IB Environmental systems and societies 1.2D Models Notes

A model is a simplified version of a system we can study more easily

1. Environmental systems are dynamic and non-linear, so we cannot experiment on them directly.
2. A model represents the system so we can visualise, analyse, communicate, and predict how it behaves.
   1. It shows the links between inputs, outputs, and storages without every real-world detail.
   2. It lets scientists test how a change in one variable affects the others.
   3. It communicates system behaviour clearly to policymakers and the public.

Types of Models

1. Diagrams show systems visually, such as food webs or nutrient cycles.
2. Mathematical models use equations to describe relationships, such as population growth.
3. Simulations are computer programs that mimic real processes, such as climate models.
4. Physical models are scaled versions of real systems, such as wind tunnels or aquariums.

Example

• A forest ecosystem model shows storages like biomass and soil nutrients alongside flows like photosynthesis, respiration, and decomposition.

Different models suit different environmental questions

Every model trades realism for usefulness

Strengths of Models

1. Simplification makes a complex system easier to work with.
2. Prediction lets us forecast effects of change, such as rising CO₂ levels.
3. Transferability means insights from one model can apply to similar systems.
4. Visualisation makes abstract ideas like nutrient cycling tangible.

Limitations of Models

1. Oversimplification means a model cannot capture every variable in a real system.
   1. Leaving out key feedback loops can distort the outcome.
2. Uncertainty arises because results depend on assumptions that may be wrong.
   1. It grows for long-term projections, such as climate change to 2100.
3. Data limitations mean poor input data produce misleading results.

Simplification trades accuracy for clarity

1. We simplify because real systems have countless interactions we cannot fully capture.
2. Simplifying lets us focus on the most important components and makes the model usable for decisions.
3. The more we simplify, the clearer the model becomes but the less accurate it is.
   1. Each assumption adds uncertainty, so different models can reach contradictory conclusions.

Calibration keeps models reliable

Calibration means adjusting a model and testing it against real-world data so its outputs stay reliable, and updating it as new data arrive.

Bias and Ethics in Modelling

1. Bias can enter through data selection, funding source, or interpretation.
2. Stakeholders may pick models that support their agenda, such as underestimating climate sensitivity.
3. Ethical practice means communicating a model's uncertainty clearly to prevent misinformation.

The Daisyworld model is covered in 1.2B, and the planetary boundaries and doughnut models in 1.3.