MimiFAIRv1_6_2.jl

This is a work-in-progress repository for a Julia-Mimi implementation of the FAIRv1_6_2 simple climate model.

To add the package to your current environment, run the following command at the julia package REPL:

pkg> add https://github.com/FrankErrickson/MimiFAIRv1_6_2.jl.git

You probably also want to install the Mimi package into your julia environment, so that you can use some of the tools in there:

pkg> add Mimi

The model uses the Mimi framework and it is highly recommended to read the Mimi documentation first to understand the code structure. The basic way to access a copy of the default MimiFAIRv2 model and explore the resuts is the following:

using Mimi 
using MimiFAIRv1_6_2

# Create an instance of MimiFAIRv1_6_2.
m = MimiFAIRv1_6_2.get_model() 

# Run the model.
run(m)

# Access the temperature output.
fair_temps = m[:temperature, :T]

# Explore interactive plots of all the model output.
explore(m)

The get_model() function currently has the following keyword arguments:

• ar6_scenario: One of the RCMIP scenarios from the original FAIRv2.0 paper. Current options include "ssp119", "ssp126", "ssp245", "ssp370", and"ssp585". The default is "ssp245".
• start_year: The model has an option to be initialized at different time periods, however this is only currently set up to start in 1750.
• end_year: The model can be run out to 2500 (the default final year).

See mcs/AR6_Monte_Carlo.jl for the script and details on running a Monte Carlo Simulation, and don't hesitate to contact the developers with any questions, or post an Issue on Github.

This file contains functions to run a Monte Carlo with the AR6 implementation of MimiFAIRv1_6_2 using the constrained parameters from AR6. The primary function, run_mcs, loads the constrained parameter samples and then performs the analysis.

Note that the original parameter samples are stored in model_data/fair-1.6.2-wg3-params.json and then are parsed into mcs_params using the function parse_mcs_params in mcs/utils.jl. This does not need to be repeated, but can be useful for replication and understanding.

The run_mcs function is the primary user-facing function provided for the monte carlo simulation and has the signature and function arguments as follows:

    run_mcs(;trials::Int64 = 2237, 
        output_dir::Union{String, Nothing} = nothing, 
        save_trials::Bool = false,
        m::Mimi.Model = get_model())

This function returns the results of a Monte Carlo Simulation with the defined number of trials and save data into the output_dir folder, optionally also saving trials if save_trials is set to true. If no model is provided, use the default model returned by get_model(). If an output_dir is not provided, data will be saved to the output folder in this repository in a subfolder named based on the Date, Time, and number of trials.

Call this function as follows:

results = MimiFAIRv1_6_2.run_mcs(trials = 100, output_dir = path, save_trials = true)
explore(results)
Mimi.plot(results, :temperature, :T; interactive = true)

will run a Monte Carlo simulation with 100 trials, and return a Mimi.SimulationInstance object that can be explored with a UI (note this is fairly slow at the moment it is under improvement), or display a particular plot for an output variable. Output variable data and trials data will be saved in path, or if this isn't provided in the output folder in this repository in a subfolder named based on the Date, Time, and number of trials.

The output variables, currently temperature and co2, will be saved to the output_directory as will all trials values in trials.csv. Adding more variables to output is a matter of augmenting the following section of code. Feel free to contact the authors with requests on more outputs, or open a PR doing so yourself.

# define the Monte Carlo Simulation
mcs = @defsim begin
    save(temperature.T, co2_cycle.co2)
end