92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Thursday, 26 January 2012: 11:45 AM
Seasonal Prediction to Support Climate Change Adaptation - Capacity Building in Pacific Island Countries and East Timor
Room 354 (New Orleans Convention Center )
Andrew Charles, National Climate Centre, Melbourne, Australia

The effects of climate change on climate variability challenge the assumption of stationarity implicit in empirical seasonal prediction models. We describe the development of systems and the provision of tools and training to enable the adoption of dynamical model-based seasonal outlooks by Pacific Island Countries (PICs) and East Timor.

Since 2004 the Pacific Island-Climate Prediction Project (PI-CPP) managed by the Australian Bureau of Meteorology (BoM) has built seasonal prediction capabilities within National Meteorological Services of PICs through the development and provision of decision support software (SCOPIC) and training in seasonal prediction. The high predictability of seasonal climate in the tropical Pacific provides opportunities for using seasonal forecasts to improve the resilience of climate sensitive sectors including water management disaster preparedness, agriculture and hydroelectricity. The outlooks provided under this project use discriminant analysis using El Niņo-Southern Oscillation (ENSO) based predictors to generate probabilistic seasonal outlooks for sites, trained on the observational record.

Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record; an issue that is highlighted by recent unusually high sea surface temperatures which are used as predictors under PI-CPP. Dynamical physics-based models implicitly include the effects of a changing climate. There exist clear opportunities for improvement of seasonal outlook capability in the Pacific through the adoption of Coupled Global Circulation Model (CGCM) based seasonal outlooks. The primitive equations solved by CGCMS are not sensitive to a particular climate regime and so dynamical models are less compromised by climate change than statistical models. CGCMs allow longer lead-time prediction of major climate drivers like ENSO, and therefore can support longer lead seasonal forecasts, and can potentially provide climate information at a range of time scales. As part of the Pacific Adaptation Strategy Assistance Program (PASAP), the BoM led a project to improve available seasonal climate forecasts for partner countries by supporting the transition from statistical to dynamical climate predictions. Assessment of the hindcast predictability for seasonal mean rainfall, minimum and maximum temperature is performed using an operational version of the seasonal time scale Australian coupled model, POAMA (Predictive Ocean-Atmosphere Model for Australia). As expected a high degree of predictability compared to statistical models is evident, though the location of the South Pacific Convergence Zone is misrepresented.

Assessment of model grid point based outlooks for individual sites reveals positive skill for many locations, while topographic effects are identified as a limiting factor for the larger islands. A simple regression based variance inflation calibration scheme is applied to correct the variability of the model and adjust for hindcast skill. This scheme implicitly adjusts for topographic effects when applied using site observations and thus functions as a simple but effective downscaling technique.

Seasonal outlooks for the broad scale Pacific and for selected sites in PICs are generated and made available via an interactive web portal and via the software tools already deployed by PI-CPP which is a PC based package used within National Meteorological Services. A series of workshops bringing together climate services staff from the National Meteorological Services of partner countries were held to gather requirements and later to provide training in the scientific basis of seasonal prediction and in the interpretation of the dynamical model-based outlooks provided in the web portal. Partner countries will benefit from ongoing upgrades to the POAMA assimilation scheme and model configuration, including the extension to the prediction of extremes such as coral bleaching and tropical cyclones.

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