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Improving projections of rainfall trends through regional climate modeling and wide-ranging assessment

Citation

Grose, MR and Corney, SP and Katzfey, JJ and Bennett, JC and Bindoff, NL, Improving projections of rainfall trends through regional climate modeling and wide-ranging assessment, SUSTAINING OUR FUTURE: understanding and living with uncertainty, 12-16 December 2011, Perth, Western Australia, pp. 2726-2732. ISBN 978-0-9872143-1-7 (2011) [Refereed Conference Paper]


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Copyright Statement

Copyright 2011 The Modelling and Simulation Society of Australia and New Zealand Inc.

Official URL: http://www.mssanz.org.au/modsim2011/F5/grose.pdf

Abstract

General Circulation Models (GCMs) are our best tool for assessing potential changes to our climate on a global scale into the future. However, certain physical processes that influence rainfall at any particular location operate at finer spatial scales than can be simulated by GCMs. Dynamical downscaling using regional climate models (RCMs) addresses this problem by simulating the relevant processes at finer scales, whilst retaining the important large-scale features of the original GCM. Assessment of the RCM simulations is an essential task before using them as a guide for potential future rainfall trends. Assessing the outputs of models both in terms of their simulation of relevant output variables (e.g. rainfall) and also the dynamics gives a more comprehensive assessment than examining the variables alone. Similarly, understanding the dynamics driving the projected trends from the model simulation can be used to gauge the plausibility of that projected trend. Here we present dynamically downscaled simulations of rainfall over the study site of Tasmania, Australia (~10 km grid scale). These fine-scale projections were produced using a dynamical downscaling regional climate model. Rainfall in the simulations is validated against a gridded climate dataset based on observations, and the model simulation of broad climate fields such as mean pressure and wind fields were assessed by comparison to reanalysis datasets. Additionally, the projected rainfall trends are interpreted in terms of known climate drivers. While GCM projections to 2100 show fairly uniform rainfall trends over all of Tasmania, the RCM projections reveal distinct trends in the different districts of Tasmania. The spatial pattern of rainfall changes also varies greatly in each season. The change in each district is driven by a unique combination of drivers and processes. The main climate drivers of change include the alteration to mean circulation in response to surface warming, changes to the strength and position of the subtropical ridge, shifts in atmospheric blocking, the southern annular mode, as well as changes to the synoptic climatology of significant systems. At a scale relevant to Tasmania the climate response to these processes is often poorly resolved in GCM simulations, and is improved through regional modelling. In this paper we present a holistic view of rainfall changes over Tasmania, linking large-scale drivers and finer scale processes to spatial and temporal changes in rainfall. Such analyses are only possible with fine-scale dynamical downscaling. We conclude that including new information from increased resolution using fine-scale dynamical downscaling provides more useful projections of rainfall changes at a local scale. We propose that analysis of the relevant rainfall mechanisms also helps to assess the confidence in which the climate simulations should be used as a tool for understanding future changes in rainfall.

Item Details

Item Type:Refereed Conference Paper
Keywords:Regional climate modeling, Downscaling, Climate change
Research Division:Earth Sciences
Research Group:Atmospheric Sciences
Research Field:Climate Change Processes
Objective Division:Environment
Objective Group:Climate and Climate Change
Objective Field:Climate Change Models
Author:Grose, MR (Dr Michael Grose)
Author:Corney, SP (Dr Stuart Corney)
Author:Bennett, JC (Mr James Bennett)
Author:Bindoff, NL (Professor Nathan Bindoff)
ID Code:80763
Year Published:2011
Deposited By:CRC-Antarctic Climate & Ecosystems
Deposited On:2012-11-12
Last Modified:2017-11-06
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