Large-scale data from observations and simulations are central to understanding the Earth system and mitigating risks associated with environmental change. Models are powerful tools that embody our understanding of the interacting physical, chemical, and biological processes that govern the behavior of the Earth system. Given the growing body of observational data, the increasing complexity of Earth system models (ESMs), and the copious output they produce, integrating models and data to extract patterns of ecological shifts and other emergent behavior under anticipated warming has become a grand challenge. With each subsequent climate assessment, more direct coupling between physical and socioeconomic systems is employed to better incorporate interactions between environmental change and changes in human systems and the built environment. Such actionable knowledge can inform policy and help society better anticipate and manage resources for consequences of global environmental change. Yet Earth system models diverge in their simulations of environmental change, particularly for plausible futures of land use and food, energy, and water needs. This divergence has persisted despite order of magnitude increases in data velocity, volume and variety available for model calibration and validation, the incorporation of ever more Earth system processes, and vast increases in computational efficiency. Such uncertainty has downstream effects on constraining impacts on human systems under future scenarios.
This topic focuses on original research to explore, explain and ultimately decrease model divergence and uncertainties, while simultaneously improving model skill and overall model utility to detect ecological shifts and inform resource policy. Submissions addressing one or more of the following are welcome:
1) Improvements in scientific workflow to execute multiple ESMs and confront simulated Earth system behavior with appropriate observationally based benchmarks.
2) Development of strategies to link ESM fidelity to model divergence and identifiable parameters or process representations.
3) Integration of physical, chemical, and biological models of the Earth system with human system simulation, i.e., end-to-end analysis of the full Earth system using a single modeling framework.
4) Detection and attribution of ecological shifts and other emergent behavior in Earth system function or state under a range of plausible futures, including probabilistic assessments of reliability.
This article collection welcomes diverse article types, including Original Research, Reviews, and Perspective Papers. Upon consultation with the Editors, we may also include Hypothesis & Theory papers, Technology Reports, Mini Reviews, Code, Data Report, General Commentaries, and other article types.
Large-scale data from observations and simulations are central to understanding the Earth system and mitigating risks associated with environmental change. Models are powerful tools that embody our understanding of the interacting physical, chemical, and biological processes that govern the behavior of the Earth system. Given the growing body of observational data, the increasing complexity of Earth system models (ESMs), and the copious output they produce, integrating models and data to extract patterns of ecological shifts and other emergent behavior under anticipated warming has become a grand challenge. With each subsequent climate assessment, more direct coupling between physical and socioeconomic systems is employed to better incorporate interactions between environmental change and changes in human systems and the built environment. Such actionable knowledge can inform policy and help society better anticipate and manage resources for consequences of global environmental change. Yet Earth system models diverge in their simulations of environmental change, particularly for plausible futures of land use and food, energy, and water needs. This divergence has persisted despite order of magnitude increases in data velocity, volume and variety available for model calibration and validation, the incorporation of ever more Earth system processes, and vast increases in computational efficiency. Such uncertainty has downstream effects on constraining impacts on human systems under future scenarios.
This topic focuses on original research to explore, explain and ultimately decrease model divergence and uncertainties, while simultaneously improving model skill and overall model utility to detect ecological shifts and inform resource policy. Submissions addressing one or more of the following are welcome:
1) Improvements in scientific workflow to execute multiple ESMs and confront simulated Earth system behavior with appropriate observationally based benchmarks.
2) Development of strategies to link ESM fidelity to model divergence and identifiable parameters or process representations.
3) Integration of physical, chemical, and biological models of the Earth system with human system simulation, i.e., end-to-end analysis of the full Earth system using a single modeling framework.
4) Detection and attribution of ecological shifts and other emergent behavior in Earth system function or state under a range of plausible futures, including probabilistic assessments of reliability.
This article collection welcomes diverse article types, including Original Research, Reviews, and Perspective Papers. Upon consultation with the Editors, we may also include Hypothesis & Theory papers, Technology Reports, Mini Reviews, Code, Data Report, General Commentaries, and other article types.
