NOAA National Weather Service Alaska Region

Abstract Alaska encompasses several climate types because of its vast size, high-latitude location, proximity to oceans, and complex topography. There is a great need to understand how climate varies regionally for climatic research and forecasting applications. Although climate-type zones have been established for Alaska on the basis of seasonal climatological mean behavior, there has been little attempt to construct climate divisions that identify regions with consistently homogeneous climatic variability. In this study, cluster analysis was applied to monthly-average temperature data from 1977 to 2010 at a robust set of weather stations to develop climate divisions for the state. Mean-adjusted Advanced Very High Resolution Radiometer surface temperature estimates were employed to fill in missing temperature data when possible. Thirteen climate divisions were identified on the basis of the cluster analysis and were subsequently refined using local expert knowledge. Divisional boundary lines were drawn that encompass the grouped stations by following major surrounding topographic boundaries. Correlation analysis between station and gridded downscaled temperature and precipitation data supported the division placement and boundaries. The new divisions north of the Alaska Range were the North Slope, West Coast, Central Interior, Northeast Interior, and Northwest Interior. Divisions south of the Alaska Range were Cook Inlet, Bristol Bay, Aleutians, Northeast Gulf, Northwest Gulf, North Panhandle, Central Panhandle, and South Panhandle. Correlations with various Pacific Ocean and Arctic climatic teleconnection indices showed numerous significant relationships between seasonal division average temperature and the Arctic Oscillation, Pacific–North American pattern, North Pacific index, and Pacific decadal oscillation.

Abstract 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.

Photo by Joe Raedle/Getty Images-A vehicle drives through flooded streets caused by a combination of the lunar orbit which caused seasonal high tides and what many believe is the rising sea levels due to climate change on September 30, 2015, in Fort Lauderdale, Florida.South Florida is projected to continue to feel the effects of climate change, and many of the cities have begun programs such as installing pumps or building up sea walls to try and combat the rising oceans.

Mount Cleveland, Alaska (52°49′N, 169°57′W), located on Chuginadak Island, erupted on 19 February 2001. The atmosphere–volcanic plume interactions that occurred as part of this event led to several serious encounters of commercial aircraft with the ash. A number of continental and oceanic air traffic control areas were affected. Here, a detailed case study of the eruption, subsequent movement of the airborne plume, and operational response is presented. The likelihood of such encounters in the future may be reduced as a result of lessons learned from this event. Some potential new assets for improving the detection of and response to the airborne volcanic ash hazard to aviation also are discussed.

Abstract The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

<p>Disruptive environmental change in the Arctic continued in 2021. While few indicators were at<br>record levels, the ongoing trends provide a stark illustration of an Arctic that is a very different<br>place than the Arctic of the twentieth century. Air and ocean temperatures in the Arctic are intimately<br>linked with sea ice and are directly connected to the biological productivity of the region.<br>Terrestrial snow cover, or the lack thereof, plays an important role in modulating air temperatures<br>and the hydrologic cycle. During the winter, lower latitude drivers such as the El Niño-Southern<br>Oscillation, the Madden-Julian Oscillation, and the evolution of the stratospheric polar vortex<br>affect regional conditions and sub-seasonal variability. These processes add to the complexity<br>of annually assessing the state of the Arctic, despite numerous examples of observed broadscale<br>directional change across the region.</p>

Combined digital data from multiple satellites and Doppler radar can provide fire weather meteorologists and resource managers with accurate information on forest fire location, intensity, growth, smoke plumes, and associated mesoscale weather. An integrated application using real-time satellite and radar is described for the Miller’s Reach forest fire that occurred in south-central Alaska in June 1996. Generated data and products were made available immediately on-scene via point-to-point high-speed portable satellite communications. This fire consumed over 15 000 ha and destroyed 344 structures.

<p>Journal article: Arctic observations in 2023 provided clear evidence of rapid and pronounced climate and <br>environmental change, shaped by past and ongoing human activities that release greenhouse <br>gases into the atmosphere and push the broader Earth system into uncharted territory. This <br>chapter provides a snapshot of 2023 and summarizes decades-long trends observed across the <br>Arctic, including warming surface air and sea-surface temperatures, decreasing snow cover, <br>diminishing sea ice, thawing permafrost, and continued mass loss from the Greenland Ice Sheet <br>and Arctic glaciers. These changes are driving a transition to a wetter, greener, and less frozen <br>Arctic, with serious implications for Arctic peoples and ecosystems, as well as for low- and <br>midlatitudes</p>

Abstract Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis.

Abstract 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.

Patterned Ground in the Arctic by Ina Timling.

An analog forecast method designed for monthly and seasonal outlooks is applied to the Arctic. The analog selection process uses pattern matches based on agreement with historical data to identify past years with similar distributions of sea level pressure, upper-air geopotential height, surface and upper-air temperatures, precipitation, and sea surface temperatures. The evolution of the atmosphere in the analog years is then the basis of a prediction for the target year. Users can choose the predictor domain, the predictand domain, the variable to be predicted, and the number of antecedent months on which the analog selection is based. We provide an example of a monthly forecast generated by the analog forecast tool. In comparisons with operational dynamical model forecasts over the period 2012-2019, the analog system underperforms the dynamical models in middle latitudes but generally outperforms the dynamical models in monthly forecasts of surface air temperatures in the Arctic. The improvement over the dynamical models is especially apparent in the late summer and early autumn (August-October).

The Arctic environment in 2024 continued on a trajectory that has put it in a state far different from that of the twentieth century. Ongoing accumulation of greenhouse gases in the atmosphere continues to quickly warm the Arctic, resulting in rapid changes in the cryosphere that are driving cascading impacts to climate, ecological, and societal systems.

Quinhagak, and Goodnews Bay (fig. 1). Elevation data collected between 2015 and 2016 were used to create color-indexed maps for the first time at these communities.Tschetter and others (2014) outlines the original methodology used to create color-indexed maps, while Overbeck and others (2017) discusses similar datasets to those used in this update and show useful schematics of the terminology used in this series.Color-Indexed maps show elevation intervals at individual communities that might be used to communicate forecasted storm surge elevations.Each map sheet is associated with a tide staff reference page used to convert between local land and tidal datums, as well as to show the elevation interval corresponding to modeled water level elevations or low elevation infrastructure.Community infrastructure, boundary, and land-use delineations used in the color-indexed maps were provided by the Alaska Department of Community and Regional Affairs (DCRA), which were originally published in the community profile map series (DCRA, 2017).Other data sources and the accuracy of data used to create the color-indexed maps and tide staffs are referenced throughout this document.

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