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Merge pull request #135 from OMS-NetZero/docs-cal-1.2
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Docs cal 1.2
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@chrisroadmap
chrisroadmap authored Oct 3, 2023
2 parents 31ed7a9 + 8fec915 commit 03aac4f
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89 changes: 40 additions & 49 deletions docs/examples/calibrated_constrained_ensemble.rst
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Expand Up @@ -14,7 +14,7 @@ IPCC Sixth Assessement Report.

**Note**: if you are reading this tutorial online and want to reproduce
the results, you will need one additional file. Grab this from
https://github.com/OMS-NetZero/FAIR/blob/master/examples/data/species_configs_properties_calibration1.1.0.csv.
https://github.com/OMS-NetZero/FAIR/blob/master/examples/data/species_configs_properties_calibration1.2.0.csv.
In Step 5 below, this is read in from the ``data/`` directory relative
to here. This does not apply if you are running this notebook from
Binder or have cloned it from GitHub - it should run out of the box.
Expand All @@ -33,15 +33,15 @@ DOI link below.

A two-step constraining process is produced. The first step ensures that
historical simulations match observed climate change to a
root-mean-square error of less than 0.16°C. The second step
root-mean-square error of less than 0.17°C. The second step
simultaneously distribution-fits to the following assessed ranges:

- equilibrium climate sensitivity (ECS), very likely range 2-5°C, best
estimate 3°C
- transient climate response (TCR), very likely range 1.2-2.4°C, best
estimate 1.8°C
- global mean surface temperature change 1850-1900 to 1995-2014, very
likely range 0.67-0.98°C, best estimate 0.85°C
- global mean surface temperature change 1850-1900 to 2003-2022, very
likely range 0.87-1.13°C, best estimate 1.03°C
- effective radiative forcing from aerosol-radiation interactions 1750
to 2005-2014, very likely range -0.6 to 0 W/m², best estimate -0.3
W/m²
Expand All @@ -50,13 +50,10 @@ simultaneously distribution-fits to the following assessed ranges:
W/m²
- effective radiative forcing from aerosols 1750 to 2005-2014, very
likely range -2.0 to -0.6 W/m², best estimate -1.3 W/m²
- ocean heat content change 1971 to 2018, likely range 329-463 ZJ, best
estimate 396 ZJ
- CO2 concentrations in 2014, very likely range 396.95-398.15 ppm, best
estimate 397.55 ppm
- future warming in SSP245 1995-2014 to 2081-2100, 1.24-2.59°C, best
estimate 1.81°C. Note the IPCC constraint was concentration driven,
in fair-calibrate v1.1.0 an emissions-driven constraint was used.
- earth energy uptake change 1971 to 2020, very likely range 358-573
ZJ, best estimate 465 ZJ
- CO2 concentrations in 2014, very likely range 416.2-417.8 ppm, best
estimate 417.0 ppm

1001 posterior ensemble members are produced from an initial prior of
1.5 million.
Expand Down Expand Up @@ -163,14 +160,14 @@ constraining code.

.. code:: ipython3
fair_params_1_1_0_obj = pooch.retrieve(
url = 'https://zenodo.org/record/7694879/files/calibrated_constrained_parameters.csv',
known_hash = 'md5:9f236c43dd18a36b7b63b94e05f3caab',
fair_params_1_2_0_obj = pooch.retrieve(
url = 'https://zenodo.org/record/8399112/files/calibrated_constrained_parameters.csv',
known_hash = 'md5:de3b83432b9d071efdd1427ad31e9076',
)
.. code:: ipython3
df_configs = pd.read_csv(fair_params_1_1_0_obj, index_col=0)
df_configs = pd.read_csv(fair_params_1_2_0_obj, index_col=0)
configs = df_configs.index # this is used as a label for the "config" axis
f.define_configs(configs)
Expand Down Expand Up @@ -205,7 +202,7 @@ are.

.. code:: ipython3
species, properties = read_properties(filename='data/species_configs_properties_calibration1.1.0.csv')
species, properties = read_properties(filename='data/species_configs_properties_calibration1.2.0.csv')
f.define_species(species, properties)
6. Modify run options
Expand Down Expand Up @@ -326,8 +323,8 @@ https://github.com/OMS-NetZero/FAIR/issues/126).
.. code:: ipython3
volcanic_obj = pooch.retrieve(
url = 'https://raw.githubusercontent.com/chrisroadmap/fair-add-hfc/main/data/volcanic_ERF_monthly_174901-201912.csv',
known_hash = 'md5:d3ac469ee7d2c2c75fbb656c2c67c4aa',
url = 'https://raw.githubusercontent.com/chrisroadmap/fair-calibrate/main/data/forcing/volcanic_ERF_1750-2101_timebounds.csv',
known_hash = 'md5:c0801f80f70195eb9567dbd70359219d',
)
.. code:: ipython3
Expand All @@ -350,13 +347,7 @@ little bit of ninja skill with indexing is needed.
solar_forcing = np.zeros(551)
volcanic_forcing = np.zeros(551)
for i, year in enumerate(np.arange(1750, 2021)):
volcanic_forcing[i] = np.mean(
df_volcanic.loc[
((year - 1) <= df_volcanic["year"]) & (df_volcanic["year"] < year)
].erf
)
volcanic_forcing[271:281] = np.linspace(1, 0, 10) * volcanic_forcing[270]
volcanic_forcing[:352] = df_volcanic.erf.values
solar_forcing = df_solar["erf"].loc[1750:2300].values
trend_shape = np.ones(551)
Expand All @@ -369,13 +360,13 @@ the volcanic forcing time series, and the solar amplitude and trend:
fill(
f.forcing,
volcanic_forcing[:, None, None] * df_configs["scale Volcanic"].values.squeeze(),
volcanic_forcing[:, None, None] * df_configs["fscale_Volcanic"].values.squeeze(),
specie="Volcanic",
)
fill(
f.forcing,
solar_forcing[:, None, None] * df_configs["solar_amplitude"].values.squeeze()
+ trend_shape[:, None, None] * df_configs["solar_trend"].values.squeeze(),
solar_forcing[:, None, None] * df_configs["fscale_solar_amplitude"].values.squeeze()
+ trend_shape[:, None, None] * df_configs["fscale_solar_trend"].values.squeeze(),
specie="Solar",
)
Expand All @@ -391,19 +382,19 @@ dataset.

.. code:: ipython3
fill(f.climate_configs["ocean_heat_capacity"], df_configs.loc[:, "c1":"c3"].values)
fill(f.climate_configs["ocean_heat_capacity"], df_configs.loc[:, "clim_c1":"clim_c3"].values)
fill(
f.climate_configs["ocean_heat_transfer"],
df_configs.loc[:, "kappa1":"kappa3"].values,
df_configs.loc[:, "clim_kappa1":"clim_kappa3"].values,
)
fill(f.climate_configs["deep_ocean_efficacy"], df_configs["epsilon"].values.squeeze())
fill(f.climate_configs["gamma_autocorrelation"], df_configs["gamma"].values.squeeze())
fill(f.climate_configs["sigma_eta"], df_configs["sigma_eta"].values.squeeze())
fill(f.climate_configs["sigma_xi"], df_configs["sigma_xi"].values.squeeze())
fill(f.climate_configs["deep_ocean_efficacy"], df_configs["clim_epsilon"].values.squeeze())
fill(f.climate_configs["gamma_autocorrelation"], df_configs["clim_gamma"].values.squeeze())
fill(f.climate_configs["sigma_eta"], df_configs["clim_sigma_eta"].values.squeeze())
fill(f.climate_configs["sigma_xi"], df_configs["clim_sigma_xi"].values.squeeze())
fill(f.climate_configs["seed"], df_configs["seed"])
fill(f.climate_configs["stochastic_run"], True)
fill(f.climate_configs["use_seed"], True)
fill(f.climate_configs["forcing_4co2"], df_configs["F_4xCO2"])
fill(f.climate_configs["forcing_4co2"], df_configs["clim_F_4xCO2"])
8c. Fill in species_configs
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Expand All @@ -413,7 +404,7 @@ species/properties/configs file

.. code:: ipython3
f.fill_species_configs(filename='data/species_configs_properties_calibration1.1.0.csv')
f.fill_species_configs(filename='data/species_configs_properties_calibration1.2.0.csv')
Then, we overwrite the ``species_configs`` that are varies as part of
the probablistic sampling. This makes heavy use of the ``fill()``
Expand All @@ -422,16 +413,16 @@ convenience function.
.. code:: ipython3
# carbon cycle
fill(f.species_configs["iirf_0"], df_configs["r0"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_airborne"], df_configs["rA"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_uptake"], df_configs["rU"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_temperature"], df_configs["rT"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_0"], df_configs["cc_r0"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_airborne"], df_configs["cc_rA"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_uptake"], df_configs["cc_rU"].values.squeeze(), specie="CO2")
fill(f.species_configs["iirf_temperature"], df_configs["cc_rT"].values.squeeze(), specie="CO2")
# aerosol indirect
fill(f.species_configs["aci_scale"], df_configs["beta"].values.squeeze())
fill(f.species_configs["aci_shape"], df_configs["shape Sulfur"].values.squeeze(), specie="Sulfur")
fill(f.species_configs["aci_shape"], df_configs["shape BC"].values.squeeze(), specie="BC")
fill(f.species_configs["aci_shape"], df_configs["shape OC"].values.squeeze(), specie="OC")
fill(f.species_configs["aci_scale"], df_configs["aci_beta"].values.squeeze())
fill(f.species_configs["aci_shape"], df_configs["aci_shape_so2"].values.squeeze(), specie="Sulfur")
fill(f.species_configs["aci_shape"], df_configs["aci_shape_bc"].values.squeeze(), specie="BC")
fill(f.species_configs["aci_shape"], df_configs["aci_shape_oc"].values.squeeze(), specie="OC")
# aerosol direct
for specie in [
Expand All @@ -445,7 +436,7 @@ convenience function.
"VOC",
"Equivalent effective stratospheric chlorine"
]:
fill(f.species_configs["erfari_radiative_efficiency"], df_configs[f"ari {specie}"], specie=specie)
fill(f.species_configs["erfari_radiative_efficiency"], df_configs[f"ari_{specie}"], specie=specie)
# forcing scaling
for specie in [
Expand All @@ -457,7 +448,7 @@ convenience function.
"Light absorbing particles on snow and ice",
"Land use"
]:
fill(f.species_configs["forcing_scale"], df_configs[f"scale {specie}"].values.squeeze(), specie=specie)
fill(f.species_configs["forcing_scale"], df_configs[f"fscale_{specie}"].values.squeeze(), specie=specie)
# the halogenated gases all take the same scale factor
for specie in [
"CFC-11",
Expand Down Expand Up @@ -501,16 +492,16 @@ convenience function.
"HFC-365mfc",
"HFC-4310mee",
]:
fill(f.species_configs["forcing_scale"], df_configs["scale minorGHG"].values.squeeze(), specie=specie)
fill(f.species_configs["forcing_scale"], df_configs["fscale_minorGHG"].values.squeeze(), specie=specie)
# ozone
for specie in ["CH4", "N2O", "Equivalent effective stratospheric chlorine", "CO", "VOC", "NOx"]:
fill(f.species_configs["ozone_radiative_efficiency"], df_configs[f"o3 {specie}"], specie=specie)
fill(f.species_configs["ozone_radiative_efficiency"], df_configs[f"o3_{specie}"], specie=specie)
# initial value of CO2 concentration (but not baseline for forcing calculations)
fill(
f.species_configs["baseline_concentration"],
df_configs["co2_concentration_1750"].values.squeeze(),
df_configs["cc_co2_concentration_1750"].values.squeeze(),
specie="CO2"
)
Expand Down
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