A framework for interpreting climate model outputs

Nadja A. Leith and Richard E. Chandler

Journal of the Royal Statistical Society Series C, 2010, vol. 59, issue 2, 279-296

Abstract: Summary. Projections of future climate are often based on deterministic models of the Earth’s atmosphere and oceans. However, these projections can vary widely between models, with differences becoming more pronounced at the relatively fine spatial and temporal scales that are relevant in many applications. We suggest that the resulting uncertainty can be handled in a logically coherent and interpretable way by using a hierarchical statistical model, implemented in a Bayesian framework. Model fitting using Markov chain Monte Carlo techniques is feasible but moderately time consuming; the computational efficiency can, however, be improved dramatically by substituting maximum likelihood estimates for the original data. The work was motivated by the need for future precipitation scenarios in the UK, in applications such as flood risk assessment and water resource management. We illustrate the methodology by considering the generation of multivariate time series of atmospheric variables, that can be used to drive stochastic simulations of high resolution precipitation for risk assessment purposes.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9876.2009.00694.x

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:59:y:2010:i:2:p:279-296

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().