Running scenariosΒΆ
Here we show how multiple scenarios can be run using the OpenSCM Two Layer Model package.
[1]:
# NBVAL_IGNORE_OUTPUT
import os.path
import numpy as np
import pandas as pd
from openscm_units import unit_registry as ur
import tqdm.autonotebook as tqdman
from scmdata import ScmRun, run_append
from openscm_twolayermodel import TwoLayerModel
import matplotlib.pyplot as plt
/home/docs/checkouts/readthedocs.org/user_builds/openscm-two-layer-model/envs/stable/lib/python3.7/site-packages/ipykernel_launcher.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
import sys
For this we use RCMIP effective radiative forcing data.
[2]:
DATA_PATH = os.path.join(
"..",
"..",
"..",
"tests",
"test-data",
"rcmip-radiative-forcing-annual-means-v4-0-0.csv",
)
DATA_PATH
[2]:
'../../../tests/test-data/rcmip-radiative-forcing-annual-means-v4-0-0.csv'
[3]:
# NBVAL_IGNORE_OUTPUT
scenarios = ScmRun(DATA_PATH, lowercase_cols=True).filter(
scenario="historical", keep=False
)
scenarios
[3]:
<scmdata.ScmRun (timeseries: 480, timepoints: 751)>
Time:
Start: 1750-01-01T00:00:00
End: 2500-01-01T00:00:00
Meta:
activity_id mip_era model region scenario unit \
0 not_applicable CMIP5 AIM World rcp60 W/m^2
1 not_applicable CMIP5 AIM World rcp60 W/m^2
2 not_applicable CMIP5 AIM World rcp60 W/m^2
3 not_applicable CMIP5 AIM World rcp60 W/m^2
4 not_applicable CMIP5 AIM World rcp60 W/m^2
.. ... ... ... ... ... ...
494 not_applicable CMIP5 unspecified World historical-cmip5 W/m^2
495 not_applicable CMIP5 unspecified World historical-cmip5 W/m^2
496 not_applicable CMIP5 unspecified World historical-cmip5 W/m^2
497 not_applicable CMIP5 unspecified World historical-cmip5 W/m^2
498 not_applicable CMIP5 unspecified World historical-cmip5 W/m^2
variable
0 Radiative Forcing
1 Radiative Forcing|Anthropogenic
2 Radiative Forcing|Anthropogenic|Aerosols
3 Radiative Forcing|Anthropogenic|Aerosols|Aeros...
4 Radiative Forcing|Anthropogenic|Aerosols|Aeros...
.. ...
494 Radiative Forcing|Anthropogenic|Stratospheric ...
495 Radiative Forcing|Anthropogenic|Tropospheric O...
496 Radiative Forcing|Natural
497 Radiative Forcing|Natural|Solar
498 Radiative Forcing|Natural|Volcanic
[480 rows x 7 columns]
We can then run them, for a number of parameter settings, as shown.
[4]:
# NBVAL_IGNORE_OUTPUT
a_values = np.array([0, 0.01]) * ur("W/m^2/delta_degC^2")
a_values
[4]:
| Magnitude | [0.0 0.01] |
|---|---|
| Units | watt/(delta_degree_Celsius2 meter2) |
[5]:
# NBVAL_IGNORE_OUTPUT
runner = TwoLayerModel()
output = []
for a in tqdman.tqdm(a_values, desc="Parameter settings"):
runner.a = a
output.append(runner.run_scenarios(scenarios))
output = run_append(output)
output
[5]:
<scmdata.ScmRun (timeseries: 80, timepoints: 751)>
Time:
Start: 1750-01-01T00:00:00
End: 2500-01-01T00:00:00
Meta:
a (watt / delta_degree_Celsius ** 2 / meter ** 2) activity_id \
0 0.00 not_applicable
1 0.00 not_applicable
2 0.00 not_applicable
3 0.00 not_applicable
4 0.00 not_applicable
.. ... ...
75 0.01 not_applicable
76 0.01 not_applicable
77 0.01 not_applicable
78 0.01 not_applicable
79 0.01 not_applicable
climate_model dl (meter) du (meter) efficacy (dimensionless) \
0 two_layer 1200 50 1.0
1 two_layer 1200 50 1.0
2 two_layer 1200 50 1.0
3 two_layer 1200 50 1.0
4 two_layer 1200 50 1.0
.. ... ... ... ...
75 two_layer 1200 50 1.0
76 two_layer 1200 50 1.0
77 two_layer 1200 50 1.0
78 two_layer 1200 50 1.0
79 two_layer 1200 50 1.0
eta (watt / delta_degree_Celsius / meter ** 2) \
0 0.8
1 0.8
2 0.8
3 0.8
4 0.8
.. ...
75 0.8
76 0.8
77 0.8
78 0.8
79 0.8
lambda0 (watt / delta_degree_Celsius / meter ** 2) mip_era model \
0 1.246667 CMIP6 AIM/CGE
1 1.246667 CMIP6 AIM/CGE
2 1.246667 CMIP6 AIM/CGE
3 1.246667 CMIP6 AIM/CGE
4 1.246667 CMIP6 AIM/CGE
.. ... ... ...
75 1.246667 CMIP6 REMIND-MAGPIE
76 1.246667 CMIP6 REMIND-MAGPIE
77 1.246667 CMIP6 REMIND-MAGPIE
78 1.246667 CMIP6 REMIND-MAGPIE
79 1.246667 CMIP6 REMIND-MAGPIE
region run_idx scenario unit \
0 World 0 ssp370 W/m^2
1 World 0 ssp370 delta_degC
2 World 0 ssp370 delta_degC
3 World 0 ssp370 W/m^2
4 World 1 ssp370-lowNTCF-aerchemmip W/m^2
.. ... ... ... ...
75 World 8 ssp534-over W/m^2
76 World 9 ssp585 W/m^2
77 World 9 ssp585 delta_degC
78 World 9 ssp585 delta_degC
79 World 9 ssp585 W/m^2
variable
0 Effective Radiative Forcing
1 Surface Temperature|Upper
2 Surface Temperature|Lower
3 Heat Uptake
4 Effective Radiative Forcing
.. ...
75 Heat Uptake
76 Effective Radiative Forcing
77 Surface Temperature|Upper
78 Surface Temperature|Lower
79 Heat Uptake
[80 rows x 15 columns]
[6]:
# NBVAL_IGNORE_OUTPUT
pkwargs = dict(
hue="scenario", style="a (watt / delta_degree_Celsius ** 2 / meter ** 2)",
)
fig = plt.figure(figsize=(12, 18))
ax = fig.add_subplot(211)
output.filter(variable="Surface Temperature|Upper").lineplot(**pkwargs, ax=ax)
ax = fig.add_subplot(212)
output.filter(variable="Heat Uptake").lineplot(**pkwargs, ax=ax)
[6]:
<AxesSubplot:xlabel='time', ylabel='W/m^2'>
