Subsurface Oxygen \(O_2\)

Figure. Change in Subsurface Oxygen from hindcast. The map (top) shows the change in subsurface oxygen concentration (µmol/L) in the vicinity of Palau over the period 1993-2022. The grey line is the Palau EEZ. The line plot (bottom) shows the change in subsurface oxygen concentration (µmol/L) averaged over the area within the top plot. The black line represents the trend, which is statistically significant (p < 0.05). The colored dots represent the 10 years with the lowest O2 on record.

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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from myst_nb import glue 

sys.path.append("../../../../indicators_setup")

from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize

sys.path.append("../../../functions")
from data_downloaders import download_oni_index
from ocean import process_trend_with_nan

Setup

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

path_figs = "../../../matrix_cc/figures"

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Load Data

Superficial depth

data_xr = xr.open_dataset(op.join(os.getcwd(), '..', '..','..', 'data/data_phyc_o2_ph.nc'))#.isel(depth = 0).drop('depth')
dataset_id = 'o2'
label = 'O2 [µmol/l]'

Analysis

Plotting

Average

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'BuPu', 
                vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label= label)
glue("average_map", fig, display=False)
plt.savefig(op.join(path_figs, 'F17_O2_mean_map.png'), dpi=300, bbox_inches='tight')

Change

trend_m, _, _, _, _ = process_trend_with_nan(data_xr[dataset_id])

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, 
               trend_m,
                transform=ccrs.PlateCarree(), 
                cmap = 'RdBu_r', 
                vmin = -3,
                vmax = 3,
                )
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label=  dataset_id)
plt.savefig(op.join(path_figs, 'F17_O2_mean_map_trend.png'), dpi=300, bbox_inches='tight')

Seasonal average

data_month = data_xr.groupby('time.season').mean().sel(season = ['DJF', 'MAM', 'JJA', 'SON'])
im = plot_map_subplots(data_month, dataset_id, shp_eez = shp_eez, cmap = 'BuPu', 
                  vmin = np.nanpercentile(data_month.min(dim = 'season')[dataset_id], 1), 
                  vmax = np.nanpercentile(data_month.max(dim = 'season')[dataset_id], 99),
                  figsize = (15,11), sub_plot = [1, 4], cbar_pad = 0.05, cbar = 1)

Seasonal anomaly

data_month = data_xr.groupby('time.season').mean().sel(season = ['DJF', 'MAM', 'JJA', 'SON']) - data_xr.mean(dim='time')
im = plot_map_subplots(data_month, dataset_id, shp_eez = shp_eez, 
                  cmap = 'RdBu_r', vmin=-1.2, vmax=1.2,
                  figsize = (15,11), sub_plot = [1, 4], cbar_pad = 0.05,
                  cbar = 1)

Annual average

data_y = data_xr.resample(time='1YE').mean()
im = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'BuPu', 
                  vmin = np.nanpercentile(data_xr.min(dim = 'time')[dataset_id], 1), 
                  vmax = np.nanpercentile(data_xr.max(dim = 'time')[dataset_id], 99),
                  cbar = 1)

Annual anomaly

data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-2, vmax=2, cbar = 1)

Average over area

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'{label} - MEAN AREA'},]

fig, trend = plot_timeseries_interactive(dict_plot, trendline=True, return_trend=True, scatter_dict = None, figsize = (25, 12), label_yaxes = label);
fig.write_html(op.join(path_figs, 'F17_O2_mean_trend.html'), include_plotlyjs="cdn")

Seasonal variability

fig, ax = plt.subplots(figsize=(12,2))
data_xr.mean(dim = ['longitude', 'latitude']).groupby('time.month').mean()[dataset_id].plot(ax = ax, marker = 'o', color = 'k')

[<matplotlib.lines.Line2D at 0x1887074d0>]

Timeseries at a given point

loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'{label} at [{loc[0]}, {loc[1]}]'},]

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x187ced760>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = label);

ONI index analysis

p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)

lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)

Group by ONI category

df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()

Average

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'BuPu', 
                  vmin = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 1) - 1, 
                  vmax = np.nanpercentile(data_xr.mean(dim = 'time')[dataset_id], 99) + 1,
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F17_O2_ENSO.png'), dpi=300, bbox_inches='tight')

Anomaly

data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-1, vmax=1,
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)

Table

from ind_setup.tables import style_matrix, table_ocean
style_matrix(table_ocean(data_xr, trend[0], data_oni, dataset_id))

Key Metrics Summary

Metric	Value
Monthly Average	200.018
Monthly Maximum 01/02/2019	203.530
Monthly Minimum 01/11/1998	196.795
Maximum Annual Average	201.180
Minimum Annual Average	198.524
Rate of change [µmol/l/year]	-0.037
Change between 1993 and 2025 [µmol/l]	-1.184
Average La Niña o2	199.736
Average El Niño o2	200.643
Average Neutral o2	199.932