Abstract
A 29-year variable resolution climate simulation is conducted from January 1988 to December 2016 using the Model for Prediction Across Scale-Atmosphere (MPAS-A), with prescribed sea surface temperatures obtained from ERA-Interim reanalysis. The global variable resolution configuration employs a mesh refinement of 92–25 km centered over East Asia. Model validations against combined observed datasets highlight that MPAS-A demonstrated advantages over three selected Regional Climate Models (RCMs) in terms of the spatial distribution of precipitation and spatial variability of the near-surface air temperature but struggled with accurately depicting temporal precipitation patterns. MPAS-A’s anomalies in mid-latitude circulation and wave activity fluxes explained the weaker cold air activities during winter in eastern China and the northward shift of the Meiyu rain belt. Common issues with reference RCMs exist in MPAS-A, such as excessive zonal moisture transport over the ocean and unrealistic interannual variability over the northwest Pacific Ocean. The wet biases over the ocean are associated with systematically higher Convective Available Potential Energy (CAPE) for MPAS-A. However, the extreme rainfall indices such as R95pTOT and R99pTOT are not completely dominated by these wet biases and still exhibit reasonable results. This finding underscores the robustness and potential of the variable resolution (VR) approach in obtaining regional information within a single model framework.
Key Points
• A global variable resolution simulation is conducted from 1988 to 2016 following CORDEX-East Asia framework.
• MPAS-A demonstrates superior spatial correlation in precipitation and better spatial variability in near-surface air temperature compared to three RCMs from CORDEX-EA-II.
• MPAS-A and RCMs exhibit similar anomalies in moisture transport, but MPAS-A performs better over the northwest Pacific Ocean.
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Data availability
The released version of MPAS-A v7.0 can be downloaded from https://doi.org/10.5281/zenodo.3241875. The 92-25 km variable resolution mesh used in this study can be downloaded from http://www2.mmm.ucar.edu/projects/mpas/atmosphere_meshes/x4.163842.tar.gz The CN05.1 datasets can be obtained from http://ccrc.iap.ac.cn/resource/detailid=228. CMORPH datasets can be obtained from https://www.cpc.ncep.noaa.gov/products/janowiak/cmorph_description.html. The GPCP V3.2 could be accessed via https://measures.gesdisc.eosdis.nasa.gov/data/GPCP/GPCPMON.3.2/.
The GPCP daily precipitation v1.3 could be downloaded via https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily. APHRODITE V1901 and V1808 datasets could be downloaded via https://www.chikyu.ac.jp/precip/english/downloads.html. We declare that we have no conflict of interest. Interpolated monthly-averaged simulations and reference datasets are available at https://zenodo.org/record/8362716. The ERA-Interim reanalysis data (Dee et al. 2011) used for driving the MPAS-A experiments were obtained from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim.
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Acknowledgements
This work is jointly funded by the National Key Research and Development Program of China (grant no. 2023YFF0805404), the National Natural Science Foundation of China (U2242204, 42130602), and the Jiangsu Collaborative Innovation Center for Climate Change. The numerical calculations in this paper have been done on the computing facilities in the High Performance Computing Center (HPCC) of Nanjing University. The authors acknowledge with thanks to the ECMWF for providing the ERA-interim reanalysis data as driving fields in the simulations, NOAA for providing the GPCP data, and NOAA’s Climate Precipitation Center for providing the CMORPH observational data. We also thank Dr. Jia Wu from the National Climate Center (NCC) of China Meteorological Administration (CMA) for providing the CN05.1 gridded dataset and Dr. Akiyo YATAGAI for providing the APHRODITE dataset. The authors express gratitude towards the developers of the scientific software pivotal to this study, specifically acknowledging xarray (Hoyer and Hamman 2017), xclim (Logan et al. 2021), and proplot Luke L. B. Davis (2021).
Funding
This work is jointly funded by the National Key Research and Development Program of China (grant no. 2023YFF0805404), the National Natural Science Foundation of China (U2242204, 42130602), and the Jiangsu Collaborative Innovation Center for Climate Change.
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Yiyuan Cheng, Jianping Tang and Juan Fang contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yiyuan Cheng, Jianping Tang and Juan Fang. Jianping Tang, Yixiong Lu, and Juan Fang helped perform the analysis with constructive discussions. Juan Fang provided financial and resource support, ensuring the smooth progression of the project. The first draft of the manuscript was written by Yiyuan Cheng and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Cheng, Y., Tang, J., Lu, Y. et al. Assessment of dynamical downscaling performance over cordex east Asia using MPAS-A global variable resolution model: climatology, seasonal cycle, and extreme events. Clim Dyn (2024). https://doi.org/10.1007/s00382-024-07265-4
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DOI: https://doi.org/10.1007/s00382-024-07265-4