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Abstract Restoring and preserving the world's forests are promising natural pathways to mitigate some aspects of 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 questions about reforestation's contribution to local cooling and climate mitigation. Using new cross‐scale approaches and multiple independent sources of data, we 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 compared to nearby grasslands and croplands, with the strongest cooling effect during midday in the growing season, when cooling is 2–5°C. Young forests (20–40 years) have the strongest cooling effect on surface temperature. Surface cooling extends to the near‐surface air, with forests reducing midday air temperature by up to 1°C compared to nearby non‐forests. Analyses of historical land cover and air temperature trends showed that the cooling benefits of reforestation extend across the landscape. Locations 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 reforestation contributed to the historically slow pace of warming in the EUS, underscoring reforestation's potential as a local climate adaptation strategy in temperate regions. 

Climate change is reducing snowpack across temperate regions with negative consequences for human and natural systems. Because forest canopies create microclimates that preserve snowpack, managing forests to support snow refugia—defined here as areas that remain relatively buffered from contemporary climate change over time that sustain snow quality, quantity, and/or timing appropriate to the landscape—could reduce climate change impacts on snow cover, sustaining the benefits of snow. We review the current understanding of how forest canopies affect snow, finding that while closed‐conifer forests and snow interactions have been extensively studied in western North America, there are knowledge gaps for deciduous and mixed forests with dormant season leaf loss. We propose that there is an optimal, intermediate zone along a gradient of dormant season canopy cover (DSCC; the proportion of the ground area covered by the canopy during the dormant season), where peak snowpack depth and the potential for snow refugia will be greatest because the canopy‐mediated effects of snowpack sheltering (which can preserve snowpack) outweigh those of snowfall interception (which can limit snowpack). As an initial test of our hypothesis, we leveraged snowpack measurements in the northeastern United States spanning the DSCC gradient (low, <25% DSCC; medium, 25%–50% DSCC; and high, >50% DSCC), including from 2 sites in Old Town, Maine; 12 sites in Acadia National Park, Maine; and 30 sites in the northern White Mountains of New Hampshire. Medium DSCC forests (typically mature mixed coniferous–deciduous forests) exhibited the deepest peak snowpacks, likely due to reduced snowfall interception compared to high DSCC forests and reduced snowpack loss compared to low DSCC forests. Many snow accumulation or snowpack studies focus on the contrast between coniferous and open sites, but our results indicate a need for enhanced focus on mixed canopy sites that could serve as snow refugia. Measurements of snowpack depth and timing across a wider range of forest canopies would advance understanding of canopy–snow interactions, expand the monitoring of changing winters, and support management of forests and snow‐dependent species in the face of climate change. 

Abstract. We present a simple method that allows snow depth measurements tobe converted to snow water equivalent (SWE) estimates. These estimates areuseful to individuals interested in water resources, ecological function,and avalanche forecasting. They can also be assimilated into models to helpimprove predictions of total water volumes over large regions. Theconversion of depth to SWE is particularly valuable since snow depthmeasurements are far more numerous than costlier and more complex SWEmeasurements. Our model regresses SWE against snow depth (h), day of wateryear (DOY) and climatological (30-year normal) values for winter (December,January, February) precipitation (PPTWT), and the difference (TD) between meantemperature of the warmest month and mean temperature of the coldest month,producing a power-law relationship. Relying on climatological normals ratherthan weather data for a given year allows our model to be applied atmeasurement sites lacking a weather station. Separate equations are obtainedfor the accumulation and the ablation phases of the snowpack. The model isvalidated against a large database of snow pillow measurements and yields abias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE ofless than 60 mm. The model is additionally validated against two completelyindependent sets of data: one from western North America and one from thenortheastern United States. Finally, the results are compared with three othermodels for bulk density that have varying degrees of complexity and thatwere built in multiple geographic regions. The results show that the modeldescribed in this paper has the best performance for the validation datasets.