MLAWind: a monthly sea surface wind dataset derived from an interpretable machine learning approach integrating in-situ observations and satellite data

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. Quantitative validations against satellite and in-situ data indicate that MLAWind achieves better consistency with observations than most reanalysis datasets, maintaining robust performance during both training (1993–2022) and testing periods (2023). 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 reanalysis datasets, even during the non-satellite period prior to 1993. Independent coral records from 1950–1982 are employed as proxy indicators to evaluate long-term sea surface wind trends. The trend derived from MLAWind is corroborated by these proxies, exhibiting significantly higher temporal correlation than those obtained from reanalysis products. MLAWind demonstrates a more reliable reconstruction of long-term sea surface wind variability during historical periods since 1950. The results indicate that MLAWind serves as a robust data resource for global climate change research. The reconstructed MLAWind dataset is publicly accessible at https://doi.org/10.12157/IOCAS.20250707.001 (Guo et al., 2025c).