print('calculate dta')
dta = fourxCO2['ta'] - PiCon['ta']
CMIP_File = Dataset('/praid/users/jgvirgin/Radiative_Kernels/CAM3_Kernels.nc')
lat = np.squeeze(CMIP_File.variables['lat'])
lon = np.squeeze(CMIP_File.variables['lon'])
CMIP_plevs_scalar = np.squeeze(CMIP_File.variables['lev'])
CMIP_plevs = np.tile(CMIP_plevs_scalar[None, :, None, None],
(12, 1, lat.size, lon.size))
print('Calculating...')
print('saturdation specific humidity for the baseline')
Sat_Hum_base = jf.Calc_SatSpec_Hum(Ta=PiCon['ta'], P=CMIP_plevs)
print('saturation specific humidity for the response')
Sat_Hum_resp = jf.Calc_SatSpec_Hum(Ta=fourxCO2['ta'], P=CMIP_plevs)
print(
'rate of change of saturation specific humidity with respect to temperature'
)
dqsdt = (Sat_Hum_resp - Sat_Hum_base) / dta
print('relative humidity for the model')
Rel_Hum = (
1000 * PiCon['hus']
) / Sat_Hum_base #swap specific humidity units to g/kg to match specific humidity
print('rate of change of specific humidity with respect to temperature')
Kernels_interpolated.pop('CloudSat')
# In[11]:
ps_path = '/praid/users/jgvirgin/CanESM_Data/CanESM2/Raw/vars_interp_Amon_CanESM2_piControl_r1i1p1_201501-241012.nc'
PS = np.squeeze(nc.Dataset(ps_path).variables['ps'])[:12, :, :]
#Calculate pressure level thickness using CMIP pressure levels and surface pressure
dp = np.zeros([12, 17, 64, 128])
for i in range(12):
for j in range(64):
for k in range(128):
dp[i, :, j, k] = jf.PlevThck(PS=PS[i, j, k] / 100,
plevs=CMIP_plevs_scalar,
p_top=min(CMIP_plevs_scalar))
dp = dp / 100 #Kernel units are per 100 hPa
# In[12]:
print(
'calculate stratosphere flux perturbations due to adjustments & feedbacks separately'
)
#multiply all nonlinear & linear strato temperature perturbations by the proper kernels
#integrate throughout the stratosphere
#take the annual mean
temp_fb_strato_tot = dict()
temp_fb_strato_clr = dict()
print('unpack and store as a single time series array')
RCP45_PS_TmSrs = np.zeros([720,96,144])
wAVD_PS_TmSrs = np.zeros([720,96,144])
RCP45_PS_TmSrs = np.stack((np.hstack(RCP45_PS_TmSrs_Stacked[0:60,0:12])[0:720]),axis=0)
wAVD_PS_TmSrs = np.stack((np.hstack(wAVD_PS_TmSrs_Stacked[0:60,0:12])[0:720]),axis=0)
print('Calculate Pressure levels')
#calculate pressure levels
plevs_RCP45 = np.zeros([720,47,96,144])
plevs_wAVD = np.zeros([720,47,96,144])
for i in range(720):
plevs_RCP45[i,:,:,:] = JF.HybSig2Plev(P0=p0,PS=RCP45_PS_TmSrs[i,:,:],\
coef_A=hyam,coef_B=hybm)/100 #in hPa
plevs_wAVD[i,:,:,:] = JF.HybSig2Plev(P0=p0,PS=wAVD_PS_TmSrs[i,:,:],\
coef_A=hyam,coef_B=hybm)/100 #in hPa
print('Tropopause estimation')
p_tropopause_zonalmean_linear = np.zeros([96])
for y in range(96):
if y <= len(WACCM4_Lat)/2:
if y == 0:
p_tropopause_zonalmean_linear[y] = 300
elif y == len(WACCM4_Lat)/2:
p_tropopause_zonalmean_linear[y] = 100
else: