MimiFAIRv1_6_2.jl

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

Preparing the Software Environment

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

Running the Model

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).

Running a Monte Carlo Simulation

Overview

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.

Function Details

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