Understanding and predicting seasonal-to-interannual climate variability - the producer perspective

Stockdale, Timothy; Alves, Oscar; Boer, George; Déqué , Michel; Ding, Yihui; Kumar, Arun; Kumar, Krishna; Landman, Willem; Mason, Simon J.; Nobre, Paulo; Scaife, Adam; Tomoaki, Ose; Yun, Won-Tae

Seasonal prediction is based on changes in the probability of weather statistics due to changes in slowly varying forcings such as sea surface temperature anomalies, most notably those associated with El Niňo–Southern Oscillation (ENSO). However, seasonal weather can be perturbed by many factors, and is very much influenced by internal variability of the atmosphere, so comprehensive models are needed to identify what can be predicted. The predictability and probabilistic nature of seasonal forecasts is explained with suitable examples. Current capabilities for seasonal prediction that have grown out of work done in the research community at both national and international levels are described. Dynamical seasonal prediction systems are operational or quasi-operational at a number of forecasting centres around the world. Requirements for seasonal prediction include initial conditions, particularly for the upper ocean but also other parts of the climate system; high quality models of the ocean-atmosphere-land system; and data for verification and calibration. The wider context of seasonal prediction and seamless forecasting is explained. Recommendations for the future of seasonal prediction and climate services are given.


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Procedia Environmental Sciences

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Academic Units
International Research Institute for Climate and Society
Published Here
March 16, 2020


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