Selecting CMIP5 GCMs for downscaling over multiple regions

TLDR

The CMIP5 archive’s 6‑hourly data enable high‑resolution downscaling, but fully downscaling the entire ensemble is costly, so a representative subset is needed for regional climate risk assessments. This study presents a method to select 8–10 CMIP5 GCMs for regional climate change assessments. The selection prioritizes models that are realistic for Southeast Asia, Europe, and Africa, excludes the least realistic ones, and retains only those whose removal would markedly shrink the projected temperature and precipitation range. After excluding three implausible models (MIROC‑ESM, MIROC‑ESM‑CHEM, IPSL‑CM5B‑LR), the resulting set provides a suitable basis for generating downscaled climate change information across multiple regions.

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.