Abstract
The seasonal prediction skills in the CAMS-CSM (the acronym stands for the Chinese Academy of Meteorological Sciences Climate System Model) climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981–2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphere–ocean–land–sea ice fully coupled general circulation model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components. The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Pacific, in association with El Niño-Southern Oscillation (ENSO) events. The anomaly correlation coefficient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3- and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Pacific (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time.
Highlights
Climate events on seasonal time scales are essentially related to social economy and people’s living life
Unlike the predictable source of the traditional weather forecast within about 10 days, which mainly comes from the atmospheric initial conditions, the predictable source of seasonal, or even longer time period, forecasts mainly is attributed to the slow-varying processes of the climate system, which is largely resided in the ocean memory
The global distributions of prediction correlation skill of SST anomalies between the observation and predictions are shown in Fig. 1 for 1, 3- and 6-month lead times during the period 1981–2019
Summary
Climate events on seasonal time scales are essentially related to social economy and people’s living life. How to get accurate seasonal forecast has always been an important issue. Many operational and scientific centers have established seasonal climate prediction system based on dynamical methods (Becker et al 2014; Johnson et al 2019; Liu et al 2015; Molteni et al 2011; Saha et al 2006). Unlike the predictable source of the traditional weather forecast within about 10 days, which mainly comes from the atmospheric initial conditions, the predictable source of seasonal, or even longer time period, forecasts mainly is attributed to the slow-varying processes of the climate system, which is largely resided in the ocean memory. The ocean initialization plays a vital role in the seasonal forecast system
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