Revealing Risks in Adaptation Planning: expanding Uncertainty Treatment and dealing with Large Projection Ensembles during Planning Scenario development

Wednesday, 16 December 2015: 10:24
3020 (Moscone West)
Levi D Brekke1, Martyn P Clark2, Ethan D Gutmann2, Andrew Wood2, Naoki Mizukami2, Pablo A. Mendoza2, Roy Rasmussen3, Kyoko Ikeda2, Tom Pruitt4, J R Arnold5 and Balaji Rajagopalan6, (1)Bureau of Reclamation Denver, Denver, CO, United States, (2)National Center for Atmospheric Research, Boulder, CO, United States, (3)University Corporation for Atmospheric Research, Boulder, CO, United States, (4)U.S. Bureau of Reclamation, Denver, CO, United States, (5)US Army Corps of Engineers, Climate Preparedness and Resilience Programs, Jacksonville, FL, United States, (6)University of Colorado at Boulder, Boulder, CO, United States
Adaptation planning assessments often rely on single methods for climate projection downscaling and hydrologic analysis, do not reveal uncertainties from associated method choices, and thus likely produce overly confident decision-support information. Recent work by the authors has highlighted this issue by identifying strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic impacts. This work has shown that many of the methodological choices made can alter the magnitude, and even the sign of the climate change signal. Such results motivate consideration of both sources of method uncertainty within an impacts assessment. Consequently, the authors have pursued development of improved downscaling techniques spanning a range of method classes (quasi-dynamical and circulation-based statistical methods) and developed approaches to better account for hydrologic analysis uncertainty (multi-model; regional parameter estimation under forcing uncertainty).

This presentation summarizes progress in the development of these methods, as well as implications of pursuing these developments. First, having access to these methods creates an opportunity to better reveal impacts uncertainty through multi-method ensembles, expanding on present-practice ensembles which are often based only on emissions scenarios and GCM choices. Second, such expansion of uncertainty treatment combined with an ever-expanding wealth of global climate projection information creates a challenge of how to use such a large ensemble for local adaptation planning. To address this challenge, the authors are evaluating methods for ensemble selection (considering the principles of fidelity, diversity and sensitivity) that is compatible with present-practice approaches for abstracting change scenarios from any “ensemble of opportunity”. Early examples from this development will also be presented.