Part 1: Engage (Anchoring Phenomenon)
Atmospheric CO₂ concentrations are rising due to human emissions, which disrupt the balance of the global carbon cycle and affect climate. The simulation models carbon pools and fluxes between atmosphere, biosphere, hydrosphere, and geosphere.
1. Observations and Questions:
- How do rising human emissions change atmospheric carbon over decades in the model?
- What role do large interventions (reforestation, net-zero transitions) play in the simulated budget?
Part 2: Explore (Simulation Investigation)
Open the Global Carbon Cycle Model simulation.
2. Data Collection and Modeling Tasks
- Using the human emissions slider and the Global Events buttons, run the following scenarios for 50 simulated years (use Play Time):
- Baseline: keep human emissions at the default value.
- High emissions: increase human emissions to 2x default.
- Mitigation: enact Net Zero Transition and reforestation separately and together.
- For each scenario record (annual or end-of-run): Atmosphere (GtC), Biosphere (GtC), Hydrosphere (GtC), Geosphere (GtC), and Total Mass (mass conservation check).
- Compute approximate annual fluxes implied by the changes in pool sizes (Δpool / years).
Data table: | Scenario | Year | Atmosphere (GtC) | Biosphere (GtC) | Hydrosphere (GtC) | Geosphere (GtC) | Total Mass (GtC) | Annual Flux (GtC/yr) | |—|—:|—:|—:|—:|—:|—:|—:|
Part 3: Explain (Sensemaking)
3. Model Interpretation
- Explain how feedbacks (e.g., carbon fertilization) and saturation of sinks are represented in the model outputs.
- Discuss which fluxes drive major changes in atmospheric carbon for each scenario and why.
Part 4: Elaborate/Evaluate (Design & Argumentation)
4. Policy Design Challenge
- Your community must reach an atmospheric carbon target (for example, a 50% reduction relative to baseline in 30 years). Design a combined mitigation strategy (emission reductions, reforestation timeline) and test it in the model. Present a rationale and quantify expected changes.
- Discuss model limitations and which assumptions would need real-world evidence to validate your recommendation.