def test_w_out_config(self, test_scenarios):
with pytest.raises(NotImplementedError):
run(
scenarios=test_scenarios.filter(scenario=["ssp126"]),
climate_models_cfgs={
"CiceroSCM": [
{
"model_end": 2100,
"Index": 30040,
"lambda": 0.540,
"akapa": 0.341,
"cpi": 0.556,
"W": 1.897,
"rlamdo": 16.618,
"beto": 3.225,
"mixed": 107.277,
"dirso2_forc": -0.457,
"indso2_forc": -0.514,
"bc_forc": 0.200,
"oc_forc": -0.103,
},
]
},
output_variables=("Surface Air Temperature Change", ),
out_config={"CiceroSCM": ("With ECS", )},
)
def test_return_config_clash_error(test_scenarios, cfgs):
with pytest.raises(ValueError):
run(
climate_models_cfgs={"MAGICC7": cfgs},
scenarios=test_scenarios.filter(scenario=["ssp126"]),
output_variables=("Surface Air Temperature Change", ),
out_config={"MAGICC7": ("pf_apply", )},
)
def test_run_out_config_type_error():
error_msg = re.escape(
"`out_config` values must be tuples, this isn't the case for "
"climate_model: 'model_a'")
with pytest.raises(TypeError, match=error_msg):
run(
climate_models_cfgs={"model_a": ["config list"]},
scenarios="not used",
out_config={"model_a": "hi"},
)
def test_run_out_config_conflict_error():
error_msg = re.escape("Found model(s) in `out_config` which are not in "
"`climate_models_cfgs`: {'another model'}")
with pytest.raises(NotImplementedError):
with pytest.warns(UserWarning, match=error_msg):
run(
climate_models_cfgs={"model_a": ["config list"]},
scenarios="not used",
out_config={"another model": ("hi", )},
)
def test_variable_naming(self, test_scenarios):
missing_from_fair = (
"Effective Radiative Forcing|Aerosols|Direct Effect|BC|MAGICC AFOLU",
"Effective Radiative Forcing|Aerosols|Direct Effect|BC|MAGICC Fossil and Industrial",
"Effective Radiative Forcing|Aerosols|Direct Effect|OC|MAGICC AFOLU",
"Effective Radiative Forcing|Aerosols|Direct Effect|OC|MAGICC Fossil and Industrial",
"Effective Radiative Forcing|Aerosols|Direct Effect|SOx|MAGICC AFOLU",
"Effective Radiative Forcing|Aerosols|Direct Effect|SOx|MAGICC Fossil and Industrial",
"Net Atmosphere to Ocean Flux|CO2",
"Net Atmosphere to Land Flux|CO2",
)
common_variables = [
c for c in self._common_variables if c not in missing_from_fair
]
res = run(
climate_models_cfgs={"FaIR": ({
"startyear": 1750
}, )},
scenarios=test_scenarios.filter(scenario="ssp126"),
output_variables=common_variables,
)
missing_vars = set(common_variables) - set(res["variable"])
if missing_vars:
raise AssertionError(missing_vars)
def test_variable_naming(self, test_scenarios):
"""
Test that variable naming is implemented as expecteds
"""
# pseudo-code
# a model might not report all outputs
missing_variables = (
"Net Atmosphere to Ocean Flux|CO2",
"Net Atmosphere to Land Flux|CO2",
)
common_variables = [
c for c in self._common_variables if c not in missing_variables
]
# run the model and request all common variables the model reportss
res = run(
climate_models_cfgs={"climate_model": ({"para1": 3.43},)},
scenarios=test_scenarios.filter(scenario="ssp126"),
output_variables=common_variables,
)
# check that all expected outputs were returned
missing_vars = set(common_variables) - set(res["variable"])
if missing_vars:
raise AssertionError(missing_vars)
def test_fair_ocean_factors(test_scenarios):
res_default_factors = run(
climate_models_cfgs={"FaIR": [{}]},
scenarios=test_scenarios.filter(scenario=["ssp585"]),
output_variables=(
"Surface Air Ocean Blended Temperature Change",
"Heat Uptake|Ocean",
"Heat Content|Ocean",
),
)
res_custom_factors = run(
climate_models_cfgs={
"FaIR": [
{
"gmst_factor": np.linspace(0.90, 1.00, 351), # test with array
"ohu_factor": 0.93,
}
]
},
scenarios=test_scenarios.filter(scenario=["ssp585"]),
output_variables=(
"Surface Air Ocean Blended Temperature Change",
"Heat Uptake|Ocean",
"Heat Content|Ocean",
),
)
assert (
res_default_factors.filter(
variable="Surface Air Ocean Blended Temperature Change",
region="World",
year=2100,
scenario="ssp585",
).values
!= res_custom_factors.filter(
variable="Surface Air Ocean Blended Temperature Change",
region="World",
year=2100,
scenario="ssp585",
).values
)
def test_run(self, test_scenarios):
"""
Test that the model can be run, ideally with more than one configuration
"""
# pseudo-code
res = run(
climate_models_cfgs={
"climate_model": [
{
"para1": "value1",
"para2": 1.1
},
{
"para1": "value3",
"para2": 1.2
},
{
"para1": "value5",
"para2": 1.3
},
],
},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"output",
"var",
"list",
),
out_config=None,
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 0
assert res["run_id"].max() == 8
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "model_name"
assert set(res.get_unique_meta("variable")) == set(
["expected", "output", "variables"])
# output value checks e.g.
npt.assert_allclose(
2.31,
res.filter(
variable="Surface Air Temperature Change",
region="World",
year=2100,
scenario="ssp126",
).values.max(),
rtol=self._rtol,
)
def test_variable_naming(self, test_scenarios):
common_variables = self._common_variables
res = run(
climate_models_cfgs={
"MAGICC7": ({
"core_climatesensitivity": 3
}, )
},
scenarios=test_scenarios.filter(scenario="ssp126"),
output_variables=common_variables,
)
missing_vars = set(common_variables) - set(res["variable"])
if missing_vars:
raise AssertionError(missing_vars)
def test_return_config(test_scenarios, out_config):
core_climatesensitivities = [2, 3]
rf_total_runmoduses = ["ALL", "CO2"]
cfgs = []
for cs in core_climatesensitivities:
for runmodus in rf_total_runmoduses:
cfgs.append({
"out_dynamic_vars": [
"DAT_TOTAL_INCLVOLCANIC_ERF",
"DAT_SURFACE_TEMP",
],
"core_climatesensitivity":
cs,
"rf_total_runmodus":
runmodus,
})
res = run(
climate_models_cfgs={"MAGICC7": cfgs},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Air Temperature Change",
"Effective Radiative Forcing",
),
out_config={"MAGICC7": out_config},
)
for k in out_config:
assert k in res.meta.columns
ssp126 = res.filter(scenario="ssp126")
# check all the configs were used and check that each scenario
# has all the configs included in the metadata too
if k == "core_climatesensitivity":
assert set(
res.get_unique_meta(k)) == set(core_climatesensitivities)
assert set(
ssp126.get_unique_meta(k)) == set(core_climatesensitivities)
elif k == "rf_total_runmodus":
assert set(res.get_unique_meta(k)) == set(rf_total_runmoduses)
assert set(ssp126.get_unique_meta(k)) == set(rf_total_runmoduses)
else:
raise NotImplementedError(k)
def test_run(
self,
test_scenarios,
test_data_dir,
update_expected_values,
shuffle_column_order,
):
expected_output_file = os.path.join(
test_data_dir,
"expected-integration-output",
"expected_ciceroscm_test_run_output.json",
)
if shuffle_column_order:
tmp = test_scenarios.data
cols = tmp.columns.tolist()
tmp = tmp[cols[1:] + cols[:1]]
test_scenarios = ScmRun(test_scenarios)
res = run(
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
climate_models_cfgs={
"CICEROSCM": [
{
"model_end": 2100,
"Index": 30040,
"lambda": 0.540,
"akapa": 0.341,
"cpi": 0.556,
"W": 1.897,
"rlamdo": 16.618,
"beto": 3.225,
"mixed": 107.277,
"dirso2_forc": -0.457,
"indso2_forc": -0.514,
"bc_forc": 0.200,
"oc_forc": -0.103,
},
{
"model_end": 2100,
"Index": 1,
"lambda": 0.3925,
"akapa": 0.2421,
"cpi": 0.3745,
"W": 0.8172,
"rlamdo": 16.4599,
"beto": 4.4369,
"mixed": 35.4192,
"dirso2_forc": -0.3428,
"indso2_forc": -0.3856,
"bc_forc": 0.1507,
"oc_forc": -0.0776,
},
]
},
output_variables=(
"Surface Air Temperature Change",
"Surface Air Ocean Blended Temperature Change",
"Heat Content|Ocean",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Anthropogenic",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|Greenhouse Gases",
"Heat Uptake",
"Atmospheric Concentrations|CO2",
"Atmospheric Concentrations|CH4",
"Atmospheric Concentrations|N2O",
"Emissions|CO2",
"Emissions|CH4",
"Emissions|N2O",
),
out_config=None,
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 1
assert res["run_id"].max() == 30040
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "CICERO-SCM"
assert set(res.get_unique_meta("variable")) == set([
"Surface Air Temperature Change",
"Surface Air Ocean Blended Temperature Change",
"Heat Content|Ocean",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Anthropogenic",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|Greenhouse Gases",
"Heat Uptake",
"Atmospheric Concentrations|CO2",
"Atmospheric Concentrations|CH4",
"Atmospheric Concentrations|N2O",
"Emissions|CO2",
"Emissions|N2O",
"Emissions|CH4",
])
# check we can also calcluate quantiles
assert "run_id" in res.meta
quantiles = calculate_quantiles(res,
[0, 0.05, 0.17, 0.5, 0.83, 0.95, 1])
assert "run_id" not in quantiles.meta
assert (res.filter(
variable="Atmospheric Concentrations|CO2").get_unique_meta(
"unit", True) == "ppm")
assert (res.filter(
variable="Atmospheric Concentrations|CH4").get_unique_meta(
"unit", True) == "ppb")
assert (res.filter(
variable="Atmospheric Concentrations|N2O").get_unique_meta(
"unit", True) == "ppb")
assert (res.filter(variable="Emissions|CO2").get_unique_meta(
"unit", True) == "PgC / yr")
assert (res.filter(variable="Emissions|CH4").get_unique_meta(
"unit", True) == "TgCH4 / yr")
assert (res.filter(variable="Emissions|N2O").get_unique_meta(
"unit", True) == "TgN2ON / yr")
# check that emissions were passed through correctly
for (scen, variable, unit, exp_val) in (
("ssp126", "Emissions|CO2", "PgC/yr", -2.3503),
("ssp370", "Emissions|CO2", "PgC/yr", 22.562),
("ssp126", "Emissions|CH4", "TgCH4/yr", 122.195),
("ssp370", "Emissions|CH4", "TgCH4/yr", 777.732),
("ssp126", "Emissions|N2O", "TgN2ON/yr", 5.318),
("ssp370", "Emissions|N2O", "TgN2ON/yr", 13.144),
):
res_scen_2100_emms = res.filter(variable=variable,
year=2100,
scenario=scen).convert_unit(unit)
if res_scen_2100_emms.empty:
raise AssertionError("No {} data for {}".format(
variable, scen))
npt.assert_allclose(
res_scen_2100_emms.values,
exp_val,
rtol=1e-4,
)
for (scen, variable, unit, exp_val14, exp_val16) in (
("ssp126", "Emissions|CH4", "TgCH4/yr", 387.874, 379.956),
("ssp370", "Emissions|CH4", "TgCH4/yr", 387.874, 394.149),
("ssp126", "Emissions|N2O", "TgN2ON/yr", 6.911, 6.858),
("ssp370", "Emissions|N2O", "TgN2ON/yr", 6.911, 7.0477),
):
res_scen_2014_emms = res.filter(variable=variable,
year=2014,
scenario=scen).convert_unit(unit)
if res_scen_2014_emms.empty:
raise AssertionError("No {} data for {}".format(
variable, scen))
res_scen_2016_emms = res.filter(variable=variable,
year=2016,
scenario=scen).convert_unit(unit)
if res_scen_2016_emms.empty:
raise AssertionError("No {} data for {}".format(
variable, scen))
npt.assert_allclose(
res_scen_2014_emms.values,
exp_val14,
rtol=1e-4,
)
npt.assert_allclose(
res_scen_2016_emms.values,
exp_val16,
rtol=1e-4,
)
for (scen, variable) in (
("ssp126", "Effective Radiative Forcing|Aerosols"),
("ssp370", "Effective Radiative Forcing|Aerosols"),
):
res_scen_2015_emms = res.filter(variable=variable,
year=2015,
scenario=scen)
if res_scen_2015_emms.empty:
raise AssertionError(
"No CO2 emissions data for {}".format(scen))
assert not np.equal(res_scen_2015_emms.values, 0).all()
ssp245_ghg_erf_2015 = res.filter(
variable="Effective Radiative Forcing|Greenhouse Gases",
year=2015,
scenario="ssp245",
run_id=1,
)
ssp245_ghg_erf_2014 = res.filter(
variable="Effective Radiative Forcing|Greenhouse Gases",
year=2014,
scenario="ssp245",
run_id=1,
)
# check that jump in GHG ERF isn't there
assert (ssp245_ghg_erf_2015.values.squeeze() -
ssp245_ghg_erf_2014.values.squeeze()) < 0.1
ssp245_ch4_conc_2015 = res.filter(
variable="Atmospheric Concentrations|CH4",
year=2015,
scenario="ssp245",
run_id=1,
)
ssp245_ch4_conc_2014 = res.filter(
variable="Atmospheric Concentrations|CH4",
year=2014,
scenario="ssp245",
run_id=1,
)
# ch
# check that jump in GHG ERF isn't there
assert (ssp245_ch4_conc_2014.values.squeeze() -
ssp245_ch4_conc_2015.values.squeeze()) < 0.1
self._check_output(res, expected_output_file, update_expected_values)
scenarios = pyam.IamDataFrame(data_file_scenarios)
assert MAGICC7.get_version() == EXPECTED_MAGICC_VERSION, MAGICC7.get_version()
res = run(
climate_models_cfgs={
"MAGICC7": [
{
"core_climatesensitivity": 3,
"rf_soxi_dir_wm2": -0.2,
"out_temperature": 1,
},
{
"core_climatesensitivity": 2,
"rf_soxi_dir_wm2": -0.1,
"out_temperature": 1,
},
{
"core_climatesensitivity": 5,
"rf_soxi_dir_wm2": -0.35,
"out_temperature": 1,
},
],
},
scenarios=scenarios,
)
if UPDATE:
res.to_csv(data_file_regression)
else:
expected_res = pyam.IamDataFrame(data_file_regression)
def test_magicc7_run(test_scenarios, magicc7_is_available):
debug_run = False
res = run(
climate_models_cfgs={
"MAGICC7": [
{
"core_climatesensitivity":
3,
"rf_soxi_dir_wm2":
-0.2,
"out_temperature":
1,
"out_forcing":
1,
"out_dynamic_vars": [
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_CO2_AIR2LAND_FLUX",
],
"out_ascii_binary":
"BINARY",
"out_binary_format":
2,
},
{
"core_climatesensitivity": 2,
"rf_soxi_dir_wm2": -0.1,
"out_temperature": 1,
"out_forcing": 1,
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
{
"core_climatesensitivity": 5,
"rf_soxi_dir_wm2": -0.35,
"out_temperature": 1,
"out_forcing": 1,
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
],
},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Temperature",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"Heat Content|Ocean",
"Net Atmosphere to Land Flux|CO2",
),
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 0
assert res["run_id"].max() == 8
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "MAGICC{}".format(
MAGICC7.get_version())
assert set(res.get_unique_meta("variable")) == set([
"Surface Temperature",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"Heat Content|Ocean",
"Net Atmosphere to Land Flux|CO2",
])
# check ocean heat content unit conversion comes through correctly
_check_res(
1824.05,
res.filter(
unit="ZJ",
variable="Heat Content|Ocean",
region="World",
year=2100,
scenario="ssp126",
).values.max(),
not debug_run,
rtol=RTOL,
)
_check_res(
0.472378,
res.filter(
unit="GtC / yr",
variable="Net Atmosphere to Land Flux|CO2",
region="World",
year=2100,
scenario="ssp126",
).values.max(),
not debug_run,
rtol=RTOL,
)
_check_res(
2.756034,
res.filter(variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126").values.max(),
not debug_run,
rtol=RTOL,
)
_check_res(
1.2195495,
res.filter(variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126").values.min(),
not debug_run,
rtol=RTOL,
)
_check_res(
5.5226571,
res.filter(variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370").values.max(),
not debug_run,
rtol=RTOL,
)
_check_res(
2.733369581,
res.filter(variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370").values.min(),
not debug_run,
rtol=RTOL,
)
# check we can also calcluate quantiles
quantiles = calculate_quantiles(res, [0.05, 0.17, 0.5, 0.83, 0.95])
_check_res(
1.27586919,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.05,
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
2.6587052,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.95,
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
2.83627686,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.05,
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
5.34663565,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.95,
).values,
not debug_run,
rtol=RTOL,
)
if debug_run:
assert False, "Turn off debug"
def test_forcing_categories(test_scenarios):
forcing_categories = [
"Effective Radiative Forcing|CO2",
"Effective Radiative Forcing|CH4",
"Effective Radiative Forcing|N2O",
"Effective Radiative Forcing|CF4",
"Effective Radiative Forcing|C2F6",
"Effective Radiative Forcing|C6F14",
"Effective Radiative Forcing|HFC23",
"Effective Radiative Forcing|HFC32",
"Effective Radiative Forcing|HFC125",
"Effective Radiative Forcing|HFC134a",
"Effective Radiative Forcing|HFC143a",
"Effective Radiative Forcing|HFC227ea",
"Effective Radiative Forcing|HFC245fa",
"Effective Radiative Forcing|HFC4310mee",
"Effective Radiative Forcing|SF6",
"Effective Radiative Forcing|CFC11",
"Effective Radiative Forcing|CFC12",
"Effective Radiative Forcing|CFC113",
"Effective Radiative Forcing|CFC114",
"Effective Radiative Forcing|CFC115",
"Effective Radiative Forcing|CCl4",
"Effective Radiative Forcing|CH3CCl3",
"Effective Radiative Forcing|HCFC22",
"Effective Radiative Forcing|HCFC141b",
"Effective Radiative Forcing|HCFC142b",
"Effective Radiative Forcing|Halon1211",
"Effective Radiative Forcing|Halon1202",
"Effective Radiative Forcing|Halon1301",
"Effective Radiative Forcing|Halon2402",
"Effective Radiative Forcing|CH3Br",
"Effective Radiative Forcing|CH3Cl",
"Effective Radiative Forcing|Tropospheric Ozone",
"Effective Radiative Forcing|Stratospheric Ozone",
"Effective Radiative Forcing|CH4 Oxidation Stratospheric H2O",
"Effective Radiative Forcing|Contrails",
"Effective Radiative Forcing|Aerosols|Direct Effect|SOx",
"Effective Radiative Forcing|Aerosols|Direct Effect|Secondary Organic Aerosol",
"Effective Radiative Forcing|Aerosols|Direct Effect|Nitrate",
"Effective Radiative Forcing|Aerosols|Direct Effect|BC",
"Effective Radiative Forcing|Aerosols|Direct Effect|OC",
"Effective Radiative Forcing|Aerosols|Indirect Effect",
"Effective Radiative Forcing|Black Carbon on Snow",
"Effective Radiative Forcing|Land-use Change",
"Effective Radiative Forcing|Volcanic",
"Effective Radiative Forcing|Solar",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Anthropogenic",
"Effective Radiative Forcing|Greenhouse Gases",
"Effective Radiative Forcing|Kyoto Gases",
"Effective Radiative Forcing|CO2, CH4 and N2O",
"Effective Radiative Forcing|F-Gases",
"Effective Radiative Forcing|Montreal Protocol Halogen Gases",
"Effective Radiative Forcing|Aerosols|Direct Effect",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|Ozone",
"Effective Radiative Forcing",
]
res = run(
climate_models_cfgs={"FaIR": [{}]},
scenarios=test_scenarios.filter(scenario=["ssp245"]),
output_variables=tuple(forcing_categories),
out_config=None,
)
# storing results in a dict makes this a bit more compact
forcing = {}
for variable in forcing_categories:
forcing[variable] = res.filter(variable=variable,
region="World").values
npt.assert_allclose(
forcing["Effective Radiative Forcing|CO2"] +
forcing["Effective Radiative Forcing|CH4"] +
forcing["Effective Radiative Forcing|N2O"] +
forcing["Effective Radiative Forcing|CF4"] +
forcing["Effective Radiative Forcing|C2F6"] +
forcing["Effective Radiative Forcing|C6F14"] +
forcing["Effective Radiative Forcing|HFC23"] +
forcing["Effective Radiative Forcing|HFC32"] +
forcing["Effective Radiative Forcing|HFC125"] +
forcing["Effective Radiative Forcing|HFC134a"] +
forcing["Effective Radiative Forcing|HFC143a"] +
forcing["Effective Radiative Forcing|HFC227ea"] +
forcing["Effective Radiative Forcing|HFC245fa"] +
forcing["Effective Radiative Forcing|HFC4310mee"] +
forcing["Effective Radiative Forcing|SF6"] +
forcing["Effective Radiative Forcing|CFC11"] +
forcing["Effective Radiative Forcing|CFC12"] +
forcing["Effective Radiative Forcing|CFC113"] +
forcing["Effective Radiative Forcing|CFC114"] +
forcing["Effective Radiative Forcing|CFC115"] +
forcing["Effective Radiative Forcing|CCl4"] +
forcing["Effective Radiative Forcing|CH3CCl3"] +
forcing["Effective Radiative Forcing|HCFC22"] +
forcing["Effective Radiative Forcing|HCFC141b"] +
forcing["Effective Radiative Forcing|HCFC142b"] +
forcing["Effective Radiative Forcing|Halon1211"] +
forcing["Effective Radiative Forcing|Halon1202"] +
forcing["Effective Radiative Forcing|Halon1301"] +
forcing["Effective Radiative Forcing|Halon2402"] +
forcing["Effective Radiative Forcing|CH3Br"] +
forcing["Effective Radiative Forcing|CH3Cl"],
forcing[
"Effective Radiative Forcing|Greenhouse Gases"], # should this be "well mixed" greenhouse gases?
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|CO2"] +
forcing["Effective Radiative Forcing|CH4"] +
forcing["Effective Radiative Forcing|N2O"] +
forcing["Effective Radiative Forcing|CF4"] +
forcing["Effective Radiative Forcing|C2F6"] +
forcing["Effective Radiative Forcing|C6F14"] +
forcing["Effective Radiative Forcing|HFC23"] +
forcing["Effective Radiative Forcing|HFC32"] +
forcing["Effective Radiative Forcing|HFC125"] +
forcing["Effective Radiative Forcing|HFC134a"] +
forcing["Effective Radiative Forcing|HFC143a"] +
forcing["Effective Radiative Forcing|HFC227ea"] +
forcing["Effective Radiative Forcing|HFC245fa"] +
forcing["Effective Radiative Forcing|HFC4310mee"] +
forcing["Effective Radiative Forcing|SF6"],
forcing["Effective Radiative Forcing|Kyoto Gases"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|CO2"] +
forcing["Effective Radiative Forcing|CH4"] +
forcing["Effective Radiative Forcing|N2O"],
forcing["Effective Radiative Forcing|CO2, CH4 and N2O"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|CF4"] +
forcing["Effective Radiative Forcing|C2F6"] +
forcing["Effective Radiative Forcing|C6F14"] +
forcing["Effective Radiative Forcing|HFC23"] +
forcing["Effective Radiative Forcing|HFC32"] +
forcing["Effective Radiative Forcing|HFC125"] +
forcing["Effective Radiative Forcing|HFC134a"] +
forcing["Effective Radiative Forcing|HFC143a"] +
forcing["Effective Radiative Forcing|HFC227ea"] +
forcing["Effective Radiative Forcing|HFC245fa"] +
forcing["Effective Radiative Forcing|HFC4310mee"] +
forcing["Effective Radiative Forcing|SF6"],
forcing["Effective Radiative Forcing|F-Gases"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|CFC11"] +
forcing["Effective Radiative Forcing|CFC12"] +
forcing["Effective Radiative Forcing|CFC113"] +
forcing["Effective Radiative Forcing|CFC114"] +
forcing["Effective Radiative Forcing|CFC115"] +
forcing["Effective Radiative Forcing|CCl4"] +
forcing["Effective Radiative Forcing|CH3CCl3"] +
forcing["Effective Radiative Forcing|HCFC22"] +
forcing["Effective Radiative Forcing|HCFC141b"] +
forcing["Effective Radiative Forcing|HCFC142b"] +
forcing["Effective Radiative Forcing|Halon1211"] +
forcing["Effective Radiative Forcing|Halon1202"] +
forcing["Effective Radiative Forcing|Halon1301"] +
forcing["Effective Radiative Forcing|Halon2402"] +
forcing["Effective Radiative Forcing|CH3Br"] +
forcing["Effective Radiative Forcing|CH3Cl"],
forcing["Effective Radiative Forcing|Montreal Protocol Halogen Gases"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|Tropospheric Ozone"] +
forcing["Effective Radiative Forcing|Stratospheric Ozone"],
forcing["Effective Radiative Forcing|Ozone"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|Aerosols|Direct Effect|SOx"] +
forcing[
"Effective Radiative Forcing|Aerosols|Direct Effect|Secondary Organic Aerosol"]
+
forcing["Effective Radiative Forcing|Aerosols|Direct Effect|Nitrate"] +
forcing["Effective Radiative Forcing|Aerosols|Direct Effect|BC"] +
forcing["Effective Radiative Forcing|Aerosols|Direct Effect|OC"],
forcing["Effective Radiative Forcing|Aerosols|Direct Effect"],
)
# If up to here is fine, then we only need to check previouly defined aggregates against "super-aggregates"
npt.assert_allclose(
forcing["Effective Radiative Forcing|Aerosols|Direct Effect"] +
forcing["Effective Radiative Forcing|Aerosols|Indirect Effect"],
forcing["Effective Radiative Forcing|Aerosols"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|Greenhouse Gases"] +
forcing["Effective Radiative Forcing|Ozone"] +
forcing["Effective Radiative Forcing|CH4 Oxidation Stratospheric H2O"]
+ forcing["Effective Radiative Forcing|Contrails"] +
forcing["Effective Radiative Forcing|Aerosols"] +
forcing["Effective Radiative Forcing|Black Carbon on Snow"] +
forcing["Effective Radiative Forcing|Land-use Change"],
forcing["Effective Radiative Forcing|Anthropogenic"],
)
npt.assert_allclose(
forcing["Effective Radiative Forcing|Anthropogenic"] +
forcing["Effective Radiative Forcing|Volcanic"] +
forcing["Effective Radiative Forcing|Solar"],
forcing["Effective Radiative Forcing"],
)
def test_startyear(test_scenarios, test_scenarios_2600):
# we can't run different start years in the same ensemble as output files will differ in shape.
# There is a separate test to ensure this does raise an error.
res_1850 = run(
climate_models_cfgs={"FaIR": [{
"startyear": 1850
}]},
scenarios=test_scenarios.filter(scenario=["ssp245"]),
output_variables=("Surface Air Temperature Change", ),
out_config=None,
)
res_1750 = run(
climate_models_cfgs={"FaIR": [{
"startyear": 1750
}]},
scenarios=test_scenarios.filter(scenario=["ssp245"]),
output_variables=("Surface Air Temperature Change", ),
out_config=None,
)
res_default = run(
climate_models_cfgs={"FaIR": [{}]},
scenarios=test_scenarios.filter(scenario=["ssp245"]),
output_variables=("Surface Air Temperature Change", ),
out_config=None,
)
gsat2100_start1850 = res_1850.filter(
variable="Surface Air Temperature Change",
region="World",
year=2100,
).values
gsat2100_start1750 = res_1750.filter(
variable="Surface Air Temperature Change",
region="World",
year=2100,
).values
gsat2100_startdefault = res_default.filter(
variable="Surface Air Temperature Change",
region="World",
year=2100,
).values
assert gsat2100_start1850 != gsat2100_start1750
assert gsat2100_start1750 == gsat2100_startdefault
with pytest.raises(ValueError):
run(
climate_models_cfgs={"FaIR": [{
"startyear": 1650
}]},
scenarios=test_scenarios.filter(scenario=["ssp245"]),
output_variables=("Surface Air Temperature Change", ),
out_config=None,
)
with pytest.raises(ValueError):
run(
climate_models_cfgs={"FaIR": [{}]},
scenarios=test_scenarios_2600.filter(scenario=["ssp245"]),
output_variables=("Surface Air Temperature Change", ),
out_config=None,
)
with pytest.raises(ValueError):
run(
climate_models_cfgs={
"FaIR": [{
"startyear": 1750
}, {
"startyear": 1850
}]
},
scenarios=test_scenarios.filter(scenario=["ssp245"]),
output_variables=("Surface Air Temperature Change", ),
out_config=None,
)
def test_run(
self,
test_scenarios,
monkeypatch,
nworkers,
test_data_dir,
update_expected_values,
):
expected_output_file = os.path.join(
test_data_dir,
"expected-integration-output",
"expected_fair1X_test_run_output.json",
)
monkeypatch.setenv("FAIR_WORKER_NUMBER", "{}".format(nworkers))
res = run(
climate_models_cfgs={
"FaIR": [
{},
{
"q": np.array([0.3, 0.45]),
"r0": 30.0,
"lambda_global": 0.9
},
{
"q": np.array([0.35, 0.4]),
"r0": 25.0,
"lambda_global": 1.1
},
],
},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Air Temperature Change",
"Atmospheric Concentrations|CO2",
"Heat Content",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"CO2 Air to Land Flux", # should be ignored
),
out_config=None,
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 0
assert res["run_id"].max() == 8
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "FaIRv{}".format(
FAIR.get_version())
assert set(res.get_unique_meta("variable")) == set([
"Surface Air Temperature Change",
"Atmospheric Concentrations|CO2",
"Heat Content",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
])
# check we can also calcluate quantiles
assert "run_id" in res.meta
quantiles = calculate_quantiles(res,
[0, 0.05, 0.17, 0.5, 0.83, 0.95, 1])
assert "run_id" not in quantiles.meta
self._check_output(res, expected_output_file, update_expected_values)
def test_multimodel_run(test_scenarios, magicc7_is_available):
debug_run = False
res = run(
climate_models_cfgs={
"FaIR": [
{},
{
"q": np.array([0.3, 0.45]),
"r0": 30.0,
"lambda_global": 0.9
},
{
"q": np.array([0.35, 0.4]),
"r0": 25.0,
"lambda_global": 1.1
},
],
"MAGICC7": [
{
"core_climatesensitivity":
3,
"rf_soxi_dir_wm2":
-0.2,
"out_temperature":
1,
"out_forcing":
1,
"out_dynamic_vars": [
"DAT_CO2_CONC",
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_HEATUPTK_AGGREG",
],
"out_ascii_binary":
"BINARY",
"out_binary_format":
2,
},
{
"core_climatesensitivity":
2,
"rf_soxi_dir_wm2":
-0.1,
"out_temperature":
1,
"out_forcing":
1,
"out_dynamic_vars": [
"DAT_CO2_CONC",
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_HEATUPTK_AGGREG",
],
"out_ascii_binary":
"BINARY",
"out_binary_format":
2,
},
{
"core_climatesensitivity":
5,
"rf_soxi_dir_wm2":
-0.35,
"out_temperature":
1,
"out_forcing":
1,
"out_dynamic_vars": [
"DAT_CO2_CONC",
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_HEATUPTK_AGGREG",
],
"out_ascii_binary":
"BINARY",
"out_binary_format":
2,
},
],
},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Temperature",
"Atmospheric Concentrations|CO2",
"Heat Content|Ocean",
"Heat Uptake|Ocean",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
),
out_config=None,
)
assert isinstance(res, ScmRun)
climate_models = res.get_unique_meta("climate_model")
assert any(["MAGICC" in m for m in climate_models])
assert any(["FaIR" in m for m in climate_models])
for climate_model in climate_models:
res_cm = res.filter(climate_model=climate_model)
assert set(res_cm.get_unique_meta("variable")) == set([
"Surface Temperature",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"Heat Content|Ocean",
"Heat Uptake|Ocean",
"Atmospheric Concentrations|CO2",
])
quantiles = calculate_quantiles(res, [0.05, 0.17, 0.5, 0.83, 0.95])
_check_res(
1.3603486,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.05,
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
2.61950384,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.95,
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
2.99063777,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.05,
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
5.34821243,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.95,
).values,
not debug_run,
rtol=RTOL,
)
quantiles_cm = calculate_quantiles(
res,
[0.05, 0.17, 0.5, 0.83, 0.95],
process_over_columns=("run_id", "ensemble_member"),
)
_check_res(
1.27586919,
quantiles_cm.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.05,
climate_model="MAGICC*",
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
5.34663565,
quantiles_cm.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.95,
climate_model="MAGICC*",
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
1.80924238,
quantiles_cm.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.05,
climate_model="FaIR*",
).values,
not debug_run,
rtol=RTOL,
)
_check_res(
4.76399179,
quantiles_cm.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.95,
climate_model="FaIR*",
).values,
not debug_run,
rtol=RTOL,
)
if debug_run:
assert False, "Turn off debug"
def test_run(
self,
test_scenarios,
test_data_dir,
update_expected_values,
):
expected_output_file = os.path.join(
test_data_dir,
"expected-integration-output",
"expected_magicc7_test_run_output.json",
)
res = run(
climate_models_cfgs={
"MAGICC7": [
{
"core_climatesensitivity":
3,
"rf_soxi_dir_wm2":
-0.2,
"out_temperature":
1,
"out_forcing":
1,
"out_dynamic_vars": [
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_CO2_AIR2LAND_FLUX",
],
"out_ascii_binary":
"BINARY",
"out_binary_format":
2,
},
{
"core_climatesensitivity": 2,
"rf_soxi_dir_wm2": -0.1,
"out_temperature": 1,
"out_forcing": 1,
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
{
"core_climatesensitivity": 5,
"rf_soxi_dir_wm2": -0.35,
"out_temperature": 1,
"out_forcing": 1,
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
],
},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Air Temperature Change",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"Heat Content|Ocean",
"Net Atmosphere to Land Flux|CO2",
),
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 0
assert res["run_id"].max() == 8
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "MAGICC{}".format(
MAGICC7.get_version())
assert set(res.get_unique_meta("variable")) == set([
"Surface Air Temperature Change",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"Heat Content|Ocean",
"Net Atmosphere to Land Flux|CO2",
])
# check we can also calcluate quantiles
assert "run_id" in res.meta
quantiles = calculate_quantiles(res,
[0, 0.05, 0.17, 0.5, 0.83, 0.95, 1])
assert "run_id" not in quantiles.meta
self._check_output(res, expected_output_file, update_expected_values)
def test_multimodel_run(test_scenarios, test_data_dir, update_expected_values):
expected_output_file = os.path.join(
test_data_dir,
"expected-integration-output",
"expected_run_multimodel_output.json",
)
res = run(
climate_models_cfgs={
"FaIR": [
{},
{"q": np.array([0.3, 0.45]), "r0": 30.0, "lambda_global": 0.9},
{"q": np.array([0.35, 0.4]), "r0": 25.0, "lambda_global": 1.1},
],
"MAGICC7": [
{
"core_climatesensitivity": 3,
"rf_soxi_dir_wm2": -0.2,
"out_temperature": 1,
"out_forcing": 1,
"out_dynamic_vars": [
"DAT_CO2_CONC",
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_HEATUPTK_AGGREG",
],
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
{
"core_climatesensitivity": 2,
"rf_soxi_dir_wm2": -0.1,
"out_temperature": 1,
"out_forcing": 1,
"out_dynamic_vars": [
"DAT_CO2_CONC",
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_HEATUPTK_AGGREG",
],
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
{
"core_climatesensitivity": 5,
"rf_soxi_dir_wm2": -0.35,
"out_temperature": 1,
"out_forcing": 1,
"out_dynamic_vars": [
"DAT_CO2_CONC",
"DAT_AEROSOL_ERF",
"DAT_HEATCONTENT_AGGREG_TOTAL",
"DAT_HEATUPTK_AGGREG",
],
"out_ascii_binary": "BINARY",
"out_binary_format": 2,
},
],
},
scenarios=test_scenarios.filter(scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Air Temperature Change",
"Atmospheric Concentrations|CO2",
"Heat Content|Ocean",
"Heat Uptake|Ocean",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
),
out_config=None,
)
assert isinstance(res, ScmRun)
climate_models = res.get_unique_meta("climate_model")
assert any(["MAGICC" in m for m in climate_models])
assert any(["FaIR" in m for m in climate_models])
for climate_model in climate_models:
res_cm = res.filter(climate_model=climate_model)
assert set(res_cm.get_unique_meta("variable")) == set(
[
"Surface Air Temperature Change",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"Heat Content|Ocean",
"Heat Uptake|Ocean",
"Atmospheric Concentrations|CO2",
]
)
# check we can also calcluate quantiles
assert "run_id" in res.meta
quantiles = calculate_quantiles(res, [0, 0.05, 0.17, 0.5, 0.83, 0.95, 1])
assert "run_id" not in quantiles.meta
openscm_runner.testing._check_output(
res, expected_output_file, rtol=RTOL, update=update_expected_values
)
def test_fair_run(test_scenarios):
res = run(
climate_models_cfgs={
"FaIR": [
{},
{
"q": np.array([0.3, 0.45]),
"r0": 30.0,
"lambda_global": 0.9
},
{
"q": np.array([0.35, 0.4]),
"r0": 25.0,
"lambda_global": 1.1
},
],
},
scenarios=test_scenarios.filter(
scenario=["ssp126", "ssp245", "ssp370"]),
output_variables=(
"Surface Temperature",
"Atmospheric Concentrations|CO2",
"Heat Content",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
"CO2 Air to Land Flux", # should be ignored
),
out_config=None,
)
assert isinstance(res, ScmRun)
assert res["run_id"].min() == 0
assert res["run_id"].max() == 8
assert res.get_unique_meta("climate_model",
no_duplicates=True) == "FaIRv{}".format(
FAIR.get_version())
assert set(res.get_unique_meta("variable")) == set([
"Surface Temperature",
"Atmospheric Concentrations|CO2",
"Heat Content",
"Effective Radiative Forcing",
"Effective Radiative Forcing|Aerosols",
"Effective Radiative Forcing|CO2",
])
npt.assert_allclose(
2.2099132544445927,
res.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
).values.max(),
rtol=RTOL,
)
npt.assert_allclose(
1.7945341435607594,
res.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
).values.min(),
rtol=RTOL,
)
npt.assert_allclose(
4.824878345585957,
res.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
).values.max(),
rtol=RTOL,
)
npt.assert_allclose(
4.07184261082167,
res.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
).values.min(),
rtol=RTOL,
)
# check we can also calcluate quantiles
quantiles = calculate_quantiles(res, [0.05, 0.17, 0.5, 0.83, 0.95])
npt.assert_allclose(
1.80924238,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.05,
).values,
rtol=RTOL,
)
npt.assert_allclose(
2.18308358,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp126",
quantile=0.95,
).values,
rtol=RTOL,
)
npt.assert_allclose(
4.08625963,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.05,
).values,
rtol=RTOL,
)
npt.assert_allclose(
4.76399179,
quantiles.filter(
variable="Surface Temperature",
region="World",
year=2100,
scenario="ssp370",
quantile=0.95,
).values,
rtol=RTOL,
)
