Linked Life Data

21115841

Source:http://linkedlifedata.com/resource/pubmed/id/21115841

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
50
pubmed:dateCreated
2011-3-22
pubmed:abstractText
Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1091-6490
pubmed:author
pubmed:issnType
Electronic
pubmed:day
14
pubmed:volume
107
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
21349-54
pubmed:dateRevised
2011-7-26
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Considerations for parameter optimization and sensitivity in climate models.
pubmed:affiliation
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.