The starting point of the analysis is a distinction that sounds obvious but is routinely blurred in practice: the difference between a prediction and a forecast. In the authors’ terminology, a prediction is a deterministic statement that a specific hazard intensity will or will not occur in a defined region and time window, while a forecast assigns a probability to that occurrence. Even predictions, the authors note, implicitly carry uncertainty because of unavoidable false alarms and missed events, which makes them probabilistic whether scientists admit it or not. The task force argues that issuing forecasts is preferable because probabilities clarify the division of labor in risk management: scientists provide probabilities that describe scientific uncertainty, while decision-makers choose the probability thresholds that trigger action. This hazard-risk separation principle, advocated by Jordan and colleagues more than a decade ago, remains surprisingly neglected in operational practice.

Why can natural systems not be predicted deterministically in the first place? The paper points to intrinsic unpredictability arising from strong nonlinearities such as chaos, fine-scale aggregation in space and time, high dimensionality, and the open nature of Earth systems, which exchange energy and matter with their surroundings in often uncontrolled ways. On top of this natural variability sits a second, equally pervasive source of uncertainty: our limited knowledge of the underlying processes and the limitations of their numerical representation. These two categories are conventionally labeled aleatory variability and epistemic uncertainty, and the task force found that every hazard domain they examined struggles with how to define them unambiguously and how to include both in a single forecast. The proliferation of competing terms, shallow and deep, internal and external, structural and model uncertainty, likelihood and confidence, reveals fields tackling similar problems in isolation.

The technical heart of the framework lies in how forecasting models express uncertainty. Each model, whether physics-based, machine-learning, empirical, or rooted in expert judgment, produces a forecast distribution, a survival function showing the exceedance probability for each hazard intensity value. When knowledge is strong and data abundant, a single forecast distribution may suffice; the paper cites ocean biogeochemical modeling, where point estimates are compared with sparse observations to derive error statistics, and the Bluecat approach in hydrology, which converts deterministic predictions into stochastic forecasts. But in many domains, from long-term flood frequency analysis to seismic hazard assessment, no single trustable model exists. Scientists then work with an ensemble of alternative forecast distributions, each reflecting a different working hypothesis or numerical approximation. The standard practice of collapsing these into one mixture distribution, often loosely called the mean, is where the authors see a critical loss of information.

Collapsing the ensemble, they argue, discards the dispersion of the individual distributions around the mixture, and that dispersion is precisely the measure of how much scientists believe in their own assessment. A statement that an event has a 15 percent probability conveys nothing about whether it comes from a large dataset, from models in close agreement, or from averaging wildly divergent assessments. The same mixture distribution can emerge from a tightly clustered ensemble or from one spread across implausibly wide alternatives, and stakeholders deserve to know which. The proposed solution is the complete forecast: a forecast that explicitly represents and keeps distinct all recognized uncertainties. Quantitatively, it is built from what the authors call the extended experts’ distribution, a probability distribution over the exceedance probability itself, obtained by fitting the ensemble of model-derived probabilities, for example with a Beta distribution. Its spread mimics the epistemic uncertainty; its center tracks the aleatory variability.

This construction rests on a unified probabilistic framework developed by Marzocchi and Thomas Jordan, which represents both objective frequency-based information and subjective expert judgment as a distribution of probability rather than a single number. The framework is anchored in the concept of an experimental concept, a collection of observations judged to be stochastically exchangeable, whose long-run frequency defines the true, unknown forecast distribution that every model attempts to estimate. The authors are candid about the philosophical difficulties: what appears to be irreducible randomness may simply reflect incomplete understanding, and as knowledge improves, apparent aleatory variability can be reclassified as epistemic. Their resolution is to define aleatory variability not as a property of the physical process but of the data-generating process specified by the experimental concept, a definition that is model-independent in principle. If the true probability falls outside the extended experts’ distribution, the framework registers an ontological error, an unknown unknown in Donald Rumsfeld’s memorable phrase.

Testing forecasts against reality is the second pillar of the framework, and here the distinctions become operationally consequential. A single forecast distribution can be calibrated by comparing it with independent observations, for instance through the probability integral transform, which should yield uniformly distributed values for a well-calibrated model. A complete forecast, by contrast, is validated by testing whether observed exceedance frequencies are coherent with the extended experts’ distribution. When observations were used to build the model, only the humbler term consistency applies, and the authors stress that a flawed distribution can pass a consistency test through overfitting while failing calibration; only rejection is truly informative in that case. Relative skill among competing models is measured with proper scoring rules, which rank models without implying reliability. Where data are scarce or absent, as is common for the highest and most societally relevant hazard intensities, expert judgment and structured elicitation protocols take on a fundamental role in assessing whether the ensemble spans scientifically plausible interpretations.

Communication emerges as perhaps the least mature component of the entire risk management cycle. The RETURN project interviewed 32 professionals involved in risk communication across municipalities, civil protection agencies, research institutions, and consultancy, and the interviews revealed large differences in terminology and skillsets that suggest a pioneering phase rather than an established discipline. The challenges are formidable: low probabilistic literacy among the public, a tendency to mistake detail for precision, deterministic expectations of science, and technical terms such as uncertainty, error, and moderate alert that shed their precise meanings outside the laboratory. Simplifications like traffic-light color schemes feel intuitive but can produce sharp, misleading discontinuities between assessments just above or below a threshold. The task force identifies two complementary approaches, an operational one built on digestible data and a narrative one built on storytelling, noting that narratives can make tiny probabilities tangible but risk sliding into persuasion.

The framework’s implications for decision-making are illustrated by tsunami warning systems in the North-East Atlantic and Mediterranean region, where traditional decision matrices leave no room for quantifying uncertainty. A probabilistic system that links alert levels to percentiles of a forecast distribution can make explicit the trade-offs between false alarms and missed alerts, choices that are ultimately political judgments about acceptable risk rather than purely scientific ones. The authors also recommend pre-defined protocols as a transparent audit trail for emergencies, when there is no time to weigh every uncertainty but ignoring them can erode public trust. Their vision, spanning eighteen Italian universities and research centers alongside the Civil Protection Department, State Railways, and insurers, is a unified cross-hazard framework in which probabilistic forecasting serves as the common language bridging science and society, complete forecasts keep epistemic and aleatory uncertainties distinct, rigorous evaluation establishes credibility, and communication experts ensure the last mile to decision-makers is never left uncovered.

Subject of Research: A cross-hazard framework for quantifying and communicating uncertainty in natural hazard forecasting

Article Title: Invited perspective: Uncertainties in natural systems may be uncomfortable, but ignoring them would be absurd

Article References: Marzocchi, W., Montanari, A., & the RETURN-uncertainty task force (2026). Invited perspective: Uncertainties in natural systems may be uncomfortable, but ignoring them would be absurd. Natural Hazards and Earth System Sciences, 26(10), 4861-4880. https://doi.org/10.5194/nhess-26-4861-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4861-2026

Keywords: uncertainty, natural hazards, probabilistic forecasting, epistemic uncertainty, aleatory variability, risk communication, complete forecast, seismic hazard, flood forecasting, model evaluation, decision-making, RETURN project