Peter A. Bieniek

Recent Declines in Warming and Vegetation Greening Trends over Pan-Arctic Tundra

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

Climate Divisions for Alaska Based on Objective Methods

AbstractAlaska 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 th...

Using Climate Divisions to Analyze Variations and Trends in Alaska Temperature and Precipitation

AbstractBy 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,...

Large-Scale Climate Controls of Interior Alaska River Ice Breakup

Frozen rivers in the Arctic serve as critical highways because of the lack of roads; therefore, it is important to understand the key mechanisms that control the timing of river ice breakup. The relationships between springtime Interior Alaska river ice breakup date and the large-scale climate are investigated for the Yukon, Tanana, Kuskokwim, and Chena Rivers for the 1949‐2008 period. The most important climate factor that determines breakup is April‐May surface air temperatures (SATs). Breakup tends to occur earlier when Alaska April‐May SATs and riverflow are above normal. Spring SATs are influenced by storms approaching thestatefromtheGulfofAlaska,whicharepartoflarge-scaleclimateanomaliesthatcomparefavorably with ENSO. During the warm phase of ENSO fewer storms travel into the Gulf of Alaska during the spring, resulting in a decrease of cloud cover over Alaska, which increases surface solar insolation. This results in warmer-than-average springtime SATs and an earlier breakup date. The opposite holds true for the cold phaseofENSO.IncreasedwintertimeprecipitationoverAlaskahasasecondaryimpactonearlierbreakupby increasing spring river discharge. Improved springtime Alaska temperature predictions would enhance the ability to forecast the timing of river ice breakup.

Climate Drivers Linked to Changing Seasonality of Alaska Coastal Tundra Vegetation Productivity

AbstractThe 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 inla...

Dynamical Downscaling of ERA-Interim Temperature and Precipitation for Alaska

AbstractThe European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) has been downscaled using a regional model covering Alaska at 20-km spatial and hourly temporal resolution for 1979–2013. Stakeholders can utilize these enhanced-resolution data to investigate climate- and weather-related phenomena in Alaska. Temperature and precipitation are analyzed and compared among ERA-Interim, WRF Model downscaling, and in situ observations. Relative to ERA-Interim, the downscaling is shown to improve the spatial representation of temperature and precipitation around Alaska’s complex terrain. Improvements include increased winter and decreased summer higher-elevation downscaled seasonal average temperatures. Precipitation is also enhanced over higher elevations in all seasons relative to the reanalysis. These spatial distributions of temperature and precipitation are consistent with the few available gridded observational datasets that account for topography. The downscaled precipitation ...

Two-Meter Temperature and Precipitation from Atmospheric Reanalysis Evaluated for Alaska

AbstractAlaska 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 Re...

An Assessment of the Role of Anthropogenic Climate Change in the Alaska Fire Season of 2015

Introduction. The 2015 Alaska fire season burned 5.1 million acres, the second largest burned area since 1940, exceeded only by the 2004 Alaska fire season when 6.2 million acres burned (Fig. 4.1a). Despite a below normal end-of-winter snowpack and an unseasonably warm spring with earlier snowmelt, which dried fuels early in the season, scattered showers and cool temperatures kept 2015 fire activity near normal through early June. During the first half of June, several days of maximum temperatures exceeded 30 ̊C, relative humidity (RH) values were in the teens, and long daylight hours quickly dried surface and subsurface (duff) forest-floor fuels. Beginning June 19, a period of vigorous thunderstorm activity resulted in an unprecedented weeklong lightning event with 36 000 strikes in three days. During this period, 65 000+ strikes in Alaska gave rise to nearly 270 ignitions of the preconditioned fuels. Burned acreage increased by 3.8 million acres (Fig. 4.1b) in the two and a half weeks following those starts (Fig. 4.1c). Lightning ignitions caused 99.5% of the acreage burned in Alaska in 2015. A westerly shift in upper-level winds by mid-July brought cool and damp weather that curtailed fire growth, and most extant fires burned little acreage after July 15. This pattern highlights a significant difference between Alaska’s top two fire seasons: 2004 burned significant acreage in July and again in August during extended warm and dry late summer weather, while 2015 saw the bulk of fire activity concentrated from mid-June to mid-July. These different pathways to large fire seasons demonstrate the importance of intraseasonal weather variability and the timing of dynamical features. Yet, underlying each case are the common requirements of: heat, extremely dry fuels, and ignition. One question that arises is whether the extremely warm and dry, yet convective, conditions of 2015 might be driven by anthropogenic climate change. This attribution study is a model-based test of the hypothesis that anthropogenic climate change increases the likelihood of fire seasons as extreme as 2015 through increasing flammability of fuels.

The Exceptionally Warm Winter of 2015/16 in Alaska

AbstractAlaska 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 f...

Projections of Twenty-First-Century Climate Extremes for Alaska via Dynamical Downscaling and Quantile Mapping

AbstractClimate 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, versio...