In recent decades, extreme fire events have become increasingly common, exemplified by the recent fire seasons in Greece, Canada, Hawaii, California, Australia, Amazonia, the Arctic and the Pantanal. While these extremes and megafires have an exponential impact on society and all aspects of the Earth system, there is much to learn about their characteristics, drivers, links to climate change, and how to quantify their impacts, as well as mitigation and prevention strategies and tools.
One area of attention is how extreme fires are currently represented by different fire models. Due to their stochastic nature, uncertainty in observations, and the challenge of representing local processes within global models, extreme fires and their impacts still present a challenge to coupled modelling. The big data science models and machine learning approaches show promise in representing extremes but are weak in coupling feedbacks to vegetation, soils and the wider Earth System.
We also welcome case studies of regional extreme wildfire events, their impacts, and prevention and mitigation strategy experiences worldwide. We encourage contributions from a wide range of disciplines, including global, regional, and landscape modelling, statistical and process-based modelling, observations and field studies, science and social science studies on all temporal scales. In this session, we aim to share knowledge across multiple disciplines, from science to decision-makers and practitioners, to help overcome the challenges that wildfires pose to our models and our society.
We aim to explore the significance and interactions of extreme wildfires and their impacts on society and the earth system and identify the current gaps in our understanding to help us prepare for and mitigate future extreme wildfire events.
One area of attention is how extreme fires are currently represented by different fire models. Due to their stochastic nature, uncertainty in observations, and the challenge of representing local processes within global models, extreme fires and their impacts still present a challenge to coupled modelling. The big data science models and machine learning approaches show promise in representing extremes but are weak in coupling feedbacks to vegetation, soils and the wider Earth System.
We also welcome case studies of regional extreme wildfire events, their impacts, and prevention and mitigation strategy experiences worldwide. We encourage contributions from a wide range of disciplines, including global, regional, and landscape modelling, statistical and process-based modelling, observations and field studies, science and social science studies on all temporal scales. In this session, we aim to share knowledge across multiple disciplines, from science to decision-makers and practitioners, to help overcome the challenges that wildfires pose to our models and our society.
We aim to explore the significance and interactions of extreme wildfires and their impacts on society and the earth system and identify the current gaps in our understanding to help us prepare for and mitigate future extreme wildfire events.