Attributing Extreme Events to Climate Change: A New Frontier in a Warming World

Abstract

The emerging field of extreme-event attribution (EEA) seeks to answer the question: “Has climate change influenced the frequency, likelihood, and/or severity of individual extreme events?” Methodological advances over the past 15 years have transformed what was once an unanswerable hypothetical into a tractable scientific question—and for certain types of extreme events, the influence of anthropogenic climate change has emerged beyond a reasonable doubt. Several challenges remain, particularly those stemming from structural limitations in process-based climate models and the temporal and geographic limitations of historical observations. However, the growing use of large climate-model ensembles that capture natural climate variability, fine-scale simulations that better represent underlying physical processes, and the lengthening observational record could obviate some of these concerns in the near future. EEA efforts have important implications for risk perception, public policy, infrastructure design, legal liability, and climate adaptation in a warming world. The emerging field of extreme-event attribution (EEA) seeks to answer the question: “Has climate change influenced the frequency, likelihood, and/or severity of individual extreme events?” Methodological advances over the past 15 years have transformed what was once an unanswerable hypothetical into a tractable scientific question—and for certain types of extreme events, the influence of anthropogenic climate change has emerged beyond a reasonable doubt. Several challenges remain, particularly those stemming from structural limitations in process-based climate models and the temporal and geographic limitations of historical observations. However, the growing use of large climate-model ensembles that capture natural climate variability, fine-scale simulations that better represent underlying physical processes, and the lengthening observational record could obviate some of these concerns in the near future. EEA efforts have important implications for risk perception, public policy, infrastructure design, legal liability, and climate adaptation in a warming world. There is now an extremely high level of scientific confidence that human activities are the only plausible explanation for the observed ∼1.2°C rise in global mean temperature, and a human fingerprint has likewise been found in numerous other changes in climate. However, although the mean climate is a useful metric of overall climate change, it remains a statistical construct: no place actually experiences its local mean. Moreover, the aspects of climate change that have the greatest effects on society and ecosystems—such as heatwaves, downpours, hurricanes, droughts, and wildfires—are inherently far from the mean. Therefore, to understand, mitigate, and adapt to climate changes that could harm the health and well-being of humans and ecosystems, it is imperative to understand how (and why) these climate-related extremes are changing in a warming world. This branch of climate science, often referred to as extreme-event attribution (EEA), has evolved rapidly in recent years. This evolution has faced a number of challenges. In particular, structural limitations in process-based climate models, as well as temporal and geographic limitations of historical observations, lead to substantial challenges in quantification and validation. However, recent methodological advances, coupled with longer observational records and improved climate models, have opened the door to systematically addressing the question of whether climate change has influenced the likelihood and/or severity of individual extreme events. The news media and public often ask: “Did climate change cause this specific extreme weather event?” In a very literal sense, the answer to such a rigidly posed question will always be “no.” All events in the dynamically coupled Earth system are ultimately the product of numerous complex, interrelated processes acting across a wide range of spatiotemporal scales. There will thus rarely (if ever) be a traceable singular cause for any specific event, and variability will always play an important role. Indeed, as recently as a decade ago, a common response from scientists was that “no single weather event can be attributed to climate change.” Weather and climate, of course, are not the same. Weather describes variations on very short day-to-day timescales, whereas climate integrates over much longer time horizons. A key step forward in the development of EEA has been the acknowledgment that weather and climate exist on a continuum. Because climate describes the aggregate statistical properties of weather—in other words, the plausible envelope of weather conditions at a particular point in time—it encompasses not only “typical” conditions but also rare, high-magnitude weather extremes. From this perspective, understanding multi-decadal climate change can reasonably be framed as an exercise in quantifying shifts in the overall probability distribution of day-to-day weather conditions. As a result, climate scientists have increasingly recognized that the strict question of binary causality is ill posed. Because climate is inherently a probabilistic descriptor of largely stochastic underlying weather processes, it stands to reason that scientific investigations into the influence of climate change upon extreme weather events should also be framed in probabilistic terms. Additionally, a considerable body of evidence suggests that human-caused changes in the low-probability, high-consequence “tails” of the weather distribution could be considerably different from what might be inferred from extrapolating shifts in the mean. Therefore, a growing number of studies have instead begun to ask a more nuanced question: “Has climate change influenced the frequency, likelihood, and/or severity of the extreme event?” This seemingly subtle shift in perspective transforms an essentially unanswerable question about absolute causality into one that is both scientifically tractable and practically actionable—and that can be directly addressed with existing observational and numerical modeling tools. As the field of EEA has rapidly expanded over the past decade, different research groups have pioneered a range of novel approaches. Virtually all approaches share a common epistemology: using some combination of real-world observations, numerical climate-model simulations, and rigorous statistical techniques to separate the effects of actual human influence on the climate system from a counterfactual “climate without human influence.” It is critical to understand both this general scientific framing and the specific methodological variations because results can be strongly dependent on the assumptions and analysis techniques employed. In the sections that follow, we first outline the basic methodological steps that are shared across most EEA studies (Figure 1) and then more deeply explore the range of approaches and assumptions that have historically been employed in different contexts. 1.Define the event. What spatiotemporal scale and physical variable(s) best characterize the event? Given an extreme heatwave, for instance, appropriate metrics might include daily maximum temperatures for a specific city, weekly average temperatures for a region, combined heat and humidity metrics, or underlying event drivers such as the strength of the atmospheric underlying high-pressure system.2.Estimate the “counterfactual” climate. Quantifying the influence of global warming requires quantification of the magnitude and/or likelihood of the event in a counterfactual climate without human influence. One approach is to quantify changes in the probability of the event in climate-model simulations without anthropogenic climate forcing. Alternative approaches include removing the long-term trend from the historical climate time series, using statistical relationships between the climate variable and global temperature, and using observational data from a time period with little anthropogenic influence.3.Compare actual and counterfactual climate. Are there statistically distinguishable differences in the probability and/or severity of the event between the actual and counterfactual climates? A number of different metrics have been used, including the fractional difference in event magnitude, the ratio of event probability (often called the “risk ratio”), and the portion of the total risk contributed by anthropogenic activities (i.e., the “fraction of attributable risk”). In addition, uncertainty quantification is a critical priority for both model- and observation-focused approaches. Key sources of uncertainty include the statistical quantification of the probability of the event, the ability of climate models to accurately simulate the observed variability of the climate variable, the magnitude of the “forced response” simulated by different climate models, and the “irreducible uncertainty” in the forced response contributed by internal climate variability.4.Make a formal attribution statement. Most EEA approaches use a very high bar for attribution: the typical null hypothesis is that human-caused climate change did not influence the magnitude or probability of the event, and rejecting that null requires a “beyond a reasonable doubt” standard. If there is sufficient evidence of a statistically distinguishable difference in the actual versus counterfactual climate, the null hypothesis can be rejected, and an affirmative attribution statement can be made at a specific confidence level. Given the multiple sources of uncertainty, attribution statements often include multiple components (i.e., “there is a 95% likelihood that global warming increased the probability of the event by at least a factor of 2.86”). New frameworks have been suggested to simplify the final attribution statement (Figure 1D). Initial decisions regarding how to define the event can influence the entire EEA process described in Figure 1. In addition to the decisions regarding appropriate physical metrics and spatiotemporal scales, there is also a deeper philosophical choice regarding which aspects of the event are most important and how far down the chain of complex physical causality the attribution methodology can be reasonably extended. These decisions can ultimately shape the final EEA conclusion. Consider an attribution study focused on the coastal inundation produced by a large hurricane making landfall at some specific location. One possible approach would be to consider the full sample of all hurricanes that affected the region and ask whether there has been a change in the likelihood of flooding exceeding the observed threshold. This might be referred to as an “absolute” approach because it considers overall changes in event likelihood without accounting for the specific initial conditions (i.e., the study is not preconditioned on the fact that a large hurricane occurred at that specific location and at that specific time) or the contribution of any particular contributing factor (e.g., sea level, precipitation intensity, and storm strength). As a result, absolute approaches can complicate efforts to understand which specific aspect of climate change has contributed to changes in the probability or severity of the extreme event. For example, without methods to isolate specific conditions, it would be difficult to differentiate between contributions from sea-level rise (which increase background water levels), increasing atmospheric water-vapor content (which contributes to the precipitation intensity of a given storm), and warming ocean temperatures and decreasing vertical wind shear (both of which act to intensify hurricanes). Another approach, often referred to as the conditional or “storyline” approach, takes certain aspects of the event conditions as given (such as the large-scale atmospheric conditions at the time of the event) and asks whether climate change has had a detectable effect upon modulating the outcome of the event. Often, such attribution studies involve perturbing a subset of relevant physical variables characterizing the state of the real-world atmosphere and/or ocean by an increment commensurate with the effect of climate change. In the hurricane example, a conditional approach might involve using the real-world atmospheric conditions from 5 days before the storm made landfall as initial conditions in a model simulation but prescribing sea surface temperatures with the anthropogenic ocean warming trend removed. A key strength is that the conditional approach can help isolate the influence of specific physical aspects of climate change. A significant weakness is that this approach cannot diagnose changes in the overall probability of the event or the probability of individual constituent physical conditions. An alternative to the absolute and conditional frameworks is the “ingredient-based” approach (Figure 2). Here, investigators first ascertain the most essential physical conditions known to contribute to the severity of a given event and then assess changes in the probability of these conditions. This approach aims to combine some of the key strengths of the absolute and conditional approaches because it (1) enhances understanding of how anthropogenic climate change is influencing the underlying physical drivers of extreme events, including the probability that they co-occur; (2) makes no assumptions regarding the specific set of initial conditions that produced the event; and (3) potentially enables attribution of event types that are poorly simulated in climate models and/or sparsely sampled in observational datasets. Fundamentally, two aspects of extreme events are typically assessed in attribution studies: the probability and the severity (Figure 2). The probability of an event is often defined as a rate of exceedance of a fixed threshold defined with a historical baseline—for instance, exceeding the 99.99th quantile of daily precipitation during the years 1920–1980. Conversely, the severity of an event is often defined as a magnitude associated with a given probability, such as “design floods” that are based on the magnitude of the 100-year recurrence interval. The probability and severity definitions can be two sides of the same analytical coin (Figure 2). However, the differences between these definitions are sometimes highly consequential for both broader communication and practical decision making. For example, regional sea-level rise over the past two centuries increased the severity of Superstorm Sandy's flooding in New York City by 22% (from ∼2.3 to ∼2.8 m for an event of Sandy's observed probability). According to the same analysis (Lin et al., 2016Lin N. Kopp R.E. Horton B.P. Donnelly J.P. Hurricane Sandy’s flood frequency increasing from year 1800 to 2100.Proc. Natl. Acad. Sci. U S A. 2016; 113: 12071-12075Crossref PubMed Scopus (116) Google Scholar), that same sea-level rise tripled the probability of the observed flooding (from ∼1,200- to ∼400-year return period for an event of Sandy's observed severity). In colloquial terms, a ∼20% increase might sound modest, whereas a tripling sounds very large indeed—perhaps leading to a wide divergence in public perception regarding a study's outcome. Yet, both of these are equally valid—and statistically consistent—metrics for quantifying the role of climate change, and both are potentially useful in different contexts. The probability-based metric, for example, could be highly relevant in a civil engineering context. Given that water infrastructure ranging from drainage culverts to large dams is typically designed to accommodate events defined by fixed historical thresholds (e.g., the amount of precipitation associated with a 100-year recurrence interval), increases in the probability of exceeding the original design threshold imply increased risk that the existing design capacity could be exceeded. The magnitude-based metric, on the other hand, is of heightened relevance in a legal and public policy context—instances in which it could be important to know the fraction of known losses contributed by climate change. Another key point of distinction is the difference between individual event attribution and what can be described as “collective event attribution.” Individual event attribution seeks to answer the question: “Has global warming influenced the likelihood or severity of a specific observed historical event?” Conversely, collective event attribution seeks to answer the question: “Has global warming influenced the overall likelihood or severity of extreme events of a certain type?” (Figure 3). Individual event attribution might focus, for example, on whether the vegetation flammability in the vicinity of Paradise, California, in November 2018 (the time and location of California's deadliest and most destructive wildfire in modern history) was made more likely or more severe by global warming. Collective event attribution, on the other hand, might focus on whether climate change has increased the overall likelihood of high vegetation flammability in the western United States (and, hence, that the record-setting vegetation flammability was “consistent with” changes that would be expected from climate change). Recently, research groups have begun to offer “rapid response” climate attribution targeted toward real-time weather events and sometimes make a formal attribution statement before the event even takes place. Emerging methods that apply an anthropogenic signal to numerical weather forecasts enable evaluations that are highly specific to the conditions of a given individual event. In addition, rapid statements can also be predicated on precomputed metrics via collective event-attribution methodologies that use large samples of observations and climate-model simulations to evaluate a particular type of extreme. Similar collective attribution methodologies have also been used to quantify the fraction of a region or the globe over which anthropogenic forcing has already influenced the probability of record-setting events (Figure 3) and to verify event-attribution methodologies by using out-of-sample prediction-verification frameworks. Although the science of EEA has advanced dramatically since the benchmark attribution study of the 2003 European heatwave (Stott et al., 2004Stott P.A. Stone D.A. Allen M.R. Human contribution to the European heatwave of 2003.Nature. 2004; 432: 610-614Crossref PubMed Scopus (1144) Google Scholar), several substantial challenges remain. The most prominent relate to uncertainties surrounding the creation and analysis of the counterfactual climate. Researchers have used both statistical and climate-modeling approaches to quantify the counterfactual, although there is no consensus on which of these methods is the most suitable representation of event probability or severity in the absence of human influence. The challenge of the counterfactual is exacerbated by the fact in it remains difficult to the event probability in the climate. For severe events, the existing observational record might be and/or to enable probability One is to use or other statistical techniques from extreme to the recurrence of the event. However, multiple studies have that such statistical approaches are extremely to the of the underlying distribution and of probability that by of climate-model which offer much sample can help the to make such assumptions about the underlying this alternative is to the that climate models cannot always capture the underlying physical processes for certain types of events. This to the question of whether climate models are for in the of A challenge is the between the model that is for the physical that certain types of extreme weather and the large ensembles and that are for characterizing internal variability and the signal of climate change. For instance, climate models are to represent high-pressure for extreme heatwaves, but most are to capture the full intensity and of and even challenges in extreme precipitation events, which can on that are a single global climate model These climate-model limitations are a key reason the level of confidence associated with EEA statements considerably by the type of extreme event (e.g., very high confidence for versus only confidence for Figure these limitations the that studies no influence of climate change are the limitations of the observational record or climate-modeling A key philosophical thus an of regarding the role of climate change mean that there is of it can be difficult to between these two possible of a attribution is an important aspect of the results of such studies to decision and the in climate modeling and Earth system science the for rapid of the most important development has been the of the EEA which has expanded the number of and attribution methods to a wide of extreme events human and natural the world. to systematically methods have begun to of these efforts and to underlying and data will help EEA In addition, efforts to and shared the specific or of the event with associated scientific will help the public and decision better understand the role of anthropogenic climate change. in has in climate-model and for increased physical in processes that are critical in the evolution of extreme events. 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