A framework for investigating largescale patterns

Authors: Erin Towler Debasish PaiMazumder Greg Holland

Publish Date: 2016/04/18

Volume: 48, Issue: 3-4, Pages: 881-892

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Abstract

Global Climate Model GCM projections suggest that drought will increase across large areas of the globe but lack skill at simulating climate variations at localscales where adaptation decisions are made As such GCMs are often downscaled using statistical methods This study develops a 3step framework to assess the use of largescale environmental patterns to assess local precipitation in statistically downscaling to local drought In Step 1 two Statistical Downscaling models are developed one based on temperature and precipitation and another based on temperature and a largescale predictor that serves as a proxy for precipitation A key component is identifying the largescale predictor which is customized for the location of interest In Step 2 the statistical models are evaluated using NCEP/NCAR Reanalysis data In Step 3 we apply a large ensemble of future GCM projections to the statistical models The technique is demonstrated for predicting drought as measured by the Palmer Drought Severity Index in Southcentral Oklahoma but the framework is general and applicable to other locations Case study results using the Reanalysis show that the largescale predictor explains slightly more variance than precipitation when predicting local drought Applying future GCM projections to both statistical models indicates similar drying trends but demonstrates notable internal variability The case study demonstrates 1 where a largescale predictor performs comparably or better than precipitation directly then it is an appealing predictor choice to use with future projections 2 when statistically downscaling to local scales it is critical to consider internal variability as it may be more important than predictor selectionDrought is a critical challenge facing societies around the world exacerbated by growing demands on water resources in an era of climate variability and change Eriyagama et al 2009 Allen et al 2010 As such there is great interest in how drought may change in the future especially at the local scale where adaptation decisions are made Global Climate Models GCMs provide a fundamental starting point for investigating how climate may change in the future and have shown ability to reproduce surface temperatures over large scales as well as some improvements in simulating global precipitation patterns Flato et al 2013 GCM projections suggest that drought will increase across large areas of the globe Dai 2011 2013 but GCMs lack skill at simulating climate variations at regional or localscales because of their coarse spatial resolution and the increasing importance of internal variability at smaller scales Deser et al 2014One effective way to counter this is to downscale the GCMs using dynamical downscaling approaches that embed limited area highresolution climate models ie Regional Climate Models RCMs RCMs are forced by boundary conditions from the GCM but are free to develop their own climate Because of their smaller domain and higher resolution RCMs can resolve finer scale physical processes and enhance the representation of land surface characteristics leading to improved simulation of smaller scales Dickinson et al 1989 Giorgi et al 1994 Wang et al 2004 Lo et al 2008 Heikkila et al 2010 Maraun et al 2010 Qian et al 2009 PaiMazumder et al 2013 Harding et al 2013 However these dynamical models remain expensive and therefore limited in their use especially in terms of running ensembles to make an assessment of the uncertainty from internal variability at local scalesAs an alternative to dynamical downscaling Statistical Downscaling can also be an effective way to simulate weather phenomena at local scale Maraun et al 2010 Wilby and Wigley 1997 Wilby et al 1998 A detailed comparison of the advantages and disadvantages of statistical and dynamical downscaling is provided by Fowler et al 2007 The main advantages of Statistical Downscaling are that it is computationally efficient and that it can be readily used for any location to derive the local variable of interest Fowler et al 2007 The main disadvantage is that Statistical Downscaling does not include feedback from the climate system and makes the assumption that the relationship will hold into the future Fowler et al 2007There are several approaches to Statistical Downscaling including “perfect prognosis downscaling” Maraun et al 2010 whereby a statistical relationship is developed between observed largescale predictors and localscale weather phenomena eg Bruyère et al 2012 Drought is an extreme that requires skilful prediction of both temperature and precipitation Bonsal et al 2012 PaiMazumder and Done 2014 so a logical starting point is to relate local drought to the coarser temperature and precipitation variables from the GCMs this is often what is done for impact assessments that require finer scale inputs see Gutmann et al 2014 and references therein However using precipitation as a predictor has been criticized as regional precipitation is not simulated well by GCMs Flato et al 2013 Thus using precipitation may violate a central tenant of Statistical Downscaling which is that the climate model reliably simulates variables from which to downscale Maraun et al 2010 As such one appealing alternative is to use largescale environmental patterns from GCMs as predictors which have been shown to be skillfully reproduced Flato et al 2013 and have increasing credibility for use in climate change studies Christensen et al 2013 For drought using remote largescale patterns as predictors is appealing given the increasing literature on the role of teleconnections on drought and precipitation in the United States US For instance several studies reveal the link between US precipitation and sea surface temperature SST anomalies such as those associated with the El Niño Southern Oscillation ENSO Ting and Wang 1997 Montroy 1997 Montroy et al 1998 As a specific example cold SST anomalies in the tropical Pacific helped establish the largescale conditions for the 1988 drought over the Great Plains Trenberth et al 1988 Trenberth and Branstator 1992 Palmer and Brankovic 1989 Namias 1991 Lyon and Dole 1995 Chen and Newman 1998 Hong and Kalnay 2000 2002 McCabe et al 2004 found that North Atlantic warming and tropical Pacific cooling helped to explain droughts over the US in 1996 and from 1999–2002 However while many of these largescale phenomenon have been found to be relevant at regionalscales most stakeholders make decisions at the local level and are only concerned with a relatively small geographic region As such it has been found that these largescale predictors may need to be customized so that they are relevant to a particular location eg Grantz et al 2005The goal of this study is to develop a generalized framework to investigate the use of largescale environmental patterns as an alternative to precipitation in statistically downscaling to local drought This responds to increasing calls for generalized approaches that can help to add value and customize climate information for specific decisionmaking contexts Lemos et al 2012 The framework has threesteps In Step 1 two Statistical Downscaling models are developed one based on temperature and precipitation and another based on temperature and a remote largescale predictor that serves as a proxy for precipitation A key component is identifying the largescale predictor which is customized for the location of interest In Step 2 the statistical models are evaluated using NCEP/NCAR Reanalysis data which serves as a “ground truth” for a climate model as done in other downscaling studies eg Gutmann et al 2014 Wilby et al 2000 Finally as previously mentioned an important consideration when predicting at local scales is the increasing importance of internal variability Deser et al 2014 As such a secondary goal of the paper is to examine future changes in light of internal variability which is accomplished in Step 3 by applying a large ensemble of future GCM projections to the statistical models The technique is demonstrated for predicting late summer drought in Southcentral Oklahoma but the framework is general and could be applied to other areas where local drought is associated with largescale environmental patterns

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