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Some of the largest climatic changes in the Arctic have been observed in Alaska and surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here we provide an updated, long-term trend analysis (1957-2021; n=65 years) of such parameters in ERA5, NOAA NClimGrid, NOAA NCEI Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m>0.50°C decade−1). P has also increased across climate divisions, and appears strongly interrelated with temperature-sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends, and show ~50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. sub-region, positioning Alaska at the forefront of U.S. climate warming.

Parts of southeast Alaska experienced record drought in 2019, followed by record daily precipitation in late 2020 with substantial impacts to human health and safety, energy resources, and fisheries. To help ascertain whether these types of events can be expected more frequently, this study investigated observed trends and projected changes of hydroclimatic extremes indices across southeast Alaska, including measures of precipitation variability, seasonality, magnitude, and type. Observations indicated mixed tendencies of interannual precipitation variability, but there were consistent trends toward warmer and wetter conditions. Projected changes were assessed using dynamically downscaled climate model simulations at 4-km spatial resolution from 2031 to 2060 that were compared with a historical period from 1981 to 2010 using two models—NCAR CCSM4 and GFDL CM3. Consistent directional changes were found for five of the analyzed indices. The CCSM indicated increased maximum 1-day precipitation (RX1; 12.6%), increased maximum consecutive 5-day precipitation (RX5; 7.4%), longer periods of consecutive dry days (CDD; 11.9%), fewer snow cover days (SNC; −21.4%) and lower snow fraction (SNF; −24.4%); for GFDL these changes were 19.8% for RX1, 16.0% for RX5, 20.1% for CDD, −21.9% for SNC, and −26.5% for SNF. Although both models indicated substantial snow losses, they also projected annual snowfall increases at high elevations; this occurred above 1500 m for CCSM and above 2500 m for GFDL. Significance testing was assessed at the 95% confidence level using Theil–Sen’s slope estimates for the observed time series and the Wilcoxon–Mann–Whitney U test for projected changes of the hydroclimatic extremes indices relative to their historical distributions.

In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.

Alaska’s Yukon–Kuskokwim Delta (YKD) is among the Arctic’s warmest, most biologically productive regions, but regional decline of the normalized difference vegetation index (NDVI) has been a striking feature of spaceborne Advanced High Resolution Radiometer (AVHRR) observations since 1982. This contrast with “greening” prevalent elsewhere in the low Arctic raises questions concerning climatic and biophysical drivers of tundra productivity along maritime–continental gradients. We compared NDVI time series from AVHRR, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat for 2000–19 and identified trend drivers with reference to sea ice and climate datasets, ecosystem and disturbance mapping, field measurements of vegetation, and knowledge exchange with YKD elders. All time series showed increasing maximum NDVI; however, whereas MODIS and Landsat trends were very similar, AVHRR-observed trends were weaker and had dissimilar spatial patterns. The AVHRR and MODIS records for time-integrated NDVI were dramatically different; AVHRR indicated weak declines, whereas MODIS indicated strong increases throughout the YKD. Disagreement largely arose from observations during shoulder seasons, when there is partial snow cover and very high cloud frequency. Nonetheless, both records shared strong correlations with spring sea ice extent and summer warmth. Multiple lines of evidence indicate that, despite frequent disturbances and high interannual variability in spring sea ice and summer warmth, tundra productivity is increasing on the YKD. Although climatic drivers of tundra productivity were similar to more continental parts of the Arctic, our intercomparison highlights sources of uncertainty in maritime areas like the YKD that currently, or soon will, challenge historical concepts of “what is Arctic.”

Warming temperatures across southeast Alaska are affecting the region’s energy and transportation sectors, marine ecosystems, and forest health. More frequent above-freezing temperatures lead a transition from snow- to rain-dominant precipitation regimes, accelerating glacial mass balance loss and a leading to a greater risk for warm-season drought. Southeast Alaska has steep topographical gradients, which necessitate the use of downscaled climate information to study historical and projected periods. This study used regional dynamical downscaling at 4-km spatial resolution with the Weather Research and Forecasting Model to assess historical (1981–2010) and projected (2031–60) climate states for southeast Alaska. These simulations were driven by one reanalysis (i.e., the Climate Forecast System Reanalysis) and two climate models (i.e., the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4), which each included a historical simulation and a projected simulation. The future simulations used the representative concentration pathway 8.5 emissions scenario. Bias-corrected projections (2031–60 minus 1981–2010) indicated seasonal warming of 1°–3°C, increased precipitation during autumn (4%–12%) and winter (7%–12%), and decreased snowfall in all seasons (up to 60% in autumn). The average number of days annually with a minimum temperature below freezing dropped by more than 30. The average annual maximum consecutive 3-day precipitation amounts increased by 11%–16%, but analogous extreme snowfall amounts dropped by 5%–11%. The most substantial snow losses occurred at low-elevation and coastal locations; at many high elevations (e.g., above 1000 m), extreme snowfall amounts increased.

Profound changes in Arctic sea-ice, a growing desire to utilize the Arctic’s abundant natural resources, and the potential competitiveness of Arctic shipping routes, all provide for increased industry marine activity throughout the Arctic Ocean. This is anticipated to result in further challenges for maritime safety. Those operating in ice-infested waters require various types of information for sea-ice and iceberg hazards. Ice information requirements depend on regional needs and whether the stakeholder wants to avoid ice all together, operate near or in the Marginal Ice Zone, or areas within the ice pack. An insight into user needs demonstrates how multiple spatial and temporal resolutions for sea-ice information and forecasts are necessary to provide information to the marine operating community for safety, planning, and situational awareness. Although ship-operators depend on sea-ice information for tactical navigation, stakeholders working in route and capacity planning can benefit from climatological and long-range forecast information at lower spatial and temporal resolutions where the interest is focused on open-water season. The advent of the Polar Code has brought with it additional information requirements, and exposed gaps in capacity and knowledge. Thus, future satellite data sources should be at resolutions that support both tactical and planning activities.

Lightning is a key driver of wildfire activity in Alaska. Quantifying its historical variability and trends has been challenging because of changes in the observational network, but understanding historical and possible future changes in lightning activity is important for fire management planning. Dynamically downscaled reanalysis and global climate model (GCM) data were used to statistically assess lightning data in geographic zones used operationally by fire managers across Alaska. Convective precipitation was found to be a key predictor of weekly lightning activity through multiple regression analysis, along with additional atmospheric stability, moisture, and temperature predictor variables. Model-derived estimates of historical June–July lightning since 1979 showed increasing but lower-magnitude trends than the observed record, derived from the highly heterogeneous lightning sensor network, over the same period throughout interior Alaska. Two downscaled GCM projections estimate a doubling of lightning activity over the same June–July season and geographic region by the end of the twenty-first century. Such a substantial increase in lightning activity may have significant impacts on future wildfire activity in Alaska because of increased opportunities for ignitions, although the final outcome also depends on fire weather conditions and fuels.

Landslides along road corridors in Alaska national parks pose threats to public safety, visitor access, subsistence activities, and result in costly remediation of damaged infrastructure. Landslide risk in these areas, which contain near-surface permafrost, is associated with mean annual air temperatures (MAATs) above freezing and heavy precipitation events. Historical (1981–2020) values of MAAT and summer precipitation (JJA PCPT) from the fifth generation European Centre for Medium-Range Weather Forecasts (Reading, UK) atmospheric reanalysis (ERA5) were compared to mid-century (2021–2060) and late-century (2061–2100) downscaled climate model projections across Gates of the Arctic National Park and Preserve (GAAR), Denali National Park and Preserve (DENA), and Wrangell-St. Elias National Park and Preserve (WRST). ERA5 showed that all locations historically had MAAT values below freezing, but all three parks were warming significantly (0.3–0.6 °C per decade). Observed trends of MAAT from 18 stations showed warming trends with 11 of the 18 being significant at the 95% confidence level using the Mann–Kendall non-parametric test. Road corridor values are given for the: (1) proposed Ambler Road through GAAR, (2) Denali Park Road in DENA, and (3) McCarthy Road in WRST. Elevated risk from MAAT was projected in the mid-century period for the Denali Park Road and McCarthy Road and across all three park road corridors in the late-century period; elevated risk from JJA PCPT was projected in all periods for all road corridors.

Seasonality in photosynthetic activity is a critical component of seasonal carbon, water and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated. However, how photosynthetic seasonality evolved to its current state, and what role climatic constraints and their variability played in this process and ultimately in carbon cycle is still poorly understood due to its complexity. Here, we take the 'laws of minimum' as a basis and introduce a new framework where the timing (Day of Year) of peak photosynthetic activity (DOYP max ) acts as a proxy for plant's adaptive state to climatic constraints on its growth. Our analyses confirm that spatial variations in DOYP max reflect spatial gradients in climatic constraints as well as seasonal maximum and total productivity. We find a widespread warming-induced advance in DOYP max (-1.66 ± 0.30 days decade-1 , P < 0.001) across northern lands, indicating a spatio-temporal dynamism of climatic constraints to plant growth. We show that the observed changes in DOYP max are associated with an increase in total gross primary productivity through enhanced carbon assimilation early in the growing season, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. Such changes are expected to continue in the future based on our analysis of Earth System Model (ESM) projections. Our study provides a simplified, yet realistic framework based on first principles for the complex mechanisms by which various climatic factors constrain plant growth in northern ecosystems.

Climate change impacts the electric power system by affecting both the load and generation. It is paramount to understand this impact in the context of renewable energy as their market share has increased and will continue to grow. This study investigates the impact of climate change on the supply of renewable energy through applying novel metrics of intermittency, power production and storage required by the renewable energy plants as a function of historical climate data variability. Here we focus on and compare two disparate locations, Palma de Mallorca in the Balearic Islands and Cordova, Alaska. The main results of this analysis of wind, solar radiation and precipitation over the 1950–2020 period show that climate change impacts both the total supply available and its variability. Importantly, this impact is found to vary significantly with location. This analysis demonstrates the feasibility of a process to evaluate the local optimal mix of renewables, the changing needs for energy storage as well as the ability to evaluate the impact on grid reliability regarding both penetration of the increasing renewable resources and changes in the variability of the resource. This framework can be used to quantify the impact on both transmission grids and microgrids and can guide possible mitigation paths., P.C. and D.G. acknowledge financial support from Ministerio de Ciencia e Innovación (Spain), the Agencia Estatal de Investigación (AEI, Spain), and the Fondo Europeo de Desarrollo Regional (FEDER, EU) under grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711). D.N. gratefully acknowledges support from DOE Project GMLC 1.5.02—Resilient Alaskan Distribution system Improvements using Automation, Network analysis, Control, and Energy storage (RADIANCE). U.S.B. acknowledges support from the National Science Foundation under award #OIA-1753748 and by the State of Alaska for material which this work is based upon.

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

Climate change is generating sufficient risk for nation-states and citizens throughout the Arctic to warrant potentially radical geoengineering solutions. Currently, geoengineering solutions such as surface albedo modification or aerosol deployment are in the early stages of testing and development. Due to the scale of deployments necessary to enact change, and their preliminary nature, these methods are likely to result in unforeseen consequences. These consequences may range in severity from local ecosystem impacts to large scale changes in available solar energy. The Arctic is an area that is experiencing rapid change, increased development, and exploratory interest, and proposed solutions have the potential to produce new risks to both natural and human systems. This article examines potential security and ethical considerations of geoengineering solutions in the Arctic from the perspectives of securitization, consequentialism, and risk governance approaches, and argues that proactive and preemptive frameworks at the international level, and especially the application of risk governance approaches, will be needed to prevent or limit negative consequences resulting from geoengineering efforts. Utilizing the unique structures already present in Arctic governance provides novel options for addressing these concerns from both the perspective of inclusive governance and through advancing the understanding of uncertainty analysis and precautionary principles.

Accepted manuscript version, licensed CC BY-NC-ND 4.0. " The Arctic Ocean is overwhelmingly forced by its lateral boundaries, and interacts with, the global system. For the development of nested conceptual models of the Arctic Ocean ecosystem we here choose the full pan-Arctic as our focal scale. Understanding the pan-Arctic scale, however, requires that we look at the underlying scales of its major components, by considering regionality, connectivity and seasonality. Six regions are identified on the basis of hydro-morphological characteristics, which subsequently reflect ecological function and traits. Regions are static, tied to geography, but are linked by contiguous domains of shared function that facilitate material transports and share key ecological features. The pan-Arctic scale also requires attention to forcing by the seasonal light intensity, wherein the maximum length of a single day varies from near 24 h at the Arctic Circle to about 4400 h (183 days) at the North Pole. The light climate forces a strong phenology in the Arctic, as reflected in the periodic life cycle events of organisms. In addition to light climate, Arctic Ocean ecosystems are dominated by three fundamental variables: ice cover, nutrient/food availability and advection. The conditions under which each of these variables play out in the course of a year are set by the regions and contiguous domains within which they operate and interact. Together, the defined regions and their seasonality, the contiguous domains and their connectivity, and the three fundamental variables allow unambiguous application of scale-nested, parsimonious and adaptive, conceptual models, from which to 1) create testable hypotheses, 2) plan and then modify field campaigns, and 3) communicate essential results to managers and the general public. The development of these nested conceptual pan-Arctic scale models creates a vital step into the future of unifying, integrative oceanographic and ecological work.

In Alaska, wildfire map products have traditionally been generated from lower spatial and spectral resolution Landsat imagery such as LANDFIRE Program's Existing Vegetation Type (EVT) resulting in products that do not accurately assess fire fuel types for local sites. In this study we demonstrate the efficacy of AVIRIS-NG hyperspectral data for mapping Interior Alaska's vegetation and fuel type. Based on an evaluation of field plot data collected by the project team in 2019, the new vegetation map derived from AVIRIS-NG at Viereck IV level resulted in a 73% classification accuracy compared to the 32% accuracy of the LANDFIRE's product EVT derived from Landsat 8. Not only did our product more accurately classify fire fuels, it was also able to identify 20 dominant vegetation classes (percent cover > 1%) while the EVT product only identified eight dominant classes within the study area.

Thirty models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their performances in reproducing two summertime atmospheric circulation patterns in the Arctic: the Arctic Oscillation (AO) and Arctic dipole (AD). The reference AO and AD are extracted from the ERA-Interim dataset (1979–2016). Model evaluation is conducted during the historical period (1901–2005). Models are ranked by a combined metrics approach based on two pattern correlation coefficients (PCCs) and two explained variances for the AO and AD, respectively. In the projected period (2006–2100), most models produce a positive trend for the AO index and a negative trend for the AD index in summer. The models ranked higher based on the combined metrics ranking show greater consistency and smaller values in the magnitudes of trends of AO and AD than the lower-ranked ones. The projected trends in the AO and AD contribute to a slight increase, if not a decrease, of the air temperature and an acceleration of precipitation increase in the twenty-first century over Arctic Alaska, which is the reverse of over the Barents and Kara Seas. Changes in the AO and AD are relatively minor contributing factors to the projected temperature and precipitation changes in the Arctic, among which the changes in the AD play a bigger role than those in the AO. The summer AO and AD have a stronger impact on the spatial asymmetry of the precipitation field than on the air temperature field.

Climate warming is expected to disproportionately affect crop yields in the southern United States due to excessive heat stress, while presenting new farming opportunities through a longer growing season farther north. Few studies have investigated the impact of this warming on agro-climate indices that link meteorological data with important field dates in northern regions. This study employs regional dynamical downscaling using the Weather Research and Forecasting (WRF) Model to assess changes in growing season length (GSL), spring planting dates, and occurrences of plant heat stress (PHS) for five regions in Alaska. Differences between future representative concentration pathway 8.5 (RCP8.5; 2011–40, 2041–70, 2071–2100) and historical (1981–2010) periods are obtained using boundary forcing from the Geophysical Fluid Dynamics Laboratory Climate Model, version 3, and the NCAR Community Climate System Model, version 4. The model output is bias corrected using ERA-Interim. Median GSL shows increases of 48–87 days by 2071–2100, with the largest changes in northern Alaska. Similarly, by 2071–2100, planting dates advance 2–4 weeks, and PHS days increase from near 0 to 5–10 instances per summer in the hottest areas. The largest GSL changes occur in the mid- (2041–70) and late century (2071–2100), when a warming signal emerges from the historical interannual variability. These periods coincide with the greatest divergence of the RCPs, suggesting that near-term decision-making may affect substantial future changes. Early-century (2011–40) projections show median GSL increases of 8–27 days, which is close to the historical standard deviation of GSL. Thus, internal variability will remain an important source of uncertainty into the midcentury, despite a trend for longer growing seasons.

The ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

The Arctic has warmed more than twice as fast as the global average since the late twentieth century, a phenomenon known as Arctic amplification (AA). Recently, there have been considerable advances in understanding the physical contributions to AA, and progress has been made in understanding the mechanisms that link it to midlatitude weather variability. Observational studies overwhelmingly support that AA is contributing to winter continental cooling. Although some model experiments support the observational evidence, most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes. Divergent conclusions between model and observational studies, and even intramodel studies, continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change. acceptedVersion

Alaska is experiencing effects of global climate change that are due, in large part, to the positive feedback mechanisms associated with polar amplification. The major risk factors include loss of sea ice and glaciers, thawing permafrost, increased wildfires, and ocean acidification. Reanalyses, integral to understanding mechanisms of Alaska’s past climate and to helping to calibrate modeling efforts, are based on the output of weather forecast models that assimilate observations. This study evaluates temperature and precipitation from five reanalyses at monthly and daily time scales for the period 1979–2009. Monthly data are evaluated spatially at grid points and for six climate zones in Alaska. In addition, daily maximum temperature, minimum temperature, and precipitation from reanalyses are compared with meteorological-station data at six locations. The reanalyses evaluated in this study include the NCEP–NCAR reanalysis (R1), North American Regional Reanalysis (NARR), Climate Forecast System Reanalysis (CFSR), ERA-Interim, and the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Maps of seasonal bias and standard deviation, constructed from monthly data, show how the reanalyses agree with observations spatially. Cross correlations between the monthly gridded and daily station time series are computed to provide a measure of confidence that data users can assume when selecting reanalysis data in a region without many surface observations. A review of natural hazards in Alaska indicates that MERRA is the top reanalysis for wildfire and interior-flooding applications. CFSR is the recommended reanalysis for North Slope coastal erosion issues and, along with ERA-Interim, for heavy precipitation in southeastern Alaska.