PICO
(1) Innovative and practical tools (e.g. from social or statistical research) for communicating uncertainty
(2) Pitfalls, challenges and solutions to communicating uncertainty with non-experts
(3) Communicating uncertainty in risk and crisis situations (e.g., natural hazards, climate change, public health crises)
Examples of research fitting into the categories above include a) new, creative ways to visualize different aspects of uncertainty, b) new frameworks to communicate the level of confidence associated with research, c) testing the effectiveness of existing tools and frameworks, such as the categories of “confidence” used in expert reports (e.g., IPCC), or d) research addressing the challenges of communicating high-uncertainty high-impact events.
This session encourages you to share your work and join a community of practice to inform and advance the effective communication of uncertainty in earth and space science.
Whether its memories of a cold, frosty Christmas or an August bank holiday beach trip interrupted by rain, many cultural, sporting, and social events in the United Kingdom have strong associations with particular weather conditions. As the average global temperature increases, the impacts of a changing climate are likely to be felt across many aspects of British life, including in the public’s experiences of these popular events. Several recent works conducted by the UK Met Office have sought to make the local day-to-day impacts of climate change more understandable for the public by exploring likely climatic conditions of popular events by the 2050s. These works have received strong engagement from the public, demonstrating the demand for relevant and understandable climate information.
We serve this demand by using the 2018 UK Climate Projections (UKCP18) and HadUK-Grid observations data to evaluate how climate change will affect the climatology of a diverse range of British social, cultural, and sporting events. To explore and communicate the uncertainties in UKCP18 due to inherent model biases, several bias correction methods are applied to the data and the resulting data is analysed together to give an improved uncertainty range. The research will focus on assessing changes to temperature variables at global warming levels of 1.5°C and 3.0°C to illustrate these two future scenarios and the uncertainty within each scenario.
We will show that some events are likely to have a significantly altered climatology which is likely to substantially change the nature of these events or force them to change when they occur during the year to give the best chance of having favourable climatic conditions. By assessing the impact of climate change on popular British events such as the London Marathon and Glastonbury Festival the findings of this research will prove useful in communicating the impacts of climate change in a way which will resonate with the British public.
How to cite: Woods, L., Pope, J., and Fung, F.: Impacting on our Lives: Using British sports and culture to explain uncertainty in climate projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9081, https://doi.org/10.5194/egusphere-egu25-9081, 2025.
Sub-seasonal weather forecasting is notoriously difficult, particularly for the extra-tropics. Predictions must be probabilistic, and from weeks 3 or 4 onwards forecast distributions are often very close to model-climate distributions. Together, these facts make conveying a meaningful forecast to customers extremely difficult, and those forecasts are then very vulnerable to misinterpretation. Standard map-based graphical output can show little more than whether the forecast mean is for average, or above average or below average conditions – ostensibly a 3-category classification. And indeed “average” in this scheme can be interpreted variously as a genuine forecast of average, or a “no-signal” prediction, which cannot both be right.
So ECMWF is working towards a new two-layer brand of map-based sub-seasonal forecast products, that succinctly represent both the mean anomaly and the forecast uncertainty. We plan to call these “quantile-based weekly guidance maps”. The overarching aim has been to exploit much better than hitherto the information content of the sub-seasonal forecast system in a compact format. Once these first go public they will be classed as an “experimental product”. We hope for wide-ranging uptake, providing greater outreach for our forecasts than hitherto, to benefit multiple sectors of society.
The new graphical output can be summarised in a 3-by-3 matrix form where one dimension represents the mean anomaly and the other relative spread. So for example a mean anomaly around zero can either represent a high confidence, narrow distribution forecast of average conditions (a true forecast of “average”), or more commonly a no-signal forecast where forecast and climate distributions are much the same (= “we don’t know”), or less often an odd scenario in which forecast spread exceeds climate spread (= “very uncertain indeed”). The graphical versions of the new system, and the 9 classes, will be demonstrated using real ECMWF forecast examples. These will highlight how translating appropriately chosen mathematical metrics into suitable graphics, and on into plain language text, can lie at the heart of successful uncertainty communication. Clear documentation for users is another key requirement.
How to cite: Hewson, T.: Making Uncertainty in Sub-seasonal Weather Forecasts Intelligible, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19375, https://doi.org/10.5194/egusphere-egu25-19375, 2025.
Skilful weather forecasts help users make sound decisions when faced with potentially hazardous climatic conditions. However, this beneficial result may be reduced or negated in the absence of an effective communication of forecast uncertainty. On average, forecast skill improves for shorter lead times, which implies that we expect differences between successive forecasts. While there is a vast literature on the communication and visualisation of weather forecast uncertainty, little attention has been dedicated to communicating forecast changes to non-specialist audiences. Nonetheless, this is a key dimension of forecast uncertainty, and there are several user cases in which providing information about possible future changes in weather forecasts can improve their use.
An illustrative example is the situation in which a user has to decide whether to act now or wait for the next forecast. This can be as simple as a professional deciding whether to drive or not to a client on a day for which extremely heavy rainfall is forecasted, potentially leading to flash flooding. Cancelling well-ahead of time makes rescheduling easier, yet the forecast has a larger chance of being wrong. Cancelling on short notice minimises the chance of a false alarm, but poses greater logistical challenges for both the professional and the client. Something as simple as knowing how often the later forecast is better – for example knowing that 9 times out of 10 a heavy rainfall forecast issued one day ahead is better than one issued 5 days ahead – can qualitatively help the non-specialist users in this fictitious example to make a more informed decision.
In this contribution, we consider a variety of cases in which information on forecast changes may be of value. We then present a set of easily interpretable metrics making information on such changes accessible to non-specialist users.
How to cite: Messori, G., Jewson, S., and Scher, S.: Communicating uncertainty in weather forecasts: the role of forecast changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4471, https://doi.org/10.5194/egusphere-egu25-4471, 2025.
This work examines the extent and form in which uncertainty of Extreme Event Attribution (EEA) results is best communicated to stakeholders. To achieve this, we develop communication materials in both text and graphics and test them for accuracy and accessibility through guided interviews with scientists and stakeholders.
Extreme weather events pose significant challenges for human civilization. Climate change can influence both the intensity and probability of specific extreme weather events, such as heatwaves or heavy rainfall. EEA has become an established tool to answer public questions about the contribution of climate change to such events. However, the results of EEA studies are often accompanied by considerable uncertainties. Communication of results, including an accessible representation of uncertainty, is therefore a fundamental necessity in this field of research, extending beyond the general effort to make scientific findings accessible to the public. Media representatives, who often bridge the gap between attribution scientists and the public, are therefore key stakeholders in this research.
We present the current state of research on communicating uncertainties in this field and outline our iterative approach to working with attribution scientists and media representatives alike to determine what should be communicated and how to communicate it effectively. Finally, we evaluate which communication materials are both relevant and accessible, and we reflect on the lessons learned for future communication efforts concerning EEA results.
This study is part of ClimXchange, which aims to enhance the usability of climate science for societal stakeholders. ClimXchange is embedded within the ClimXtreme research consortium, funded by the German Federal Ministry of Education and Research (BMBF), which focuses on extreme weather events in the context of climate change.
How to cite: Knauf, J., Zimmermann, T., Schröter, J., Tivig, M., and Kreienkamp, F.: Communicating uncertainty in extreme event attribution to the media, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2164, https://doi.org/10.5194/egusphere-egu25-2164, 2025.
Working with environmental data means dealing with complex processes, limited data (in space and/or time) and the impossibility of setting up controlled experiments, leading to uncertain predictions of system behaviour.
In the field of statistical hydrology, many efforts have been made during the last decades to provide methods to quantify uncertainty, but the common practice of infrastructure design has not yet incorporated them. This may be due to several reasons, including the complexity of the methods, which are often difficult to apply in most everyday cases, and regulations that "favour" well-established requirements.
Here we present the "uncertainty compliant design flood estimator" (UNCODE) method, which accounts for aleatory uncertainty in the estimation of the design flood value. The method provides a corrected design value and is easy to use for practical purposes as simplified formulae are provided to quantify the correction factor. However, in addition to its practical application, it can also be used to compare different models with different levels of uncertainty and to highlight the "cost" of uncertainty.
Finally, its mathematical formulation allows a direct link to be made between the classical approach to hydrological design, based on a fixed hazard level (or return period), and a risk-based design approach, which is widely recognised as a more flexible method but is not usually included in regulations.
How to cite: Ganora, D.: Uncertainty in flood frequency analysis and its implications for infrastructure design, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15189, https://doi.org/10.5194/egusphere-egu25-15189, 2025.
Flood frequency analysis is a cornerstone of hydrologic studies, providing a probabilistic framework to relate the magnitude of extreme events to their frequency of occurrence. This methodology is critical for designing flood-related infrastructure, conducting economic evaluations of flood control projects, and delineating floodplains. However, its utility depends heavily on data quality, model selection, and parameter estimation, each of which introduces uncertainties that become especially significant for rare events.
This presentation will address key sources of uncertainty, including model choice, parameter inference methods, and sample size limitations. Strategies for incorporating these uncertainties into engineering practice are discussed, with an emphasis on probabilistic representations and innovative design approaches. An exceptional flood, a "black swan" event, is used to illustrate the paradox of increased uncertainty despite improved information. This case underscores the importance of expanding flood analyses through historical records, regionalization, and causal modeling, particularly in the context of a changing climate.
The presentation will be designed to foster cross-discipline exchange in the quantification of uncertainty in Earth Sciences.
How to cite: Viglione, A.: Flood Frequency Hydrology: Navigating Uncertainty in Flood Design, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11466, https://doi.org/10.5194/egusphere-egu25-11466, 2025.
Uncertainties are an unavoidable part of scientific research. Practical limits with regard to the number, accuracy and precision of available observations as well as limitations in terms of methodological accuracy and modelling contribute to the fact that even the most elaborate and meticulous forecasts can never be deterministic and no completely reliable and accurate predictions for decision-making can be achieved. In concrete applications, a sufficient understanding of the accuracy and reliability of scientifically based predictions is important, for example in disaster prevention or resource planning. For example, natural hazard maps are primarily intended for those who have the necessary expertise to understand them. However, they are also used in their unaltered form by non-experts for decision-making, many of whom are unfamiliar with the scientific background and implications of the map.
We address this problem using an earthquake hazard map which can be relevant to non-expert audiences when seeking advice on purchasing a house or obtaining insurance. In order to understand how non-experts perceive a scientifically compiled earthquake hazard map, we conducted an online survey with 229 participants. This was done as part of the 2024 Science & Innovation Days (a public engagement event) in Tübingen, Germany. Participants were asked about their first impression of the map in terms of information content, any need for further explanation and possible actions to take. Other questions assessed participants’ previous experiences and self-assessment of hazard perceptions.
In this presentation, we will discuss the survey results and share lessons learned when communicating information that contains uncertainty with non-expert audiences.
How to cite: Dietrich, P., Dietrich, M., Pelzer, M., and Mohadjer, S.: Non-expert understanding of hazard maps: Insights from an online survey, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13260, https://doi.org/10.5194/egusphere-egu25-13260, 2025.
Maps are the most commonly used means of visualizing and communicating natural hazard information to support decisions about risk mitigation. They are a product of hazard assessment studies which involve different input parameters with uncertainties relevant to decision making. This process is further complicated by the subjective uncertainties that arise in the audience when confronted with the visualization of hazard information.
Natural hazard maps are primarily designed to be used by experts, but they are also used in their unaltered form to communicate with non-experts, many of whom are unfamiliar with the map’s scientific background and implications. Previous studies focus mainly on evaluating such maps with expert groups (e.g., directly involved stakeholders and authorities), with less attention on non-experts (e.g., the public audiences) who are confronted with these maps before purchasing a house, getting insurance or making other critical decisions.
To address this gap, our study investigates how well hazard maps are understood and interpreted by non-expert audiences. We tested two earthquake hazard maps of Germany that differ in color palettes (rainbow vs. colorblind-friendly and perception-optimized yellow-orange-red-brown color palettes) and data classification schemes (algorithmic Fisher vs. quasi-logarithmic classification schemes). We showed both maps to 20 non-expert participants during the 2024 Science & Innovation Days (a public engagement event) in Tübingen, Germany. Participants answered map-reading and hazard perception questions (e.g., participants were asked to read off the hazard level for a given city, and to compare hazard levels between for a pair of cities) while their eye movements were monitored with eye-tracking software.
To identify if either map improved map reading and hazard perception, participants’ responses were scored, analyzed and compared using a two-sample Mann–Whitney U and Fisher’s Exact tests. In general, the differences detected in participants’ responses were not statistically significant, perhaps due to the small sample size. Still, we observed that nearly all participants who used the redesigned map (8 out of 9) correctly read the hazard level for a city while only 33% (3 out of 9 participants) who used the rainbow color map responded correctly.
Eye-tracking data were used to analyze focus-metrics. Composite heatmaps accumulating the duration of eye fixations of all participants indicate that their eye movements were focused more on the high hazard zones and the corresponding values shown on map legend when answering questions using a hazard map redesigned to use best practices for hazard perception.
To quantify these differences, the ratio of fixations on high-hazard zones to total fixations on the map were calculated for both map versions. The data were tested for normality and the statistical significance of the differences were evaluated using Independent Samples t-tests for equal variances. While the results were not statistically significant, participants viewing the redesigned map showed a greater number of fixations on high-hazard zones compared to the participants viewing the original map, with a moderate effect size. We note tendencies in the data that encourage the repetition of the experiment with a larger sample size.
How to cite: Mohadjer, S., Ergün, G., Mutz, S. G., Schneider, M., Schürmann, T., Pelzer, M., and Dietrich, P.: Non-Expert Understanding of Hazard Maps: An Eye-Tracking Study , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17779, https://doi.org/10.5194/egusphere-egu25-17779, 2025.
A presentation of emerging themes and lessons learnt from examples of best practice in uncertainty quantification and communication relevant to climate services. Drawn from existing literature and reports, and from a community engagement workshop.
- Consider the climate risks that are of most concern to the audience.
- Use language the audience is familiar with (don’t say uncertainty).
- The precision of uncertainty information should be relevant to the situation.
- Understand existing narratives about climate uncertainty.
- Use communication about uncertainty to build trust.
- Be aware of deep uncertainty.
Standardised approaches to uncertainty communication should consider not only the climate science component, but also the complexities regarding socio-economic vulnerability.
Climateurope2, is a Horizon Europe project with a consortium of 33 parties from 13 countries that includes intergovernmental institutions such as the World Meteorological Organisation, social sciences, humanities and STEM expertise, assurance providers, SMEs, and standardisation bodies. Together we are building a community of practice for the standardisation and support of climate services.
How to cite: Pascoe, C., Dankers, R., Domingo, X., and Pagé, C.: Don't say uncertainty: preliminary best practices and emerging themes for uncertainty quantification and communication in climate services from the Climateurope2 project., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18364, https://doi.org/10.5194/egusphere-egu25-18364, 2025.
Two-dimensional (2D) images are often used to communicate the results of scientific investigations and predictions. Examples are weather maps, earthquake hazard maps and MRI slices. In contrast to statistical analyses of individual variables or time series, there are currently no established methods for visualizing the uncertainties in the 2D images. However, this would be necessary to make the information in the 2D images clear to scientists as well as to the non-expert public audiences in order to avoid misinterpretation and over-interpretation.
In this study, we demonstrate the challenges and approaches to uncertainty visualization using the case study of drought forecasting, which is relevant for climate adaptations and mitigations. A drought is a deviation (anomaly) from the parameter value expected from long-term data. In our case, the parameter under consideration is soil moisture, which is an important parameter for various environmental processes. The soil moisture can be used in combination with soil type to estimate the amount of water available to plants in the topsoil. If the amount of water available to plants according to the so-called percentile approach deviates significantly from the value expected from long-term data, this is referred to as an agricultural drought.
The drought forecast is based on ensemble modelling. This means that the results of various weather forecast models are used to predict the development of soil moisture for the period of the weather forecast. For each weather model used, a possible soil moisture development is predicted. Each of these is used for a drought forecast. The result of the ensemble modelling is therefore several forecasts, which can differ significantly. Due to the use of different weather models and the consideration of uncertainties in the models, the result of ensemble modelling is therefore a large number of drought forecast maps. When visualising the results, often only a map of the mean values resulting from the predictions is shown. If only the mean value is displayed, however, the information about a possible difference and thus the uncertainty of the predictions is lost. In other words: If individual cases from the ensemble predict the possibility of drought, this will not be clearly visible in the mean value map.
In this presentation, we will demonstrate and discuss different approaches to visualize the uncertainty in the prediction.
How to cite: Dietrich, P., Najafi, H., Pelzer, M., and Mohadjer, S.: Visualization of uncertainties in 2D images, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13135, https://doi.org/10.5194/egusphere-egu25-13135, 2025.
How to cite: Stepanovic, J., Claes, S., and Sermeus, J.: Immersed in Uncertainty: Discussing Uncertainty in Science in a Planetarium, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21809, https://doi.org/10.5194/egusphere-egu25-21809, 2025.
The recent COVID-19 pandemic highlighted the need to effectively communicate forecasts and their uncertainty. This is especially important if the aim is to minimize the risk of misinformation and poorly-informed decision-making. Both the IPCC and the Sendai Framework for Disaster Risk Reduction have identified risk communication, complexity and uncertainty as major challenges to decision-making, and call for better understanding of how existing risk communication practices are perceived by those affected and those making decisions.
Despite these calls, many geoscientists, especially early career researchers, lack opportunities to discuss scientific uncertainty and explore ways to communicate uncertainty to different audiences, including the non-scientific publics. To address this demand, we organize the international training school “Understanding the Unknowns: Communicating Uncertainty as a Driving Force for Geosciences”, which is co-sponsored by the EGU and set to take place at the University of Tübingen in Germany from March 17 to 19, 2025. This in-person, three-day training school aims to equip Early Career Researchers with knowledge and skills needed to effectively account for and communicate uncertainty in geosciences with their peers as well as public audiences.
Some of the biggest challenges of training programs on uncertainty relate to the interdisciplinary nature of the concept: understanding and effectively communicating uncertainties requires knowledge and skill sets typically taught and researched across a range of diverse fields. Highlighting this interdisciplinary background, we combine insights from geoscientific uncertainty assessment and outputs (e.g., maps, interpretations, models, simulations, time series) with approaches from (visual) rhetoric, science communication, presentation research, and multimedia competence.
Building on existing good practice, the training strives to equip geoscientists with the tools and skills they need to communicate uncertainty, help reduce misinformation, and enhance future decision-making. This will be done collaboratively through an interdisciplinary partnership between the Department of Geosciences, the Research Center for Science Communication at the Department of General Rhetoric, and Global Awareness Education at the University of Tübingen. The new approaches and exercises developed for this training will not only be practically applied in the training school, but also reflected and evaluated, including a pre-workshop survey addressing expectations and needs identified by the participants and a concluding qualitative evaluation.
In this presentation, we will discuss our multifaceted practices and strategies applied to foster skills in communicating uncertainty in geosciences, present the results of the accompanying survey and evaluation used in this training, and conclude with lessons learned and best practices recommended to further develop similar opportunities in the future.
How to cite: Pelzer, M., Dietrich, P., and Mohadjer, S.: Fostering Skills in Communicating Uncertainty in the Geosciences: a review of concepts, strategies and approaches applied in the training school “Understanding the Unknowns: Communicating Uncertainty as a Driving Force for Geosciences”, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18655, https://doi.org/10.5194/egusphere-egu25-18655, 2025.
