Comparing and Evaluating Statistical and Dynamical Downscaling Projections
Future rainfall patterns have been projected for the Hawaiian Islands using different downscaling techniques. Both statistical and dynamical downscaling utilize results from global climate models to make spatially detailed projections of future changes. However, the approaches are very different and can produce conflicting results, which make communicating the results and understanding the confidence of the projections complex. Stakeholders in policy and management positions need to know both how and why the projections differ. While both downscaling methods have inherent uncertainties, each has advantages and disadvantages. Therefore, it is important that continuing efforts to improve regional climate projections should be done using both approaches. In this study, researchers from the University of Hawai‘i and the Pacific RISA will analyze the downscaling results to determine sensitivities to technical assumptions and to ascertain the reasons for apparent discrepancies.
Previous work developed a set of statistically downscaled multi-model ensemble rainfall change scenarios for the Hawaiian Islands for the mid and late 21st century. Overall, the wet season (November-April) shows an enhanced drying trend in regions with climatological low rainfall amounts, and slightly enhanced precipitation in the wet windward regions.
Interpolated maps of the statistically downscaled wet-season rainfall for scenarios RCP4.5 and RCP 8.5 for the period 2041–2071 (31-yr time-mean). Shown is the ensemble median result from 32-members from CMIP5. Units are in percent.
The general pattern of the projected rainfall changes appears consistent with recent dynamical downscaling results. However, the drying trend is less severe in the dynamical results, and a larger increase in the wet region rainfall is projected in the dynamical downscaling scenario. This project will examine how robust the statistical model results are and seeking ways to improve the reliability of this approach. The goals of the first part of this study are to test: (1) the sensitivity to changes in large-scale climate information; (2) the use of new large-scale climate variables as predictors; and (3) the robustness against changes in the statistical downscaling method. In the second part, statistically and dynamically downscaled historical rainfall anomalies will be compared to identify the weaknesses in each approach to provide guidelines for improving projections from both methods.
Research Team Tom Giambelluca, Professor, Department of Geography, University of Hawai’i|808.956.7390 Henry Diaz, Affiliate Faculty, Department of Geography, University of Hawai‘i Oliver Elison Timm, Associate Professor, Department of Atmospheric and Environmental Sciences, University at Albany Mami LeMaster, Post-Masters Researcher, Department of Geography, University of Hawai‘i
Resilient and sustainable Pacific Island communities using climate information to manage risks and support practical decision-making about climate variability and change.
Comparing and Evaluating Statistical and Dynamical Downscaling Projections
Future rainfall patterns have been projected for the Hawaiian Islands using different downscaling techniques. Both statistical and dynamical downscaling utilize results from global climate models to make spatially detailed projections of future changes. However, the approaches are very different and can produce conflicting results, which make communicating the results and understanding the confidence of the projections complex. Stakeholders in policy and management positions need to know both how and why the projections differ. While both downscaling methods have inherent uncertainties, each has advantages and disadvantages. Therefore, it is important that continuing efforts to improve regional climate projections should be done using both approaches. In this study, researchers from the University of Hawai‘i and the Pacific RISA will analyze the downscaling results to determine sensitivities to technical assumptions and to ascertain the reasons for apparent discrepancies.
Previous work developed a set of statistically downscaled multi-model ensemble rainfall change scenarios for the Hawaiian Islands for the mid and late 21st century. Overall, the wet season (November-April) shows an enhanced drying trend in regions with climatological low rainfall amounts, and slightly enhanced precipitation in the wet windward regions.
The general pattern of the projected rainfall changes appears consistent with recent dynamical downscaling results. However, the drying trend is less severe in the dynamical results, and a larger increase in the wet region rainfall is projected in the dynamical downscaling scenario. This project will examine how robust the statistical model results are and seeking ways to improve the reliability of this approach. The goals of the first part of this study are to test: (1) the sensitivity to changes in large-scale climate information; (2) the use of new large-scale climate variables as predictors; and (3) the robustness against changes in the statistical downscaling method. In the second part, statistically and dynamically downscaled historical rainfall anomalies will be compared to identify the weaknesses in each approach to provide guidelines for improving projections from both methods.
Research Team
Tom Giambelluca, Professor, Department of Geography, University of Hawai’i|808.956.7390
Henry Diaz, Affiliate Faculty, Department of Geography, University of Hawai‘i
Oliver Elison Timm, Associate Professor, Department of Atmospheric and Environmental Sciences, University at Albany
Mami LeMaster, Post-Masters Researcher, Department of Geography, University of Hawai‘i
References
Elison Timm, O., Giambelluca, T.W. & Diaz, H.F. 2015. Statistical Downscaling of Rainfall Changes in Hawai‘i based on the CMIP5 Global Model Projections. Journal of Geophysical Research—Atmospheres, v. 120, no. 1, p. 92–112, doi:10.1002/2014JD022059.
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Resilient and sustainable Pacific Island communities using climate information to manage risks and support practical decision-making about climate variability and change.
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