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Prof. Song Yang’s Research Group First Proves that Inter-model Warming Projection Spread Inherits Diversity from the Control Climate

Last updated :2017-07-10

Source: School of Atmospheric Sciences
Written by: School of Atmospheric Sciences
Edited by: Wang Dongmei

An article recently published in Scientific Reports by the research group of Prof. Song Yang from the Sun Yat-sen University (Dr. Xiaoming Hu and Prof. Song Yang), Dr. Patrick Taylor and Dr. Sergio Sejas from the U.S. National Aeronautics and Space Administration, Prof. Ming Cai from the Florida State University, and Prof. Yi Deng from the Georgia Institute of Technology, found that the inter-model spread of global warming projections was inherited traits from the diversity of the control climate. It shed new light on the warming projection spread by indicating that climate feedbacks inherit diversity from the model control climate, besides the intrinsic climate feedback diversity of models that is independent of the control climate state.

The global warming due to anthropogenic greenhouse radiative forcing is robust across all climate models (Fig. 1). However, the exact amount of warming predicted by different models shows a large spread (referred to as global warming projection uncertainty) that has not been narrowed significantly for several decades. The underlying assumption of using global climate models for global warming projection is that the difference between the perturbation simulations that are subject to anthropogenic greenhouse radiative forcing and the control simulations solely or mainly reflects the effect of the anthropogenic greenhouse radiative forcing. As a result, the global warming projection uncertainties have been solely or mainly attributed to different sensitivities of different climate models to the same anthropogenic radiative forcing via climate feedback processes.

This study shows that the global warming projection uncertainties are strongly coupled with the uncertainties of projecting changes in global ice coverage, in amount of atmospheric water vapor, and in global hydrological cycle (Fig. 2). The latter uncertainties are found to be associated with or inherited from the large spreads in control climate mean states. The differences in the global ice coverage and atmospheric water vapor of the control climate mean states of different climate models contribute a majority of the uncertainties, explaining nearly 89% of the global warming projection uncertainties reported in the Intergovernmental Panel on Climate Change the Fifth Assessment Report. The current study points out an urgent need to reduce the differences among different climate models in simulating the observed climate state for narrowing down the range of global warming projections.

It should also be indicated that a series of recent related studies by the research group of Prof. Song Yang (Hu et al. 2016, Climate Dynamics; Hu et al. 2017, Climate Dynamics; Hu et al. 2017, Journal of Climate) has been conducted to gain a better understanding of the relative contribution of individual coupled ocean-atmosphere dynamic and thermodynamic processes to i) the different spatial distributions of surface temperature anomalies between the eastern and central types of El Niño events, ii) the decadal climate difference between 2002–13 and 1984–95, and iii) the atmospheric response to surface temperature forcing associated with El Niño.

Figure 1. Time series of global mean surface temperature change of the 31 CMIP5 1pctCO2 experiments relative to their corresponding first 10-year averages (labeled as “Year 0” which has been set to zero for each curve). The color scheme for these 31 curves represents the global and time mean surface temperature of the first 10-year simulations of the 31 CMIP5 1pctCO2 experiments. The color scheme is arranged in such a way that the control climate state ranges from the coldest to the warmest as the color changes from blue to red.
 
Figure 2. Correlation coefficients between the warming projection spread and (a) spreads in the eight key control climate state variables, (b) spreads in the key climate variable transient responses to 4xCO2. Numbers in orange and blue colored (black) circles indicate the correlation coefficients (do not) exceed 90% confidence level.
 
Hu, X., S. Yang, and M. Cai, 2016: Contrasting the eastern Pacific El Niño and the central Pacific El Niño: process-based feedback attribution. Climate Dynamics, 47, 2413-2424, doi: 10.1007/s00382-015-2971-9.
Hu, X., Y. Li, S. Yang, Y. Deng, and M. Cai, 2017a: Process-Based decomposition of the decadal climate difference between 2002–13 and 1984–95. Journal of Climate, 30, 4373–4393, doi:10.1175/JCLI-D-15-0742.1.
Hu, X., M. Cai, S. Yang, and Z. Wu, 2017b: Delineation of thermodynamic and dynamic responses to sea surface temperature forcing associated with El Niño. Climate Dynamics, in press, doi:10.1007/s00382-017-3711-0.
Hu, X., P. Taylor, M. Cai, S. Yang, Y. Deng, and S. Sejas, 2017c: Inter-model warming projection spread: inherited traits from control climate diversity. Scientific Reports, doi:10.1038/s41598-017-04623-7. (URL: https://www.nature.com/articles/s41598-017-04623-7)

The research was in part supported by the National Key Research Program of China (2014CB953900) and the National Natural Science Foundation of China (41375081).