Tim Palmer

Abstract:

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. This journal is © 2011 The Royal Society.

Abstract:

The probabilistic skill of ensemble forecasts for the first month and the first season of the forecasts is assessed, where model uncertainty is represented by the a) multi-model, b) perturbed parameters, and c) stochastic parameterisation ensembles. The main foci of the assessment are the Brier Skill Score for near-surface temperature and precipitation over land areas and the spread-skill relationship of sea surface temperature in the tropical equatorial Pacific. On the monthly timescale, the ensemble forecast system with stochastic parameterisation provides overall the most skilful probabilistic forecasts. On the seasonal timescale the results depend on the variable under study: for near surface temperature the multi-model ensemble is most skilful for most land regions and for global land areas. For precipitation, the ensemble with stochastic parameterisation most often produces the highest scores on global and regional scales. Our results indicate that stochastic parameterisations should now be developed for multi-decadal climate predictions using earth-system models. Copyright 2011 by the American Geophysical Union.

Abstract:

How accurate are predictions of climate change from a model which is biased against contemporary observations? If a model bias can be thought of as a state-independent linear offset, then the signal of climate change derived from a biased climate model should not be affected substantially by that model's bias. By contrast, if the processes which cause model bias are highly nonlinear, we could expect the accuracy of the climate change signal to degrade with increasing bias. Since we do not yet know the late 21st Century climate change signal, we cannot say at this stage which of these two paradigms describes best the role of model bias in studies of climate change. We therefore study this question using time-slice projections from a global climate model run at two resolutions - a resolution typical of contemporary climate models and a resolution typical of contemporary numerical weather prediction - and treat the high-resolution model as a surrogate of truth, for both 20th and 21st Century climate. We find that magnitude of the regionally varying model bias is a partial predictor of the accuracy of the regional climate change signal for both wind and precipitation. This relationship is particularly apparent for the 850 mb wind climate change signal. Our analysis lends some support to efforts to weight multi-model ensembles of climate change according to 20th Century bias, though note that the optimal weighting appears to be a nonlinear function of bias. Copyright © 2011 by the American Geophysical Union.