fixed={
'mod_Fland_preind': 'ORCHIDEE-CNP',
'mod_Efire_preind': 'off_',
'mod_Eharv_preind': 'ORCHIDEE-CNP',
'mod_Egraz_preind': 'off_',
'mod_Eluc_agb': 'ORCHIDEE-CNP',
'mod_Fland_trans': 'IPSL-CM5A-LR',
'mod_Efire_trans': 'IPSL-CM5A-LR'
})
Par = Par_mc.mean('config')
## load raw drivers
For_luc = load_landuse_hist(mod_region, LCC='gross')
For_clim = aggreg_region(load_data('CRU-TS'),
mod_region,
weight_var={
'Tl': 'area',
'Pl': 'area'
}).drop('area')
For_co2 = xr.concat([
load_data('concentrations_AR5').CO2.interp({'year': np.arange(1750, 1959)
}),
load_data('NOAA-ESRL').CO2.sel(site='Mauna Loa', drop=True)
],
dim='year').to_dataset(name='CO2')
## format drivers
For = xr.merge([For_luc, For_clim, For_co2]).sel(year=slice(ind0, ind1))
For['D_CO2'] = For.CO2 - Par.CO2_0
For['D_Tl'] = For.Tl - For.Tl.sel(year=slice(1901, 1930)).mean('year')
For['D_Pl'] = For.Pl - For.Pl.sel(year=slice(1901, 1930)).mean('year')
For = For.drop(['CO2', 'Tl', 'Pl'])
def load_emissions_hist(mod_region,
datasets=['ACCMIP', 'CDIAC', 'CEDS', 'EDGAR-HYDEv13', 'EDGAR-HYDEv14', 'EDGARv42', 'EDGARv42-FT2010', 'EDGARv432', 'EDGARv432-FT2016', 'EPA', 'Meinshausen_2011', 'PRIMAP'],
dropped_species=['PM10', 'PM2p5'],
**useless):
'''
Function to load and format primary historical emissions datasets, taken from the 'input_data' folder.
Input:
------
mod_region (str) regional aggregation name
Output:
-------
For (xr.Dataset) dataset that contains the loaded datasets aggregated over 'mod_region'
Options:
--------
datasets (list) names of primary datasets to be loaded;
default = ['ACCMIP', 'CDIAC', 'CEDS', 'EDGAR-HYDEv13', 'EDGAR-HYDEv14',
'EDGARv42', 'EDGARv42-FT2010', 'EDGARv432', 'EDGARv432-FT2016',
'EPA', 'Meinshausen_2011', 'PRIMAP']
dropped_species (list) species to be excluded from the loaded datasets;
default = ['PM10', 'PM2p5']
'''
## list of missing halogenated species taken from Meinshausen_2011
missing_halo = ['CFC-11', 'CFC-12', 'CFC-113', 'CFC-114', 'CFC-115', 'CCl4', 'CH3CCl3', 'HCFC-22', 'Halon-1211', 'Halon-1202', 'Halon-1301', 'Halon-2402', 'CH3Br', 'CH3Cl']
## dictionaries for ignoring sectors
## CO2 emissions
CO2_ignored_sectors = {
'ACCMIP':['agr', 'awb', 'for', 'gra', 'wst'],
'CEDS':['3B_Manure-management', '3D_Rice-Cultivation', '3D_Soil-emissions', '3E_Enteric-fermentation', '3I_Agriculture-other', '5A_Solid-waste-disposal', '5C_Waste-combustion', '5D_Wastewater-handling', '5E_Other-waste-handling'],
'EDGAR-HYDEv13':['agr', 'bfc', 'liv', 'sav', 'def', 'awb', 'lfl'],
'EDGAR-HYDEv14':['BF3', 'AGL', 'ANM', 'SAV', 'DEF', 'AGR', 'LAN'],
'EDGARv42':['4A', '4B', '4C', '4D1', '4D2', '4D3', '4D4', '4E', '4F', '5A', '5C', '5D', '5F', '5F1', '5F2', '5FL', '5FL1', '6A', '6B', '6C', '6D'],
'EDGARv42-FT2010':['5A', '5C', '5D', '5F2'],
'EDGARv432':['4A', '4B', '4C', '4D1', '4D2', '4D3', '4D4', '4F', '6A', '6B', '6C', '6D'],
'EDGARv432-FT2016':[],
'EPA':['agr1', 'agr2', 'agr3', 'agr4', 'agr5', 'was1', 'was2', 'was3', 'was4'],
'PRIMAP':['4', '5', '6']}
## non-CO2 emissions
nonCO2_ignored_sectors = {
'ACCMIP':['for', 'gra'],
'CEDS':[],
'EDGAR-HYDEv13':['sav', 'def'],
'EDGAR-HYDEv14':['SAV', 'DEF'],
'EDGARv42':['4E', '5A', '5C', '5D', '5F', '5F1', '5F2', '5FL', '5FL1'],
'EDGARv42-FT2010':['4E', '5A', '5C', '5D', '5F2'],
'EDGARv432':[],
'EDGARv432-FT2016':[],
'EPA':['agr5'], # slightly inconsistent as mixing e.g. agricultural waste burning & savannah burning
'PRIMAP':['5']}
## main loading loop
For0 = []
units = {}
for data in datasets:
## load data if available
if os.path.isfile('input_data/drivers/emissions_' + data + '.nc'):
with xr.open_dataset('input_data/drivers/emissions_' + data + '.nc') as TMP:
For1 = TMP.load()
## display message otherwise
else: raise IOError('{0} not available'.format(data))
## get and check units
for VAR in For1:
if 'units' in For1[VAR].attrs:
if VAR not in units.keys():
units[VAR] = For1[VAR].units
else:
if units[VAR] != For1[VAR].units:
raise RuntimeWarning('inconsistent units: {0} (internal dic) vs. {1} ({2} in {3})'.format(units[VAR], For1[VAR].units, "'"+VAR+"'", "'"+data+"'"))
##---
## take only historical period
if data in ['ACCMIP', 'Meinshausen_2011']:
For1 = For1.sel(scen='historical', drop=True).dropna('year', how='all')
## take only national-based data
if data in ['CDIAC']:
For1 = For1.sel(data='national', drop=True)
## aggregate over fuels
if data in ['CDIAC']:
For1 = For1.sum('fuel', min_count=1)
## aggregate over sectors (ignoring some)
if data in ['ACCMIP', 'CEDS', 'EDGAR-HYDEv13', 'EDGAR-HYDEv14', 'EDGARv42', 'EDGARv42-FT2010', 'EDGARv432', 'EDGARv432-FT2016', 'EPA', 'PRIMAP']:
## selection and aggregation
for VAR in For1:
if VAR == 'E_CO2':
For1['E_CO2'] = For1['E_CO2'].sel(sector=[sec for sec in For1.sector.values if sec not in CO2_ignored_sectors[data]]).sum('sector', min_count=1)
else:
For1[VAR] = For1[VAR].sel(sector=[sec for sec in For1.sector.values if sec not in nonCO2_ignored_sectors[data]]).sum('sector', min_count=1)
## dropping useless coords
For1 = For1.drop('sector')
if 'sector_long_name' in For1.coords:
For1 = For1.drop('sector_long_name')
## take only missing halogenated species
if data in ['Meinshausen_2011']:
For1 = For1.drop([VAR for VAR in For1 if VAR != 'E_Xhalo'])
For1 = For1.sel(spc_halo=missing_halo)
## put global data on regional axis
if data in ['Meinshausen_2011']:
For1 = For1.expand_dims('reg_iso', -1).assign_coords(reg_iso=[999])
##---
## interpolate to yearly data
if not all(np.diff(For1.year.values) == 1):
For1 = For1.interp({'year':np.arange(int(For1.year[0]), int(For1.year[-1])+1, 1)})
## rename CO2 emissions
if 'E_CO2' in For1:
For1 = For1.rename({'E_CO2':'Eff'})
## aggregate to model regions
if 'reg_iso' in For1.coords:
For1 = aggreg_region(For1, mod_region)
else:
For1 = aggreg_region(For1, mod_region, old_axis='region', dataset=data)
## append to final list (with new dimension)
For0.append(For1.expand_dims('data', -1).assign_coords(data=[data]))
del For1
## merge into one xarray
For0 = xr.merge(For0)
## create one data axis per driver
For = xr.Dataset()
for VAR in For0:
TMP = [For0[VAR].sel(data=data).rename({'data':'data_'+VAR}) for data in For0.data.values if not np.isnan(For0[VAR].sel(data=data).sum(min_count=1))]
For[VAR] = xr.concat(TMP, dim='data_'+VAR)
del TMP
## order dimensions
For = For.transpose(*(['year', 'reg_land', 'spc_halo'] + [var for var in For.coords if 'data' in var]))
## drop requested species
for vars_then_coords in [For, For.coords]:
for VAR in vars_then_coords:
if any([spc in VAR for spc in dropped_species]):
For = For.drop(VAR)
## reapply units
for VAR in For:
if VAR in units.keys():
For[VAR].attrs['units'] = units[VAR]
## return
return For
def load_emissions_scen(mod_region,
datasets=['Meinshausen_2011', 'RCPdb', 'SRES', 'ScenarioMIP'],
all_SRES=False, all_SSPdb=False,
dropped_species=['CCS'],
Xhalo_offset={'CF4':{}, 'C2F6':{}, 'HFC-23':{}, 'CH3Br':{},
'CH3Cl':{'RCPdb':3100.211, 'Meinshausen_2011':3100.211}},
**useless):
'''
Function to load and format primary scenario emissions datasets, taken from the 'input_data' folder.
Input:
------
mod_region (str) regional aggregation name
Output:
-------
For (xr.Dataset) dataset that contains the loaded datasets tentatively aggregated over 'mod_region'
Options:
--------
datasets (list) names of primary datasets to be loaded;
default = ['Meinshausen_2011', 'RCPdb', 'SRES', 'ScenarioMIP']
all_SRES (bool) whether to take all SRES scenarios (if loaded) or just markers;
default = False
all_SSPdb (bool) whether to take all SSP database scenarios (if loaded) or just markers;
default = False
dropped_species (list) species to be excluded from the loaded datasets;
default = ['CCS']
Xhalo_offset (dict) how the offset by Xhalo preindustrial emissions is handled;
keys are species whose emissions must be offset;
values are another dict being:
either empty, in which case the offset is made on RCP2.6;
or whose keys are dataset names and values are floats, for offset by the specified values;
default = {'CF4':{}, 'C2F6':{}, 'HFC-23':{}, 'CH3Br':{},
'CH3Cl':{'RCPdb':3100.211, 'Meinshausen_2011':3100.211}}
'''
## dictionaries for ignoring sectors
## non-CO2 emissions
nonCO2_ignored_sectors = {
'RCPdb':['for', 'gra'],
'ScenarioMIP':['Forest Burning', 'Grassland Burning', 'Peat Burning']}
## main loading loop
For0 = []
units = {}
for data in datasets:
## load data if available
if os.path.isfile('input_data/drivers/emissions_' + data + '.nc'):
with xr.open_dataset('input_data/drivers/emissions_' + data + '.nc') as TMP:
For1 = TMP.load()
## display message otherwise
else: raise IOError('{0} not available'.format(data))
## get and check units
for VAR in For1:
if 'units' in For1[VAR].attrs:
if VAR not in units.keys():
units[VAR] = For1[VAR].units
else:
if units[VAR] != For1[VAR].units:
raise RuntimeWarning('inconsistent units: {0} (internal dic) vs. {1} ({2} in {3})'.format(units[VAR], For1[VAR].units, "'"+VAR+"'", "'"+data+"'"))
##---
## take only halogenated species over right scenario
## note: all RCPs are supposed to be the same, however some inconsistent values appear but not in RCP2.6
if data in ['Meinshausen_2011']:
For1 = For1['E_Xhalo'].to_dataset(name='E_Xhalo')
For1 = For1.sel(scen='RCP2.6').dropna('year', how='all')
For1 = For1.assign_coords(scen='CMIP5').expand_dims('scen', -1)
## put global data on regional axis
if data in ['Meinshausen_2011']:
For1 = For1.expand_dims('reg_iso', -1).assign_coords(reg_iso=[999])
## offset with preindustrial emissions level
if data in ['Meinshausen_2011', 'RCPdb']:
for VAR in Xhalo_offset.keys():
if data in Xhalo_offset[VAR].keys():
For1['E_Xhalo'] = xr.where(For1.spc_halo == VAR, For1['E_Xhalo'].sel(spc_halo=VAR) - Xhalo_offset[VAR][data], For1['E_Xhalo'])
else:
if 'RCP2.6' in For1.scen:
For1['E_Xhalo'] = xr.where(For1.spc_halo == VAR, For1['E_Xhalo'].sel(spc_halo=VAR) - For1['E_Xhalo'].sel(spc_halo=VAR, scen='RCP2.6').dropna('year', how='all').isel(year=-1), For1['E_Xhalo'])
else:
For1['E_Xhalo'] = xr.where(For1.spc_halo == VAR, For1['E_Xhalo'].sel(spc_halo=VAR) - For1['E_Xhalo'].sel(spc_halo=VAR).dropna('year', how='all').isel(year=-1), For1['E_Xhalo'])
## aggregate over sectors (ignoring some)
if data in ['RCPdb', 'ScenarioMIP']:
## selection and aggregation
for VAR in For1:
if 'sector' in For1[VAR].coords:
For1[VAR] = For1[VAR].sel(sector=[sec for sec in For1.sector.values if sec not in nonCO2_ignored_sectors[data]]).sum('sector', min_count=1)
## dropping useless coords
For1 = For1.drop('sector')
if 'sector_long_name' in For1.coords:
For1 = For1.drop('sector_long_name')
## select SRES scenarios
if data in ['SRES']:
## take all scenarios (flatten array)
if all_SRES:
For1 = For1.stack(new_scen=('scen', 'model'))
For1['new_scen'] = ['SRES-'+var1+' ('+var2+')' for var1, var2 in For1.new_scen.values]
For1 = For1.dropna('new_scen').rename({'new_scen':'scen'})
## take only markers
else:
For1 = For1.where(For1.is_marker, drop=True).sum('model', min_count=1)
For1['scen'] = [data+'-'+var+' (marker)' for var in For1.scen.values]
## select SSP scenarios
if data in ['SSPdb']:
## take all scenarios (flatten array)
if all_SSPdb:
For1 = For1.stack(new_scen=('scen_ssp', 'scen_rcp', 'model'))
For1['new_scen'] = [var1+'-'+var2+' ('+var3+')' for var1, var2, var3 in For1.new_scen.values]
For1 = For1.dropna('new_scen').rename({'new_scen':'scen'})
## take only markers (also flattened)
else:
For1 = For1.where(For1.is_marker, drop=True).sum('model', min_count=1)
For1 = For1.stack(new_scen=('scen_ssp', 'scen_rcp'))
For1['new_scen'] = [var1+'-'+var2+' (marker)' for var1, var2 in For1.new_scen.values]
For1 = For1.dropna('new_scen').rename({'new_scen':'scen'})
##---
## interpolate to yearly data
if not all(np.diff(For1.year.values) == 1):
For1 = For1.interp({'year':np.arange(int(For1.year[0]), int(For1.year[-1])+1, 1)})
## aggregate to model regions
if 'reg_iso' in For1.coords:
For1 = aggreg_region(For1, mod_region)
else:
For1 = aggreg_region(For1, mod_region, old_axis='region', dataset=data)
## append to final list
For0.append(For1)
del For1
## merge into one xarray
For0 = xr.merge(For0)
## create one data axis per driver
For = xr.Dataset()
for VAR in For0:
TMP = [For0[VAR].sel(scen=scen).rename({'scen':'scen_'+VAR}) for scen in For0.scen.values if not np.isnan(For0[VAR].sel(scen=scen).sum(min_count=1))]
For[VAR] = xr.concat(TMP, dim='scen_'+VAR)
del TMP
## order dimensions
For = For.transpose(*(['year', 'reg_land'] + ['spc_halo']*('spc_halo' in For.coords) + [var for var in For.coords if 'scen' in var]))
## drop requested species
for vars_then_coords in [For, For.coords]:
for VAR in vars_then_coords:
if any([spc in VAR for spc in dropped_species]):
For = For.drop(VAR)
## reapply units
for VAR in For:
if VAR in units.keys():
For[VAR].attrs['units'] = units[VAR]
## return
return For
def load_landuse_scen(mod_region,
datasets=['LUH1', 'LUH2'],
LCC='all',
**useless):
'''
Function to load and format primary scenario land-use datasets, taken from the 'input_data' folder.
Input:
------
mod_region (str) regional aggregation name
Output:
-------
For (xr.Dataset) dataset that contains the loaded datasets aggregated over 'mod_region'
Options:
--------
datasets (list) names of primary datasets to be loaded;
default = ['LUH1', 'LUH2']
LCC (str) which of 'gross' or 'net' land-cover transitions should be kept;
unless both are kept ('all'), the driver is renamed to 'd_Acover';
default = 'all'
'''
## main loading loop
For0 = []
units = {}
for data in datasets:
## load data if available
if os.path.isfile('input_data/drivers/land-use_' + data + '.nc'):
with xr.open_dataset('input_data/drivers/land-use_' + data + '.nc') as TMP:
For1 = TMP.load()
## display message otherwise
else: raise IOError('{0} not available'.format(data))
## get and check units
for VAR in For1:
if 'units' in For1[VAR].attrs:
if VAR not in units.keys():
units[VAR] = For1[VAR].units
else:
if units[VAR] != For1[VAR].units:
raise RuntimeWarning('inconsistent units: {0} (internal dic) vs. {1} ({2} in {3})'.format(units[VAR], For1[VAR].units, "'"+VAR+"'", "'"+data+"'"))
##---
## take scenarios
if data in ['LUH1', 'LUH2']:
For1 = For1.sel(scen=[sc for sc in For1.scen.values if 'historical' not in sc]).dropna('year', how='all')
##---
## interpolate to yearly data
if not all(np.diff(For1.year.values) == 1):
For1 = For1.interp({'year':np.arange(int(For1.year[0]), int(For1.year[-1])+1, 1)})
## aggregate to model regions
if 'reg_iso' in For1.coords:
For1 = aggreg_region(For1, mod_region)
else:
For1 = aggreg_region(For1, mod_region, old_axis='region', dataset=data)
## append to final list
For0.append(For1)
del For1
## merge into one xarray (and rename scenario dimension)
For0 = xr.merge(For0)
For0 = For0.rename({'scen':'scen_LULCC'})
## order dimensions
For0 = For0.transpose(*(['year', 'reg_land', 'bio_land', 'bio_from', 'bio_to'] + [var for var in For0.coords if 'scen' in var]))
## reapply units
for VAR in For0:
if VAR in units.keys():
For0[VAR].attrs['units'] = units[VAR]
## return (with selected net/gross LCC, if requested)
For0 = For0.drop('Aland_0')
if LCC == 'net': return For0.rename({'d_Anet':'d_Acover'}).drop('d_Agross')
elif LCC == 'gross': return For0.rename({'d_Agross':'d_Acover'}).drop('d_Anet')
else: return For0
def calib_land_TRENDYv7(mod_region, biome_specific_process=True,
path_in='calib_data/', path_out='input_data/parameters/', **useless):
## load original data
with xr.open_dataset(path_in + 'land_TRENDYv7.nc') as TMP:
ds = TMP.sel(sim=['S0', 'S4']).sel(weight='area3', drop=True).load()
## aggregate over regions
ds = aggreg_region(ds, mod_region)
## separate natural and anthropogenic biomes
ds_nat = ds.sel(bio_land=['Forest', 'Non-Forest']).sel(sim='S0', drop=True).mean('year')
ds_ant = ds.sel(bio_land=['Cropland', 'Pasture', 'Urban']).sel(sim='S4', drop=True).sel(year=slice(1990, 2010)).mean('year')
ds2 = xr.merge([ds_nat, ds_ant]).sel(bio_land=['Forest', 'Non-Forest']+['Cropland', 'Pasture', 'Urban'])
## test existing variables per model
exist = xr.Dataset()
for var in ['area', 'npp', 'cVeg', 'cLitter', 'cSoil', 'fFire', 'fHarvest', 'fGrazing', 'fVegLitter', 'fVegSoil', 'fLitterSoil', 'rh', 'fDOC', 'cRoot', 'cWood']:
exist[var] = ds2[var].sum('reg_land', min_count=1).sum('bio_land', min_count=1).notnull() & (ds2[var].sum('reg_land', min_count=1).sum('bio_land', min_count=1) != 0)
## test critical variables
for var in ['npp', 'cVeg', 'cSoil']:
if exist[var].sum() < len(exist.model):
raise RuntimeError("'{0}' must be defined for all models!".format(var))
## tests whether 3- or 2-box model
is_3box = exist.cLitter & exist.fVegLitter & exist.fLitterSoil
## calculate Fsoil2, Fmort and Rh depending on existing data
fSoilIn = (ds2.fVegSoil.fillna(0) + ds2.fLitterSoil.fillna(0)).where(ds2.fVegSoil.notnull() | ds2.fLitterSoil.notnull())
fMort = (ds2.fVegLitter.fillna(0) + ds2.fVegSoil.fillna(0)).where(ds2.fVegLitter.notnull() | ds2.fVegSoil.notnull())
fMort = fMort.where(exist.fVegLitter | exist.fVegSoil, ds2.npp - ds2.fFire.fillna(0) - ds2.fHarvest.fillna(0) - ds2.fGrazing.fillna(0))
Rh = ds2.rh.where(exist.rh, ds2.npp - ds2.fFire.fillna(0) - ds2.fHarvest.fillna(0) - ds2.fGrazing.fillna(0) - ds2.fDOC.fillna(0))
cSoilTot = (ds2.cLitter.fillna(0) + ds2.cSoil.fillna(0)).where(ds2.cLitter.notnull() | ds2.cSoil.notnull())
## initialisation of final array
Par = xr.Dataset()
## areal net primary productivity
Par['npp_0'] = ds2.npp / ds2.area
## wildfire emission rate
Par['igni_0'] = (ds2.fFire / ds2.cVeg).where(exist.fFire)
## harvest index
Par['harv_0'] = (ds2.fHarvest / ds2.cVeg).where(exist.fHarvest)
## grazing rate
Par['graz_0'] = (ds2.fGrazing / ds2.cVeg).where(exist.fGrazing)
## mortality rates
Par['mu1_0'] = (ds2.fVegLitter / ds2.cVeg).where(is_3box, 0)
Par['mu2_0'] = (ds2.fVegSoil.where(exist.fVegSoil, 0) / ds2.cVeg).where(is_3box, 0) + (fMort / ds2.cVeg).where(~is_3box, 0)
## metabolization rate
Par['muM_0'] = (ds2.fLitterSoil / ds2.cLitter).where(is_3box, 0)
## respiration rates
Par['rho1_0'] = ((ds2.fVegLitter - ds2.fLitterSoil) / ds2.cLitter).where(is_3box, 0)
Par['rho2_0'] = (fSoilIn / ds2.cSoil).where(is_3box, 0) + (Rh / cSoilTot).where(~is_3box, 0)
## above-ground biomass fraction
Par['p_agb'] = 1 - ds2.cRoot / ds2.cVeg
## additional processing (some conditional)
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
## fill missing anthropogenic biomes:
## using Non-Forest parameters (except Urban NPP set to zero)
not_fill = Par.npp_0.notnull()
for var in Par:
Par[var] = Par[var].where(not_fill, Par[var].sel(bio_land='Non-Forest', drop=True))
Par['npp_0'] = Par.npp_0.where((Par.bio_land != 'Urban') | not_fill, 0)
## fill 'Unknown' region if needed:
## using average of other regions
if 'Unknown' in Par.reg_land_long_name:
for var in Par:
Par[var] = Par[var].where(Par[var].notnull(), Par[var].where(Par.reg_land_long_name != 'Unknown').mean('reg_land'))
## assume biome-specific processes:
## no wildfire on Urban, harvest only on Cropland, grazing only on Pasture
if biome_specific_process:
Par['igni_0'] = Par.igni_0.where(Par.bio_land != 'Urban', 0).where(exist.fFire)
Par['harv_0'] = Par.harv_0.where(Par.bio_land == 'Cropland', 0).where(exist.fHarvest)
Par['graz_0'] = Par.graz_0.where(Par.bio_land == 'Pasture', 0).where(exist.fGrazing)
## add units
Par['npp_0'].attrs['units'] = 'PgC yr-1 Mha-1'
Par['p_agb'].attrs['units'] = '1'
for var in ['igni_0', 'harv_0', 'graz_0', 'mu1_0', 'mu2_0', 'muM_0', 'rho1_0', 'rho2_0']:
Par[var].attrs['units'] = 'yr-1'
## create model option axes
Par['igni_0'] = Par.igni_0.dropna('model', how='all').rename({'model':'mod_Efire_preind'})
Par['harv_0'] = Par.harv_0.dropna('model', how='all').rename({'model':'mod_Eharv_preind'})
Par['graz_0'] = Par.graz_0.dropna('model', how='all').rename({'model':'mod_Egraz_preind'})
Par['p_agb'] = Par.p_agb.dropna('model', how='all').rename({'model':'mod_Eluc_agb'})
Par = Par.rename({'model':'mod_Fland_preind'})
## create multi-model mean values
try:
mmm = xr.Dataset()
for mod in ['mod_Fland_preind', 'mod_Efire_preind', 'mod_Eharv_preind', 'mod_Egraz_preind', 'mod_Eluc_agb']:
mmm = xr.merge([mmm, xr.Dataset({var:Par[var].mean(mod) for var in Par if mod in Par[var].dims}).assign_coords({mod:'mean_TRENDYv7'}).expand_dims(mod, -1)])
except:
mmm = xr.Dataset()
mmm = xr.merge([mmm, xr.Dataset({var:Par[var].mean('mod_Fland_preind') for var in Par if 'mod_Fland_preind' in Par[var].dims}).assign_coords(mod_Fland_preind='mean_TRENDYv7').expand_dims('mod_Fland_preind', -1)])
mmm = xr.merge([mmm, xr.Dataset({var:Par[var].mean('mod_Efire_preind') for var in Par if 'mod_Efire_preind' in Par[var].dims}).assign_coords(mod_Efire_preind='mean_TRENDYv7').expand_dims('mod_Efire_preind', -1)])
mmm = xr.merge([mmm, xr.Dataset({var:Par[var].mean('mod_Eharv_preind') for var in Par if 'mod_Eharv_preind' in Par[var].dims}).assign_coords(mod_Eharv_preind='mean_TRENDYv7').expand_dims('mod_Eharv_preind', -1)])
mmm = xr.merge([mmm, xr.Dataset({var:Par[var].mean('mod_Egraz_preind') for var in Par if 'mod_Egraz_preind' in Par[var].dims}).assign_coords(mod_Egraz_preind='mean_TRENDYv7').expand_dims('mod_Egraz_preind', -1)])
mmm = xr.merge([mmm, xr.Dataset({var:Par[var].mean('mod_Eluc_agb') for var in Par if 'mod_Eluc_agb' in Par[var].dims}).assign_coords(mod_Eluc_agb='mean_TRENDYv7').expand_dims('mod_Eluc_agb', -1)])
## create off values
try:
off = xr.Dataset()
for mod in ['mod_Efire_preind', 'mod_Eharv_preind', 'mod_Egraz_preind']:
off = xr.merge([off, xr.Dataset({var:Par[var].mean(mod) * 0 for var in Par if mod in Par[var].dims}).assign_coords({mod:'off_'}).expand_dims(mod, -1)])
except:
off = xr.Dataset()
off = xr.merge([off, xr.Dataset({var:Par[var].mean('mod_Efire_preind') * 0 for var in Par if 'mod_Efire_preind' in Par[var].dims}).assign_coords(mod_Efire_preind='off_').expand_dims('mod_Efire_preind', -1)])
off = xr.merge([off, xr.Dataset({var:Par[var].mean('mod_Eharv_preind') * 0 for var in Par if 'mod_Eharv_preind' in Par[var].dims}).assign_coords(mod_Eharv_preind='off_').expand_dims('mod_Eharv_preind', -1)])
off = xr.merge([off, xr.Dataset({var:Par[var].mean('mod_Egraz_preind') * 0 for var in Par if 'mod_Egraz_preind' in Par[var].dims}).assign_coords(mod_Egraz_preind='off_').expand_dims('mod_Egraz_preind', -1)])
## merge, save and return
Par = Par.combine_first(mmm).combine_first(off)
Par.to_netcdf(path_out + 'land_TRENDYv7__' + mod_region + '.nc')
return Par