Checking the Models by Comparing Results to Observations

An essential step in the modeling process is making sure the model is producing realistic results. You probably hear a lot in the media about predictions of climate change related to global warming. You might wonder, "How can we know if these predictions are realistic?"
The answer is by comparing model results to observations, or by comparing the results to what we expect based on our knowledge of the atmosphere. Climate models that are used to study future climate change are usually started with atmospheric conditions that existed more than 100 years ago. The model is allowed to run to present day, and the results are checked against the current climate to make sure the model is able to produce a realistic climate. Once a model is able to represent present day properties of the atmosphere, it is ready to predict future climates.

Although climate models are constantly being testing against observations and improved upon, there is still uncertainty in their results. A lot of this uncertainty is related to the difficulties with representing clouds in the models. The uncertainty motivates us to improve the models, so that as time goes on, we become more and more confident in their results.

There are several types of observations available to help us learn more about our climate and to compare to model results. We can get observations from satellites, surface stations, radiosondes, reanalysis, and field programs.

Let's take a look at how these help us make better models.

Next page   -> model accuracy, continued

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