The societal impact of a weather event increases in proportion to its rarity yet our current ability to model extreme events is limited by not only rarity but also by the fidelity of current data and approaches and a lack of understanding of the way many of the underlying physical processes may vary in time. This challenging conflict is driving fresh approaches to assessing the way that high impact weather adjusts to climate variability and change. Here I discuss lessons learnt, lessons yet to be learnt, and promising avenues of exploration following several years’ experience with the NCAR Nested Regional Climate Modeling System.

Lessons learnt include: the importance of handling bias in the global driving model; the need for considerable care in selecting the domain size for high-resolution simulation; the substantial benefits from adopting a hybrid dynamical statistical approach; and the simple fact that the uncertainty level goes up with increased resolution. Lessons yet to be learnt (aka known unknowns) include: useable approaches to assessing uncertainty; the consequences of upscale interactions, where the local scale influences the global; and how to best integrate statistical-dynamical modeling with direct impacts assessment (societal, commercial and ecological). Each of these challenges opens up promising avenues of exploration. We are already exploring some of these, but there are many more opportunities, all of which hold the potential for opening up future research collaborations between ANL and NCAR.