HR: 1340h
AN: GC43A-0934    [Abstracts]
TI: Quantifying the change in extreme seasonal precipitation events under global warming using a grand ensemble experiment
AU: * Fowler, H J
EM: h.j.fowler@ncl.ac.uk
AF: Newcastle University, Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, Cassie Building, Newcastle upon Tyne, NE1 7RU, United Kingdom
AU: Sain, S R
EM: ssain@ucar.edu
AF: National Center for Atmospheric Research, Geophysical Statistics Project, Institute for Mathematics Applied to Geosciences, P.O. Box 3000, Boulder, CO 80307-3000, United States
AU: Allen, M
EM: allen@atm.ox.ac.uk
AF: Oxford University, Dept of Physics, Atmospheric Oceanic & Planetary Physics, University of Oxford, Parks Road, Oxford, OX1 3PU, United Kingdom
AU: Lopez, A
EM: ana.lopez@ouce.ox.ac.uk
AF: Oxford University, School of Geography, Oxford University Centre for the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, United Kingdom
AB: Estimates of future precipitation extremes are subject to much uncertainty. Uncertainties in emission rates, climate model structures, parameterisations and initial conditions add to the uncertainties in the prediction of extremes simply due to rarity of such events. Here, we produce probabilistic predictions of seasonal changes in extreme precipitation for regions across the globe using results from the climateprediction.net experiment. In this experiment, a coupled atmosphere-ocean global climate model was run in ‘grand ensemble' mode for a transient integration from 1920 to 2080, varying parameter values, initial conditions and forcing scenarios. Here, we examine changes separately for each forcing scenario but examine the uncertainties introduced by different parameterisations and initial conditions. We use extreme value analysis to define extremes of precipitation. We fit the Generalized Extreme Value distribution to annual maxima using L-moments, to estimate extremes with return values of between 5 and 50 years for moving 30-yr time slices within the transient integration. We then apply the principle of equal weighting of the results from different models in the production of probability distributions of change for different regions of the globe. This allows us to establish which regions are most sensitive to the impacts of global warming on precipitation extremes, and the likely rates of change.
DE: 1626 Global climate models (3337, 4928)
DE: 1637 Regional climate change
DE: 1817 Extreme events
DE: 3354 Precipitation (1854)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting