Earth System Reconstructions provide vital insights across all geological spatiotemporal scales, from the depths of deep time to future projections, crucial for understanding the complex interplay among the geosphere, atmosphere, and biosphere. These reconstructions are underpinned by paleogeographic research at regional to global scales. They leverage emerging modeling techniques and expanding databases to elucidate the interactions and feedback driving major past environmental crises and long-term evolutionary changes. In the current era of climate, biodiversity, and energy crises, such reconstructions are increasingly pivotal in shaping informed decision-making, with applications spanning environmental risk assessments, climate forecasting, and resource exploration.
The field is witnessing significant advancements through the application of machine learning, large language models, and other sophisticated statistical and nonlinear optimization techniques. These methods enhance our ability to interpret complex and often obscure geological, environmental, and geophysical data. By integrating approaches from various disciplines, we enhance the quantifiability of geological processes over a broad spectrum of spatial and temporal scales. This integration is critical for incorporating better quantifications of uncertainty in both parameter values and model choice, as well as the fusion between geophysical, geological and environmental sensing constraints with data analyses and numerical modelling of Earth Systems.
We invite contributions from all disciplines focused on modeling or constraining Earth Systems, from deep geological times to anticipated future scenarios, whether regional or global in scope. We welcome submissions that are analytical or lab-focused, field-based, or involve numerical modelling. This session also aims to explore cutting-edge methods, tools, and approaches that push the boundaries of inference and uncertainty analysis, and interdisciplinary model-data fusion. We ask the question `Where to next?’ in our collective quest to develop digital twins of our planet.
We also celebrate the contributions of early career researchers, open/community research philosophy, and innovations that have adopted interdisciplinary approaches.
The field is witnessing significant advancements through the application of machine learning, large language models, and other sophisticated statistical and nonlinear optimization techniques. These methods enhance our ability to interpret complex and often obscure geological, environmental, and geophysical data. By integrating approaches from various disciplines, we enhance the quantifiability of geological processes over a broad spectrum of spatial and temporal scales. This integration is critical for incorporating better quantifications of uncertainty in both parameter values and model choice, as well as the fusion between geophysical, geological and environmental sensing constraints with data analyses and numerical modelling of Earth Systems.
We invite contributions from all disciplines focused on modeling or constraining Earth Systems, from deep geological times to anticipated future scenarios, whether regional or global in scope. We welcome submissions that are analytical or lab-focused, field-based, or involve numerical modelling. This session also aims to explore cutting-edge methods, tools, and approaches that push the boundaries of inference and uncertainty analysis, and interdisciplinary model-data fusion. We ask the question `Where to next?’ in our collective quest to develop digital twins of our planet.
We also celebrate the contributions of early career researchers, open/community research philosophy, and innovations that have adopted interdisciplinary approaches.