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Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings: Part I
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Science, Department of Physical Geography.
Stockholm University, Faculty of Science, Department of Mathematics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

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) factors. Here, in Part I, we suggest several latent factor models of different complexity that can be used for evaluation of temperature data from climate model simulations against climate proxy data from the last millennium. Each factor model is developed for use with data from a single region, which can be of any size. To be able to test the hypotheses of interest, we have applied the technique of confirmatory factor analysis. We also elucidate the link between our factor models and the statistical methods used in Detection and Attribution (D\&A) studies. In particular, we demonstrate that our factor models can be used as an alternative approach to the methods used in D\&A studies. An additional advantage of their use is that they, in contrast to the commonly used D\&A methods, make it, in principle, possible to investigate whether the forcings of interest act additively or if any interaction effects exist.In Part II we investigate and illustrate the expansion of factor models to structural equation models, which permits the statistical modelling of more complicated climatological relationships. The performance of some of our statistical models suggested in Part I and Part is evaluated and compared in a numerical experiment, whose results are presented in Part III.

Keyword [en]
Confirmatory Factor Analysis, Structural Equation models, Measurement Error models, Climate model simulations, Climate forcings, Climate proxy data, Detection and Attribution
National Category
Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:su:diva-148202OAI: oai:DiVA.org:su-148202DiVA: diva2:1150156
Available from: 2017-10-18 Created: 2017-10-18 Last updated: 2017-10-31Bibliographically approved
In thesis
1. Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings
Open this publication in new window or tab >>Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis, using the principles of confirmatory factor analysis (CFA) and the cause-effect concept associated with structural equation modelling (SEM), a new flexible statistical framework for evaluation of climate model simulations against observational data is suggested. The design of the framework also makes it possible to investigate the magnitude of the influence of different forcings on the temperature as well as to investigate a general causal latent structure of temperature data. In terms of the questions of interest, the framework suggested here can be viewed as a natural extension of the statistical approach of 'optimal fingerprinting', employed in many Detection and Attribution (D&A) studies. Its flexibility means that it can be applied under different circumstances concerning such aspects as the availability of simulated data, the number of forcings in question, the climate-relevant properties of these forcings, and the properties of the climate model under study, in particular, those concerning the reconstructions of forcings and their implementation. It should also be added that although the framework involves the near-surface temperature as a climate variable of interest and focuses on the time period covering approximately the last millennium prior to the industrialisation period, the statistical models, included in the framework, can in principle be generalised to any period in the geological past as soon as simulations and proxy data on any continuous climate variable are available.  Within the confines of this thesis, performance of some CFA- and SEM-models is evaluated in pseudo-proxy experiments, in which the true unobservable temperature series is replaced by temperature data from a selected climate model simulation. The results indicated that depending on the climate model and the region under consideration, the underlying latent structure of temperature data can be of varying complexity, thereby rendering our statistical framework, serving as a basis for a wide range of CFA- and SEM-models, a powerful and flexible tool. Thanks to these properties, its application ultimately may contribute to an increased confidence in the conclusions about the ability of the climate model in question to simulate observed climate changes.

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2017
Keyword
Confirmatory Factor Analysis, Measurement Error models, Structural Equation models, Wald confidence interval, Fieller confidence set, Climate model simulations, Climate forcings, Climate proxy data, Detection and Attribution
National Category
Mathematics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-148208 (URN)978-91-7797-055-2 (ISBN)978-91-7797-056-9 (ISBN)
Public defence
2017-12-12, sal 14, hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 3: Manuscript.

Available from: 2017-11-17 Created: 2017-10-18 Last updated: 2017-11-15Bibliographically approved

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