An Evaluation of Decadal Probability Forecasts from State-of-the-Art Climate Models

• Published in: Journal of climate Vol. 26; no. 23; pp. 9334 - 9347
• Main Authors: Suckling, Emma B., Smith, Leonard A.
• Format: Journal Article
• Language: English
• Published: Boston, MA American Meteorological Society 01.12.2013
• Subjects: Archives & records; Climate; Climate change; Climate models; Climate prediction; Climate system; Climatology; Climatology. Bioclimatology. Climate change; Computer simulation; Dynamic climatology; Dynamics; Earth; Earth, ocean, space; Empirical analysis; Empirical modeling; Empirical models; Exact sciences and technology; Experiments; External geophysics; Forecasting; Forecasting models; Geophysics. Techniques, methods, instrumentation and models; Mathematical independent variables; Mathematical models; Mean temperatures; Meteorology; Modeling; Modelling; Parametric models; Probability; Probability forecasts; Probability theory; Seasonal forecasting; Simulation; Simulation models; Simulations; Skills; Statistical analysis; Statistical forecasts; Temperature; Weather forecasting; Performance evaluation; Multimodel; Hindcast; Ensemble forecast; Decadal prediction; Climate models; Probabilistic model; Climate prediction; climate change
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-D-12-00485.1

While state-of-the-art models of Earth’s climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a “dynamic climatology” empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today’s state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model–model intercomparisons.