Climate change, climate justice and the application of probabilistic event attribution to summer heat extremes in the California Central Valley

Roberto Mera,Neil Massey,Philip Mote,Peter C. Frumhoff,Myles Allen,David E. Rupp

doi:10.1007/s10584-015-1474-3

Abstract

Probabilistic event attribution (PEA) is an important tool for assessing the contribution of climate change to extreme weather events. Here, PEA is applied to explore the climate attribution of recent extreme heat events in California’s Central Valley. Heat waves have become progressively more severe due to increasing relative humidity and nighttime temperatures, which increases the health risks of exposed communities, especially Latino farmworkers and other socioeconomically disadvantaged communities. Using a superensemble of simulations with the Hadley Centre Regional Model (HadRM3P), we find that (1) simulations of the hottest summer days during the 2000s were twice as likely to occur using observed levels of greenhouse gases than in a counterfactual world without major human activities, suggesting a strong relationship between heat extremes and the increase in human emissions of greenhouse gases, (2) detrimental impacts of heat on public health-relevant variables, such as the number of days above 40 °C, can be quantified and attributed to human activities using PEA, and (3) PEA can serve as a tool for addressing climate justice concerns of populations within developed nations who are disproportionately exposed to climate risks.

Highlights

Recent advances in probabilistic event attribution (PEA) have allowed for enhanced exploration of the role of human activities in meteorological extremes (Otto et al 2013; Pall et al 2011; Rupp et al 2015)
In order to quantify the climate attribution of extreme heat events in the model analysis, this study uses the fraction of attributable risk (FAR), defined as the fraction of the current risk that is attributable to past greenhouse gas (GHG) emissions (Allen 2003), and is computed as follows: FAR 1⁄4 1 – P0 = P1
The present study examines a comprehensive record of observed temperature extremes dating back to 1900 for validation and bias-correction of the superensemble model results

Summary

Introduction

Recent advances in probabilistic event attribution (PEA) have allowed for enhanced exploration of the role of human activities in meteorological extremes (Otto et al 2013; Pall et al 2011; Rupp et al 2015). Heat extremes in the United States disproportionately affect specific populations, including those in urban areas (Fischer et al 2012), areas lacking air conditioning (O’Neill et al 2005), lower economic background (Hajat and Kosatky 2010), elderly, and specific ethnic groups (White-Newsome et al 2009). These factors need to be taken into consideration when exploring the human impact of heat extremes in order to inform policy-makers and affected communities where a case can be made for lack of preparation and adaptation. A large fraction of the population in the United States is inadequately prepared for extreme heat (White-Newsome et al 2014)

Methods

Results

Conclusion