm <- sd(CESM_with_float$temp_anomaly, na.rm = TRUE)

cesm_hist<- ggplot(CESM_with_float, aes(x=temp_anomaly)) +
  geom_histogram(aes(y=..density..), bins=30, fill="blue", alpha=0.5) +
  stat_function(fun=dnorm, args=list(mean=mean_anomaly_cesm, sd=sd_anomaly_cesm), color="red", size=1) +
  labs(subtitle='CESM - Argo extent', 
    x="Temperature Anomaly",
       y="Density") 

combined_plot<-argo_hist+cesm_hist+  cesm_hist_full+
  plot_layout(ncol = 3, guides = 'collect') +
  plot_annotation(
    title = 'Temperature Anomaly Distribution with Gaussian Curve',
    theme = theme(
      plot.title = element_text(size = 18),
      plot.subtitle = element_text(size = 14))
    )

combined_plot

Comparison CESM- ARGO

# Defining period of 2months and calculating monthly mean anomaly over the east area 
anomaly_CESM_2month_avg<-CESM_with_float %>% #CESM
  mutate(period=(as.numeric(month)+1)%/%2)

anomaly_CESM_2month_avg<-anomaly_CESM_2month_avg %>% #CESM
  group_by(depth, period) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(temp_anomaly , na.rm = TRUE),
            temp_anomaly_sd = sd(temp_anomaly , na.rm = TRUE))

anomaly_ARGO_2month_avg <- core_anomaly_2023_natlantic_subset %>% #ARGO
  mutate(period=(as.numeric(month)+1)%/%2)

anomaly_ARGO_2month_avg<-anomaly_ARGO_2month_avg %>% #CESM
  group_by(depth, period) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(anomaly , na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly , na.rm = TRUE))

anomaly_CESM_entire_extent <- CESM_natlantic_2023_subset %>% #entire extent
    mutate(period=(as.numeric(month)+1)%/%2)

anomaly_CESM_entire_extent<-anomaly_CESM_entire_extent %>% #CESM
  group_by(depth, period) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(temp_anomaly , na.rm = TRUE),
            temp_anomaly_sd = sd(temp_anomaly , na.rm = TRUE))

anomaly_extents_diff_2month_avg<-difference_extent %>% #different extents
  mutate(period=(as.numeric(month)+1)%/%2)%>% 
  group_by(depth, period) %>% 
  summarise(temp_count = n(),
            temp_anomaly_mean = mean(diff_temp_anomaly_mean , na.rm = TRUE),
            temp_anomaly_sd = sd(diff_temp_anomaly_mean , na.rm = TRUE))


#PLot
ggplot() +
  #---ribbons
   # geom_ribbon(data=anomaly_CESM_entire_extent, aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
   #                                              xmin = temp_anomaly_mean - temp_anomaly_sd,
   #                                              y = depth), fill = "#880D1E", alpha = 0.3) +
  geom_ribbon(data=anomaly_CESM_2month_avg, aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                                                xmin = temp_anomaly_mean - temp_anomaly_sd,
                                                y = depth), fill = "#FFBE0B", alpha = 0.2) +
  geom_ribbon(data=anomaly_ARGO_2month_avg, aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                                                xmin = temp_anomaly_mean - temp_anomaly_sd,
                                                y = depth), fill = "#7FC6A4", alpha = 0.2) +
  
  #---paths
  geom_path(data=anomaly_CESM_entire_extent , aes(x = temp_anomaly_mean, y = depth, color = "3"), linetype="dashed")+ #anomaly_extents_diff_2month_avg
  geom_path(data=anomaly_ARGO_2month_avg, aes(x = temp_anomaly_mean, y = depth, color = "1"), linetype="solid")+
  geom_path(data=anomaly_CESM_2month_avg, aes(x = temp_anomaly_mean, y = depth, color = "2"), linetype="solid") +

  #---settings (legend, ticks...)
  geom_vline(xintercept = 0) +
  scale_y_reverse() +
  coord_cartesian(xlim = c(-4, 4), ylim = c(200, 0)) +
  labs(title = paste('Propagation of SST anomalies in the water column'), 
       subtitle = paste0("Extent: ", name_extent, " North Atlantic bassin in 2023"),
       x = 'Temperature (°C)', y = 'Depth (m)') +
  theme(plot.title = element_text(size = 20), 
        plot.subtitle = element_text(size = 14),
    legend.position = "bottom",
        legend.title = element_blank(), 
        legend.key.height = unit(5, "lines"), 
    legend.text = element_text(size = 12)) +
  scale_color_manual(values = c("1" = "#4CA97C", 
                                "2" = "#F3712B", 
                                "3" = "#880D1E"),
                     labels = c( "ARGO", "CESM (ARGO extent)","CESM (full extent)")) + #CESM extents difference (full -ARGO)
  facet_wrap(~period, labeller = labeller(period = c("1"="Jan-Feb", 
                                                     "2"="Mar-Apr", 
                                                     "3"="May-Jun", 
                                                     "4"="Jul-Aug", 
                                                     "5"="Sep-Oct", 
                                                     "6"="Nov-Dec")), nrow=1)+
    guides(color = guide_legend(nrow = 1, override.aes = list(linetype = c("solid", "solid", "dashed"))))

Version Author Date
b21e0e6 mlarriere 2024-05-20 

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] scico_1.3.1        patchwork_1.1.2    collapse_2.0.13    tidync_0.3.0      
 [5] marelac_2.1.10     shape_1.4.6        RColorBrewer_1.1-3 stars_0.6-0       
 [9] sf_1.0-9           abind_1.4-5        paletteer_1.6.0    cluster_2.1.6     
[13] gridExtra_2.3      viridis_0.6.2      viridisLite_0.4.1  lubridate_1.9.0   
[17] timechange_0.1.1   forcats_0.5.2      stringr_1.5.0      dplyr_1.1.3       
[21] purrr_1.0.2        readr_2.1.3        tidyr_1.3.0        tibble_3.2.1      
[25] ggplot2_3.4.4      tidyverse_1.3.2    workflowr_1.7.0   

loaded via a namespace (and not attached):
 [1] googledrive_2.0.0   colorspace_2.0-3    ellipsis_0.3.2     
 [4] class_7.3-20        rprojroot_2.0.3     fs_1.5.2           
 [7] rstudioapi_0.15.0   proxy_0.4-27        farver_2.1.1       
[10] fansi_1.0.3         xml2_1.3.3          ncdf4_1.22         
[13] cachem_1.0.6        knitr_1.41          jsonlite_1.8.3     
[16] gsw_1.1-1           broom_1.0.5         dbplyr_2.2.1       
[19] compiler_4.2.2      httr_1.4.4          backports_1.4.1    
[22] assertthat_0.2.1    fastmap_1.1.0       gargle_1.2.1       
[25] cli_3.6.1           later_1.3.0         htmltools_0.5.8.1  
[28] tools_4.2.2         gtable_0.3.1        glue_1.6.2         
[31] maps_3.4.1          Rcpp_1.0.10         cellranger_1.1.0   
[34] jquerylib_0.1.4     RNetCDF_2.6-1       vctrs_0.6.4        
[37] lwgeom_0.2-10       xfun_0.35           ps_1.7.2           
[40] rvest_1.0.3         lifecycle_1.0.3     ncmeta_0.3.5       
[43] googlesheets4_1.0.1 oce_1.7-10          getPass_0.2-2      
[46] scales_1.2.1        hms_1.1.2           promises_1.2.0.1   
[49] parallel_4.2.2      rematch2_2.1.2      yaml_2.3.6         
[52] sass_0.4.4          stringi_1.7.8       highr_0.9          
[55] e1071_1.7-12        rlang_1.1.1         pkgconfig_2.0.3    
[58] evaluate_0.18       SolveSAPHE_2.1.0    labeling_0.4.2     
[61] processx_3.8.0      tidyselect_1.2.0    seacarb_3.3.1      
[64] magrittr_2.0.3      R6_2.5.1            generics_0.1.3     
[67] DBI_1.2.2           pillar_1.9.0        haven_2.5.1        
[70] whisker_0.4         withr_2.5.0         units_0.8-0        
[73] modelr_0.1.10       crayon_1.5.2        KernSmooth_2.23-20 
[76] utf8_1.2.2          tzdb_0.3.0          rmarkdown_2.18     
[79] grid_4.2.2          readxl_1.4.1        callr_3.7.3        
[82] git2r_0.30.1        reprex_2.0.2        digest_0.6.30      
[85] classInt_0.4-8      httpuv_1.6.6        munsell_0.5.0      
[88] bslib_0.4.1