GC41G-01
Implications of dynamics underlying temperature and precipitation distributions for changes in extremes
Thursday, 17 December 2015: 08:00
3003 (Moscone West)
J David Neelin1, Paul C Loikith2, Samuel N Stechmann3, Sandeep Sahany4, Diana N Bernstein1, Kevin Martin Quinn5, Joyce Meyerson6, Katrina Hales1 and Baird Langenbrunner1, (1)University of California Los Angeles, Los Angeles, CA, United States, (2)Portland State University, Portland, OR, United States, (3)University of Wisconsin Madison, Madison, WI, United States, (4)National Environment Agency Singapore, Singapore, Singapore, (5)University of California, Beverly Hills, CA, United States, (6)UCLA, Los Angeles, CA, United States
Abstract:
Characterizing present-day probability distributions of temperature and precipitation measures are an important part of the pathway to improving quantitative assessment of changes in their extremes. In some cases, relatively simple prototypes for the dynamics underlying these distributions can assist in this characterization, pointing to key physical factors and measures to evaluate even in more complex distributions. In the case of daily temperature distributions, quantifying the widespread occurrence of non-Gaussian tails is motivated in part by tracer-advection across a maintained gradient prototypes. Substantial implications of the shape of these tails for regional changes in probabilities of temperature extremes with large-scale warming motivate measures of non-Gaussianity specific to this problem for assessing climate model present-day simulations. In the case of distributions of precipitation accumulations, simple prototypes yield insights into the form of the present-day distribution and predictions for the form of the global warming changes that can be evaluated in models and observations. Probability drops relatively slowly over a substantial range of accumulation size, followed by a key cutoff scale that limits large event probabilities in current climate but changes under global warming. Precipitation integrated over spatial clusters exhibits similar distribution features.
