Climate change is expected to alter the frequencies and intensities of at least some types of extreme events. Although Alaska is already experiencing an amplified response to climate change, studies of extreme event occurrences have lagged those for other regions. Forced migration due to coastal erosion, failing infrastructure on thawing permafrost, more severe wildfire seasons, altered ocean chemistry, and an ever-shrinking season for snow and ice are among the most devastating effects, many of which are related to extreme climate events. This study uses regional dynamical downscaling with the Weather Research and Forecasting (WRF) Model to investigate projected twenty-first-century changes of daily maximum temperature, minimum temperature, and precipitation over Alaska. The forcing data used for the downscaling simulations include the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim; 1981–2010), Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL CM3), historical (1976–2005), and GFDL CM3 representative concentration pathway 8.5 (RCP8.5; 2006–2100). Observed trends of temperature and sea ice coverage in the Arctic are large, and the present trajectory of global emissions makes a continuation of these trends plausible. The future scenario is bias adjusted using a quantile-mapping procedure. Results indicate an asymmetric warming of climate extremes; namely, cold extremes rise fastest, and the greatest changes occur in winter. Maximum 1- and 5-day precipitation amounts are projected to increase by 53% and 50%, which is larger than the corresponding increases for the contiguous United States. When compared with the historical period, the shifts in temperature and precipitation indicate unprecedented heat and rainfall across Alaska during this century.

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.

Snowfall and snow season length across Alaska control the surface hydrology and underlying soil properties and also influence near‐surface air temperature by changing the energy balance. Current projections of warming suggest that considerable change will occur to key snow parameters, possibly contributing to extensive infrastructure damage from thawing permafrost, an increased frequency of rain‐on‐snow events and reduced soil recharge in the spring due to shallow end‐of‐winter snowpack. This study investigates projected changes to mean annual snowfall, dates of snow onset and snowmelt and extreme snowfall for Alaska, using dynamically downscaled reanalysis and climate model simulations. These include the ERA‐Interim reanalysis from 1981 to 2010, and two Coupled Model Intercomparison Project Phase 5 models: Community Climate System Model version 4 (CCSM4) and Geophysical Fluid Dynamics Laboratory Climate Model version 3 (GFDL‐CM3) from 1981 to 2100. The analysis is presented in 30‐year periods (i.e., 1981–2010, 2011–2040, 2041–2070 and 2071–2100) with the future scenarios from Representative Concentration Pathway 8.5. Late‐century projections of average annual snowfall at low elevations (0–1,000 m) show decreases of 41.3 and 40.6% for CCSM4 and GFDL‐CM3, respectively. At high elevations (1,000–2,000 m), the reductions are smaller at 13.5 and 14.2%, respectively. End‐of‐winter snow‐water equivalent displays reductions at all elevations in the future periods. Snow season length is shortened due to later snow onset and earlier snowmelt; many locations in southwest Alaska no longer experience continuous winter snowpack by the late‐century period. Maximum 2‐day snowfall amounts are projected to decrease near Anchorage and Nome, while Fairbanks and Utqiaġvik (Barrow) show no significant trend.

The late-season extreme fire activity in Southcentral Alaska during 2019 was highly unusual and consequential. Firefighting operations had to be extended by a month in 2019 due to the extreme conditions of hot summer temperature and prolonged drought. The ongoing fires created poor air quality in the region containing most of Alaska’s population, leading to substantial impacts to public health. Suppression costs totaled over $70 million for Southcentral Alaska. This study’s main goals are to place the 2019 season into historical context, provide an attribution analysis, and assess future changes in wildfire risk in the region. The primary tools are meteorological observations and climate model simulations from the NCAR CESM Large Ensemble (LENS). The 2019 fire season in Southcentral Alaska included the hottest and driest June–August season over the 1979–2019 period. The LENS simulation analysis suggests that the anthropogenic signal of increased fire risk had not yet emerged in 2019 because of the CESM’s internal variability, but that the anthropogenic signal will emerge by the 2040–2080 period. The effect of warming temperatures dominates the effect of enhanced precipitation in the trend towards increased fire risk.

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.

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.