Assessment of the Decadal Prediction Skill of Sahel Rainfall in CMIP5 and CMIP6

Yujun He aState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid, Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Bin Wang aState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid, Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bMinistry of Education, Key Laboratory for Earth System Modelling and Department of Earth System Science, Tsinghua University, Beijing, China
cInnovation Group 311020008, Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China
dCollege of Ocean Science, University of Chinese Academy of Sciences, Beijing, China

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Juanjuan Liu aState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid, Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
dCollege of Ocean Science, University of Chinese Academy of Sciences, Beijing, China

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Yong Wang bMinistry of Education, Key Laboratory for Earth System Modelling and Department of Earth System Science, Tsinghua University, Beijing, China

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Lijuan Li aState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid, Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Li Liu bMinistry of Education, Key Laboratory for Earth System Modelling and Department of Earth System Science, Tsinghua University, Beijing, China

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Shiming Xu bMinistry of Education, Key Laboratory for Earth System Modelling and Department of Earth System Science, Tsinghua University, Beijing, China

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Wenyu Huang bMinistry of Education, Key Laboratory for Earth System Modelling and Department of Earth System Science, Tsinghua University, Beijing, China

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Hui Lu bMinistry of Education, Key Laboratory for Earth System Modelling and Department of Earth System Science, Tsinghua University, Beijing, China

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Abstract

Accurately predicting the decadal variations in Sahel rainfall has important implications for the lives and economy in the Sahel. Previous studies found that the decadal variations in sea surface temperature (SST) in the Atlantic, Mediterranean Sea, Indian Ocean, and Pacific contribute to those in Sahel rainfall. This study evaluates the decadal prediction skills of Sahel rainfall from all the available hindcasts contributing to phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), in comparison with the related uninitialized simulations. A majority of the prediction systems show high skill with regard to Sahel rainfall. The high skill may be partly attributed to external forcings, which are reflected in good performance of the respective uninitialized simulations. The decadal prediction skills of the key SST drivers and their relationships with the Sahel rainfall are also assessed. Both the hindcasts and the uninitialized simulations generally present high skill for the Atlantic multidecadal variability (AMV) and Mediterranean Sea SST indices and low skill for the Indian Ocean basin mode (IOBM) and interdecadal Pacific variability (IPV) indices. The relationship between the Sahel rainfall and the AMV or Mediterranean Sea SST index is reasonably captured by most prediction systems and their uninitialized simulations, while that between the Sahel rainfall and the IOBM or IPV index is captured by only a few systems and their uninitialized simulations. The high skill of the AMV and Mediterranean Sea SST indices as well as the reasonable representations of their relationships with the Sahel rainfall by both the hindcasts and uninitialized simulations probably plays an important role in predicting the Sahel rainfall successfully.

Significance Statement

Predicting Sahel rainfall on the decadal time scale is of great importance. This study provides a thorough evaluation of the decadal prediction skills of Sahel rainfall in the current decadal prediction systems participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). A majority of the systems achieve high prediction skill of Sahel rainfall, which probably results from the high prediction skill of some key sea surface temperature (SST) drivers, especially in the Atlantic and Mediterranean Sea SST, and their relationships with Sahel rainfall. This study provides a reference for better understanding the predictability of Sahel rainfall.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bin Wang, wab@lasg.iap.ac.cn

Abstract

Accurately predicting the decadal variations in Sahel rainfall has important implications for the lives and economy in the Sahel. Previous studies found that the decadal variations in sea surface temperature (SST) in the Atlantic, Mediterranean Sea, Indian Ocean, and Pacific contribute to those in Sahel rainfall. This study evaluates the decadal prediction skills of Sahel rainfall from all the available hindcasts contributing to phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), in comparison with the related uninitialized simulations. A majority of the prediction systems show high skill with regard to Sahel rainfall. The high skill may be partly attributed to external forcings, which are reflected in good performance of the respective uninitialized simulations. The decadal prediction skills of the key SST drivers and their relationships with the Sahel rainfall are also assessed. Both the hindcasts and the uninitialized simulations generally present high skill for the Atlantic multidecadal variability (AMV) and Mediterranean Sea SST indices and low skill for the Indian Ocean basin mode (IOBM) and interdecadal Pacific variability (IPV) indices. The relationship between the Sahel rainfall and the AMV or Mediterranean Sea SST index is reasonably captured by most prediction systems and their uninitialized simulations, while that between the Sahel rainfall and the IOBM or IPV index is captured by only a few systems and their uninitialized simulations. The high skill of the AMV and Mediterranean Sea SST indices as well as the reasonable representations of their relationships with the Sahel rainfall by both the hindcasts and uninitialized simulations probably plays an important role in predicting the Sahel rainfall successfully.

Significance Statement

Predicting Sahel rainfall on the decadal time scale is of great importance. This study provides a thorough evaluation of the decadal prediction skills of Sahel rainfall in the current decadal prediction systems participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). A majority of the systems achieve high prediction skill of Sahel rainfall, which probably results from the high prediction skill of some key sea surface temperature (SST) drivers, especially in the Atlantic and Mediterranean Sea SST, and their relationships with Sahel rainfall. This study provides a reference for better understanding the predictability of Sahel rainfall.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bin Wang, wab@lasg.iap.ac.cn

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