Extreme Temperature Profiles

workflowr

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Last updated: 2022-05-12

Checks: 7 0

Knit directory: bgc_argo_r_argodata/

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---

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/extreme_temp.Rmd) and HTML (docs/extreme_temp.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	944e0a2	jens-daniel-mueller	2022-05-12	revised color scale for argo location map
Rmd	1bdcd6e	jens-daniel-mueller	2022-05-12	revised color scale for argo location map
html	4173c20	jens-daniel-mueller	2022-05-12	Build site.
Rmd	78acca9	jens-daniel-mueller	2022-05-12	run with DIC clim scaled to 2016
html	dfe89d7	jens-daniel-mueller	2022-05-12	Build site.
html	710edd4	jens-daniel-mueller	2022-05-11	Build site.
Rmd	2f20a76	jens-daniel-mueller	2022-05-11	rebuild all after subsetting AB profiles and code cleaning
html	b917bd0	jens-daniel-mueller	2022-05-11	Build site.
Rmd	86144c6	jens-daniel-mueller	2022-05-11	rerun with flag A and B subset
html	ca30beb	pasqualina-vonlanthendinenna	2022-05-05	Build site.
Rmd	bb146f4	pasqualina-vonlanthendinenna	2022-05-05	updated map colors and plotting
html	4cf88e4	pasqualina-vonlanthendinenna	2022-05-05	Build site.
Rmd	3bde57b	pasqualina-vonlanthendinenna	2022-05-05	added argo profile locations
html	2751f13	pasqualina-vonlanthendinenna	2022-05-05	Build site.
Rmd	8e115be	pasqualina-vonlanthendinenna	2022-05-05	added argo profile locations
html	f46b9da	pasqualina-vonlanthendinenna	2022-05-05	Build site.
Rmd	ebcc576	pasqualina-vonlanthendinenna	2022-05-05	added argo profile locations
html	6572988	pasqualina-vonlanthendinenna	2022-05-04	Build site.
Rmd	8d56775	pasqualina-vonlanthendinenna	2022-05-04	updated plot labels
html	708f923	pasqualina-vonlanthendinenna	2022-05-04	Build site.
Rmd	d569024	pasqualina-vonlanthendinenna	2022-05-04	added number of profiles to plot
html	6a6e874	pasqualina-vonlanthendinenna	2022-04-29	Build site.
html	2d44f8a	pasqualina-vonlanthendinenna	2022-04-29	Build site.
Rmd	8b582f0	pasqualina-vonlanthendinenna	2022-04-29	added broullon climatology page, argo locations
html	e61c08e	pasqualina-vonlanthendinenna	2022-04-27	Build site.
Rmd	9664e0e	pasqualina-vonlanthendinenna	2022-04-27	added temp data page, changed double extremes
html	10036ed	pasqualina-vonlanthendinenna	2022-04-26	Build site.
html	c03dd24	pasqualina-vonlanthendinenna	2022-04-20	Build site.
html	f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14	Build site.
Rmd	c2fa269	pasqualina-vonlanthendinenna	2022-04-14	added full temperature climatology
html	8805f99	pasqualina-vonlanthendinenna	2022-04-11	Build site.
Rmd	d21c526	pasqualina-vonlanthendinenna	2022-04-11	cleaned up code
Rmd	f3ca885	pasqualina-vonlanthendinenna	2022-04-07	added OceanSODA-Argo SST comparison
html	c541171	pasqualina-vonlanthendinenna	2022-04-07	Build site.
Rmd	9437f81	pasqualina-vonlanthendinenna	2022-04-07	cleaned loading data page
html	9875dd0	pasqualina-vonlanthendinenna	2022-04-05	Build site.
Rmd	72a65a7	pasqualina-vonlanthendinenna	2022-04-05	added new biomes to extreme pH
html	48573c4	pasqualina-vonlanthendinenna	2022-03-31	Build site.
Rmd	11915d8	pasqualina-vonlanthendinenna	2022-03-31	loaded in Mayot biomes and Roemmich temp climatology
html	eb8e3be	pasqualina-vonlanthendinenna	2022-03-31	Build site.
Rmd	c4d4031	pasqualina-vonlanthendinenna	2022-03-31	extended OceanSODA to 1995 for extreme detection
html	a2271df	pasqualina-vonlanthendinenna	2022-03-30	Build site.
Rmd	25d5eed	pasqualina-vonlanthendinenna	2022-03-30	updated figure aspects
html	dfd75e9	pasqualina-vonlanthendinenna	2022-03-29	Build site.
Rmd	b9a42f9	pasqualina-vonlanthendinenna	2022-03-29	added january plots and changed pH anomaly detection to mean
html	65e609a	pasqualina-vonlanthendinenna	2022-03-28	Build site.
Rmd	a22e2f4	pasqualina-vonlanthendinenna	2022-03-28	re-build extreme temp page
html	cbb2360	jens-daniel-mueller	2022-03-28	Build site.
Rmd	c07ce42	jens-daniel-mueller	2022-03-28	rerun with mean instead of lm anomaly detection
html	fa1b6de	jens-daniel-mueller	2022-03-28	Build site.
Rmd	c53aa88	jens-daniel-mueller	2022-03-28	rerun with lm instead of mean anomaly detection
html	749e005	jens-daniel-mueller	2022-03-28	Build site.
Rmd	9ed3727	jens-daniel-mueller	2022-03-28	cleaned code
html	8173cdb	jens-daniel-mueller	2022-03-28	Build site.
Rmd	d7e3599	jens-daniel-mueller	2022-03-28	reviewed depth binning for profile averaging
html	f27d454	pasqualina-vonlanthendinenna	2022-03-25	Build site.
Rmd	b9c4426	pasqualina-vonlanthendinenna	2022-03-25	read in temp climatology in loading data
html	7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25	Build site.
Rmd	becbfe0	pasqualina-vonlanthendinenna	2022-03-25	corrected anomaly profile calculation
html	27a52f8	pasqualina-vonlanthendinenna	2022-03-25	Build site.
Rmd	a6aad60	pasqualina-vonlanthendinenna	2022-03-25	added january anomaly profiles for each year
html	6dd0945	pasqualina-vonlanthendinenna	2022-03-25	Build site.
html	d9caaae	pasqualina-vonlanthendinenna	2022-03-22	Build site.
Rmd	9daebcf	pasqualina-vonlanthendinenna	2022-03-22	removed climatology from temperature profiles (anomaly profiles section)
html	5e36bb4	pasqualina-vonlanthendinenna	2022-03-18	Build site.
Rmd	44a9ba6	pasqualina-vonlanthendinenna	2022-03-18	removed eval false from anomaly maps
html	650ef68	pasqualina-vonlanthendinenna	2022-03-18	Build site.
Rmd	792f3f0	pasqualina-vonlanthendinenna	2022-03-18	removed climatology from oceansoda temperature
html	e12a216	pasqualina-vonlanthendinenna	2022-03-15	Build site.
Rmd	e4d1d1e	pasqualina-vonlanthendinenna	2022-03-15	updated to new only flag A data
html	c8451b9	pasqualina-vonlanthendinenna	2022-03-14	Build site.
html	1ffe07f	pasqualina-vonlanthendinenna	2022-03-11	Build site.
Rmd	f0fde29	pasqualina-vonlanthendinenna	2022-03-11	changed anomaly detection to 1x1 grid with old data
html	7540ae4	pasqualina-vonlanthendinenna	2022-03-08	Build site.
Rmd	18dff1b	pasqualina-vonlanthendinenna	2022-03-08	subsetted profiles with flag A only for extremes
html	9d97f25	pasqualina-vonlanthendinenna	2022-03-02	Build site.
Rmd	9ccabc6	pasqualina-vonlanthendinenna	2022-03-02	removed facet wrap
html	e4188d2	pasqualina-vonlanthendinenna	2022-03-01	Build site.
Rmd	6ca535c	pasqualina-vonlanthendinenna	2022-03-01	updated profiles
html	da665ab	pasqualina-vonlanthendinenna	2022-03-01	Build site.
Rmd	57ada58	pasqualina-vonlanthendinenna	2022-03-01	updated figure aspects
html	5ef4df2	pasqualina-vonlanthendinenna	2022-03-01	Build site.
Rmd	8ef1277	pasqualina-vonlanthendinenna	2022-03-01	plotted Atlantic mean seasonal profiles
Rmd	73463cc	pasqualina-vonlanthendinenna	2022-03-01	changed line thickness for H and L raw profiles
html	c4362e5	pasqualina-vonlanthendinenna	2022-02-28	Build site.
Rmd	5b0901d	pasqualina-vonlanthendinenna	2022-02-28	corrected dates and titles
html	d299359	pasqualina-vonlanthendinenna	2022-02-28	Build site.
Rmd	aad1df4	pasqualina-vonlanthendinenna	2022-02-28	plotted specific profiles
html	fd521d1	pasqualina-vonlanthendinenna	2022-02-25	Build site.
Rmd	64c2c71	pasqualina-vonlanthendinenna	2022-02-25	plotted line profiles and changed HNL colors
html	7d7874c	pasqualina-vonlanthendinenna	2022-02-24	Build site.
Rmd	58d2846	pasqualina-vonlanthendinenna	2022-02-24	added st dev for temp profiles
html	c68163a	pasqualina-vonlanthendinenna	2022-02-22	Build site.
Rmd	818ac54	pasqualina-vonlanthendinenna	2022-02-22	updated regression and merging for extreme_temp
html	f98c744	pasqualina-vonlanthendinenna	2022-02-18	Build site.
Rmd	8b99ab3	pasqualina-vonlanthendinenna	2022-02-18	updates
html	19aa73d	pasqualina-vonlanthendinenna	2022-02-16	Build site.
Rmd	d955d28	pasqualina-vonlanthendinenna	2022-02-16	updated extreme temperature
html	905d82f	pasqualina-vonlanthendinenna	2022-02-15	Build site.
Rmd	01ae9da	pasqualina-vonlanthendinenna	2022-02-15	added OceanSODA-Argo SST comparison
html	54ea512	pasqualina-vonlanthendinenna	2022-02-10	Build site.
html	f2fa56a	pasqualina-vonlanthendinenna	2022-02-10	Build site.
Rmd	eda8ca8	pasqualina-vonlanthendinenna	2022-02-10	code review

---

Task

Compare depth profiles of normal temperature and of extreme temperature, as identified in the surface OceanSODA data product

theme_set(theme_bw())
HNL_colors <- c("H" = "#b2182b",
                "N" = "#636363",
                "L" = "#2166ac")

HNL_colors_map <- c('H' = 'red3',
                    'N' = 'transparent',
                    'L' = 'blue3')

Load data

path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_updata <- '/nfs/kryo/work/updata'
path_argo_clim_temp <- paste0(path_updata, "/argo_climatology/temperature")

# RECCAP2-ocean region mask

#  region_masks_all_2x2 <- read_rds(file = paste0(path_argo_preprocessed,
#                                                 "/region_masks_all_2x2.rds"))
# #   
# region_masks_all_2x2 <- region_masks_all_2x2 %>%
#   rename(biome = value) %>%
#   mutate(coast = as.character(coast))

# updated biomes of Nicolas Mayot 

nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))

# WOA 18 basin mask

basinmask <-
  read_csv(
    paste(path_emlr_utilities,
          "basin_mask_WOA18.csv",
          sep = ""),
    col_types = cols("MLR_basins" = col_character())
  )

basinmask <- basinmask %>%
  filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>% 
  select(-c(MLR_basins, basin))

# OceanSODA temperature (from 1995 to 2020)

OceanSODA_temp <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_temp.rds"))

OceanSODA_temp <- OceanSODA_temp %>%
  mutate(month = month(date))

# Argo flag A profiles

full_argo <-
  read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_AB.rds"))

full_argo <- full_argo %>%
  select(-c(ph_in_situ_total_adjusted:ph_in_situ_total_adjusted_error,
            profile_ph_in_situ_total_qc))

# change the date format for compatibility with OceanSODA data
full_argo <- full_argo %>%
  mutate(year = year(date),
         month = month(date)) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))

# base map for plotting
map <-
  read_rds(paste(path_emlr_utilities,
                 "map_landmask_WOA18.rds",
                 sep = ""))
# restrict base map to Southern Ocean
map <- map +
  lims(y = c(-85, -30))

Regions

Biome grid reduction

map+
  geom_tile(data = nm_biomes,
            aes(x = lon,
                y = lat,
                fill = biome_name))+
  scale_fill_brewer(palette = 'Dark2')+
  labs(title = 'Mayot biomes (pre-grid reduction)')

Past versions of new_mayot_biomes_pre_grid_reduction-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
48573c4	pasqualina-vonlanthendinenna	2022-03-31

basemap(limits = -30)+
  geom_spatial_tile(data = nm_biomes,
                    aes(x = lon,
                        y = lat,
                        fill = biome_name),
                    col = NA)+
  scale_fill_brewer(palette = 'Dark2')+
  labs(title = 'Mayot biomes (pre-grid reduction)')

nm_biomes_2x2 <- nm_biomes %>% 
  mutate(lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
         lon = as.numeric(as.character(lon)),
         lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
         lat = as.numeric(as.character(lat)))

nm_biomes_2x2 <- nm_biomes_2x2 %>% 
  count(lon, lat, biome_name) %>% 
  group_by(lon, lat) %>% 
  slice_max(n, with_ties = FALSE) %>% 
  ungroup()

rm(nm_biomes)

map+
  geom_tile(data = nm_biomes_2x2,
            aes(x = lon,
                y = lat,
                fill = biome_name))+
  scale_fill_brewer(palette = 'Dark2')+
  labs('Mayot biomes post-grid reduction')

Past versions of new_mayot_biomes_map-1.png

Version	Author	Date
48573c4	pasqualina-vonlanthendinenna	2022-03-31

basemap(limits = -30)+
  geom_spatial_tile(data = nm_biomes_2x2,
                    aes(x = lon, 
                        y = lat,
                        fill = biome_name),
                    col = NA)+
  scale_fill_brewer(palette = 'Dark2')+
  labs(title = 'Mayot biomes (post-grid reduction)')

Basin grid reduction

basinmask <- basinmask %>%
  filter(lat < -30)

map +
  geom_tile(data = basinmask, 
            aes(x = lon, 
                y = lat, 
                fill = basin_AIP))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of basin_map_pre_grid_reduction-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

basinmask_2x2 <- basinmask %>%
  mutate(
    lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
    lat = as.numeric(as.character(lat)),
    lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
    lon = as.numeric(as.character(lon))
   )

# assign basins from each pixel to to each 2 Lon x Lat pixel, based on the majority of basins in each 2x2 grid

basinmask_2x2 <- basinmask_2x2 %>%
  count(lon, lat, basin_AIP) %>%
  group_by(lon, lat) %>%
  slice_max(n, with_ties = FALSE) %>%
  ungroup() %>%
  select(-n)

rm(basinmask)

map+
  geom_tile(data = basinmask_2x2 %>% filter(lat < -30),
            aes(x = lon,
                y = lat,
                fill = basin_AIP))+
  scale_fill_brewer(palette = 'Dark2')

Past versions of basin_map_post_grid_reduction-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

basemap(limits = -32)+
  geom_spatial_tile(data = basinmask_2x2 %>% filter(lat < -32),
                    aes(x = lon,
                        y = lat,
                        fill = basin_AIP),
                    col = NA)+
  scale_fill_brewer(palette = 'Dark2')

OceanSODA

Monthly clim

OceanSODA_temp <- OceanSODA_temp %>% 
  group_by(lon, lat, month) %>% 
  mutate(clim_temp = mean(temperature, na.rm = TRUE),
         clim_diff = temperature - clim_temp,
         .after = temperature) %>% 
  ungroup()

Grid reduction

# Note: While reducing lon x lat grid,
# we keep the original number of observations

OceanSODA_temp_2x2 <- OceanSODA_temp %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon,
    lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
    lat = as.numeric(as.character(lat)),
    lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
    lon = as.numeric(as.character(lon))) # regrid into 2x2º grid

rm(OceanSODA_temp)

Apply region masks

# keep only Southern Ocean data
OceanSODA_temp_2x2_SO <- inner_join(OceanSODA_temp_2x2, nm_biomes_2x2 %>% 
                                      select(-n)) 

rm(OceanSODA_temp_2x2)

# add in basin separations
OceanSODA_temp_2x2_SO <- inner_join(OceanSODA_temp_2x2_SO, basinmask_2x2)
# expected number of rows from -30 to -70º latitude, 360º longitude, for 12 months, 8 years:
# 40 lat x 360 lon x 12 months x 8 years = 1 382 400 rows 
# actual number of rows: 925 260 (in line with expectations)

OceanSODA_temp_2x2_SO <- OceanSODA_temp_2x2_SO %>% 
  filter(!is.na(temperature))
# no NA clim_diff values 

Maps

OceanSODA_temp_2x2_SO %>% 
  filter(year == 2020) %>% 
  ggplot(aes(lon_raw, lat_raw, fill = clim_temp)) +
  geom_tile() +
  scale_fill_viridis_c() +
  facet_wrap(~ month, ncol = 2) +
  coord_quickmap(expand = 0)

Past versions of map_OceanSODA_temp_clim_diff-1.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11

OceanSODA_temp_2x2_SO %>% 
  group_split(month) %>% 
  #head(1) %>% 
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = clim_temp))+
      scale_fill_viridis_c()+
      labs(title = paste0('month:', unique(.x$month)))+
      theme(legend.position = 'right')
  )

[[1]]

Past versions of monthly_clim_OceanSODA_temp_map-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[2]]

Past versions of monthly_clim_OceanSODA_temp_map-2.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[3]]

Past versions of monthly_clim_OceanSODA_temp_map-3.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[4]]

Past versions of monthly_clim_OceanSODA_temp_map-4.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[5]]

Past versions of monthly_clim_OceanSODA_temp_map-5.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[6]]

Past versions of monthly_clim_OceanSODA_temp_map-6.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[7]]

Past versions of monthly_clim_OceanSODA_temp_map-7.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[8]]

Past versions of monthly_clim_OceanSODA_temp_map-8.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[9]]

Past versions of monthly_clim_OceanSODA_temp_map-9.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[10]]

Past versions of monthly_clim_OceanSODA_temp_map-10.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[11]]

Past versions of monthly_clim_OceanSODA_temp_map-11.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[12]]

Past versions of monthly_clim_OceanSODA_temp_map-12.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

OceanSODA_temp_2x2_SO %>% 
  group_split(month) %>% 
  #head(1) %>% 
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = clim_diff))+
      scale_fill_divergent(mid = 'grey80')+
      facet_wrap(~year, ncol = 3)+
      labs(title = paste0('month:', unique(.x$month)))+
      theme(legend.position = 'right')
  )

[[1]]

Past versions of OceanSODA_temp_anomaly_map-1.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[2]]

Past versions of OceanSODA_temp_anomaly_map-2.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[3]]

Past versions of OceanSODA_temp_anomaly_map-3.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[4]]

Past versions of OceanSODA_temp_anomaly_map-4.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[5]]

Past versions of OceanSODA_temp_anomaly_map-5.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[6]]

Past versions of OceanSODA_temp_anomaly_map-6.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[7]]

Past versions of OceanSODA_temp_anomaly_map-7.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[8]]

Past versions of OceanSODA_temp_anomaly_map-8.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[9]]

Past versions of OceanSODA_temp_anomaly_map-9.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[10]]

Past versions of OceanSODA_temp_anomaly_map-10.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[11]]

Past versions of OceanSODA_temp_anomaly_map-11.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[12]]

Past versions of OceanSODA_temp_anomaly_map-12.png

Version	Author	Date
710edd4	jens-daniel-mueller	2022-05-11
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

SST anomalies

Through setting only one of the following two code chunks to “eval=FALSE” you can choose between a lm- and a mean-based anomaly detection.

Fit lm models

# fit a linear regression of OceanSODA pH against time (temporal trend)
# in each lat/lon/month grid

OceanSODA_temp_regression <- OceanSODA_temp_2x2_SO %>% 
  # filter(basin_AIP == "Indian",
  #        biome_name == "SPSS",
  #        lon < 40) %>%
  nest(data = -c(lon, lat, month)) %>%
  mutate(fit = map(.x = data,
                   .f = ~ lm(clim_diff ~ year, data = .x)),
         tidied = map(.x = fit, .f = tidy),
         glanced = map(.x = fit, .f = glance),
         augmented = map(.x = fit, .f = augment)) 


OceanSODA_temp_regression_tidied <- OceanSODA_temp_regression %>%
  select(-c(data, fit, augmented, glanced)) %>%
  unnest(tidied) 

OceanSODA_temp_regression_tidied <- OceanSODA_temp_regression_tidied %>% 
  select(lon:estimate) %>% 
  pivot_wider(names_from = term,
              values_from = estimate) %>% 
  rename(intercept = `(Intercept)`,
         slope = year)

OceanSODA_temp_regression_augmented <- OceanSODA_temp_regression %>%
  select(-c(fit, tidied, glanced, data)) %>%
  unnest(augmented) %>% 
  select(lon:year, .resid) 

OceanSODA_temp_regression_data <- OceanSODA_temp_regression %>% 
  select(-c(fit, tidied, glanced, augmented)) %>% 
  unnest(data) 

OceanSODA_temp_regression_augmented <- bind_cols(
  OceanSODA_temp_regression_augmented,
  OceanSODA_temp_regression_data %>% 
    select(date,
           basin_AIP, biome_name,
           temperature, clim_temp, 
           lat_raw, lon_raw))

OceanSODA_temp_regression_glanced <- OceanSODA_temp_regression %>%
  select(-c(data, fit, tidied, augmented)) %>%
  unnest(glanced) 

Fit mean

# identify the mean value
# in each lat/lon/month grid

OceanSODA_temp_regression_tidied <- OceanSODA_temp_2x2_SO %>% 
  # filter(basin_AIP == "Indian",
  #        biome_name == "SPSS",
  #        lon < 40) %>%
  group_by(lon, lat, month) %>%
  summarise(slope = 0,
            intercept = mean(clim_diff, na.rm = TRUE)) %>% 
  ungroup()

OceanSODA_temp_regression_glanced <- OceanSODA_temp_2x2_SO %>% 
  # filter(basin_AIP == "Indian",
  #        biome_name == "SPSS",
  #        lon < 40) %>%
  group_by(lon, lat, month) %>%
  summarise(sigma = sd(clim_diff, na.rm = TRUE)) %>% 
  ungroup()

OceanSODA_temp_regression_augmented <- OceanSODA_temp_2x2_SO %>% 
  # filter(basin_AIP == "Indian",
  #        biome_name == "SPSS",
  #        lon < 40) %>%
  mutate(.resid = clim_diff)

Slope maps

map+
  geom_tile(data = OceanSODA_temp_regression_tidied,
            aes(x = lon,
                y = lat, 
                fill = slope))+
  scale_fill_scico(palette = 'vik', midpoint = 0)+
  facet_wrap(~month, ncol = 2)

Past versions of map_temp_regression_slopes_flat-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Sigma maps

map+
  geom_tile(data = OceanSODA_temp_regression_glanced,
            aes(x = lon,
                y = lat, 
                fill = sigma))+
  scale_fill_viridis_c()+
  facet_wrap(~month, ncol = 2)+
  labs(fill = '1 residual \nst. dev.')

Past versions of temp_map_sigma-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

Anomaly identification

Calculate OceanSODA surface temperature anomalies; L for abnormally low, H for abnormally high, and N for normal

# when the in-situ OceanSODA temperature is lower than the 5th percentile (predicted - 2*residual.st.dev), assign 'L' for low extreme
# when the in-situ OceanSODA temperature exceeds the 95th percentile (predicted + 2*residual.st.dev), assign 'H' for high extreme
# when the in-situ OceanSODA temperature is within 95% of the range, then assign 'N' for normal pH

# combine observations and regression statistics

OceanSODA_temp_SO_extreme_grid <-
  full_join(
    OceanSODA_temp_regression_augmented,
    OceanSODA_temp_regression_glanced %>%
      select(lon:month, sigma)
  )
# results in 925 056 rows 

# identify observations in anomaly classes

OceanSODA_temp_SO_extreme_grid <- OceanSODA_temp_SO_extreme_grid %>%
  mutate(
    temp_extreme = case_when(
      .resid < -sigma*2 ~ 'L',
      .resid > sigma*2 ~ 'H',
      TRUE ~ 'N'
    )
  ) 

OceanSODA_temp_SO_extreme_grid <- OceanSODA_temp_SO_extreme_grid %>%
  mutate(temp_extreme = fct_relevel(temp_extreme, "H", "N", "L"))


# combine with regression coefficients

OceanSODA_temp_SO_extreme_grid <-
  full_join(OceanSODA_temp_SO_extreme_grid,
            OceanSODA_temp_regression_tidied) 
# 925 056 rows, in line with expectations for 40 lat x 360 lon x 12 months x 8 years (1 382 400 obs minus NA values)

Write anomalies to file

OceanSODA_temp_SO_extreme_grid %>% 
  write_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_SST_anomaly_field.rds"))

OceanSODA_temp_SO_extreme_grid %>%
  group_split(lon, lat, month) %>%
  head(6) %>%
  map(~ ggplot(data = .x) +
        geom_point(aes(x = year,
                       y = clim_diff,
                       col = temp_extreme)) +
        geom_abline(data = .x, aes(slope = slope,
                    intercept = intercept)) +
        geom_abline(data = .x, aes(slope = slope,
                    intercept = intercept + 2*sigma),
                    linetype = 2) +
        geom_abline(data = .x, aes(slope = slope,
                    intercept = intercept - 2*sigma),
                    linetype = 2) +
        labs(title = paste(fititle = paste(
          "lon:", unique(.x$lon),
          "| lat:", unique(.x$lat),
          "| month:", unique(.x$month)
          ))) +
        scale_color_manual(values = HNL_colors))

[[1]]

Past versions of temp_anomaly_test_plot_grid-1.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of temp_anomaly_test_plot_grid-2.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of temp_anomaly_test_plot_grid-3.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[4]]

Past versions of temp_anomaly_test_plot_grid-4.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[5]]

Past versions of temp_anomaly_test_plot_grid-5.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[6]]

Past versions of temp_anomaly_test_plot_grid-6.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Anomaly maps

# anomaly maps on a 1x1 grid 

# OceanSODA_temp_SO_extreme_grid %>%
#   group_split(year) %>%
#   # head(1) %>%
#   map(
#     ~map +
#       geom_tile(data = .x,
#                 aes(x = lon_raw,
#                     y = lat_raw,
#                     fill = temp_extreme),
#                 width = 1,
#                 height = 1)+
#       scale_fill_manual(values = HNL_colors_map)+
#       facet_wrap(~month, ncol = 2)+
#       labs(title = paste('Year:', unique(.x$year)),
#            fill = 'temperature')
#   )

OceanSODA_temp_SO_extreme_grid %>% 
  filter(year >= 2013) %>% 
  group_split(month) %>% 
  #head(1) %>% 
  map(
    ~map +
      geom_tile(data = .x,
                aes(x = lon_raw,
                    y = lat_raw,
                    fill = temp_extreme),
                width = 1,
                height = 1)+
      scale_fill_manual(values = HNL_colors_map)+
      facet_wrap(~year, ncol = 2)+
      labs(title = paste('month:', unique(.x$month)),
           fill = 'temperature')
  )

[[1]]

Past versions of map_temp_extremes_1x1_plot-1.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[2]]

Past versions of map_temp_extremes_1x1_plot-2.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[3]]

Past versions of map_temp_extremes_1x1_plot-3.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[4]]

Past versions of map_temp_extremes_1x1_plot-4.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[5]]

Past versions of map_temp_extremes_1x1_plot-5.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[6]]

Past versions of map_temp_extremes_1x1_plot-6.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[7]]

Past versions of map_temp_extremes_1x1_plot-7.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[8]]

Past versions of map_temp_extremes_1x1_plot-8.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[9]]

Past versions of map_temp_extremes_1x1_plot-9.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[10]]

Past versions of map_temp_extremes_1x1_plot-10.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[11]]

Past versions of map_temp_extremes_1x1_plot-11.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

[[12]]

Past versions of map_temp_extremes_1x1_plot-12.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

Anomaly timeseries

# calculate a regional mean temperature for each biome, basin, and ph extreme (H/L/N) and plot a timeseries 

OceanSODA_temp_SO_extreme_grid %>% 
  group_by(year, biome_name, basin_AIP, temp_extreme) %>% 
  summarise(temp_regional = mean(temperature, na.rm = TRUE)) %>% 
  ungroup() %>% 
  ggplot(aes(x = year, y = temp_regional, col = temp_extreme))+
  geom_point(size = 0.3)+
  geom_line()+
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP~biome_name)+
  theme(legend.position = 'bottom')

Past versions of extreme_temp_timeseries_grid-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
c68163a	pasqualina-vonlanthendinenna	2022-02-22
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Anomaly histogram

# histograms for each extreme level 

OceanSODA_temp_SO_extreme_grid %>%
  ggplot(aes(temperature, col = temp_extreme)) +
  geom_density() +
  scale_color_manual(values = HNL_colors) +
  facet_grid(basin_AIP ~ biome_name) +
  coord_cartesian(xlim = c(-2, 28)) +
  labs(x = 'value',
       y = 'density',
       col = 'temp anomaly') +
  theme(legend.position = 'bottom')

Past versions of extreme_temp_histogram_grid-1.png

Version	Author	Date
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
c68163a	pasqualina-vonlanthendinenna	2022-02-22
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

Argo

Grid reduction

# Note: While reducing lon x lat grid,
# we keep the original number of observations

full_argo_2x2 <- full_argo %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon,
    lat = cut(lat, seq(-90, 90, 2), seq(-89, 89, 2)),
    lat = as.numeric(as.character(lat)),
    lon = cut(lon, seq(20, 380, 2), seq(21, 379, 2)),
    lon = as.numeric(as.character(lon)))  # re-grid to 2x2

Apply region masks

# add in new Mayot biome information
full_argo_2x2_SO <- inner_join(full_argo_2x2, nm_biomes_2x2)

# add in basin separations
full_argo_2x2_SO <- inner_join(full_argo_2x2_SO, basinmask_2x2)

Join OceanSODA

# revert OceanSODA to regular 1x1 grid
OceanSODA_temp_SO_extreme_grid <- OceanSODA_temp_SO_extreme_grid %>%
  select(-c(lon, lat)) %>%
  rename(OceanSODA_temp = temperature,
         lon = lon_raw,
         lat = lat_raw) %>% 
  filter(year >=2013)
# 925 056 obs 

# combine the argo profile data to the surface extreme data
profile_temp_extreme <- inner_join(
  full_argo %>% 
    select(c(year, month, date, lon, lat, depth,
           temp_adjusted,
           platform_number,
           cycle_number)),                 # 567 327 obs 
  OceanSODA_temp_SO_extreme_grid %>% 
    select(c(year, month, date, lon, lat,
           OceanSODA_temp, temp_extreme,
           clim_temp, clim_diff,
           basin_AIP, biome_name)))

profile_temp_extreme <- profile_temp_extreme %>% 
  unite('platform_cycle', platform_number:cycle_number, sep = '_', remove = FALSE)

Location of Argo profiles

OceanSODA_temp_SO_extreme_grid %>%
  group_split(month) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(
        data = .x,
        aes(x = lon,
            y = lat,
            fill = temp_extreme),
        alpha = 0.5
      ) +
      scale_fill_manual(values = HNL_colors_map) +
      new_scale_fill() +
      geom_tile(
        data = profile_temp_extreme %>%
          distinct(lon, lat, platform_cycle, year, month),
        aes(
          x = lon,
          y = lat,
          fill = 'argo\nprofiles',
          height = 1,
          width = 1
        ),
        alpha = 0.5
      ) +
      scale_fill_manual(values = "springgreen4",
                        name = "") +
      facet_wrap(~ year, ncol = 1) +
      lims(y = c(-85, -30)) +
      labs(title = paste('month:', unique(.x$month))
      )
  )

[[1]]

Past versions of map_location_argo_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[2]]

Past versions of map_location_argo_profiles-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[3]]

Past versions of map_location_argo_profiles-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[4]]

Past versions of map_location_argo_profiles-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[5]]

Past versions of map_location_argo_profiles-5.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[6]]

Past versions of map_location_argo_profiles-6.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[7]]

Past versions of map_location_argo_profiles-7.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[8]]

Past versions of map_location_argo_profiles-8.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[9]]

Past versions of map_location_argo_profiles-9.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[10]]

Past versions of map_location_argo_profiles-10.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[11]]

Past versions of map_location_argo_profiles-11.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

[[12]]

Past versions of map_location_argo_profiles-12.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
4cf88e4	pasqualina-vonlanthendinenna	2022-05-05
2751f13	pasqualina-vonlanthendinenna	2022-05-05
f46b9da	pasqualina-vonlanthendinenna	2022-05-05
2d44f8a	pasqualina-vonlanthendinenna	2022-04-29

Plot profiles

Argo profiles plotted according to the surface OceanSODA temperature

L profiles correspond to a low surface temperature event, as recorded in OceanSODA

H profiles correspond to an event of high surface temperature, as recorded in OceanSODA

N profiles correspond to normal surface OceanSODA temperature

Raw

By Mayot biomes

profile_temp_extreme %>%
  group_split(biome_name, basin_AIP, year) %>% 
  head(6) %>%
  map(
    ~ ggplot() +
      geom_path(data = .x %>% filter(temp_extreme == 'N'),
                aes(x = temp_adjusted, 
                    y = depth,
                    group = platform_cycle,
                    col = temp_extreme),
                size = 0.3) +
      geom_path(data = .x %>% filter(temp_extreme == 'H' | temp_extreme == 'L'),
                aes(x = temp_adjusted,
                    y = depth,
                    group = platform_cycle,
                    col = temp_extreme),
                size = 0.5)+
      scale_y_reverse() +
      scale_color_manual(values = HNL_colors) +
      facet_wrap(~ month, ncol = 6) +
      labs(
        x = 'Argo temperature (ºC)',
        y = 'depth (m)',
        title = paste(
          unique(.x$basin_AIP),
          "|",
          unique(.x$year),
          "| biome:",
          unique(.x$biome_name)
        ),
        col = 'OceanSODA temp \nanomaly'
      )
  )

[[1]]

Past versions of temp_profiles_raw_Mayot_biomes-1.png

Version	Author	Date
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[2]]

Past versions of temp_profiles_raw_Mayot_biomes-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[3]]

Past versions of temp_profiles_raw_Mayot_biomes-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[4]]

Past versions of temp_profiles_raw_Mayot_biomes-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[5]]

Past versions of temp_profiles_raw_Mayot_biomes-5.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[6]]

Past versions of temp_profiles_raw_Mayot_biomes-6.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

Atl, STSS biome, Oct17

# Temperature extreme: 
# Atlantic biome 1, 2018, months 2 and 3 

OceanSODA_temp_SO_extreme_grid_2017 <- OceanSODA_temp_SO_extreme_grid %>% 
  filter(date == '2017-10-15')  

map+
  geom_tile(data = OceanSODA_temp_SO_extreme_grid_2017,
            aes(x = lon,
                y = lat,
                fill = temp_extreme))+
  scale_fill_manual(values = HNL_colors_map)+
  labs(title = 'October 2017',
       fill = 'OceanSODA SST \nextreme')

Past versions of select_specific_temp_profiles_2017-1.png

Version	Author	Date
ca30beb	pasqualina-vonlanthendinenna	2022-05-05
c541171	pasqualina-vonlanthendinenna	2022-04-07
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

profile_temp_Atl_2017 <- profile_temp_extreme %>% 
  filter(date == '2017-10-15',
         basin_AIP == 'Atlantic',
         biome_name == 'STSS') 

profile_temp_Atl_2017 %>% 
  ggplot(aes(x = temp_adjusted,
             y = depth,
             group = platform_cycle,
             col = temp_extreme))+
  geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'N'),
            aes(x = temp_adjusted,
                y = depth,
                group = platform_cycle,
                col = temp_extreme),
            size = 0.3)+
  geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
            aes(x = temp_adjusted,
                y = depth,
                group = platform_cycle,
                col = temp_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic, STSS biome, October 2017',
       col = 'OceanSODA SST\nextreme',
       x = 'Argo temperature (ºC)')

Past versions of select_specific_temp_profiles_2017-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
dfd75e9	pasqualina-vonlanthendinenna	2022-03-29

rm(profile_temp_Atl_2017, OceanSODA_temp_SO_extreme_grid_2017)

Atl, SPSS biome, Jul16

# Atlantic biome 2, 2016 month 7 

OceanSODA_temp_SO_extreme_grid_2016 <- OceanSODA_temp_SO_extreme_grid %>% 
  filter(date == '2016-07-15')  

map+
  geom_tile(data = OceanSODA_temp_SO_extreme_grid_2016,
            aes(x = lon,
                y = lat,
                fill = temp_extreme))+
  scale_fill_manual(values = HNL_colors_map)+
  labs(title = 'July 2016',
       fill = 'OceanSODA SST \nextreme')

profile_temp_Atl_2016 <- profile_temp_extreme %>% 
  filter(date == '2016-07-15',
         basin_AIP == 'Atlantic',
         biome_name == 'SPSS') 

profile_temp_Atl_2016 %>% 
  ggplot(aes(x = temp_adjusted,
             y = depth,
             group = platform_cycle,
             col = temp_extreme))+
  geom_path(data = profile_temp_Atl_2016 %>% filter(temp_extreme == 'N'),
            aes(x = temp_adjusted,
                y = depth, 
                group = platform_cycle,
                col = temp_extreme),
            size = 0.3)+
  geom_path(data = profile_temp_Atl_2016 %>% filter(temp_extreme == 'H'|temp_extreme == 'L'),
            aes(x = temp_adjusted,
                y = depth, 
                group = platform_cycle,
                col = temp_extreme),
            size = 0.5)+
  scale_y_reverse()+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Atlantic, SPSS biome, July 2016',
       col = 'OceanSODA SST\nextreme',
       x = 'Argo temperature (ºC)')

rm(profile_temp_Atl_2016, OceanSODA_temp_SO_extreme_grid_2016)

Averaged profiles

# cut depth levels at 10, 20, .... etc m
# add seasons 
# Dec, Jan, Feb <- summer 
# Mar, Apr, May <- autumn 
# Jun, Jul, Aug <- winter 
# Sep, Oct, Nov <- spring 

profile_temp_extreme <- profile_temp_extreme %>%
  mutate(
    depth = Hmisc::cut2(
      depth,
      cuts = c(10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000, 2500),
      levels.mean = TRUE,
      digits = 3
    ),
    depth = as.numeric(as.character(depth))
  ) %>%
  mutate(
    season = case_when(
      between(month, 3, 5) ~ 'autumn',
      between(month, 6, 8) ~ 'winter',
      between(month, 9, 11) ~ 'spring',
      month == 12 | 1 | 2 ~ 'summer'
    ),
    .after = date
  )

Overall mean

profile_temp_extreme_mean <- profile_temp_extreme %>%
  group_by(temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_mean %>%
  arrange(depth) %>%
  ggplot(aes(y = depth)) +
  geom_ribbon(aes(xmin = temp_mean - temp_std,
                  xmax = temp_mean + temp_std,
                  fill = temp_extreme), 
              alpha = 0.2)+
  geom_path(aes(x = temp_mean,
                col = temp_extreme))+
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(title = "Overall mean",
       col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'mean Argo temperature (ºC)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Past versions of mean_temp_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
7d7874c	pasqualina-vonlanthendinenna	2022-02-24
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

rm(profile_temp_extreme_mean)

Number of profiles

profile_temp_count_mean <- profile_temp_extreme %>% 
  distinct(temp_extreme, platform_number, cycle_number) %>% 
  count(temp_extreme)

profile_temp_count_mean %>% 
  ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
  geom_col(width = 0.5)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles')

Past versions of mean_temp_profile_count-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

# rm(profile_temp_count_mean)

Surface Argo temperature vs surface OceanSODA temperature (20 m)

# calculate surface-mean argo SST, for each profile 
surface_temp_mean <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(temp_extreme, platform_number, cycle_number) %>% 
  summarise(argo_surf_temp = mean(temp_adjusted, na.rm = TRUE),
            OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))

surface_temp_mean %>% 
  group_by(temp_extreme) %>%
  group_split() %>% 
  # head(1) %>%
  map(
  ~ggplot(data = .x, aes(x = OceanSODA_surf_temp, 
             y = argo_surf_temp))+
  geom_bin2d(data = .x, aes(x = OceanSODA_surf_temp, 
                 y = argo_surf_temp), size = 0.3, bins = 60) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1,
              xlim = c(-3, 28),
              ylim = c(-3, 28))+
    labs(title = paste('temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_mean-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_mean-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_mean-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

rm(surface_temp_mean)

January overall mean profiles

profile_temp_extreme_mean_jan <- profile_temp_extreme %>%
  filter(month == 1) %>% 
  group_by(temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_mean_jan %>%
  arrange(depth) %>%
  ggplot(aes(y = depth)) +
  geom_ribbon(aes(xmin = temp_mean - temp_std,
                  xmax = temp_mean + temp_std,
                  fill = temp_extreme), 
              alpha = 0.2)+
  geom_path(aes(x = temp_mean,
                col = temp_extreme))+
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(title = "January mean",
       col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'mean Argo temperature (ºC)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

rm(profile_temp_extreme_mean_jan)

Season x Mayot biome

profile_temp_extreme_biome <- profile_temp_extreme %>% 
  group_by(season, biome_name, temp_extreme, depth) %>% 
  summarise(temp_biome = mean(temp_adjusted, na.rm = TRUE),
            temp_std_biome = sd(temp_adjusted, na.rm = TRUE)) %>% 
  ungroup()
  

profile_temp_extreme_biome %>%
  ggplot(aes(
    x = temp_biome,
    y = depth,
    group = temp_extreme,
    col = temp_extreme
  )) +
  geom_ribbon(aes(xmax = temp_biome + temp_std_biome,
                  xmin = temp_biome - temp_std_biome,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA, 
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'biome mean Argo temperature (ºC)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  lims(x = c(-3, 18))+
  facet_grid(season ~ biome_name)

Past versions of mean_temp_profiles_season_new_biomes-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

rm(profile_temp_extreme_biome)

Number of profiles per season per Mayot biome

profile_temp_count_biome <- profile_temp_extreme %>% 
  distinct(season, biome_name, temp_extreme, platform_number, cycle_number) %>%
  group_by(season, biome_name, temp_extreme) %>% 
  count(temp_extreme)

profile_temp_count_biome %>% 
  ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
  geom_col(width = 0.5)+
  facet_grid(season ~ biome_name)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles season x Mayot biome')

Past versions of count_temp_profiles_season_new_biome-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

# rm(profile_temp_count_biome)

Surface Argo temp vs surface OceanSODA temp season x Mayot biome (20 m)

surface_temp_biome <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, biome_name, temp_extreme, platform_number, cycle_number) %>% 
  summarise(argo_surf_temp = mean(temp_adjusted, na.rm=TRUE),
            OceanSODA_surf_temp = mean(OceanSODA_temp, na.rm = TRUE))

surface_temp_biome %>% 
  group_by(temp_extreme) %>% 
  group_split(temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = OceanSODA_surf_temp, 
             y = argo_surf_temp))+
  geom_bin2d(data = .x, aes(x = OceanSODA_surf_temp, 
                 y = argo_surf_temp)) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 25),
              ylim = c(-3, 25))+
  facet_grid(season~biome_name) +
    labs(title = paste( 'Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_new_biome-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[2]]

Past versions of argo_vs_OceanSODA_temp_new_biome-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[3]]

Past versions of argo_vs_OceanSODA_temp_new_biome-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

rm(surface_temp_biome)

Season x basin

profile_temp_extreme_basin <- profile_temp_extreme %>% 
  group_by(season, basin_AIP, temp_extreme, depth) %>% 
  summarise(temp_basin = mean(temp_adjusted, na.rm = TRUE),
            temp_basin_std = sd(temp_adjusted, na.rm = TRUE)) %>% 
  ungroup()

profile_temp_extreme_basin %>% 
  ggplot(aes(x = temp_basin, 
             y = depth, 
             group = temp_extreme, 
             col = temp_extreme))+
  geom_ribbon(aes(xmin = temp_basin - temp_basin_std,
                  xmax = temp_basin + temp_basin_std,
                  group = temp_extreme, 
                  fill = temp_extreme),
              col = NA, 
              alpha = 0.2)+
  geom_path()+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  labs(col = 'OceanSODA\ntemp anomaly\n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly\n(mean ± st dev)',
       y = 'depth (m)',
       x = 'basin-mean Argo temperature (ªC)')+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) +
  facet_grid(season~basin_AIP)

Past versions of mean_basin_temp_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
7d7874c	pasqualina-vonlanthendinenna	2022-02-24
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

rm(profile_temp_extreme_basin)

Number of profiles season x basin

profile_temp_count_basin <- profile_temp_extreme %>% 
  distinct(season, basin_AIP, temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, basin_AIP, temp_extreme) %>% 
  count(temp_extreme)

profile_temp_count_basin %>% 
  ggplot(aes(x = temp_extreme, y = n, fill = temp_extreme))+
  geom_col(width = 0.5)+
  facet_grid(season~basin_AIP)+
  scale_y_continuous(trans = 'log10')+
  labs(y = 'log(number of profiles)',
       title = 'Number of profiles season x basin')

Past versions of count_basin_temp_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

# rm(profile_temp_count_basin)

Surface Argo temperature vs surface OceanSODA temperature (20 m) season x basin

# calculate surface-mean argo pH to compare against OceanSODA surface pH (one value)
surface_temp_basin <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, basin_AIP, temp_extreme, platform_number, cycle_number) %>% 
  summarise(surf_argo_temp = mean(temp_adjusted, na.rm=TRUE),
            surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE)) 

surface_temp_basin %>% 
  group_by(temp_extreme) %>% 
  group_split(temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = surf_OceanSODA_temp, 
             y = surf_argo_temp))+
  geom_bin2d(data = .x, aes(x = surf_OceanSODA_temp, 
                 y = surf_argo_temp)) +
    scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 25),
              ylim = c(-3, 25))+
  facet_grid(season~basin_AIP) +
    labs(title = paste('Temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_basin-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[2]]

Past versions of argo_vs_OceanSODA_temp_basin-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

[[3]]

Past versions of argo_vs_OceanSODA_temp_basin-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01
8ef1277	pasqualina-vonlanthendinenna	2022-03-01
fd521d1	pasqualina-vonlanthendinenna	2022-02-25
c68163a	pasqualina-vonlanthendinenna	2022-02-22
19aa73d	pasqualina-vonlanthendinenna	2022-02-16
f2fa56a	pasqualina-vonlanthendinenna	2022-02-10

rm(surface_temp_basin)

Season x Mayot biome x Basin

profile_temp_extreme_season <- profile_temp_extreme %>%
  group_by(season, biome_name, basin_AIP, temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_season %>%
  arrange(depth) %>%
  group_split(season) %>%
  # head(1) %>%
  map(
    ~ ggplot(
      data = .x,
      aes(x = temp_mean,
          y = depth,
          group = temp_extreme,
          col = temp_extreme)) +
      geom_ribbon(aes(xmax = temp_mean + temp_std,
                      xmin = temp_mean - temp_std,
                      group = temp_extreme,
                      fill = temp_extreme),
                  col = NA,
                  alpha = 0.2)+
      geom_path() +
      scale_color_manual(values = HNL_colors) +
      scale_fill_manual(values = HNL_colors) +
      labs(title = paste("season:", unique(.x$season)),
           col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
           fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
           y = 'depth (m)',
           x = 'mean Argo temperature (ºC)') +
      scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))
      ) +
      facet_grid(basin_AIP ~ biome_name)
  )

[[1]]

Past versions of temp_profiles_new_biomes_basin-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[2]]

Past versions of temp_profiles_new_biomes_basin-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[3]]

Past versions of temp_profiles_new_biomes_basin-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

[[4]]

Past versions of temp_profiles_new_biomes_basin-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31

Number of profiles season x Mayot biome x basin

profile_temp_count_season <- profile_temp_extreme %>% 
  distinct(season, biome_name, basin_AIP,
           temp_extreme, platform_number, cycle_number) %>% 
  group_by(season, biome_name, basin_AIP, temp_extreme) %>% 
  count(temp_extreme)


profile_temp_count_season %>% 
  group_by(season) %>% 
  group_split(season) %>% 
  map(
    ~ggplot()+
      geom_col(data =.x, 
               aes(x = temp_extreme,
                   y = n,
                   fill = temp_extreme),
               width = 0.5)+
      facet_grid(basin_AIP ~ biome_name)+
      scale_y_continuous(trans = 'log10')+
      labs(y = 'log(number of profiles)',
           title = paste('season:', unique(.x$season)))
  )

[[1]]

Past versions of count_number_of_profiles_season_new_biomes-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[2]]

Past versions of count_number_of_profiles_season_new_biomes-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[3]]

Past versions of count_number_of_profiles_season_new_biomes-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[4]]

Past versions of count_number_of_profiles_season_new_biomes-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

# rm(profile_temp_count_season)

Surface Argo temperature vs surface OceanSODA temperature (20m) in each season, Mayot biome, basin

# calculate surface-mean argo pH, for each season x biome x basin x ph extreme 
surface_temp_season <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season, 
           basin_AIP, 
           biome_name, 
           temp_extreme,  
           platform_number, 
           cycle_number) %>%  
  summarise(surf_argo_temp = mean(temp_adjusted, na.rm=TRUE),
            surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE)) 

surface_temp_season %>% 
  group_by(season, temp_extreme) %>% 
  group_split(season, temp_extreme) %>% 
  map(
  ~ggplot(data = .x, aes(x = surf_OceanSODA_temp, 
             y = surf_argo_temp))+
  geom_bin2d(data = .x, aes(x = surf_OceanSODA_temp, 
                 y = surf_argo_temp)) +
  scale_fill_viridis_c()+
  geom_abline(slope = 1, intercept = 0)+
  coord_fixed(ratio = 1, 
              xlim = c(-3, 25),
              ylim = c(-3, 25))+
  facet_grid(basin_AIP ~ biome_name) +
    labs(title = paste('season:', unique(.x$season), 
                        '| temp extreme:', unique(.x$temp_extreme)),
         x = 'OceanSODA temp',
         y = 'Argo temp')
  )

[[1]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[2]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[3]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[4]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[5]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-5.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[6]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-6.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[7]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-7.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[8]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-8.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[9]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-9.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[10]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-10.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[11]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-11.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

[[12]]

Past versions of argo_vs_OceanSODA_temp_season_new_biomes-12.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05

rm(surface_temp_season)

Atlantic, SPSS biome, winter

profile_temp_extreme_season %>%
  filter(basin_AIP == 'Atlantic',
         biome_name == 'SPSS',
         season == 'winter') %>% 
  arrange(depth) %>%
  ggplot(aes(x = temp_mean,
             y = depth,
             group = temp_extreme,
             col = temp_extreme)) +
  geom_ribbon(aes(xmax = temp_mean + temp_std,
                  xmin = temp_mean - temp_std,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA,
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors) +
  labs(title = 'Atlantic basin, SPSS biome, winter',
       col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'mean Argo temperature (ºC)') +
  scale_y_continuous(
  trans = trans_reverser("sqrt"),
  breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) 

Past versions of temp_profile_season_biome_Atlantic-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18
e12a216	pasqualina-vonlanthendinenna	2022-03-15
1ffe07f	pasqualina-vonlanthendinenna	2022-03-11
7540ae4	pasqualina-vonlanthendinenna	2022-03-08
e4188d2	pasqualina-vonlanthendinenna	2022-03-01
5ef4df2	pasqualina-vonlanthendinenna	2022-03-01

Atlantic, STSS biome, spring

profile_temp_extreme_season %>%
  filter(basin_AIP == 'Atlantic',
         biome_name == 'STSS',
         season == 'spring') %>% 
  arrange(depth) %>%
  ggplot(aes(x = temp_mean,
             y = depth,
             group = temp_extreme,
             col = temp_extreme)) +
  geom_ribbon(aes(xmax = temp_mean + temp_std,
                  xmin = temp_mean - temp_std,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA,
              alpha = 0.2)+
  geom_path() +
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors) +
  labs(title = 'Atlantic basin, STSS biome, spring',
       col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'mean Argo temperature (ºC)') +
  scale_y_continuous(
  trans = trans_reverser("sqrt"),
  breaks = c(10, 100, 250, 500, seq(1000, 5000, 500))) 

Past versions of Atlantic_biome_1_spring_mean_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
8173cdb	jens-daniel-mueller	2022-03-28
650ef68	pasqualina-vonlanthendinenna	2022-03-18

rm(profile_temp_extreme_season)

Mayot biome - basin January mean profiles

profile_temp_extreme_biome_basin_jan <- profile_temp_extreme %>%
  filter(month == 1) %>% 
  group_by(biome_name, basin_AIP, temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp_adjusted, na.rm = TRUE),
            temp_std = sd(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_biome_basin_jan %>%
  arrange(depth) %>%
  ggplot(aes(x = temp_mean,
             y = depth)) +
  geom_ribbon(aes(xmin = temp_mean - temp_std,
                  xmax = temp_mean + temp_std,
                  fill = temp_extreme), 
              alpha = 0.2)+
  geom_path(aes(x = temp_mean,
                col = temp_extreme))+
  facet_grid(basin_AIP~biome_name)+
  scale_color_manual(values = HNL_colors) +
  scale_fill_manual(values = HNL_colors)+
  labs(title = "Basin-Mayot biome-mean January profiles",
       col = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       fill = 'OceanSODA\ntemp anomaly \n(mean ± st dev)',
       y = 'depth (m)',
       x = 'mean Argo temperature (ºC)') +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))

Remove climatology

Plot the H/L/N profiles as anomalies relative to the CSIO-MNR Argo temperature climatology

Argo profiles

profile_temp_extreme_binned <- profile_temp_extreme %>%
  group_by(lon, lat, year, month, platform_cycle,
           biome_name, basin_AIP, temp_extreme,
           depth) %>%
  summarize(temp_adjusted_binned = mean(temp_adjusted, na.rm = TRUE)) %>%
  ungroup()

ARGO climatology

boa_temp_clim <- read_rds(file = paste0(path_argo_preprocessed, '/boa_temp_clim.rds'))

# compatibility with profile_temp_extreme_jan
boa_temp_clim_SO <- boa_temp_clim %>% 
  filter(lat <= -30) %>% 
  mutate(depth_boa = depth)

# grid average climatological temp into the argo depth bins 
boa_temp_clim_SO <- boa_temp_clim_SO %>%
  mutate(
    depth = cut(
      depth_boa,
      breaks = c(0, 10, 20, 30, 50, 70, 100, 300, 500, 800, 1000, 1500, 2000),
      include.lowest = TRUE,
      labels = as.factor(unique(profile_temp_extreme$depth))[1:12]
    ),
    depth = as.numeric(as.character(depth))
  )


# calculate mean climatological pH per depth bin
boa_temp_clim_SO_binned <- boa_temp_clim_SO %>% 
  group_by(lon, lat, depth, month) %>% 
  summarise(clim_temp_binned = mean(clim_temp, na.rm = TRUE)) %>%
  ungroup()


# join climatology and ARGO profiles

remove_clim <- inner_join(profile_temp_extreme_binned,
                              boa_temp_clim_SO_binned)

Profiles - Jan

Points are the January climatological temperature, lines are the depth-binned Argo profiles colored by H/N/L classification

Absolute

remove_clim %>%
  group_split(biome_name, basin_AIP, year) %>%
  head(6) %>%
  map(
    ~ ggplot() +
      geom_path(
        data = .x %>%
          filter(temp_extreme == 'N'),
        aes(
          x = temp_adjusted_binned,
          y = depth,
          group = platform_cycle,
          col = temp_extreme
        ),
        size = 0.3
      ) +
      geom_path(
        data = .x %>%
          filter(temp_extreme == 'H' | temp_extreme == 'L'),
        aes(
          x = temp_adjusted_binned,
          y = depth,
          group = platform_cycle,
          col = temp_extreme
        ),
        size = 0.5
      ) +
      geom_point(
        data = .x,
        aes(x = clim_temp_binned,
            y = depth,
            col = temp_extreme),
        size = 0.5
      ) +
      scale_y_reverse() +
      scale_color_manual(values = HNL_colors) +
      labs(
        x = 'Argo temperature (ºC)',
        y = 'depth (m)',
        title = paste(
          "Biome:",
          unique(.x$biome_name),
          "| basin:",
          unique(.x$basin_AIP),
          " | ",
          unique(.x$year)
        ),
        col = 'OceanSODA temp \nanomaly'
      )
  )

[[1]]

Past versions of plot_raw_profiles_with_clim-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

[[2]]

Past versions of plot_raw_profiles_with_clim-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

[[3]]

Past versions of plot_raw_profiles_with_clim-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

[[4]]

Past versions of plot_raw_profiles_with_clim-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

[[5]]

Past versions of plot_raw_profiles_with_clim-5.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

[[6]]

Past versions of plot_raw_profiles_with_clim-6.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

# calculate the difference between the binned climatological argo and in-situ argo for each depth level and grid cell 

remove_clim <- remove_clim %>% 
  mutate(argo_temp_anomaly = temp_adjusted_binned - clim_temp_binned,
         season = case_when(between(month, 3, 5) ~ 'autumn',
                            between(month, 6, 8) ~ 'winter',
                            between(month, 9, 11) ~ 'spring',
                            month == 12 | 1 | 2 ~ 'summer'))

Anomaly

remove_clim %>% 
  group_split(month) %>% 
  #head(6) %>% 
  map(
    ~ggplot()+
      geom_path(data = .x %>% filter(temp_extreme == 'N'),
                aes(x = argo_temp_anomaly,
                    y = depth,
                    group = platform_cycle,
                    col = temp_extreme),
                size = 0.2)+
      geom_path(data = .x %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
                 aes(x = argo_temp_anomaly,
                     y = depth,
                     group = platform_cycle,
                     col = temp_extreme),
                 size = 0.3)+
      geom_vline(xintercept = 0)+
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
      scale_color_manual(values = HNL_colors)+
      scale_fill_manual(values = HNL_colors)+
      facet_grid(basin_AIP~biome_name)+
      labs(title = paste0('month: ', unique(.x$month)))
  )

[[1]]

Past versions of plot_january_anomaly_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
27a52f8	pasqualina-vonlanthendinenna	2022-03-25

[[2]]

Past versions of plot_january_anomaly_profiles-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
27a52f8	pasqualina-vonlanthendinenna	2022-03-25

[[3]]

Past versions of plot_january_anomaly_profiles-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
27a52f8	pasqualina-vonlanthendinenna	2022-03-25

[[4]]

Past versions of plot_january_anomaly_profiles-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
27a52f8	pasqualina-vonlanthendinenna	2022-03-25

[[5]]

Past versions of plot_january_anomaly_profiles-5.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
27a52f8	pasqualina-vonlanthendinenna	2022-03-25

[[6]]

Past versions of plot_january_anomaly_profiles-6.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
27a52f8	pasqualina-vonlanthendinenna	2022-03-25

[[7]]

Past versions of plot_january_anomaly_profiles-7.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14

[[8]]

Past versions of plot_january_anomaly_profiles-8.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04

[[9]]

Past versions of plot_january_anomaly_profiles-9.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04

[[10]]

Past versions of plot_january_anomaly_profiles-10.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04

[[11]]

Past versions of plot_january_anomaly_profiles-11.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04

[[12]]

Past versions of plot_january_anomaly_profiles-12.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04

Overall mean anomaly

remove_clim_overall_mean <- remove_clim %>% 
  group_by(temp_extreme, depth) %>% 
  summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(argo_temp_anomaly, na.rm = TRUE))

remove_clim_overall_mean %>% 
  ggplot()+
  geom_path(aes(x = temp_anomaly_mean,
                y = depth,
                group = temp_extreme,
                col = temp_extreme))+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  y = depth,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA,
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  geom_text_repel(data = profile_temp_count_mean,
            aes(x = 1, 
                y = 1500, 
                label = paste0(n), 
                col = temp_extreme),
            size = 7,
            segment.color = 'transparent')+
  labs(title = 'Overall mean anomaly profiles')

Past versions of plot_overall_mean_clim_removed_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

rm(remove_clim_overall_mean, profile_temp_count_mean)

Biome x season mean anomaly

remove_clim_biome_mean <- remove_clim %>% 
  group_by(temp_extreme, depth, season, biome_name) %>% 
  summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(argo_temp_anomaly, na.rm = TRUE))

remove_clim_biome_mean %>% 
  ggplot(aes(x = temp_anomaly_mean,
                y = depth,
                group = temp_extreme,
                col = temp_extreme))+
  geom_path()+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA,
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
  scale_fill_manual(values = HNL_colors)+
  scale_color_manual(values = HNL_colors)+
  labs(title = 'Biome-mean anomaly profiles')+
  geom_text_repel(data = profile_temp_count_biome,
                  aes(x = 3,
                      y = 1500,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4,
                  segment.color = 'transparent')+
  facet_grid(season~biome_name)

Past versions of plot_biome_mean_clim_removed_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
6572988	pasqualina-vonlanthendinenna	2022-05-04
708f923	pasqualina-vonlanthendinenna	2022-05-04
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

rm(remove_clim_biome_mean, profile_temp_count_biome)

Basin x season mean anomaly

remove_clim_basin_mean <- remove_clim %>% 
  group_by(basin_AIP, temp_extreme, depth, season) %>% 
  summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(argo_temp_anomaly, na.rm = TRUE))

remove_clim_basin_mean %>% 
  ggplot(aes(x = temp_anomaly_mean,
             y = depth, 
             group = temp_extreme,
             col = temp_extreme))+
  geom_path()+
  geom_ribbon(aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA,
              alpha = 0.2)+
  geom_vline(xintercept = 0)+
  facet_grid(season~basin_AIP)+
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
  scale_color_manual(values = HNL_colors)+
  scale_fill_manual(values = HNL_colors)+
  geom_text_repel(data = profile_temp_count_basin,
                  aes(x = 2,
                      y = 1500,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4,
                  segment.color = 'transparent')+
  labs(title = 'Basin-mean anomaly profiles')

Past versions of plot_basin_mean_anomaly_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
708f923	pasqualina-vonlanthendinenna	2022-05-04
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28
8173cdb	jens-daniel-mueller	2022-03-28
7f5c5c6	pasqualina-vonlanthendinenna	2022-03-25
d9caaae	pasqualina-vonlanthendinenna	2022-03-22

rm(remove_clim_basin_mean, profile_temp_count_basin)

Basin x biome x season mean anomaly

remove_clim_basin_biome_mean <- remove_clim %>% 
  group_by(basin_AIP, biome_name, temp_extreme, season, depth) %>% 
  summarise(temp_anomaly_mean = mean(argo_temp_anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(argo_temp_anomaly, na.rm = TRUE))

remove_clim_basin_biome_mean %>% 
  group_by(season) %>% 
  group_split(season) %>% 
  map(
    ~ggplot(data = .x, 
            aes(x = temp_anomaly_mean,
                y = depth,
                group = temp_extreme,
                col = temp_extreme))+
      geom_path()+
      geom_ribbon(data = .x,
                  aes(xmax = temp_anomaly_mean + temp_anomaly_sd,
                  xmin = temp_anomaly_mean - temp_anomaly_sd,
                  group = temp_extreme,
                  fill = temp_extreme),
              col = NA,
              alpha = 0.2)+
      geom_vline(xintercept = 0)+
      facet_grid(basin_AIP~biome_name)+
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(10, 100, 250, 500, seq(1000, 5000, 500)))+
      scale_color_manual(values = HNL_colors)+
      scale_fill_manual(values = HNL_colors)+
      # geom_text_repel(data = profile_temp_count_season,
      #                 aes(x = 1,
      #                     y = 1400,
      #                     label = paste0(n),
      #                     col = temp_extreme,
      #                     group = temp_extreme),
      #                 size = 4,
      #                 segment.color = 'transparent')+
      labs(title = paste0('biome-basin mean anomaly profiles ', unique(.x$season)))
    )

[[1]]

Past versions of plot_basin_biome_mean_anomaly_profiles-1.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14
9875dd0	pasqualina-vonlanthendinenna	2022-04-05
48573c4	pasqualina-vonlanthendinenna	2022-03-31
eb8e3be	pasqualina-vonlanthendinenna	2022-03-31
a2271df	pasqualina-vonlanthendinenna	2022-03-30
cbb2360	jens-daniel-mueller	2022-03-28
fa1b6de	jens-daniel-mueller	2022-03-28
749e005	jens-daniel-mueller	2022-03-28

[[2]]

Past versions of plot_basin_biome_mean_anomaly_profiles-2.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14

[[3]]

Past versions of plot_basin_biome_mean_anomaly_profiles-3.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14

[[4]]

Past versions of plot_basin_biome_mean_anomaly_profiles-4.png

Version	Author	Date
b917bd0	jens-daniel-mueller	2022-05-11
e61c08e	pasqualina-vonlanthendinenna	2022-04-27
f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14

rm(remove_clim_basin_biome_mean, profile_temp_count_season)

Session information

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] ggnewscale_0.4.5  ggrepel_0.9.1     oce_1.5-0         gsw_1.0-6        
 [5] ggforce_0.3.3     metR_0.11.0       scico_1.3.0       ggOceanMaps_1.2.6
 [9] ggspatial_1.1.5   broom_0.7.11      lubridate_1.8.0   forcats_0.5.1    
[13] stringr_1.4.0     dplyr_1.0.7       purrr_0.3.4       readr_2.1.1      
[17] tidyr_1.1.4       tibble_3.1.6      ggplot2_3.3.5     tidyverse_1.3.1  
[21] workflowr_1.7.0  

loaded via a namespace (and not attached):
  [1] colorspace_2.0-2    ellipsis_0.3.2      class_7.3-20       
  [4] rgdal_1.5-28        rprojroot_2.0.2     htmlTable_2.4.0    
  [7] base64enc_0.1-3     fs_1.5.2            rstudioapi_0.13    
 [10] proxy_0.4-26        farver_2.1.0        bit64_4.0.5        
 [13] fansi_1.0.2         xml2_1.3.3          splines_4.1.2      
 [16] codetools_0.2-18    knitr_1.37          polyclip_1.10-0    
 [19] Formula_1.2-4       jsonlite_1.7.3      cluster_2.1.2      
 [22] dbplyr_2.1.1        png_0.1-7           rgeos_0.5-9        
 [25] compiler_4.1.2      httr_1.4.2          backports_1.4.1    
 [28] Matrix_1.4-0        assertthat_0.2.1    fastmap_1.1.0      
 [31] cli_3.1.1           later_1.3.0         tweenr_1.0.2       
 [34] htmltools_0.5.2     tools_4.1.2         gtable_0.3.0       
 [37] glue_1.6.0          Rcpp_1.0.8          cellranger_1.1.0   
 [40] jquerylib_0.1.4     raster_3.5-11       vctrs_0.3.8        
 [43] xfun_0.29           ps_1.6.0            rvest_1.0.2        
 [46] lifecycle_1.0.1     terra_1.5-12        getPass_0.2-2      
 [49] MASS_7.3-55         scales_1.1.1        vroom_1.5.7        
 [52] hms_1.1.1           promises_1.2.0.1    parallel_4.1.2     
 [55] RColorBrewer_1.1-2  yaml_2.2.1          gridExtra_2.3      
 [58] sass_0.4.0          rpart_4.1-15        latticeExtra_0.6-29
 [61] stringi_1.7.6       highr_0.9           e1071_1.7-9        
 [64] checkmate_2.0.0     rlang_1.0.2         pkgconfig_2.0.3    
 [67] evaluate_0.14       lattice_0.20-45     sf_1.0-5           
 [70] htmlwidgets_1.5.4   labeling_0.4.2      bit_4.0.4          
 [73] processx_3.5.2      tidyselect_1.1.1    magrittr_2.0.1     
 [76] R6_2.5.1            generics_0.1.1      Hmisc_4.6-0        
 [79] DBI_1.1.2           foreign_0.8-82      pillar_1.6.4       
 [82] haven_2.4.3         whisker_0.4         withr_2.4.3        
 [85] units_0.7-2         nnet_7.3-17         survival_3.2-13    
 [88] sp_1.4-6            modelr_0.1.8        crayon_1.4.2       
 [91] KernSmooth_2.23-20  utf8_1.2.2          tzdb_0.2.0         
 [94] rmarkdown_2.11      jpeg_0.1-9          grid_4.1.2         
 [97] readxl_1.3.1        data.table_1.14.2   callr_3.7.0        
[100] git2r_0.29.0        reprex_2.0.1        digest_0.6.29      
[103] classInt_0.4-3      httpuv_1.6.5        munsell_0.5.0      
[106] viridisLite_0.4.0   bslib_0.3.1