What might we learn from climate forecasts?

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.

authors

Smith LA

doi

10.1073/pnas.012580599

keywords:

subject

Has Abstract

pub_date

2002-02-19 00:00:00

pages

2487-92

eissn

0027-8424

issn

1091-6490

pii

99/suppl_1/2487

journal_volume

99 Suppl 1

pub_type

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