Abstract
The unprecedented availability of 6-hourly data from a multi-model GCM ensemble in the CMIP5 data archive presents the new opportunity to dynamically downscale multiple GCMs to develop high-resolution climate projections relevant to detailed assessment of climate vulnerability and climate change impacts. This enables the development of high resolution projections derived from the same set of models that are used to characterise the range of future climate changes at the global and large-scale, and as assessed in the IPCC AR5. However, the technical and human resource required to dynamically-downscale the full CMIP5 ensemble are significant and not necessary if the aim is to develop scenarios covering a representative range of future climate conditions relevant to a climate change risk assessment. This paper illustrates a methodology for selecting from the available CMIP5 models in order to identify a set of 8–10 GCMs for use in regional climate change assessments. The selection focuses on their suitability across multiple regions—Southeast Asia, Europe and Africa. The selection (a) avoids the inclusion of the least realistic models for each region and (b) simultaneously captures the maximum possible range of changes in surface temperature and precipitation for three continental-scale regions. We find that, of the CMIP5 GCMs with 6-hourly fields available, three simulate the key regional aspects of climate sufficiently poorly that we consider the projections from those models ‘implausible’ (MIROC-ESM, MIROC-ESM-CHEM, and IPSL-CM5B-LR). From the remaining models, we demonstrate a selection methodology which avoids the poorest models by including them in the set only if their exclusion would significantly reduce the range of projections sampled. The result of this process is a set of models suitable for using to generate downscaled climate change information for a consistent multi-regional assessment of climate change impacts and adaptation.
Highlights
Modelling centres participating in the fifth Coupled Model Inter-comparison Project (CMIP5) experiment (Taylor et al 2012) agreed to make available the 6-hourly instantaneous fields of prognostic variables from General Circulation Models (GCMs) for use as lateral boundary conditions (LBCs) for driving regional climate models (RCMs)
This provides the opportunity for those interested in higher-resolution baseline and future climates derived by downscaling with multiple combinations of global and regional climate models or statistical downscaling techniques, allowing exploration of a wide range of high-resolution projections for one or more regions of the world consistent with the latest GCM-based climate projections assessed in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5 (AR5)
Our selection of evaluation criteria includes key aspects of the large-scale climate of the regions and takes advantage of specific CMIP5 regional assessments documented in the existing literature
Summary
Modelling centres participating in the fifth Coupled Model Inter-comparison Project (CMIP5) experiment (Taylor et al 2012) agreed to make available the 6-hourly instantaneous fields of prognostic variables from GCMs for use as lateral boundary conditions (LBCs) for driving regional climate models (RCMs). The human and computational resource-intensive nature of high-resolution downscaling places a restriction on the size of ensembles generated and downscaling the full ensemble may not be desirable or necessary to generate a representative range of future climate conditions relevant to assessing risks associated with future climate change This implies the need to develop strategies to sample from the available General Circulation Models (GCMs) and Representative Concentration Pathway (RCP) scenarios in order to generate projections that are policy relevant and manageable to develop, analyse and disseminate. Such an approach could be adopted by large model intercomparison projects such as the Cordinated Regional Downscaling Intercomparison Project (CORDEX) (Giorgi et al 2009)
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