Coupling solar radiation and cloud cover data for enhanced temperature predictions over topographically complex mountain terrain

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AbstractFine‐scale climate information is critical to understand species–climate relationships. It is usually obtained by interpolating meteorological station data or by downscaling coarse‐gridded climate data. In mountain areas, however, the low station density and macroclimate variables used in coarse‐gridded climate products cannot reproduce fine‐scale variations caused by the complex topography and thus result in biased predictions of species responses to climate change. Here, we present an innovative method to estimate daily local air temperature at 100 m spatial resolution in the mountain region of South Tyrol (Central Alps, Italy), called the cloud‐corrected model. We introduce a correction factor that couples solar radiation and cloud cover data to improve air temperature predictions. Results are compared to models that either consider elevation only (lapse‐rate model) or elevation and solar radiation but not cloud cover (clear‐sky model) using a set of independent meteorological stations for validation. Moreover, all models were tested to predict critical phenological stages of the climate‐sensitive species Vitis vinifera. Over the vegetative period, the cloud‐corrected model significantly reduced the mean absolute error of predicted temperature compared with the lapse rate and clear‐sky model by 10 and 23%, respectively, and the bias by 93 and 90%, respectively. Solar radiation and cloud cover both strongly influenced local air temperature and their inclusion in temperature estimates greatly reduced systematic biases and improved predictions of important plant phenological stages by multiple days. Our method therefore provides a new way to include easily accessible topography and climate data into fine‐scale temperature predictions and accounts for important climate forcing factors that otherwise are often neglected. It combines efficiency and accuracy, because it limits data requirements but still operates on an ecologically relevant spatial scale. Thus, our approach offers promising opportunities to improve the understanding of species–climate relationships, especially in regions sensitive to the effects of climate change.