Restoring and preventing losses of the world’s forests are promising natural pathways to mitigate climate change. In addition to regulating atmospheric carbon dioxide concentrations, forests modify surface and near-surface air temperatures through biophysical processes. In the eastern United States (EUS), widespread reforestation during the 20th century coincided with an anomalous lack of warming, raising the question of whether reforestation contributed to biophysical cooling and slowed local climate change. Using new cross-scale approaches and multiple independent sources of data, our analysis uncovered links between reforestation and the response of both surface and air temperature in the EUS. Ground- and satellite-based observations showed that EUS forests cool the land surface by 1-2 °C annually, with the strongest cooling effect during midday in the growing season, when cooling is 2 to 5 °C. Young forests aged 25-50 years have the strongest cooling effect on surface temperature, which extends to the near-surface air, with forests reducing midday air temperature by up to 1 °C. Our analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations predominantly surrounded by reforestation were up to 1 °C cooler than neighboring locations that did not undergo land cover change, and areas dominated by regrowing forests were associated with cooling temperature trends in much of the EUS. Our work indicates that reforestation contributed to the historically slow pace of warming in the EUS, highlighting the potential for reforestation to provide local climate adaptation benefits in temperate regions worldwide.

Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as wind speed and direction, solar radiation, and soil moisture that occur over a range of spatial scales. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenged due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) framework to identify primary controls over open terrain and forests at the Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in MaxEnt to predicting locations of deeper or shallower snowpacks, respectively, across the landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, downscaling remotely sensed snow products, and understanding field scale snow states.