vqnt[jt] = nmp.sum(
QNT_m[jt, :-2, :-2] * Xarea_t[:-2, :-2]) * 1.E-9
# to PW
if l_qsr:
vqsr[jt] = nmp.sum(
QSR_m[jt, :-2, :-2] * Xarea_t[:-2, :-2]) * 1.E-9
# to PW
cf_out = vdic['DIAG_D'] + '/mean_htf_' + CONFEXP + '_GLO.nc'
bnc.wrt_appnd_1d_series(
vtime,
vqnt,
cf_out,
'Qnet',
cu_t='year',
cu_d='PW',
cln_d='Globally averaged net heat flux (nemo:' + vdic['NN_QNET'] +
')',
vd2=vqsr,
cvar2='Qsol',
cln_d2='Globally averaged net solar heat flux (nemo:' +
vdic['NN_QSOL'] + ')')
print ' +++ ' + cnexec + ' => Done with heat flux diags!\n'
# Freshwater fluxes
if l_fwf:
print '\n\n +++ ' + cnexec + ' => Starting freshwater flux diags!'
#cv_fwf = vdic['NN_FWF']
#cv_emp = vdic['NN_EMP']
##########################################
# Writing/Appending in output netcdf file
##########################################
# Appending only 1 record for 1 year into the netcdf file!
cf_out = vdic['DIAG_D']+'/DMV_'+czcrit+'_box_'+cbox+'_'+CONFRUN+'.nc'
cv_dmv_m = 'DMV_'+czcrit+'_'+ccold
cv_dmv_jfm = 'DMV_'+czcrit+'_'+cvinter
long_name1 = 'Deep Mixed Volume (crit = '+czcrit+'m) for '+ccold_ln+' on box '+cbox
long_name2 = 'Deep Mixed Volume (crit = '+czcrit+'m) for '+cvinter+' on box '+cbox
long_name3 = 'Deepest ML point in '+ccold_ln+' on box '+cbox
long_name4 = 'Mean MLD in '+ccold_ln+' where MLD > '+czcrit+'m on box '+cbox
bnc.wrt_appnd_1d_series([float(jyear)], [VDMV[2]], cf_out, cv_dmv_m, cu_t='year', cu_d='10^3 km^3', cln_d=long_name1,
vd2=[rc_WINT], cvar2=cv_dmv_jfm, cln_d2=long_name2,
vd3=[rML_max], cvar3='ML_max', cln_d3=long_name3,
vd4=[rML_deep_mean], cvar4='ML_deep_mean', cln_d4=long_name4)
print '\n Z_crit '+str(int(czcrit))+'m done!\n\n'
print '\nBye!\n'
jb = jb + 1 ; # next basin!
print '\n +++ '+cnexec+' => Done with 2D-summing of variable '+cvar+'!\n'
del Xd_m
jv = jv + 1 ; # next variable!
# Time to write netcdf file:
vtime = nmp.zeros(12)
for jt in range(12): vtime[jt] = float(jyear) + (float(jt)+0.5)*1./float(12)
#print ' * Calendar: ', vtime[:]
c1 = '2D-integral of '
jb = 0
for cbasin in list_basin_names[:]:
c2 = ') on region '+list_basin_lgnms[jb]
cf_out = cdir_out+'/fwf_int_'+CONFEXP+'_'+cbasin+'.nc'
bnc.wrt_appnd_1d_series(vtime, v_fwf_m[:,jb,0], cf_out, vnnm[0],
cu_t='year', cu_d=cunit, cln_d =c1+vnnm[0]+' ('+vvar[0]+c2,
vd2=v_fwf_m[:,jb,1], cvar2=vnnm[1], cln_d2=c1+vnnm[1]+' ('+vvar[1]+c2,
vd3=v_fwf_m[:,jb,2], cvar3=vnnm[2], cln_d3=c1+vnnm[2]+' ('+vvar[2]+c2,
vd4=v_fwf_m[:,jb,3], cvar4=vnnm[3], cln_d4=c1+vnnm[3]+' ('+vvar[3]+c2,
vd5=v_fwf_m[:,jb,4], cvar5=vnnm[4], cln_d5=c1+vnnm[4]+' ('+vvar[4]+c2)
jb = jb + 1
print '\n *** EXITING '+cnexec+' for year '+cyear+' and variable '+cvar+'!\n'
##########################################
# Writing/Appending in output netcdf file
##########################################
# Appending only 1 record for 1 year into the netcdf file!
cf_out = vdic['DIAG_D']+'/DMV_'+czcrit+'_box_'+cbox+'_'+CONFEXP+'.nc'
cv_dmv_m = 'DMV_'+czcrit+'_'+ccold
cv_dmv_jfm = 'DMV_'+czcrit+'_'+cvinter
long_name1 = 'Deep Mixed Volume (crit = '+czcrit+'m) for '+ccold_ln+' on box '+cbox
long_name2 = 'Deep Mixed Volume (crit = '+czcrit+'m) for '+cvinter+' on box '+cbox
long_name3 = 'Deepest ML point in '+ccold_ln+' on box '+cbox
long_name4 = 'Mean MLD in '+ccold_ln+' where MLD > '+czcrit+'m on box '+cbox
bnc.wrt_appnd_1d_series([float(jyear)], [VDMV[2]], cf_out, cv_dmv_m, cu_t='year', cu_d='10^3 km^3', cln_d=long_name1,
vd2=[rc_WINT], cvar2=cv_dmv_jfm, cln_d2=long_name2,
vd3=[rML_max], cvar3='ML_max', cln_d3=long_name3, cun3='m',
vd4=[rML_deep_mean], cvar4='ML_deep_mean', cln_d4=long_name4, cun4='m')
print '\n Z_crit '+str(int(czcrit))+'m done!\n\n'
print '\nBye!\n'
# ========================================
Vts_tot = bo.mean_3d(Xd_m[:,:,j1:j2,i1:i2], mask[joce,:,j1:j2,i1:i2], Xv[:,j1:j2,i1:i2]) ; # Top to bottom
Vts_0_100 = bo.mean_3d(Xd_m[:,:j100m,j1:j2,i1:i2], mask[joce,:j100m,j1:j2,i1:i2], Xv[:j100m,j1:j2,i1:i2])
Vts_100_1000 = bo.mean_3d(Xd_m[:,j100m:j1000m,j1:j2,i1:i2], mask[joce,j100m:j1000m,j1:j2,i1:i2], Xv[j100m:j1000m,j1:j2,i1:i2])
Vts_1000_bot = bo.mean_3d(Xd_m[:,j1000m:,j1:j2,i1:i2], mask[joce,j1000m:,j1:j2,i1:i2], Xv[j1000m:,j1:j2,i1:i2])
cf_out = vdic['DIAG_D']+'/3d_'+cvar+'_'+CONFEXP+'_'+cocean+'.nc'
cv1 = cvar+'_0-bottom'
cv2 = cvar+'_0-100'
cv3 = cvar+'_100-1000'
cv4 = cvar+'_1000-bottom'
bnc.wrt_appnd_1d_series(vtime, Vts_tot, cf_out, cv1,
cu_t='year', cu_d='Unknown', cln_d ='3D-average of '+cvar+': surface to bottom, '+colnm,
vd2=Vts_0_100, cvar2=cv2, cln_d2='3D-average of '+cvar+': surface to 100m, '+colnm,
vd3=Vts_100_1000, cvar3=cv3, cln_d3='3D-average of '+cvar+': 100m to 1000m, '+colnm,
vd4=Vts_1000_bot, cvar4=cv4, cln_d4='3D-average of '+cvar+': 1000m to bottom, '+colnm)
# II) Annual mean vertical profile
# ================================
Vf = nmp.zeros(nk)
for jk in range(nk):
[ rf ] = bo.mean_2d(Xd_y[:,jk,j1:j2,i1:i2], mask[joce,jk,j1:j2,i1:i2], Xa[j1:j2,i1:i2])
Vf[jk] = rf
l_htf = l_qnt ; # Forgeting heat flux if both Qnet is missing...
if l_htf:
[ nt, nj0, ni0 ] = QNT_m.shape
vtime = nmp.zeros(nt)
for jt in range(nt): vtime[jt] = float(jyear) + (float(jt)+0.5)*1./12.
vqnt = [] ; vqsr = []
if l_qnt: vqnt = nmp.zeros(nt)
if l_qsr: vqsr = nmp.zeros(nt)
for jt in range(nt):
if l_qnt: vqnt[jt] = nmp.sum( QNT_m[jt,:,:]*Xarea_t ) * 1.E-9 ; # to PW
if l_qsr: vqsr[jt] = nmp.sum( QSR_m[jt,:,:]*Xarea_t ) * 1.E-9 ; # to PW
cf_out = vdic['DIAG_D']+'/mean_htf_'+CONFRUN+'_global.nc'
bnc.wrt_appnd_1d_series(vtime, vqnt, cf_out, 'Qnet',
cu_t='year', cu_d='PW', cln_d ='Globally averaged net heat flux (nemo:'+cv_qnt+')',
vd2=vqsr, cvar2='Qsol', cln_d2='Globally averaged net solar heat flux (nemo:'+cv_qsr+')',
)
# Freshwater fluxes
if l_fwf:
cv_fwf = vdic['NN_FWF']
cv_emp = vdic['NN_EMP']
cv_prc = vdic['NN_P']
cv_rnf = vdic['NN_RNF']
cv_clv = vdic['NN_CLV']
cv_evp = vdic['NN_E']
# cv_evb (top of file...)
bnc.wrt_appnd_1d_series(Vtime,
ssh_m,
cf_out,
'ssh',
cu_t='year',
cu_d='m',
cln_d=cc + 'sea surface height',
vd2=sst_m,
cvar2='sst',
cln_d2=cc + 'sea surface temperature',
cun2='deg.C',
vd3=sss_m,
cvar3='sss',
cln_d3=cc + 'sea surface salinity',
cun3='PSU',
vd4=surf_T_m,
cvar4='surf_T',
cln_d4=cc + 'Temperature (first ' + czs + 'm)',
cun4='deg.C',
vd5=T_m,
cvar5='theta',
cln_d5=cc + 'potential temperature',
cun5='deg.C',
vd6=H_m,
cvar6='HC',
cln_d6=cc + 'heat content',
cun6='PJ',
vd7=surf_S_m,
cvar7='surf_S',
cln_d7=cc + 'salinity (first ' + czs + 'm)',
cun7='PSU',
vd8=S_m,
cvar8='S',
cln_d8=cc + 'salinity',
cun8='PSU',
vd9=ss0_m,
cvar9='SSs0',
cln_d9=cc + 'sea surface sigma0 (sst&sss)',
cun9='',
vd10=surf_s0_m,
cvar10='surf_s0',
cln_d10=cc + 'surface sigma0 (first ' + czs + 'm)',
cun10='')
print 'Volume associated with SSH (km^3) = ', Vol_m[jm], '\n'
Vtime = nmp.zeros(Nt)
for jm in range(Nt): Vtime[jm] = float(jy) + (float(jm)+0.5)/12.
cc = 'Box-averaged '
cf_out = vdic['DIAG_D']+'/budget_'+CONFRUN+'_box_'+cbox+'.nc'
bnc.wrt_appnd_1d_series(Vtime, ssh_m, cf_out, 'ssh', cu_t='year', cu_d='m', cln_d=cc+'sea surface height',
vd2=sst_m, cvar2='sst', cln_d2=cc+'sea surface temperature', cun2='deg.C',
vd3=sss_m, cvar3='sss', cln_d3=cc+'sea surface salinity', cun3='PSU',
vd4=surf_T_m, cvar4='surf_T', cln_d4=cc+'Temperature (first '+czs+'m)', cun4='deg.C',
vd5=T_m, cvar5='theta', cln_d5=cc+'potential temperature', cun5='deg.C',
vd6=H_m, cvar6='HC', cln_d6=cc+'heat content', cun6='PJ',
vd7=surf_S_m, cvar7='surf_S', cln_d7=cc+'salinity (first '+czs+'m)', cun7='PSU',
vd8=S_m, cvar8='S', cln_d8=cc+'salinity', cun8='PSU',
vd9=ss0_m, cvar9='SSs0', cln_d9=cc+'sea surface sigma0 (sst&sss)', cun9='',
vd10=surf_s0_m,cvar10='surf_s0', cln_d10=cc+'surface sigma0 (first '+czs+'m)', cun10=''
)
cf_out = vdic['DIAG_D']+'/budget_srf_flx_'+CONFRUN+'_box_'+cbox+'.nc'
bnc.wrt_appnd_1d_series(Vtime, Qnet_m, cf_out, 'Qnet', cu_t='year', cu_d='W/m^2', cln_d=cc+'net heat flux',
vd2=Qnet_x_S_m, cvar2='Qnet_x_S', cln_d2=cc+'net heat flux x Surface', cun2='PW',
vd3=Qsw_m, cvar3='Qsw', cln_d3=cc+'solar radiation', cun3='W/m^2',
vd4=Qsw_x_S_m, cvar4='Qsw_x_S', cln_d4=cc+'solar radiation x Surface', cun4='PW',
vd5=PmE_m, cvar5='PmE', cln_d5=cc+'net freshwater flux', cun5='Sv',
vd6=Tau_m, cvar6='Tau', cln_d6=cc+'wind stress module', cun6='N/m^2'
if l_qsr: vqsr = nmp.zeros(nt)
for jt in range(nt):
if l_qnt:
vqnt[jt] = nmp.sum(QNT_m[jt, :, :] * Xarea_t) * 1.E-9
# to PW
if l_qsr:
vqsr[jt] = nmp.sum(QSR_m[jt, :, :] * Xarea_t) * 1.E-9
# to PW
cf_out = vdic['DIAG_D'] + '/mean_htf_' + CONFRUN + '_global.nc'
bnc.wrt_appnd_1d_series(
vtime,
vqnt,
cf_out,
'Qnet',
cu_t='year',
cu_d='PW',
cln_d='Globally averaged net heat flux (nemo:' + cv_qnt + ')',
vd2=vqsr,
cvar2='Qsol',
cln_d2='Globally averaged net solar heat flux (nemo:' + cv_qsr + ')',
)
# Freshwater fluxes
if l_fwf:
cv_fwf = vdic['NN_FWF']
cv_emp = vdic['NN_EMP']
cv_prc = vdic['NN_P']
cv_rnf = vdic['NN_RNF']
cv_clv = vdic['NN_CLV']
cv_evp = vdic['NN_E']
