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1. Future Changes of Snow in Alaska and the Arctic under Stabilized Global Warming Scenarios

   https://doi.org/10.3390/atmos13040541  

   Bigalke, Siiri; Walsh, John E. (April 2022, Atmosphere)

   Manifestations of global warming in the Arctic include amplifications of temperature increases and a general increase in precipitation. Although topography complicates the pattern of these changes in regions such as Alaska, the amplified warming and general increase in precipitation are already apparent in observational data. Changes in snow cover are complicated by the opposing effects of warming and increased precipitation. In this study, high-resolution (0.25°) outputs from simulations by the Community Atmosphere Model, version 5, were analyzed for changes in snow under stabilized global warming scenarios of 1.5 °C, 2.0 °C and 3.0 °C. Future changes in snowfall are characterized by a north–south gradient over Alaska and an east–west gradient over Eurasia. Increased snowfall is projected for northern Alaska, northern Canada and Siberia, while milder regions such as southern Alaska and Europe receive less snow in a warmer climate. Overall, the results indicate that the majority of the land area poleward of 55°N will experience a reduction in snow. The approximate threshold of global warming for a statistically significant increase in temperature over 50% of the pan-Arctic land area is 1.5 °C. The corresponding threshold for precipitation is approximately 2.0 °C. The global warming threshold for the loss of high-elevation snow in Alaska is approximately 2.0 °C. The results imply that limiting global warming to the Paris Agreement target is necessary to prevent significant changes in winter climates in Alaska and the Arctic. 

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2. Mapping and modeling the impact of climate change on recreational ecosystem services using machine learning and big data

   https://doi.org/10.1088/1748-9326/ac65a3  

   Manley, Kyle; Egoh, Benis N (April 2022, Environmental Research Letters)

   Abstract The use of recreational ecosystem services is highly dependent on the surrounding environmental and climate conditions. Due to this dependency, future recreational opportunities provided by nature are at risk from climate change. To understand how climate change will impact recreation we need to understand current recreational patterns, but traditional data is limited and low resolution. Fortunately, social media data presents an opportunity to overcome those data limitations and machine learning offers a tool to effectively use that big data. We use data from the social media site Flickr as a proxy for recreational visitation and random forest to model the relationships between social, environmental, and climate factors and recreation for the peak season (summer) in California. We then use the model to project how non-urban recreation will change as the climate changes. Our model shows that current patterns are exacerbated in the future under climate change, with currently popular summer recreation areas becoming more suitable and unpopular summer recreation areas becoming less suitable for recreation. Our model results have land management implications as recreation regions that see high visitation consequently experience impacts to surrounding ecosystems, ecosystem services, and infrastructure. This information can be used to include climate change impacts into land management plans to more effectively provide sustainable nature recreation opportunities for current and future generations. Furthermore, our study demonstrates that crowdsourced data and machine learning offer opportunities to better integrate socio-ecological systems into climate impacts research and more holistically understand climate change impacts to human well-being. 

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3. 70-year retrospective of remote sensing applied to cumulative impact assessments, Prudhoe Bay Oilfield, Alaska

   Walker, DA (May 2022, NSF PAR)

   Cumulative impact assessments (CIAs) for new Arctic oilfields have not adequately addressed the potential landscape impacts of climate change or the indirect impacts of infrastructure in areas with ice-rich permafrost (IRP) (e.g., Raynolds et al. 2020). The main goals of this paper are: (1) trace the history of remote sensing for assessing past cumulative impacts in the Prudhoe Bay Oilfield (PBO), Alaska; (2) discuss some promising new remote-sensing and modeling tools; and (3) point toward improved capability to predict future changes. We first define IRP and cumulative impacts (CIs) and distinguish direct impacts (footprint) of infrastructure from the indirect impacts that follow construction. Aerial photographs (U.S. Navy 1948–1949) provided images of PBO landscapes before development occurred. The oil industry initiated annual high-resolution aerial-photograph missions of the PBO in 1968. In the same year, the International Biological Program (IBP) Tundra Biome started geoecological investigations that used these images to map landforms, soils, and vegetation of the PBO (Walker et al. 1980). The maps were later adapted to GIS approaches in three highly impacted 25-km2 areas of the PBO, which included several years of changes to tundra areas adjacent to infrastructure (Walker et al. 1987). The National Research Council later updated these three landscape-scale maps to 2001 and contracted the oil companies and Quantum Spatial Inc. to produce a regional-scale historical analysis of the network of roads, pipelines and other forms of infrastructure in all the North Slope Oilfields (NRC 2003). The regional- and landscape-scale maps used for NRC analysis were updated again in 2010 when unexpected rapid expansion of ice-wedge thermokarst was detected (Raynolds et al. 2014, 2016). Up to this time, CIAs of the PBO relied on aerial photographs and maps produced by the oil industry. The spatial resolution of available satellite-based remote-sensing data was insufficient to discern the details of periglacial landforms (e.g., ice-wedge polygons and nonsorted circles) or of roads, pipelines, or changes to land surfaces adjacent to infrastructure. Industry-sponsored studies that used remote-sensing products included studies of oil-pipeline spills, reserve-pits leaks (e.g., Jorgenson et al. 1995), off-road vehicles trails, and recovery following removal of gravel pads. Highlighted studies for this talk include a new NSF project that is part of the NSF Navigating the New Arctic initiative that is using integrated ground-based studies, advanced remote-sensing tools, and improved modeling approaches to examine climate- and infrastructure-related changes (Walker et al. 2022, Bergstedt 2022). Other projects that use PBO datasets for calibration, include an analysis of long-term impacts from a catastrophic flood (Shur et al. 2016, Zwieback et al. 2021) and studies that are using massive amounts of high-resolution imagery and pattern-recognition tools to detect and map ice-wedge polygons, water bodies, and infrastructure across the circumpolar Arctic (Bartsch et al. 2020; Witherrana et al. 2021). These tools combined with improved modeling approaches that bridge the gap between regional and engineering scales (e.g., Deimling et al. 2021) promise to greatly improve our ability to predict and monitor future infrastructure and landscape changes in areas with IRP. 

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4. Predicted expansion of beaver pond distribution in Arctic Alaska, 1910–2090

   https://doi.org/10.1088/1748-9326/adeba2  

   Tape, Ken D; Speed, James_D M (June 2025, Environmental Research Letters)

   Abstract Ecosystem engineering by beavers is a nascent disturbance in the Arctic tundra, appearing in the 1970s in western Alaska and since expanding deeper into tundra regions. Evidence from modeling and observations indicates that beaver ponds act as biophysical oases, and we anticipate myriad changes as these disturbances are constructed along tundra streams, sloughs, and lake outlets. We used over 11 000 mapped beaver pond locations in Arctic Alaska and their climatic, geographic, and environmental attributes to understand (1) which of those attributes control the distribution of beaver ponds, and, if temperature is a factor, (2) how beaver pond distribution will change under future climate scenarios. Of the variables used in the ensemble modeling approach, mean annual temperature was the most important variable in determining beaver pond locations, with pond occurrences more likely in warmer locales (>−2 °C). The distance to water was also important in determining beaver pond locations, as expected, with higher likelihood of ponds closer to water features. Lowland topographic variables were also relevant in determining the distribution of beaver ponds. Under the current climate, beaver ponds are widespread in most of western Alaska, matching the predicted extent of potential occupancy, with the exception of areas furthest from treeline, implying possible dispersal lags or other factors. By 2050, under future climate scenarios (RCP8.5; 2090 for RCP6.0), the entire North Slope of Alaska, which currently has no beaver ponds, is predicted to be suitable for beaver ponds, comparable to western Alaska in 2016. The vast extent of future beaver engineering in tundra regions will require reenvisioning the typical tundra stream ecosystems of northern Alaska, northern Canada, northern Europe, and northern Asia to include more extensive wetlands, routine disturbances, permafrost thaw, and other features of these nascent oases that are not fully understood. 

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5. Infrastructure Adaptation to Climate Change: Institutional Support in Rural Alaska

   Taylor, J; Poleacovschi, C; Perez, M. (October 2020, Engineering Project Organization Conference)

   Faust, K; Kanjanabootra, S (Ed.)

   As climate change impacts intensify, communities in rural Alaska are undergoing and adapting to changes to infrastructure from increased permafrost thawing, flooding, and coastal erosion. Climate change adaptation, defined as a process, action, or outcome in a system to better adjust to actual or expected climate change impacts, is needed to address significant structural failures and safety concerns. Despite the recognition of the need for support from stakeholders and adaptation of infrastructure, the level of adaptation activity remains limited and inconsistent across regions and communities in rural Alaska. We address this need by identifying drivers and barriers of adaptation based on stakeholder perspectives (N=25). Stakeholders included people who work for government agencies, non-profits, engineering firms, or academic institutions in rural Alaska. Results show that strong community leadership and flexibility of funding conditions were drivers to adaptation of infrastructure. Further, results show that the high cost of technology and infrastructure and lack of access to and stipulations on funding were barriers to adaptation of infrastructure. These drivers and barriers emphasize the importance of adaptation processes that effectively accommodate the unique contexts of addressing impacts in rural Alaska. Results demonstrate the need for national adaptation funding and policy that encourages local decision-making power. Specifically, results outline the need for adaptation funding and policy that supports the collaboration of Alaska based institutions and rural Alaska communities in adaptation. 

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