Improvement of grand multi‐model ensemble prediction skills for the coupled models of APCC/ENSEMBLES using a climate filter

Abstract

Twelve coupled model simulations of two multi-model ensemble (MME) systems for boreal winters from 1983 to 2005 are used to improve the climate prediction. From grading the relative capability of each simulation in reproducing the observed link between the tropical El Niño-Southern Oscillation (ENSO)-related Walker circulation and the Pacific rainfall, we find an optimal MME suite with improved prediction skills. This study demonstrates that the climate filter concept, proposed by us in a recent work, is not only useful in improving the MME prediction skills as compared to a single MME system, but also the skills of a grand MME that encompasses two well-performing MMEs.

Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection

“…We adopt a simple composite MME method (Peng et al 2002;Kang et al 2009;Lee et al 2008Lee et al , 2009Lee et al , 2011aLee et al , 2013a, which assigns equal weights to the ensemble mean predictions of individual models. The performance of this method is on par with the best available operational MME techniques ).…”

“…affected by the MPIs represented as the western tropical convection describes the major features of the East Asian rainfall pattern and its interannual variability. The regression results of the summer rainfall on the EASM Index (Wang and Fan 1999;Wang et al 2008b;Lee et al 2013a;henceforth, EASMI), which is defined as the difference of the area-averaged zonal wind at 850 hPa between the southern (5°-15°N, 90°-130°E) and northern (22.5°-32.5°N, 110°-140°E) portion of the monsoon domain, and MPIs are compared with the spatial pattern of the first EOF mode (fraction of variance is 26.56 %) of rainfall over East Asia (Fig. 9).…”

Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real‐time forecasts (2008–2013)

“…As a result, various MME prediction systems are currently utilized at several operational centers (e.g., APCC, the European Centre for Medium-Range Weather Forecasts (ECMWF), the International Research Institute for Climate and Society (IRI), National Center for Environmental Prediction (NCEP), Meteorological Service of Canada (MSC), World Meteorological Organization Lead Center, and the North American Multimodel Ensemble) that routinely provide MME seasonal forecasts. Since its inception, the APCC has devoted considerable effort to developing a MME prediction system for producing improved and wellvalidated seasonal and regional forecasts in both probabilistic and deterministic framework for research and operational purposes [e.g., Kang et al, 2009;Min et al, 2009;Lee et al, 2011;Min et al, 2011;Sohn et al, 2012;Lee et al, 2013aLee et al, , 2013bKang et al, 2014]. Currently, four deterministic MME methods based on the ensemble means of participating models are operationally exploited for seasonal forecasts at the APCC.…”

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

“…Particularly, climate prediction over inland continental regions such as Mongolia is one of the most challenging issues for state-of-the-art meteorology due to the limitations of current observation that hinder our understanding of the interaction between land surface and atmosphere and due to the difficulties in modelling the complex and inhomogeneous distribution of the terrestrial surface in detail. Thus, in spite of their relatively low climate prediction skill over continents, state-of-the-art CGCMs are still widely used for seasonal forecasting in many meteorological research centres and laboratories, such as the National Centers for Environmental Prediction (NCEP; Toth et al, 2001), the European Centre for Medium-Range Weather Forecast (ECMWF; Anderson et al, 2007), the Asia-Pacific Economic Cooperation Climate Center (APCC; Lee et al, 2013;Lee et al, 2014) and Pusan National University (PNU; Sun and Ahn, 2014;Kim and Ahn, 2015). Thus, in spite of their relatively low climate prediction skill over continents, state-of-the-art CGCMs are still widely used for seasonal forecasting in many meteorological research centres and laboratories, such as the National Centers for Environmental Prediction (NCEP; Toth et al, 2001), the European Centre for Medium-Range Weather Forecast (ECMWF; Anderson et al, 2007), the Asia-Pacific Economic Cooperation Climate Center (APCC; Lee et al, 2013;Lee et al, 2014) and Pusan National University (PNU; Sun and Ahn, 2014;Kim and Ahn, 2015).…”

“…Meehl (1995) claimed that the coupled general circulation model (CGCM) is the ultimate tool for predicting the longterm weather and climate. Thus, in spite of their relatively low climate prediction skill over continents, state-of-the-art CGCMs are still widely used for seasonal forecasting in many meteorological research centres and laboratories, such as the National Centers for Environmental Prediction (NCEP; Toth et al, 2001), the European Centre for Medium-Range Weather Forecast (ECMWF; Anderson et al, 2007), the Asia-Pacific Economic Cooperation Climate Center (APCC; Lee et al, 2013;Lee et al, 2014) and Pusan National University (PNU; Sun and Ahn, 2014;Kim and Ahn, 2015).…”