How to run the FAIR climate model:

Run in Google Colab

(Based on this example.)

Create a new Colab notebook, and install the FAIR python library.

!pip install fair

Import the necessary libraries for numerical operations, visualization, and data handling.

import numpy as np
import matplotlib.pyplot as pl
import pandas as pd

from fair import FAIR
from fair.io import read_properties
from fair.interface import fill, initialise
from fair.earth_params import seconds_per_year

Create an instance of the FAIR model.

f = FAIR(ch4_method='thornhill2021')

Dfine the model’s time range from 1750 to 2100 with a yearly step.

f.define_time(1750, 2100, 1)

Specify Shared Socioeconomic Pathway (SSP) scenarios for different emission trajectories.

scenarios = ['ssp119', 'ssp126', 'ssp245', 'ssp370', 'ssp434', 'ssp460', 'ssp534-over', 'ssp585']
f.define_scenarios(scenarios)

Read external CSV data containing climate model configurations.

df = pd.read_csv('https://raw.githubusercontent.com/OMS-NetZero/FAIR/refs/heads/master/tests/test_data/4xCO2_cummins_ebm3.csv')
models = df['model'].unique()
configs = []

# Generate unique configuration names
for imodel, model in enumerate(models):
    for run in df.loc[df['model'] == model, 'run']:
        configs.append(f"{model}_{run}")
f.define_configs(configs)

Load greenhouse gas species and their characteristics.

species, properties = read_properties()
f.define_species(species, properties)

Prepare internal data structures for the simulation.

f.allocate()

Define baseline emissions and lifetimes for methane (CH4) and nitrous oxide (N2O).

f.fill_species_configs()
fill(f.species_configs['unperturbed_lifetime'], 10.8537568, specie='CH4')
fill(f.species_configs['baseline_emissions'], 19.01978312, specie='CH4')
fill(f.species_configs['baseline_emissions'], 0.08602230754, specie='N2O')

Retrieve and preprocess volcanic radiative forcing data.

df_volcanic = pd.read_csv('https://raw.githubusercontent.com/OMS-NetZero/FAIR/refs/heads/master/tests/test_data/volcanic_ERF_monthly_175001-201912.csv', index_col='year')
f.fill_from_rcmip()

# Overwrite volcanic forcing using averaged yearly values
volcanic_forcing = np.zeros(351)
volcanic_forcing[:271] = df_volcanic[1749:].groupby(np.ceil(df_volcanic[1749:].index) // 1).mean().squeeze().values
fill(f.forcing, volcanic_forcing[:, None, None], specie="Volcanic")

Set initial values for greenhouse gas concentrations, forcing, temperature, and emissions.

initialise(f.concentration, f.species_configs['baseline_concentration'])
initialise(f.forcing, 0)
initialise(f.temperature, 0)
initialise(f.cumulative_emissions, 0)
initialise(f.airborne_emissions, 0)

Assign ocean heat capacity, heat transfer, and stochastic parameters to the model for each configuration.

df = pd.read_csv("https://raw.githubusercontent.com/OMS-NetZero/FAIR/refs/heads/master/tests/test_data/4xCO2_cummins_ebm3.csv")
models = df['model'].unique()

seed = 1355763  # Set an initial random seed

for config in configs:
    model, run = config.split('_')
    condition = (df['model'] == model) & (df['run'] == run)

    # Assign climate configuration parameters
    fill(f.climate_configs['ocean_heat_capacity'], df.loc[condition, 'C1':'C3'].values.squeeze(), config=config)
    fill(f.climate_configs['ocean_heat_transfer'], df.loc[condition, 'kappa1':'kappa3'].values.squeeze(), config=config)
    fill(f.climate_configs['deep_ocean_efficacy'], df.loc[condition, 'epsilon'].values[0], config=config)
    fill(f.climate_configs['gamma_autocorrelation'], df.loc[condition, 'gamma'].values[0], config=config)
    fill(f.climate_configs['sigma_eta'], df.loc[condition, 'sigma_eta'].values[0], config=config)
    fill(f.climate_configs['sigma_xi'], df.loc[condition, 'sigma_xi'].values[0], config=config)
    fill(f.climate_configs['stochastic_run'], True, config=config)
    fill(f.climate_configs['use_seed'], True, config=config)
    fill(f.climate_configs['seed'], seed, config=config)

    seed += 399  # Update the seed for the next configuration

Execute the model to simulate future climate projections.

f.run()

Visualize the temperature anomaly over time for the ssp119 scenario.

pl.plot(f.timebounds, f.temperature.loc[dict(scenario='ssp119', layer=0)], label=f.configs)
pl.title('ssp119: temperature')
pl.xlabel('year')
pl.ylabel('Temperature anomaly (K)')

Video

(Coming soon.)