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Implications of Model Error for Numerical Climate Prediction : Volume 1, Issue 1 (07/03/2014)

By Martínez-alvarado, O.

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Book Id: WPLBN0004020066
Format Type: PDF Article :
File Size: Pages 23
Reproduction Date: 2015

Title: Implications of Model Error for Numerical Climate Prediction : Volume 1, Issue 1 (07/03/2014)  
Author: Martínez-alvarado, O.
Volume: Vol. 1, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Martínez-Alvarado, O. (2014). Implications of Model Error for Numerical Climate Prediction : Volume 1, Issue 1 (07/03/2014). Retrieved from

Description: Department of Meteorology, University of Reading, Earley Gate, P.O. Box 243, Reading RG6 6BB, UK. Numerical climate models constitute the best available tools to tackle the problem of climate prediction. Two assumptions lie at the heart of their suitability: (1) a climate attractor exists, and (2) the numerical climate model's attractor lies on the actual climate attractor, or at least on the projection of the climate attractor on the model's phase space. In this contribution, the Lorenz '63 system is used both as a prototype system and as an imperfect model to investigate the implications of the second assumption. By comparing results drawn from the Lorenz '63 system and from numerical weather and climate models, the implications of using imperfect models for the prediction of weather and climate are discussed. It is shown that the imperfect model's orbit and the system's orbit are essentially different, purely due to model error and not to sensitivity to initial conditions. Furthermore, if a model is a perfect model, then the attractor, reconstructed by sampling a collection of initialised model orbits (forecast orbits), will be invariant to forecast lead time. This conclusion provides an alternative method for the assessment of climate models.

Implications of model error for numerical climate prediction

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