In the past decade, the field of extreme event attribution has made great strides in understanding and explaining extreme events in the context of climate change. This is still an emerging science, however; thus, continued research is required to increase the reliability of event attribution results, particularly for event types that are presently poorly understood. The need for improved understanding is coming at a time when there is increasing inquiry by the public, policy makers, and practitioners about the relationship between specific weather events and climate change (e.g., the question, “Is it caused or affected by climate change?”). Advances in the field will depend not only on addressing scientific problems specific to attribution but also on advances in the basic underlying science, including observations, weather and climate modeling, statistical methodology, and theoretical understanding of extreme events and their relation to climate.
This chapter builds on the information presented in the preceding chapters to provide guidance for framing questions about event attribution and approaches to ensuring the robustness and reliability of event attribution studies and information. The committee also recommends future research that would improve extreme event attribution capabilities and discusses the future of event attribution in an operational context.
Event attribution is more reliable when based on sound physical principles, consistent evidence from observations, and numerical models that can replicate the event. The ability to attribute the causes of some extreme event types has advanced rapidly since the emergence of event attribution science a little over a decade ago, while attribution of other event types remains challenging. In general, confidence in attribution results is strongest for extreme event types that
- have a long-term historical record of observations to place the event in an appropriate historical context;
- are simulated adequately in climate models; and
- are either purely meteorological in nature (i.e., the nature of the event is not strongly influenced by the built infrastructure, resource management actions,
- etc.) or occur in circumstances where these confounding factors can be carefully and reliably considered.
Non-meteorological factors confound observational records and can limit the accuracy of model simulations of extreme events. Drought and wildfire are examples of events for which non-meteorological factors can be especially challenging in attribution studies.
Furthermore, confidence in attribution results that indicate an influence from anthropogenic climate change is strongest when
- there is an understood and robustly simulated physical mechanism that relates a given class of extreme events to long-term anthropogenic climate changes such as global-scale temperature increase or increases in water content of a warmer atmosphere.
Confidence in attribution findings of anthropogenic influence is greatest for those extreme events that are related to an aspect of temperature, such as the observed long-term warming of the regional or global climate, where there is little doubt that human activities have caused an observed change. For example, a warmer atmosphere is associated with higher evapotranspiration rates and heavier precipitation events through changes in the air’s capacity to absorb moisture. Atmospheric circulation and dynamics play some role, however, which is different for different event types. Changes in atmospheric circulation and dynamics are generally less directly controlled by temperature, less robustly simulated by climate models, and less well understood. Event attribution can be further complicated by the existence of other factors that contribute to the severity of impacts.
Confidence in attribution analyses of specific extreme events is highest for extreme heat and cold events, followed by hydrological drought and heavy precipitation. There is little or no confidence in the attribution of severe convective storms and extratropical cyclones. Confidence in the attribution of specific events generally increases with our understanding of the effect of climate change in the event type. Nevertheless, the gap between this understanding and confidence in attribution of specific events varies among event types.
Attribution of events to anthropogenic climate change may be complicated by low-frequency natural variability, which influences the frequencies of extreme events on decadal to multidecadal timescales. The Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation are examples of such variability. Characterization of these influences is uncertain because the observed record is too short to do so reliably, and it also is too short to assess whether climate models simulate these modes of variability correctly.
There is no single best method or set of assumptions for event attribution because these depend heavily on the framing of the question and the amount of time available to answer it. Time constraints may themselves affect framing and methodological choices by limiting analyses to approaches that can be undertaken quickly.
A definitive answer to the commonly asked question of whether climate change “caused” a particular event to occur cannot usually be provided in a deterministic sense because natural variability almost always plays a role. Many conditions must align to set up a particular event. Extreme events are generally influenced by a specific weather situation, and all events occur in a climate system that has been changed by human influences. Event attribution studies generally estimate how the intensity or frequency of an event or class of events has been altered by climate change (or by another factor, such as low-frequency natural variability).
Statements about attribution are sensitive to the way the questions are posed and the context within which they are posed. For example, when defining an event, choices must be made about defining the duration of the event (when did it begin and when did it end) and the geographic area it impacted, but this may not be straightforward for some events (e.g., heat waves). Furthermore, different physical variables may be studied (e.g., drought might be characterized by a period with insufficient precipitation, excessively dry soil, or reduced stream flow), and different metrics can be used to determine how extreme an event was (e.g., frequency, magnitude). Whether an observation- or model-based approach is used, and the sorts of observations and/or models available for studying the event, also will constrain the sorts of questions that can be posed.
Attribution studies of individual events should not be used to draw general conclusions about the impact of climate change on extreme events as a whole. Events that have been selected for attribution studies to date are not a representative sample (e.g., events affecting areas with high population and extensive infrastructure will attract the greatest demand for information from stakeholders). In addition, events that are becoming less likely because of climate change (e.g., cold extremes) will be studied less often because they occur less often than events whose frequency is increasing because of climate change. Furthermore, attribution of individual events is generally more difficult than characterizing the statistical distribution of an event of a given type and its dependence on climate. For all of these reasons, counts of available attribution studies with either positive or negative or neutral results are not expected to give a reliable indication of the overall importance of human influence on extreme events.
Unambiguous interpretation of an event attribution study is possible only when the assumptions and choices that were made in conducting the study are clearly stated and the uncertainties are carefully estimated. The framing of event attribution questions, which may depend strongly on the intended application of the study results, determines how the event will be studied and can lead to large differences in the interpretation of the results. Event attribution studies presented in the following manner are less likely to be misinterpreted:
- Assumptions about the state of one or more aspects of the climate system at the time of the event (e.g., sea-surface temperature [SST] anomalies, atmospheric circulation regimes, specific synoptic situations) are clearly communicated.
- Estimates of changes in both magnitude and frequency are provided, with accompanying estimates of uncertainty, so users can understand the estimated degree of change from the different perspectives.
- Estimates of changes in frequency are presented as a risk ratio: that is, in terms of the ratio of the probability of the event in a world with human-caused climate change to its probability in a world without human-caused climate change. Equivalently, one can compare the return periods of the event (i.e., how rarely an event occurs) in the world without climate change to that in the world with climate change.
- The impact of assumptions (e.g., of how estimates of changes in magnitude and frequency depend on SST anomalies or atmospheric circulation regimes) is discussed.
- Statements of confidence accompany results so users understand the strength of the evidence.
Bringing multiple scientifically appropriate approaches together, including multiple models and multiple studies, helps distinguish results that are robust from those that are much more sensitive to how the question is posed and the approach taken. Utilizing multiple methods to estimate human influences on a given event also partially addresses the challenge of characterizing the many sources of uncertainty in event attribution.
Examples of multiple components that can lead to more robust conclusions include:
- Estimates of event probabilities or effect magnitudes based on an appropriate modeling tool that has been shown to reasonably reproduce the event and its circumstances, such as the dynamic situation leading to the event.
- Reliable observations against which the model has been evaluated and that give an indication of whether the event in question has changed over time in a manner that is consistent with the model-based attribution.
- Assessment of the extent to which the result is consistent with the physical understanding of climate change’s influence on the class of events in question.
- Clear communication of remaining uncertainties and assumptions made or conditions imposed on the analysis.
Improving Extreme Event Attribution Capabilities
A focused effort to improve understanding of specific aspects of weather and climate extremes could improve the ability to perform extreme event attribution. The World Climate Research Programme (WCRP) has identified climate extremes as one of its grand challenges, suggesting major areas of scientific research, modeling, analysis, and observations for WCRP in the next decade. Because extreme event attribution relies on all aspects of the understanding of extremes and their challenges, the committee endorses the recommendations from the white paper “WCRP Grand Challenge: Understanding and Predicting Weather and Climate Extremes” (Box 5.1; Zhang et al., 2014) as necessary to make advances in event attribution. Advances made in understanding the physical mechanisms and in improving the realism of extreme events in weather and climate models will benefit event attribution studies.
The committee recommends that research that specifically aims to improve event attribution capabilities include increasing the understanding of
- the role of dynamics and thermodynamics in the development of extreme events;
- the model characteristics that are required to reliably reproduce extreme events of different types and scales;
- changes in natural variability, including the interplay between a changing climate and natural variability, and improved characterization of the skill of models to represent low-frequency natural variability in regional climate phenomena and circulation;
- the various sources of uncertainty that arise from the use of models in event attribution;
- how different levels of conditioning (i.e., the process of limiting an attribution analysis to particular types of weather or climate situations) lead to apparently different results when studying the same event;
- the statistical methods used for event attribution, objective criteria for event selection, and development of event attribution evaluation methods;
- the effects of non-climate causes—such as changes in the built environment (e.g., increasing area of urban impervious surfaces and heat island effects), land cover changes, natural resource management practices (e.g., fire suppression), coastal and river management (e.g., dredging, seawalls), agricultural practices (e.g., tile drainage), and other human activities—in determining the impacts of an extreme event;
- expected trends in future extreme events to help inform adaptation or mitigation strategies (e.g., calculating changes in return periods to show how the risk from extreme events may change in the future); and
- the representation of a counterfactual world that reliably characterizes the probability, magnitude, and circumstances of events in the absence of human influence on climate.
Research that is targeted specifically at extreme events, including event attribution, could rapidly improve capabilities and lead to more reliable results. In particular, there are opportunities to better coordinate existing research efforts to further accelerate the development of the science and to improve and quantify event attribution reliability. Examples of event attribution research coordination include EUropean CLimate and weather Events: Interpretation and Attribution (EUCLEIA), weather@home, World Weather Attribution (see Box 3.4 for additional information on these), and the International Detection and Attribution Group (IDAG), all of which also coordinate with one another. Furthermore, given that event attribution spans climate and weather, the field would benefit from interdisciplinary research at the interface between the climate, weather, and statistical sciences to improve analysis methods. Event attribution capabilities would be improved with better observational records, both near–real time and for historical context. Long, homogeneous observed
records are essential for placing events into a historical context and for evaluating to what extent climate models reliably simulate the effect of decadal climate variability on extremes.
Event attribution could be improved by the development of transparent community standards for attributing classes of extreme events. Such standards could include an assessment of model quality in relation to the event/event class. Community agreement is needed on when a model represents a given event type well enough for attribution studies to be possible. At present, such standards do not clearly exist, and some model-based attribution studies do not even attempt to assess model adequacy. Such standards are critical for enhancing confidence in event attribution studies. Other examples of necessary community standards include use of multiple lines of evidence, development of a transparent link to a detected change that influences events in question, and clear communication of sensitivities of the result to framing of the event attribution question.
Systematic criteria for selecting events to be analyzed would minimize selection bias and permit systematic evaluation of event attribution performance, which is important for enhancing confidence in attribution results. Studies of a representative sample of extreme events would allow stakeholders to use such studies as a tool for understanding how individual events fit into the broader picture of climate change. Irrespective of the method or related choices, it would be useful to develop a set of objective criteria to guide event selection. A simple example of an objective approach might be to select events based on their rarity in the historical record using a fixed threshold, such as 24-hour precipitation events throughout a given domain that exceed the local 99th percentile of historical precipitation events. It should be noted, however, that even in this case, subtleties associated with historical quantile definition would need to be considered. The development of objective criteria for event selection would help both to reduce selection bias and to lead to methodological improvements. A path forward to avoiding selection bias is to perform event attribution on a predefined set of events of several different types that could reasonably be expected to occur in the current climate. This could involve systematic definition of events or consideration of events based on the full historical record and not just current events. Christidis and colleagues (2014) describe one example of such an approach: namely, a method for precomputing estimates of how human influence has changed the odds of extremely warm regional seasonal mean temperatures based on a formal detection and attribution methodology (see Chapter 3). Another example is the approach of trying to identify “grey swan tropical cyclones” (events not seen before, but theoretically possible) before they occur (Lin and Emanuel, 2015).
Event selection criteria also is a prerequisite for the development of a formalized approach to evaluating event attribution results and uncertainty estimates. Such evaluation is important for establishing confidence in event attribution statements. Development of such an approach could be modeled after existing approaches used to evaluate weather forecasts. One possible approach to evaluation would be to use a large sample of objectively selected events on a global scale to evaluate if, on average, model predictions or simulations of extreme events are on target. This could involve seasonal and decadal predictions of the number of events of a certain type based on simulations with external drivers only. Events that become more frequent with global warming, as well as events that become less frequent, such as cold spells, would be included in such an approach.
Event Attribution in an Operational Context
As more researchers begin to attempt event attribution, their efforts can benefit from coordination to improve analysis methods and work toward exploring uncertainties across methods and framing. Event attribution can benefit from links to operational numerical weather prediction where available. As discussed in Chapter 3 (see also Box 3.4), some groups are moving toward the development of operational extreme event attribution systems to systematically evaluate the causes of extreme events based on predefined and tested methods. Objective approaches to compare and contrast the analyses among multiple different research groups based on agreed event selection criteria are yet to be developed.
In the committee’s view, a successful operational event attribution system would have several key characteristics. First is the development and use of objective event selection criteria to reduce selection bias so stakeholders understand how individual events fit into the broader picture of climate change. Second is the provision of stakeholder information about causal factors within days of an event, followed by updates as more data and analysis results become available. This is analogous to such other fields as public health and economics, where it is acceptable to revise initial forecasts and analyses as more data become available (e.g., Gross Domestic Product estimates, recession start and stop dates, etc.). A third characteristic of a successful event attribution system is clear communication of key messages to stakeholders about the methods and framing choices as well as the associated uncertainties and probabilities. Finally, reliable assessments of performance of the event attribution system are needed. Such assessments could be developed through processes utilizing regular forecasts of event probability and intensity, observations, and skill scores similar to those used routinely in weather forecasting for evaluation. Rigorous approaches to
managing and implementing system improvements also are a critical element of these assessments.
Some future event attribution activities could benefit from being linked to an integrated weather-to-climate forecasting effort on a range of timescales. The development of such an activity could be modeled from concepts and practices within the Numerical Weather Prediction (NWP) and seasonal forecasting community. NWP, which dates back to the 1950s, is focused on taking current observations of weather and processing these data with computer models to forecast the future state of weather. A project linking attribution and weather-to-climate forecasting likewise could build on recent efforts to increase national and international capacity to forecast the likelihood of extreme events at subseasonal-to-seasonal timescales1 (WMO, 2013).
Ultimately the goal would be to provide predictive (probabilistic) forecasts of future extreme events at lead times of days to seasons, or longer, accounting for natural variability and anthropogenic influences. These forecasts would be verified and evaluated utilizing observations, and their routine production would enable the development and application of appropriate skill scores (using appropriate metrics to define and track the skill). The activity would involve rigorous approaches to managing and implementing system enhancements to continually improve models, physical understanding, and observations focused on extreme events.
Correctly done, attribution of extreme weather events can provide an additional line of evidence that demonstrates the changing climate as well as its impacts and consequences. An accurate scientific understanding of extreme weather event attribution can be an additional piece of evidence needed to inform decisions on climate change–related actions.
The committee also encourages continued research in event attribution outside of an operational context to ensure further innovation in the field. This would facilitate better understanding of a breadth of approaches, framings, modeling systems, and the performance of event attribution methods across past events, including in the longer historical context.
1 Another National Academies of Sciences, Engineering, and Medicine committee is studying this topic and will produce a report in the spring of 2016: http://dels.nas.edu/Study-In-Progress/Developing-Research-Agenda/DELS-BASCPR-13-05.