Predictability of summer climate anomalies over East Asia and the northwestern Pacific is investigated using observations and a multimodel hindcast ensemble initialized on 1 May for the recent 20-30 yr. Summertime East Asia is under the influence of the northwestern Pacific subtropical high (PASH). The Pacific-Japan (PJ) teleconnection pattern, a meridional dipole of sea level pressure variability, affects the northwestern PASH. The forecast models generally capture the association of the PJ pattern with the El Niñ o-Southern Oscillation (ENSO).The Silk Road pattern, a wave train along the summer Asian jet, is another dominant teleconnection that influences the northwestern PASH and East Asia. In contrast to the PJ pattern, observational analysis reveals a lack of correlations between the Silk Road pattern and ENSO. Coupled models cannot predict the temporal phase of the Silk Road pattern, despite their ability to reproduce its spatial structure as the leading mode of atmospheric internal variability. Thus, the pattern is rather unpredictable at monthly to seasonal lead, limiting the seasonal predictability for summer in East Asia.The anomalous summer of 2010 in East Asia is a case in point, illustrating the interference by the Silk Road pattern. Canonical anomalies associated with a decayed El Niñ o and developing La Niñ a would have the PJ pattern bring a cold summer to East Asia in 2010. In reality, the Silk Road pattern overwhelmed this tendency, bringing a record-breaking hot summer instead. A dynamical model experiment indicates that European blocking was instrumental in triggering the Silk Road pattern in the 2010 summer.

Since 2007, the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective and real-time (2008-2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single-model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression-based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.

A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

[1] A regression-based method of statistical downscaling from global multimodel ensemble (MME) forecasts has been developed. This method is appropriate for seasonal forecasts at stations with the use of model outputs as predictors; it refers to a technique that is known as "model output statistics" (MOS). Downscaled forecasts are formulated in terms of tercile probabilities based on the probabilistic interpretation of the forecast uncertainty. The novelty of the method is in the estimation of uncertainties originating from both regression and ensemble spread of model forecasts within the framework of the regression analysis. The method has been tested on the prediction of wintertime temperature and precipitation for 60 Korean stations by downscaling from the MME forecasts of 850 hPa temperature, sea level pressure, and 500 hPa geopotential height. Different sources of uncertainty associated with regression and ensemble spread have been evaluated and their contributions compared. It is shown that although the uncertainty associated with the deviation from the linear model is usually the largest, a comparable contribution to the uncertainty can come from the ensemble spread. Verification assessments of the method show that downscaled probabilistic MME forecasts essentially outperform the forecasts interpolated from the raw model predicted anomalies for both temperature and precipitation.