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Evaluation of ERA5 reanalysis over the deserts in northern China

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Abstract

Deserts cover a vast area of the world’s land surface, but the study of desert climate has been impeded by the lack of ground meteorological observations. In recent years, the climate reanalysis products provide an important data source to investigate climate change in observation-limited areas. However, their accuracy in desert regions has been poorly investigated. Here, we evaluated the performance of the latest ERA5 reanalysis datasets for the climatic conditions over the deserts of northern China (DNC), including temperature and precipitation variations, climate extremes, and detection skills of daily precipitation. The results show that ERA5 well captures the observed pattern of annual and seasonal temperatures, as well as the warming trend during the past four decades in DNC, compared to the ground observations. However, both annual and seasonal precipitations are greatly overestimated over DNC, and large uncertainties exist in precipitation trends. In addition, the variability of interannual precipitation, precipitation intensity, maximum 1-day precipitation, and the number of continuous dry days are underestimated by ERA5. The bias of precipitation estimates may be traced to the overestimation of rainfall occurrence in ERA5. Comparing to other reanalysis (such as MERRA2, NCEP/NCAR, and NOAA-20C), the ERA5 outperforms them for precipitation and precipitation extremes over DNC, although its performance is not as good as gridded gauge-based precipitation datasets (such as CPC). Overall, this study provides references for the use of ERA5 over DNC and facilitates the applications of ERA5 over other observation-limited desert regions.

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Data availability

The ground observation data were derived from China Meteorological Administration (http://cdc.cma.gov.cn/). ERA5 reanalysis data were obtained from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/#!/home). NCEP1, NCEP2, NOAA-20C, PREC/L, and CPC data are derived from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded/index.html), MERRA2 is derived from the National Aeronautics and Space Administration Goddard Space Flight Center (https://disc.gsfc.nasa.gov/datasets/M2TMNXFLX_5.12.4/summary), and GPCP is derived from the Global Precipitation Climatology Project (https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-monthly/access/).

Code availability

The programs used to generate all the results are Python 3.8 and ArcGIS 10.8.

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Acknowledgements

We gratefully acknowledge the China Meteorological Data Service Center for the supply of meteorological data. And we thank the ECMWF, NOAA, and NASA for the reanalysis datasets and gauge-based precipitation datasets. We appreciate the helpful and constructive comments by the reviewers.

Funding

This study was supported by the National Natural Science Foundation of China (Nos. 41871012, 42075020), the Regional Key Program of Science and Technology Development Plan (Nos. 2022E01014, 2019BFG02024), the Fundamental Research Funds for the Central Universities of China (0209–14380091), and the Chinese Academy of Sciences Interdisciplinary Innovation Team.

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Authors and Affiliations

  1. School of Geography and Ocean Science, Nanjing University, Nanjing, China

    Chengzhi Hou & Zhiwei Xu

  2. School of Atmospheric Sciences, Nanjing University, Nanjing, China

    Danqing Huang

  3. Institute of Forestry and Grassland Ecology, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan, China

    Hao Xu

  4. Ningxia Key Laboratory of Desertification Control and Soil and Water Conservation, Yinchuan, China

    Hao Xu

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  1. Chengzhi Hou
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Zhiwei Xu and Danqing Huang collected the data, conceptualized, and lead the project. Chengzhi Hou analyzed the data and wrote the draft. All authors contributed to the interpretation of the results and revised the paper.

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Correspondence to Danqing Huang or Zhiwei Xu.

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Hou, C., Huang, D., Xu, H. et al. Evaluation of ERA5 reanalysis over the deserts in northern China. Theor Appl Climatol 151, 801–816 (2023). https://doi.org/10.1007/s00704-022-04306-y

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