Extreme BGC-Temperature Profiles

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Last updated: 2024-05-13

Checks: 7 0

Knit directory: bgc_argo_r_argodata/analysis/

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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
html	af44bcd	mlarriere	2024-04-26	Build site.
html	780276c	mlarriere	2024-04-19	Build site.
html	1a72545	mlarriere	2024-04-12	Build site.
html	c076fba	mlarriere	2024-04-12	Build site.
html	91f08a6	mlarriere	2024-04-07	Build site.
html	db21f55	mlarriere	2024-04-06	Build site.
html	c0a5b90	mlarriere	2024-04-01	Build site.
Rmd	42a66ee	mlarriere	2024-04-01	extreme_temp and temp_core building test
html	f9de50e	ds2n19	2024-01-01	Build site.
html	07d4eb8	ds2n19	2023-12-20	Build site.
html	fa6cf38	ds2n19	2023-12-14	Build site.
Rmd	64fd104	ds2n19	2023-12-14	revised coverage analysis and SO focused cluster analysis.
html	f110b74	ds2n19	2023-12-13	Build site.
Rmd	fa9795c	ds2n19	2023-12-12	dependencies listed are start of markdown files.
Rmd	a434982	ds2n19	2023-12-11	test run of coverage maps
html	e60ebd2	ds2n19	2023-12-07	Build site.
html	4942ace	ds2n19	2023-12-06	Build site.
Rmd	237c3ec	ds2n19	2023-12-06	Cluster under surface extreme.
html	c00711b	ds2n19	2023-12-06	Build site.
Rmd	4c83bc4	ds2n19	2023-12-06	Cluster under surface extreme.
html	cf5dd20	ds2n19	2023-12-04	Build site.
Rmd	3cb4b17	ds2n19	2023-12-04	Cluster under surface extreme.
Rmd	fa1083d	ds2n19	2023-12-01	Additional analysis to cluster process.
html	cec2a2a	ds2n19	2023-11-24	Build site.
Rmd	3dc557d	ds2n19	2023-11-24	Switched to new profile details.
Rmd	59f5cc4	ds2n19	2023-11-23	Moved spatiotemporal analysis to use aligned profiles.
html	80c16c2	ds2n19	2023-11-15	Build site.
html	56c8f49	ds2n19	2023-10-20	Build site.
html	1cd9ec1	ds2n19	2023-10-19	Build site.
html	15c1d68	ds2n19	2023-10-19	Build site.
Rmd	81b3d3c	ds2n19	2023-10-19	moved from month by month regression to annual with monthly
html	2f4ea7e	ds2n19	2023-10-19	Build site.
Rmd	fbd34e7	ds2n19	2023-10-19	moved from month by month regression to annual with monthly
html	879821d	ds2n19	2023-10-18	Build site.
Rmd	dba28d5	ds2n19	2023-10-18	Clean up BGC load and re-run coverage and extreme packages.
html	93b4545	ds2n19	2023-10-18	Build site.
html	7004f76	ds2n19	2023-10-17	Build site.
Rmd	86e3764	ds2n19	2023-10-17	standard range v climatology, season order resolved and count labels to
html	4b55c43	ds2n19	2023-10-12	Build site.
Rmd	ce19a66	ds2n19	2023-10-04	Revised version of OceanSODA product -v2023
html	7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29	Build site.
Rmd	8e81570	pasqualina-vonlanthendinenna	2022-08-29	load and add in core-argo data (1 month)
html	bdd516d	pasqualina-vonlanthendinenna	2022-05-23	Build site.
Rmd	b41e65f	pasqualina-vonlanthendinenna	2022-05-23	recreate data in bgc_argo_preprocessed_data
html	71e58d6	jens-daniel-mueller	2022-05-12	Build site.
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

Dependencies

OceanSODA_temp.rds - bgc preprocessed folder, created by OceanSODA_argo_pH.

temp_bgc_va.rds - bgc preprocessed folder, created by temp_align_climatology.

temp_anomaly_va.rds - bgc preprocessed folder, created by temp_align_climatology.

Outputs

OceanSODA_SST_anomaly_field_01.rds (or _02.rds)

OceanSODA_global_SST_anomaly_field_01.rds (or _02.rds)

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

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

# opt_min_profile_range
# profiles with profile_range >= opt_min_profile_range will be selected 1 = profiles of at least 600m, 2 = profiles of at least 1200m, 3 = profiles of at least 1500m
opt_min_profile_range = 3

# opt_extreme_determination
# 1 - based on the trend of de-seasonal data - we believe this results in more summer extremes where variation tend to be greater.
# 2 - based on the trend of de-seasonal data by month. grouping is by lat, lon and month.
opt_extreme_determination <- 2

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")

path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
# /nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo/preprocessed_bgc_data
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")

# 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))

# load validated and vertically aligned temp profiles, 
full_argo <-
  read_rds(file = paste0(path_argo_preprocessed, "/temp_bgc_va.rds")) %>%
  filter(profile_range >= opt_min_profile_range) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))


# base map for plotting
map <-
  read_rds(paste(path_emlr_utilities,
                 "map_landmask_WOA18.rds",
                 sep = ""))

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
c00711b	ds2n19	2023-12-06
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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)')

# Commented
# 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()

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

# Commented
#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')

# 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)')

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
c00711b	ds2n19	2023-12-06
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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

# Commented
# 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)

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

# commented
#rm(basinmask)

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

# 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

# Commented
# 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

# Added
OceanSODA_temp <- OceanSODA_temp %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon)

# commented
#rm(OceanSODA_temp)

Apply region masks

Propose not to add biomes as these restrict to SO

# 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 


# OceanSODA_temp_SO <- inner_join(OceanSODA_temp, nm_biomes %>% 
#                                        select(-n)) 

# add in basin separations
OceanSODA_temp <- inner_join(OceanSODA_temp, basinmask)

OceanSODA_temp <- OceanSODA_temp %>% 
   filter(!is.na(temperature))

Maps

OceanSODA_temp %>% 
  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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
710edd4	jens-daniel-mueller	2022-05-11

OceanSODA_temp %>% 
  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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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 %>% 
  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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
c00711b	ds2n19	2023-12-06
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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 lr- and a mean-based anomaly detection.

Fit lr models

# create a decimal_year column to use are the parameter to the regression function
OceanSODA_temp <- OceanSODA_temp %>%
  mutate(decimal_year = decimal_date(date), .after = year)

# fit a linear regression of OceanSODA temp against time (temporal trend)
# in each lat/lon/month grid, month is used depending on opt_extreme_determination
if (opt_extreme_determination == 1){
  OceanSODA_temp_regression <- OceanSODA_temp %>% 
    nest(data = -c(lon, lat)) %>%
    mutate(fit = map(.x = data,
                     .f = ~ lm(clim_diff ~ decimal_year, data = .x)),
           tidied = map(.x = fit, .f = tidy),
           glanced = map(.x = fit, .f = glance),
           augmented = map(.x = fit, .f = augment)) 
} else if (opt_extreme_determination == 2){
  OceanSODA_temp_regression <- OceanSODA_temp %>% 
    nest(data = -c(lon, lat, month)) %>%
    mutate(fit = map(.x = data,
                     .f = ~ lm(clim_diff ~ decimal_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 = decimal_year)

OceanSODA_temp_regression_augmented <- OceanSODA_temp_regression %>%
  select(-c(fit, tidied, glanced, data)) %>%
  unnest(augmented) %>% 
  select(lon:decimal_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, 
           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 %>% 
  group_by(lon, lat, month) %>%
  summarise(slope = 0,
            intercept = mean(clim_diff, na.rm = TRUE)) %>% 
  ungroup()

OceanSODA_temp_regression_glanced <- OceanSODA_temp_2x2_SO %>% 
  group_by(lon, lat, month) %>%
  summarise(sigma = sd(clim_diff, na.rm = TRUE)) %>% 
  ungroup()

OceanSODA_temp_regression_augmented <- OceanSODA_temp_2x2_SO %>% 
  mutate(.resid = clim_diff)

Slope maps

if (opt_extreme_determination == 1){
  map +
    geom_tile(data = OceanSODA_temp_regression_tidied,
              aes(x = lon,
                  y = lat,
                  fill = slope)) +
    scale_fill_scico(palette = 'vik', midpoint = 0)
} else if (opt_extreme_determination == 2){
  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
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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

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

Past versions of temp_map_sigma-1.png

Version	Author	Date
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
if (opt_extreme_determination == 1){
  OceanSODA_temp_extreme_grid <-
    full_join(
      OceanSODA_temp_regression_augmented,
      OceanSODA_temp_regression_glanced %>%
        select(lon:lat, sigma)
    )
} else if (opt_extreme_determination == 2){
  OceanSODA_temp_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_extreme_grid <- OceanSODA_temp_extreme_grid %>%
  mutate(
    temp_extreme = case_when(
      .resid < -sigma*2 ~ 'L',
      .resid > sigma*2 ~ 'H',
      TRUE ~ 'N'
    )
  ) 

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


# combine with regression coefficients

OceanSODA_temp_extreme_grid <-
  full_join(OceanSODA_temp_extreme_grid,
            OceanSODA_temp_regression_tidied) 

OceanSODA_temp_extreme_grid <- OceanSODA_temp_extreme_grid %>%
  mutate(year = year(date),
         month = month(date),
         .after = decimal_year)

# 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

# Restrict to SO by inner join to nm_biomes
OceanSODA_temp_SO_extreme_grid <- inner_join(OceanSODA_temp_extreme_grid, nm_biomes %>% 
                                        select(-n)) 

# if (opt_extreme_determination == 1){
#   OceanSODA_temp_SO_extreme_grid %>%
#     write_rds(file = paste0(
#       path_argo_preprocessed,
#       "/OceanSODA_SST_anomaly_field_01.rds"
#     ))
#   OceanSODA_temp_extreme_grid %>%
#     write_rds(file = paste0(
#       path_argo_preprocessed,
#       "/OceanSODA_global_SST_anomaly_field_01.rds"
#     ))
# } else if (opt_extreme_determination == 2){
#   OceanSODA_temp_SO_extreme_grid %>%
#     write_rds(file = paste0(
#       path_argo_preprocessed,
#       "/OceanSODA_SST_anomaly_field_02.rds"
#     ))
#   OceanSODA_temp_extreme_grid %>%
#     write_rds(file = paste0(
#       path_argo_preprocessed,
#       "/OceanSODA_global_SST_anomaly_field_02.rds"
#     ))
# }

if (opt_extreme_determination == 1){
  OceanSODA_temp_SO_extreme_grid %>%
    group_split(lon, lat) %>%
    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))
        )) +
        scale_color_manual(values = HNL_colors)
    )
} else if (opt_extreme_determination == 2){
  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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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

Global anomaly identification

# Check valid temperature
OceanSODA_temp <- OceanSODA_temp %>% 
   filter(!is.na(temperature))

# create a decimal_year column to use are the parameter to the regression function
OceanSODA_temp <- OceanSODA_temp %>%
  mutate(decimal_year = decimal_date(date), .after = year)

# fit a linear regression of OceanSODA temp against time (temporal trend)
# in each lat/lon/month grid, month is used depending on opt_extreme_determination
if (opt_extreme_determination == 1){
  OceanSODA_temp_regression <- OceanSODA_temp %>%
    nest(data = -c(lon, lat)) %>%
    mutate(
      fit = map(
        .x = data,
        .f = ~ lm(clim_diff ~ decimal_year, data = .x)
      ),
      tidied = map(.x = fit, .f = tidy),
      glanced = map(.x = fit, .f = glance),
      augmented = map(.x = fit, .f = augment)
    )
} else if (opt_extreme_determination == 2){
  OceanSODA_temp_regression <- OceanSODA_temp %>%
    nest(data = -c(lon, lat, month)) %>%
    mutate(
      fit = map(
        .x = data,
        .f = ~ lm(clim_diff ~ decimal_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 = decimal_year)

OceanSODA_temp_regression_augmented <- OceanSODA_temp_regression %>%
  select(-c(fit, tidied, glanced, data)) %>%
  unnest(augmented) %>% 
  select(lon:decimal_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,
           temperature, clim_temp, 
           lat_raw, lon_raw))

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

# Anomally identification

# 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
if (opt_extreme_determination == 1){
  OceanSODA_temp_extreme_grid <-
    full_join(
      OceanSODA_temp_regression_augmented,
      OceanSODA_temp_regression_glanced %>%
        select(lon:lat, sigma)
    )
} else if (opt_extreme_determination == 2){
  OceanSODA_temp_extreme_grid <-
    full_join(
      OceanSODA_temp_regression_augmented,
      OceanSODA_temp_regression_glanced %>%
        select(lon:month, sigma)
    )
}

# identify observations in anomaly classes
OceanSODA_temp_extreme_grid <- OceanSODA_temp_extreme_grid %>%
  mutate(
    temp_extreme = case_when(
      .resid < -sigma*2 ~ 'L',
      .resid > sigma*2 ~ 'H',
      TRUE ~ 'N'
    )
  ) 

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

# combine with regression coefficients
OceanSODA_temp_extreme_grid <-
  full_join(OceanSODA_temp_extreme_grid,
            OceanSODA_temp_regression_tidied) 

OceanSODA_temp_extreme_grid <- OceanSODA_temp_extreme_grid %>%
  mutate(year = year(date),
         month = month(date),
         .after = decimal_year)


# Write anomalies to file
# if (opt_extreme_determination == 1){
#   OceanSODA_temp_extreme_grid %>%
#     write_rds(file = paste0(
#       path_argo_preprocessed,
#       "/OceanSODA_global_SST_anomaly_field_01.rds"
#     ))
# } else if (opt_extreme_determination == 2){
#   OceanSODA_temp_extreme_grid %>%
#     write_rds(file = paste0(
#       path_argo_preprocessed,
#       "/OceanSODA_global_SST_anomaly_field_02.rds"
#     ))
# }

Anomaly maps

# anomaly maps on a 1x1 grid 

OceanSODA_temp_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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
4942ace	ds2n19	2023-12-06
c00711b	ds2n19	2023-12-06
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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 temp 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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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 (BGC-temperature)

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

full_argo <- full_argo %>%
  mutate(
    lat_raw = lat,
    lon_raw = lon)

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(
      file_id, 
      year, 
      month, 
      date, 
      lon, 
      lat, 
      depth, 
      temp
      ),
  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 BGC-temperature 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, file_id, 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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
71e58d6	jens-daniel-mueller	2022-05-12
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, 
                    y = depth,
                    group = file_id,
                    col = temp_extreme),
                linewidth = 0.3) +
      geom_path(data = .x %>% filter(temp_extreme == 'H' | temp_extreme == 'L'),
                aes(x = temp,
                    y = depth,
                    group = file_id,
                    col = temp_extreme),
                linewidth = 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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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)+
  lims(y = c(-85, -30)) +
  labs(title = 'October 2017',
       fill = 'OceanSODA SST \nextreme')

Past versions of select_specific_temp_profiles_2017-1.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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,
             y = depth,
             group = file_id,
             col = temp_extreme))+
  geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'N'),
            aes(x = temp,
                y = depth,
                group = file_id,
                col = temp_extreme),
            linewidth = 0.3)+
  geom_path(data = profile_temp_Atl_2017 %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
            aes(x = temp,
                y = depth,
                group = file_id,
                col = temp_extreme),
            linewidth = 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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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)+
  lims(y = c(-85, -30)) +
  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,
             y = depth,
             group = file_id,
             col = temp_extreme))+
  geom_path(data = profile_temp_Atl_2016 %>% filter(temp_extreme == 'N'),
            aes(x = temp,
                y = depth, 
                group = file_id,
                col = temp_extreme),
            linewidth = 0.3)+
  geom_path(data = profile_temp_Atl_2016 %>% filter(temp_extreme == 'H'|temp_extreme == 'L'),
            aes(x = temp,
                y = depth, 
                group = file_id,
                col = temp_extreme),
            linewidth = 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'
    ),
    season_order = case_when(
      between(month, 3, 5) ~ 2,
      between(month, 6, 8) ~ 3,
      between(month, 9, 11) ~ 4,
      month == 12 | 1 | 2 ~ 1
    ),
    .after = date
  )

Overall mean

profile_temp_extreme_mean <- profile_temp_extreme %>%
  group_by(temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp, na.rm = TRUE),
            temp_std = sd(temp, 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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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, file_id) %>% 
  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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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, file_id) %>% 
  summarise(argo_surf_temp = mean(temp, 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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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, na.rm = TRUE),
            temp_std = sd(temp, 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_order, season, biome_name, temp_extreme, depth) %>% 
  summarise(temp_biome = mean(temp, na.rm = TRUE),
            temp_std_biome = sd(temp, na.rm = TRUE)) %>% 
  ungroup()
  
facet_label <- as_labeller(c("1"="summer", 
                             "2"="autumn", 
                             "3"="winter", 
                             "4"="spring", 
                             "ICE" = "ICE", 
                             "SPSS" = "SPSS",
                             "STSS" = "STSS",
                             "Atlantic" = "Atlantic",
                             "Indian" = "Indian",
                             "Pacific" = "Pacific"
                             ))

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_order ~ biome_name, labeller = facet_label)

Past versions of mean_temp_profiles_season_new_biomes-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, biome_name, temp_extreme, file_id) %>%
  group_by(season_order, 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_order ~ biome_name, labeller = facet_label)+
  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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, biome_name, temp_extreme, file_id) %>% 
  summarise(argo_surf_temp = mean(temp, 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_order~biome_name, labeller = facet_label) +
    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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, basin_AIP, temp_extreme, depth) %>% 
  summarise(temp_basin = mean(temp, na.rm = TRUE),
            temp_basin_std = sd(temp, 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_order~basin_AIP, labeller = facet_label)

Past versions of mean_basin_temp_profiles-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, basin_AIP, temp_extreme, file_id) %>% 
  group_by(season_order, 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_order~basin_AIP, labeller = facet_label)+
  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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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 temp to compare against OceanSODA surface temp (one value)
surface_temp_basin <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season_order, season, basin_AIP, temp_extreme, file_id) %>% 
  summarise(surf_argo_temp = mean(temp, 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_order~basin_AIP, labeller = facet_label) +
    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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, biome_name, basin_AIP, temp_extreme, depth) %>%
  summarise(temp_mean = mean(temp, na.rm = TRUE),
            temp_std = sd(temp, na.rm = TRUE)) %>%
  ungroup()

profile_temp_extreme_season %>%
  arrange(depth) %>%
  group_split(season_order) %>%
  # 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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, biome_name, basin_AIP,
           temp_extreme, file_id) %>% 
  group_by(season_order, season, biome_name, basin_AIP, temp_extreme) %>% 
  count(temp_extreme)


profile_temp_count_season %>% 
  group_by(season_order) %>% 
  group_split(season_order) %>% 
  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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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 temp, for each season x biome x basin x temp extreme 
surface_temp_season <- profile_temp_extreme %>% 
  filter(depth <= 20) %>% 
  group_by(season_order, 
           season, 
           basin_AIP, 
           biome_name, 
           temp_extreme,  
           file_id) %>%  
  summarise(surf_argo_temp = mean(temp, na.rm=TRUE),
            surf_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE)) 

surface_temp_season %>% 
  group_by(season_order, temp_extreme) %>% 
  group_split(season_order, 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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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, na.rm = TRUE),
            temp_std = sd(temp, 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, file_id,
#            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)

remove_clim <-
  read_rds(file = paste0(path_argo_preprocessed, "/temp_anomaly_va.rds")) %>%
  filter(profile_range >= opt_min_profile_range) %>%
  mutate(date = ymd(format(date, "%Y-%m-15")))

remove_clim <- inner_join(
  remove_clim %>%
    select(
      file_id, 
      year, 
      month, 
      date, 
      lon, 
      lat, 
      depth, 
      temp,
      clim_temp,
      anomaly
      ),
  OceanSODA_temp_SO_extreme_grid %>%
    select(
      year,
      month,
      date,
      lon,
      lat,
      OceanSODA_temp,
      temp_extreme,
      biome_name,
      basin_AIP
    )
)

remove_clim <- remove_clim %>%
  mutate(
    season = case_when(
      between(month, 3, 5) ~ 'autumn',
      between(month, 6, 8) ~ 'winter',
      between(month, 9, 11) ~ 'spring',
      month == 12 | 1 | 2 ~ 'summer'
    ),
    season_order = case_when(
      between(month, 3, 5) ~ 2,
      between(month, 6, 8) ~ 3,
      between(month, 9, 11) ~ 4,
      month == 12 | 1 | 2 ~ 1
    ),
    .after = date
  ) 

Profiles

Points are the 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,
          y = depth,
          group = file_id,
          col = temp_extreme
        ),
        size = 0.3
      ) +
      geom_path(
        data = .x %>%
          filter(temp_extreme == 'H' | temp_extreme == 'L'),
        aes(
          x = temp,
          y = depth,
          group = file_id,
          col = temp_extreme
        ),
        size = 0.5
      ) +
      geom_point(
        data = .x,
        aes(x = clim_temp,
            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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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'),
#           season_order = case_when(
#                 between(month, 3, 5) ~ 2,
#                 between(month, 6, 8) ~ 3,
#                 between(month, 9, 11) ~ 4,
#                 month == 12 | 1 | 2 ~ 1
#           )
#          )

Anomaly

remove_clim %>% 
  group_split(month) %>% 
  #head(6) %>% 
  map(
    ~ggplot()+
      geom_path(data = .x %>% filter(temp_extreme == 'N'),
                aes(x = anomaly,
                    y = depth,
                    group = file_id,
                    col = temp_extreme),
                size = 0.2)+
      geom_path(data = .x %>% filter(temp_extreme == 'H'| temp_extreme == 'L'),
                 aes(x = anomaly,
                     y = depth,
                     group = file_id,
                     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_anomaly_profiles-1.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[2]]

Past versions of plot_anomaly_profiles-2.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[3]]

Past versions of plot_anomaly_profiles-3.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[4]]

Past versions of plot_anomaly_profiles-4.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[5]]

Past versions of plot_anomaly_profiles-5.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[6]]

Past versions of plot_anomaly_profiles-6.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[7]]

Past versions of plot_anomaly_profiles-7.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[8]]

Past versions of plot_anomaly_profiles-8.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[9]]

Past versions of plot_anomaly_profiles-9.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[10]]

Past versions of plot_anomaly_profiles-10.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[11]]

Past versions of plot_anomaly_profiles-11.png

Version	Author	Date
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

[[12]]

Past versions of plot_anomaly_profiles-12.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
7b3d8c5	pasqualina-vonlanthendinenna	2022-08-29

Overall mean anomaly

remove_clim_overall_mean <- remove_clim %>% 
  group_by(temp_extreme, depth) %>% 
  summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(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(data = profile_temp_count_mean[2,],
          aes(x = -4.0, 
              y = 1200, 
              label = paste0(n), 
              col = temp_extreme),
          size = 6)+
  geom_text(data = profile_temp_count_mean[1,],
          aes(x = -4.0, 
              y = 1400, 
              label = paste0(n), 
              col = temp_extreme),
          size = 6)+
  geom_text(data = profile_temp_count_mean[3,],
          aes(x = -4.0, 
              y = 1600, 
              label = paste0(n), 
              col = temp_extreme),
          size = 6)+
  coord_cartesian(xlim = c(-4.5, 4.5))+
  scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
  labs(title = 'Overall mean anomaly profiles')

Past versions of plot_overall_mean_clim_removed_profiles-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, biome_name) %>% 
  summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(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(data = profile_temp_count_biome %>% filter (temp_extreme == 'N'),
                  aes(x = -4,
                      y = 800,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4)+
  geom_text(data = profile_temp_count_biome %>% filter (temp_extreme == 'H'),
                  aes(x = -4,
                      y = 1200,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4)+
  geom_text(data = profile_temp_count_biome %>% filter (temp_extreme == 'L'),
                  aes(x = -4,
                      y = 1600,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4)+
  coord_cartesian(xlim = c(-4.5, 4.5))+
  scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
  facet_grid(season_order~biome_name, labeller = facet_label)

Past versions of plot_biome_mean_clim_removed_profiles-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season) %>% 
  summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(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')+
  geom_text(data = profile_temp_count_basin %>% filter (temp_extreme == 'N'),
                  aes(x = -4,
                      y = 800,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4)+
  geom_text(data = profile_temp_count_basin %>% filter (temp_extreme == 'H'),
                  aes(x = -4,
                      y = 1200,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4)+
  geom_text(data = profile_temp_count_basin %>% filter (temp_extreme == 'L'),
                  aes(x = -4,
                      y = 1600,
                      label = paste0(n),
                      col = temp_extreme),
                  size = 4)+
  coord_cartesian(xlim = c(-4.5, 4.5))+
  scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
  labs(title = 'Basin-mean anomaly profiles')

Past versions of plot_basin_mean_anomaly_profiles-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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_order, season, depth) %>% 
  summarise(temp_anomaly_mean = mean(anomaly, na.rm = TRUE),
            temp_anomaly_sd = sd(anomaly, na.rm = TRUE))

remove_clim_basin_biome_mean %>% 
  group_by(season_order) %>% 
  group_split(season_order) %>% 
  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')+
      geom_text(data = profile_temp_count_season %>% filter (temp_extreme == 'N' & season == unique(.x$season)),
                      aes(x = -4,
                          y = 800,
                          label = paste0(n),
                          col = temp_extreme),
                      size = 4)+
      geom_text(data = profile_temp_count_season %>% filter (temp_extreme == 'H' & season == unique(.x$season)),
                      aes(x = -4,
                          y = 1200,
                          label = paste0(n),
                          col = temp_extreme),
                      size = 4)+
      geom_text(data = profile_temp_count_season %>% filter (temp_extreme == 'L' & season == unique(.x$season)),
                      aes(x = -4,
                          y = 1600,
                          label = paste0(n),
                          col = temp_extreme),
                      size = 4)+
      coord_cartesian(xlim = c(-4.5, 4.5))+
      scale_x_continuous(breaks = c(-4, -2, 0, 2, 4))+
      labs(title = paste0('biome-basin mean anomaly profiles ', unique(.x$season)))
    )

[[1]]

Past versions of plot_basin_biome_mean_anomaly_profiles-1.png

Version	Author	Date
f9de50e	ds2n19	2024-01-01
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
15c1d68	ds2n19	2023-10-19
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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
cf5dd20	ds2n19	2023-12-04
cec2a2a	ds2n19	2023-11-24
2f4ea7e	ds2n19	2023-10-19
879821d	ds2n19	2023-10-18
7004f76	ds2n19	2023-10-17
4b55c43	ds2n19	2023-10-12
1ae81b3	ds2n19	2023-10-11
44f5720	ds2n19	2023-10-09
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)

Argo (BGC-temperature irrespective of pH)

all_profile_temp_extreme <- inner_join(
  argo_temp %>% 
    select(c(year, month, date, lon, lat, depth,
           temp_adjusted,
           file_id)),                 # 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)))

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

Location of all BGC-temp profiles (regardless of pH)

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 = all_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))
      )
  )

Session information

sessionInfo()

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

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggnewscale_0.4.8  ggrepel_0.9.2     oce_1.7-10        gsw_1.1-1        
 [5] ggforce_0.4.1     metR_0.13.0       scico_1.3.1       ggOceanMaps_1.3.4
 [9] ggspatial_1.1.7   broom_1.0.5       lubridate_1.9.0   timechange_0.1.1 
[13] forcats_0.5.2     stringr_1.5.0     dplyr_1.1.3       purrr_1.0.2      
[17] readr_2.1.3       tidyr_1.3.0       tibble_3.2.1      ggplot2_3.4.4    
[21] tidyverse_1.3.2   workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] googledrive_2.0.0   colorspace_2.0-3    ellipsis_0.3.2     
 [4] class_7.3-20        rprojroot_2.0.3     fs_1.5.2           
 [7] rstudioapi_0.15.0   proxy_0.4-27        farver_2.1.1       
[10] bit64_4.0.5         fansi_1.0.3         xml2_1.3.3         
[13] codetools_0.2-18    cachem_1.0.6        knitr_1.41         
[16] polyclip_1.10-4     jsonlite_1.8.3      dbplyr_2.2.1       
[19] rgeos_0.5-9         compiler_4.2.2      httr_1.4.4         
[22] backports_1.4.1     assertthat_0.2.1    fastmap_1.1.0      
[25] gargle_1.2.1        cli_3.6.1           later_1.3.0        
[28] tweenr_2.0.2        htmltools_0.5.8.1   tools_4.2.2        
[31] gtable_0.3.1        glue_1.6.2          Rcpp_1.0.10        
[34] cellranger_1.1.0    jquerylib_0.1.4     raster_3.6-11      
[37] vctrs_0.6.4         xfun_0.35           ps_1.7.2           
[40] rvest_1.0.3         lifecycle_1.0.3     googlesheets4_1.0.1
[43] terra_1.7-65        getPass_0.2-2       MASS_7.3-58.1      
[46] scales_1.2.1        vroom_1.6.0         hms_1.1.2          
[49] promises_1.2.0.1    parallel_4.2.2      RColorBrewer_1.1-3 
[52] yaml_2.3.6          memoise_2.0.1       sass_0.4.4         
[55] stringi_1.7.8       highr_0.9           e1071_1.7-12       
[58] checkmate_2.1.0     rlang_1.1.1         pkgconfig_2.0.3    
[61] evaluate_0.18       lattice_0.20-45     sf_1.0-9           
[64] labeling_0.4.2      bit_4.0.5           processx_3.8.0     
[67] tidyselect_1.2.0    magrittr_2.0.3      R6_2.5.1           
[70] generics_0.1.3      DBI_1.2.2           pillar_1.9.0       
[73] haven_2.5.1         whisker_0.4         withr_2.5.0        
[76] units_0.8-0         sp_1.5-1            modelr_0.1.10      
[79] crayon_1.5.2        KernSmooth_2.23-20  utf8_1.2.2         
[82] tzdb_0.3.0          rmarkdown_2.18      grid_4.2.2         
[85] readxl_1.4.1        data.table_1.14.6   callr_3.7.3        
[88] git2r_0.30.1        reprex_2.0.2        digest_0.6.30      
[91] classInt_0.4-8      httpuv_1.6.6        munsell_0.5.0      
[94] viridisLite_0.4.1   bslib_0.4.1