The orography of anthropogenic climate change in the western USA

Tuesday, 15 December 2015
Poster Hall (Moscone South)
David Earl Rupp1, Sihan Li1, Philip Mote2, Neil Massey3 and Myles Robert Allen4,5, (1)Oregon State University, Corvallis, OR, United States, (2)Oregon State University, Oregon Climate Change Research Institute, Corvallis, OR, United States, (3)University of Oxford, Oxford, United Kingdom, (4)University of Oxford, ECI/School of Geography and the Environment, Oxford, United Kingdom, (5)University of Oxford, Physics, Oxford, United Kingdom
Though orography is expected to modulate the larger-scale climate response to increased greenhouse gases, the understanding of meso-scale climate changes in regions of highly varying topography has faced two major challenges. The first is that it requires simulating climate at spatial resolutions finer than those used by current global climate models (GCMs). The second challenge is the ever-decreasing signal-to-noise ratio as one examines climate change patterns at finer and finer granularity. Whereas regional climate models (RCMs) at high resolutions (~4 to 32 km) are being increasingly used, computational demands have placed practical constraints on their utility: few ensemble members and high internal variability leads to limited powers of detection, and even less of attribution. To explore orographic controls on meso-scale changes, climate of the western USA was simulated of the recent past (1986-2014) and of the mid-21st century (2031-2058) with RCP4.5 forcings assumed for the future. To overcome challenge #1, an RCM (HadRM3P; 25-km horizontal resolution) was nested in an atmospheric GCM (HadAM3P; 1.875° lon. by 1.25° lat.). Challenge #2 was overcome by averaging over a large ensemble (100 members per year) of simulations. The ensemble averaging reveals clear, non-random, meso-scale patterns in the spatial distribution of both seasonal temperature and precipitation changes (some surprising) that can be linked to concomitant land-surface and atmospheric processes (e.g., land surface-atmosphere energy exchanges, snow pack depletion, and shifting wind patterns).