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weddell_polynya_paper.py
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weddell_polynya_paper.py
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# -*- coding: utf-8 -*-
print('Script is loading dependencies.')
# external imports
import os
from numpy import *
import scipy.io as sio
import scipy.interpolate as spin
from scipy import stats
import pandas as pd
import pandas.plotting._converter as pandacnv # FIXME: only necessary due to Pandas 0.21.0 bug with Datetime plotting
pandacnv.register() # FIXME: only necessary due to Pandas 0.21.0 bug with Datetime plotting
import xarray as xr
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.cbook as mcbook
import matplotlib.dates as mdates
import matplotlib.ticker as pltick
import matplotlib.legend as mlegend
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.patches import Polygon, Rectangle, ConnectionPatch
if os.path.isdir('/Applications/anaconda/share/proj'): # needed for Basemap import on my machine, but probably not yours
os.environ['PROJ_LIB'] = '/Applications/anaconda/share/proj'
from mpl_toolkits.basemap import Basemap
from matplotlib import gridspec
from datetime import datetime, timedelta
from collections import OrderedDict
from Circles.circles import circle # from https://github.com/urschrei/Circles
import pickle
import warnings
import time
import gsw
# import custom functions
import download_product as dlp
import load_product as ldp
import time_tools as tt
import plot_tools as pt
import geo_tools as gt
# custom settings
set_printoptions(threshold=100) # speeds up printing of large dicts containing NumPy arrays during debugging
plt.ion() # necessary for interactive contour label placement
# prettier font for plots
# note: before uncommenting, have to 'install' Helvetica using fondu (see instructions at https://goo.gl/crpbt2)
# mpl.rc('font',**{'family':'sans-serif','sans-serif':'Helvetica'})
####################################################################################################
# download data
download_argo = False
download_soccom = False
download_amsr2 = False
download_amsre = False
download_dmsp_nrt = False
download_dmsp_v3 = False
download_nimbus5 = False
download_ecmwf = False
download_isd = False
# data preparation/processing routines
argo_cal_5904468 = False
process_ecmwf = False
load_erai_land_sea_mask = True # always keep this True!
# analyze data and generate paper figures
plot_fig_1 = False # note: this routine requires manual input for contour labels
plot_fig_2_ED_figs_3_4 = False
plot_fig_3 = False
plot_fig_4_ED_figs_6_7 = False
plot_fig_5_ED_fig_9_ED_table_1 = False
plot_ED_fig_1 = False
plot_ED_figs_2_8 = False
plot_ED_fig_5 = False
# directory for plotting output
current_results_dir = os.getcwd() + '/Results/'
# directory for h4toh5 executable
script_dir = os.getcwd() + '/'
# root directory for data files
data_dir = os.getcwd() + '/Data/'
# sub-directories for data files
argo_gdac_dir = data_dir + 'Argo/'
soccom_dir = argo_gdac_dir + 'SOCCOM/'
uw_o2_dir = argo_gdac_dir + 'UW-O2/'
shipboard_dir = data_dir + 'Shipboard/'
wod_dir = shipboard_dir + 'WOD/'
waghc_dir = shipboard_dir + 'WAGHC2017/'
amsr2_dir = data_dir + 'Sea ice concentration/AMSR2/'
amsre_dir = data_dir + 'Sea ice concentration/AMSR-E/'
dmsp_nrt_dir = data_dir + 'Sea ice concentration/DMSP_NRT/'
dmsp_v3_dir = data_dir + 'Sea ice concentration/DMSP_v3/'
nimbus5_dir = data_dir + 'Sea ice concentration/Nimbus-5/'
amsr_gridfile = data_dir + 'Sea ice concentration/AMSR_grid/LongitudeLatitudeGrid-s6250-Antarctic.h5'
amsr_areafile = data_dir + 'Sea ice concentration/AMSR_grid/pss06area_v3.dat'
nsidc_ps25_grid_dir = data_dir + 'Sea ice concentration/NSIDC_polar_stereo_25km_grid/'
coastline_filename_prefix = data_dir + 'GSHHG coast shapefiles/l/GSHHS_l_L5'
climate_indices_dir = data_dir + 'Climate indices/'
reanalysis_dir = data_dir + 'Reanalysis/'
era_new_dir = data_dir + 'Reanalysis/ECMWF_Weddell_unprocessed/'
era_custom_dir = data_dir + 'Reanalysis/ECMWF_Weddell_processed/'
era_processed_gwk_moore_dir = data_dir + 'Reanalysis/ECMWF_processed_GWKMoore/'
isd_dir = data_dir + 'ISD station records/'
reader_dir = data_dir + 'READER station records/'
# sub-directories for serialized ("pickled") processed data
figure_pickle_dir = data_dir + 'Processed_pickle_archives/'
argo_index_pickle_dir = argo_gdac_dir + 'Argo_index_pickles/'
############################################ DATA DOWNLOAD ##########################################################
print('Script is starting.')
# download Argo data from GDAC and update pickles
if download_argo:
# note: set overwrite_global_index to True when checking for new/updated profiles
dlp.argo_gdac((1990,1,1),(2018,10,1),[-80,-55],[-70,50],argo_gdac_dir,overwrite_global_index=False,
overwrite_profs=False,bypass_download=False,
only_download_wmoids=[])
argo_gdac_index = ldp.argo_gdac_load_index(argo_gdac_dir)
pickle.dump(argo_gdac_index,open(argo_index_pickle_dir + 'argo_gdac_index.pickle','wb'))
# correct float 5904468 for salinity drift
# note: run this after downloading new Argo data; see #FIXME notes in ldp.argo_float_data()
if argo_cal_5904468:
argo_gdac_index = pickle.load(open(argo_index_pickle_dir + 'argo_gdac_index.pickle','rb'))
argo_soccom_index = pickle.load(open(argo_index_pickle_dir + 'argo_soccom_index.pickle','rb'))
# export calibration based on average salinity between 1600-1700 m
gdac_data = ldp.argo_float_data(5904468,argo_gdac_dir,argo_gdac_index,argo_soccom_index,
prof_nums='all',verbose=False,use_unadjusted=False,correct_5904468_interim=False)
gdac_num_profs = len(gdac_data['profiles'])
gdac_prof_nums = array([gdac_data['profiles'][p]['prof_num'] for p in range(gdac_num_profs)])
gdac_time_coord = array([tt.convert_tuple_to_datetime(tt.convert_14_to_tuple(gdac_data['profiles'][p]['datetime']))
for p in range(gdac_num_profs)])
gdac_sal_series = array([gt.vert_prof_eval(gdac_data['profiles'][p],'psal',(1600,1700),z_coor='depth',
interp_method='linear',extrap='NaN',avg_method='interp',avg_spacing=1.0,
avg_nan_tolerance=1.0) for p in range(gdac_num_profs)])
last_good_sal = gdac_sal_series[where(gdac_prof_nums == 83)[0][0]]
prof_idx_mask_for_trend = logical_and(gdac_prof_nums >= 83, gdac_prof_nums <= 118)
prof_idx_mask_to_cal = logical_and(gdac_prof_nums >= 84, gdac_prof_nums <= 118)
[cal_slope,cal_intercept] = stats.linregress(mdates.date2num(gdac_time_coord[prof_idx_mask_for_trend]),
gdac_sal_series[prof_idx_mask_for_trend])[0:2]
sal_trend = cal_intercept + cal_slope * mdates.date2num(gdac_time_coord)
sal_deltas = -1 * (sal_trend - sal_trend[where(gdac_prof_nums == 83)[0][0]])
# note: to not zero the trend at its leftmost y-value, subtract last_good_sal instead (a subjective choice)
sal_deltas_within_cal_period_only = zeros(gdac_num_profs)
sal_deltas_within_cal_period_only[prof_idx_mask_to_cal] = sal_deltas[prof_idx_mask_to_cal]
pickle.dump([gdac_prof_nums,sal_deltas_within_cal_period_only],
open(argo_index_pickle_dir + 'argo_5904468_cal.pickle','wb'))
# download and/or process SOCCOM data and update pickles
# note: it is preferred to download quarterly SOCCOM snapshots, which have a DOI, rather than the latest data
# so this offers two options:
# 1) to just process already-downloaded quarterly DOI snapshots, uncomment the ldp.argo_soccom() routine
# 2) to download near-real-time FTP files and process them, uncomment the dlp.argo_soccom() routine
if download_soccom:
# dlp.argo_soccom(argo_gdac_dir,overwrite_profs=True) # OPTION 2
ldp.argo_soccom(soccom_dir) # OPTION 1
argo_soccom_index = ldp.argo_soccom_load_index(soccom_dir,uw_o2_dir,verbose=True)
pickle.dump(argo_soccom_index,open(argo_index_pickle_dir + 'argo_soccom_index.pickle','wb'))
# download AMSR2 sea ice data and convert from HDF4 to HDF5
if download_amsr2:
dlp.amsr(2,(2012,7,4),(2019,2,26),amsr2_dir,get_pdfs=False,overwrite=False,convert=True,
conversion_script_dir=script_dir)
# download AMSR-E sea ice data and convert from HDF4 to HDF5
if download_amsre:
dlp.amsr(1,(2002,6,1),(2011,10,4),amsre_dir,get_pdfs=False,overwrite=False,convert=True,
conversion_script_dir=script_dir)
# download NSIDC v1 NRT CDR sea ice data
# note: check FTP link for new data availability; update satellite abbreviations/dates accordingly within functions,
# including gt.identify_polynyas_magic()
if download_dmsp_nrt:
dlp.dmsp_nrt((2018,1,1),(2019,2,26),dmsp_nrt_dir,overwrite=True)
# download NSIDC v3 GSFC Merged/CDR sea ice data
# note: check FTP link for new data availability; update satellite abbreviations/dates accordingly within functions,
# including gt.identify_polynyas_magic()
# note: if a new year of data is released, delete the corresponding NSIDC NRT CDR data
if download_dmsp_v3:
dlp.dmsp_v3((1978,11,1),(2017,12,31),dmsp_v3_dir,overwrite=False)
# download and unzip Nimbus-5 sea ice data
# note: this requires an EarthData login username and password and a local key; see dlp.nimbus5() for instructions
if download_nimbus5:
dlp.nimbus5((1972,12,12),(1977,5,11),nimbus5_dir,convert=True,conversion_script_dir=script_dir)
# download station meteorological data from Integrated Surface Database
if download_isd:
dlp.isd_station(895120,1973,2019,isd_dir,overwrite=True) # Novolazarevskaya
dlp.isd_station(890010,1973,1994,isd_dir,overwrite=True) # SANAE SAF
dlp.isd_station(890040,1997,2019,isd_dir,overwrite=True) # SANAE AWS
dlp.isd_station(890020,1981,2019,isd_dir,overwrite=True) # Neumayer
dlp.isd_station(895140,1990,2019,isd_dir,overwrite=True) # Maitri
# submit MARS request for ERA-Interim reanalysis fields
# note: submit one at a time; cancel using Ctrl-C (or "stop" button) immediately after seeing "Request is queued"
# then download using Chrome from: http://apps.ecmwf.int/webmars/joblist/
# and save using filenames in comments in folder 'ECMWF_Weddell_unprocessed'
# then run 'process_ecmwf' routine below
if download_ecmwf:
which_to_download = 1 # change to submit one at a time (see note above) - recommend order 3, 4, 1, 2
# daily fields
if which_to_download == 1: # analysis; save as 'erai_daily_weddell.nc'
dlp.ecmwf(date_range='1979-01-01/to/2018-12-31',area='-40/-90/-90/90',output_filename=None,type='an',
step='0',time='00/06/12/18',params=['msl','sst','skt','t2m','d2m','u10','v10'])
elif which_to_download == 2: # forecast; save as 'erai_daily_weddell_forecast.nc'
dlp.ecmwf(date_range='1979-01-01/to/2018-12-31',area='-40/-90/-90/90',output_filename=None,type='fc',
step='6/12',time='00/12',params=['sf','sshf','slhf','ssr','str','strd','e','tp','iews','inss'])
# monthly means
elif which_to_download == 3: # analysis; save as 'erai_monthly_mean_weddell.nc'
dlp.ecmwf(date_range=[datetime(1979,1,1),datetime(2018,12,1)],area='-40/-90/-90/90',output_filename=None,
type='an',step=None,time=None,params=['msl','sp','sst','skt','t2m','u10','v10','si10'])
elif which_to_download == 4: # forecast; save as 'erai_monthly_mean_weddell_forecast.nc'
dlp.ecmwf(date_range=[datetime(1979,1,1),datetime(2018,12,1)],area='-40/-90/-90/90',output_filename=None,
type='fc',step=None,time=None,params=['iews','inss'])
# process newly downloaded ECMWF reanalysis files (calculate derived quantities, de-accumulate, and re-export)
# note: once finished, manually delete unprocessed files and any processed chunks
if process_ecmwf:
for filename in os.listdir(path=era_new_dir):
if filename == '.DS_Store': continue
ldp.load_ecmwf(era_new_dir,filename,export_to_dir=era_custom_dir,verbose=True)
# load ERA-Interim land-sea mask
if load_erai_land_sea_mask:
erai_mask = ldp.load_ecmwf_mask(reanalysis_dir,'erai_land_sea_mask.nc')
###################################### ANALYSIS ROUTINES ######################################################
# Fig. 1. Polynyas of 1974, 2016 and 2017 in relation to profiling float trajectories near Maud Rise.
if plot_fig_1:
# NOTE: bathymetry contours require manual input to select locations (click, then Return)
plot_fig_1a = True
plot_fig_1b = True
plot_fig_1c = True
use_fig_1a_pickle = True # must be False after changing polynya dates for contour (not a big slowdown)
# establish Antarctic coastline
circumant_lons,circumant_lats = gt.establish_coastline(coastline_filename_prefix)
# load sea ice concentration metadata
[sea_ice_grids,sea_ice_data_avail,sea_ice_all_dates] = ldp.sea_ice_data_prep(nimbus5_dir,dmsp_v3_dir,dmsp_nrt_dir,
amsre_dir,amsr2_dir,amsr_gridfile,
amsr_areafile,nsidc_ps25_grid_dir)
# plot parameters
open_sic_plotting = 50 # SIC to be plotted as open
open_sic_polynya = 50 # SIC threshold for polynyas
extent_threshold_weddell = 30000 # minimum polynya extent in km^2 to identify/plot
map_params_weddell = [1700000,1800000,-67.0,0] # width,height,lat_center,lon_center
map_params_floats_2016 = [475000,520000,-65.25,4.25]
map_params_floats_2017 = [465000,520000,-65.25,2.75]
labelsize = 5
station_names = ['Novolazarevskaya Station']
station_locs = [[11.8,-70.8]] # lon, lat
station_colors = ['w']
station_markers = ['^']
motoi_fuji_loc = [1.2,-66.5] # lon, lat of R/V Fuji MLS observation on 1974-02-27 from Motoi et al. 1987
mr_center = [-65.0,3.0] # summit of Maud Rise (65.0°S, 3.0°E)
mr_hydro_radius = 250 # search radius (km) from Maud Rise
mr_bathy_contours = arange(-3250,760,750)
ocean_color = '#bce6fc' # light blue
ocean_color_light = '#ddf2fd' # lighter version of the above
polynya_2016_date = (2016,8,6)
polynya_color_2016 = '#ffae1a'
polynya_color_2016_light = '#ffce75'
polynya_color_2016_dark = '#AA7411'
polynya_line_2016 = '-'
polynya_2017_date_sic = (2017,11,21)
polynya_2017_date_string = '2017-11-21'
polynya_2017_date = (2017,9,25)
polynya_color_2017 = '#0000cd'
polynya_color_2017_medium = '#4c4cdc'
polynya_color_2017_light = '#8484e7'
polynya_color_2017_dark = '#000089'
polynya_line_2017 = '-'
polynya_1974_date = (1974,10,12)
polynya_color_1974 = 'maroon'
polynya_line_1974 = '-'
float_wmoids = [5903616,5904468,5904471]
float_markers = ['^','o','s']
float_lines = [(0,(1,1)),'-.','--']
float_line_colors = ['0.2','k','k']
float_start_colors = ['lime','lime','lime']
float_start_sizes = [10,10,10]
polynya_marker_sizes = [None,25,25]
toi_5903616 = [20111218000000,20160603000000]
toi_5904468a = [20150118000000,20170110000000]
toi_5904468b = [20161231000000,20180509000000]
toi_5904471a = [20141220000000,20170110000000]
toi_5904471b = [20161231000000,20180623000000]
float_polynya_plot_dates = [polynya_2016_date,polynya_2017_date]
polynya_colors = [polynya_color_2016,polynya_color_2017]
# establish figure
if plot_fig_1a or plot_fig_1b or plot_fig_1c:
fig = plt.figure(figsize=(6.4,2.5))
subplot_grid_left \
= gridspec.GridSpec(1,3,width_ratios=[1.1*map_params_weddell[0]*map_params_floats_2016[1]/map_params_weddell[1],
map_params_floats_2016[0],map_params_floats_2017[0]],wspace=0.20)
subplot_grid_right \
= gridspec.GridSpec(1,3,width_ratios=[1.1*map_params_weddell[0]*map_params_floats_2016[1]/map_params_weddell[1],
map_params_floats_2016[0],map_params_floats_2017[0]],wspace=0.05)
# 1974, 2016, and 2017 polynyas
if plot_fig_1a:
ax1 = plt.subplot(subplot_grid_left[0])
sic_grid = sea_ice_grids['amsr2']
sic_field = ldp.load_amsr(sea_ice_data_avail['amsr2'][polynya_2017_date_sic][0],regrid_to_25km=False)
m1,pcm = pt.sea_ice_argo_spatial(data_dir,polynya_2017_date_sic,sic_grid,sic_field,None,None,None,None,None,
*map_params_weddell,plot_floats=False,polynya_grid=None,open_sic=0,
rasterized=True,as_subplot=True,create_subplot=False,
subplot_add_colorbar=False,
bathy_contours=[],subplot_lon_labels=[0,0,1,0],subplot_lat_labels=[1,0,0,0],
grid_lats=arange(-80,60,5),grid_lons=arange(-80,50,10),
subplot_labelsize=labelsize,grid_color='0.7',
which_ice_cmap=5,cmap_bad_color=ocean_color,cmap_ocean_color=ocean_color,
continent_color='0.7',boundary_width=0.5,coastline_width=0.5,
return_basemap=True,return_pcolor=True)
sic_grid = sea_ice_grids['nimbus5']
polynya_grid_1974 = gt.identify_polynyas_magic('nimbus5',polynya_1974_date,sea_ice_grids,sea_ice_data_avail,
circumant_lons,circumant_lats,open_threshold=open_sic_polynya,
extent_threshold=extent_threshold_weddell,identify_bad=True)[7]
m1.contour(*m1(sic_grid['lons'],sic_grid['lats']),polynya_grid_1974,levels=[0.999],
colors=polynya_color_1974,linestyles=polynya_line_1974,linewidths=1.0,alpha=0.9,zorder=3)
sic_grid = sea_ice_grids['amsr2']
if use_fig_1a_pickle:
[polynya_grid_2016,polynya_grid_2017] = pickle.load(open(figure_pickle_dir + 'fig_1a','rb'))
else:
polynya_grid_2016 = gt.identify_polynyas_magic('amsr2',polynya_2016_date,sea_ice_grids,sea_ice_data_avail,
circumant_lons,circumant_lats,
open_threshold=open_sic_polynya,
extent_threshold=extent_threshold_weddell,identify_bad=True,
regrid_amsr_to_25km=False)[7]
polynya_grid_2017 = gt.identify_polynyas_magic('amsr2',polynya_2017_date,sea_ice_grids,sea_ice_data_avail,
circumant_lons,circumant_lats,
open_threshold=open_sic_polynya,
extent_threshold=extent_threshold_weddell,identify_bad=True,
regrid_amsr_to_25km=False)[7]
pickle.dump([polynya_grid_2016,polynya_grid_2017],open(figure_pickle_dir + 'fig_1a','wb'))
m1.contour(*m1(sic_grid['lons'],sic_grid['lats']),polynya_grid_2017,levels=[0.999],
colors=polynya_color_2017_dark,linestyles=polynya_line_2017,linewidths=0.5,alpha=0.9,zorder=3)
m1.contourf(*m1(sic_grid['lons'],sic_grid['lats']),polynya_grid_2017,levels=[0.999,1.1],
colors=polynya_color_2017,alpha=0.25,zorder=3)
m1.contour(*m1(sic_grid['lons'],sic_grid['lats']),polynya_grid_2016,levels=[0.999],
colors=polynya_color_2016,linestyles=polynya_line_2016,linewidths=0.5,alpha=0.9,zorder=3)
m1.contourf(*m1(sic_grid['lons'],sic_grid['lats']),polynya_grid_2016,levels=[0.999,1.1],
colors=polynya_color_2016,alpha=0.25,zorder=3)
m1.scatter(*m1(*station_locs[0]),s=14,c=station_colors[0],marker=station_markers[0],
edgecolors='k',linewidths=0.25,zorder=3)
m1.scatter(*m1(*motoi_fuji_loc),s=17,c='maroon',marker='*',
edgecolors='maroon',linewidths=0.25,zorder=3)
c1, = plt.plot(0,NaN,ls=polynya_line_1974,color=polynya_color_1974,linewidth=1.0,alpha=0.8,
label='{0}-{1:02d}-{2:02d}'.format(*polynya_1974_date)) # dummy handles for legend
c4, = plt.plot([0,0],[NaN,NaN],c='maroon',marker='*',ms=5,mec='maroon',mew=0.25,ls='none',
label=r'R/V $\it{Fuji}$, 1974-02-27')
c5, = plt.plot([0,0],[NaN,NaN],c=station_colors[0],marker=station_markers[0],ms=3,mec='k',mew=0.25,ls='none',
label=station_names[0])
leg1 = ax1.legend(handles=[c4,c1],ncol=2,loc='upper center',bbox_to_anchor=(0.5,-0.010),
fontsize=labelsize,columnspacing=1.5,handletextpad=0.6,handlelength=1.5,frameon=False)
leg2 = mlegend.Legend(ax1,[c5],[station_names[0]],ncol=1,fontsize=labelsize,
columnspacing=1.5,handletextpad=0.6,handlelength=1.5,frameon=False)
leg1._legend_box._children.append(leg2._legend_box._children[1])
leg1._legend_box.align = 'center'
m1_ax = plt.gca()
plt.gca().set_anchor('C')
plt.text(0.96,0.97,polynya_2017_date_string,color='0.2',size=labelsize+1,fontweight='bold',
horizontalalignment='right',verticalalignment='top',transform=ax1.transAxes)
# 2016 polynya
if plot_fig_1b:
ax2 = plt.subplot(subplot_grid_right[1])
argo_gdac_index = pickle.load(open(argo_index_pickle_dir + 'argo_gdac_index.pickle','rb'))
float_data_all = []
for wmoid in float_wmoids:
this_float_meta = ldp.argo_gdac_float_meta(argo_gdac_index['local_prof_index'],wmoid)
if wmoid == 5903616: toi_mask = logical_and(this_float_meta['prof_datetimes'] >= toi_5903616[0],
this_float_meta['prof_datetimes'] <= toi_5903616[1])
if wmoid == 5904468: toi_mask = logical_and(this_float_meta['prof_datetimes'] >= toi_5904468a[0],
this_float_meta['prof_datetimes'] <= toi_5904468a[1])
if wmoid == 5904471: toi_mask = logical_and(this_float_meta['prof_datetimes'] >= toi_5904471a[0],
this_float_meta['prof_datetimes'] <= toi_5904471a[1])
float_data_all.append([wmoid,this_float_meta['prof_lons'][toi_mask],this_float_meta['prof_lats'][toi_mask],
this_float_meta['prof_position_flags'][toi_mask],
this_float_meta['prof_datetimes'][toi_mask]])
_,m2 = pt.bathy_basemap(data_dir,*map_params_floats_2016,create_new_fig=False,
labelsize=labelsize,boundary_width=0.5,lon_labels_on_top=True,
grid_color='0.4',label_contours=True,cmap='Greys_r',bathy_alpha=0.6,
grid_lats=arange(-70,-60,2),grid_lons=arange(-80,50,5))
for f in range(len(float_data_all)):
zob = f * 7 # zorder baseline
mk = float_markers[f]
fl = float_line_colors[f]
fls = float_lines[f]
start_mark = float_markers[f]
start_color = float_start_colors[f]
start_size = float_start_sizes[f]
polynya_marker_size = polynya_marker_sizes[f]
wmoid = float_data_all[f][0]
lons = float_data_all[f][1]
lats = float_data_all[f][2]
position_flags = float_data_all[f][3]
datetimes = array([datetime(*tt.convert_14_to_tuple(dt)[0:3]) for dt in float_data_all[f][4]])
lonx,laty = m2(lons,lats)
plt.plot(lonx[position_flags != 9],laty[position_flags != 9],color=fl,lw=0.75,ls=fls,zorder=zob+3)
plt.scatter(lonx[0],laty[0],s=start_size,c=start_color,marker=start_mark,edgecolors='none',zorder=zob+6)
if wmoid == 5904468 or wmoid == 5904471:
date_int = tt.convert_tuple_to_8_int(polynya_2016_date) * 1000000
if sum((float_data_all[f][4] - date_int) == 0) >= 1:
polynya_prof_idx = where((float_data_all[f][4] - date_int) == 0)[0][0]
else:
polynya_prof_idx = where((float_data_all[f][4] - date_int) < 0)[0][-1]
polynya_lon = lons[polynya_prof_idx]
polynya_lat = lats[polynya_prof_idx]
plt.scatter(*m2(polynya_lon,polynya_lat),s=polynya_marker_size,c=polynya_color_2016,marker='*',
edgecolor='k',linewidths=0.5,zorder=zob+6)
c1, = plt.plot([0,0],[NaN,NaN],lw=0.75,ls=float_lines[0],c=float_line_colors[0],label='{0}'.format(float_wmoids[0]))
c2, = plt.plot([0,0],[NaN,NaN],lw=0.75,ls=float_lines[1],c=float_line_colors[1],label='{0}'.format(float_wmoids[1]))
c3, = plt.plot([0,0],[NaN,NaN],lw=0.75,ls=float_lines[2],c=float_line_colors[2],label='{0}'.format(float_wmoids[2]))
leg1 = ax2.legend(handles=[c1,c2,c3],ncol=3,loc='upper center',bbox_to_anchor=(1.025,-0.010),
fontsize=labelsize,columnspacing=2.5,handletextpad=0.6,handlelength=2.25,frameon=False,
markerscale=1,scatterpoints=1)
c4, = plt.plot([0,0],[NaN,NaN],c=polynya_color_2016_light,ls='-',lw=3,
label='{0}-{1:02d}-{2:02d}'.format(*polynya_2016_date),
marker='*',ms=5,markerfacecolor=polynya_color_2016,markeredgecolor='k',markeredgewidth=0.5)
c5, = plt.plot([0,0],[NaN,NaN],c=polynya_color_2017_light,ls='-',lw=3,
label='{0}-{1:02d}-{2:02d}'.format(*polynya_2017_date),
marker='*',ms=5,markerfacecolor=polynya_color_2017,markeredgecolor='k',markeredgewidth=0.5)
leg2 = mlegend.Legend(ax2,[c4,c5],['{0}-{1:02d}-{2:02d}'.format(*polynya_2016_date),
'{0}-{1:02d}-{2:02d}'.format(*polynya_2017_date)],ncol=2,fontsize=labelsize,
columnspacing=3.0,handletextpad=0.6,handlelength=2.25,frameon=False)
leg1._legend_box._children.append(leg2._legend_box._children[1])
leg1._legend_box.align = 'center'
sic_grid = sea_ice_grids['amsr2']
sic_field = ldp.load_amsr(sea_ice_data_avail['amsr2'][polynya_2016_date][0],regrid_to_25km=False)
m2.contour(*m2(sic_grid['lons'],sic_grid['lats']),sic_field,levels=[open_sic_plotting+1],
colors=polynya_color_2016_dark,linewidths=0.5,alpha=0.9,zorder=4)
sic_field = ma.masked_where(sic_field > open_sic_plotting,sic_field)
m2.contourf(*m2(sic_grid['lons'],sic_grid['lats']),sic_field,levels=[0,open_sic_plotting-1],
colors=polynya_color_2016,alpha=0.40,zorder=3)
plt.text(0.96,0.97,'2011–2016',color='w',size=labelsize+1,fontweight='bold',
horizontalalignment='right',verticalalignment='top',transform=ax2.transAxes)
# 2017 polynya
if plot_fig_1c:
ax3 = plt.subplot(subplot_grid_right[2])
argo_gdac_index = pickle.load(open(argo_index_pickle_dir + 'argo_gdac_index.pickle','rb'))
float_data_all = []
for wmoid in float_wmoids:
this_float_meta = ldp.argo_gdac_float_meta(argo_gdac_index['local_prof_index'],wmoid)
if wmoid == 5903616: toi_mask = logical_and(this_float_meta['prof_datetimes'] >= toi_5903616[0],
this_float_meta['prof_datetimes'] <= toi_5903616[1]) # dummy
if wmoid == 5904468: toi_mask = logical_and(this_float_meta['prof_datetimes'] >= toi_5904468b[0],
this_float_meta['prof_datetimes'] <= toi_5904468b[1])
if wmoid == 5904471: toi_mask = logical_and(this_float_meta['prof_datetimes'] >= toi_5904471b[0],
this_float_meta['prof_datetimes'] <= toi_5904471b[1])
float_data_all.append([wmoid,this_float_meta['prof_lons'][toi_mask],this_float_meta['prof_lats'][toi_mask],
this_float_meta['prof_position_flags'][toi_mask],
this_float_meta['prof_datetimes'][toi_mask]])
_,m3 = pt.bathy_basemap(data_dir,*map_params_floats_2017,create_new_fig=False,
labelsize=labelsize,boundary_width=0.5,lon_labels_on_top=True,
grid_color='0.4',label_contours=False,cmap='Greys_r',bathy_alpha=0.6,
grid_lats=arange(-70,-60,2),grid_lons=arange(-80,50,5),force_lat_labels=[0,0,0,0])
for f in range(len(float_data_all)):
if f == 0: continue
zob = f * 7 # zorder baseline
mk = float_markers[f]
fl = float_line_colors[f]
fls = float_lines[f]
start_mark = float_markers[f]
start_color = float_start_colors[f]
start_size = float_start_sizes[f]
polynya_marker_size = polynya_marker_sizes[f]
wmoid = float_data_all[f][0]
lons = float_data_all[f][1]
lats = float_data_all[f][2]
position_flags = float_data_all[f][3]
datetimes = array([datetime(*tt.convert_14_to_tuple(dt)[0:3]) for dt in float_data_all[f][4]])
lonx,laty = m3(lons,lats)
plt.plot(lonx[position_flags != 9],laty[position_flags != 9],color=fl,lw=0.75,ls=fls,zorder=zob+3)
plt.scatter(lonx[0],laty[0],s=start_size-6,c=start_color,marker=start_mark,edgecolors='none',zorder=zob+6)
if wmoid == 5904468 or wmoid == 5904471:
date_int = tt.convert_tuple_to_8_int(polynya_2017_date) * 1000000
if sum((float_data_all[f][4] - date_int) == 0) >= 1:
polynya_prof_idx = where((float_data_all[f][4] - date_int) == 0)[0][0]
else:
polynya_prof_idx = where((float_data_all[f][4] - date_int) < 0)[0][-1]
polynya_lon = lons[polynya_prof_idx]
polynya_lat = lats[polynya_prof_idx]
plt.scatter(*m3(polynya_lon,polynya_lat),s=polynya_marker_size,c=polynya_color_2017_medium,marker='*',
edgecolor='k',linewidths=0.5,zorder=zob+6)
sic_grid = sea_ice_grids['amsr2']
sic_field = ldp.load_amsr(sea_ice_data_avail['amsr2'][polynya_2017_date][0],regrid_to_25km=False)
m3.contour(*m3(sic_grid['lons'],sic_grid['lats']),sic_field,levels=[open_sic_plotting+1],
colors=polynya_color_2017_dark,linewidths=0.5,alpha=0.70,zorder=4)
sic_field = ma.masked_where(sic_field > open_sic_plotting,sic_field)
m3.contourf(*m3(sic_grid['lons'],sic_grid['lats']),sic_field,levels=[0,open_sic_plotting-1],
colors=polynya_color_2017,alpha=0.30,zorder=3)
plt.text(0.04,0.97,'2017–2018',color='w',size=labelsize+1,fontweight='bold',
horizontalalignment='left',verticalalignment='top',transform=ax3.transAxes)
# inset box on first subplot
if plot_fig_1a and plot_fig_1b and plot_fig_1c:
slon = 3.25 # switchover longitude for right frame representing Fig. 1b and left frame representing Fig. 1c
# representing Fig. 1b: top, right, bottom
top_lons,top_lats = m2(linspace(0,map_params_floats_2016[0],100),
linspace(map_params_floats_2016[1],map_params_floats_2016[1],100),inverse=True) # left to right
right_lons,right_lats = m2(linspace(map_params_floats_2016[0],map_params_floats_2016[0],100),
linspace(map_params_floats_2016[1],0,100),inverse=True) # downwards
bottom_lons,bottom_lats = m2(linspace(map_params_floats_2016[0],0,100),
linspace(0,0,100),inverse=True) # right to left
patch_lons = concatenate((top_lons[top_lons >= slon],right_lons,bottom_lons[bottom_lons >= slon]))
patch_lats = concatenate((top_lats[top_lons >= slon],right_lats,bottom_lats[bottom_lons >= slon]))
plonx,platy = m1(patch_lons,patch_lats)
m1_ax.plot(plonx,platy,c='k',lw=0.5,ls='-',alpha=0.7,zorder=2)
# representing Fig. 1c: bottom, left, top
top_lons,top_lats = m3(linspace(0,map_params_floats_2017[0],100),
linspace(map_params_floats_2017[1],map_params_floats_2017[1],100),inverse=True) # left to right
bottom_lons,bottom_lats = m3(linspace(map_params_floats_2017[0],0,100),
linspace(0,0,100),inverse=True) # right to left
left_lons,left_lats = m3(linspace(0,0,100),
linspace(0,map_params_floats_2017[1],100),inverse=True) # upwards
patch_lons = concatenate((bottom_lons[bottom_lons <= slon],left_lons,top_lons[top_lons <= slon]))
patch_lats = concatenate((bottom_lats[bottom_lons <= slon],left_lats,top_lats[top_lons <= slon]))
plonx,platy = m1(patch_lons,patch_lats)
m1_ax.plot(plonx,platy,c='k',lw=0.5,ls='-',alpha=0.7,zorder=2)
# add ice colorbar to Fig. 1a
if plot_fig_1a:
original_axes = plt.gca()
cbar_ax = inset_axes(m1_ax,width='100%',height='100%',loc=3,
bbox_to_anchor=(0.25,0.0,0.60,0.017),bbox_transform=m1_ax.transAxes)
# left, bottom, width, height
cbar = plt.gcf().colorbar(pcm,ticks=arange(0,101,20),format='%.0f%%',extend='neither',
orientation='horizontal',cax=cbar_ax)
cbar.ax.tick_params(labelsize=labelsize,top='on',bottom=False,labeltop='on',labelbottom=False)
cbar.outline.set_linewidth(0.5)
plt.sca(original_axes)
# add inset map to Fig. 1a
if plot_fig_1a:
original_axes = plt.gca()
m4_ax = inset_axes(m1_ax,width='100%',height='100%',
bbox_to_anchor=(0.08,0.70,0.32,0.32),bbox_transform=m1_ax.transAxes)
m4 = Basemap(projection='spstere',boundinglat=-56,lon_0=180,resolution='i',round=True)
circle = m4.drawmapboundary(linewidth=0.5,fill_color=ocean_color_light)
circle.set_clip_on(False)
m4.fillcontinents(color='0.7')
m4.drawcoastlines(linewidth=0.25,color='k')
m4.drawparallels(arange(-80,0,10),color='0.6',linewidth=0.25,labels=[0,0,0,0],zorder=2)
m4.drawmeridians(arange(0,359,30),color='0.6',linewidth=0.25,labels=[0,0,0,0],zorder=2)
top_lons,top_lats = m1(linspace(0,map_params_weddell[0],100),
linspace(map_params_weddell[1],map_params_weddell[1],100),inverse=True) # left to right
right_lons,right_lats = m1(linspace(map_params_weddell[0],map_params_weddell[0],100),
linspace(map_params_weddell[1],0,100),inverse=True) # downwards
bottom_lons,bottom_lats = m1(linspace(map_params_weddell[0],0,100),
linspace(0,0,100),inverse=True) # right to left
left_lons,left_lats = m1(linspace(0,0,100),
linspace(0,map_params_weddell[1],100),inverse=True) # upwards
patch_lons = concatenate((top_lons,right_lons,bottom_lons,left_lons))
patch_lats = concatenate((top_lats,right_lats,bottom_lats,left_lats))
plonx,platy = m4(patch_lons,patch_lats)
patchxy = list(zip(plonx,platy))
poly = Polygon(patchxy,linewidth=0.5,linestyle='-',edgecolor='k',facecolor='none',alpha=0.7,zorder=3)
m4_ax.add_patch(poly)
plt.sca(original_axes)
# save figure
if plot_fig_1a or plot_fig_1b or plot_fig_1c:
plt.savefig(current_results_dir + 'figure_1.pdf',dpi=450)
plt.close()
# Fig. 2. Storms, sea ice concentration and mixed-layer salinity at Maud Rise in 2016 and 2017.
# Extended Data Fig. 3. Evolution of sea ice concentration, air temperature and upper ocean properties at Maud Rise in
# 2016 and 2017.
# Extended Data Fig. 4. Correspondence of sea ice loss episodes and major storms near Maud Rise.
if plot_fig_2_ED_figs_3_4:
verbose = True
use_ice_pickle = True
use_hydro_pickle = True
use_erai_pickle = True
mr_box_small = [0,10,-67,-63] # for reanalysis series
erai_toi_2016 = [datetime(2016,1,1),datetime(2016,12,31)]
erai_toi_2017 = [datetime(2017,1,1),datetime(2017,12,31)]
polynya_datetimes = [[datetime(2016,7,27),datetime(2016,8,17)],[datetime(2017,9,3),datetime(2017,11,28)]]
mr_obs_dist = 250 # maximum obs distance (in km) from Maud Rise center
e_weddell_obs_dist = 500 # for comparison
mr_center = [-65.0,3.0] # summit of Maud Rise (65.0°S, 3.0°E)
dpb = 21 # days per bin for climatological envelope
si10_crit = 20 # storm criterion (wind speed ≥ 20 m/s)
msl_crit = 950 # storm criterion (surface pressure ≤ 950 hPa)
polynya_sats = ['dmsp','amsr2']
pickle_names = ['fig_2_ice','fig_2_ice_with_amsr_polynya_extent']
if not use_ice_pickle:
for run_idx, polynya_sat in enumerate(polynya_sats):
sic_lon_bounds = [0,10]
sic_lat_bounds = [-67,-63]
polynya_lon_bounds = array([-15,20])
polynya_lat_bounds = array([-68,-62])
circumant_lons,circumant_lats = gt.establish_coastline(coastline_filename_prefix)
[sea_ice_grids,sea_ice_data_avail,sea_ice_all_dates] = ldp.sea_ice_data_prep(nimbus5_dir,dmsp_v3_dir,
dmsp_nrt_dir,
amsre_dir,amsr2_dir,amsr_gridfile,
amsr_areafile,nsidc_ps25_grid_dir)
sic_doy_2016_dmsp = pd.Series()
sic_doy_2017_dmsp = pd.Series()
sic_doy_dmsp = dict()
sic_doy_2016_amsr = pd.Series()
sic_doy_2017_amsr = pd.Series()
sic_doy_amsr = dict()
polynya_extent_doy_2016 = pd.Series()
polynya_extent_doy_2017 = pd.Series()
for index, date in enumerate(tt.dates_in_range((1978,11,1),tt.now())):
if verbose: print(date)
date_as_datetime = datetime(*date)
doy = date_as_datetime.timetuple().tm_yday
# SIC average
[sic_dmsp,open_area,day_offset] = ldp.sea_ice_concentration(date,sic_lat_bounds,sic_lon_bounds,sea_ice_grids,
sea_ice_data_avail,use_only=['dmsp'])
[sic_amsr,open_area,day_offset] = ldp.sea_ice_concentration(date,sic_lat_bounds,sic_lon_bounds,sea_ice_grids,
sea_ice_data_avail,use_only=['amsre','amsr2'])
if doy not in sic_doy_dmsp: sic_doy_dmsp[doy] = []
if doy not in sic_doy_amsr: sic_doy_amsr[doy] = []
sic_doy_dmsp[doy].append(sic_dmsp)
sic_doy_amsr[doy].append(sic_amsr)
if date[0] == 2016: sic_doy_2016_dmsp = sic_doy_2016_dmsp.append(pd.Series(index=[doy],data=[sic_dmsp]))
if date[0] == 2016: sic_doy_2016_amsr = sic_doy_2016_amsr.append(pd.Series(index=[doy],data=[sic_amsr]))
if date[0] == 2017: sic_doy_2017_dmsp = sic_doy_2017_dmsp.append(pd.Series(index=[doy],data=[sic_dmsp]))
if date[0] == 2017: sic_doy_2017_amsr = sic_doy_2017_amsr.append(pd.Series(index=[doy],data=[sic_amsr]))
if date[0] == 2016 or date[0] == 2017:
# polynya identification
if verbose: print('polynya ID for ',date)
sat_string,polynya_string,filename_abbrev,sic_grid,sic_field, \
polynya_stats,polynya_grid,polynya_grid_binary,open_ocean_grid,error_code \
= gt.identify_polynyas_magic(polynya_sat,date,sea_ice_grids,sea_ice_data_avail,circumant_lons,
circumant_lats,open_threshold=50,
extent_threshold=0,regrid_amsr_to_25km=True)
# no errors in identifying polynyas
if error_code == 0:
total_polynya_extent = 0
for polynya_index in range(len(polynya_stats)):
if polynya_lat_bounds[0] <= polynya_stats[polynya_index]['centroid'][0] <= polynya_lat_bounds[1] \
and polynya_lon_bounds[0] <= polynya_stats[polynya_index]['centroid'][1] <= \
polynya_lon_bounds[1]:
total_polynya_extent += polynya_stats[polynya_index]['total_extent']
if date[0] == 2016: polynya_extent_doy_2016 \
= polynya_extent_doy_2016.append(pd.Series(index=[doy],data=[total_polynya_extent]))
if date[0] == 2017: polynya_extent_doy_2017 \
= polynya_extent_doy_2017.append(pd.Series(index=[doy],data=[total_polynya_extent]))
if verbose: print('>>> polynya extent: ',total_polynya_extent)
# fully or partially bad SIC field
else:
if date[0] == 2016: polynya_extent_doy_2016 \
= polynya_extent_doy_2016.append(pd.Series(index=[doy],data=[NaN]))
if date[0] == 2017: polynya_extent_doy_2017 \
= polynya_extent_doy_2017.append(pd.Series(index=[doy],data=[NaN]))
for doy in sic_doy_dmsp.keys():
sic_doy_dmsp[doy] = [nanmean(sic_doy_dmsp[doy]),nanstd(sic_doy_dmsp[doy]),nanmedian(sic_doy_dmsp[doy]),
stats.iqr(sic_doy_dmsp[doy],rng=(25,50),nan_policy='omit'),
stats.iqr(sic_doy_dmsp[doy],rng=(50,75),nan_policy='omit')]
for doy in sic_doy_amsr.keys():
sic_doy_amsr[doy] = [nanmean(sic_doy_amsr[doy]),nanstd(sic_doy_amsr[doy]),nanmedian(sic_doy_amsr[doy]),
stats.iqr(sic_doy_amsr[doy],rng=(25,50),nan_policy='omit'),
stats.iqr(sic_doy_amsr[doy],rng=(50,75),nan_policy='omit')]
pickle.dump([sic_doy_dmsp,sic_doy_amsr,sic_doy_2016_dmsp,sic_doy_2016_amsr,sic_doy_2017_dmsp,sic_doy_2017_amsr,
polynya_extent_doy_2016,polynya_extent_doy_2017],
open(figure_pickle_dir + pickle_names[run_idx],'wb'))
sic_doy_dmsp,sic_doy_amsr,sic_doy_2016_dmsp,sic_doy_2016_amsr,sic_doy_2017_dmsp,sic_doy_2017_amsr, \
polynya_extent_doy_2016,polynya_extent_doy_2017 \
= pickle.load(open(figure_pickle_dir + pickle_names[0],'rb'))
_,_,_,_,_,_,polynya_extent_doy_2016_amsr,polynya_extent_doy_2017_amsr \
= pickle.load(open(figure_pickle_dir + pickle_names[1],'rb'))
sic_doys = arange(1,366+1)
sic_doy_dmsp_mean = pd.Series(index=sic_doys,data=[sic_doy_dmsp[doy][0] for doy in sic_doys])
sic_doy_dmsp_median \
= pd.Series(index=sic_doys,data=[sic_doy_dmsp[doy][2] for doy in sic_doys]).rolling(window=7,center=True).mean()
sic_doy_dmsp_iqr_25 \
= pd.Series(index=sic_doys,data=[sic_doy_dmsp[doy][3] for doy in sic_doys]).rolling(window=7,center=True).mean()
sic_doy_dmsp_iqr_75 \
= pd.Series(index=sic_doys,data=[sic_doy_dmsp[doy][4] for doy in sic_doys]).rolling(window=7,center=True).mean()
sic_doy_amsr_median \
= pd.Series(index=sic_doys,data=[sic_doy_amsr[doy][2] for doy in sic_doys]).rolling(window=7,center=True).mean()
sic_doy_amsr_iqr_25 \
= pd.Series(index=sic_doys,data=[sic_doy_amsr[doy][3] for doy in sic_doys]).rolling(window=7,center=True).mean()
sic_doy_amsr_iqr_75 \
= pd.Series(index=sic_doys,data=[sic_doy_amsr[doy][4] for doy in sic_doys]).rolling(window=7,center=True).mean()
# pickle SIC climatology for other purposes...
pickle.dump(sic_doy_dmsp_mean,open(figure_pickle_dir + 'fig_2_sic_climatology_dmsp','wb'))
if use_hydro_pickle:
mr_obs = pickle.load(open(figure_pickle_dir + 'fig_2_mr_obs','rb'))
e_weddell_obs = pickle.load(open(figure_pickle_dir + 'fig_2_e_weddell_obs','rb'))
else:
mr_obs \
= ldp.compile_hydrographic_obs(argo_index_pickle_dir,argo_gdac_dir,wod_dir,lon_bounds=[-20,25],
lat_bounds=[-75,-59],toi_bounds=[datetime(1900,1,1),datetime.today()],
distance_check=mr_obs_dist,distance_center=mr_center,
include_argo=True,include_wod=True,params=['ptmp','psal','sigma_theta'],
compute_extras=False,max_cast_min_depth=30,min_cast_max_depth=250,
reject_mld_below=250,interp_spacing=0.1,interp_depths=(0,500),calc_mld=True,
calc_ml_avg=True,calc_at_depths=[20,200,250],
calc_depth_avgs=[(0,200),(0,250),(200,500),(250,500)],
calc_sd=[200,250,(0,250,34.8),(250,1650,34.8)],calc_tb=[200,250],
pickle_dir=figure_pickle_dir,pickle_filename='fig_2_mr_obs',
prof_count_dir=current_results_dir,
prof_count_filename='figure_2_prof_counts_mr',verbose=verbose)
e_weddell_obs \
= ldp.compile_hydrographic_obs(argo_index_pickle_dir,argo_gdac_dir,wod_dir,lon_bounds=[-20,25],
lat_bounds=[-75,-59],toi_bounds=[datetime(1900,1,1),datetime.today()],
distance_check=[mr_obs_dist,e_weddell_obs_dist],distance_center=mr_center,
include_argo=True,include_wod=True,params=['ptmp','psal','sigma_theta'],
compute_extras=False,max_cast_min_depth=30,min_cast_max_depth=250,
reject_mld_below=250,interp_spacing=0.1,interp_depths=(0,500),calc_mld=True,
calc_ml_avg=True,calc_at_depths=[20,200,250],
calc_depth_avgs=[(0,200),(0,250),(200,500),(250,500)],
calc_sd=[250,(0,250,34.8)],calc_tb=None,
pickle_dir=figure_pickle_dir,pickle_filename='fig_2_e_weddell_obs',
prof_count_dir=current_results_dir,
prof_count_filename='figure_2_prof_counts_e_weddell',verbose=verbose)
# calculate and export statistics on compiled hydrography
text_file = open(current_results_dir + 'figure_2_mr_hydrography_stats.txt','w')
text_file.write('Statistics on Maud Rise hydrography compiled for MLS, MLT, SD climatologies:\n'
'- Number of obs: {0}\n'
'- Fraction of obs collected before 1970: {1:.2f}%\n'
'- Mean date: {2}\n'
'- Median date: {3}\n'
.format(len(mr_obs['datetimes']),
100 * sum(mr_obs['datetimes'] < datetime(1970,1,1)) / len(mr_obs['datetimes']),
datetime.fromtimestamp(mean(array([dt.timestamp() for dt in mr_obs['datetimes']]))),
datetime.fromtimestamp(median(array([dt.timestamp() for dt in mr_obs['datetimes']])))))
text_file.close()
text_file = open(current_results_dir + 'figure_2_ew_hydrography_stats.txt','w')
text_file.write('Statistics on Eastern Weddell hydrography compiled for MLS, MLT, SD climatologies:\n'
'- Number of obs: {0}\n'
'- Fraction of obs collected before 1970: {1:.2f}%\n'
'- Mean date: {2}\n'
'- Median date: {3}\n'
.format(len(e_weddell_obs['datetimes']),
100 * sum(e_weddell_obs['datetimes'] < datetime(1970,1,1)) / len(e_weddell_obs['datetimes']),
datetime.fromtimestamp(mean(array([dt.timestamp() for dt in e_weddell_obs['datetimes']]))),
datetime.fromtimestamp(median(array([dt.timestamp() for dt in e_weddell_obs['datetimes']])))))
text_file.close()
# load reanalysis data and create time series of interest
if use_erai_pickle:
erai_series = pickle.load(open(figure_pickle_dir + 'fig_2_erai_series','rb'))
else:
erai_daily = ldp.load_ecmwf(era_custom_dir,'erai_daily_weddell.nc')
erai_daily_toi_2016 = erai_daily.sel(time=slice(erai_toi_2016[0],erai_toi_2016[1]))
erai_daily_toi_2017 = erai_daily.sel(time=slice(erai_toi_2017[0],erai_toi_2017[1]))
erai_daily_toi_2016_2017 = erai_daily.sel(time=slice(erai_toi_2016[0],erai_toi_2017[1]))
erai_series = dict()
erai_series['msl_min_2016'] \
= ldp.create_reanalysis_index(erai_daily_toi_2016,param_name='msl',avg_box=mr_box_small,
min_not_mean=True)[0]
erai_series['msl_min_2017'] \
= ldp.create_reanalysis_index(erai_daily_toi_2017,param_name='msl',avg_box=mr_box_small,
min_not_mean=True)[0]
erai_series['msl_min_climo_2016'], erai_series['msl_min_climo_iqr_25_2016'], erai_series['msl_min_climo_iqr_75_2016'] \
= ldp.create_reanalysis_index(erai_daily,param_name='msl',avg_box=mr_box_small,
min_not_mean=True,create_climo_iqr=True,make_year=2016)
erai_series['msl_min_climo_2017'], erai_series['msl_min_climo_iqr_25_2017'], erai_series['msl_min_climo_iqr_75_2017'] \
= ldp.create_reanalysis_index(erai_daily,param_name='msl',avg_box=mr_box_small,
min_not_mean=True,create_climo_iqr=True,make_year=2017)
erai_series['si10_max_2016'] \
= ldp.create_reanalysis_index(erai_daily_toi_2016,param_name='si10',avg_box=mr_box_small,
max_not_mean=True)[0]
erai_series['si10_max_2017'] \
= ldp.create_reanalysis_index(erai_daily_toi_2017,param_name='si10',avg_box=mr_box_small,
max_not_mean=True)[0]
erai_series['si10_max_climo_2016'], erai_series['si10_max_climo_iqr_25_2016'], erai_series['si10_max_climo_iqr_75_2016'] \
= ldp.create_reanalysis_index(erai_daily,param_name='si10',avg_box=mr_box_small,
max_not_mean=True,create_climo_iqr=True,make_year=2016)
erai_series['si10_max_climo_2017'], erai_series['si10_max_climo_iqr_25_2017'], erai_series['si10_max_climo_iqr_75_2017'] \
= ldp.create_reanalysis_index(erai_daily,param_name='si10',avg_box=mr_box_small,
max_not_mean=True,create_climo_iqr=True,make_year=2017)
erai_series['t2m_2016'] \
= ldp.create_reanalysis_index(erai_daily_toi_2016,param_name='t2m',avg_box=mr_box_small)[0]
erai_series['t2m_2017'] \
= ldp.create_reanalysis_index(erai_daily_toi_2017,param_name='t2m',avg_box=mr_box_small)[0]
erai_series['t2m_2016_2017'] \
= ldp.create_reanalysis_index(erai_daily_toi_2016_2017,param_name='t2m',avg_box=mr_box_small)[0]
erai_series['t2m_climo_2016'], erai_series['t2m_climo_iqr_25_2016'], erai_series['t2m_climo_iqr_75_2016'] \
= ldp.create_reanalysis_index(erai_daily,param_name='t2m',avg_box=mr_box_small,
create_climo_iqr=True,make_year=2016)
erai_series['t2m_climo_2017'], erai_series['t2m_climo_iqr_25_2017'], erai_series['t2m_climo_iqr_75_2017'] \
= ldp.create_reanalysis_index(erai_daily,param_name='t2m',avg_box=mr_box_small,
create_climo_iqr=True,make_year=2017)
pickle.dump(erai_series,open(figure_pickle_dir + 'fig_2_erai_series','wb'))
erai_daily_toi_2016.close()
erai_daily_toi_2017.close()
erai_daily.close()
##################################################################################################################
################################### FIG. 2 PLOTTING ROUTINE ##################################################
##################################################################################################################
fontsize = 5
shade_color='k'
shade_alpha=0.15
shade_alpha_dark=0.25
shade_centerline_alpha=0.5
ew_shade_color='saddlebrown'
ew_shade_centerline_alpha=0.5
color_accent='k'
color_polynya='navy'
lw=0.5
ms=1.5
mew=0.1 # marker edge width
# create multiyear series that repeats a single-year climatology
def plot_doy_series(series,ref_datetime=datetime(2013,12,31),N_years=5,smooth_N_days_betw_yrs=7,
just_return_series=False):
if N_years == 1: smooth_N_days_betw_yrs = 0
full_index = array([])
full_data = array([])
for y in range(N_years):
new_index = array([timedelta(days=int(doy)) for doy in series.index]) \
+ ref_datetime.replace(year=ref_datetime.year+y)
single_year_mask = array([dt.year == ref_datetime.year+1+y for dt in new_index])
full_index = append(full_index,new_index[single_year_mask])
if smooth_N_days_betw_yrs != 0: full_data[-1*smooth_N_days_betw_yrs:] = NaN
new_data = series.values[single_year_mask]
if smooth_N_days_betw_yrs != 0: new_data[:smooth_N_days_betw_yrs] = NaN
full_data = append(full_data,new_data)
full_series = pd.Series(index=full_index,data=full_data).interpolate(method='linear').bfill().ffill()
if just_return_series: return full_series
return full_series.index,full_series.values
def series_change_year(series,new_year):
new_index = array([dt.replace(year=new_year) for dt in series.index])
return pd.Series(index=new_index,data=series.values)
def series_daily_interp(orig_series,new_index_toi=[datetime(2016,1,1),datetime(2018,1,1)]):
interpolator = spin.interp1d(tt.datetime_to_datenum(orig_series.index),orig_series.values,
bounds_error=False,fill_value=NaN)
new_index = arange(*new_index_toi,timedelta(days=1))
interp_data = interpolator(tt.datetime_to_datenum(new_index))
return pd.Series(index=new_index,data=interp_data)
def fig_2_axis_prep(ylabel='',xlabel_top=False,xlabel_bottom=False,add_xtick_top=False,add_xtick_bottom=False,
spines=[],ylabel_pos='left',ylabel_color='k',remove_yticks=[],
xgrid=False,ygrid=False,ylabel_offset=0.0,ylabel_pad=0.0,
xbounds=[datetime(2016,1,1),datetime(2017,12,31)]):
plt.xlim(xbounds)
plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b'))
if add_xtick_top: xtick_top = True
else: xtick_top = xlabel_top
if add_xtick_bottom: xtick_bottom = True
else: xtick_bottom = xlabel_bottom
plt.tick_params(axis='x',which='both',bottom=xtick_bottom,top=xtick_top,
labeltop=xlabel_top,labelbottom=xlabel_bottom)
if xgrid: plt.gca().grid(which='major',axis='x',linewidth=0.5,alpha=0.3)
if ygrid: plt.gca().grid(which='major',axis='y',linewidth=0.5,alpha=0.3)
plt.gca().tick_params(axis='both',which='major',labelsize=fontsize)
[plt.gca().spines[side].set_linewidth(0.25) for side in plt.gca().spines.keys()]
plt.gca().tick_params(width=0.25)
if 'top' not in spines: plt.gca().spines['top'].set_visible(False)
if 'bottom' not in spines: plt.gca().spines['bottom'].set_visible(False)
for tick in remove_yticks:
plt.gca().get_yticklabels()[tick].set_visible(False)
plt.gca().yaxis.get_major_ticks()[tick].set_visible(False)
if ylabel_pos == 'right':
plt.gca().yaxis.tick_right()
plt.gca().yaxis.set_label_position('right')
plt.gca().patch.set_alpha(0.0)
if ylabel_pos == 'left': ylabel_rot = 90
if ylabel_pos == 'right': ylabel_rot = 90 # previously -90
plt.ylabel(ylabel,fontsize=fontsize,rotation=ylabel_rot,color=ylabel_color)
if ylabel_pos == 'left': plt.gca().get_yaxis().set_label_coords(-0.07-ylabel_pad,0.5+ylabel_offset)
if ylabel_pos == 'right': plt.gca().get_yaxis().set_label_coords( 1.09+ylabel_pad,0.5+ylabel_offset)
plt.setp(plt.gca().get_yticklabels(),color=ylabel_color)
def fig_2_plot_sic(plot_2016=True,plot_2017=True,plot_legend=True,legend_bbox=(0.003,1.00),
xbounds=[datetime(2016,1,1),datetime(2017,12,31)]):
factor = 1.7
sic_doy_dmsp_low = sic_doy_dmsp_median-sic_doy_dmsp_iqr_25
sic_doy_dmsp_low[sic_doy_dmsp_low < 0.0] = 0.0
plt.fill_between(*plot_doy_series(sic_doy_dmsp_low**factor),
plot_doy_series(sic_doy_dmsp_median+sic_doy_dmsp_iqr_75)[1]**factor,
facecolor=shade_color,alpha=shade_alpha,zorder=4)
plt.plot(*plot_doy_series(sic_doy_dmsp_median**factor),
c='k',linewidth=0.5,alpha=shade_centerline_alpha,zorder=5)
# han1 = plt.fill_between([0,1],[nan,nan],facecolor=shade_color,alpha=shade_alpha,
# label='Maud Rise') # dummy handle # (±1$\sigma$)
han2, = plt.plot([0,1],[nan,nan],c='k',lw=lw,ls='-',label='NSIDC Merged') # dummy handle
han3, = plt.plot([0,1],[nan,nan],c='k',lw=lw,ls=':',label='AMSR2-ASI') # dummy handle
if plot_2016:
# han4, = plt.plot([0,1],[nan,nan],c=color_accent,lw=1.0,ls='-',label='2016') # dummy handle
plt.plot(*plot_doy_series(sic_doy_2016_dmsp**factor,N_years=1,ref_datetime=datetime(2015,12,31)),
c=color_accent,lw=lw,ls='-',zorder=6)
plt.plot(*plot_doy_series(sic_doy_2016_amsr**factor,N_years=1,ref_datetime=datetime(2015,12,31)),
c=color_accent,lw=lw,ls=':',zorder=7)
if plot_2017:
# han5, = plt.plot([0,1],[nan,nan],c=color_accent,lw=1.0,ls='-',label='2017') # dummy handle
plt.plot(*plot_doy_series(sic_doy_2017_dmsp**factor,N_years=1,ref_datetime=datetime(2016,12,31)),
c=color_accent,lw=lw,ls='-',zorder=6)
plt.plot(*plot_doy_series(sic_doy_2017_amsr**factor,N_years=1,ref_datetime=datetime(2016,12,31)),
c=color_accent,lw=lw,ls=':',zorder=7)
# plt.plot([0,1],[nan,nan],c='k',lw=lw,ls='-',label=' ',visible=False)
plt.plot([datetime(2016,1,1),datetime(2017,12,31)],[0,0],c='k',linestyle='-',linewidth=0.5,zorder=15)
plt.ylim([-0.01,1.0])
plt.gca().set_yticks(array([0,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])**factor)
plt.gca().set_yticklabels(['','30','40','50','60','70','80','90','100'])
if plot_legend:
plt.legend(handles=[han2,han3],bbox_to_anchor=legend_bbox,handlelength=1.75,loc='upper left',
ncol=1,frameon=False,fontsize=fontsize - 1) # labelspacing=1.075
fig_2_axis_prep(ylabel='Sea ice\nconcentration (%)',spines=['top'],xbounds=xbounds,add_xtick_bottom=True)
def find_storms(si10_series,msl_series,si10_crit=si10_crit,msl_crit=msl_crit,after=datetime(2016,5,1)):
storm_dates_all = []
for date in si10_series.index:
if date < after: continue
if si10_series[date] >= si10_crit or msl_series[date] <= msl_crit:
storm_dates_all.append(date)
return storm_dates_all
# NOTE: storms identified using find_storms(), then dates are manually aggregated (if applicable) and listed below:
storm_datetimes_2016 = [[datetime(2016,5,20,0),datetime(2016,5,26,0)],
[datetime(2016,6,12,0),datetime(2016,6,15,0)],
[datetime(2016,6,30,0),datetime(2016,7,1,0)],
[datetime(2016,7,9,0),datetime(2016,7,13,0)],
[datetime(2016,7,26,0),datetime(2016,7,28,0)],
[datetime(2016,8,2,0),datetime(2016,8,6,0)],
[datetime(2016,8,21,0),datetime(2016,8,23,0)],
[datetime(2016,8,28,0),datetime(2016,8,31,0)],
[datetime(2016,10,17,0),datetime(2016,10,19,0)],
[datetime(2016,10,26,0),datetime(2016,10,28,0)]]
storm_datetimes_2017 = [[datetime(2017,7,3,0),datetime(2017,7,8,0)],
[datetime(2017,7,23,0),datetime(2017,7,27,0)],
[datetime(2017,7,30,0),datetime(2017,8,3,0)],
[datetime(2017,8,7,0),datetime(2017,8,8,0)],
[datetime(2017,8,14,0),datetime(2017,8,15,0)],
[datetime(2017,8,18,0),datetime(2017,8,19,0)],
[datetime(2017,8,30,0),datetime(2017,9,2,0)],
[datetime(2017,9,13,0),datetime(2017,9,19,0)],
[datetime(2017,11,4,0),datetime(2017,11,5,0)],
[datetime(2017,11,30,0),datetime(2017,12,1,0)]]
# extract hydrographic quantities
mr_obs_mask_5903616 = mr_obs['platforms'] == str(5903616)
mr_obs_mask_5904471 = mr_obs['platforms'] == str(5904471)
mr_obs_mask_5904468 = mr_obs['platforms'] == str(5904468)
ml_avg_ptmp_median \
= ldp.hydro_obs_to_doy_series(mr_obs,'ptmp','ml_avg',doy_median=True,days_per_bin=dpb,rm_days=dpb)
ml_avg_ptmp_iqr_25 \
= ldp.hydro_obs_to_doy_series(mr_obs,'ptmp','ml_avg',doy_iqr_25=True,days_per_bin=dpb,rm_days=dpb)
ml_avg_ptmp_iqr_75 \
= ldp.hydro_obs_to_doy_series(mr_obs,'ptmp','ml_avg',doy_iqr_75=True,days_per_bin=dpb,rm_days=dpb)