Search Results
Abstract
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.
Abstract
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.
Abstract
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.
Abstract
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.
Abstract
The 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 generally exceeds observationally derived estimates in all seasons over mainland Alaska, and it is less than observations in the southeast. Temperature biases tended to be more mixed, and the downscaling reduces absolute bias at higher elevations, especially in winter. Careful selection of data for local site analysis from the downscaling can help to reduce these biases, especially those due to inconsistencies in elevation. Improved meteorological station coverage at higher elevations will be necessary to better evaluate gridded downscaled products in Alaska because biases vary and may even change sign with elevation.
Abstract
The 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 generally exceeds observationally derived estimates in all seasons over mainland Alaska, and it is less than observations in the southeast. Temperature biases tended to be more mixed, and the downscaling reduces absolute bias at higher elevations, especially in winter. Careful selection of data for local site analysis from the downscaling can help to reduce these biases, especially those due to inconsistencies in elevation. Improved meteorological station coverage at higher elevations will be necessary to better evaluate gridded downscaled products in Alaska because biases vary and may even change sign with elevation.
Abstract
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.
Abstract
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.
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.
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.
