Reliability of simulating internal precipitation variability over multi-timescales using multiple global climate model large ensembles in China

Authors: LIU, JIAHE - Wuhan University; CHEN, JIE - Wuhan University; Zhang, Xunchang

Submitted to: International Journal of Climatology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/30/2023
Publication Date: 9/10/2023
Citation: Liu, J., Chen, J., Zhang, X.J. 2023. Reliability of simulating internal precipitation variability over multi-timescales using multiple global climate model large ensembles in China. International Journal of Climatology. 43(14):6383-6401. https://doi.org/10.1002/joc.8210.
DOI: https://doi.org/10.1002/joc.8210

Interpretive Summary: Large ensembles (SMILEs) are generated by running a single model under different initial conditions, which provide valuable means to quantify internal climate variability (ICV) under the climate change. However, the ability of SMILEs to represent the multi-timescale ICV is not fully understood, especially for the region of China. The main objective of this work is to assess the ability of six advanced SMILEs with ensemble members ranging from 30 to 90 in simulating ICV of precipitation at various timescales in China, using a set of observational datasets as the benchmark. To have a first insight into the selected models, the mean states and trends of annual precipitation were evaluated at first. The ICV of precipitation was then calculated at multiple timescales by two widely used methods of representing temporal variability and inter-member variability. The minimum required ensemble size for each climate model to robustly capture the real value of ICV is investigated at last. The results show that the SMILEs have the ability to capture the basic spatial pattern and the magnitude of mean states and trends, as well as ICV of precipitation at all selected timescales. The ICV represented by temporal variability and inter-member variability are both close to observations at the inter-annual and the inter-decadal timescales. However, the ICV calculated by the inter-member variability obviously overestimate ICV of observational datasets at multi-decadal scales for most grids. Within ± 10 % error limit, 20~30 members are sufficient for all climate models at the inter-annual timescale while ensemble sizes of 76% or more of the full sizes for each climate model are needed at multi-decadal timescales. Overall, the results of this study indicate that multi-SMILEs are capable of capturing ICV of annual precipitation at the selected timescales in the mainland of China. This work would be useful to climatologists and climate modelers for quantifying and understanding ICV of climate model simulation under climate change.

Technical Abstract: Single model initial-condition large ensembles (SMILEs) are valuable means to study the role of internal climate variability (ICV) in the climate change. However, the ability of SMILEs to represent the multi-timescale ICV is not fully understood, especially for the region of China. The main objective of this work is to assess the ability of six advanced SMILEs with ensemble members ranging from 30 to 90 in simulating ICV of precipitation at various timescales in China, using a set of observational datasets as the benchmark. To have a first insight into the selected models, the mean states and trends of annual precipitation were evaluated at the very beginning. The ICV of precipitation was then calculated at multiple timescales by two widely used methods of representing temporal variability and inter-member variability. The minimum required ensemble size for each climate model to robustly capture the real value of ICV is investigated at last. The results show that the SMILEs have the ability to capture the basic spatial pattern and the magnitude of mean states and trends, as well as ICV of precipitation at all selected timescales. The ICV represented by temporal variability and inter-member variability are both close to observations at the inter-annual and the inter-decadal timescales. However, the ICV calculated by the inter-member variability obviously overestimate ICV of observational datasets at multi-decadal scales for most grids. Within ± 10 % error limit, 20~30 members are sufficient for all climate models at the inter-annual timescale while ensemble sizes of 76% or more of the full sizes for each climate model are needed at multi-decadal timescales. Overall, the results of this study indicate that multi-SMILEs are capable of capturing ICV of annual precipitation at the selected timescales in the mainland of China.