Projecting the Most Likely Annual Urban Heat Extremes in the Central United States

Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period.

[1]  T. Oke City size and the urban heat island , 1973 .

[2]  T. Oke The energetic basis of the urban heat island , 1982 .

[3]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[4]  M. G. Estes,et al.  A Decision Support Information System for Urban Landscape Management Using Thermal Infrared Data , 2000 .

[5]  H. Kondo,et al.  A Simple Single-Layer Urban Canopy Model For Atmospheric Models: Comparison With Multi-Layer And Slab Models , 2001 .

[6]  A. Arnfield Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island , 2003 .

[7]  R. Arritt,et al.  Altered hydrologic feedback in a warming climate introduces a “warming hole” , 2004 .

[8]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[9]  F. Kimura,et al.  Coupling a Single-Layer Urban Canopy Model with a Simple Atmospheric Model: Impact on Urban Heat Island Simulation for an Idealized Case , 2004 .

[10]  Anton H. Westveld,et al.  Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation , 2005 .

[11]  Durga L. Shrestha,et al.  Machine learning approaches for estimation of prediction interval for the model output , 2006, Neural Networks.

[12]  E. Maurer,et al.  Fine‐resolution climate projections enhance regional climate change impact studies , 2007 .

[13]  Tilmann Gneiting,et al.  Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression , 2010 .

[14]  educhenin Heat-waves: risks and responses , 2010 .

[15]  Yu Ding,et al.  A classification procedure for highly imbalanced class sizes , 2010 .

[16]  A. Thomson,et al.  The representative concentration pathways: an overview , 2011 .

[17]  M. Simpson Global Climate Change Impacts in the United States , 2011 .

[18]  Amir F. Atiya,et al.  Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.

[19]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[20]  T. Thorarinsdottir,et al.  Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction , 2012, 1204.1022.

[21]  Gerald A. Meehl,et al.  Mechanisms Contributing to the Warming Hole and the Consequent U.S. East–West Differential of Heat Extremes , 2012 .

[22]  F. Zwiers,et al.  Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate , 2013 .

[23]  T. Fearn Ridge Regression , 2013 .

[24]  C. Tebaldi,et al.  Long-term Climate Change: Projections, Commitments and Irreversibility , 2013 .

[25]  Eunshin Byon,et al.  BAYESIAN SPLINE METHOD FOR ASSESSING EXTREME LOADS ON WIND TURBINES , 2013, 1401.2760.

[26]  J. Minx,et al.  Climate Change 2014 : Synthesis Report , 2014 .

[27]  S. Baran,et al.  Log‐normal distribution based Ensemble Model Output Statistics models for probabilistic wind‐speed forecasting , 2014, 1407.3252.

[28]  G. Yohe,et al.  Climate Change Impacts in the United States: The Third National Climate Assessment , 2014 .

[29]  Jean-Pascal van Ypersele de Strihou Climate Change 2014 - Synthesis Report , 2015 .

[30]  N. Holbrook,et al.  Cooling of US Midwest summer temperature extremes from cropland intensification , 2016 .

[31]  S. Baran,et al.  Mixture EMOS model for calibrating ensemble forecasts of wind speed , 2015, Environmetrics.

[32]  A. Fiore,et al.  Timing and seasonality of the United States ‘warming hole’ , 2017 .

[33]  X. Labandeira,et al.  Impact of Cold Waves and Heat Waves on the Energy Production Sector , 2017 .

[34]  S. Witherspoon Research Program , 2018, Research quarterly for exercise and sport.