def loadRetPerAllYears(ncDir='/ClimateRun4/multi-hazard/eva', rlVarName='rl', retPer=100,
flpattern='projection_dis_{scen}_{mdl}_wuConst_statistics.nc', nmodels=-1, excludedModels=[]):
wlyR8 = getWarmingLevels('rcp85', 2.0)
models = wlyR8.keys()
if nmodels > -1:
models = models[:nmodels]
for mdl in excludedModels:
if mdl in models:
models.remove(mdl)
dsuparea = netCDF4.Dataset('upArea.nc')
upArea = dsuparea.variables['upArea'][:].transpose()
dsuparea.close()
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask.transpose()
vlsR8 = []
vlsR4 = []
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flr8 = flpattern.format(scen='rcp85', mdl=mdl)
flr8pth = os.path.join(ncDir, flr8)
ds = netCDF4.Dataset(flr8pth)
yr = ds.variables['year'][:]
upAreaMtx = np.tile(upArea, [yr.shape[0], 1, 1])
tamaskMtx = np.tile(tamask, [yr.shape[0], 1, 1])
retper_ = ds.variables['return_period'][:]
rpIndx = np.where(retper_==retPer)[0][0]
vl = ds.variables[rlVarName][rpIndx, :, :, :]
vl[upAreaMtx < 1e9] = np.nan
vl[~tamaskMtx] = np.nan
vlsR8.append(vl)
ds.close()
flr4 = flpattern.format(scen='rcp45', mdl=mdl)
flr4pth = os.path.join(ncDir, flr4)
ds = netCDF4.Dataset(flr4pth)
vl = ds.variables[rlVarName][rpIndx, :, :, :]
vl[upAreaMtx < 1e9] = np.nan
vl[~tamaskMtx] = np.nan
vlsR4.append(vl)
ds.close()
vlsR8 = np.array(vlsR8)
vlsR4 = np.array(vlsR4)
return yr, vlsR8, vlsR4, models
def loadWlVsScenChange(
ncDir='/ClimateRun4/multi-hazard/eva',
bslnYear=1995,
warmingLev=2,
retPer=100,
threshold=0,
rlVarName='rl',
flpattern='projection_dis_{scen}_{mdl}_wuConst_statistics.nc',
nmodels=-1,
shapeParamNcVarName='',
minNumModels=8):
# computes the mean relative change
wlyR8 = getWarmingLevels('rcp85', warmingLev)
wlyR4 = getWarmingLevels('rcp45', warmingLev)
dsuparea = netCDF4.Dataset('upArea.nc')
upArea = dsuparea.variables['upArea'][:].transpose()
dsuparea.close()
maskInvalidShapeParamPts = shapeParamNcVarName != ''
models = wlyR8.keys()
if nmodels > -1:
models = models[:nmodels]
# menta
#models = [models[0], models[1]]
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask.transpose()
if minNumModels > nmodels:
minNumModels = nmodels
rl_r4 = []
rl_r8 = []
mdlCountR4 = np.zeros(upArea.shape)
mdlCountR8 = np.zeros(upArea.shape)
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flr8 = flpattern.format(scen='rcp85', mdl=mdl)
flr8pth = os.path.join(ncDir, flr8)
flr4 = flpattern.format(scen='rcp45', mdl=mdl)
flr4pth = os.path.join(ncDir, flr4)
r8year = wlyR8[mdl]
r8yearInf = int(np.floor(r8year / float(5)) * 5)
print(' rcp85 w.l. year: ' + str(r8year))
r4year = wlyR4[mdl]
r4yearInf = int(np.floor(r4year / float(5)) * 5)
print(' rcp45 w.l. year: ' + str(r4year))
print(' loading file ' + flr8pth)
ds = netCDF4.Dataset(flr8pth)
retper_ = ds.variables['return_period'][:]
rpIndx = np.where(retper_ == retPer)[0][0]
year_ = ds.variables['year'][:]
yIndxBsln = np.where(year_ == bslnYear)[0][0]
rlBslnR8 = ds.variables[rlVarName][rpIndx, yIndxBsln, :, :]
yIndx = np.where(year_ == r8yearInf)[0][0]
rlR8_ = ds.variables[rlVarName][rpIndx, yIndx:yIndx + 2, :, :]
rlR8 = interp1d(year_[yIndx:yIndx + 2], rlR8_, axis=0)(r8year)
if maskInvalidShapeParamPts:
shapeParam = ds.variables[shapeParamNcVarName][:]
cnd = shapeParam <= -.4
rlBslnR8[cnd] = np.nan
rlR8[cnd] = np.nan
ds.close()
r8RelChng = (rlR8 - rlBslnR8) / rlBslnR8
if threshold > 0:
cnd = rlBslnR8 < threshold
r8RelChng[cnd] = np.nan
#r8RelChng[r8RelChng < -.15] = np.nan
print(' loading file ' + flr4pth)
ds = netCDF4.Dataset(flr4pth)
rlBslnR4 = ds.variables[rlVarName][rpIndx, yIndxBsln, :, :]
#rlBslnR4 = rlBslnR8
yIndx = np.where(year_ == r4yearInf)[0][0]
rlR4_ = ds.variables[rlVarName][rpIndx, yIndx:yIndx + 2, :, :]
rlR4 = interp1d(year_[yIndx:yIndx + 2], rlR4_, axis=0)(r4year)
if maskInvalidShapeParamPts:
shapeParam = ds.variables[shapeParamNcVarName][:]
cnd = shapeParam <= -.4
rlBslnR4[cnd] = np.nan
rlR4[cnd] = np.nan
ds.close()
r4RelChng = (rlR4 - rlBslnR4) / rlBslnR4
if threshold > 0:
cnd = rlBslnR4 < threshold
r4RelChng[cnd] = np.nan
#r4RelChng[r4RelChng < -.15] = np.nan
r8RelChng[upArea < 1e9] = np.nan
r4RelChng[upArea < 1e9] = np.nan
r8RelChng[~tamask] = np.nan
r4RelChng[~tamask] = np.nan
rl_r8.append(r8RelChng)
rl_r4.append(r4RelChng)
mdlCountR8 += ~np.isnan(r8RelChng)
mdlCountR4 += ~np.isnan(r4RelChng)
rl_r8 = np.array(rl_r8)
rl_r4 = np.array(rl_r4)
mdlCountMskR8 = mdlCountR8 >= minNumModels
mdlCountMskR4 = mdlCountR4 >= minNumModels
mskR8 = np.tile(mdlCountMskR8, [rl_r8.shape[0], 1, 1])
mskR4 = np.tile(mdlCountMskR4, [rl_r8.shape[0], 1, 1])
rl_r8[mskR8 != 1] = np.nan
rl_r4[mskR4 != 1] = np.nan
relChngDiff = np.nanmean(rl_r8 - rl_r4, 0)
relChngDiff[~tamask] = np.nan
return relChngDiff, np.nanmean(rl_r8, 0), np.nanmean(rl_r4,
0), rl_r8, rl_r4
def loadMeanPrecipitationChangesAtWl(
ncRootDir='/DATA/ClimateData/cordexEurope/yearlymeans/',
bslnYear=1995,
warmingLev=2,
threshold=0,
flname='pr.nc',
nmodels=-1,
timeWindowHalfSize=15):
# computes the mean relative change
wlyR8 = getWarmingLevels('rcp85', warmingLev)
wlyR4 = getWarmingLevels('rcp45', warmingLev)
models = wlyR8.keys()
if nmodels > -1:
models = models[:nmodels]
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask
rlVarName1 = 'pr'
rlVarname2 = 'prAdjust'
rl_r4 = []
rl_r8 = []
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flhistpth = os.path.join(ncRootDir, mdl, 'historical', flname)
flr8pth = os.path.join(ncRootDir, mdl, 'rcp85', flname)
flr4pth = os.path.join(ncRootDir, mdl, 'rcp45', flname)
r8year = wlyR8[mdl]
r4year = wlyR4[mdl]
print(' loading file ' + flhistpth)
ds = netCDF4.Dataset(flhistpth)
rlVarName = 'pr' if 'pr' in ds.variables else 'prAdjust'
tmnc = ds.variables['time']
tm = netCDF4.num2date(tmnc[:], tmnc.units, tmnc.calendar)
yearHist = np.array([t.year for t in tm])
yIndxBsln = np.where(yearHist == bslnYear)[0][0]
minIndx = np.max([yIndxBsln - timeWindowHalfSize, 0])
maxIndx = np.min([yIndxBsln + timeWindowHalfSize, len(yearHist)])
rlBsln = np.nanmean(ds.variables[rlVarName][minIndx:maxIndx, :, :], 0)
ds.close()
print(' loading file ' + flr8pth)
ds = netCDF4.Dataset(flr8pth)
rlVarName = 'pr' if 'pr' in ds.variables else 'prAdjust'
tmnc = ds.variables['time']
tm = netCDF4.num2date(tmnc[:], tmnc.units, tmnc.calendar)
yearR = np.array([t.year for t in tm])
yIndx = np.where(yearR == r8year)[0][0]
minIndx = np.max([yIndx - timeWindowHalfSize, 0])
maxIndx = np.min([yIndx + timeWindowHalfSize, len(yearR)])
rlR8 = np.nanmean(ds.variables[rlVarName][minIndx:maxIndx, :, :], 0)
r8RelChng = (rlR8 - rlBsln) / rlBsln
if threshold > 0:
cnd = rlBslnR8 < threshold
r8RelChng[cnd] = np.nan
#r8RelChng[r8RelChng < -.15] = np.nan
ds.close()
print(' loading file ' + flr4pth)
ds = netCDF4.Dataset(flr4pth)
rlVarName = 'pr' if 'pr' in ds.variables else 'prAdjust'
tmnc = ds.variables['time']
tm = netCDF4.num2date(tmnc[:], tmnc.units, tmnc.calendar)
yearR = np.array([t.year for t in tm])
yIndx = np.where(yearR == r4year)[0][0]
minIndx = np.max([yIndx - timeWindowHalfSize, 0])
maxIndx = np.min([yIndx + timeWindowHalfSize, len(yearR)])
rlR4 = np.nanmean(ds.variables[rlVarName][minIndx:maxIndx, :, :], 0)
r4RelChng = (rlR4 - rlBsln) / rlBsln
if threshold > 0:
cnd = rlBslnR4 < threshold
r4RelChng[cnd] = np.nan
#r4RelChng[r4RelChng < -.15] = np.nan
ds.close()
#r8RelChng[upArea < 1e9] = np.nan
#r4RelChng[upArea < 1e9] = np.nan
r8RelChng[~tamask] = np.nan
r4RelChng[~tamask] = np.nan
rl_r8.append(r8RelChng)
rl_r4.append(r4RelChng)
rl_r8 = np.array(rl_r8)
rl_r4 = np.array(rl_r4)
relChngDiff = np.nanmean(rl_r8 - rl_r4, 0)
relChngDiff[~tamask] = np.nan
return relChngDiff, np.nanmean(rl_r8, 0), np.nanmean(rl_r4,
0), rl_r8, rl_r4
def loadWlVsScenChangeYMax(
ncDir='/ClimateRun4/multi-hazard/eva',
bslnYear=1995,
warmingLev=2,
retPer=100,
threshold=0,
rlVarName='year_max',
windowHalfSize=10,
flpattern='projection_dis_{scen}_{mdl}_wuConst_statistics.nc'):
# computes the mean relative change
wlyR8 = getWarmingLevels('rcp85', warmingLev)
wlyR4 = getWarmingLevels('rcp45', warmingLev)
dsuparea = netCDF4.Dataset('upArea.nc')
upArea = dsuparea.variables['upArea'][:].transpose()
dsuparea.close()
models = wlyR8.keys()
# menta
#models = [models[0], models[1]]
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask.transpose()
rl_r4 = []
rl_r8 = []
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flr8 = flpattern.format(scen='rcp85', mdl=mdl)
flr8pth = os.path.join(ncDir, flr8)
flr4 = flpattern.format(scen='rcp45', mdl=mdl)
flr4pth = os.path.join(ncDir, flr4)
r8year = wlyR8[mdl]
print(' rcp85 w.l. year: ' + str(r8year))
r4year = wlyR4[mdl]
print(' rcp45 w.l. year: ' + str(r4year))
hsz = windowHalfSize
print(' loading file ' + flr8pth)
ds = netCDF4.Dataset(flr8pth)
year_ = ds.variables['year_all'][:]
yIndxBsln = np.where(year_ == bslnYear)[0][0]
rlBslnR8 = np.nanmean(
ds.variables[rlVarName][yIndxBsln - hsz:yIndxBsln + hsz, :, :], 0)
yIndx = np.where(year_ == r8year)[0][0]
rlR8 = np.nanmean(
ds.variables[rlVarName][yIndx - hsz:yIndx + hsz, :, :], 0)
ds.close()
r8RelChng = (rlR8 - rlBslnR8) / rlBslnR8
if threshold > 0:
cnd = rlBslnR8 < threshold
r8RelChng[cnd] = np.nan
#r8RelChng[r8RelChng < -.15] = np.nan
print(' loading file ' + flr4pth)
ds = netCDF4.Dataset(flr4pth)
#rlBslnR4 = ds.variables[rlVarName][rpIndx, yIndxBsln, :, :]
rlBslnR4 = rlBslnR8
yIndx = np.where(year_ == r4year)[0][0]
rlR4 = np.nanmean(
ds.variables[rlVarName][yIndx - hsz:yIndx + hsz, :, :], 0)
ds.close()
r4RelChng = (rlR4 - rlBslnR4) / rlBslnR4
if threshold > 0:
cnd = rlBslnR4 < threshold
r4RelChng[cnd] = np.nan
#r4RelChng[r4RelChng < -.15] = np.nan
r8RelChng[upArea < 1e9] = np.nan
r4RelChng[upArea < 1e9] = np.nan
r8RelChng[~tamask] = np.nan
r4RelChng[~tamask] = np.nan
rl_r8.append(r8RelChng)
rl_r4.append(r4RelChng)
rl_r8 = np.array(rl_r8)
rl_r4 = np.array(rl_r4)
relChngDiff = np.nanmean(rl_r8 - rl_r4, 0)
relChngDiff[~tamask] = np.nan
return relChngDiff, np.nanmean(rl_r8, 0), np.nanmean(rl_r4,
0), rl_r8, rl_r4
def getGrossEnsembleAtYear(ryear,
ncDir='/ClimateRun4/multi-hazard/eva',
bslnYear=1995,
retPer=100,
threshold=200):
flpattern = 'projection_dis_{scen}_{mdl}_wuConst_statistics.nc'
wlyR8 = getWarmingLevels('rcp85', 2.0)
#wlyR4 = getWarmingLevels('rcp45', warmingLev)
models = wlyR8.keys()
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask.transpose()
rl_all = []
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flr8 = flpattern.format(scen='rcp85', mdl=mdl)
flr8pth = os.path.join(ncDir, flr8)
flr4 = flpattern.format(scen='rcp45', mdl=mdl)
flr4pth = os.path.join(ncDir, flr4)
#r8year = wlyR8[mdl] + wlOffset
#r8yearInf = int(np.floor(r8year/float(5))*5)
#print(' rcp85 w.l. year: ' + str(r8year))
#r4year = wlyR4[mdl] + wlOffset
#r4yearInf = int(np.floor(r4year/float(5))*5)
#print(' rcp45 w.l. year: ' + str(r4year))
ryearInf = int(np.floor(ryear / float(5)) * 5)
print(' loading file ' + flr8pth)
ds = netCDF4.Dataset(flr8pth)
retper_ = ds.variables['return_period'][:]
rpIndx = np.where(retper_ == retPer)[0][0]
year_ = ds.variables['year'][:]
yIndxBsln = np.where(year_ == bslnYear)[0][0]
rlBslnR8 = ds.variables['rl'][rpIndx, yIndxBsln, :, :]
#yIndx = np.where(year_==r8yearInf)[0][0]
yIndx = np.where(year_ == ryearInf)[0][0]
rlR8_ = ds.variables['rl'][rpIndx, yIndx:yIndx + 2, :, :]
ds.close()
#rlR8 = interp1d(year_[yIndx:yIndx+2], rlR8_, axis=0)(r8year)
rlR8 = interp1d(year_[yIndx:yIndx + 2], rlR8_, axis=0)(ryear)
r8RelChng = (rlR8 - rlBslnR8) / rlBslnR8
if threshold > 0:
cnd = rlBslnR8 < threshold
r8RelChng[cnd] = np.nan
print(' loading file ' + flr4pth)
ds = netCDF4.Dataset(flr4pth)
rlBslnR4 = ds.variables['rl'][rpIndx, yIndxBsln, :, :]
#yIndx = np.where(year_==r4yearInf)[0][0]
yIndx = np.where(year_ == ryearInf)[0][0]
rlR4_ = ds.variables['rl'][rpIndx, yIndx:yIndx + 2, :, :]
ds.close()
#rlR4 = interp1d(year_[yIndx:yIndx+2], rlR4_, axis=0)(r4year)
rlR4 = interp1d(year_[yIndx:yIndx + 2], rlR4_, axis=0)(ryear)
r4RelChng = (rlR4 - rlBslnR4) / rlBslnR4
if threshold > 0:
cnd = rlBslnR4 < threshold
r4RelChng[cnd] = np.nan
r8RelChng[~tamask] = np.nan
r4RelChng[~tamask] = np.nan
rl_all.append(r8RelChng)
rl_all.append(r4RelChng)
rl_all = np.array(rl_all)
ensembleRelChngAll = np.nanmean(rl_all, 0)
return ensembleRelChngAll
def loadWlVsScenChange2(ncDir='/ClimateRun4/multi-hazard/eva',
bslnYear=1995,
warmingLev=2,
retPer=100,
threshold=0,
rlVarName='rl'):
# computes the relative change of the ensemble
flpattern = 'projection_dis_{scen}_{mdl}_wuConst_statistics.nc'
wlyR8 = getWarmingLevels('rcp85', warmingLev)
wlyR4 = getWarmingLevels('rcp45', warmingLev)
dsuparea = netCDF4.Dataset('upArea.nc')
upArea = dsuparea.variables['upArea'][:].transpose()
dsuparea.close()
models = wlyR8.keys()
# menta
#models = [models[0], models[1]]
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask.transpose()
rl_bsln = []
rl_r4 = []
rl_r8 = []
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flr8 = flpattern.format(scen='rcp85', mdl=mdl)
flr8pth = os.path.join(ncDir, flr8)
flr4 = flpattern.format(scen='rcp45', mdl=mdl)
flr4pth = os.path.join(ncDir, flr4)
r8year = wlyR8[mdl]
r8yearInf = int(np.floor(r8year / float(5)) * 5)
print(' rcp85 w.l. year: ' + str(r8year))
r4year = wlyR4[mdl]
r4yearInf = int(np.floor(r4year / float(5)) * 5)
print(' rcp45 w.l. year: ' + str(r4year))
print(' loading file ' + flr8pth)
ds = netCDF4.Dataset(flr8pth)
retper_ = ds.variables['return_period'][:]
rpIndx = np.where(retper_ == retPer)[0][0]
year_ = ds.variables['year'][:]
yIndxBsln = np.where(year_ == bslnYear)[0][0]
rlBslnR8 = ds.variables[rlVarName][rpIndx, yIndxBsln, :, :]
yIndx = np.where(year_ == r8yearInf)[0][0]
rlR8_ = ds.variables[rlVarName][rpIndx, yIndx:yIndx + 2, :, :]
ds.close()
rlR8 = interp1d(year_[yIndx:yIndx + 2], rlR8_, axis=0)(r8year)
print(' loading file ' + flr4pth)
ds = netCDF4.Dataset(flr4pth)
yIndx = np.where(year_ == r4yearInf)[0][0]
rlR4_ = ds.variables[rlVarName][rpIndx, yIndx:yIndx + 2, :, :]
ds.close()
rlR4 = interp1d(year_[yIndx:yIndx + 2], rlR4_, axis=0)(r4year)
rlR8[upArea < 1e9] = np.nan
rlR4[upArea < 1e9] = np.nan
rlR8[~tamask] = np.nan
rlR4[~tamask] = np.nan
rl_r8.append(rlR8)
rl_r4.append(rlR4)
rl_bsln.append(rlBslnR8)
rl_r8 = np.array(rl_r8)
rl_r4 = np.array(rl_r4)
rl_bsln = np.array(rl_bsln)
mnbsln = np.nanmean(rl_bsln, 0)
mnbsln[mnbsln < threshold] = np.nan
rc_r8 = (np.nanmean(rl_r8, 0) - mnbsln) / mnbsln
rc_r4 = (np.nanmean(rl_r4, 0) - mnbsln) / mnbsln
relChngDiff = rc_r8 - rc_r4
relChngDiff[~tamask] = np.nan
nmdl = rl_bsln.shape[0]
std_r8 = np.std(rl_r8 - rl_bsln, 0) / np.sqrt(
np.sum(rl_bsln**2., 0) / nmdl)
std_r4 = np.std(rl_r4 - rl_bsln, 0) / np.sqrt(
np.sum(rl_bsln**2., 0) / nmdl)
std_diff = np.std(rl_r8 - rl_r4, 0) / np.sqrt(
np.sum(rl_bsln**2., 0) / nmdl)
_, pval_r8 = stats.ttest_ind(rl_r8, rl_bsln, 0)
_, pval_r4 = stats.ttest_ind(rl_r4, rl_bsln, 0)
_, pval_diff = stats.ttest_ind(rl_r8, rl_r4, 0)
return relChngDiff, rc_r8, rc_r4, std_r8, std_r4, std_diff, pval_r8, pval_r4, pval_diff
def loadMeanChangesAtWl_nYearsAroundWLYear(ncDir='/ClimateRun4/multi-hazard/eva', bslnYear=1995, warmingLev=2,
threshold=0, rlVarName='year_mean',
flpattern='projection_dis_{scen}_{mdl}_wuConst_statistics.nc',
nmodels=-1, timeWindowHalfSize=15, excludedModels=[]):
# computes the mean relative change
wlyR8 = getWarmingLevels('rcp85', warmingLev)
wlyR4 = getWarmingLevels('rcp45', warmingLev)
dsuparea = netCDF4.Dataset('upArea.nc')
upArea = dsuparea.variables['upArea'][:].transpose()
dsuparea.close()
models = wlyR8.keys()
if nmodels > -1:
models = models[:nmodels]
for mdl in excludedModels:
if mdl in models:
models.remove(mdl)
tamask, _, _ = getAfricaAndTurkeyMask()
tamask = tamask.transpose()
rl_r4 = []
rl_r8 = []
for mdl, imdl in zip(models, range(len(models))):
print('model ' + mdl)
flr8 = flpattern.format(scen='rcp85', mdl=mdl)
flr8pth = os.path.join(ncDir, flr8)
flr4 = flpattern.format(scen='rcp45', mdl=mdl)
flr4pth = os.path.join(ncDir, flr4)
r8year = wlyR8[mdl]
r4year = wlyR4[mdl]
print(' loading file ' + flr8pth)
ds = netCDF4.Dataset(flr8pth)
year_ = ds.variables['year_all'][:]
yIndxBsln = np.where(year_ == bslnYear)[0][0]
minIndx = np.max([yIndxBsln-timeWindowHalfSize, 0])
maxIndx = np.min([yIndxBsln+timeWindowHalfSize+1, len(year_)])
rlBslnR8 = np.nanmean(ds.variables[rlVarName][minIndx:maxIndx, :, :], 0)
yIndx = np.where(year_==r8year)[0][0]
minIndx = np.max([yIndx-timeWindowHalfSize*2, 0])
maxIndx = np.min([yIndx+timeWindowHalfSize*2, len(year_)])
rlR8All = ds.variables[rlVarName][minIndx:maxIndx, :, :]
convFnct = np.ones([timeWindowHalfSize*2])/(timeWindowHalfSize*2)
rlR8RunningMean = convolve1d(rlR8All, convFnct, 0)
minIndx, maxIndx = timeWindowHalfSize, timeWindowHalfSize*3+1
rlR8RunningMean = rlR8RunningMean[minIndx:maxIndx, :, :]
indx = np.arange(0,31,5)
rlR8RunningMean = rlR8RunningMean[indx, :, :]
rlBslnR8Mtx = np.tile(rlBslnR8, [rlR8RunningMean.shape[0], 1, 1])
r8RelChng = (rlR8RunningMean-rlBslnR8Mtx)/rlBslnR8Mtx
if threshold > 0:
cnd = rlBslnR8 < threshold
r8RelChng[cnd] = np.nan
#r8RelChng[r8RelChng < -.15] = np.nan
print(' loading file ' + flr4pth)
ds = netCDF4.Dataset(flr4pth)
rlBslnR4 = rlBslnR8
yIndx = np.where(year_==r4year)[0][0]
minIndx = np.max([yIndx-timeWindowHalfSize*2, 0])
maxIndx = np.min([yIndx+timeWindowHalfSize*2, len(year_)])
rlR4All = ds.variables[rlVarName][minIndx:maxIndx, :, :]
convFnct = np.ones([timeWindowHalfSize*2])/(timeWindowHalfSize*2)
rlR4RunningMean = convolve1d(rlR4All, convFnct, 0)
minIndx, maxIndx = timeWindowHalfSize, timeWindowHalfSize*3+1
rlR4RunningMean = rlR4RunningMean[minIndx:maxIndx, :, :]
indx = np.arange(0,31,5)
rlR4RunningMean = rlR4RunningMean[indx, :, :]
rlBslnR4Mtx = np.tile(rlBslnR4, [rlR4RunningMean.shape[0], 1, 1])
r4RelChng = (rlR4RunningMean-rlBslnR4Mtx)/rlBslnR4Mtx
upAreaMtx = np.tile(upArea, [r8RelChng.shape[0], 1, 1])
r8RelChng[upAreaMtx < 1e9] = np.nan
r4RelChng[upAreaMtx < 1e9] = np.nan
tamaskMtx = np.tile(tamask, [r8RelChng.shape[0], 1, 1])
r8RelChng[~tamaskMtx] = np.nan
r4RelChng[~tamaskMtx] = np.nan
rl_r8.append(r8RelChng)
rl_r4.append(r4RelChng)
rl_r8 = np.array(rl_r8)
rl_r4 = np.array(rl_r4)
relChngDiff = np.nanmean(rl_r8-rl_r4, 0)
return relChngDiff, np.nanmean(rl_r8, 0), np.nanmean(rl_r4, 0), rl_r8, rl_r4