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Climate Dynamics

, Volume 50, Issue 3–4, pp 1373–1391 | Cite as

On the influence of simulated SST warming on rainfall projections in the Indo-Pacific domain: an AGCM study

  • Huqiang Zhang
  • Y. Zhao
  • A. Moise
  • H. Ye
  • R. Colman
  • G. Roff
  • M. Zhao
Article
  • 216 Downloads

Abstract

Significant uncertainty exists in regional climate change projections, particularly for rainfall and other hydro-climate variables. In this study, we conduct a series of Atmospheric General Circulation Model (AGCM) experiments with different future sea surface temperature (SST) warming simulated by a range of coupled climate models. They allow us to assess the extent to which uncertainty from current coupled climate model rainfall projections can be attributed to their simulated SST warming. Nine CMIP5 model-simulated global SST warming anomalies have been super-imposed onto the current SSTs simulated by the Australian climate model ACCESS1.3. The ACCESS1.3 SST-forced experiments closely reproduce rainfall means and interannual variations as in its own fully coupled experiments. Although different global SST warming intensities explain well the inter-model difference in global mean precipitation changes, at regional scales the SST influence vary significantly. SST warming explains about 20–25% of the patterns of precipitation changes in each of the four/five models in its rainfall projections over the oceans in the Indo-Pacific domain, but there are also a couple of models in which different SST warming explains little of their precipitation pattern changes. The influence is weaker again for rainfall changes over land. Roughly similar levels of contribution can be attributed to different atmospheric responses to SST warming in these models. The weak SST influence in our study could be due to the experimental setup applied: superimposing different SST warming anomalies onto the same SSTs simulated for current climate by ACCESS1.3 rather than directly using model-simulated past and future SSTs. Similar modelling and analysis from other modelling groups with more carefully designed experiments are needed to tease out uncertainties caused by different SST warming patterns, different SST mean biases and different model physical/dynamical responses to the same underlying SST forcing.

Notes

Acknowledgements

Part of the analysis was conducted when Dr. Yong Zhao visited the Bureau of Meteorology (BoM) in January–March 2015 under a BoM–CMA bilateral agreement on climate change collaborations. The study was also partially supported by the Australian Climate Change Science Programme, a collaboration between the Bureau of Meteorology, CSIRO and the Department of the Environment. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and the climate modelling groups (see Table 1) for producing and making available their model output. For CMIP the US Department of Energy’s PCMDI provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge thoughtful comments and suggestions from Drs. Jo Brown, Hongyan Zhu and Tony Hirst during the internal review process. Very thoughtful and constructive comments from the two anonymous reviewers are much appreciated. Inquiries for model and analysis data in this study should be directed to the corresponding author.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Bureau of MeteorologyMelbourneAustralia
  2. 2.School of Atmospheric SciencesChengdu University of Information TechnologyChengduChina
  3. 3.Institute of Desert MeteorologyChina Meteorological AdministrationXinjiangChina

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