Downscaled Precipitation Sensitivity to Gridded Observation Data and Downscaling Technique
Abstract
Climate modeling and downscaling have become important tools in impact assessments related to anthropogenic climate change, but providing information regarding regional climate change remains a challenge. Adaptation plans and impact assessments are regional and local in nature are affected by physical processes which are non-existent or approximated in global climate models. Statistical downscaling is often used to translate the projected changes to regional and local levels. Numerous studies exist which demonstrate the added value of statistical downscaling, but there are questions of interest related to the sensitivity and uncertainty of climate projections connected to downscaling and gridded observations. These questions regarding the sensitivity and uncertainty are important to address to improve modeling of the regional climate and the information provided to stakeholders. This study focuses the sensitivity / uncertainty of downscaled precipitation in the South Central United States to downscaling techniques trained and observation datasets as applied to downscale three GCMs forced with representative concentration pathway (RCP) 8.5. Specifically, this study focuses on the precipitation adjustment used to create gridded observations, the delta ratio downscaling, and equi-ratio quantile mapping. This study also focuses on the how the different downscaling techniques capture changes through the precipitation distribution and the impact to projections of variables such as the annual number of days with precipitation and the annual one day maximum precipitation. Results of this study indicate that the precipitation adjustment used in the creation observation data can be translated through downscaling into the change signal for projections of the wet day frequency, intensity of precipitation extremes, and length of multi-day wet and dry periods. The choice of downscaling technique can also effect the change signal for variables of interest, in some cases causing change signals to reverse sign between techniques. The choice of observations and downscaling technique provide sources of uncertainty to downscaled precipitation connected to the decisions made with regards to correcting measurement errors and capturing change signals from a GCM. While these are potentially reducible sources of uncertainty with respect to regional climate change, they also have potential to influence impact assessments that rely upon daily outputs of precipitation from downscaling.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMGC43J1675W
- Keywords:
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- 1626 Global climate models;
- GLOBAL CHANGEDE: 1968 Scientific reasoning/inference;
- INFORMATICSDE: 1990 Uncertainty;
- INFORMATICSDE: 3275 Uncertainty quantification;
- MATHEMATICAL GEOPHYSICS