When a plaintiff sues an emitter for a specific climate-related harm, courts must decide what attribution science can legitimately show about that singular harm. Lloyd and Shepherd (2021) argued that the conditional, deductive 'storyline' approach to extreme event attribution aligns naturally with legal evidence, but noted their analysis had never been tested in court and that the integration of storyline and probabilistic attribution for litigation — including the partition of probabilistic results into thermodynamic and dynamical factors and the treatment of the uncertain dynamical factor — remained undeveloped. Subsequent literature and case law (Held v. Montana; Lliuya v. RWE; end-to-end probabilistic attribution frameworks) confirm that the question is still open: no court has yet adjudicated the admissibility and probative weight of storyline-based attribution evidence, and no accepted integrated evidentiary framework exists.

Background

Extreme event attribution is the science of quantifying how anthropogenic climate change altered the probability or severity of a specific weather event, such as a heat wave, flood, or wildfire season. Two complementary framings exist. The probabilistic approach estimates a risk ratio Pf(H,D)/Pc(H,D)P_f(H,D)/P_c(H,D) — the probability of the hazard HH occurring together with the dynamical situation DD conducive to it (the prevailing atmospheric circulation, e.g. a blocking anticyclone — a persistent stationary high-pressure system) under factual conditions with climate change (ff), divided by the corresponding joint probability under counterfactual conditions without climate change (cc). The storyline approach, by contrast, is conditional and deductive: it takes the dynamical conditions as given (contingent) and asks how climate change, mainly through thermodynamic effects such as warming and moistening, altered the realized event's magnitude or impact; it reasons from the general (well-established knowledge about climate change) to the specific (a particular local harm), rather than inductively from observations to general conclusions.

By the laws of probability the risk ratio factors as

Pf(H,D)Pc(H,D)=Pf(H∣D)Pc(H∣D)×Pf(D)Pc(D).\frac{P_f(H,D)}{P_c(H,D)}=\frac{P_f(H\mid D)}{P_c(H\mid D)}\times\frac{P_f(D)}{P_c(D)}.

The first factor is exactly the storyline (thermodynamic) calculation, for which confidence is relatively high. The second factor — the change in probability of the dynamical conditions themselves due to climate change — is much more uncertain, because how atmospheric circulation responds to warming remains a leading source of uncertainty in climate models (Shepherd 2016; this decomposition was recommended as good practice by the 2016 US National Academies report on extreme event attribution).

In tort law (civil liability for harm), a plaintiff must establish causation on the balance of probabilities ('preponderance of the evidence', i.e. more likely than not) — a lower standard than scientific proof — and courts act as gatekeepers for the admissibility of scientific evidence (in US federal courts under the Daubert framework). In the case Juliana v. United States (a constitutional climate suit brought by young plaintiffs against the US federal government), climate scientist Kevin Trenberth gave expert testimony using the storyline approach to connect greenhouse warming to plaintiffs' specific regional harms; the government's rebuttal expert argued that no scientifically valid attribution down to the scale of a specific injury was possible without a fully quantitative modeling chain of matching geographic and temporal scope. The case was dismissed on standing and justiciability grounds — that is, on whether the plaintiffs were the right parties to bring suit and whether the dispute was one a court could decide — leaving that evidentiary dispute undecided. Lloyd and Shepherd (2021) argued that deductive, general-to-specific storyline reasoning is a legitimate scientific method, well suited to legal concepts of evidence, and that tort claims would be largely precluded if such reasoning were excluded. Prior and contemporaneous legal-scholarship (Burger, Wentz and Horton 2020; Pfrommer et al. 2019; Stuart-Smith et al. 2021) had mapped how attribution science enters litigation, finding that submitted evidence generally lagged the state of the art and that causation remained the central obstacle. The glacier-lake case Lliuya v. RWE (a Peruvian farmer suing a German utility for a proportional share of flood-protection costs at Huaraz, Peru) illustrates the two strands: a probabilistic study of the lake's outburst-flood hazard (Stuart-Smith et al. 2021, Nature Geoscience) and a storyline-style causal-chain analysis (Huggel et al. 2020), which Lloyd and Shepherd held up as complementary halves of a single evidentiary narrative.

Problem Statement

Under what conditions can storyline-based extreme-event attribution evidence — the conditional, deductive reasoning from general attribution knowledge (e.g. thermodynamic change as assessed by the Intergovernmental Panel on Climate Change, IPCC) to a specific, singular local harm, without an attribution study of matching geographic and temporal scope — be admitted and weighed in legal proceedings on climate-change liability, and how can it be integrated with probabilistic attribution (whose results should be partitioned into thermodynamic and dynamical factors) into a single evidentiary narrative that satisfies legal standards of causation and proof, such as 'but-for' causation (whether the harm would not have occurred but for the defendant's emissions), risk increase, and the preponderance-of-the-evidence ('more likely than not') standard of tort law? The question comprises three jointly linked requirements that together serve one objective — establishing a defensible causal chain from an emitter's greenhouse-gas emissions to a plaintiff's specific harm: (1) admissibility and probative weight of general-to-specific (deductive) attribution reasoning for singular events, the dispute left undecided by the dismissal of Juliana v. United States; (2) an integration of the storyline and probabilistic approaches in which each covers the other's weaknesses, probabilistic studies partitioning attribution into thermodynamic and dynamical factors; and (3) a defensible treatment of the second risk-ratio factor Pf(D)/Pc(D)P_f(D)/P_c(D) — the change in probability of the dynamical conditions, the locus of persistent scientific uncertainty — in a form legal fact-finders can weigh.

References

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