MLAWind: A Monthly Sea Surface Wind Dataset Derived from an Interpretable Machine Learning Approach Integrating In-Situ Observations and Satellite Data

Abstract. A gridded sea surface wind dataset with long temporal coverage is crucial for understanding atmospheric circulation changes and air-sea interactions at different time scales. This study employs an interpretable machine learning model based on random forest algorithm to generate a 1°×1° monthly sea surface wind dataset (MLAWind) from 1950 to 2023, covering the near-global ocean within 60° S–60° N. The data reconstruction model integrates the Cross-Calibrated Multi-Platform (CCMP) satellite data and the spatially sparse long-term International Comprehensive Ocean-Atmosphere Data Set (ICOADS), exhibiting robust interpretability and generalization capability. Evaluations demonstrate that the MLAWind dataset exhibits better agreement with remote sensing observations than existing reanalysis datasets during the training period (1993–2022), while maintaining robust performance during the independent testing period in 2023. Moreover, the performance of MLAWind since 1950 is assessed across multiple time scales. Its characteristics in climatology, annual cycle, and inter-annual variability are comparable to those of existing reanalysis datasets, even during the non-satellite period prior to 1993. Uncertainties remain in the long-term trends of different datasets. The trend derived from MLAWind is corroborated by independent coral records during 1950–1982, which demonstrates its strong capability in reconstructing historical sea surface wind variations. The results indicate that MLAWind serves as a reliable data resource for global climate change research. The reconstructed MLAWind dataset is publicly accessible at https://doi.org/10.5281/zenodo.17354864 (Guo et al., 2025b).