Literature DB >> 17502623

Irreducible imprecision in atmospheric and oceanic simulations.

James C McWilliams1.   

Abstract

Atmospheric and oceanic computational simulation models often successfully depict chaotic space-time patterns, flow phenomena, dynamical balances, and equilibrium distributions that mimic nature. This success is accomplished through necessary but non-unique choices for discrete algorithms, parameterizations, and coupled contributing processes that introduce structural instability into the model. Therefore, we should expect a degree of irreducible imprecision in quantitative correspondences with nature, even with plausibly formulated models and careful calibration (tuning) to several empirical measures. Where precision is an issue (e.g., in a climate forecast), only simulation ensembles made across systematically designed model families allow an estimate of the level of relevant irreducible imprecision.

Mesh:

Year:  2007        PMID: 17502623      PMCID: PMC1868592          DOI: 10.1073/pnas.0702971104

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  2 in total

1.  Uncertainty in predictions of the climate response to rising levels of greenhouse gases.

Authors:  D A Stainforth; T Aina; C Christensen; M Collins; N Faull; D J Frame; J A Kettleborough; S Knight; A Martin; J M Murphy; C Piani; D Sexton; L A Smith; R A Spicer; A J Thorpe; M R Allen
Journal:  Nature       Date:  2005-01-27       Impact factor: 49.962

2.  Tropical drying trends in global warming models and observations.

Authors:  J D Neelin; M Münnich; H Su; J E Meyerson; C E Holloway
Journal:  Proc Natl Acad Sci U S A       Date:  2006-04-10       Impact factor: 11.205

  2 in total
  7 in total

1.  Considerations for parameter optimization and sensitivity in climate models.

Authors:  J David Neelin; Annalisa Bracco; Hao Luo; James C McWilliams; Joyce E Meyerson
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-29       Impact factor: 11.205

2.  High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

Authors:  Andrew J Majda; Rafail Abramov; Boris Gershgorin
Journal:  Proc Natl Acad Sci U S A       Date:  2009-12-22       Impact factor: 11.205

3.  Rough parameter dependence in climate models and the role of Ruelle-Pollicott resonances.

Authors:  Mickaël David Chekroun; J David Neelin; Dmitri Kondrashov; James C McWilliams; Michael Ghil
Journal:  Proc Natl Acad Sci U S A       Date:  2014-01-17       Impact factor: 11.205

4.  Addressing partial identification in climate modeling and policy analysis.

Authors:  Charles F Manski; Alan H Sanstad; Stephen J DeCanio
Journal:  Proc Natl Acad Sci U S A       Date:  2021-04-13       Impact factor: 11.205

5.  Tipping points induced by parameter drift in an excitable ocean model.

Authors:  Stefano Pierini; Michael Ghil
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.996

6.  On estimating local long-term climate trends.

Authors:  S C Chapman; D A Stainforth; N W Watkins
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2013-04-15       Impact factor: 4.226

7.  Climate modelling and structural stability.

Authors:  Vincent Lam
Journal:  Eur J Philos Sci       Date:  2021-10-19       Impact factor: 1.753

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.