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Copy pathOHC_T_S_trends_vsdepth.py
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OHC_T_S_trends_vsdepth.py
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from __future__ import absolute_import, division, print_function, \
unicode_literals
import os
import xarray
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as cols
from matplotlib.pyplot import cm
from matplotlib.colors import from_levels_and_colors
from matplotlib.colors import BoundaryNorm
import cmocean
from mpas_analysis.shared.io import open_mpas_dataset, write_netcdf
from mpas_analysis.shared.io.utility import get_files_year_month, decode_strings
from geometric_features import FeatureCollection, read_feature_collection
from common_functions import hovmoeller_plot, add_inset
startYear = 1
endYear = 54
calendar = 'gregorian'
# Settings for cori
meshName = 'ARRM60to10'
restartFile = '/global/cfs/projectdirs/e3sm/inputdata/ocn/mpas-o/oARRM60to10/ocean.ARRM60to10.180715.nc'
regionMaskFile = '/global/cfs/projectdirs/m1199/milena/mpas-region_masks/{}_oceanOHCRegions.nc'.format(meshName)
featureFile = '/global/cfs/projectdirs/m1199/milena/mpas-region_masks/oceanOHCRegions.geojson'
#runName = 'ARRM60to10_JRA_GM_ramp'
#runNameShort = 'E3SM-Arctic-OSI'
#modeldir = '/global/cscratch1/sd/milena/E3SM_simulations/ARRM60to10_JRA_GM_ramp/run'
runName = '20210204.A_WCYCL1850S_CMIP6.ne30pg2_oARRM60to10_ICG.beta1.cori-knl'
runNameShort = 'E3SM-Arctic-coupled-beta1'
modeldir = '/global/cscratch1/sd/dcomeau/e3sm_scratch/cori-knl/20210204.A_WCYCL1850S_CMIP6.ne30pg2_oARRM60to10_ICG.beta1.cori-knl/run'
# Settings for blues
#meshName = 'EC30to60E2r2'
#restartFile = '/lcrc/group/e3sm/public_html/inputdata/ocn/mpas-o/{}/ocean.EC30to60E2r2.200908.nc'.format(meshName)
#meshName = 'oEC60to30v3'
#restartFile = '/lcrc/group/e3sm/public_html/inputdata/ocn/mpas-o/{}/oEC60to30v3_60layer.170506.nc'.format(meshName)
#meshName ='SOwISC12to60E2r4'
#restartFile = '/home/ac.dcomeau/cryo/SOwISC12to60E2r4/ocean.SOwISC12to60E2r4.210107.nc'
#regionMaskFile = '/lcrc/group/e3sm/ac.milena/mpas-region_masks/{}_oceanOHCRegions.nc'.format(meshName)
#featureFile = '/lcrc/group/e3sm/ac.milena/mpas-region_masks/oceanOHCRegions.geojson'
#runName = '20210127_JRA_POPvertMix_EC30to60E2r2'
#runNameShort = 'JRA_POPvertMix_noSSSrestoring'
#modeldir = '/lcrc/group/e3sm/ac.vanroekel/scratch/anvil/20210127_JRA_POPvertMix_EC30to60E2r2/run'
#runName = '20210212.v2_newICs.piControl.ne30pg2_EC30to60E2r2.chrysalis'
#runNameShort = 'v2_newICs.piControl.ne30pg2_EC30to60E2r2'
#modeldir = '/lcrc/group/acme/ac.golaz/E3SM_simulations/20210212.v2_newICs.piControl.ne30pg2_EC30to60E2r2.chrysalis/archive/ocn/hist'
#runName = '20201202_JRA_brokenAdv_EC30to60E2r2'
#runNameShort = 'JRA_brokenAdv_EC30to60E2r2'
#modeldir = '/lcrc/group/acme/ac.vanroekel/acme_scratch/anvil/20201202_JRA_brokenAdv_EC30to60E2r2/run'
#runName = '20200812_CORE_GMPAS-IAF.T62_oECv3_anvil'
#runNameShort = 'CORE_GMPAS-IAF.T62_oECv3'
#modeldir = '/lcrc/group/acme/ac.vanroekel/acme_scratch/anvil/20200812_CORE_GMPAS-IAF.T62_oECv3_anvil/run'
#runName = 'SORRMr4.Btest'
#runNameShort = 'SOwISC12to60E2r4.beta1'
#modeldir = '/lcrc/group/acme/ac.dcomeau/scratch/anvil/20210120.A_WCYCL1850S_CMIP6.ne30pg2_SOwISC12to60E2r4.beta1.maptest.anvil/run'
# Settings for compy
#meshName = 'EC30to60E2r2'
#restartFile = '/compyfs/inputdata/ocn/mpas-o/{}/ocean.EC30to60E2r2.200908.nc'.format(meshName)
#regionMaskFile = '/compyfs/vene705/mpas-region_masks/{}_oceanOHCRegions20201120.nc'.format(meshName)
#featureFile = '/compyfs/vene705/mpas-region_masks/oceanOHCRegions.geojson'
#modeldir = '/compyfs/zhen797/E3SM_simulations/20201108.alpha5_55_fallback.piControl.ne30pg2_r05_EC30to60E2r2-1900_ICG.compy/archive/ocn/hist'
#runName = '20201108.alpha5_55_fallback.piControl.ne30pg2_r05_EC30to60E2r2-1900_ICG.compy'
#runNameShort = 'alpha5_55_fallback'
#modeldir = '/compyfs/zhen797/E3SM_simulations/20201124.alpha5_59_fallback.piControl.ne30pg2_r05_EC30to60E2r2-1900_ICG.compy/archive/ocn/hist/'
#runName = '20201124.alpha5_59_fallback.piControl.ne30pg2_r05_EC30to60E2r2-1900_ICG.compy'
#runNameShort = 'alpha5_59_fallback'
outdir = './timeseries_data/{}'.format(runNameShort)
if not os.path.isdir(outdir):
os.makedirs(outdir)
figdir = './timeseries/{}'.format(runNameShort)
if not os.path.isdir(figdir):
os.makedirs(figdir)
if os.path.exists(restartFile):
dsRestart = xarray.open_dataset(restartFile)
dsRestart = dsRestart.isel(Time=0)
else:
raise IOError('No MPAS restart/mesh file found')
areaCell = dsRestart.areaCell
if 'landIceMask' in dsRestart:
# only the region outside of ice-shelf cavities
openOceanMask = dsRestart.landIceMask == 0
else:
openOceanMask = None
refBottomDepth = dsRestart.refBottomDepth
maxLevelCell = dsRestart.maxLevelCell
nVertLevels = dsRestart.sizes['nVertLevels']
vertIndex = xarray.DataArray.from_dict(
{'dims': ('nVertLevels',), 'data': np.arange(nVertLevels)})
depthMask = (vertIndex < maxLevelCell).transpose('nCells', 'nVertLevels')
if os.path.exists(regionMaskFile):
dsRegionMask = xarray.open_dataset(regionMaskFile)
regionNames = decode_strings(dsRegionMask.regionNames)
regionNames.append('Global')
nRegions = np.size(regionNames)
else:
raise IOError('No regional mask file {} found'.format(regionMaskFile))
variables = [{'name': 'ohc',
'title': 'OHC',
'units': 'x10$^{22}$ J',
'mpas': 'timeMonthly_avg_activeTracers_temperature',
'colormap': cmocean.cm.balance,
'clevels': [-2.4, -0.8, -0.4, -0.2, 0, 0.2, 0.4, 0.8, 2.4],
'colorIndices': [0, 28, 57, 85, 113, 142, 170, 198, 227, 255],
'fac': 1e-22*1026.0*3996.0}, # 1e-22*rho0*cp (where rho0=config_density0 and cp=config_specific_heat_sea_water)
{'name': 'temperature',
'title': 'Temperature',
'units': '$^\circ$C',
'mpas': 'timeMonthly_avg_activeTracers_temperature',
'colormap': cmocean.cm.balance,
'clevels': [-2.0, -1.0, -0.5, -0.25, -0.1, 0, 0.1, 0.25, 0.5, 1.0, 2.0],
'colorIndices': [0, 28, 57, 85, 113, 125, 130, 142, 170, 198, 227, 255],
'fac': 1},
{'name': 'salinity',
'title': 'Salinity',
'units': 'psu',
'mpas': 'timeMonthly_avg_activeTracers_salinity',
'colormap': cmocean.cm.balance,
'clevels': [-1.0, -0.5, -0.2, -0.1, -0.02, 0, 0.02, 0.1, 0.2, 0.5, 1.0],
'colorIndices': [0, 28, 57, 85, 113, 125, 130, 142, 170, 198, 227, 255],
'fac': 1}]
startDate = '{:04d}-01-01_00:00:00'.format(startYear)
endDate = '{:04d}-12-31_23:59:59'.format(endYear)
years = range(startYear, endYear + 1)
variableList = [var['mpas'] for var in variables] + \
['timeMonthly_avg_layerThickness']
timeSeriesFile0 = '{}/OHC_T_S_trends_vsdepth'.format(outdir)
# Compute regional averages one year at a time
for year in years:
timeSeriesFile = '{}_year{:04d}.nc'.format(timeSeriesFile0, year)
if not os.path.exists(timeSeriesFile):
print('\nComputing regional time series for year={}'.format(year))
datasets = []
for month in range(1, 13):
#inputFile = '{}/{}.mpaso.hist.am.timeSeriesStatsMonthly.{:04d}-{:02d}-01.nc'.format(
# modeldir, runName, year, month)
inputFile = '{}/mpaso.hist.am.timeSeriesStatsMonthly.{:04d}-{:02d}-01.nc'.format(
modeldir, year, month)
if not os.path.exists(inputFile):
raise IOError('Input file: {} not found'.format(inputFile))
dsTimeSlice = open_mpas_dataset(fileName=inputFile,
calendar=calendar,
variableList=variableList,
startDate=startDate,
endDate=endDate)
datasets.append(dsTimeSlice)
# combine data sets into a single data set
dsIn = xarray.concat(datasets, 'Time')
# Global depth-masked layer thickness and layer volume
layerThickness = dsIn.timeMonthly_avg_layerThickness
layerThickness = layerThickness.where(depthMask, drop=False)
layerVol = areaCell*layerThickness
datasets = []
regionIndices = []
for regionName in regionNames:
print(' region: {}'.format(regionName))
# Compute region total area and, for regionName
# other than Global, compute regional mask and
# regionally masked layer volume
if regionName=='Global':
regionIndices.append(nRegions-1)
totalArea = areaCell.sum()
if year==years[0]:
print(' totalArea: {} mil. km^2'.format(1e-12*totalArea.values))
else:
regionIndex = regionNames.index(regionName)
regionIndices.append(regionIndex)
dsMask = dsRegionMask.isel(nRegions=regionIndex)
cellMask = dsMask.regionCellMasks == 1
if openOceanMask is not None:
cellMask = np.logical_and(cellMask, openOceanMask)
localArea = areaCell.where(cellMask, drop=True)
totalArea = localArea.sum()
if year==years[0]:
print(' totalArea: {} mil. km^2'.format(1e-12*totalArea.values))
localLayerVol = layerVol.where(cellMask, drop=True)
# Temporary dsOut (xarray dataset) containing results for
# all variables for one single region
dsOut = xarray.Dataset()
# Compute layer-volume weighted averages (or sums for OHC)
for var in variables:
outName = var['name']
mpasVarName = var['mpas']
units = var['units']
factor = var['fac']
description = var['title']
timeSeries = dsIn[mpasVarName]
timeSeries = timeSeries.where(depthMask, drop=False)
if regionName=='Global':
timeSeries = (layerVol*timeSeries).sum(dim='nCells')
if outName!='ohc':
timeSeries = timeSeries / layerVol.sum(dim='nCells')
else:
timeSeries = timeSeries.where(cellMask, drop=True)
timeSeries = (localLayerVol*timeSeries).sum(dim='nCells')
if outName!='ohc':
timeSeries = timeSeries / localLayerVol.sum(dim='nCells')
dsOut[outName] = factor*timeSeries
dsOut[outName].attrs['units'] = units
dsOut[outName].attrs['description'] = description
dsOut['totalArea'] = totalArea
dsOut.totalArea.attrs['units'] = 'm^2'
datasets.append(dsOut)
# Combine data sets into a single data set for all regions
dsOut = xarray.concat(datasets, 'nRegions')
dsOut['refBottomDepth'] = refBottomDepth
write_netcdf(dsOut, timeSeriesFile)
else:
print('Time series file already exists for year {}. Skipping it...'.format(year))
# Make plot
timeSeriesFiles = []
for year in years:
timeSeriesFile = '{}_year{:04d}.nc'.format(timeSeriesFile0, year)
timeSeriesFiles.append(timeSeriesFile)
if os.path.exists(featureFile):
fcAll = read_feature_collection(featureFile)
else:
raise IOError('No feature file found')
for regionIndex, regionName in enumerate(regionNames):
print(' region: {}'.format(regionName))
fc = FeatureCollection()
for feature in fcAll.features:
if feature['properties']['name'] == regionName:
fc.add_feature(feature)
break
dsIn = xarray.open_mfdataset(timeSeriesFiles, combine='nested',
concat_dim='Time', decode_times=False).isel(nRegions=regionIndex)
movingAverageMonths = 12
depths = dsIn.refBottomDepth.values[0]
z = np.zeros(depths.shape)
z[0] = -0.5 * depths[0]
z[1:] = -0.5 * (depths[0:-1] + depths[1:])
Time = dsIn.Time.values
for var in variables:
varName = var['name']
clevels = var['clevels']
colormap0 = var['colormap']
colorIndices0 = var['colorIndices']
underColor = colormap0(colorIndices0[0])
overColor = colormap0(colorIndices0[-1])
if len(clevels)+1 == len(colorIndices0):
# we have 2 extra values for the under/over so make the colormap
# without these values
colorIndices = colorIndices0[1:-1]
else:
colorIndices = colorIndices0
colormap = cols.ListedColormap(colormap0(colorIndices))
colormap.set_under(underColor)
colormap.set_over(overColor)
cnorm = mpl.colors.BoundaryNorm(clevels, colormap.N)
field = dsIn[varName]
# Compute first-year average (note that this assumes monthly fields)
fieldMean = field.isel(Time=range(12)).mean(dim='Time')
# Compute moving average of the anomaly with respect to first-year average
if movingAverageMonths != 1:
N = movingAverageMonths
movingAverageDepthSlices = []
for nVertLevel in range(nVertLevels):
depthSlice = field.isel(nVertLevels=nVertLevel) - fieldMean.isel(nVertLevels=nVertLevel)
mean = pd.Series.rolling(depthSlice.to_series(), N,
center=True).mean()
mean = xarray.DataArray.from_series(mean)
mean = mean[int(N / 2.0):-int(round(N / 2.0) - 1)]
movingAverageDepthSlices.append(mean)
field = xarray.DataArray(movingAverageDepthSlices)
xLabel = 'Time (yr)'
yLabel = '{} ({})'.format(var['title'], var['units'])
title = '{} Anomaly, {}'.format(var['title'], regionName)
figFileName = '{}/{}vsTimeDepth_{}.png'.format(figdir, varName,
regionName[0].lower()+regionName[1:].replace(' ', ''))
fig = hovmoeller_plot(Time[N-1:], z, field.values, colormap, cnorm, clevels,
title, xLabel, yLabel, calendar, colorbarLabel=var['units'],
titleFontSize=None, figsize=(15, 6), dpi=None)
# do this before the inset because otherwise it moves the inset
# and cartopy doesn't play too well with tight_layout anyway
plt.tight_layout()
if regionName!='Global':
add_inset(fig, fc, width=1.5, height=1.5, xbuffer=0.5, ybuffer=-1)
plt.savefig(figFileName, dpi='figure', bbox_inches='tight',
pad_inches=0.1)
plt.close()