Climate Modeling 101

Type of Resource: Link

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Description

This resource is a website that is a self-contained, multi-part introduction to how climate models work. The materials include videos and animations about understanding, constructing and applying climate models.

The foundation for Earth’s global climate systems is the electromagnetic radiation from the sun, as well as its reflection, absorption, storage, and redistribution among the atmosphere, ocean, and land systems, and this energy’s re-radiation into space.

Current models predict that, although future regional climate changes will be complex and varied, average global temperatures will continue to rise. The outcomes predicted by global climate models strongly depend on the amounts of human-generated greenhouse gases added to the atmosphere each year and by the ways in which these gases are absorbed by the ocean and biosphere.

Changes in the atmosphere due to human activity have increased carbon dioxide concentrations and thus affect climate.

Both physical models and computers can be used in various ways to aid in the engineering design process. Computers are useful for a variety of purposes, such as running simulations to test different ways of solving a problem or to see which one is most efficient or economical; and in making a persuasive presentation to a client about how a given design will meet his or her needs.

Empirical evidence is required to differentiate between cause and correlation and make claims about specific causes and effects.

Changes of energy and matter in a system can be described in terms of energy and matter flows into, out of, and within that system.

Much of science deals with constructing explanations of how things change and how they remain stable.

Cause and effect relationships can be suggested and predicted for complex natural and human designed systems by examining what is known about smaller scale mechanisms within the system.

Feedback (negative or positive) can stabilize or destabilize a system.

Algebraic thinking is used to examine scientific data and predict the effect of a change in one variable on another (e.g., linear growth vs. exponential growth).

Empirical evidence is needed to identify patterns.

Change and rates of change can be quantified and modeled over very short or very long periods of time. Some system changes are irreversible.

Patterns of performance of designed systems can be analyzed and interpreted to reengineer and improve the system.

Some systems can only be studied indirectly as they are too small, too large, too fast, or too slow to observe directly.