Winter Arctic sea ice state variability (updates through to April 2022)#
Summary: In this notebook, we provide updates to the original winter Arctic sea ice thickness notebooks with an additional winter (2021-2022) of data collection. Here we read the IS2SITMOGR4 files from our AWS S3 bucket, instead of our aggregated Jupyter Book dataset. I generally focus on showing the ‘’smoothed/interpolated’ variables, whereas Petty et al., (2023) focussed more on the non-interpolated gridded data. Once we produce an updated aggregated Jupyter Book dataset we can update this notebook with those additional variables.
Version history: Version 1 (01/23/2023)
Import notebook dependencies#
# For working with gridded climate data
import xarray as xr
# Helper function for reading the data from the bucket
from utils.read_data_utils import read_IS2SITMOGR4
from utils.plotting_utils import static_winter_comparison_lineplot, staticArcticMaps, staticArcticMaps_overlayDrifts, interactiveArcticMaps, compute_gridcell_winter_means, interactive_winter_comparison_lineplot # Plotting utils
import numpy as np
# Plotting dependencies
#%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
# Sets figure size in the notebook
mpl.rcParams['figure.dpi'] = 150
# Remove warnings to improve display
import warnings
warnings.filterwarnings('ignore')
# Read in the raw monthly gridded winter Arctic sea ice data from S3 (still using V2)
IS2SITMOGR4_all = read_IS2SITMOGR4(data_type='zarr-s3',
local_data_path='./data/IS2SITMOGR4/', version='V2',persist=True)
IS2SITMOGR4_all
load zarr from S3 bucket
zarr_path: s3://icesat-2-sea-ice-us-west-2/IS2SITMOGR4_V3/IS2SITMOGR4_V3_201811-202404.zarr
<xarray.Dataset>
Dimensions: (time: 46, y: 448, x: 304)
Coordinates:
latitude (y, x) float32 31.1 31.2 ... 34.58 34.47
longitude (y, x) float32 168.3 168.1 ... -10.18 -9.999
* time (time) datetime64[ns] 2018-11-01 ... 2024...
* x (x) float32 -3.838e+06 ... 3.738e+06
* y (y) float32 5.838e+06 ... -5.338e+06
Data variables: (12/27)
crs (time) int32 dask.array<chunksize=(46,), meta=np.ndarray>
freeboard (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
freeboard_int (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
ice_density (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
ice_density_j22 (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
ice_thickness (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
... ...
snow_density_sm (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
snow_density_w99 (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
snow_depth (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
snow_depth_int (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
snow_depth_mw99 (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
snow_depth_sm (time, y, x) float32 dask.array<chunksize=(1, 448, 304), meta=np.ndarray>
Attributes:
contact: Alek Petty (akpetty@umd.edu)
description: Aggregated IS2SITMOGR4 V3 dataset.
history: Created 28/11/23
reference: Official NSIDC data doi: 10.5067/CV6JEXEE31HF. Derived data...# Years over which to perform analysis
years = [2018,2019,2020, 2021]
Winter mean Arctic maps (static)#
Compute and map (static) mean winter sea ice conditions across the years of analysis chosen
freeboard_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.freeboard_int, years=years)
staticArcticMaps(freeboard_winter_means, dates=freeboard_winter_means.time.values, set_cbarlabel = "Sea ice freeboard (m)", cmap="YlOrRd", col_wrap=4, vmin=0, vmax=0.8, out_str='freeboard_winter_2018_2022')
snow_depth_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.snow_depth_int, years=years)
staticArcticMaps(snow_depth_winter_means, dates=snow_depth_winter_means.time.values,set_cbarlabel = "Snow depth (m)", cmap="inferno", col_wrap=4, vmin=0, vmax=0.5, out_str='snowdepth_winter_2018_2022')
thickness_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.ice_thickness_int, years=years)
staticArcticMaps(thickness_winter_means, dates=thickness_winter_means.time.values,title="", set_cbarlabel = "Sea ice thickness (m)", col_wrap=4, cmap="viridis", vmin=0, vmax=5, out_str='thickness_winter_2018_2022')
snow_density_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.snow_density, years=years)
staticArcticMaps(snow_density_winter_means, dates=snow_density_winter_means.time.values,set_cbarlabel = "Snow density (kg/m3)", cmap="GnBu", col_wrap=4, vmin=240, vmax=330, out_str='snowdensity_winter_2018_2022')
ice_type_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.ice_type, years=years)
staticArcticMaps(ice_type_winter_means, dates=ice_type_winter_means.time.values, set_cbarlabel = "Sea ice type (0 = FYI, 1 = MYI)", col_wrap=4, cmap="YlOrRd", vmin=0, vmax=1, out_str='icetype_winter_2018_2022')
Winter anomaly plots#
Compute and map (static) winter anomaly maps (relative to the mean across the three winters by default) for given variables across the four winters
freeboard_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.freeboard, years=years)
staticArcticMaps(freeboard_winter_means-freeboard_winter_means.mean(axis=0), dates=freeboard_winter_means.time.values,title="", col_wrap=4, set_cbarlabel = "Sea ice freeboard anomaly (m)", cmap="BrBG", vmin=-0.2, vmax=0.2, out_str='freeboard_winter_anomaly_2018_2022')
snow_depth_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.snow_depth, years=years)
staticArcticMaps(snow_depth_winter_means-snow_depth_winter_means.mean(axis=0), dates=snow_depth_winter_means.time.values,col_wrap=4, set_cbarlabel = "Snow depth anomaly (m)", cmap="PRGn", vmin=-0.2, vmax=0.2, out_str='snowdepth_winter_anomaly_2018_2022')
thickness_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.ice_thickness_int, years=years)
staticArcticMaps(thickness_winter_means-thickness_winter_means.mean(axis=0), dates=thickness_winter_means.time.values,col_wrap=4, set_cbarlabel = "Sea ice thickness anomaly (m)", cmap="RdBu", vmin=-1.5, vmax=1.5, out_str='thickness_winter_anomaly_2018_2022')
Monthly mean timeseries#
Next we’ll compute monthly means by averaging over all gridcells within a given region. We’ll use this to generate a lineplot to compare across the three winter seasons for each variable.
# Here is where we might also want to set a region mask, e.g. to avoid including some of the more uncertain data in the peripheral seas
innerArctic = [1,2,3,4,5,6]
IS2SITMOGR4_all_innerArctic = IS2SITMOGR4_all.where(IS2SITMOGR4_all.region_mask.isin(innerArctic))
# Uncomment out to set an additional ice type mask too and change the save_label accordingly (0 = FYI, 1 = MYI)
#IS2SITMOGR4_all_innerArctic = IS2SITMOGR4_all_innerArctic.where(IS2SITMOGR4_all_innerArctic.ice_type==1)
save_label='Inner_Arctic'
static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.freeboard, years=years, start_month="Sep", figsize=(5,3), annotation='(a)', set_ylabel=r'Total freeboard (m)', save_label=save_label, loc_pos=4, legend=True)
static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.snow_depth, years=years, start_month="Sep", figsize=(5,3), annotation='(b)',set_ylabel='Snow depth (m)', save_label=save_label, legend=False)
static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.snow_density, years=years, start_month="Sep", figsize=(5,3), annotation='(c)',set_ylabel=r'Snow density (kg/m$^3$)', save_label=save_label, legend=False)
static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.ice_thickness,
da_unc = IS2SITMOGR4_all_innerArctic.ice_thickness_unc,
years=years, start_month="Sep", figsize=(5,3), set_ylabel='Sea ice thickness (m)', save_label=save_label, annotation='', legend=True)
static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.sea_ice_conc, years=years, start_month="Sep", figsize=(5,3), annotation='(e)', set_ylabel='Sea ice concentration', save_label=save_label, legend=False)
static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.ice_type, years=years, start_month="Sep", figsize=(5,3), annotation='(f)',set_ylabel='Multi-year ice fraction', save_label=save_label, legend=False)
Winter mean Arctic maps (interactive, preliminary)#
Interactively map the mean winter sea ice conditions across the most recent winter months.
NB: interactive plotting is slow but can be useful for the zoom functionality and for looking at exacty grid-cell/regional values. Plotting all months in the dataset pushes the notebook above 100 Mb which triggers a GitHubt warning
interactiveArcticMaps(IS2SITMOGR4_all.ice_thickness_int.sel(time=slice('2021-09-01', '2022-04-01')), title='IS2SITMOGR4', frame_width=600)