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Climate Dynamics

, Volume 50, Issue 3–4, pp 1115–1128 | Cite as

Understanding the Central Equatorial African long-term drought using AMIP-type simulations

  • Wenjian Hua
  • Liming Zhou
  • Haishan Chen
  • Sharon E. Nicholson
  • Yan Jiang
  • Ajay Raghavendra
Article

Abstract

Previous studies show that Indo-Pacific sea surface temperature (SST) variations may help to explain the observed long-term drought during April–May–June (AMJ) since the 1990s over Central equatorial Africa (CEA). However, the underlying physical mechanisms for this drought are still not clear due to observation limitations. Here we use the AMIP-type simulations with 24 ensemble members forced by observed SSTs from the ECHAM4.5 model to explore the likely physical processes that determine the rainfall variations over CEA. We not only examine the ensemble mean (EM), but also compare the “good” and “poor” ensemble members to understand the intra-ensemble variability. In general, EM and the “good” ensemble member can simulate the drought and associated reduced vertical velocity and anomalous anti-cyclonic circulation in the lower troposphere. However, the “poor” ensemble members cannot simulate the drought and associated circulation patterns. These contrasts indicate that the drought is tightly associated with the tropical Walker circulation and atmospheric teleconnection patterns. If the observational circulation patterns cannot be reproduced, the CEA drought will not be captured. Despite the large intra-ensemble spread, the model simulations indicate an essential role of SST forcing in causing the drought. These results suggest that the long-term drought may result from tropical Indo-Pacific SST variations associated with the enhanced and westward extended tropical Walker circulation.

Keywords

Central Equatorial Africa Drought AMIP Sea surface temperature 

Notes

Acknowledgements

We acknowledge the International Research Institute for Climate and Society (IRI) working group on modeling, and we thank the modeling groups for producing and making their model output available. This study was supported by National Science Foundation (NSF AGS-1535426 and AGS-1535439). W.H. was jointly funded by the National Natural Science Foundation of China (41605034), the National Natural Science Foundation of Jiangsu Province (BK20160948) and the Natural Science Foundation for Higher Education Institutions in Jiangsu Province (16KJB170007) as well as project supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). We also thank Prof. Aiguo Dai (SUNY at Albany, USA) for insightful discussion.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Atmospheric and Environmental Sciences, University at AlbanyState University of New YorkAlbanyUSA
  2. 2.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  3. 3.Earth, Ocean and Atmospheric ScienceFlorida State UniversityTallahasseeUSA

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