Professor Myles Allen CBE FRS

Abstract:

A methodology for constraining climate forecasts, developed for application to the multithousand member perturbed physics ensemble of simulations completed by the distributed computing project ClimatePrediction.net, is here presented in detail. The methodology is extended to produce constrained forecasts of mean surface temperature and precipitation within 21 land-based regions and is validated with climate simulations from other models available from the IPCC (AR4) data set. The mean forecasted values of temperature and precipitation largely confirm prior results for the same regions. In particular, precipitation in the Mediterranean basin is shown to decrease and temperature over northern Europe is shown to increase with comparatively little uncertainty in the forecast (i.e., with tight constraints). However, in some cases the forecasts show large uncertainty, and there are a few cases where the forecasts cannot be constrained at all. These results illustrate the effectiveness of the methodology and its applicability to regional climate variables. Copyright 2007 by the American Geophysical Union.

Abstract:

Over the last 20 years, climate models have been developed to an impressive level of complexity. They are core tools in the study of the interactions of many climatic processes and justifiably provide an additional strand in the argument that anthropogenic climate change is a critical global problem. Over a similar period, there has been growing interest in the interpretation and probabilistic analysis of the output of computer models; particularly, models of natural systems. The results of these areas of research are being sought and utilized in the development of policy, in other academic disciplines, and more generally in societal decision making. Here, our focus is solely on complex climate models as predictive tools on decadal and longer time scales. We argue for a reassessment of the role of such models when used for this purpose and a reconsideration of strategies for model development and experimental design. Building on more generic work, we categorize sources of uncertainty as they relate to this specific problem and discuss experimental strategies available for their quantification. Complex climate models, as predictive tools for many variables and scales, cannot be meaningfully calibrated because they are simulating a never before experienced state of the system; the problem is one of extrapolation. It is therefore inappropriate to apply any of the currently available generic techniques which utilize observations to calibrate or weight models to produce forecast probabilities for the real world. To do so is misleading to the users of climate science in wider society. In this context, we discuss where we derive confidence in climate forecasts and present some concepts to aid discussion and communicate the state-of-the-art. Effective communication of the underlying assumptions and sources of forecast uncertainty is critical in the interaction between climate science, the impacts communities and society in general.

Abstract:

The development of ensemble-based 'probabilistic' climate forecasts is often seen as a promising avenue for climate scientists. Ensemble-based methods allow scientists to produce more informative, nuanced forecasts of climate variables by reflecting uncertainty from various sources, such as similarity to observation and model uncertainty. However, these developments present challenges as well as opportunities, particularly surrounding issues of experimental design and interpretation of forecast results. This paper discusses different approaches and attempts to set out what climateprediction.net and other large ensemble, complex model experiments might contribute to this research programme.