|
|||||
|
|
Assessment of the benefits of climate model weights for ensemble analysis in three urban precipitation frequency studies |
|
| Authors: | Kevin Grady Momcilo Markus Shu Wu Fuyao Wang Seid Koric |
| Institution: | 1. Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
Contribution: Formal analysis, Investigation, Validation, Writing - review & editing;2. Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA;3. Nelson Institute for Environmental Studies, University of Wisconsin–Madison, Madison, Wisconsin, USA Contribution: Data curation, Software, Validation, Writing - review & editing;4. Nelson Institute for Environmental Studies, University of Wisconsin–Madison, Madison, Wisconsin, USA Contribution: Investigation, Resources, Software, Writing - review & editing;5. National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA Contribution: Investigation, Project administration, Resources, Writing - review & editing |
| Abstract: | In hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal-weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal-weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid-box averages is provided. |
| Keywords: | climate change adaptation climate models precipitation frequency projected frequency urban adaptation urban hydrology weighted ensemble |