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Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

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  • 1 Microsoft Research, Cambridge, United Kingdom
  • 2 Microsoft Research, Cambridge, and School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
  • 3 Microsoft Research, Cambridge, United Kingdom
  • 4 Microsoft Research, Cambridge, United Kingdom, and Department of Biology, University of Florida, Gainesville, Florida
  • 5 Microsoft Research, Cambridge, United Kingdom
  • 6 Department of Statistical Science, University College London, London, United Kingdom
  • 7 Microsoft Research, Cambridge, United Kingdom
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New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies. The present bias toward increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model tractability. This bias makes it difficult to quantify the uncertainty associated with the projections from any one model or to the distribution of projections from different models. This in turn limits the utility of climate model outputs for deriving useful information such as in the design of effective climate change mitigation and adaptation strategies or identifying and prioritizing sources of uncertainty for reduction. Here we argue that a new approach to model development is needed, focused on the delivery of information to support specific policy decisions or science questions. The central tenet of this approach is the assessment and justification of the overall balance of model detail that reflects the question posed, current knowledge, available data, and sources of uncertainty. This differs from contemporary practices by explicitly seeking to quantify both the benefits and costs of details at a systemic level, taking into account the precision and accuracy with which predictions are made when compared to existing empirical evidence. We specify changes to contemporary model development practices that would help in achieving this goal.

CORRESPONDING AUTHOR: Matthew J. Smith, Computational Science Laboratory, Microsoft Research, 21 Station Road, Cambridge CB1 2FB, United Kingdom, Email: matthew.smith@microsoft.com

New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies. The present bias toward increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model tractability. This bias makes it difficult to quantify the uncertainty associated with the projections from any one model or to the distribution of projections from different models. This in turn limits the utility of climate model outputs for deriving useful information such as in the design of effective climate change mitigation and adaptation strategies or identifying and prioritizing sources of uncertainty for reduction. Here we argue that a new approach to model development is needed, focused on the delivery of information to support specific policy decisions or science questions. The central tenet of this approach is the assessment and justification of the overall balance of model detail that reflects the question posed, current knowledge, available data, and sources of uncertainty. This differs from contemporary practices by explicitly seeking to quantify both the benefits and costs of details at a systemic level, taking into account the precision and accuracy with which predictions are made when compared to existing empirical evidence. We specify changes to contemporary model development practices that would help in achieving this goal.

CORRESPONDING AUTHOR: Matthew J. Smith, Computational Science Laboratory, Microsoft Research, 21 Station Road, Cambridge CB1 2FB, United Kingdom, Email: matthew.smith@microsoft.com
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