• Bieniek, P. A., and Coauthors, 2012: Climate divisions for Alaska based on objective methods. J. Appl. Meteor. Climatol., 51, 12761289, https://doi.org/10.1175/JAMC-D-11-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bieniek, P. A., J. E. Walsh, R. L. Thoman, and U. S. Bhatt, 2014: Using climate divisions to analyze variations and trends in Alaska temperature and precipitation. J. Climate, 27, 28002818, https://doi.org/10.1175/JCLI-D-13-00342.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bieniek, P. A., U. S. Bhatt, J. E. Walsh, T. S. Rupp, J. Zhang, J. R. Krieger, and R. Lader, 2016: Dynamical downscaling of ERA-Interim temperature and precipitation for Alaska. J. Appl. Meteor. Climatol., 55, 635654, https://doi.org/10.1175/JAMC-D-15-0153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Biswas, A., and K. Jayaweera, 1976: NOAA-3 satellite observations of thunderstorms in Alaska. Mon. Wea. Rev., 104, 292297, https://doi.org/10.1175/1520-0493(1976)104<0292:NSOOTI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Budescu, D. V., 1993: Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychol. Bull., 114, 542551, https://doi.org/10.1037/0033-2909.114.3.542.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calef, M. P., A. D. McGuire, and F. S. Chapin, 2008: Human influences on wildfire in Alaska from 1988 through 2005: An analysis of the spatial patterns of human impacts. Earth Interact., 12, https://doi.org/10.1175/2007EI220.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dissing, D., and D. L. Verbyla, 2003: Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation. Can. J. For. Res., 33, 770782, https://doi.org/10.1139/x02-214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duffy, P. A., J. E. Walsh, J. M. Graham, D. H. Mann, and T. S. Rupp, 2005: Impacts of large-scale atmospheric–ocean variability on Alaskan fire season severity. Ecol. Appl., 15, 13171330, https://doi.org/10.1890/04-0739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farukh, M. A., and H. Hayasaka, 2012: Active forest fire occurrences in severe lightning years in Alaska. J. Nat. Disaster Sci., 33, 7184, https://doi.org/10.2328/jnds.33.71.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farukh, M. A., H. Hayasaka, and K. Kimura, 2011a: Characterization of lightning occurrence in Alaska using various weather indices for lightning forecasting. J. Disaster Res., 6, 343355, https://doi.org/10.20965/jdr.2011.p0343.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farukh, M. A., H. Hayasaka, and K. Kimura, 2011b: Recent anomalous lightning occurrences in Alaska—The case of June 2005. J. Disaster Res., 6, 321330, https://doi.org/10.20965/jdr.2011.p0321.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fronterhouse, B. A., 2012: Alaska Lightning Detection Network (ALDN) briefing document. Bureau of Land Management Alaska Fire Service Doc., 4 pp.

  • Giannaros, T. M., V. Kotroni, and K. Lagouvardos, 2015: Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model. Atmos. Res., 156, 113, https://doi.org/10.1016/j.atmosres.2014.12.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grice, G. K., and A. L. Comiskey, 1976: Thunderstorm climatology of Alaska. NOAA (NWS Anchorage) Tech. Memo. NWS AR-14, NWS, 36 pp.

  • Gungle, B., and E. P. Krider, 2006: Cloud-to-ground lightning and surface rainfall in warm-season Florida thunderstorms. J. Geophys. Res., 111, D19203, https://doi.org/10.1029/2005JD006802.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henry, D. M., 1978: Fire occurrence using 500mb map correlation. NOAA (NWS Anchorage) Tech. Memo. NWS AR-21, 31 pp.

  • Hess, J. C., C. A. Scott, G. L. Hufford, and M. D. Fleming, 2001: El Niño and its impact on fire weather conditions in Alaska. Int. J. Wildland Fire, 10 (1), 113, https://doi.org/10.1071/WF01007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jayaratne, R., and E. Kuleshov, 2006: The relationship between lightning activity and surface wet bulb temperature and its variation with latitude in Australia. Meteor. Atmos. Phys., 91, 1724, https://doi.org/10.1007/s00703-004-0100-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kasischke, E. S., and Coauthors, 2010: Alaska’s changing fire regime—Implications for the vulnerability of its boreal forests. Can. J. For. Res., 40, 13131324, https://doi.org/10.1139/X10-098.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koshak, W. J., K. L. Cummins, D. E. Buechler, B. Vant-Hull, R. J. Blakeslee, E. R. Williams, and H. S. Peterson, 2015: Variability of CONUS lightning in 2003–12 and associated impacts. J. Appl. Meteor. Climatol., 54, 1541, https://doi.org/10.1175/JAMC-D-14-0072.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krause, A., S. Kloster, S. Wilkenskjeld, and H. Paeth, 2014: The sensitivity of global wildfires to simulated past, present, and future lightning frequency. J. Geophys. Res. Biogeosci., 119, 312322, https://doi.org/10.1002/2013JG002502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krider, E., R. Noggle, A. Pifer, and D. Vance, 1980: Lightning direction-finding systems for forest fire detection. Bull. Amer. Meteor. Soc., 61, 980986, https://doi.org/10.1175/1520-0477(1980)061<0980:LDFSFF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lader, R., U. S. Bhatt, J. E. Walsh, T. S. Rupp, and P. A. Bieniek, 2016: Two-meter temperature and precipitation from atmospheric reanalysis evaluated for Alaska. J. Appl. Meteor. Climatol., 55, 901922, https://doi.org/10.1175/JAMC-D-15-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lynch, J. A., J. L. Hollis, and F. S. Hu, 2004: Climatic and landscape controls of the boreal forest fire regime: Holocene records from Alaska. J. Ecol., 92, 477489, https://doi.org/10.1111/j.0022-0477.2004.00879.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Macias Fauria, M., and E. A. Johnson, 2006: Large-scale climatic patterns control large lightning fire occurrence in Canada and Alaska forest regions. J. Geophys. Res., 111, G04008, https://doi.org/10.1029/2006JG000181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Magi, B. I., 2015: Global lightning parameterization from CMIP5 climate model output. J. Atmos. Oceanic Technol., 32, 434452, https://doi.org/10.1175/JTECH-D-13-00261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markon, C., and Coauthors, 2018: Alaska. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, D. R. Reidmiller et al., Eds., Vol. II. U. S. Global Change Research Program, 1185–1241, https://doi.org/10.7930/NCA4.2018.CH26.

    • Crossref
    • Export Citation
  • Melvin, A. M., J. Murray, B. Boehlert, J. A. Martinich, L. Rennels, and T. S. Rupp, 2017: Estimating wildfire response costs in Alaska’s changing climate. Climatic Change, 141, 783795, https://doi.org/10.1007/s10584-017-1923-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mölders, N., and G. Kramm, 2007: Influence of wildfire induced land-cover changes on clouds and precipitation in interior Alaska—A case study. Atmos. Res., 84, 142168, https://doi.org/10.1016/j.atmosres.2006.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Partain, J. L., and Coauthors, 2016: An assessment of the role of anthropogenic climate change in the Alaska fire season of 2015. Bull. Amer. Meteor. Soc., 97, S14S18, https://doi.org/10.1175/BAMS-D-16-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G. P., and Coauthors, 2013: The challenge to keep global warming below 2°C. Nat. Climate Change, 3, 46, https://doi.org/10.1038/nclimate1783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, D., J. Wang, C. Ichoku, and L. A. Remer, 2010: Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: Implications for fire weather forecasting. Atmos. Chem. Phys., 10, 68736888, https://doi.org/10.5194/acp-10-6873-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, C., and D. Rind, 1994: Possible implications of global climate change on global lightning distributions and frequencies. J. Geophys. Res., 99, 10 82310 831, https://doi.org/10.1029/94JD00019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reap, R. M., 1991: Climatological characteristics and objective prediction of thunderstorms over Alaska. Wea. Forecasting, 6, 309319, https://doi.org/10.1175/1520-0434(1991)006<0309:CCAOPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romps, D. M., J. T. Seeley, D. Vollaro, and J. Molinari, 2014: Projected increase in lightning strikes in the United States due to global warming. Science, 346, 851854, https://doi.org/10.1126/science.1259100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shulski, M., and G. Wendler, 2007: The Climate of Alaska. University of Alaska Press, 216 pp.

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Sullivan, W. G., 1963: Low-level convergence and thunderstorms in Alaska. Mon. Wea. Rev., 91, 8992, https://doi.org/10.1175/1520-0493(1963)091<0089:LCATIA>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Veraverbeke, S., B. M. Rogers, M. L. Goulden, R. R. Jandt, C. E. Miller, E. B. Wiggins, and J. T. Randerson, 2017: Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Climate Change, 7, 529534, https://doi.org/10.1038/nclimate3329.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., and Coauthors, 2018: Downscaling of climate model output for Alaskan stakeholders. Environ. Modell. Software, 110, 3851, https://doi.org/10.1016/j.envsoft.2018.03.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wendler, G., J. Conner, B. Moore, M. Shulski, and M. Stuefer, 2011: Climatology of Alaskan wildfires with special emphasis on the extreme year of 2004. Theor. Appl. Climatol., 104, 459472, https://doi.org/10.1007/s00704-010-0357-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Williams, E., and N. Renno, 1993: An analysis of the conditional instability of the tropical atmosphere. Mon. Wea. Rev., 121, 2136, https://doi.org/10.1175/1520-0493(1993)121<0021:AAOTCI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yair, Y., B. Lynn, C. Price, V. Kotroni, K. Lagouvardos, E. Morin, A. Mugnai, and M. C. Llasat, 2010: Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields. J. Geophys. Res., 115, D04205, https://doi.org/10.1029/2008JD010868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A. M., P. E. Higuera, P. A. Duffy, and F. S. Hu, 2017: Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. Ecography, 40, 606617, https://doi.org/10.1111/ecog.02205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A. M., P. E. Higuera, J. T. Abatzoglou, P. A. Duffy, and F. S. Hu, 2019: Consequences of climatic thresholds for projecting fire activity and ecological change. Global Ecol. Biogeogr., 28, 521532, https://doi.org/10.1111/geb.12872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., and J. Zhang, 2001: Heat and freshwater budgets and pathways in the Arctic Mediterranean in a coupled ocean/sea-ice model. J. Oceanogr., 57, 207234, https://doi.org/10.1023/A:1011147309004.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Lightning Variability in Dynamically Downscaled Simulations of Alaska’s Present and Future Summer Climate

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  • 1 International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska
  • 2 Department of Atmospheric Sciences and Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska
  • 3 Alaska Fire Science Consortium, University of Alaska Fairbanks, Fairbanks, Alaska
  • 4 Alaska Interagency Coordination Center, Fairbanks, Alaska
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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.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter A. Bieniek, pbieniek@alaska.edu

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.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter A. Bieniek, pbieniek@alaska.edu
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