def climate_model(fossil_gtc_carbon):
data = scenarios['RCP26']['WORLD']
copy_row = data.loc[[2007]]
for index, row in data.iterrows():
if index > 2007:
data = data.drop(index)
for i in range(2008, 2101):
temp = copy.deepcopy(copy_row)
for index, row in temp.iterrows():
data.loc[i] = row.tolist()
break
# All future years have this amount of CO2
for i in range(2019, 2101):
data.at[i, "FossilCO2"] = float(fossil_gtc_carbon)
# Funnel into model and parse results
results = pymagicc.run(data)
results_df = results['SURFACE_TEMP']['GLOBAL']
ret_json = {}
ret_json['start_year'] = str(results_df.index[0])
ret_json['global_temps'] = list(results_df.values)
results_df = results['SURFACE_TEMP']['NH-LAND']
# Specific data used by the sea model
ret_json['NH_temps'] = list(results_df.values)
return ret_json
def diagnose_tcr_tcre(direction, **kwargs):
postcrtest = {
'file_co2_conc': '1PCTCO2_CO2_CONC.IN',
'testscen': '1pctCO2'
}
negtcrtest = {'file_co2_conc': onepctcdr, 'testscen': '1pctCO2-cdr'}
if 'pos' in direction:
selectedtst = postcrtest
elif 'neg' in direction:
selectedtst = negtcrtest
print('Calculating TCR & TCRE from {}.'.format(selectedtst['testscen']))
timeseries = pymagicc.run(scenario=None,
only=[
'Atmospheric Concentrations|CO2',
'INVERSEEMIS',
'Radiative Forcing',
'Surface Temperature',
],
**kwargs,
file_co2_conc=selectedtst['file_co2_conc'])
# drop all the irrelevant inverse emissions
timeseries = timeseries.filter(variable='Inverse Emissions*',
level=1,
keep=False)
timeseries['scenario'] = selectedtst['testscen']
tcr, tcre = get_tcr_tcre_from_diagnosis_results(timeseries)
return {'tcr': tcr, 'tcre': tcre, 'timeseries': timeseries}
def diagnose_ecs(direction, **kwargs):
posecstest = {
'file_co2_conc': 'ABRUPT2XCO2_CO2_CONC.IN',
'testscen': 'abrupt-2xCO2'
}
negecstest = {'file_co2_conc': abrupt0p5, 'testscen': 'abrupt-0p5xCO2'}
if 'pos' in direction:
selectedtst = posecstest
elif 'neg' in direction:
selectedtst = negecstest
print('Calculating ECS from {}.'.format(selectedtst['testscen']))
timeseries = pymagicc.run(scenario=None,
only=[
'Atmospheric Concentrations|CO2',
'INVERSEEMIS',
'Radiative Forcing',
'Surface Temperature',
],
**kwargs,
file_co2_conc=selectedtst['file_co2_conc'])
# drop all the irrelevant inverse emissions
timeseries = timeseries.filter(variable='Inverse Emissions*',
level=1,
keep=False)
timeseries['scenario'] = selectedtst['testscen']
ecs = get_ecs_from_diagnosis_results(timeseries)
return {'ecs': ecs, 'timeseries': timeseries}
def test_set_years():
results, conf = run(rcp26,
return_config=True,
startyear=1900,
endyear=2000)
assert conf["years"]["startyear"] == 1900
assert conf["years"]["endyear"] == 2000
assert results["SURFACE_TEMP"].GLOBAL.index[0] == 1900
assert results["SURFACE_TEMP"].GLOBAL.index[-1] == 2000
def test_run_rcp85(package):
if package.version == 7:
pytest.skip(magicc7_not_included_msg)
results = run(rcp85, magicc_version=package.version)
result_temp = (results.filter(variable="Surface Temperature",
year=2100,
region="World").timeseries().squeeze())
np.testing.assert_allclose(result_temp, 4.676012, rtol=1e-5)
do_basic_run_checks(results)
def test_run_rcp60(package):
if package.version == 7:
pytest.skip(MAGICC7_NOT_INCLUDED_MSG)
results = run(rcp60, magicc_version=package.version)
result_temp = (results.filter(variable="Surface Temperature",
year=2100,
region="World").timeseries().squeeze())
np.testing.assert_allclose(result_temp, 3.102484, rtol=1e-5)
do_basic_run_checks(results)
def test_run_rcp85():
results = run(rcp85)
surface_temp = pd.read_csv(os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"./original_data/RCP85/DAT_SURFACE_TEMP.OUT"),
delim_whitespace=True,
skiprows=19,
index_col=0)
assert surface_temp.GLOBAL.equals(results["SURFACE_TEMP"].GLOBAL)
def test_parameters(package):
results = run(
rcp26,
magicc_version=package.version,
out_parameters=True,
core_climatesensitivity=1.5,
)
assert results.metadata["parameters"]["allcfgs"][
"core_climatesensitivity"] == 1.5
# Test removal of newlines in PARAMETERS.out
assert "H\nFC134a" not in results.metadata["parameters"]["allcfgs"][
"fgas_names"]
def test_set_years(package):
results = run(
rcp26,
magicc_version=package.version,
out_parameters=True,
startyear=1900,
endyear=2000,
)
assert results.metadata["parameters"]["years"]["startyear"] == 1900
assert results.metadata["parameters"]["years"]["endyear"] == 2000
assert results["time"].min().year == 1900
assert results["time"].max().year == 2000
def test_default_config():
_, conf = run(rcp26, return_config=True)
assert conf["allcfgs"]["core_climatesensitivity"] == 3
assert conf["years"]["startyear"] == 1765
def test_parameters():
_, params = run(rcp26, return_config=True, core_climatesensitivity=1.5)
assert params['allcfgs']["core_climatesensitivity"] == 1.5
# Test removal of newlines in PARAMETERS.out
assert 'H\nFC134a' not in params['allcfgs']["fgas_names"]
def test_out_carboncycle():
out = run(rcp26, out_carboncycle=1)
assert "CARBONCYCLE" in out.keys()
import os
import matplotlib.pyplot as plt
import pymagicc
from pymagicc import scenarios
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 10, 5
plt.rcParams["font.family"] = "serif"
plt.rcParams["font.size"] = 12
path = os.path.join(os.path.dirname(__file__),
'./example-plot.png')
for name, scen in sorted(scenarios.items(), reverse=True):
results, params = pymagicc.run(scen, return_config=True)
temp = (results["SURFACE_TEMP"].GLOBAL.loc[1850:] -
results["SURFACE_TEMP"].GLOBAL.loc[1850:1900].mean())
temp.plot(label=name)
plt.legend()
plt.title("Global Mean Temperature Projection")
plt.ylabel(u"°C over pre-industrial (1850-1900 mean)")
plt.legend(loc="best")
plt.savefig(path, dpi=96)
def test_set_years():
results = run(rcp26, out_parameters=True, startyear=1900, endyear=2000)
assert results.metadata["parameters"]["years"]["startyear"] == 1900
assert results.metadata["parameters"]["years"]["endyear"] == 2000
assert results["time"].min().year == 1900
assert results["time"].max().year == 2000
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 10, 5
plt.rcParams["font.family"] = "serif"
plt.rcParams["font.size"] = 12
output_path = os.path.join(
os.path.dirname(__file__),
'./example-plot.png'
)
results = []
for scen in rcps.groupby("scenario"):
results_scen = pymagicc.run(scen)
results.append(results_scen)
results = scmdata.run_append(results)
temperature_rel_to_1850_1900 = (
results
.filter(variable="Surface Temperature", region="World")
.relative_to_ref_period_mean(year=range(1850, 1900 + 1))
)
temperature_rel_to_1850_1900.lineplot()
plt.title("Global Mean Temperature Projection")
plt.ylabel("°C over pre-industrial (1850-1900 mean)")
plt.savefig(output_path, dpi=96)
import os
import matplotlib.pyplot as plt
import pymagicc
from pymagicc import scenarios
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 10, 5
plt.rcParams["font.family"] = "serif"
plt.rcParams["font.size"] = 12
output_path = os.path.join(os.path.dirname(__file__), './example-plot.png')
for name, scen in scenarios.items():
results = pymagicc.run(scen)
results_df = results.df
results_df.set_index("time", inplace=True)
global_temp_time_rows = ((results_df.variable == "Surface Temperature")
& (results_df.region == "World"))
temp = (results_df.value[global_temp_time_rows].loc[1850:] -
results_df.value[global_temp_time_rows].loc[1850:1900].mean())
temp.plot(label=name)
plt.legend()
plt.title("Global Mean Temperature Projection")
plt.ylabel("°C over pre-industrial (1850-1900 mean)")
plt.legend(loc="best")
plt.savefig(output_path, dpi=96)
def test_default_config():
results = run(rcp26, out_parameters=True)
assert results.metadata["parameters"]["allcfgs"][
"core_climatesensitivity"] == 3
assert results.metadata["parameters"]["years"]["startyear"] == 1765
import pymagicc from pathlib import Path root = Path(__file__).parents[1] _, conf = pymagicc.run(pymagicc.rcp45, return_config=True) conf["allcfgs"]["rundate"] = "No date specified." conf.write(str(root / "pymagicc/default_config.nml"), force=True)
