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Evaluating climate model simulations of precipitation: Methods, problems and performance

TitleEvaluating climate model simulations of precipitation: Methods, problems and performance
Publication TypeManual Entry
Year of Publication1995
AuthorsAirey, M., and M. Hulme
Progress in Physical Geography
Volume19
Pagination427-448
Abstract

Climate system modelling has been used extensively to investigate the role of human activities in causing global change. Model evaluation assesses the ability of the models used to simulate current climate. This article reviews the methodology of model evaluation with examples from recent studies involving precipitation. This crucial element of climate is difficult to model since the majority of precipitation occurs at scales less than that of the gridboxes of the highest resolution models. Detailed and reliable evaluation requires investigation of interannual variability as well as of climatological means on a variety of spatial scales. This sort of detailed analysis requires time-series of observed global precipitation at monthly time-steps or less. No single currently available global dataset of precipitation fulfils all the requirements for model evaluation, making the comparison of modelled global precipitation fields with 'reality' difficult. A number of recent precipitation evaluation projects are reviewed and a hierarchy of evaluation methods is provided based on spatial and temporal scale and whether or not tests for statistical significance are applied. Most studies to date have not tested for statistical significance, although when models improve with higher resolution and better physical parameterizations, statistical significance testing of differences will become increasingly more essential. The problems of evaluating modelled precipitation are being tackled by international projects such as the Global Precipitation Climatology Project, the WetNet Precipitation Intercomparison Projects and the Atmospheric Model Intercomparison Project. The results of evaluation studies to date emphasize that model simulations of future changes to the magnitude, timing and spatial pattern of global precipitation be viewed as scenarios and not as predictions.

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Community Notes

"this review concentrates on only one variable, precipitation. Evaluation
normally involves comparison of model and observed hemispheric means, seasonal cycles
and spatial patterns. This concentration on the mean precipitation of model simulations
results largely from the lack of any globally gridded time-series which provide measures of
the variability of observed precipitation."

"Each technique needs to address two problems: first, how to measure objectively the
goodness-of-fit between model and observed fields, and secondly, how to assign statistical
significance to that measure of fit"

"To evaluate the performance of a climate
model at simulating precipitation, simple comparison of the mean fields is a good starting
point. More detailed analysis of the variability about these means may then highlight some
inadequacies of the model. Evaluating the model at the regional level is vital since it is at
this spatial scale that climate impacts are felt.  Wigley and Santer (1990) state that
statistical significance testing is important if the difference between model and observed fields is small. This level of evaluation is the most detailed and is therefore ranked the
highest (1)"