The World Economic Forum’s 2023 Global Risks Perception Report identifies, for the first time, ‘failure to mitigate climate change’ and ‘failure of climate-change adaptation’ as the most severe risks on a global scale, followed by ‘natural disasters and extreme weather events’ (Heading and Zahidi 2023).
In Australia for example, natural disasters, primarily wildfires and floods, have cost over $13 billion every year (1.2% of GDP), and are ‘expected to rise to $39 billion per year by 2050’ (Slatyer et al. 2017). Over nine million Australians have been directly impacted by extreme weather events since 1990 (ibid.). The Australian Government’s National Strategy for Disaster Resilience (2011) and its National Climate Resilience and Adaptation Strategy (2021) emphasise the critical importance of employing all possible means to mitigate the impacts of climate emergencies on communities.
In reference to the findings of the Royal Commission into National Natural Disaster Arrangements (Binskin 2020), Emergency Leaders for Climate Action (2022) urged that ‘unprecedented is not a reason to be unprepared. We need to be prepared for the future’. As detailed by Cunningham et al. (2024).
Australia’s policy settings have thus far failed to facilitate a level of effective preparedness, adaptation and mitigation that can pave a way to sustaining quality of life (or life itself) on our continent.
The Australian Productivity Commission (2014) reports that 97% of Australia’s investment in climate emergencies is on recovery and only 3% on preparedness and mitigation. As fire scientist David Bowman (2023) points out, Australia is currently ‘sleepwalking’ into a future of extreme and escalating climate events that will inevitably change how we can live on this ‘most fire-prone continent[-] on Earth’ (page). As Cunningham et al. argue, investing in preparedness is key to meeting this challenge. However, despite its importance, tangible examples of enhancing preparedness are rare, partly because it often requires deep levels of experiential and intellectual engagement across disciplinary boundaries (Lazo et al. 2015).
The climate crisis requires fundamental shifts in how these events are envisioned. Creative arts and technology are increasingly being used to depict and model these world-defining events, using evocative imagery and abstract graphics to communicate with audiences (see for example, Altintas et al. 2015; Calkin 2021; Smith 2015; Roelstraete et al. 2023).
By depicting complex climate information in dynamic visual form, art and technology enable a more immediate and intuitive understanding of extreme events in ways that are not apparent in raw data (Shepherd and Truong 2023).
The arts can achieve this by depicting plausible scenarios while technology undertakes it by modelling probable scenarios. Yet, both currently tend to provide episodes that address non-localised uncertainties and render citizens as passive witnesses to the aftermaths, or the events as abstract forces, constraining the capacity to anticipate reliable scenarios (Jasanoff 2010; Sheppard 2019). This leads to detached generic observations, whereas credible and meaningful preparedness emerges from embedded sensorial experiences that can inform decision making, since preparedness is a function of the prior experience of a perceived threat (Lazo et al. 2015). Most importantly, current art and technology approaches are unable to experientially vivify the increasingly uncertain interaction between geo-located events and situated communities, amplifying a profound existential vulnerability (Sheppard 2005).
Towards Climate Scenario Visualisation
The iFire (since 2021, and including authors in this issue Del Favero, Davidson, Oswald, Pagnucco, Song, and others ) explores the visualisation of dynamic wildfire scenarios. It is an experimental prototype for climate scenario visualisation, integrating the advances in immersive visualisation and AI aesthetics. It is being developed in collaboration with a range of national and international partners, including the Australian Broadcasting Corporation, AFAC (Australasian Fire and Emergency Service Authorities Council), CSIRO/Data61, Fire and Rescue NSW, Royal College of Art London, San José State University’s Interdisciplinary Wildfire Center and The University of Melbourne. It assembles a team of Australian, European and US artists, AI, computer, fire and climate scientists at international universities and partner organisations to develop immersive visualisations of extreme wildfires and their uncertain dynamics. It utilises an AI-based landscape prototype that not only interprets but learns from human interaction and behaves autonomously. It sketches a scenario visualisation system that can depict unpredictable climate event interactions.
iFire’s goals are to:
- Establish synthetic landscapes that can envision unpredictable wildfires and explore imaginative risk perception.
- Create improvised narratives that can dramatise unanticipated fire behaviour and embody visceral decision making.
- Enact interactive experiences in rural settings where users can virtually rehearse unexpected fiery encounters.
- Generate geo-located scenarios that can model unanticipated fire-laden landscapes and probe readiness.
The iFire prototype is being developed for application as an artistic and scientific series, titled Penumbra and Umbra. It consists of a Database of atmospheres, flora, pyro-histories and topographies, and AI landscapes. To ensure geophysical reliability it uses both the SPARK and WRF-SFIRE simulation engines applied to geographical databases. To implement a rich sensorial credibility for these worlds, it uses the Unreal game simulation engine and a customised interface specific to each series. This enables higher-fidelity texturing and modelling of the landscape at scale. The interfaces are venue domain-specific, such as for museums, scientific laboratories and emergency centres. For artistic applications, it utilises cinematic environments where interaction is driven by motion tracking. For science and emergency applications, it uses either a tablet for physical screen environments, or a mouse for online environments. A main window display is used for landscape navigation, with smaller-inset informational windows to convey variables such as wind speed. Each series is being developed in collaboration with domain-specific stakeholders. Both series are explored through three geo-located case studies: a pine plantation fire and a grasslands fire in the Australian state of Victoria (2022) and the historical Bridger Foothills Fire (Montana, USA; 2021). The case studies progressively explore the intensifying dynamics of fire from the irregular rhythms of a low-intensity fire, to the violent vorticity driven lateral spread of an extreme fire.
The artistic Penumbra series explores the multi-sensorial qualities of unexpected wildfire experiences for creative industry audiences. The scientific Umbra series investigates the dynamic interplay between unanticipated wildfire processes and users for scientific analysis and emergency training. Both series are remotely accessible. They are developed across a networked, fully immersive system that can translate multi-layered wildfire data into 3D scenarios, allowing exploration of situations across distributed locations. This comprises immersive cinemas, wall displays, desktops and tablets. It generates hyper-realistic immersive visualisations that can stage a geo-located wildfire as it unfolds on the ground in interaction with users. The visualisations operate in two modalities – the first enables users to create compelling hypothetical scenarios, the second allows users to recreate historical fires and generate probable future scenarios.
The AI system that underpins the wildfire landscape has three distinct functions. Firstly, its ML system addresses shortcomings in existing empirical and physical models. For physical models, the computation complexity grows exponentially with higher resolution and takes an inordinate amount of time to simulate, which is prohibitive for operational use. On the other hand, empirical models are mostly used in operations because of their simplicity. However, due to simplified modelling of the processes and dynamics of wildfires, empirical models often fail to achieve the accuracy required. To address the disadvantages of both physical and empirical simulations, iFire’s ML-based simulations learn multi-dimensional representations of data in latent space and apply physics-related constraints to a Neural Network. Once trained, it provides instant and accurate inference.
Secondly, the AI system implements algorithms for the interpretation of motion capture and tracking data derived from individual users and groups. It applies algorithms to enable the AI landscape to set goals, learn from and autonomously respond to user behaviour. This furnishes the landscape with the ability to develop and realise its goals while integrating what it learns from human behaviour through exceptionally fast execution enabled by the programming language verified in the Scenario and Nebula projects. This interprets past user behavioural patterns, models and anticipates future behaviour and makes inferences about how to act in response. It is achieved by using open-ended rules that allow the AI setting to infer from unknown situations, make predictions, decide how to act, learn from the interaction and adapt its reasoning. This ensures that the setting can make independent decisions, a critical attribute for its improvised and reciprocal interaction with users so that it acts in unanticipated ways. Thirdly, the AI goal-oriented system analyses user interactive decision making to both support and challenge it, in order to optimise user response to scenario uncertainty.
The AI is applied in different ways for the two series. The artistic Penumbra series leverages AI to explore an open-ended user and landscape dialogue that co-evolves through reciprocal transactions between the user and the fire-laden terrain.
For example, the landscape may choose to collaborate with the user by enlivening the landscape and propagating fire-resistant trees, or it may challenge the user’s attempt to control its spread by generating chaotic ember storms. The unpredictable nature of its behaviour opens pathways for new forms of interactive encounter that are open to user and AI co-creativity while circumscribed by physics. By translating wildfire behaviour into discrete actions, Penumbra facilitates an experimental imaginary where possibilities and vulnerabilities can be generated and investigated. Opening a new horizon for reimagining extreme fires, it enables prototyping an anticipatory life-saving imaginary. By interactively exploring and transforming the dynamic range of possibilities involved, it offers artists and audiences a new genre of collaborative human and machine co-creativity through which they will be able to compose a wide spectrum of previously unforeseen and mutating encounters with evolving fire landscapes unrestricted in aesthetic form, complexity and inventiveness.
The scientific Umbra series leverages the AI to furnish an analytical laboratory for scientists and a training platform for emergency personnel, such as fire crews, incident controllers and operations officers.
The focus for the first is to facilitate 3D-animated models of extreme fires in geo-located landscapes where scientists can either forensically assess historical fires or test their hypotheses for up-stream scenarios by manipulating variables such as wind speed and fuel load. For the second, the focus is to train emergency service personnel’s situational awareness and decision making in a safe environment. Actual wildfire grounds are impossible to use as training environments due to their clear and present dangers. Emergency personnel often have very short time spans to spot key indicators of impending conflagrations and to make life-saving decisions. This series allows personnel to adjust the situational variables to focus on a specific critical variable, such as wind direction, and to viscerally experience the dramatic changes these can trigger.
Emergency personnel are able to explore and practise the efficacy of a range of different responses, training their perceptual skills.
This allows them to momentarily step away from the hyper attention required in the real-life situation, to rethink, reflect and reconsider the complex relationships that structure and govern their action space. The AI system learns from, predicts and disrupts their reactions, while presenting a challenging range of novel fire behaviours. These features allow emergency personnel to experientially deal with unpredictable scenarios and develop proactive planning through dramatisations that safely simulate complex uncertainties. Groups of responders are physically placed inside rapidly moving wildfire landscapes and confronted with evolving situations that challenge them to collaborate across geographic locations. Emergency organisations can integrate location-specific data and protocols into the simulation to build informational complexity and provide trainees with the challenging and unanticipated experiences they need to manage and mitigate risk.
Conclusion
As extreme event preparedness is a function of prior threat experience, safely visualising threat scenarios in advance is key to enhancing survival and adaptation in an era of unpredictable extreme event emergencies. A climate scenario visualisation framework aims to model a visceral imaginary to foster community preparedness by aesthetically integrating and transforming artistic, technological and scientific approaches. It would generate the capacity to make sense of an extreme event by picturing the situation, narrating its contours, interacting with its dynamics, communicating its experiences and testing a credible response. This would enhance a community’s ability to viscerally experience and develop threat perception, situational awareness, adaptive decision making and flexible response to unexpected life-threatening situations. By integrating advances in immersive visualisation and AI aesthetics, such a framework would virtually rehearse unforeseen geo-located extreme events to facilitate readiness in the face of escalating and profound climate uncertainties.
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Dennis Del Favero is an artist, ARC Australian Laureate Fellow and Chair Professor of Digital Innovation and Executive Director of The University of New South Wales’s iCinema Centre. He has led numerous large-scale interdisciplinary art projects that explore the visceral dynamics of unpredictable climate scenarios and the aesthetics of uncertainty using AI visualisation systems.
Ursula Frohne is Professor for art history at Universität Münster (Germany). Previously, she was Professor of twentieth and twenty-first-century art history at University of Cologne, Chief Curator for ZKM and Lecturer at Karlsruhe’s State Academy of Fine Art. In Cologne, she chaired the DFG-project Cinematographic Aesthetics in Contemporary Art (2007–14), while from 2023 she has been co-leading the DFG-Centre for Advanced Study: Access to Cultural Goods in Digital Change.
Maurice Pagnucco received his PhD degree in computer science from The University of Sydney in 1996. He is currently a Professor and the Deputy Dean (Education) of the Faculty of Engineering at the University of New South Wales (UNSW). He joined UNSW in 2001 and has held the position of Deputy Dean (Education) since 2015. His research is focused on Artificial Intelligence, with particular emphasis on knowledge representation and reasoning, and cognitive robotics.
Susanne Thurow is Associate Director Research and ARC Laureate Postdoctoral Research Fellow at The University of New South Wales’s iCinema Centre, where she leads the climate aesthetics research program. Her interdisciplinary work rethinks contemporary arts through performative digital aesthetics, having co-developed multidisciplinary projects with industry partners, such as Opera Australia. Her latest book (Routledge, 2020) won the 2021 Alvie Egan Award and the 2019 UNSW Art & Design Dean’s Award for Research Excellence, Best Monograph.
Main Image Credit: Still from Umbra.
This article is based on the chapter: Del Favero, D., Thurow, S., Pagnucco, M., & Frohne, U. 2024. Reimagining Extreme Event Scenarios: The Aesthetic Visualisation of Climate Uncertainty to Enhance Preparedness. In D. Del Favero, M. Ostwald, U. Frohne, & S. Thurow (Eds.), Reimagining Extreme Event Scenarios (pp.5-24). Netherlands Springer.