Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave

In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by large margins and directly causing hundreds of fatalities. The observed 2021 daily maximum temperature across much of the U.S. Pacific Northwest exceeded upper bound estimates obtained from single-station temperature records even after accounting for anthropogenic climate change, meaning that the event could not have been predicted under standard univariate extreme value analysis assumptions. In this work, we utilize a flexible spatial extremes model that considers all stations across the Pacific Northwest domain and accounts for the fact that many stations simultaneously experience extreme temperatures. Our analysis incorporates the effects of anthropogenic forcing and natural climate variability in order to better characterize time-varying changes in the distribution of daily temperature extremes. We show that greenhouse gas forcing, drought conditions and large-scale atmospheric modes of variability all have significant impact on summertime maximum temperatures in this region. Our model represents a significant improvement over corresponding single-station analysis, and our posterior medians of the upper bounds are able to anticipate more than 96% of the observed 2021 high station temperatures after properly accounting for extremal dependence.

[1]  J. Abatzoglou,et al.  Unprecedented 21st century heat across the Pacific Northwest of North America , 2023, npj Climate and Atmospheric Science.

[2]  E. Fischer,et al.  Prediction and projection of heatwaves , 2022, Nature Reviews Earth & Environment.

[3]  M. Wehner,et al.  Anthropogenic Contributions to the 2021 Pacific Northwest Heatwave , 2022, Geophysical Research Letters.

[4]  I. Simpson,et al.  How Unexpected Was the 2021 Pacific Northwest Heatwave? , 2022, Geophysical Research Letters.

[5]  T. O'brien,et al.  Accounting for the Spatial Structure of Weather Systems in Detected Changes in Precipitation Extremes , 2022, SSRN Electronic Journal.

[6]  Yuqing Wang,et al.  Unprecedented Heatwave in Western North America during Late June of 2021: Roles of Atmospheric Circulation and Global Warming , 2022, Advances in Atmospheric Sciences.

[7]  F. Pappenberger,et al.  Predicting the unprecedented: forecasting the June 2021 Pacific Northwest heatwave , 2022, Weather.

[8]  John P. O’Brien,et al.  A framework for detection and attribution of regional precipitation change: Application to the United States historical record , 2022, Climate Dynamics.

[9]  F. Vitart,et al.  An anomalous warm-season trans-Pacific atmospheric river linked to the 2021 western North America heatwave , 2022, Communications Earth & Environment.

[10]  M. Hauer,et al.  Housing unit and urbanization estimates for the continental U.S. in consistent tract boundaries, 1940–2019 , 2022, Scientific Data.

[11]  Jordis S. Tradowsky,et al.  Rapid attribution analysis of the extraordinary heatwave on the Pacific Coast of the US and Canada June 2021 , 2021 .

[12]  Paul Berrisford,et al.  The ERA5 global reanalysis: Preliminary extension to 1950 , 2021, Quarterly Journal of the Royal Meteorological Society.

[13]  J. Thepaut,et al.  The ERA5 global reanalysis , 2020, Quarterly Journal of the Royal Meteorological Society.

[14]  Stephanie C. Herring,et al.  Development of a Submonthly Temperature Product to Monitor Near-Real-Time Climate Conditions and Assess Long-Term Heat Events in the United States , 2019, Journal of Applied Meteorology and Climatology.

[15]  Likun Zhang,et al.  Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets With Many Locations , 2019, Journal of the American Statistical Association.

[16]  P. Dirmeyer,et al.  The relative importance among anthropogenic forcings of land use/land cover change in affecting temperature extremes , 2019, Climate Dynamics.

[17]  Christina M. Patricola,et al.  Diversity of ENSO Events Unified by Convective Threshold Sea Surface Temperature: A Nonlinear ENSO Index , 2018, Geophysical Research Letters.

[18]  D. Stone,et al.  Early 21st century anthropogenic changes in extremely hot days as simulated by the C20C+ detection and attribution multi-model ensemble , 2018, Weather and Climate Extremes.

[19]  Mark D. Risser,et al.  Attributable Human‐Induced Changes in the Likelihood and Magnitude of the Observed Extreme Precipitation during Hurricane Harvey , 2017 .

[20]  P. Taylor,et al.  The spatial distribution of rainfall extremes and the influence of El Nino Southern Oscillation , 2017 .

[21]  T. Opitz,et al.  Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures , 2017 .

[22]  Michael F. Wehner,et al.  Quantifying statistical uncertainty in the attribution of human influence on severe weather , 2017, Weather and Climate Extremes.

[23]  Jennifer L. Wadsworth,et al.  Modeling Spatial Processes with Unknown Extremal Dependence Class , 2017, Journal of the American Statistical Association.

[24]  F. Zwiers,et al.  The impact of ENSO and the NAO on extreme winter precipitation in North America in observations and regional climate models , 2017, Climate Dynamics.

[25]  G. Myhre,et al.  Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing , 2016 .

[26]  R. Vautard,et al.  Attribution of Extreme Weather Events in the Context of Climate Change , 2016 .

[27]  A. Timmermann,et al.  Combination Mode Dynamics of the Anomalous Northwest Pacific Anticyclone , 2015 .

[28]  J. Fuglestvedt,et al.  Global warming potentials and radiative efficiencies of halocarbons and related compounds: A comprehensive review , 2013 .

[29]  John T. Abatzoglou,et al.  The West Wide Drought Tracker: Drought Monitoring at Fine Spatial Scales , 2013 .

[30]  R. Vose,et al.  An Overview of the Global Historical Climatology Network-Daily Database , 2012 .

[31]  A. Davison,et al.  Statistical Modeling of Spatial Extremes , 2012, 1208.3378.

[32]  Alan E Gelfand,et al.  Hierarchical Modeling for Spatial Data Problems. , 2012, Spatial statistics.

[33]  K. Oleson,et al.  An examination of urban heat island characteristics in a global climate model , 2011 .

[34]  Neal Lott,et al.  The Integrated Surface Database: Recent Developments and Partnerships , 2011 .

[35]  Chris Hans Bayesian lasso regression , 2009 .

[36]  D. Nychka,et al.  Bayesian Spatial Modeling of Extreme Precipitation Return Levels , 2007 .

[37]  S. Wood Thin plate regression splines , 2003 .

[38]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[39]  Walter H. F. Smith,et al.  New, improved version of generic mapping tools released , 1998 .

[40]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[41]  S. Philander,et al.  El Niño and La Niña , 1985 .

[42]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[43]  S. Arrhenius ON THE INFLUENCE OF CARBONIC ACID IN THE AIR UPON THE TEMPERATURE OF THE EARTH , 1897 .

[44]  A. Ruane,et al.  SYNTHESIS REPORT OF THE IPCC SIXTH ASSESSMENT REPORT (AR6) , 2023 .

[45]  Martin T. Wells,et al.  Exploring an Adaptive Metropolis Algorithm , 2010 .

[46]  C. Paciorek,et al.  Quantifying statistical uncertainty in the attribution of human in fl uence on severe weather Weather and Climate Extremes , 2022 .