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Journal of Meteorological Research

, Volume 33, Issue 2, pp 307–322 | Cite as

Assessment of the Capability of ENSEMBLES Hindcasts in Predicting Spring Climate in China

  • Yitong Lin
  • Haiming XuEmail author
  • Jing Ma
  • Haijun Li
Regular Articles
  • 2 Downloads

Abstract

Using the hindcasts provided by the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project for the period of 1980-2005, the forecast capability of spring climate in China is assessed mainly from the aspects of precipitation, 2-m air temperature, and atmospheric circulations. The ENSEMBELS can reproduce the climatology and dominant empirical orthogonal function (EOF) modes of precipitation and 2-m air temperature, with some differences arising from different initialization months. The multi-model ensemble (MME) forecast of interannual variability is of good performance in some regions such as eastern China with February initialization. The spatial patterns of the MME interannual and inter-member spreads for precipitation and 2-m air temperature are consistent with those of the observed interannual spread, indicating that internal dynamic processes have major impacts on the interannual anomaly of spring climate in China. We have identified two coupled modes between inter-member anomalies of the 850-hPa vorticity in spring and sea surface temperature (SST) both in spring and at a lead of 2 months, of which the first mode shows a significant impact on the spring climate in China, with an anomalous anticyclone located over Northwest Pacific and positive precipitation and southwesterly anomalies in eastern China. Our results also suggest that the SST at a lead of two months may be a predictor for the spring climate in eastern China. A better representation of the ocean-atmosphere interaction over the tropical Pacific, Northwest Pacific, and Indian Ocean can improve the forecast skill of the spring climate in eastern China.

Key words

ENSEMBLES seasonal forecast spring climate coupled atmosphere-ocean mode 

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Notes

Acknowledgments

The authors acknowledge the ENSEMBLES project for providing the model outputs. We also acknowledge the organizations that provided the observations for this study: the NCEP Reanalysis Derived data, NOAA_ERSST_V4 data, and GPCP precipitation data, which are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA (at https://www.esrl.noaa.gov/psd/). The authors thank the anonymous reviewers for their constructive and thoughtful comments, which have helped improve this manuscript.

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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science & TechnologyNanjingChina

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