Abstract:
:Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them "better," estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as "chaos" prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system.
journal_name
Proc Natl Acad Sci U S Aauthors
Smith LAdoi
10.1073/pnas.012580599keywords:
subject
Has Abstractpub_date
2002-02-19 00:00:00pages
2487-92eissn
0027-8424issn
1091-6490pii
99/suppl_1/2487journal_volume
99 Suppl 1pub_type
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