Land surface temperatures outperform gridded air temperatures in modeling forest growth across the Northern Hemisphere

Global warming significantly impacts forest growth. However, commonly used spatially interpolated gridded air temperature datasets may not fully capture these effects due to their coarse spatial resolution and because air temperature may not accurately reflect the conditions that influence the tree growth process. Although finer spatial resolution land surface temperature (LST) datasets may capture more detailed temperature variations, their potential to assess forest growth responses to global warming has not been thoroughly explored. We evaluated the performance of air temperature and LST datasets with various spatial resolutions, including Climatic Research Unit gridded Time Series (CRU), TerraClimate, the land component of the fifth-generation European ReAnalysis (ERA5-Land), and MODIS LST (MOD11A2), in capturing the relationships between tree radial growth and temperature variations across 555 sites in the Northern Hemisphere. Our results showed that the finer spatial resolution MOD11A2 significantly outperformed the widely used CRU air temperature in modeling tree radial growth, with mean and maximum temperatures increasing the coefficient of determination (R²) by 16.32 % and 18.14 %, respectively. This improvement was especially apparent in high-elevation areas where R² increased by 35.70 % and 36.97 %. We suggested that commonly used spatially interpolated gridded air temperature datasets (e.g., CRU and TerraClimate) may underestimate the impact of rising temperatures on forest growth. Our findings highlight the necessity of integrating high-resolution LST to accurately model forest growth responses to global warming.