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A Multimodel Ensemble Pattern Regression Method to Correct the Tropical Pacific SST Change Patterns under Global Warming

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  • 1 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, and Joint Center for Global Change Studies, Beijing, China
  • | 2 Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
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Abstract

This study develops a new observational constraint method, called multimodel ensemble pattern regression (EPR), to correct the projections of regional climate change by the conventional unweighted multimodel mean (MMM). The EPR method first extracts leading modes of historical bias using intermodel EOF analysis, then builds up the linear correlated modes between historical bias and change bias using multivariant linear regression, and finally estimates the common change bias induced by common historical bias. Along with correcting common change bias, the EPR method implicitly removes the intermodel uncertainty in the change projection deriving from the intermodel diversity in background simulation.

The EPR method is applied to correct the patterns of tropical Pacific SST changes using the historical and representative concentration pathway 8.5 (RCP8.5) runs in 30 models from phase 5 of CMIP (CMIP5) and observed SSTs. The common bias patterns of the tropical Pacific SSTs in historical runs, including the excessive cold tongue, the southeastern warm bias, and the narrower warm pool, are estimated to induce La Niña–like change biases. After the estimated common change biases are removed, the corrected SST changes display a pronounced El Niño–like pattern and have much greater zonal gradients. The bias correction decreases by around half of the intermodel uncertainties in the MMM SST projections. The patterns of corrected tropical precipitation and circulation change are dominated by the enhanced SST change patterns, displaying a pronounced warmer-get-wetter pattern and a decreased Walker circulation with decreased uncertainties.

Corresponding author address: Dr. Ping Huang, Institute of Atmospheric Physics, Chinese Academy of Sciences, Center for Monsoon System Research, Bei-Er-Tiao 6#, Zhong-Guan-Cun, Beijing 100190, China. E-mail: huangping@mail.iap.ac.cn

Abstract

This study develops a new observational constraint method, called multimodel ensemble pattern regression (EPR), to correct the projections of regional climate change by the conventional unweighted multimodel mean (MMM). The EPR method first extracts leading modes of historical bias using intermodel EOF analysis, then builds up the linear correlated modes between historical bias and change bias using multivariant linear regression, and finally estimates the common change bias induced by common historical bias. Along with correcting common change bias, the EPR method implicitly removes the intermodel uncertainty in the change projection deriving from the intermodel diversity in background simulation.

The EPR method is applied to correct the patterns of tropical Pacific SST changes using the historical and representative concentration pathway 8.5 (RCP8.5) runs in 30 models from phase 5 of CMIP (CMIP5) and observed SSTs. The common bias patterns of the tropical Pacific SSTs in historical runs, including the excessive cold tongue, the southeastern warm bias, and the narrower warm pool, are estimated to induce La Niña–like change biases. After the estimated common change biases are removed, the corrected SST changes display a pronounced El Niño–like pattern and have much greater zonal gradients. The bias correction decreases by around half of the intermodel uncertainties in the MMM SST projections. The patterns of corrected tropical precipitation and circulation change are dominated by the enhanced SST change patterns, displaying a pronounced warmer-get-wetter pattern and a decreased Walker circulation with decreased uncertainties.

Corresponding author address: Dr. Ping Huang, Institute of Atmospheric Physics, Chinese Academy of Sciences, Center for Monsoon System Research, Bei-Er-Tiao 6#, Zhong-Guan-Cun, Beijing 100190, China. E-mail: huangping@mail.iap.ac.cn
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