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Ting Ding and Zongjian Ke

Abstract

The present study focuses on two statistical approaches for improving seasonal precipitation prediction skills for Pakistan. Precipitation over Pakistan is concentrated in July–August (JA), when droughts and floods occur recurrently and cause disasters. Empirical orthogonal function (EOF) analysis is used to assess spatial patterns of precipitation, and two precipitation patterns are identified: a consistent pattern and a north–south dipole pattern. Two statistical approaches, the statistical regression method using prewinter predictors and statistical downscaling, are employed to perform rainfall predictions for JA in Pakistan. Linear regression (LR) and optimal subset regression (OSR) are used for each approach, and the regression forecast methods are compared with the raw model outputs. Historical data for large-scale variables from the NCEP–NCAR reanalysis and version 1.0 of the coupled atmosphere–ocean general circulation model from the Beijing Climate Center (CGCM1.0/BCC) outputs in 1986–2011 are used as predictors for the statistical prewinter method and statistical downscaling, respectively. In the majority of the years, the statistical prewinter method and statistical downscaling are able to correct the erroneous signs of the raw dynamical model output for the consistent pattern. The statistical prewinter method is found to provide more skillful predictions than the statistical downscaling on the prediction of the dipolelike pattern. The best prediction skills for the consistent pattern and dipolelike pattern are provided by NCEP-OSR and NCEP-LR, which have significant correlations of 0.39 and 0.40, respectively. For all the forecast methods in this study, prewinter prediction and downscaled prediction show considerable improvements when compared with model output. These statistical methods provide valuable approaches for studying local climates.

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Zongjian Ke, Peiqun Zhang, Wenjie Dong, and Laurent Li

Abstract

Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble.

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Zunya Wang, Song Yang, Zongjian Ke, and Xingwen Jiang

Abstract

Based on the observational datasets of rime and glaze from 743 stations in China and the atmospheric circulation data from the NCEP–NCAR reanalysis during 1954–2009, large-scale atmospheric and oceanic conditions for extensive and persistent rime and glaze events were examined with a composite analysis. Results show that rime events mostly occur in northern China while glaze events are mainly observed in southern China. The icing events are accompanied by low temperature and high humidity but not necessarily by above-normal precipitation. The Asian low, blocking highs, strong moisture transport, and an inversion layer related to major abnormal circulation systems contribute to the occurrence and persistence of icing events in China. The Ural blocking high plays a major role in the glaze events, and the Okhotsk blocking high is closely related to the rime events. For glaze events, extratropical circulation anomalies and the southward outbreak of cold air play a dominant role. In contrast, the strong northward transport of warm and moist airflows plays a leading role and the blocking high and the southward outbreak of extratropical cold air take a supporting role for rime events. There is nearly an equal chance for occurrences of rime events under La Niña and El Niño backgrounds. However, glaze events more likely occur under the background of La Niña. Additionally, the sea surface temperatures from the tropical Indian Ocean to the tropical northwestern Pacific Ocean also contribute to the occurrence and maintenance of icing events in China.

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Xingwen Jiang, Song Yang, Yueqing Li, Zongjian Ke, Jianping Li, and Haoran Hu

Abstract

In this study, the authors investigate the variations and predictability of wintertime upper-tropospheric temperature (UTT) over Asia, which are often linked to severe climate anomalies, and the associated features of large-scale circulation and surface climate. The ECMWF Interim Re-Analysis (ERA-Interim) and hindcast of the NCEP Climate Forecast System, version 2 (CFSv2), are mainly analyzed.

The first empirical orthogonal function mode of UTT shows a dipole structure, with a strong positive center over southern China and a weak negative center over Mongolia. The second mode is featured by a monopole variation, with a positive center appearing from the northwestern Tibetan Plateau (TP) to Japan. The third mode exhibits a tripole pattern, with two positive centers over Pakistan and the Sea of Japan and a negative center over central Asia. The first mode is linked to El Niño–Southern Oscillation, accompanied by surface warming over the southeastern TP and deficient precipitation over southern China, the Korean Peninsula, and from equatorial East Africa to the east of the TP. The second mode is associated with circulation anomalies similar to those associated with the Arctic Oscillation, with significant warming over East Asia. The third mode features two wave trains and is linked to the Middle East jet stream, and is associated with excessive precipitation from the eastern TP to southern Japan. The CFSv2 can predict the first mode skillfully by several months in advance, but it shows little skill in predicting the second and third modes.

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