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Models in the Geosciences

Bokulich, Alisa and Oreskes, Naomi (2017) Models in the Geosciences. Springer Handbook of Model-Based Science.


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The geosciences include a wide spectrum of disciplines ranging from
paleontology to climate science, and involve studies of a vast range of spatial and
temporal scales, from the deep-time history of microbial life to the future of a system no
less immense and complex than the entire earth. Modeling is thus a central and
indispensible tool across the geosciences. Here we review both the history and current
state of model-based inquiry in the geosciences. Research in these fields makes use of a
wide variety of kinds of models, including conceptual, physical, and numerical models,
and more specifically cellular automata, artificial neural networks, agent-based models,
coupled models, and hierarchical models. We note the increasing demands to incorporate
biological and social systems into geoscience modeling, challenging the traditional
boundaries of these fields. Understanding and articulating the many different sources of
scientific uncertainty--and finding tools and methods to address them--has been at the
forefront of much research in geoscience modeling. We discuss not only structural model
uncertainties, parameter uncertainties, and solution uncertainties, but also the diverse
sources of uncertainty arising from the complex nature of geoscience systems themselves.
Without an examination of the geosciences, our philosophies of science and our
understanding of the nature of model-based science are incomplete.

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Item Type: Published Article or Volume
Bokulich, Alisa
Oreskes, Naomi
Keywords: Geoscience, Earth Science, Models
Subjects: Specific Sciences > Earth Sciences
General Issues > Models and Idealization
Depositing User: Alisa Bokulich
Date Deposited: 12 Feb 2018 13:54
Last Modified: 12 Feb 2018 13:54
Item ID: 14373
Journal or Publication Title: Springer Handbook of Model-Based Science
Publisher: Springer
Subjects: Specific Sciences > Earth Sciences
General Issues > Models and Idealization
Date: 2017

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