Forecast-based attribution of a winter heatwave within the limit of predictability

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model–based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The “storyline” attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event that was predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019.

Attribution of extreme weather events is a relatively young field of research within climate science. However, it has expanded rapidly from its conceptual introduction (1) over the past 20 y; it now has an annual special issue in The Bulletin of the American Meteorological Society (2). Extreme event attribution is of particular importance for communicating the impacts of climate change to the public (3, 4), since the changing frequency of extreme weather events due to climate change is an impact that is physically experienced by society. As a result of this rapid expansion, there now exist a large number of different methodologies for carrying out an event attribution (5). Many of these rely on large ensembles of climate model simulations, the credibility of which has been questioned by recent studies (6–8). A particular issue is the dynamical response of the atmosphere to external forcing, which is highly uncertain within these models (9). As attribution studies try to provide quicker results, with an operational system a clear aim, it is vital that any such system provides trustworthy results. In this study we propose a “forecast-based” attribution methodology using medium-range weather forecasts that could provide several key advantages over traditional climate model-based approaches. First, if an event is predictable within a forecasting system, we know that that system is capable of accurately representing the event. Second, we know that any attribution performed is unequivocally an attribution of the specific event that occurred, unlike in unconditioned climate model simulations. Finally, weather forecasts are run routinely by many different national and research centers. The models used are generally state of the art and extensively verified. We propose that the attribution community could and should take advantage of the massive amount of resources that are put into these forecasts by developing methodologies that use the same type of simulation. Ideally, the experiments required for attribution with forecast models would be able to be run with little additional effort on top of the routine weather forecasts; in this way they might provide a rapid operational attribution system. We discuss these ideas further throughout the text.There have been several studies that propose or perform methodologies related to the forecast-based attribution demonstrated here. Hoerling et al. (10) used two seasonal forecast ensembles to examine the predictability of the 2011 Texas drought/heatwave within a comprehensive attribution analysis involving several different types of climate simulation. Meredith et al. (11) used a triply nested convection-permitting regional forecast model to investigate the role of historical sea surface temperature (SST) warming within an extreme precipitation event. They conditioned their analysis on the large-scale dynamics of the event through nudging in the outermost domain. More recently, Van Garderen et al. (12) employed spectrally nudged simulations to assess the contribution of human influence on the climate over the 20th century on the 2003 European and 2010 Russian heatwaves. Possibly the most similar studies to the one presented here are a series of studies by Hope et al. (13–15). They used a seasonal forecast model to assess anthropogenic CO₂ contributions to record-breaking heat and fire weather in Australia. Two more similar studies carried out forecast-based hurricane attribution studies (16, 17). Tropical cyclones are a natural candidate for forecast-based methodologies due to the high model resolution required to represent them accurately, if at all. A final distinct, but related study is Hannart et al. (18), which proposes the use of data assimilation for detection and attribution (DADA). They suggest that operational causal attribution statements could be made in a computationally efficient manner using the kind of data assimilation procedure carried out by weather centers (to initialize forecasts) to compute the likelihood of a particular weather event under different forcings (these would be observed and estimated preindustrial forcings for conventional attribution). Our forecast-based framework differs from these other studies in several regards. First, we use a state-of-the-art forecast model to perform the attribution analysis of the event in question, rather than to solely assess the predictability of the event. We use free-running coupled ocean-atmosphere global integrations here, allowing the predictable component at initialization to dynamically condition the ensemble, as opposed to nudging our simulations toward the dynamics of the event, using nested regional simulations or using the highly observationally constrained output of data assimilation procedures. A final key difference is that here we present an attribution of the direct radiative effect of CO₂ in isolation, although we hope that our approach could be extended in the future to provide an estimate of the full anthropogenic contribution to extreme weather events as in these other studies. We argue that the relative simplicity in the validation, setup, and conditioning of our simulations is desirable from an operational attribution perspective and flexible across many different types of extreme event.We begin by introducing the chosen case study, the 2019 February heatwave in Europe, describing its synoptic characteristics and formally defining the event quantitatively. We then demonstrate the predictability of the event within the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system, showing that this operational weather forecast was able to capture both the dynamical and thermodynamical features of the event. In perturbed CO₂ forecasts, we outline the experiments we have performed to quantitatively determine the direct CO₂ contribution to the heatwave. We then provide quantitative results from these experiments and finally conclude with a discussion of the strengths and potential issues of our forecast-based attribution methodology, including our proposed directions for further work.