Leading Modes of East Asian Winter Climate Variability and Their Predictability: An Assessment of the APCC Multi-Model Ensemble

Abstract

The variability and predictability of the East Asian (EA) winter climate has been studied, based on observed datasets and multi-model ensemble (MME) hindcast experiments archived at the APEC Climate Center (APCC). The focus is on the leading modes of wintertime variability over the eastern to northeastern part of Asia, which are identified based on multivariate EOF analysis of the monthly 850 hPa wind and temperature. The leading EA climate mode is characterized by continental-scale temperature anomalies covering a broad region from the northwestern flank of the Siberian high to northeast Asia. The second mode is associated with fluctuations of temperature and monsoon northerlies over the EA locations of Korea, Japan and eastern coastal China. Moreover, the first mode is found to be influenced by the Scandinavian (SCA) pattern, while the second mode is closely associated with the Eurasia (EU) pattern.In general, the dominant circulation patterns of the EA wintertime variability from each MME member model compare well with their observational counterparts. However, the temporal variations of these modes are difficult to reproduce in the model simulations. The variation of the leading mode is found to be better predicted by most models, which leads to better predictions of the winter climate over continental northeast Asia, compared to the second mode. The MME performance is further assessed in the context of circulation changes during ENSO. It is found that most models have difficulty in capturing both the timing and strength of the observed second EA climate mode variations. Analysis based on observations shows that there is Rossby wave activity from Eurasia in early boreal winter during ENSO years. The Eurasian wave train, however, is either too weak or absent in the model simulations. Overall, these results highlight the difficulties in forecasting EA winter climate in the current framework of seasonal climate prediction.

Dynamical prediction of the East Asian winter monsoon by the NCEP Climate Forecast System

“…However, most models cannot simulate the relationship between cold surges and tropical convection properly. Most recently, Sohn et al [2011] showed that the seasonal prediction models that participate in the APEC Climate Center multimodel ensemble seasonal forecast had difficulties in predicting the interannual variability of East Asian climate. On the other hand, Li and Wang [2012] showed that the EAWM can be reasonably predicted by the models from the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction [Palmer et al, 2004].…”

“…Given the possible interaction between extratropical and tropical systems, ocean-atmosphere coupled models may help improve prediction of the EAWM. Currently, both tier-1 and tier-2 climate prediction systems are applied in operational climate prediction [Bengtsson et al, 1993;Palmer et al, 2004;Sohn et al, 2011]. One of the key differences between the two systems is whether ocean-atmosphere coupling is included in models.…”

Seasonal prediction and predictability of the Asian winter temperature variability

“…Lee and Wang 2012b). Although economic and social influences are equally affected by salient variability in the Asian winter monsoon (AWM) and that in the summer, prediction of the AWM Wu et al 2011;Sohn et al 2011) has received less attention than that of the summer counterpart which has turned out to be the most challengeable in current climate models and observation shown by numerous studies (e.g. Kang et al 2002;Ha et al 2005;Kang and Shukla 2006;Yang et al 2008;Wang et al 2004Wang et al , 2008Wang et al , 2009Lee et al 2010Lee et al , 2011aLee and Wang 2012a and many others).…”

Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling

“…Despite the advances in prediction skills brought about by the MME method, there are still deficiencies and limitations in long-lead predictions of climate variability and extreme phenomena on the local scale. This is especially the case over the extratropics, due to inherently low climate predictability in this region (Lee et al 2011(Lee et al , 2013aSohn et al 2011Sohn et al , 2012. Other factors such as the relatively coarse resolution of global models, uncertainties in initial conditions, forecast lead-time and error growth also limit the skill of long-lead regional climate predictions.…”