Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
Tim Jupp
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
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Total: 2,651
BibTeX: 129
EndNote: 153
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Total article views: 2,118 (including HTML, PDF, and XML)
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1,747
267
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2,118
125
149
HTML: 1,747
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XML: 104
Total: 2,118
BibTeX: 125
EndNote: 149
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439
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Cumulative views and downloads
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Viewed (geographical distribution)
Total article views: 2,651 (including HTML, PDF, and XML)
Thereof 2,597 with geography defined
and 54 with unknown origin.
Total article views: 2,118 (including HTML, PDF, and XML)
Thereof 2,077 with geography defined
and 41 with unknown origin.
Total article views: 533 (including HTML, PDF, and XML)
Thereof 520 with geography defined
and 13 with unknown origin.
Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.
Climate models are used to predict the impact of climate change. However, poorly constrained...
