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Ice climbing is important to the culture and economies of mountain communities worldwide. However, warming winters call into question the future of livelihoods associated with ice climbing. In response, this case study presents observed and simulated ice climbing conditions in the Mount Washington Valley, New Hampshire, USA, as well as local climbing guide's experiences of and responses to these changes. First, variability in ice conditions were evaluated by classifying and summarizing ice characteristics depicted in a 20-year collection of conditions reports ( n = 372) including photos and written observations for a benchmark ice climb (Standard Route). Next, climate model ensembles were used to simulate probable changes in future ice season lengths according to intermediate and high climate forcing scenarios (i.e., RCP 4.5 & RCP 8.5). Finally, a survey and focus group were conducted with Mount Washington Valley ice climbing guides to examine observations and lived experiences of warming winters. This study, which is the first formal assessment of the implications of warming winters for ice climbing, reveals significant effects of climate change for current and projected ice climbing conditions as well as marked, and often differentiated, vulnerability and adaptability to these changes amongst climbing guides. The unique mixed-methods approach used is applicable in other locales where climate change is impacting ice climbing activities and associated livelihoods. 

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. 

Winters in northeastern North America have warmed faster than summers, with impacts on ecosystems and society. Global climate models (GCMs) indicate that winters will continue to warm and lose snow in the future, but uncertainty remains regarding the magnitude of warming. Here, we project future trends in winter indicators under lower and higher climate-warming scenarios based on emission levels across northeastern North America at a fine spatial scale (1/16°) relevant to climate-related decision making. Under both climate scenarios, winters continue to warm with coincident increases in days above freezing, decreases in days with snow cover, and fewer nights below freezing. Deep snowpacks become increasingly short-lived, decreasing from a historical baseline of 2 months of subnivium habitat to <1 month under the warmer, higher-emissions climate scenario. Warmer winter temperatures allow invasive pests such as Adelges tsugae (Hemlock Woolly Adelgid) and Dendroctonus frontalis (Southern Pine Beetle) to expand their range northward due to reduced overwinter mortality. The higher elevations remain more resilient to winter warming compared to more southerly and coastal regions. Decreases in natural snowpack and warmer temperatures point toward a need for adaptation and mitigation in the multi-million-dollar winter-recreation and forest-management economies. 

Abstract While the significance of quantifying the biophysical effects of deforestation is rarely disputed, the sensitivities of land surface temperature (LST) to deforestation-induced changes in different biophysical factors (e.g., albedo, aerodynamic resistance, and surface resistance) and the relative importance of those biophysical changes remain elusive. Based on the subgrid-scale outputs from two global Earth system models (ESMs, i.e., the Geophysical Fluid Dynamics Laboratory Earth System Model and the Community Earth System Model) and an improved attribution framework, the sensitivities and responses of LST to deforestation are examined. Both models show that changes in aerodynamic resistance are the most important factor responsible for LST changes, with other factors such as albedo and surface resistance playing secondary but important roles. However, the magnitude of the contributions from different biophysical factors to LST changes is quite different for the two ESMs. We find that the differences between the two models in terms of the sensitivities are smaller than those of the corresponding biophysical changes, indicating that the dissimilarity between the two models in terms of LST responses to deforestation is more related to the magnitude of biophysical changes. It is the first time that the attribution of subgrid surface temperature variability is comprehensively compared based on simulations with two commonly used global ESMs. This study yields new insights into the similarity and dissimilarity in terms of how the biophysical processes are represented in different ESMs and further improves our understanding of how deforestation impacts on the local surface climate. 

Abstract Ice storms are important winter weather events that can have substantial environmental, economic, and social impacts. Mapping and assessment of damage after these events could be improved by making ice accretion measurements at a greater number of sites than is currently available. There is a need for low-cost collectors that can be distributed broadly in volunteer observation networks; however, use of low-cost collectors necessitates understanding of how collector characteristics and configurations influence measurements of ice accretion. A study was conducted at the Hubbard Brook Experimental Forest in New Hampshire that involved spraying water over passive ice collectors during freezing conditions to simulate ice storms of different intensity. The collectors consisted of plates composed of four different materials and installed horizontally; two different types of wires strung horizontally; and rods of three different materials, with three different diameters, and installed at three different inclinations. Results showed that planar ice thickness on plates was 2.5–3 times as great as the radial ice thickness on rods or wires, which is consistent with expectations based on theory and empirical evidence from previous studies. Rods mounted on an angle rather than horizontally reduced the formation of icicles and enabled more consistent measurements. Results such as these provide much needed information for comparing ice accretion data. Understanding of relationships among collector configurations could be refined further by collecting data from natural ice storms under a broader range of weather conditions. 

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.