Abstract

We evaluate how hotspots of different types of extreme summertime heat change under global warming increase of up to 4,^circ hbox {C}; and which level of global warming allows us to avert the risk of these hotspots considering the irreducible range of possibilities defined by well-sampled internal variability. We use large samples of low-probability extremes simulated by the 100-member Max Planck Institute Grand Ensemble (MPI-GE) for five metrics of extreme heat: maximum absolute temperatures, return periods of extreme temperatures, maximum temperature variability, sustained tropical nights, and wet bulb temperatures. At 2,^circ hbox {C} of warming, MPI-GE projects maximum summer temperatures below 50,^circ hbox {C} over most of the world. Beyond 2,^circ hbox {C}, this threshold is overshot in all continents, with the maximum projected temperatures in hotspots over the Arabic Peninsula. Extreme 1-in-100-years pre-industrial temperatures occur every 10–25 years already at 1.5,^circ hbox {C} of warming. At 4,^circ hbox {C}, these 1-in-100-years extremes are projected to occur every 1 to 2 years over most of the world. The range of maximum temperature variability increases by 10–50% at 2,^circ hbox {C} of warming, and by 50–100% at 4,^circ hbox {C}. Beyond 2,^circ hbox {C}, heat stress is aggravated substantially over non-adapted areas by hot and humid conditions that occur rarely in a pre-industrial climate; while extreme pre-industrial tropical night conditions become common-pace already at 1.5,^circ hbox {C}. At 4,^circ hbox {C} of warming, tropical night hotspots spread polewards globally, and are sustained during more than 99% of all summer months in the tropics; whilst extreme monthly mean wet bulb temperatures beyond 26,^circ hbox {C} spread both over large tropical as well as mid-latitude regions.

Highlights

  • Extreme heat will become more likely and more extreme under global warming (Meehl and Tebaldi 2004; IPCC 2013; Mora et al 2017; Lewis et al 2019)

  • The distributions are adequately centered around the represented global mean surface temperature (GMST) levels and present no substantial overlap, indicating that each sample distribution is distinguishable from the others and offers an adequate representation of the climate conditions of each warming level

  • With this consideration in mind, we find that the observed temperature patterns are well represented in Max Planck Institute Grand Ensemble (MPI-GE), the maximum absolute temperatures under pre-industrial conditions in the MPI-GE simulations are similar or higher than those observed under current global warming levels (Fig. 4, top)

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Summary

Introduction

Extreme heat will become more likely and more extreme under global warming (Meehl and Tebaldi 2004; IPCC 2013; Mora et al 2017; Lewis et al 2019). Most previous studies evaluating how these heat stress metrics change under global warming are based on smaller multi-model ensembles (Dunne et al 2013; Fischer and Knutti 2013; Seneviratne et al 2016; Donat et al 2017; Mishra et al 2017; Matthews et al 2017; Russo et al 2017; Mora et al 2017; Newth and Gunasekera 2018; Coffel et al 2018; Li et al 2018; Bathiany et al 2018; Brouillet and Joussaume 2019), smaller regional model ensembles (Pal and Eltahir 2015; Im et al 2017), atmosphere-only ensembles (Lewis et al 2019), or smaller single-model ensembles (Sherwood and Huber 2010; Willett and Sherwood 2012; Mishra et al 2017) These smaller ensemble sizes imply a potential misrepresentation of the severity of future extremes in these studies. This diversity allows us to robustly characterize and compare the well-sampled climates at 0 ◦C , 1.5 ◦C , 2 ◦C , 3 ◦C and 4 ◦C of global warming above pre-industrial conditions

Data and methods
MPI‐GE evaluation
Results and discussion
Maximum absolute temperatures
Return periods of very extreme temperatures
Maximum temperature variability
Sustained tropical night temperatures
Extreme wet bulb temperatures
Summary and conclusions
Full Text
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