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Land‐use/cover change (LUCC) is an important driver of environmental change, occurring at the same time as, and often interacting with, global climate change. Reforestation and deforestation have been critical aspects of LUCC over the past two centuries and are widely studied for their potential to perturb the global carbon cycle. More recently, there has been keen interest in understanding the extent to which reforestation affects terrestrial energy cycling and thus surface temperature directly by altering surface physical properties (e.g., albedo and emissivity) and land–atmosphere energy exchange. The impacts of reforestation on land surface temperature and their mechanisms are relatively well understood in tropical and boreal climates, but the effects of reforestation on warming and/or cooling in temperate zones are less certain. This study is designed to elucidate the biophysical mechanisms that link land cover and surface temperature in temperate ecosystems. To achieve this goal, we used data from six paired eddy‐covariance towers over co‐located forests and grasslands in the temperate eastern United States, where radiation components, latent and sensible heat fluxes, and meteorological conditions were measured. The results show that, at the annual time scale, the surface of the forests is 1–2°C cooler than grasslands, indicating a substantial cooling effect of reforestation. The enhanced latent and sensible heat fluxes of forests have an average cooling effect of −2.5°C, which offsets the net warming effect (+1.5°C) of albedo warming (+2.3°C) and emissivity cooling effect (−0.8°C) associated with surface properties. Additional daytime cooling over forests is driven by local feedbacks to incoming radiation. We further show that the forest cooling effect is most pronounced when land surface temperature is higher, often exceeding −5°C. Our results contribute important observational evidence that reforestation in the temperate zone offers opportunities for local climate mitigation and adaptation.

 

The winter-spring shoulder season, or vernal window, is a key period for ecosystem carbon, water, and energy cycling. Sometimes referred to as mud season, in temperate forests, this transitional season opens with the melting of snowpack in seasonally snow-covered forests and closes when the canopy fills out. Sunlight pours onto the forest floor, soils thaw and warm, and there is an uptick in soil respiration. Scientists hypothesize that this window of ecological opportunity will lengthen in the future; these changes could have implications across all levels of the ecosystem, including the availability of food and water in human systems. Yet, there remains a dearth of observations that track both winter and spring indicators at the same location. Here, we present an inquiry-based, low-cost approach for elementary to high school classrooms to track environmental changes in the winter-spring shoulder season. Engagement in hypothesis generation and the use of claim, evidence, and reasoning practices are coupled with field measurement protocols, which provides teachers and students an authentic research experience that allows for a place-based understanding of local ecosystems and their connection to 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.