Scientific Modelling

Turning ideas and thoughts into scientific knowledge involves a process called investigating. Scientific modelling creates a simplified representation of a system (or part of a system) that helps scientists investigate and learn about how something works. Scientists use models to develop questions, test ideas, make predictions, and communicate explanations to others. Because many scientific systems are too large, too small, too complex, or too dangerous to study directly, models allow scientists to safely and effectively explore how these systems behave.

Scientific modelling helps us to visualise and make sense of:

• Objects that are difficult to see because of their size or position — for example, the structure of a cell or the layout of an ecosystem.

• Processes that can’t be easily observed — such as blood circulation, the water cycle, or photosynthesis.

• Abstract ideas — like energy transfer, the particulate nature of matter, or gravitational forces.

• Complex systems — such as weather patterns, global warming, or planetary motion.

For example, the greenhouse effect on Earth can be modelled using a real greenhouse. The glass in the greenhouse acts like greenhouse gases (such as carbon dioxide) in Earth’s atmosphere — it lets sunlight in but traps heat inside. This helps us understand how global temperatures can increase when more heat is trapped by the atmosphere.

Scientific modelling is essential because it allows scientists to:

• Understand systems that are too large, small, dangerous, or complex to study directly (like atoms, volcanoes, or weather systems).

• Visualise abstract or invisible processes to make them easier to explain.

• Predict future events or outcomes, such as the spread of diseases or the effects of pollution.

• Communicate ideas clearly with other scientists and the public.

• Generate new questions for further investigation and testing.

Example: NZ climate change model

Climate models help scientists predict how global temperatures might change with increased carbon dioxide. These predictions guide governments and organisations in making environmental decisions. The model below shows projected future changes to New Zealand’s temperature, rainfall, and wind under different scenarios at a 5 km resolution. It allows individuals, communities, and businesses to explore and understand what New Zealand's future climate may look like.  

Note that the climate change scenarios used in the model are: 

• Sustainability (SSP1-2.6): The world works together to protect the environment and live more fairly, keeping global warming below 2°C and reaching zero carbon emissions by 2050.

• Middle of the road (SSP2-4.5): The world mostly keeps doing things the same way as now, leading to about 2.7°C of warming by 2100.

• Regional rivalry (SSP3-7.0): Countries focus on their own safety and power instead of helping the planet, causing carbon emissions to double and global warming to reach about 3.6°C by 2100.

Creating a scientific model involves simplifying a real-world system so that it can be understood, tested, or predicted more easily. Scientists begin by making observations or gathering data about the real system. They then identify the key factors (or variables) that need to be represented in the model.

Once a model is built, it is tested by comparing its predictions to real-world data. If the model’s predictions match reality, the model is considered useful. If not, it is refined or adjusted to better reflect new evidence. This process — building, testing, and refining — is continuous and helps improve the model’s accuracy over time.

Visual Models

visual models are two-dimensional representations, such as diagrams, drawings, or digital images. They are useful for showing relationships and explaining invisible or hard-to-see processes.

Mathematical Models

Mathematical models use equations to describe relationships between variables. They can be used to predict outcomes and patterns, such as population growth, speed, or energy transfer.

For example, 𝐹 = 𝑚 𝑎 (Force = mass × acceleration) is a mathematical model that predicts how force affects motion.

In climate science, mathematical model predict how changes in greenhouse gas levels might affect global temperatures in the future.
Reference: Study Confirms Climate Models are Getting future warming projections right (NASA) 

Computational Models

Computational models use computer simulations to represent complex systems and calculate results that would be difficult or impossible to test in the real world. Examples include flight simulators, climate simulations, or virtual dissections used to teach anatomy without harming animals. PhET Interactive Simulations have amazing computational model, and are used throughout Lemonade-Ed.

Using digital modelling software has transformed science and technology. For example, pilots use flight simulators to learn how to fly safely, and surgeons use 3D simulations to practice delicate operations. These models can even save lives.

More than one model can be used to explain different aspects of the same idea. For example, there are several different models of the atom, each one showing a different level of understanding about its structure. Models are not perfect copies of reality — they are representations that help us think and ask questions about how things work.