Downscaling and Why Local Predictions are Difficult to Make Preparing Your Coast

How Models Work  Global climate models simulate the chemical and physical processes that drive Earth's climate.  These models are used to study Earth's past climate changes, and they provide an important tool for checking our understanding of how climate processes work.  Models are also the only way we have to evaluate how the climate might change in the future. * In other words, they are a big part of how we come to understand climate science.

How Models Work (cont’d)  Traditional climate models produce projections globally, revealing large-scale patterns of change.  The resolution of global models is currently too coarse to answer questions about individual locations directly, but techniques referred to as downscaling methods take global model output and refine it so as to provide information more applicable to the local level.

The Regional Approach  What if we'd like to know what might happen at a particular location?  The solution is "downscaling."  Scientists can choose from two basic methods: regional models, and statistical downscaling.  In regional climate modeling (RCM), scientists nest a regional climate model in the output from a larger global-scale climate model (GCM).

The Regional Approach (cont’d)  The nested regional climate model takes meteorological, oceanic, and land-surface data generated by a global climate model and makes a regional projection.  The advantage is that because of its limited scale, regional models can be run at much higher resolution than can be achieved globally.  Nesting regional climate models has its problems.  Small errors in the global climate model may amplify inside the nested domain and become major errors.

The Regional Approach (cont’d)  Another weakness is the lack of cross-talk between the regional climate model and the global one; the regional model can theoretically provide the global one with feedback, increasing overall accuracy, but this also increases the computing requirement substantially at the expense of resolution.  These models command huge amounts of computer power and time, which limits the resolution or the length of time the experiments can run.

The Statistical Approach  The second approach, statistical downscaling, takes advantage of statistical relationships between regional climate and global climate.  That is, scientists may notice that when global climate does A, the regional climate responds by doing B 40% of the time.  For example, if the global model can get the El Nino correct, then, because El Nino has a strong influence on Atlantic hurricanes, statistical downscaling can be used to assess their expected changes.

The Statistical Approach (cont’d)  By developing a long list of such statistical associations, scientists can input the conditions projected by global models into regional statistical downscaling systems to predict how the regional climates will respond to projected future global temperatures or greenhouse gas concentrations.

Imperfect Projections  It is important to remember that it may be difficult to develop statistical relationships in remote or topographically complex areas where good weather data have not been kept.  Local predictions are difficult to make, because at the local scale many complex and chaotic factors can come into play.  But the information that can be gleaned from downscaling can provide critical input for planning and adapting to both climate variability and change.

Imperfect Projections (cont’d)  The regional modeling and statistical downscaling techniques are not perfect, but when used correctly, are quite valuable, and uncertainty in the models should not preclude taking action.