Climate Model Projections Guidance Notes
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Key message: Climate simulation results can help inform decisions that involve climate change, but using these data is more involved than selecting and downloading data from a web portal.
Before using scenarios or climate simulation results, it is important to make sure you have formulated specific questions you want to address, and to investigate whether you can do that using the results of existing analyses, rather than performing your own. The more specific the question(s) you can formulate, the easier it will be to decide whether you need to use and analyze climate model data sets, and if so which ones. This is important, because there is no “best” data set or “best” climate model; which ones you should use will depend on what question(s) you are trying to address, in what geographical region(s), etc.
If you decide that you do need to use and analyze climate simulation results, you should keep several points in mind as you proceed:
- Each data set of climate simulation results generally contains only selected types of information, which limits the range of questions it can be applied to. For example, many datasets of downscaled climate projections include information about temperature and precipitation only; these cannot be used to address questions involving storm surge or extreme winds, for example. Also, climate data sets may contain only monthly-averaged quantities (e.g. temperature); these may not be useful for many purposes.
- Climate simulations generally make no attempt to predict the timing of natural climate variability; hence for example these datasets may contain useful information about conditions during El Nino years, but they can’t tell you when these years will occur.
- Many users are interested in information about extreme weather of some sort (heat, precipitation, etc.) These can be difficult to simulate realistically; extreme precipitation is particularly tricky.
- Simulations of future climate are all based upon assumptions about future greenhouse gas concentrations and other factors that influence climate; this is one reason why these simulations are referred to as “projections” rather than “predictions.”
- In addition, there are numerous uncertainties in the climate models themselves, due to the challenge of numerically simulating all relevant aspects of the climate system over long timescales of decades to centuries. Therefore, even given the same assumptions about future greenhouse gases, different models will often produce different results, particularly at finer spatial scales and for extremes. (Note that some of these differences also result from random weather variations, and therefore do not represent true differences among model responses to greenhouse gas increases, but nevertheless can lead to different simulation results.) For this reason, it is considered good practice to use output from multiple models to explore a range of scientifically plausible futures – to account for an envelope of future climate risk, rather than a single future pathway.
- Finally, simulations having finer spatial detail (i.e., “downscaled” climate model projections) do not necessarily have greater accuracy than coarser-resolution simulations; they add contextual detail related to factors such as regional topography and coastlines but may still retain the same basic climatic features simulated at larger scales.
