e3sm-cryo-analysis-scripts/water_mass_T-S_plot.py at master · cbegeman/e3sm-cryo-analysis-scripts · GitHub

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#!/usr/bin/env python
'''
Script to compare some scalar values from different runs of Thwaites melt variability experiment.
'''

import sys
import os
import netCDF4
import datetime
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal
from matplotlib import cm

fmesh=netCDF4.Dataset('oEC60to30v3wLI60lev.171031.nc')
latCell = fmesh.variables['latCell'][:]
lonCell = fmesh.variables['lonCell'][:]
xCell = fmesh.variables['xCell'][:]
yCell = fmesh.variables['yCell'][:]
depths = fmesh.variables['refBottomDepth'][:]
z = np.zeros(depths.shape)
z[0] = -0.5 * depths[0]
z[1:] = -0.5 * (depths[0:-1] + depths[1:])
pii=3.14159


yr=94
idx = np.nonzero(np.logical_and(np.logical_and(latCell<-75.0/180.0*pii, latCell>-78.0/180.0*pii), np.logical_and(lonCell>320.0/360.0*2.0*pii, lonCell<330.0/360.0*2*pii)))[0]  #filchner trough in front of ice shelf
#idx = np.nonzero(np.logical_and(np.logical_and(latCell<-78.0/180.0*pii, latCell>-85.0/180.0*pii), np.logical_and(lonCell>315.0/360.0*2.0*pii, lonCell<330.0/360.0*2*pii)))[0]  #filchner ice shelf
#idx = np.nonzero(np.logical_and(np.logical_and(latCell<-70.0/180.0*pii, latCell>-85.0/180.0*pii), np.logical_and(lonCell>300.0/360.0*2.0*pii, lonCell<350.0/360.0*2*pii)))[0]  #entire weddell

print "Found {} cells".format(len(idx))

fig = plt.figure(2, facecolor='w')
idx2 = np.nonzero(latCell<-60.0/180.0*pii)[0]
plt.plot(yCell[idx2], xCell[idx2], 'k.')
plt.plot(yCell[idx], xCell[idx], 'r.')

path='/global/cscratch1/sd/dcomeau/acme_scratch/cori-knl/20190225.GMPAS-DIB-IAF-ISMF.T62_oEC60to30v3wLI.cori-knl/run'
#path='/global/cscratch1/sd/dcomeau/acme_scratch/cori-knl/20190225.GMPAS-DIB-IAF.T62_oEC60to30v3wLI.cori-knl/run'
#path='/global/cscratch1/sd/hoffman2/acme_scratch/edison/archive/20190306.A_WCYCL1850-DIB-ISMF_CMIP6.ne30_oECv3wLI.edison/ocn/hist'
#path='/global/cscratch1/sd/hoffman2/acme_scratch/edison/20190423.GMPAS-DIB-IAF-ISMF.T62_oEC60to30v3wLI.edison.restrictedMelt/run'
print "running year:", yr

fig = plt.figure(1, facecolor='w')
nrow=1
ncol=1

for yr in np.arange(95,102,1):
 for mo in np.arange(1,13,1):
   print "yr=",yr, "mo=", mo
   axTS = fig.add_subplot(nrow, ncol, 1)
   plt.ylabel('temperature (deg. C)')
   plt.xlabel('salinity (psu)')
   plt.grid()


   #f=netCDF4.Dataset('/usr/projects/climate/mhoffman/e3sm/cryo-campaign-data/mpaso.hist.am.timeSeriesStatsMonthly.0070-01-01.nc')
   f=netCDF4.Dataset('{0}/mpaso.hist.am.timeSeriesStatsMonthly.{1:04d}-{2:02d}-01.nc'.format(path, yr, mo), 'r')
   T=f.variables['timeMonthly_avg_activeTracers_temperature']
   S=f.variables['timeMonthly_avg_activeTracers_salinity']

   # get data
   Ts = T[0,idx,:]
   Ss = S[0,idx,:]
   sc=axTS.scatter(Ss[:], Ts[:], s=1, c=np.tile(z, (len(idx),1)), vmin=-700, vmax=0.0, cmap='twilight')

   axTS.set_ylim([-2.1,2.0])
   axTS.set_xlim([33.0,34.8])
   plt.colorbar(sc)

   plt.plot([34.0, 34.0], [-1.85, 0.2], 'k:')
   plt.plot([34.5, 34.5], [-1.86, -1.5], 'k:')
   plt.plot([34.0, 35.0], [-1.5, -1.5], 'k:')
   plt.plot([34.0, 35.0], [0.0, 0.0], 'k:')
   plt.plot([33.5, 35.0], [-0.0575*33.5+0.0901, -0.0575*35.0+0.0901], 'k:')
   plt.title("year {}, mo {}".format(yr, mo))
   plt.draw()
   plt.savefig('TS_yr{:04d}-{:02d}.png'.format(yr, mo))
   plt.clf()


   # velo plots
   v=f.variables['timeMonthly_avg_velocityMeridional'][0,idx,:]
   u=f.variables['timeMonthly_avg_velocityZonal'][0,idx,:]
   swsv=f.variables['timeMonthly_avg_windStressMeridional'][0,idx]
   swsu=f.variables['timeMonthly_avg_windStressZonal'][0,idx]

   fig = plt.figure(3, facecolor='w')

   axUsrf = fig.add_subplot(1, 3, 1)
   axUsrf.axis('equal')
   plt.quiver(yCell[idx], xCell[idx], u[:,0], v[:,0])
   print "d={}, z={}".format(0, z[0])
   plt.title("year={}, depth={}m".format(yr, z[0]))
   plt.title


   d=np.argmin(np.absolute(z--250.0))
   print "d={}, z={}".format(d, z[d])

   axU2 = fig.add_subplot(1, 3, 2)
   axU2.axis('equal')
   plt.quiver(yCell[idx], xCell[idx], u[:,d], v[:,d])
   plt.title("year={}, depth={}m".format(yr, z[d]))

   axWS = fig.add_subplot(1, 3, 3)
   axWS.axis('equal')
   plt.quiver(yCell[idx], xCell[idx], swsu[:], swsv[:])
   plt.title("year={}, surface stress".format(yr))
   plt.draw()
   plt.savefig('velo_yr{:04d}.png'.format(yr))
   plt.clf()

#plt.show()