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cal_RadKernel_E3SM.py
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## this script is used to calculate radiative kernel following Soden et al. and Shell et al.
## some trival but important things need to be taken care:
## 1. albedo should be in percent (%) unit;
## 2. pay attention to the masking, which should be level < p_tropopause;
## created: March 12, 2020 by Yi Qin
## modified on June 30, 2020 --- change into a function used by main.py
## modified on Oct 17, 2020 -- update the method to calcuate nonclodu feedbacks
## modified on Jul 16, 2021 -- change output file name: eleminate used_models[imodel]
## modified on Aug 12, 2021 -- let read_data_e3sm also work on T and Q. Not only for ta and hus.
## modified on Aug 21, 2021 -- change caselist if 'coupled' in case_stamp to run linear regression.
import cdms2 as cdms
import cdutil
import MV2 as MV
import numpy as np
import pylab as pl
import matplotlib as mpl
mpl.use('Agg')
import sys
## qinyi
import cartopy.crs as ccrs
import cartopy
import matplotlib.pyplot as plt
import os
import pandas as pd
import cdtime
import time
import genutil
import numpy.ma as ma
from genutil import statistics
from scipy.interpolate import interp1d
import ReadData as RD
import psutil
import gc
cdms.axis.latitude_aliases.append("Y")
cdms.axis.longitude_aliases.append("X")
def read_data_e3sm(var,var2d,var3d,direc_data1,direc_data2,exp1,exp2,yrS_4d,monS_2d,yrE_4d,monE_2d,dic_invar,nyears):
for svar in var:
if svar in var:
#<qinyi 2021-08-12 #------------------
if not os.path.isfile(direc_data1+svar+'_'+exp1+'_'+yrS_4d+monS_2d+'-'+yrE_4d+monE_2d+'.nc'):
if svar == 'ta':
svar_in = 'T'
elif svar == 'hus':
svar_in = 'Q'
else:
svar_in = svar
else:
svar_in = svar
#>qinyi 2021-08-12 #------------------
print(" =================== we are processing E3SM amip data", svar, " locally ====================")
f1 = cdms.open(direc_data1+svar_in+'_'+exp1+'_'+yrS_4d+monS_2d+'-'+yrE_4d+monE_2d+'.nc','r')
dic_invar[svar+'_pi'] = f1(svar_in)[:nyears*12,:,:]
f1.close()
f2 = cdms.open(direc_data2+svar_in+'_'+exp2+'_'+yrS_4d+monS_2d+'-'+yrE_4d+monE_2d+'.nc','r')
dic_invar[svar+'_ab'] = f2(svar_in)[:nyears*12,:,:]
f2.close()
# reverse lev direction
if svar in var3d:
dic_invar[svar+'_pi'] = dic_invar[svar+'_pi'][:,::-1,:,:]
dic_invar[svar+'_ab'] = dic_invar[svar+'_ab'][:,::-1,:,:]
dic_invar[svar+'_ano'] = dic_invar[svar+'_ab'] - dic_invar[svar+'_pi']
dic_invar[svar+'_ano'].setAxisList(dic_invar[svar+'_pi'].getAxisList())
stop_here = False
else:
stop_here = True
if stop_here: ### we don't get enough data to do further processing. just skip out this loop.
print('stop_here is', stop_here)
continue
### get SWCF, LWCF and their anomalies
dic_invar['SWCRE_ano'] = dic_invar['rsutcs_ano'] - dic_invar['rsut_ano']
dic_invar['LWCRE_ano'] = dic_invar['rlutcs_ano'] - dic_invar['rlut_ano']
dic_invar['netCRE_ano'] = dic_invar['SWCRE_ano'] + dic_invar['LWCRE_ano']
delterm = ['rsutcs_pi','rsutcs_ab','rsut_pi','rsut_ab','rlutcs_pi','rlutcs_ab','rlut_pi','rlut_ab','ps_ab','ps_ano']
dic_invar = delete_vars(delterm,dic_invar)
AXL2d = dic_invar['rsus_pi'].getAxisList()
## get albedo
dic_invar['alb_pi'] = dic_invar['rsus_pi']/dic_invar['rsds_pi'] * 100.
dic_invar['alb_ab'] = dic_invar['rsus_ab']/dic_invar['rsds_ab'] *100.
dic_invar['alb_pi'] = MV.masked_outside(dic_invar['alb_pi'], 0.0, 100.)
dic_invar['alb_ab'] = MV.masked_outside(dic_invar['alb_ab'], 0.0, 100.)
dic_invar['alb_ano'] = dic_invar['alb_ab'] - dic_invar['alb_pi']
dic_invar['alb_pi'].setAxisList(AXL2d)
dic_invar['alb_ab'].setAxisList(AXL2d)
dic_invar['alb_ano'].setAxisList(AXL2d)
delterm = ['rsus_pi','rsds_pi','rsus_ab','rsds_ab','alb_pi','alb_ab','rsdt_pi']
dic_invar = delete_vars(delterm,dic_invar)
delterm = ['rsuscs_pi','rsdscs_pi','rsuscs_ab','rsdscs_ab','tas_pi','tas_ab','rsus_ano','rsds_ano','rsuscs_ano','rsdscs_ano']
dic_invar = delete_vars(delterm,dic_invar)
return dic_invar
def qsat_blend_Mark(avgta,lev_4d):
wsl=MU.qsat_water(avgta,lev_4d*100.)
wsl_plus1=MU.qsat_water(avgta+1,lev_4d*100.)
wsi=PMC_utils.qsat_ice(avgta,lev_4d*100.)
wsi_plus1=PMC_utils.qsat_ice(avgta+1,lev_4d*100.)
qsl=wsl/(1+wsl) # convert from mixing ratio (kg/kg) to specific humidity
qsl_plus1=wsl_plus1/(1+wsl_plus1)
qsi=wsi/(1+wsi) # convert from mixing ratio (kg/kg) to specific humidity
qsi_plus1=wsi_plus1/(1+wsi_plus1)
del wsl,wsl_plus1,wsi,wsi_plus1
# Compute blend of qsi and qsl between -40 and 0
blend = (avgta-233)*qsl/40 + (273-avgta)*qsi/40
blend_plus1 = (avgta-233)*qsl_plus1/40 + (273-avgta)*qsi_plus1/40
qs0 = np.where((avgta>233) & (avgta<273), blend, qsi)#[0]
qs1 = np.where((avgta>233) & (avgta<273), blend_plus1, qsi_plus1)#[0]
qs0 = np.where(avgta >= 273, qsl, qs0)#[0]
qs1 = np.where(avgta >= 273, qsl_plus1, qs1)#[0]
del blend, blend_plus1,qsi,qsi_plus1,qsl,qsl_plus1
qs0 = np.float32(qs0)
qs1 = np.float32(qs1)
return qs0, qs1
def get_fdbk(invar1,invar2,outvar,dic_invar,AXL4d, AXL3d, dp_use_method='SPC'):
for ivar,svar in enumerate(invar1):
ovar = outvar[ivar]
print('get_fdbk: invar1, invar2, outvar: ',invar1[ivar],invar2[ivar],outvar[ivar])
dic_invar[ovar] = dic_invar[svar] * dic_invar[invar2[ivar]]
dic_invar[ovar].setAxisList(AXL4d)
# vertical integral (sum) -- first get weights: dp
if dp_use_method == 'DynPs' or dp_use_method =='SPC':
dic_invar[ovar+'_psum'] = VertSum(dic_invar[ovar],dic_invar['dp_4d'])
dic_invar[ovar+'_psum'].setAxisList(AXL3d)
else:
dic_invar[ovar+'_psum'] = MV.sum(dic_invar[ovar]*dic_invar['dp_4d']/100., axis=1)
dic_invar[ovar+'_psum'].setAxisList(AXL3d)
return dic_invar
def delete_vars(delterm,dic_invar):
sub_name = 'before delete item'
print_memory_status(sub_name)
for svar in delterm:
if svar in dic_invar.keys():
print(svar,'dic_invar[svar] getrefcount is', sys.getrefcount(dic_invar[svar]))
print('delete ',svar)
del dic_invar[svar]
gc.collect()
print('after delete, dic_invar.keys() are ',dic_invar.keys())
sub_name = 'after delete item'
print_memory_status(sub_name)
return dic_invar
def Vert_RegridStd(DATA,stdlevs,std_plev):
rawlevs = DATA.getLevel()[:]
if DATA.getLevel().units == 'Pa':
rawlevs = rawlevs/100.
# "steal" from Mark's script: load_TOA_kernels.py -- March 12
# Oct 3, 2020: MV.where has more memory leak than np.where. so, use np.where here.
# DATA2=MV.where(DATA.mask,np.nan,DATA)
DATA2=np.where(DATA.mask,np.nan,DATA)
# f = interp1d(rawlevs,DATA2,axis=1,copy=False)
f = interp1d(rawlevs, DATA2, axis=1, copy=False, fill_value="extrapolate")
newdata = f(stdlevs.data)
newdata = np.float32(newdata)
newdata = cdms.asVariable(newdata)
newdata=np.ma.masked_where(np.isnan(newdata),newdata,copy=False)
newdata.setAxis(0,DATA.getAxis(0))
newdata.setAxis(1,std_plev)
newdata.setAxis(2,DATA.getAxis(2))
newdata.setAxis(3,DATA.getAxis(3))
del f,DATA2,rawlevs
return newdata
# Oct 2, 2020: add memory diagnostics functions in MB and % separately
def print_memory_status(sub_name):
mem1 = memory_usage_psutil_Percent()
mem2 = memory_usage_psutil_MB()
print('---------------------------------------------------------------')
print(sub_name+' is done.')
print('Currently using '+str(np.round(mem1,6))+'%, '+str(mem2)+' GB')
print('---------------------------------------------------------------')
del mem1, mem2
def memory_usage_psutil_MB():
# return the memory usage in MB
import psutil
process = psutil.Process(os.getpid())
mem = process.memory_info()[0]/1.024e9
return mem
def memory_usage_psutil_Percent():
# return the memory usage in percentage like top
process = psutil.Process(os.getpid())
mem = process.memory_percent()
return mem
# Sep 22, 2020: get vertical intergal at midpoints
def VertSum(varin,dp_4d):
# get midpoint var first
var1 = (varin[:,:-1,:,:] + varin[:,1:,:,:])/2.
# vertical integral
outvar = MV.sum(var1 * dp_4d/100., axis=1)
# set axis
outvar.setAxisList(varin[:,0,:,:].getAxisList())
return outvar
def get_feedback(exp_cntl,DATA,tas_ano_grd_ann,AXL2d):
if exp_cntl == 'amip':
print('---------Doing amip feedback calculation-----------------')
DATA_ann = DATA
newdata1 = MV.average(DATA_ann,axis=0)/MV.average(tas_ano_grd_ann)
newdata1.setAxisList(AXL2d)
newdata2 = cdutil.averager(newdata1,axis='xy',weights='weighted')
newdata3 = MV.average(DATA_ann,axis=0)
newdata3.setAxisList(AXL2d)
newdata4 = cdutil.averager(newdata3,axis='xy',weights='weighted')
del DATA_ann
elif exp_cntl == 'piControl':
print('---------Doing coupled feedback calculation-----------------')
cdutil.setTimeBoundsMonthly(DATA)
DATA_ann = cdutil.YEAR(DATA)
slope,intercept = genutil.statistics.linearregression(DATA_ann,x = tas_ano_grd_ann)
newdata1 = slope
newdata2 = cdutil.averager(slope,axis='xy',weights='weighted')
newdata3 = intercept
newdata4 = cdutil.averager(intercept,axis='xy',weights='weighted')
del slope, intercept, DATA_ann
return newdata1,newdata2,newdata3,newdata4
########## MAIN SUBROUTINE STARTS HERE ....
# Oct 17, 2020: add exp1 and exp2 to denote the casetag, like 'FC5' and 'FC5_4K', or 'amip' and 'amip_4K'
def RadKernel(kernel_dir,direc_data,case_stamp,yearS,yearE,fname1,fname2,outdir,figdir,exp1,exp2):
if os.path.isfile(outdir+'FDBK_CMIP6_'+case_stamp+'.csv'):
print('RadKenel is already there.')
return
yearS_4d = "{:04d}".format(yearS)
yearE_4d = "{:04d}".format(yearE)
nyears = yearE - yearS + 1
direc_data1 = direc_data+'/'+fname1+'/'
direc_data2 = direc_data+'/'+fname2+'/'
monS = 1
monE = 12
monS_2d='{:02d}'.format(monS)
monE_2d='{:02d}'.format(monE)
plotting = False
phases = ['CMIP6']
# figure font
fh = 17
sundown = True
RegridVert_method = 'StdVert' #'OldVert', 'StdVert'
get_netRH_method = 'Mark'
do_mask_kernel = 'ToZero' # 'OnlyMask'
kernel_source = 'Raw' # 'Raw','Mark'
dp_use_method = 'SPC' # # 'DynPs', 'pTrop', 'Fixed','SPC' # notion: pTrop is exactly same as Fixed.
get_tropo_method = 'Mark' # 'NoLimit'; 'Soden'; 'Mark'
dlogq_method = 'Mixed' # 'Mark','Mixed'
ta_method = 'taavg' # 'taavg','tapi'
qsat_method = 'Yi' # 'Mark', 'Yi'
log_method ='Yi' # 'Mark','Yi','angie'
for phase in phases:
print('----------Hi, here is processing ',phase,' Data----------------')
### Jan 23: merge amip and cmip experiments in the same script. here.
All_project_cntl = ['CMIP','CMIP']
All_exp_cntl = ['piControl','amip']
All_project_new = ['CMIP','CFMIP']
if phase == 'CMIP5':
All_exp_new = ['abrupt4xCO2','amip4K']
else:
All_exp_new = ['abrupt-4xCO2','amip-p4K']
if phase == 'CMIP5':
All_nyears=[150,27]
else:
All_nyears=[nyears,nyears]
comp='atmos'
freq='mon'
var2d = ["tas","rlut","rsut","rlutcs","rsutcs","rsdt","rsus","rsds","rsuscs","rsdscs","ps"]
# var2d = ['ts','rsnt','rsntcs','rlut','rlutcs','rsdscs','rsuscs','rsds','rsus']
var3d = ['ta','hus']
var = var2d + var3d
used_models = ['E3SM-1-0']
if 'coupled' in case_stamp:
caselist = range(0,1)
else:
caselist = range(1,2) # amip
for icase in caselist:
project_cntl = All_project_cntl[icase]
exp_cntl = All_exp_cntl[icase]
project_new = All_project_new[icase]
exp_new = All_exp_new[icase]
for imod in range(len(used_models)):
dic_invar = {}
dic_invar = read_data_e3sm(var,var2d,var3d,direc_data1,direc_data2,exp1,exp2,yearS_4d,monS_2d,yearE_4d,monE_2d,dic_invar,nyears)
sub_name = 'Reading all data'
print_memory_status(sub_name)
##================================================================================###
###------------------------- Read kernel data -----------------------###
if kernel_source == 'Raw':
# kernel_dir = "/work/qin4/Data/Huang_kernel_data/"
### attention: the kernel data's latitude is from north to south [90....-90]
### we need to reverse it.
f1 = cdms.open(kernel_dir+"RRTMG_ts_toa_cld_highR.nc")
dic_invar['ts_KernCld'] = f1('lwkernel')[:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_ts_toa_clr_highR.nc")
dic_invar['ts_KernClr'] = f1('lwkernel')[:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_t_toa_cld_highR.nc")
dic_invar['t_KernCld'] = f1('lwkernel')[:,:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_t_toa_clr_highR.nc")
dic_invar['t_KernClr'] = f1('lwkernel')[:,:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_wv_sw_toa_cld_highR.nc")
dic_invar['wv_sw_KernCld'] = f1('swkernel')[:,:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_wv_sw_toa_clr_highR.nc")
dic_invar['wv_sw_KernClr'] = f1('swkernel')[:,:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_wv_lw_toa_cld_highR.nc")
dic_invar['wv_lw_KernCld'] = f1('lwkernel')[:,:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_wv_lw_toa_clr_highR.nc")
dic_invar['wv_lw_KernClr'] = f1('lwkernel')[:,:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_alb_toa_cld_highR.nc")
dic_invar['alb_KernCld'] = f1('swkernel')[:,::-1,:]
f1.close()
f1 = cdms.open(kernel_dir+"RRTMG_alb_toa_clr_highR.nc")
dic_invar['alb_KernClr'] = f1('swkernel')[:,::-1,:]
f1.close()
sub_name = 'Reading kernel data'
print_memory_status(sub_name)
# =========================================================================================================#
# Vertical Regridding
# =========================================================================================================#
if RegridVert_method == 'StdVert':
stdlevs = np.array([100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000])/100.
std_plev = cdms.createAxis(stdlevs)
std_plev.designateLevel()
std_plev.units = 'hPa'
std_plev.id = 'plev'
these_vars = ['ta_ano','hus_ano','ta_pi','ta_ab','hus_pi','hus_ab','t_KernCld','t_KernClr','wv_sw_KernCld','wv_sw_KernClr','wv_lw_KernCld','wv_lw_KernClr']
sub_name = 'Start Vertical Regrid'
print_memory_status(sub_name)
for ivar in these_vars:
if kernel_source == 'Mark' and 'Kern' in ivar:
dic_invar[ivar+'_vert'] = dic_invar[ivar]
else:
dic_invar[ivar+'_vert'] = Vert_RegridStd(dic_invar[ivar],stdlevs,std_plev)
print('Vertical regrid ',ivar,'from shape',dic_invar[ivar].shape,'to shape',dic_invar[ivar+'_vert'].shape)
del dic_invar[ivar]
del stdlevs, std_plev
sub_name = 'Vertical regrid '
print_memory_status(sub_name)
# =========================================================================================================#
# expand kernel data into several years * 12 month
# =========================================================================================================#
stdtime=dic_invar['ta_ano_vert'].getTime()[:]
kern_time = cdms.createAxis(stdtime)
kern_time.designateTime()
kern_time.units = dic_invar['ta_ano_vert'].getTime().units
kern_time.id = "time"
del stdtime
these_vars = ['ts_KernCld','ts_KernClr','alb_KernCld','alb_KernClr',\
't_KernCld_vert','t_KernClr_vert','wv_sw_KernCld_vert','wv_sw_KernClr_vert','wv_lw_KernCld_vert','wv_lw_KernClr_vert']
HAX = dic_invar['t_KernCld_vert'].getAxisList()
for ivar in these_vars:
DATA = dic_invar[ivar]
if len(DATA.shape) == 3: # (time,lat,lon)
newdata2 = np.tile(DATA,(nyears,1,1))
newdata2 = np.float32(newdata2)
newdata2.setAxis(0,kern_time)
newdata2.setAxis(1,HAX[2])
newdata2.setAxis(2,HAX[3])
elif len(DATA.shape) == 4: # (time,lev,lat,lon)
newdata2 = np.tile(DATA,(nyears,1,1,1))
newdata2 = np.float32(newdata2)
newdata2.setAxis(0,kern_time)
newdata2.setAxis(1,HAX[1])
newdata2.setAxis(2,HAX[2])
newdata2.setAxis(3,HAX[3])
dic_invar[ivar+'_mon'] = newdata2
print(ivar,'expanding from shape',DATA.shape,'to shape',newdata2.shape)
del DATA,newdata2,dic_invar[ivar]
del kern_time,HAX
sub_name = 'Expanding kernel data to 12*nyears data '
print_memory_status(sub_name)
# =========================================================================================================#
# horizontally regrid the input data to kernel grid
# =========================================================================================================#
these_vars = ['tas_ano','alb_ano',\
'ta_ano_vert','hus_ano_vert','ta_pi_vert','ta_ab_vert','hus_pi_vert','hus_ab_vert',\
'rsdt_ab','rsdt_ano','rsutcs_ano','rlutcs_ano','SWCRE_ano','LWCRE_ano','netCRE_ano','rlut_ano','rsut_ano','ps_pi']
kernel_grid = dic_invar['ts_KernCld_mon'].getGrid() # get new grid info
for ivar in these_vars:
DATA = dic_invar[ivar]
newdata = DATA.regrid(kernel_grid,regridTool='esmf',regridMethod='linear')
print(ivar,'horizonally regrid from shape',DATA.shape,'to shape', newdata.shape) #(12*nyears, 64, 128)
dic_invar[ivar+'_grd'] = newdata
del newdata, DATA, dic_invar[ivar]
del kernel_grid
sub_name = 'Horizontally regrid the input data to kernel grid '
print_memory_status(sub_name)
#################################################################################
#### Temperature feedback calculation
#################################################################################
lat = dic_invar['t_KernCld_vert_mon'].getLatitude()[:]
lon = dic_invar['t_KernCld_vert_mon'].getLongitude()[:]
time = dic_invar['t_KernCld_vert_mon'].getTime()[:]
lev = dic_invar['t_KernCld_vert_mon'].getLevel()[:]
nlat = len(lat)
nlon = len(lon)
nlev = len(lev)
ntime = len(time)
# March 18: change the raw data to get axis from t_KernCld_vert_mon to ta_pi_vert_grd
# however, I don't know why the axis getting from t_KernCld_vert_mon cannot be used for two specific models: CCSM4 and CM4 in processing coupled simulation. So strange.
AXL4d = dic_invar['ta_pi_vert_grd'].getAxisList()
AXL3d = dic_invar['ta_pi_vert_grd'][:,0,:,:].getAxisList()
AXL2d = dic_invar['ta_pi_vert_grd'][0,0,:,:].getAxisList()
# =========================================================================================================#
# get tropopause height
# =========================================================================================================#
# April 27, 2020: update the calculation of tropopause pressure by using the time-varying one.
# refer to Mark et al. (2020)
if get_tropo_method == 'NoLimit':
lev_4d = np.transpose(np.tile(lev,(ntime,nlat,nlon,1)),(0,3,1,2))
lev_4d = np.float32(lev_4d)
# temporially set the tropopause height is the minimum level
dic_invar['p_tropopause'] = cdms.asVariable(np.transpose(np.tile(lev_4d[:,-1,:,:],(nlev,1,1,1)),(1,0,2,3)))
dic_invar['p_tropopause'].setAxisList(AXL4d)
elif get_tropo_method == 'Soden':
lev_4d = np.transpose(np.tile(lev,(ntime,nlat,nlon,1)),(0,3,1,2))
lev_4d = np.float32(lev_4d)
# crude tropopause estimate: 100 hPa in the tropics, lowering with
# cosine to 300 hPa at the poles.
pi = 3.1415926
x = np.cos(lat*pi/100.)
p_tropopause_zonalmean = 300-200*x
p_tropopause = np.float32(np.transpose(np.tile(p_tropopause_zonalmean,(ntime,nlev,nlon,1)),(0,1,3,2)))
dic_invar['p_tropopause'] = cdms.asVariable(p_tropopause)
dic_invar['p_tropopause'].setAxisList(AXL4d)
del x, p_tropopause_zonalmean, p_tropopause
elif get_tropo_method == 'Mark':
lev_4d = np.transpose(np.tile(lev,(ntime,nlat,nlon,1)),(0,3,1,2))
lev_4d = np.float32(lev_4d)
# I don't know why Mark uses the p_tropopause derived from ta_ab_vert_grd. I will try it here.
dic_invar['p_tropopause_3d'] = RD.get_tropopause_pressure(dic_invar['ta_ab_vert_grd'])
p_tropopause = np.float32(np.transpose(np.tile(dic_invar['p_tropopause_3d'],(nlev,1,1,1)),(1,0,2,3)))
dic_invar['p_tropopause'] = cdms.asVariable(p_tropopause)
print('p_tropopause shape is',dic_invar['p_tropopause'].shape,'minmax is',genutil.minmax(dic_invar['p_tropopause']))
del p_tropopause
# --------------------------------------------------------------------------------------------
# set the temperature change to zero in the stratosphere (mask out stratosphere)
if get_tropo_method == 'Soden' or get_tropo_method == 'Mark':
dic_invar['ta_ano_vert_grd'] = MV.masked_where(lev_4d<=dic_invar['p_tropopause'],dic_invar['ta_ano_vert_grd'])
sub_name = 'get tropopause height '
print_memory_status(sub_name)
# =========================================================================================================#
# mask kernel method
# =========================================================================================================#
if do_mask_kernel == 'ToZero':
these_vars = ['ts_KernCld_mon','ts_KernClr_mon','alb_KernCld_mon','alb_KernClr_mon',\
't_KernCld_vert_mon','t_KernClr_vert_mon','wv_lw_KernCld_vert_mon','wv_lw_KernClr_vert_mon',\
'wv_sw_KernCld_vert_mon','wv_sw_KernClr_vert_mon']
for svar in these_vars:
dic_invar[svar][dic_invar[svar].mask] = 0.0
sub_name = 'do mask kernel '
print_memory_status(sub_name)
# =========================================================================================================#
# get pressure thickness
# =========================================================================================================#
psurf = 1000.
ptop = min(lev)
if dp_use_method == 'DynPs':
# Sep 21, 2020: use dynamic surface pressure (ps_pi_grd) rather than the fixed psurf
dic_invar['dp_4d'] = RD.get_dp_DynPs(dic_invar['ps_pi_grd'],ptop,lev,dic_invar['p_tropopause'])
elif dp_use_method == 'pTrop':
# Sep 20, 2020: update to include p_tropopause
dic_invar['dp_4d'] = RD.get_dp(psurf,ptop,lev,dic_invar['p_tropopause'])
elif dp_use_method == 'Fixed':
# the oldest method
dp = RD.get_dp_old(psurf, ptop, lev)
dic_invar['dp_4d'] = np.float32(np.transpose(np.tile(np.tile(dp,[1,1,1,1]),[ntime,nlat,nlon,1]),(0,3,1,2)))
elif dp_use_method == 'SPC': # Sep 24, 2020
trop_wts, atm_wts = RD.get_weights_SPC(dic_invar['ps_pi_grd'], dic_invar['p_tropopause_3d'], dic_invar['ta_pi_vert_grd'])
dic_invar['dp_4d'] = trop_wts * 100. # convert to thickness to be consistent with my codes.
del trop_wts, atm_wts,dic_invar['p_tropopause_3d']
dic_invar['dp_4d'] = np.float32(dic_invar['dp_4d'])
del dic_invar['ps_pi_grd'],psurf,ptop
sub_name = 'Getting pressure thickness '
print_memory_status(sub_name)
# =========================================================================================================#
# set SUNDOWN case
# =========================================================================================================#
if sundown:
SUNDOWN = dic_invar['rsdt_ab_grd']
# =========================================================================================================#
# TS feedback
# =========================================================================================================#
dic_invar['tas_ano_grd'][dic_invar['tas_ano_grd'].mask] = 0
dic_invar['dLW_ts'] = dic_invar['ts_KernCld_mon'] * dic_invar['tas_ano_grd']
dic_invar['dLW_ts'].setAxisList(AXL3d)
# for clear-sky
dic_invar['dLW_ts_clr'] = dic_invar['ts_KernClr_mon'] * dic_invar['tas_ano_grd']
dic_invar['dLW_ts_clr'].setAxisList(AXL3d)
del dic_invar['ts_KernClr_mon']
sub_name = 'Getting TS feedback '
print_memory_status(sub_name)
# =========================================================================================================#
# get total water vapor kernel
# =========================================================================================================#
dic_invar['wv_KernCld_vert_mon'] = dic_invar['wv_sw_KernCld_vert_mon'] + dic_invar['wv_lw_KernCld_vert_mon']
dic_invar['wv_KernClr_vert_mon'] = dic_invar['wv_sw_KernClr_vert_mon'] + dic_invar['wv_lw_KernClr_vert_mon']
dic_invar['wv_KernCld_vert_mon'].setAxisList(AXL4d)
dic_invar['wv_KernClr_vert_mon'].setAxisList(AXL4d)
dic_invar['Tq_KernCld_vert_mon'] = dic_invar['t_KernCld_vert_mon'] + dic_invar['wv_KernCld_vert_mon']
dic_invar['Tq_KernClr_vert_mon'] = dic_invar['t_KernClr_vert_mon'] + dic_invar['wv_KernClr_vert_mon']
dic_invar['Tq_KernCld_vert_mon'].setAxisList(AXL4d)
dic_invar['Tq_KernClr_vert_mon'].setAxisList(AXL4d)
# =========================================================================================================#
# TA feedback
# =========================================================================================================#
# get air temperature feedback
dic_invar['ta_ano_vert_grd'][dic_invar['ta_ano_vert_grd'].mask]=0
invar1 = ['t_KernCld_vert_mon','t_KernClr_vert_mon','wv_KernCld_vert_mon','wv_KernClr_vert_mon']
invar2 = ['ta_ano_vert_grd','ta_ano_vert_grd','ta_ano_vert_grd','ta_ano_vert_grd']
outvar = ['dLW_ta','dLW_ta_clr','dLW_ta_fxRH','dLW_ta_clr_fxRH']
dic_invar = get_fdbk(invar1,invar2,outvar,dic_invar,AXL4d, AXL3d, dp_use_method='SPC')
print(dic_invar.keys())
dic_invar['dLW_t_clr_psum'] = dic_invar['dLW_ta_clr_psum'] + dic_invar['dLW_ts_clr']
dic_invar['dLW_t_psum'] = dic_invar['dLW_ta_psum'] + dic_invar['dLW_ts']
dic_invar['dLW_t_clr_psum'].setAxisList(AXL3d)
dic_invar['dLW_t_psum'].setAxisList(AXL3d)
# =========================================================================================================#
### Plotting temperature test
# =========================================================================================================#
if plotting:
fig = plt.figure(figsize=(18,12))
fh = 20
plt.suptitle('clear-sky linear test',fontsize=fh)
bounds = np.arange(-12,14,2)
cmap = pl.cm.RdBu_r
bounds2 = np.append(np.append(-500,bounds),500)
norm = mpl.colors.BoundaryNorm(bounds2,cmap.N)
names = ['ta_ano_vert_grd','ta_ano_vert_grd','t_KernCld_vert_mon','t_KernClr_vert_mon','dLW_ta','dLW_ta_clr']
for n,name in enumerate(names):
DATA = MV.average(MV.average(dic_invar[name],axis=0),axis=2)
# zonal-mean structure
ax1 = fig.add_subplot(3,2,n+1)
im1 = ax1.contourf(lat,lev,DATA)
pl.title(name)
cb = plt.colorbar(im1,orientation='vertical',drawedges=True)
plt.gca().invert_yaxis()
plt.tight_layout()
plt.savefig(figdir+'lat-lon-clear-sky-linear-test-temp-'+phase+'-'+case_stamp+'-'+used_models[imod]+'.png',bbox_inches='tight')
fig.clf()
plt.close()
gc.collect()
# =========================================================================================================#
# delete unnecessary vars
# =========================================================================================================#
delterm = ['dLW_ta','dLW_ta_clr','dLW_ta_fxRH','dLW_ta_clr_fxRH']
dic_invar = delete_vars(delterm,dic_invar)
sub_name = 'Getting TA feedback '
print_memory_status(sub_name)
#################################################################################
#### Planck feedback calculation
#################################################################################
# extend to 4-d
dic_invar['tas_ano_grd_4d'] = cdms.asVariable(np.float32(np.transpose(np.tile(dic_invar['tas_ano_grd'],(nlev,1,1,1)),(1,0,2,3))))
# mask stratosphere
if get_tropo_method == 'Soden' or get_tropo_method == 'Mark':
dic_invar['tas_ano_grd_4d'] = MV.masked_where(lev_4d <=dic_invar['p_tropopause'],dic_invar['tas_ano_grd_4d'])
# mask ts_ano_grd_4d where ta_ano_vert_grd is True
dic_invar['tas_ano_grd_4d'] = MV.masked_where(dic_invar['ta_ano_vert_grd'].mask==True,dic_invar['tas_ano_grd_4d'])
dic_invar['tas_ano_grd_4d'][dic_invar['tas_ano_grd_4d'].mask]=0
# note: fxRH Planck feedback: T_kernel * uniform warming anomaly + Q_kernel * uniform warming anomaly
invar1 = ['t_KernCld_vert_mon','t_KernClr_vert_mon','Tq_KernCld_vert_mon','Tq_KernClr_vert_mon']
invar2 = ['tas_ano_grd_4d','tas_ano_grd_4d','tas_ano_grd_4d','tas_ano_grd_4d']
outvar = ['dLW_planck','dLW_planck_clr','dLW_planck_fxRH','dLW_planck_clr_fxRH']
dic_invar = get_fdbk(invar1,invar2,outvar,dic_invar,AXL4d, AXL3d, dp_use_method='SPC')
# add ts feedback
dic_invar['dLW_planck_psum'] = dic_invar['dLW_planck_psum'] + dic_invar['dLW_ts']
dic_invar['dLW_planck_clr_psum'] = dic_invar['dLW_planck_clr_psum'] + dic_invar['dLW_ts_clr']
dic_invar['dLW_planck_fxRH_psum'] = dic_invar['dLW_planck_fxRH_psum'] + dic_invar['dLW_ts']
dic_invar['dLW_planck_clr_fxRH_psum'] = dic_invar['dLW_planck_clr_fxRH_psum'] + dic_invar['dLW_ts_clr']
dic_invar['dLW_planck_psum'].setAxisList(AXL3d)
dic_invar['dLW_planck_clr_psum'].setAxisList(AXL3d)
dic_invar['dLW_planck_fxRH_psum'].setAxisList(AXL3d)
dic_invar['dLW_planck_clr_fxRH_psum'].setAxisList(AXL3d)
delterm = outvar
dic_invar = delete_vars(delterm,dic_invar)
sub_name = 'Getting Planck feedback '
print_memory_status(sub_name)
#################################################################################
#### Lapse rate feedback calculation
#################################################################################
# difference between ta and ts
dic_invar['dt_ano'] = dic_invar['ta_ano_vert_grd'] - dic_invar['tas_ano_grd_4d']
dic_invar['dt_ano'] = MV.masked_where(dic_invar['ta_ano_vert_grd'].mask==True,dic_invar['dt_ano'])
dic_invar['dt_ano'].setAxisList(AXL4d)
# mask stratosphere
if get_tropo_method == 'Soden' or get_tropo_method == 'Mark':
dic_invar['dt_ano'] = MV.masked_where(lev_4d <=dic_invar['p_tropopause'],dic_invar['dt_ano'])
dic_invar['dt_ano'][dic_invar['dt_ano'].mask]=0
# note: fxRH Lapse rate feedback: T_kernel * LR warming anomaly + Q_kernel * LR warming anomaly
invar1 = ['t_KernCld_vert_mon','t_KernClr_vert_mon','Tq_KernCld_vert_mon','Tq_KernClr_vert_mon']
invar2 = ['dt_ano','dt_ano','dt_ano','dt_ano']
outvar = ['dLW_lapserate','dLW_lapserate_clr','dLW_lapserate_fxRH','dLW_lapserate_clr_fxRH']
dic_invar = get_fdbk(invar1,invar2,outvar,dic_invar,AXL4d, AXL3d, dp_use_method='SPC')
# ---------------------
delterm = outvar+['dt_ano','tas_ano_grd_4d']
dic_invar = delete_vars(delterm,dic_invar)
sub_name = 'Getting LapseRate feedback '
print_memory_status(sub_name)
#################################################################################
#### Albedo feedback calculation
#################################################################################
dic_invar['alb_ano_grd'][dic_invar['alb_ano_grd'].mask]=0
invar1 = ['alb_KernCld_mon','alb_KernClr_mon']
invar2 = ['alb_ano_grd','alb_ano_grd']
outvar = ['dSW_alb','dSW_alb_clr']
for ivar,svar in enumerate(invar1):
ovar = outvar[ivar]
dic_invar[ovar] = dic_invar[invar1[ivar]] * dic_invar[invar2[ivar]]
#<qinyi 2021-08-21 #------------------
dic_invar[ovar].setAxisList(AXL3d)
sub_name = 'Getting Albedo feedback '
print_memory_status(sub_name)
#################################################################################
#### Water vapor feedback calculation
#################################################################################
print('-------------------We use the dlogq_method from ', dlogq_method, '------------------------------')
# ===============================================================#
# dlogq method
# ===============================================================#
if dlogq_method == 'Mixed':
# ---------------------------------------------------------------#
# ta_method
# ---------------------------------------------------------------#
if ta_method == 'taavg':
avgta = (dic_invar['ta_pi_vert_grd'] + dic_invar['ta_ab_vert_grd'])/2.0
elif ta_method == 'tapi':
avgta = dic_invar['ta_pi_vert_grd']
# ---------------------------------------------------------------#
# qsat_method
# ---------------------------------------------------------------#
if qsat_method == 'Mark':
qs0,qs1 = qsat_blend_Mark(avgta,lev_4d)
rh0=dic_invar['hus_pi_vert_grd']/qs0
q1k=rh0*qs1
elif qsat_method == 'Yi':
qs0 = RD.r_star_GG(lev_4d*100.,avgta)
qs0 = np.float32(qs0)
ta1k = avgta+1.0
qs1 = RD.r_star_GG(lev_4d*100.,ta1k)
qs1 = np.float32(qs1)
rh0 = dic_invar['hus_pi_vert_grd']/qs0
q1k = rh0*qs1
del avgta
# ---------------------------------------------------------------#
# log_method
# ---------------------------------------------------------------#
if log_method == 'Mark':
dic_invar['dlogq1k'] = MV.log(qs1) - MV.log(qs0)
dlogq = MV.log(dic_invar['hus_ab_vert_grd']) - MV.log(dic_invar['hus_pi_vert_grd'])
elif log_method == 'Yi':
dic_invar['dlogq1k'] = MV.log(q1k) - MV.log(dic_invar['hus_pi_vert_grd'])
dlogq = MV.log(dic_invar['hus_ab_vert_grd']) - MV.log(dic_invar['hus_pi_vert_grd'])
elif log_method == 'angie':
dic_invar['dlogq1k'] = (q1k-dic_invar['hus_pi_vert_grd'])/dic_invar['hus_pi_vert_grd']
dlogq = dic_invar['hus_ano_vert_grd']/dic_invar['hus_pi_vert_grd']
# ---------------------------------------------------------------#
# get dlogq2 here
# ---------------------------------------------------------------#
dic_invar['dlogq2'] = np.ma.true_divide(dlogq,dic_invar['dlogq1k'])
dic_invar['dlogq2'].setAxisList(dic_invar['hus_pi_vert_grd'].getAxisList())
print('dlogq2 shape is',dic_invar['dlogq2'].shape,', minmax is',genutil.minmax(dic_invar['dlogq2']))
del qs0,qs1,rh0,q1k,dlogq
delterm = ['dlogq1k','hus_pi_vert_grd','hus_ab_vert_grd','ta_pi_vert_grd']
dic_invar = delete_vars(delterm,dic_invar)
elif dlogq_method == 'Mark':
q_norm_flag = ''
blend_flag = ''
lognorm_flag = ''
# note: Mark's function needs lev's unit Pa. I should change my lev's unit hPa to Pa. so multiply 100.
norm_hus_anom = CU.ta_normalized_qv(lev*100,dic_invar['ta_pi_vert_grd'],dic_invar['ta_ab_vert_grd'],dic_invar['hus_pi_vert_grd'],dic_invar['hus_ab_vert_grd'],q_norm_flag,blend_flag,lognorm_flag)
dic_invar['dlogq2'] = norm_hus_anom
dic_invar['dlogq2'].setAxisList(AXL4d)
print('dlogq2 shape is',dic_invar['dlogq2'].shape,', minmax is',genutil.minmax(dic_invar['dlogq2']))
del norm_hus_anom
delterm = ['hus_pi_vert_grd','hus_ab_vert_grd','ta_pi_vert_grd']
dic_invar = delete_vars(delterm,dic_invar)
# ----------------------------------------------------------------#
# mask the statrosphere
# ----------------------------------------------------------------#
if get_tropo_method == 'Soden' or get_tropo_method == 'Mark':
dic_invar['dlogq2'] = MV.masked_where(lev_4d<=dic_invar['p_tropopause'],dic_invar['dlogq2'])
del lev_4d
dic_invar['dlogq2'][dic_invar['dlogq2'].mask] = 0
# ----------------------------------------------------------------#
# get water vapor feedback here
# ----------------------------------------------------------------#
invar1 = ['wv_lw_KernCld_vert_mon','wv_sw_KernCld_vert_mon','wv_lw_KernClr_vert_mon','wv_sw_KernClr_vert_mon',\
'wv_lw_KernCld_vert_mon','wv_sw_KernCld_vert_mon','wv_lw_KernClr_vert_mon','wv_sw_KernClr_vert_mon']
invar2 = ['dlogq2','dlogq2','dlogq2','dlogq2',\
'ta_ano_vert_grd','ta_ano_vert_grd','ta_ano_vert_grd','ta_ano_vert_grd']
outvar = ['dLW_q','dSW_q','dLW_q_clr','dSW_q_clr',\
'dLW_q_fxRH','dSW_q_fxRH','dLW_q_clr_fxRH','dSW_q_clr_fxRH']
# note: fxRH LW/SW WV feedback: Q_kernel * ta warming anomaly
dic_invar = get_fdbk(invar1,invar2,outvar,dic_invar,AXL4d, AXL3d, dp_use_method='SPC')
# ---------------------------------------------------------------#
# get final RH feedback
# ---------------------------------------------------------------#
# July 7: final RH feedback related to RH change
# RH feedback = default water vapor feedback - (water vapor kernel * uniform warming anomaly (Ts) - water vapor kernel * lapse rate temperature anomaly)
# RH feedback = default water vapor feedback - water vapor kernel * atmospheric temperature change
if get_netRH_method == 'Raw':
invar1 = ['dLW_q','dSW_q','dLW_q_clr','dSW_q_clr']
outvar = ['dLW_netRH','dSW_netRH','dLW_clr_netRH','dSW_clr_netRH']
for ivar,svar in enumerate(invar1):
ovar = outvar[ivar]
dic_invar[ovar] = dic_invar[svar] - dic_invar[svar+'_fxRH']
dic_invar[ovar].setAxisList(AXL4d)
if dp_use_method == 'DynPs' or dp_use_method =='SPC':
dic_invar[ovar+'_psum'] = VertSum(dic_invar[ovar],dic_invar['dp_4d'])
else:
dic_invar[ovar+'_psum'] = MV.sum(dic_invar[ovar]*dic_invar['dp_4d']/100., axis=1)
dic_invar[ovar+'_psum'].setAxisList(AXL3d)
elif get_netRH_method == 'Mark':
invar1 = ['dLW_q','dSW_q','dLW_q_clr','dSW_q_clr']
outvar = ['dLW_netRH','dSW_netRH','dLW_clr_netRH','dSW_clr_netRH']
for ivar,svar in enumerate(invar1):
ovar = outvar[ivar]
dic_invar[ovar+'_psum'] = dic_invar[svar+'_psum'] - dic_invar[svar+'_fxRH_psum']
dic_invar[ovar+'_psum'].setAxisList(AXL3d)
# ---------------------------------------------------------------#
# set effect of SUNDOWN
# ---------------------------------------------------------------#
if sundown:
for svar in ['dSW_alb','dSW_alb_clr','dSW_q_psum','dSW_q_clr_psum',\
'dSW_q_fxRH_psum','dSW_q_clr_fxRH_psum','dSW_netRH_psum','dSW_clr_netRH_psum']:
dic_invar[svar][SUNDOWN==0] = 0.
del SUNDOWN
# ---------------------------------------------------------------#
### Plotting Water vapor test
# ---------------------------------------------------------------#
if plotting:
fig = plt.figure(figsize=(18,12))
fh = 20
plt.suptitle('clear-sky linear test',fontsize=fh)
names = ['dlogq2','wv_lw_KernCld_vert_mon','wv_sw_KernCld_vert_mon',\
'wv_lw_KernClr_vert_mon','wv_sw_KernClr_vert_mon','dLW_q','dSW_q','dLW_q_clr','dSW_q_clr']
for n,name in enumerate(names):
DATA = MV.average(MV.average(dic_invar[name],axis=0),axis=2)
# zonal-mean structure
ax1 = fig.add_subplot(4,3,n+1)
im1 = ax1.contourf(lat,lev,DATA)
pl.title(name,fontsize=fh)
cb = plt.colorbar(im1,orientation='vertical',drawedges=True)
plt.gca().invert_yaxis()
plt.tight_layout()
plt.savefig(figdir+'lat-lon-clear-sky-linear-test-wv-'+phase+'-'+case_stamp+'-'+used_models[imod]+'.png',bbox_inches='tight')
fig.clf()
plt.close()
gc.collect()
# ---------------------------------------------------------------#
# delete unnecessary vars
# ---------------------------------------------------------------#
delterm = ['dlogq2','dLW_q','dSW_q','dLW_q_clr','dSW_q_clr',\
'dLW_q_fxRH','dLW_q_clr_fxRH','dSW_q_fxRH','dSW_q_clr_fxRH',\
't_KernCld_vert_mon','t_KernClr_vert_mon','ts_KernCld_mon','ts_KernClr_mon',\
'wv_lw_KernCld_vert_mon','wv_sw_KernCld_vert_mon','wv_lw_KernClr_vert_mon','wv_sw_KernClr_vert_mon']
dic_invar = delete_vars(delterm,dic_invar)
if get_netRH_method == 'Raw':
delterm = ['dLW_netRH','dSW_netRH','dSW_clr_netRH','dSW_clr_netRH']
dic_invar = delete_vars(delterm,dic_invar)
sub_name = 'Getting water vapor feedback '
print_memory_status(sub_name)
#################################################################################
#### Adjusted cloud feedback calculation
#################################################################################
# ---------------------------------------------------------------#
# calculate cloud masking
# ---------------------------------------------------------------#
invar1 = ['dLW_t_clr_psum','dLW_q_clr_psum','dSW_q_clr_psum','dSW_alb_clr']
invar2 = ['dLW_t_psum','dLW_q_psum','dSW_q_psum','dSW_alb']
for ivar,svar in enumerate(invar1):
dic_invar[invar2[ivar]+'_mask'] = dic_invar[invar1[ivar]] - dic_invar[invar2[ivar]]
dic_invar[invar2[ivar]+'_mask'].setAxisList(AXL3d)
# ---------------------------------------------------------------#
# adjusted CRE
# ---------------------------------------------------------------#
invar1 = ['dLW_t_psum_mask','dSW_q_psum_mask','SWCRE_ano_grd','LWCRE_ano_grd']
invar2 = ['dLW_q_psum_mask','dSW_alb_mask','dSW_adj','dLW_adj']
outvar = ['dLW_adj','dSW_adj','SWCRE_ano_grd_adj','LWCRE_ano_grd_adj']
for ivar,svar in enumerate(invar1):
dic_invar[outvar[ivar]] = dic_invar[invar1[ivar]] + dic_invar[invar2[ivar]]
dic_invar[outvar[ivar]].setAxisList(AXL3d)
dic_invar['net_adj'] = dic_invar['dLW_adj'] + dic_invar['dSW_adj']
dic_invar['net_adj'].setAxisList(AXL3d)
dic_invar['netCRE_ano_grd_adj'] = dic_invar['netCRE_ano_grd'] + dic_invar['net_adj']
dic_invar['netCRE_ano_grd_adj'].setAxisList(AXL3d)
sub_name = 'Getting adjusted cloud feedback '
print_memory_status(sub_name)
# ---------------------------------------------------------------#
# get cloudy residual term
# ---------------------------------------------------------------#
# get sum of kernel effect
dic_invar['dLW_cld_sum'] = dic_invar['dLW_t_psum'] + dic_invar['dLW_q_psum'] + dic_invar['LWCRE_ano_grd_adj']
dic_invar['dSW_cld_sum'] = dic_invar['dSW_alb'] + dic_invar['dSW_q_psum'] + dic_invar['SWCRE_ano_grd_adj']
dic_invar['dLW_cld_sum'].setAxisList(AXL3d)
dic_invar['dSW_cld_sum'].setAxisList(AXL3d)
dic_invar['dnet_cld_sum'] = dic_invar['dLW_cld_sum'] + dic_invar['dSW_cld_sum']
dic_invar['dnet_cld_sum'].setAxisList(AXL3d)
dic_invar['dLW_clr_sum'] = dic_invar['dLW_t_clr_psum'] + dic_invar['dLW_q_clr_psum']
dic_invar['dSW_clr_sum'] = dic_invar['dSW_alb_clr'] + dic_invar['dSW_q_clr_psum']
dic_invar['dLW_clr_sum'].setAxisList(AXL3d)
dic_invar['dSW_clr_sum'].setAxisList(AXL3d)
dic_invar['dnet_clr_sum'] = dic_invar['dLW_clr_sum'] + dic_invar['dSW_clr_sum']