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Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings: Part III
Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings: Part III
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Abstract
- Evaluation of climate model simulations is a crucial task in climate research. In a work consisting of three parts, we propose a new statistical framework for evaluation of simulated responses to climate forcings, based on the concept of latent (unobservable) variables. In Part I, several latent factor models were suggested for evaluation of temperature data from climate model simulations, forced by a varying number of forcings, against climate proxy data from the last millennium. In Part II, focusing on climatological characteristics of forcings, we deepen the discussion by suggesting two alternative latent variable models that can be used for evaluation of temperature simulations forced by five specific forcings of natural and anthropogenic origin. The first statistical model is formulated in line with confirmatory factor analysis (CFA), accompanied by a more detailed discussion about the interpretation of latent temperature responses and their mutual relationships. Introducing further causal links between some latent variables, the CFA model is extended to a structural equation model (SEM), which allows us to reflect more complicated climatological relationships with respect to all SEM's variables. Each statistical model is developed for use with data from a single region, which can be of any size. Here, in Part III, the performance of both these statistical models and some models suggested in Part I is evaluated and compared in a pseudo-proxy experiment, in which the true unobservable temperature is replaced by temperature data from a selected climate model simulation. The present analysis involves seven regional data sets. Focusing first on the ability of the models to provide an adequate and climatologically defensible description of the unknown underlying structure, we may conclude that given the climate model under consideration, the SEM model in general performed best. As for the factor model, its assumptions turned out to be too restrictive to describe the
Details
- Database :
- OAIster
- Notes :
- application/pdf, application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1056932453
- Document Type :
- Electronic Resource