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By extending the record of Alaskan divisional temperature and precipitation back in time, regional variations and trends of temperature and precipitation over 1920–2012 are documented. The use of the divisional framework highlights the greater spatial coherence of temperature variations relative to precipitation variations. The divisional time series of temperature are characterized by large interannual variability superimposed upon low-frequency variability, as well as by an underlying trend. Low-frequency variability corresponding to the Pacific decadal oscillation (PDO) includes Alaska’s generally warm period of the 1920s and 1930s, a cold period from the late 1940s through the mid-1970s, a warm period from the late 1970s through the early 2000s, and a cooler period in the most recent decade. An exception to the cooling of the past decade is the North Slope climate division, which has continued to warm. There has been a gradual upward trend of Alaskan temperatures relative to the PDO since 1920, resulting in a statewide average warming of about 1°C. In contrast to temperature, variations of precipitation are less consistent across climate divisions and have much less multidecadal character. Thirty-year trends of both variables are highly sensitive to the choice of the subperiod within the overall 93-yr period. The trends also vary seasonally, with winter and spring contributing the most to the annual trends.

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.

Vegetation productivity trends for the Arctic tundra are updated for the 1982–2011 period and examined in the context of land surface temperatures and coastal sea ice. Understanding mechanistic links between vegetation and climate parameters contributes to model advancements that are necessary for improving climate projections. This study employs remote sensing data: Global Inventory Modeling and Mapping Studies (GIMMS) Maximum Normalized Difference Vegetation Index (MaxNDVI), Special Sensor Microwave Imager (SSM/I) sea-ice concentrations, and Advanced Very High Resolution Radiometer (AVHRR) radiometric surface temperatures. Spring sea ice is declining everywhere except in the Bering Sea, while summer open water area is increasing throughout the Arctic. Summer Warmth Index (SWI—sum of degree months above freezing) trends from 1982 to 2011 are positive around Beringia but are negative over Eurasia from the Barents to the Laptev Seas and in parts of northern Canada. Eastern North America continues to show increased summer warmth and a corresponding steady increase in MaxNDVI. Positive MaxNDVI trends from 1982 to 2011 are generally weaker compared to trends from 1982–2008. So to better understand the changing trends, break points in the time series were quantified using the Breakfit algorithm. The most notable break points identify declines in SWI since 2003 in Eurasia and 1998 in Western North America. The Time Integrated NDVI (TI-NDVI, sum of the biweekly growing season values of MaxNDVI) has declined since 2005 in Eurasia, consistent with SWI declines. Summer (June–August) sea level pressure (slp) averages from 1999–2011 were compared to those from 1982–1998 to reveal higher slp over Greenland and the western Arctic and generally lower pressure over the continental Arctic in the recent period. This suggests that the large-scale circulation is likely a key contributor to the cooler temperatures over Eurasia through increased summer cloud cover and warming in Eastern North America from more cloud-free skies.

Alaska experienced record-setting warmth during the 2015/16 cold season (October–April). Statewide average temperatures exceeded the period-of-record mean by more than 4°C over the 7-month cold season and by more than 6°C over the 4-month late-winter period, January–April. The record warmth raises two questions: 1) Why was Alaska so warm during the 2015/16 cold season? 2) At what point in the future might this warmth become typical if greenhouse warming continues? On the basis of circulation analogs computed from sea level pressure and 850-hPa geopotential height fields, the atmospheric circulation explains less than half of the anomalous warmth. The warming signal forced by greenhouse gases in climate models accounts for about 1°C of the anomalous warmth. A factor that is consistent with the seasonal and spatial patterns of the warmth is the anomalous surface state. The surface anomalies include 1) above-normal ocean surface temperatures and below-normal sea ice coverage in the surrounding seas from which air advects into Alaska and 2) the deficient snowpack over Alaska itself. The location of the maximum of anomalous warmth over Alaska and the late-winter–early-spring increase of the anomalous warmth unexplained by the atmospheric circulation implicates snow cover and its albedo effect, which is supported by observational measurements in the boreal forest and tundra biomes. Climate model simulations indicate that warmth of this magnitude will become the norm by the 2050s if greenhouse gas emissions follow their present scenario.

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 1982-2015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 1982-2015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 1999-2015 compared to 1982-1998. Trend analysis within the growing season reveals that sea ice decline was larger in spring for the 1982-1998 period compared to 1999-2015, while fall sea ice decline was larger in the later period. Land surface temperature trends for the 1982-1998 growing season are positive and for 1999-2015 are positive in May-June but weakly negative in July-August. Spring biweekly NDVI trends are positive and significant for 1982-1998, consistent with increasing open water and increased available warmth in spring. MaxNDVI trends for 1999-2015 display significant negative trends in May and the first half of June. Numerous possible drivers of early growing season NDVI decline coincident with warming temperatures are discussed, including increased standing water, delayed spring snow-melt, winter thaw events, and early snow melt followed by freezing temperatures. Further research is needed to robustly identify drivers of the spring NDVI decline.

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

There is a growing need for proactive planning for natural hazards in a changing climate. Computational modeling of climate hazards provides an opportunity to inform planning, particularly in areas approaching ecosystem state changes, such as Interior Alaska, where future hazards are expected to differ significantly from historical events in frequency and severity. This paper considers improved modeling approaches from a physical process perspective and contextualizes the results within the complexities and limitations of hazard planning efforts and management concerns. Therefore, the aim is not only to improve the understanding of potential climate impacts on streamflow within this region but also to further explore the steps needed to evaluate local-scale hazards from global drivers and the potential challenges that may be present. This study used dynamically downscaled climate forcing data from ERA-Interim reanalysis datasets and projected climate scenarios from two General Circulation Models under a single Representative Concentration Pathway (RCP 8.5) to simulate an observational gage-calibrated WRF-Hydro model to assess shifts in streamflow and flooding potential in three Interior Alaska rivers over a historical period (2008–2017) and two future periods (2038–2047 and 2068–2077). Outputs were assessed for seasonality, streamflow, extreme events, and the comparison between existing flood control infrastructure in the region. The results indicate that streamflow in this region is likely to experience increases in seasonal length and baseflow, while the potential for extreme events and variable short-term streamflow behavior is likely to see greater uncertainty, based on the divergence between the models.

Record low Bering Sea sea ice in 2018 had profound regional impacts. According to climate models, human-caused warming was an overwhelmingly likely contributor, and such low levels will likely be typical by the 2040s.

The northern coast of Alaska is experiencing significant climatic change enhancing hazards from reduced sea ice and increased coastal erosion. This same region is home to offshore oil/gas activities. Foggy Island Bay is one region along the Beaufort Sea coast with planned offshore oil/gas development that will need to account for the changing climate. High water levels impact infrastructure through coastal erosion and flooding hazards. In this study, 21 high water level events exceeding the top 95th percentile were identified at the gauge in Prudhoe Bay, Alaska (adjacent to Foggy Island Bay) over 1990–2018. All 21 events, and many non-extreme days with elevated water levels, were associated with strong westerly winds according to station records. Storm systems were generally found to be a key driver of westerly winds in the region according to downscaled reanalysis and storm track data. A dynamically downscaled global climate model projection from CMIP5 indicated that days with westerly wind events may become more frequent as storms strengthen and have longer durations by 2100 in the Foggy Island Bay region. Coupled with the anticipated continued decline in sea ice, the northern coast of Alaska may experience more frequent high water events over the next ~80 years.

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.