The Benefits of Global High-Resolution for Climate Simulation: Process-Understanding and the Enabling of Stakeholder Decisions at the Regional Scale
We now have the capability to better address extreme weather in a changing climate with global models having horizontal resolutions considerably enhanced from those typically used in previous IPCC and CMIP exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g. flooding, drought, tropical and mid-latitude storms).
The representation of the global water cycle in coupled climate models, and in particular some of its governing processes, is subject to much larger variability among models than other (thermodynamic) indicators. One can contrast the significant agreement in CMIP5, expressed by model projections of future warming rates and patterns, against the disagreement in projected precipitation changes, which showed little improvement over the earlier CMIP3 assessment. Although precipitation does not represent the whole water cycle, and our observational record is short and uncertain, such fundamental disagreements do not build confidence in future projections. Part of the reason for this uncertainty is the lack of representation of the dynamical aspects of the coupled climate system, and how these are coupled to the physical aspects of model simulation.
The computational and analysis cost of this new generation of simulations, in terms of HPC, storage, network speed and analysis platform, is clearly large. New collaborative paradigms will be needed to efficiently address some of these challenges, including use of central analysis platforms, incorporating both data storage and compute, so that algorithms can be moved to the data rather than vice versa. Better coordination of experimental design and collaboration can help to form multi-model datasets to ameliorate the cost of single model ensemble simulations, and greatly enhance the scientific understanding from community analyses of such datasets, using common tools such as TempestExtremes, TECA and climextRemes developed in DOE BER projects.