Figure 1: Urban flash flooding in Arlesey, U.K, 2004. Photo by Malcolm Campbell. Figure 2: End of the road due to coastal erosion. Skipsea Sands, U.K. Photo by Paul Glazzard. Figure 3: An example of improved climate information from a Regional v. a Global climate model over south-east Australia. The regional model is able to capture the effects of the coast, the mountains in the south-east and inland from the east coast.
All climate change impacts and adaptation research relies on climate projection information supplied by the climate research community. The required spatial and temporal scales and climate variables vary substantially, depending on the particular impact and system of interest. Quantifying impacts on large-scale water supplies requires knowledge of rainfall and evaporation at daily (or monthly) time scales over large catchments (thousands of square kilometers), while urban water infrastructure requires rainfall data derived over minutes to hours, over comparatively small areas (Figure 1).
Climate projection information originates from Global Climate Models (GCMs), and early impacts studies used direct climate data. The GCMs have several limitations when it comes to using them to study climate change impacts. The new CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs provide a step forward in terms of scale. Even so, they use spatial resolutions with grid cells of more than 100 kilometers per side. And while some variables are saved every three hours, the rest are available at daily and longer time scales. Even these new higher-resolution GCMs operate at scales too coarse to study systems of interest in impacts and adaptation research. This scale discord is addressed by downscaling using either a statistical or dynamical approach (Figure 3).
Dynamical downscaling, or the use of Regional Climate Models (RCMs), is often seen as the alternative to statistical downscaling though most statistical approaches also can be applied to RCM output to obtain point location data. RCMs have some advantages over statistical techniques: They simulate the entire climate system so that all climate variables of interest are available, rather than being limited to the well observed variables; and they simulate the climate across the landscape regardless of whether observations exist.
RCMs do however, have some significant limitations. One major limitation being that they are computationally expensive which places limits on their resolution, often to 10s of kilometers, and on the number of simulations that can be performed to characterize the uncertainty. They also contain biases, both inherited from the driving GCM and generated within the RCM itself.
The first of these limitations is being addressed through improving computer technology which has seen, and will continue to see, the capacity of large computer centers increase dramatically. These improvements facilitate higher spatial resolutions and more simulations. The need for more simulations to characterize the uncertainty is being further addressed through international initiatives to have multiple groups contribute simulations to the same ensembles.
The GCM community has been doing this for many years through the CMIP experiments and the RCM community is building on previous projects with the CORDEX (COordinated Regional climate Downscaling EXperiment) initiative. New research into model independence also is pointing toward ways to create more statistically robust ensembles. Through a combination of further increases in computational power and growing international cooperation, the scale issues that exist between climate projections and impacts and adaptation research will be addressed with RCMs.
This will not solve all the problems with RCMs, however, particularly when it comes to dealing with the inherit bias. Here, a role for statistical downscaling techniques will continue, and will remain a vital step between the RCM output and the impacts analysis.
The future of climate projections for impacts and adaptation research is with RCMs. The challenge now is to build a dialogue between regional climate modellers and impacts and adaptation researchers. The regional climate modellers need to focus some model development toward producing particular climate variables at spatial and temporal resolutions appropriate for impacts and adaptation research. The impacts and adaptation researchers need to take advantage of the full spectrum of climate variables available from RCMs rather than continuing to limit themselves to the well-observed variables. This dialogue has already begun to some extent in CORDEX, and was a major part in the development of a regional climate modelling project in Australia, NARCliM (New South Wales and Australian Capital Territory Regional Climate Modelling project). With this dialogue developing, and cooperation between these groups increasing, the coming decade promises great leaps forward for climate change impacts and adaptation research. Perhaps just in time.
Jason P. Evans is an Australian Research Council Future Fellow, the climate projection lead for the NARCliM project, and the AustralAsia domain coordinator for the CORDEX project. He completed his Ph.D. in regional climate and hydrology at Australian National University before continuing his work in regional climate modelling at Yale University (USA). He is currently at the University of New South Wales in Sydney, Australia. Evans has worked with regional climate models for 15 years, with a particular interest in water resources.
Regional Climate Modelling: The Future for Climate Change Impacts and Adaptation Research
