OceanSODA-Argo SST
Pasqualina Vonlanthen, David Stappard & Jens Daniel Müller
19 April, 2024
Last updated: 2024-04-19
Checks: 7 0
Knit directory:
bgc_argo_r_argodata/analysis/
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| html | 1ae81b3 | ds2n19 | 2023-10-11 | reworked core load process to work initially by year and then finally create consolidated all years files. |
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| Rmd | 3e1ac14 | pasqualina-vonlanthendinenna | 2022-04-26 | separated loading data pages, added mayot biomes, switched to pH and temp flag A |
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Task
Compare BGC- and Core-Argo surface temperature to OceanSODA surface temperature
Dependencies
OceanSODA_temp.rds - bgc preprocessed folder, created by load_OceanSODA.
temp_bgc_observed.rds - bgc preprocessed folder, created by temp_align_climatology. Not this file is written BEFORE the vertical alignment stage.
theme_set(theme_bw())Load BGC-Argo data
Load in surface Argo temperature, and OceanSODA temperature, on a 1ºx1º grid
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_argo_core <- '/nfs/kryo/work/updata/core_argo_r_argodata'
path_argo_core_preprocessed <- paste0(
path_argo_core, "/preprocessed_core_data")
path_argo <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/bgc_argo'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_argo_core <- '/nfs/kryo/work/datasets/ungridded/3d/ocean/floats/core_argo_r_argodata'
path_argo_core_preprocessed <- paste0(path_argo_core, "/preprocessed_core_data")# Load in surface Argo and OceanSODA temperature data
OceanSODA_temp <- read_rds(file = paste0(path_argo_preprocessed, "/OceanSODA_temp.rds"))
argo_surf_temp <- read_rds(file = paste0(path_argo_preprocessed, '/temp_bgc_observed.rds')) %>%
filter(between(depth, 0, 20))
# argo_surf_temp <-
# read_rds(file = paste0(path_argo_preprocessed, "/bgc_merge_flag_AB.rds")) %>%
# filter(between(depth, 0, 20)) %>%
# mutate(year = year(date),
# month = month(date)) %>%
# select(
# -c(
# ph_in_situ_total_adjusted,
# ph_in_situ_total_adjusted_qc,
# ph_in_situ_total_adjusted_error,
# profile_ph_in_situ_total_qc
# )
# )
# for plotting later, load in region information
nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))# read in the map from updata
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))Harmonise BGG-Argo and OceanSODA
Calculate monthly-mean Argo temperature for each lat/lon grid and each month
argo_temp_monthly <- argo_surf_temp %>%
mutate(year_month = format_ISO8601(date, precision = "ym"), .after = 'date') %>%
group_by(year, month, year_month, date, lat, lon) %>%
summarise(argo_temp_month = mean(temp_adjusted, na.rm = TRUE)) %>%
ungroup() %>%
select(
date,
year_month,
year,
month,
lon,
lat,
argo_temp_month
)Join Argo and OceanSODA
OceanSODA_temp <- OceanSODA_temp %>%
mutate(year_month = format_ISO8601(date, precision = "ym")) %>%
rename(date_OceanSODA = date)# change date format in OceanSODA to match argo date (yyyy-mm)
argo_OceanSODA_temp <- left_join(argo_temp_monthly, OceanSODA_temp) %>%
rename(OceanSODA_temp = temperature)BGC-SST - Southern Ocean
Focus on the Southern Ocean, south of 30ºS, as defined in the Mayot biome regions
# keep only Southern Ocean data
argo_OceanSODA_temp_SO <-
inner_join(argo_OceanSODA_temp, nm_biomes)Monthly climatological OceanSODA SST
Map monthly mean SST from the OceanSODA data product, where BGC-Argo SST exists
Climatological OceanSODA SST
# calculate average monthly pH, and the difference between the two (offset)
argo_OceanSODA_temp_SO_clim <- argo_OceanSODA_temp_SO %>%
group_by(lon, lat, month) %>%
summarise(
clim_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE),
clim_argo_temp = mean(argo_temp_month, na.rm = TRUE),
offset_clim = clim_OceanSODA_temp - clim_argo_temp
) %>%
ungroup()
# regrid to a 2x2 grid for mapping
argo_OceanSODA_temp_SO_clim_2x2 <- argo_OceanSODA_temp_SO_clim %>%
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))
) %>%
group_by(lon, lat, month) %>%
summarise(
clim_OceanSODA_temp = mean(clim_OceanSODA_temp, na.rm = TRUE),
clim_argo_temp = mean(clim_argo_temp, na.rm = TRUE),
offset_clim = mean(offset_clim, na.rm = TRUE)
) %>%
ungroup()
map +
geom_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(x = lon, y = lat, fill = clim_OceanSODA_temp)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'SST (ºC)',
title = 'Monthly climatological \nOceanSODA SST') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| 8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
# plot the climatological monthly OceanSODA SST on a polar projection
basemap(limits = -32, data = argo_OceanSODA_temp_SO_clim_2x2) + # change to polar projection
geom_spatial_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(x = lon,
y = lat,
fill = clim_OceanSODA_temp),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_viridis_c()+
theme(legend.position = 'right')+
labs(x = 'lon',
y = 'lat',
fill = 'SST (ºC)',
title = 'monthly climatological \nOceanSODA SST')+
facet_wrap(~month, ncol = 2)Monthly climatological BGC-Argo SST
Climatological Argo SST
map +
geom_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(lon, lat, fill = clim_argo_temp)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'SST (ºC)',
title = 'Monthly climatological \nArgo SST') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| 8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
basemap(limits = -32, data = argo_OceanSODA_temp_SO_clim_2x2) + # change to polar projection
geom_spatial_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(x = lon,
y = lat,
fill = clim_argo_temp),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_viridis_c()+
theme(legend.position = 'right')+
labs(x = 'lon',
y = 'lat',
fill = 'SST (ºC)',
title = 'monthly climatological \nArgo SST')+
facet_wrap(~month, ncol = 2)Timeseries of monthly OceanSODA SST
Evolution of monthly SST, for the three Southern Ocean Mayot biomes
map +
geom_raster(data = nm_biomes,
aes(x = lon, y = lat)) +
geom_raster(data = nm_biomes,
aes(x = lon,
y = lat,
fill = biome_name)) +
labs(title = 'Southern Ocean Mayot regions',
fill = 'biome')
# plot timeseries of monthly OceanSODA SST
argo_OceanSODA_temp_SO_clim_regional <- argo_OceanSODA_temp_SO %>%
select(year, month, biome_name, OceanSODA_temp, argo_temp_month) %>%
pivot_longer(c(OceanSODA_temp,argo_temp_month),
values_to = "temp",
names_to = "data_source") %>%
group_by(year, month, biome_name, data_source) %>%
summarise(temp = mean(temp, na.rm = TRUE)) %>%
ungroup()argo_OceanSODA_temp_SO_clim_regional %>%
ggplot(aes(x = year,
y = temp,
col = biome_name)) +
facet_grid(month ~ data_source) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'SST (ºC)',
title = 'monthly mean SST (Southern Ocean)',
col = 'biome')argo_OceanSODA_temp_SO_clim_regional %>%
# filter(year != 2021) %>%
ggplot(aes(x = month,
y = temp,
group = year,
col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(biome_name~data_source)+
lims(y = c(-5, 20))+
labs(x = 'month',
y = 'SST (ºC)',
title = 'monthly mean SST (Southern Ocean)',
col = 'year')
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| fd521d1 | pasqualina-vonlanthendinenna | 2022-02-25 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
Comparison of BGC-Argo and OceanSODA SST
Calculate the difference between Argo and OceanSODA SST values
Offset between in-situ monthly SST:
argo_OceanSODA_temp_SO <- argo_OceanSODA_temp_SO %>%
mutate(offset = OceanSODA_temp - argo_temp_month)
argo_OceanSODA_temp_SO %>%
# drop_na() %>%
# filter(year != '2021') %>%
ggplot() +
geom_hline(yintercept = 0, size = 1)+
geom_point(aes(x = year_month, y = offset, col = biome_name), size = 0.7, pch = 19) +
scale_x_discrete(breaks = c('2013-01', '2014-01', '2015-01', '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01', '2023-01'))+
labs(title = 'oceanSODA SST - Argo SST',
x = 'date',
y = 'offset (ºC)',
col = 'region')
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| acc96b0 | pasqualina-vonlanthendinenna | 2022-02-28 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
argo_OceanSODA_temp_SO %>%
# drop_na() %>%
ggplot(aes(x = OceanSODA_temp, y = argo_temp_month))+
# geom_point(pch = 19, size = 0.7)+
geom_bin2d(aes(x = OceanSODA_temp, y = argo_temp_month), size = 0.3, bins = 60)+
scale_fill_viridis_c()+
coord_fixed(ratio = 1,
xlim = c(-3, 27),
ylim= c(-3, 27)) +
geom_abline(slope = 1, intercept = 0)+
facet_wrap(~biome_name)+
labs(x = 'OceanSODA SST (ºC)',
y = 'Argo SST (ºC)',
title = 'Southern Ocean regional SST')
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
# test with basin and biome
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(lon, lat, basin_AIP)
argo_OceanSODA_temp_SO <- inner_join(argo_OceanSODA_temp_SO, basinmask)
argo_OceanSODA_temp_SO %>%
ggplot(aes(x = OceanSODA_temp, y = argo_temp_month))+
geom_bin2d(aes(x = OceanSODA_temp, y = argo_temp_month), size = 0.3, bins = 60)+
scale_fill_viridis_c()+
coord_fixed(ratio = 1,
xlim = c(-3, 27),
ylim = c(-3, 27))+
geom_abline(slope = 1, intercept = 0)+
facet_grid(basin_AIP~biome_name)+
labs(x = 'Argo SST (ºC)',
y = 'OceanSODA SST (ºC)',
title = 'Southern Ocean subregional SST')
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| c68163a | pasqualina-vonlanthendinenna | 2022-02-22 |
Mean offset between in-situ OceanSODA SST and in-situ BGC-Argo SST
mean_insitu_offset <- argo_OceanSODA_temp_SO %>%
group_by(year_month, biome_name) %>%
summarise(mean_offset = mean(offset, na.rm = TRUE),
std_offset = sd(offset, na.rm = TRUE))
mean_insitu_offset %>%
# drop_na() %>%
# filter(year != '2021') %>%
ggplot() +
geom_hline(yintercept = 0, size = 1, col = 'red')+
geom_point(aes(x = year_month, y = mean_offset, group = biome_name, col = biome_name), size = 0.7, pch = 19) +
geom_line(aes(x = year_month, y = mean_offset, group = biome_name, col = biome_name))+
geom_ribbon(aes(x = year_month,
ymin = mean_offset-std_offset,
ymax = mean_offset+std_offset,
group = biome_name,
fill =biome_name),
alpha = 0.2)+
scale_x_discrete(breaks = c('2013-01', '2014-01', '2015-01', '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01', '2023-01'))+
# facet_wrap(~year)+
labs(title = 'Mean offset (in situ oceanSODA SST - in situ Argo SST)',
x = 'date',
y = 'offset (ºC)',
col = 'region',
fill = '± 1 std')
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| acc96b0 | pasqualina-vonlanthendinenna | 2022-02-28 |
| c68163a | pasqualina-vonlanthendinenna | 2022-02-22 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
Offset between climatological Argo and climatological OceanSODA SST:
# Offset between climatological argo and climatological OceanSODA SST
argo_OceanSODA_temp_SO_clim <- inner_join(argo_OceanSODA_temp_SO_clim, nm_biomes)argo_OceanSODA_temp_SO_clim %>%
# drop_na() %>%
ggplot() +
geom_point(aes(x = month, y = offset_clim, col = biome_name), size = 0.7, pch = 19) +
geom_hline(yintercept = 0, size = 1, col = 'red')+
scale_x_continuous(breaks = seq(1, 12, 1))+
labs(title = 'clim oceanSODA SST - clim Argo SST',
x = 'month',
y = 'offset (ºC)',
col = 'region')Mean offset between climatological OceanSODA SST and climatological BGC-Argo SST
mean_clim_offset <- argo_OceanSODA_temp_SO_clim %>%
group_by(month, biome_name) %>%
summarise(mean_offset_clim = mean(offset_clim, na.rm = TRUE),
std_offset_clim = sd(offset_clim, na.rm = TRUE))
mean_clim_offset %>%
ggplot()+
geom_point(aes(x = month, y = mean_offset_clim, col = biome_name))+
geom_line(aes(x = month, y = mean_offset_clim, col = biome_name))+
geom_hline(yintercept = 0, col = 'red') +
geom_ribbon(aes(x = month,
ymin = mean_offset_clim - std_offset_clim,
ymax = mean_offset_clim + std_offset_clim,
group = biome_name,
fill = biome_name),
alpha = 0.2) +
scale_x_continuous(breaks = seq(1, 12, 1)) +
labs(x = 'month',
y = 'mean offset (ºC)',
title = 'Mean offset (clim OceanSODA SST - clim Argo SST)',
col = 'region',
fill = '± 1 std') 
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 80c16c2 | ds2n19 | 2023-11-15 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
Mapped offset between climatological OceanSODA SST and climatological BGC-Argo SST
# bin the offsets for better plotting
# plot the offsets on a map of the Southern Ocean
argo_OceanSODA_temp_SO_clim_2x2 <- argo_OceanSODA_temp_SO_clim_2x2 %>%
mutate(offset_clim_binned =
cut(offset_clim,
breaks = c(-Inf, -0.025, -0.005, 0.000, 0.005, 0.025, 0.035, 0.05, Inf))) # bin the offsets into intervals (create a discrete variable instead of continuous)
# offset_clim_binned = as.factor(as.character(offset_clim_binned))) %>%
# drop_na()
map +
geom_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(lon, lat, fill = offset_clim_binned)) +
lims(y = c(-85, -30)) +
scale_fill_brewer(palette = 'RdBu', drop = FALSE) +
labs(x = 'lon',
y = 'lat',
fill = 'offset (ºC)',
title = 'clim OceanSODA SST - clim Argo SST') +
theme(legend.position = 'right')+
facet_wrap(~month, ncol = 2)
| Version | Author | Date |
|---|---|---|
| 1a72545 | mlarriere | 2024-04-12 |
| c076fba | mlarriere | 2024-04-12 |
| f9de50e | ds2n19 | 2024-01-01 |
| cec2a2a | ds2n19 | 2023-11-24 |
| 1ae81b3 | ds2n19 | 2023-10-11 |
| 44f5720 | ds2n19 | 2023-10-09 |
| 68eff8b | jens-daniel-mueller | 2022-05-11 |
| 10036ed | pasqualina-vonlanthendinenna | 2022-04-26 |
| 8805f99 | pasqualina-vonlanthendinenna | 2022-04-11 |
| 905d82f | pasqualina-vonlanthendinenna | 2022-02-15 |
basemap(limits = -32, data = argo_OceanSODA_temp_SO_clim_2x2) + # change to polar projection
geom_spatial_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(x = lon,
y = lat,
fill = offset_clim_binned),
linejoin = 'mitre',
col = 'transparent',
detail = 60)+
scale_fill_brewer(palette = 'RdBu', drop = FALSE)+
theme(legend.position = 'right')+
labs(x = 'lon',
y = 'lat',
fill = 'offset (ºC)',
title = 'clim Ocean SODA SST - clim Argo SST')+
facet_wrap(~month, ncol = 2)Basin separation
Using full OceanSODA data (even where there is no Argo data) Each Mayot biome is separated into basins (Atlantic, Pacific, Indian)
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(lon, lat, basin_AIP)
OceanSODA_temp_SO <- inner_join(OceanSODA_temp, nm_biomes) %>%
filter(year >= 2013)
OceanSODA_temp_SO <- inner_join(OceanSODA_temp_SO, basinmask) %>%
mutate(year = year(date_OceanSODA),
month = month(date_OceanSODA)) %>%
mutate(date = format_ISO8601(date_OceanSODA, precision = 'ym'))# plot timeseries of monthly OceanSODA SST
OceanSODA_temp_SO_clim_subregional <- OceanSODA_temp_SO %>%
group_by(year, month, biome_name, basin_AIP) %>% # compute regional mean OceanSODA SST for the three biomes
summarise(temp = mean(temperature, na.rm = TRUE)) %>%
ungroup()
# plot a timeseries of monthly average OceanSODA pH, per region and per basin
OceanSODA_temp_SO_clim_subregional %>%
ggplot(aes(x = month,
y = temp,
group = year,
col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(biome_name~basin_AIP)+
labs(x = 'month',
y = 'SST (ºC)',
title = 'monthly mean OceanSODA SST (Southern Ocean basins)',
col = 'year')
OceanSODA_temp_SO_clim_subregional %>%
ggplot(aes(x = year,
y = temp,
col = biome_name)) +
facet_grid(month ~ basin_AIP) +
geom_line() +
geom_point() +
labs(x = 'year',
y = 'SST (ºC)',
title = 'monthly mean OceanSODA SST (Southern Ocean basins)',
col = 'region')
Longitudinal separation
Bin the SST data into 20º longitude bins
OceanSODA_temp_SO_lon_binned <- OceanSODA_temp_SO %>%
mutate(lon = cut(lon, seq(20, 380, 20), seq(30, 370, 20)),
lon = as.numeric(as.character(lon))
) %>%
group_by(lon, year, month, biome_name) %>%
summarise(
OceanSODA_temp_binned = mean(temperature, na.rm = TRUE)
) %>%
ungroup()OceanSODA_temp_SO_lon_binned %>%
# drop_na() %>%
ggplot(aes(x = month, y = OceanSODA_temp_binned, group = lon, col = as.factor(lon))) +
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(year~biome_name)+
labs(x = 'month',
y = 'OceanSODA SST (ºC)',
col = 'longitude bin')
rm(OceanSODA_temp_SO_lon_binned, OceanSODA_temp_SO_clim_subregional, argo_OceanSODA_temp_SO_clim_2x2, mean_clim_offset, argo_OceanSODA_temp_SO_clim, mean_insitu_offset, argo_OceanSODA_temp_SO, argo_OceanSODA_temp_SO_clim_regional)Load Core-Argo data
Repeat analysis with SST data from the Core-Argo dataset
# argo_surf_temp_core <-
# read_rds(file = paste0(
# path_argo_core_preprocessed, "/core_temp_flag_A.rds")) %>%
# filter(between(depth, 0, 20)) %>%
# mutate(year = year(date),
# month = month(date))
argo_surf_temp_core <- read_rds(file = paste0(path_argo_core_preprocessed, '/temp_core_observed.rds')) %>%
filter(between(depth, 0, 20))Harmonise Core-Argo and OceanSODA
argo_temp_monthly_core <- argo_surf_temp_core %>%
mutate(year_month = format_ISO8601(date, precision = "ym"), .after = 'date') %>%
group_by(year, month, year_month, date, lat, lon) %>%
summarise(argo_temp_month = mean(temp_adjusted, na.rm = TRUE)) %>%
ungroup() %>%
select(
date,
year_month,
year,
month,
lon,
lat,
argo_temp_month
)Join Core-Argo and OceanSODA
argo_OceanSODA_temp_core <- left_join(
argo_temp_monthly_core, OceanSODA_temp) %>%
rename(OceanSODA_temp = temperature)Core-SST - Southern Ocean
Compare Southern Ocean Core-SST to OceanSODA SST
# keep only Southern Ocean data
argo_OceanSODA_temp_SO <-
inner_join(argo_OceanSODA_temp_core, nm_biomes)Monthly climatological OceanSODA SST
Map OceanSODA SST where Core-Argo SST measurements exist
# calculate average monthly SST between, and the difference between the two (offset)
argo_OceanSODA_temp_SO_clim <- argo_OceanSODA_temp_SO %>%
group_by(lon, lat, month) %>%
summarise(
clim_OceanSODA_temp = mean(OceanSODA_temp, na.rm = TRUE),
clim_argo_temp = mean(argo_temp_month, na.rm = TRUE),
offset_clim = clim_OceanSODA_temp - clim_argo_temp
) %>%
ungroup()
# regrid to a 2x2 grid for mapping
argo_OceanSODA_temp_SO_clim_2x2 <- argo_OceanSODA_temp_SO_clim %>%
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))
) %>%
group_by(lon, lat, month) %>%
summarise(
clim_OceanSODA_temp = mean(clim_OceanSODA_temp, na.rm = TRUE),
clim_argo_temp = mean(clim_argo_temp, na.rm = TRUE),
offset_clim = mean(offset_clim, na.rm = TRUE)
) %>%
ungroup()
map +
geom_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(x = lon, y = lat, fill = clim_OceanSODA_temp)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'SST (ºC)',
title = 'Monthly climatological \nOceanSODA SST') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
Monthly climatological Core-Argo SST
map +
geom_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(lon, lat, fill = clim_argo_temp)) +
lims(y = c(-85, -25)) +
scale_fill_viridis_c() +
labs(x = 'lon',
y = 'lat',
fill = 'SST (ºC)',
title = 'Monthly climatological \nArgo SST') +
theme(legend.position = 'right') +
facet_wrap(~month, ncol = 2)
Timeseries of monthly SST
# plot timeseries of monthly OceanSODA SST
argo_OceanSODA_temp_SO_clim_regional <- argo_OceanSODA_temp_SO %>%
select(year, month, biome_name, OceanSODA_temp, argo_temp_month) %>%
pivot_longer(c(OceanSODA_temp,argo_temp_month),
values_to = "temp",
names_to = "data_source") %>%
group_by(year, month, biome_name, data_source) %>%
summarise(temp = mean(temp, na.rm = TRUE)) %>%
ungroup()argo_OceanSODA_temp_SO_clim_regional %>%
# filter(year != 2021) %>%
ggplot(aes(x = month,
y = temp,
group = year,
col = as.character(year)))+
geom_line()+
geom_point()+
scale_x_continuous(breaks = seq(1, 12, 2))+
facet_grid(biome_name~data_source)+
lims(y = c(-5, 20))+
labs(x = 'month',
y = 'SST (ºC)',
title = 'monthly mean SST (Southern Ocean)',
col = 'year')
Comparison of Core-Argo and OceanSODA SST
argo_OceanSODA_temp_SO <- argo_OceanSODA_temp_SO %>%
mutate(offset = OceanSODA_temp - argo_temp_month)
argo_OceanSODA_temp_SO %>%
# drop_na() %>%
# filter(year != '2021') %>%
ggplot() +
geom_hline(yintercept = 0, size = 1)+
geom_point(aes(x = year_month, y = offset, col = biome_name), size = 0.7, pch = 19) +
scale_x_discrete(breaks = c('2013-01', '2014-01', '2015-01', '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01', '2023-01'))+
labs(title = 'oceanSODA SST - Argo SST',
x = 'date',
y = 'offset (ºC)',
col = 'region')
argo_OceanSODA_temp_SO %>%
# drop_na() %>%
ggplot(aes(x = OceanSODA_temp, y = argo_temp_month))+
# geom_point(pch = 19, size = 0.7)+
geom_bin2d(aes(x = OceanSODA_temp, y = argo_temp_month), size = 0.3, bins = 60)+
scale_fill_viridis_c()+
coord_fixed(ratio = 1,
xlim = c(-3, 27),
ylim= c(-3, 27)) +
geom_abline(slope = 1, intercept = 0)+
facet_wrap(~biome_name)+
labs(x = 'OceanSODA SST (ºC)',
y = 'Argo SST (ºC)',
title = 'Southern Ocean regional SST')
# test with basin and biome
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(lon, lat, basin_AIP)
argo_OceanSODA_temp_SO <- inner_join(argo_OceanSODA_temp_SO, basinmask)
argo_OceanSODA_temp_SO %>%
ggplot(aes(x = OceanSODA_temp, y = argo_temp_month))+
geom_bin2d(aes(x = OceanSODA_temp, y = argo_temp_month), size = 0.3, bins = 60)+
scale_fill_viridis_c()+
coord_fixed(ratio = 1,
xlim = c(-3, 27),
ylim = c(-3, 27))+
geom_abline(slope = 1, intercept = 0)+
facet_grid(basin_AIP~biome_name)+
labs(x = 'Argo SST (ºC)',
y = 'OceanSODA SST (ºC)',
title = 'Southern Ocean subregional SST')
Mean offset between in-situ OceanSODA SST and in-situ Core-Argo SST
mean_insitu_offset <- argo_OceanSODA_temp_SO %>%
group_by(year_month, biome_name) %>%
summarise(mean_offset = mean(offset, na.rm = TRUE),
std_offset = sd(offset, na.rm = TRUE))
mean_insitu_offset %>%
# drop_na() %>%
# filter(year != '2021') %>%
ggplot() +
geom_hline(yintercept = 0, size = 1, col = 'red')+
geom_point(aes(x = year_month, y = mean_offset, group = biome_name, col = biome_name), size = 0.7, pch = 19) +
geom_line(aes(x = year_month, y = mean_offset, group = biome_name, col = biome_name))+
geom_ribbon(aes(x = year_month,
ymin = mean_offset-std_offset,
ymax = mean_offset+std_offset,
group = biome_name,
fill =biome_name),
alpha = 0.2)+
scale_x_discrete(breaks = c('2013-01', '2014-01', '2015-01', '2016-01', '2017-01', '2018-01', '2019-01', '2020-01', '2021-01', '2022-01', '2023-01'))+
# facet_wrap(~year)+
labs(title = 'Mean offset (in situ oceanSODA SST - in situ Argo SST)',
x = 'date',
y = 'offset (ºC)',
col = 'region',
fill = '± 1 std')
Offset between climatological Core-Argo and climatological OceanSODA SST
# Offset between climatological argo and climatological OceanSODA SST
argo_OceanSODA_temp_SO_clim <- inner_join(argo_OceanSODA_temp_SO_clim, nm_biomes)argo_OceanSODA_temp_SO_clim %>%
# drop_na() %>%
ggplot() +
geom_point(aes(x = month, y = offset_clim, col = biome_name), size = 0.7, pch = 19) +
geom_hline(yintercept = 0, size = 1, col = 'red')+
scale_x_continuous(breaks = seq(1, 12, 1))+
labs(title = 'clim oceanSODA SST - clim Argo SST',
x = 'month',
y = 'offset (ºC)',
col = 'region')Mean offset between climatological OceanSODA SST and climatological Core-Argo SST
mean_clim_offset <- argo_OceanSODA_temp_SO_clim %>%
group_by(month, biome_name) %>%
summarise(mean_offset_clim = mean(offset_clim, na.rm = TRUE),
std_offset_clim = sd(offset_clim, na.rm = TRUE))
mean_clim_offset %>%
ggplot()+
geom_point(aes(x = month, y = mean_offset_clim, col = biome_name))+
geom_line(aes(x = month, y = mean_offset_clim, col = biome_name))+
geom_hline(yintercept = 0, col = 'red') +
geom_ribbon(aes(x = month,
ymin = mean_offset_clim - std_offset_clim,
ymax = mean_offset_clim + std_offset_clim,
group = biome_name,
fill = biome_name),
alpha = 0.2) +
scale_x_continuous(breaks = seq(1, 12, 1)) +
labs(x = 'month',
y = 'mean offset (ºC)',
title = 'Mean offset (clim OceanSODA SST - clim Argo SST)',
col = 'region',
fill = '± 1 std') 
Mapped offset between climatological OceanSODA SST and climatological Core-Argo SST
# bin the offsets for better plotting
# plot the offsets on a map of the Southern Ocean
argo_OceanSODA_temp_SO_clim_2x2 <- argo_OceanSODA_temp_SO_clim_2x2 %>%
mutate(offset_clim_binned =
cut(offset_clim,
breaks = c(-Inf, -0.025, -0.005, 0.000, 0.005, 0.025, 0.035, 0.05, Inf))) # bin the offsets into intervals (create a discrete variable instead of continuous)
# offset_clim_binned = as.factor(as.character(offset_clim_binned))) %>%
# drop_na()
map +
geom_tile(data = argo_OceanSODA_temp_SO_clim_2x2,
aes(lon, lat, fill = offset_clim_binned)) +
lims(y = c(-85, -30)) +
scale_fill_brewer(palette = 'RdBu', drop = FALSE) +
labs(x = 'lon',
y = 'lat',
fill = 'offset (ºC)',
title = 'clim OceanSODA SST - clim Argo SST') +
theme(legend.position = 'right')+
facet_wrap(~month, ncol = 2)
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] metR_0.13.0 ggOceanMaps_1.3.4 ggspatial_1.1.7 lubridate_1.9.0
[5] timechange_0.1.1 forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[9] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0 tibble_3.2.1
[13] ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-9 bit64_4.0.5
[4] RColorBrewer_1.1-3 httr_1.4.4 rprojroot_2.0.3
[7] tools_4.2.2 backports_1.4.1 bslib_0.4.1
[10] utf8_1.2.2 R6_2.5.1 KernSmooth_2.23-20
[13] rgeos_0.5-9 DBI_1.2.2 colorspace_2.0-3
[16] raster_3.6-11 withr_2.5.0 sp_1.5-1
[19] tidyselect_1.2.0 bit_4.0.5 compiler_4.2.2
[22] git2r_0.30.1 cli_3.6.1 rvest_1.0.3
[25] xml2_1.3.3 labeling_0.4.2 sass_0.4.4
[28] scales_1.2.1 checkmate_2.1.0 classInt_0.4-8
[31] proxy_0.4-27 digest_0.6.30 rmarkdown_2.18
[34] pkgconfig_2.0.3 htmltools_0.5.8.1 highr_0.9
[37] dbplyr_2.2.1 fastmap_1.1.0 rlang_1.1.1
[40] readxl_1.4.1 rstudioapi_0.15.0 farver_2.1.1
[43] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.3
[46] vroom_1.6.0 googlesheets4_1.0.1 magrittr_2.0.3
[49] Rcpp_1.0.10 munsell_0.5.0 fansi_1.0.3
[52] lifecycle_1.0.3 terra_1.7-65 stringi_1.7.8
[55] whisker_0.4 yaml_2.3.6 grid_4.2.2
[58] parallel_4.2.2 promises_1.2.0.1 crayon_1.5.2
[61] lattice_0.20-45 haven_2.5.1 hms_1.1.2
[64] knitr_1.41 pillar_1.9.0 codetools_0.2-18
[67] reprex_2.0.2 glue_1.6.2 evaluate_0.18
[70] data.table_1.14.6 modelr_0.1.10 vctrs_0.6.4
[73] tzdb_0.3.0 httpuv_1.6.6 cellranger_1.1.0
[76] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
[79] xfun_0.35 broom_1.0.5 e1071_1.7-12
[82] later_1.3.0 viridisLite_0.4.1 class_7.3-20
[85] googledrive_2.0.0 gargle_1.2.1 memoise_2.0.1
[88] workflowr_1.7.0 units_0.8-0 ellipsis_0.3.2