MIMI DICE2013 Climate Impact

We're using the MIMI framework to create a simple climate impact simulation.

We first install all the Julia & Python dependencies in a Nextjournal runtime.

This will be backed into a docker container, which can be used in other articles, or downloaded and run locally, with everything installed.

Project.toml
Manifest.toml
apt update
apt install python3 python3-venv python3-pip
pip3 install xlrd
19.9s
install (Bash in Julia)
→
cp(
Manifest.toml
, "Manifest.toml")
cp(
Project.toml
, "Project.toml")
ENV["PYTHON"] = "python3" # use system python
0.5s
install (Julia)
→
"Project.toml"
]registry add https://github.com/mimiframework/MimiRegistry.git
2.4s
install (Julia)
→
]activate .; instantiate
23.0s
install (Julia)
→

Now, lets create the simulation & interactive visualization in another runtime:

]activate .
12.2s
Julia
install
using Markdown
using Mimi, MimiDICE2013, DataFrames, AbstractPlotting, WGLMakie
using Observables, JSServe
using JSServe: Application, Slider, styled_slider
using JSServe.DOM
98.1s
Julia
install
function create_model()
  # create a dice 2013 model
  m = MimiDICE2013.get_model()
  # Use higher climate sensitivity value
  update_param!(m, :t2xco2, 5)
  run(m)
  return m
end
m_dice1 = create_model()
m_dice2 = create_model();
12.2s
Julia
install
# Add a better styled slider
function JSServe.styled_slider(slider, value)
    JSServe.rows(DOM.div(slider, class="w-64"),
        DOM.span(value, class="p-2"),
        class="p-2 items-center"
    )
end
function run_model(param)
    update_param!(m_dice2, :MIU, fill(param, 60))
    run(m_dice2)
    something.(m_dice2[:climatedynamics, :TATM], 0.0)
end
set_theme!(font = "Dejavu Sans", resolution = (500, 400))
function handler(session, request)
    # get the temperature forecasts:
    df_dice1 = dropmissing(getdataframe(m_dice1, :climatedynamics, :TATM))
    df_dice2 = dropmissing(getdataframe(m_dice2, :climatedynamics, :TATM))
    s = JSServe.Slider(range(0, stop=1, step=0.01))
    data = map(run_model, s)
    scene = scatter(df_dice1.time, df_dice1.TATM, markersize = 5)
    scatter!(df_dice2.time, data, color = :red, markersize = 5)
    scene[Axis].names.axisnames = ("Year", "Temperature Increase")
    scene[Axis].names.textsize = (8, 8)
    t = df_dice2.time
    year = JSServe.Slider(1:length(t))
    dmg_estimated = map(year, s) do year_idx, s
        dmg = round(m_dice2[:damages, :DAMAGES][year_idx], digits = 3)
        return "$(dmg) trillion USD"
    end
    year_sel = map(i-> t[i], year)
    b = DOM.font("● baseline", color = :black)
    ec = DOM.font("● with emission control", color = :red)
    styled_s = styled_slider(s, map(x-> round(x, digits = 3), s.value))
    styled_damage = styled_slider(year, DOM.div(dmg_estimated))
    dom = md"""
    # Explore Climate
    Set amount of emission control: $styled_s
    # Temperature Increase
    $b
    $ec
    $(scene)
    # Estimated damage in year $(year_sel)
    $(styled_damage)
    """
    dom = DOM.div(JSServe.MarkdownCSS, JSServe.TailwindCSS, JSServe.Styling, dom, class="w-2/3")
  
  # Record all states of the simulation, to have them 
  # available when the julia server is offline:
  return JSServe.record_state_map(session, dom).dom
end
JSServe.with_session(handler)
2.8s
Julia
install
Runtimes (2)