Potential to Constrain Projections of Hot Temperature Extremes

Abstract

Projected changes in temperature extremes, such as regional changes in the intensity and frequency of hot extremes, differ strongly across climate models. This study shows that this disagreement can be partly explained by discrepancies in the representation of the present-day temperature distribution, motivating the evaluation of models with observations. By evaluating climate models on carefully selected metrics, the models that are more likely to be reliable for long-term projections of temperature extremes are identified. The study found that frequencies of hot extremes are likely to increase at a higher rate than the multimodel mean estimate over large parts of the Northern Hemisphere and Australia. This implies that a higher degree of adaptation is required for a given global temperature target. It also found that projected changes in the intensity of hot extremes can be constrained in several regions, including Australia, central North America, and north Asia. In many other regions, large internal variability can often hamper model evaluation. For both aspects—the intensity and the frequency of hot extremes—the total area over which the constraints can be implemented is limited by the quality and completeness of observations. Thereby, this study highlights the importance of long-term, high-quality, and easily accessible observational records for model evaluation, which are vital to ultimately reduce uncertainties in projections of temperature extremes.

Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate

“…So calibrating on the shape of the distribution leads to improved EPs and PsFB, even when training and testing periods are several decades apart. These findings are consistent with a study by Borodina et al (), who found a strong correlation between the modeled present‐day temperature distribution and the projected frequency of warm extremes (defined as future exceedance of today's 95th percentile), which they then use to constrain changes in the intensity of warm extremes in various regions. The reason the Jeon method and our subset selection method are successful (relative to no bias correction) is because shape bias tends to persist through time, as already mentioned, and EPs are strongly influenced by the shapes of the tails, which can be strikingly biased in many cases.…”

Half‐a‐Degree Matters for Reducing and Delaying Global Land Exposure to Combined Daytime‐Nighttime Hot Extremes

“…When projected results are used for decision-making, the uncertainty range sourced from intermember (Figure 5a shadings)/intermodel (Figures 2 and 3 shadings) spreads should be kept in mind and communicated explicitly. Increasingly available global-scale daily observations and growing maturation of observation-constrained techniques are promising to help reduce uncertainties of projected changes of extremes (Borodina et al, 2017;Vogel et al, 2018).…”

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

“…Another example is the projected increase in summer temperature variability over parts of Europe ( Fig. 7f; note that we have not applied the 10-year running mean to this example in order to highlight interannual variability), which is understood to arise from a future strengthening of land-atmosphere coupling (Seneviratne et al 2006;Fischer et al 2012;Borodina et al 2017).…”