Unbiased Detection of Warming Trends Through Advanced Integration of Satellite and Reanalysis Air Temperatures

Accurate detection of warming trends is crucial for both mitigation and adaptation strategies. While satellite observations provide high spatial resolution temperature data, cloud contamination creates gaps that require filling methods–a process that can bias warming trend calculations. Traditional gap-filling approaches, though accurate for absolute temperatures, consistently underestimate warming trends. This study presents a new data assimilation method that integrates MODIS-derived air temperatures with ERA5-Land reanalysis data to achieve unbiased warming trend detection while maintaining high spatial resolution. We validate our method against weather station data on the Tibetan Plateau and compare it with four mainstream gap-filling approaches: temporal, spatial, spatio-temporal, and multisource fusion-based methods. Our results show that while all methods, including ours, perform similarly in terms of absolute temperature accuracy (with RMSE around 1.66°C and R around 0.99), a critical difference emerges in warming trend estimation. Traditional gap-filling methods show negative biases in warming trends, but our assimilation-based approach almost completely eliminates these biases when validated against both station-level data and elevation-binned averages. The integrated air temperature for the Tibetan Plateau reveals significant warming patterns, particularly in glacier regions, although the overall warming rate (0.022°C/yr) is lower than that indicated by station data alone (0.026°C/yr). This difference likely reflects the ability of our method to capture warming trends across the diverse terrain of the plateau, not just at station locations. This improvement in trend estimation, combined with the method's ability to maintain high spatial resolution, represents a significant advance in the use of satellite-derived data for climate change analysis.