| 31 Mar 2026
Optimizing WRF physics for multi-decadal simulation of near-surface climate over arid Xinjiang, China
Abstract. Complex terrain, heterogeneous surfaces, and diverse moisture sources complicate mesoscale modeling in arid and semi-arid regions. To evaluate the performance of Weather Research and Forecasting (WRF) model physics in Xinjiang, we tested 34 combinations of physical parameterizations to simulate near-surface climate variables for 1960–2020 and evaluated the simulations against surface observations. Overall skill was highest for 2 m air temperature and surface pressure, moderate for 10 m wind speed and 2 m relative humidity, and lowest for precipitation. Surface downward shortwave radiation was systematically overestimated in spring and summer. Sensitivity analyses show that the Betts–Miller–Janjic (BMJ) cumulus and WRF double-moment 6-class (WDM6) microphysics schemes improve precipitation simulation, while the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer scheme improves the representation of wind speed and relative humidity. Configurations using the Simplified Arakawa–Schubert (SAS) or Tiedtke cumulus schemes perform better for surface downward shortwave radiation, and the Community Land Model version 4 (CLM4) improves simulations of temperature and surface pressure. A multi-variable composite evaluation identifies an ensemble-optimal physics suite consisting of Thompson microphysics, Tiedtke cumulus, Rapid Radiative Transfer Model (RRTM) longwave and Dudhia shortwave radiation schemes, the Grenier–Bretherton–McCaa (GBM) planetary boundary layer scheme, the Revised MM5 surface-layer scheme, and the Noah-MP land surface model. This configuration provides the most balanced performance across variables and offers a reference for regional climate simulation in arid and semi-arid regions.
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Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
First, you have archived part of the assets necessary to produce your work on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. In addition, you have not declared in the Code and Data Availability section several datasets that you use: observations from the CMA, ERA5, topographic data, and land use/cover data. Moreover, the sites that you have linked to access them (including the nasa.gov sites that you cite) do not fulfil GMD’s requirements for a persistent data archive because:
- They do not appear to have a published policy for data preservation over many years or decades (some flexibility exists over the precise length of preservation, but the policy must exist).
- They do not appear to have a published mechanism for preventing authors from unilaterally removing material. Archives must have a policy which makes removal of materials only possible in exceptional circumstances and subject to an independent curatorial decision,
- They do not appear to issue a persistent identifier such as a DOI or Handle for each precise dataset.
If we have missed a published policy which does in fact address this matter satisfactorily, please post a response linking to it. If you have any questions about this issue, please post them in a reply.
Additionally, you do not seem to have published in a permanent repository the WRF model code that you use in your work.
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The 'Code and Data Availability’ section must also be modified to cite the new repository locations, and corresponding references added to the bibliography.
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Juan A. Añel
Geosci. Model Dev. Executive Editor