Statistical Methods to Analyze Climate Model Output
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1 Research Collection Doctoral Thesis Statistical Methods to Analyze Climate Model Output Author(s): Kerkhoff, Christian Publication Date: 2014 Permanent Link: Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library
2 DISS. ETH NO Statistical Methods to Analyze Climate Model Output A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by CHRISTIAN KERKHOFF Dipl.-Met., Rheinische Friedrich-Wilhelms-Universität Bonn, Germany born on August 5, 1983 citizen of Germany accepted on the recommendation of Prof. Dr. Hans R. Künsch, examiner Prof. Dr. Christoph Schär, co-examiner Prof. Dr. Nicolai F. Meinshausen, co-examiner 2014
3 Abstract Climate science poses an interesting yet challenging field of application for state-of-the-art statistical methodology, since atmospheric phenomena include a wide range of scales spanning from microscopic (e.g. ice nucleation in clouds) to global (e.g. the Hadley Circulation) scales and exhibit complex interactions. A cornerstone in the assessment of the climate state are comprehensive computer models. Although these models are deterministic, it is natural to interpret their output from a statistical view point. This is due to the stochastic nature of the climate system including a large number of degrees of freedom and non-linear or chaotic behavior on different scales. Analyzing climate model output is challenging for manifold reasons: the enormous amount of data produced by typical climate models, the complex dependencies between different output fields and models, the long time scales that hamper validation etc. This thesis develops statistical methods to analyze climate data and thereby tackles an important issue in climate research, namely the choice of bias assumption in the post-processing of climate data. Biases or errors in climate models occur for various reasons: numerical uncertainties and mistakes in the computer code, missing processes, parameterizations of phenomena acting on scales smaller than the model s grid resolution and so on. Comparing models with observations in the past and present, these biases become evident. The output from climate models is thus only a contaminated version of reality, and because future observations are not available, extrapolation assumptions for climate model biases are indispensable to estimate true changes. This thesis investigates prominent bias assumptions and analyzes their
4 x Abstract validity under climate change. We use 2m temperatures from the EU ENSEMBLES project a large climate change research initiative for Europe and study the driving global climate models (GCMs) as well as their high-resolution regional counterparts (RCMs). Contrary to the well-established belief that bias changes are negligible compared to climate change, our analyses suggest that these changes are significant. It is therefore advisable to allow for time-dependent biases. Additionally, we find that although relations between RCMs and their driving GCM are strong and in principle linear, RCMs reduce biases of their drivers. In agreement with these findings, we have developed a Bayesian hierarchical model that allows a coherent and holistic analysis of heterogeneous RCM-GCM multi-model ensembles. All relevant assumptions on which our statistical model rests upon are transparently set out. Our approach is superior to alternative statistical modeling proposals due to its flexible functional form that allows for multidecadal variability and enables an integral analysis of climate time series. Our results have also implications for the design of prospective RCM-GCM multimodel ensembles, namely, independent RCM-GCM chains are a key to maximizing predictive power. Methods developed in this thesis are neither constrained to specific data sets nor certain climate variables but are generic and transferable.
5 Zusammenfassung Atmosphärische Phänomene laufen auf den unterschiedlichsten Skalen ab: von der mikroskopischen Ebene (z.b. Eisnukleation in Wolken) bis hin zu globalen Phänomenen (z.b. die Hadley Zirkulation) und zeigen komplexe Wechselwirkungen. Damit sind die Klimawissenschaften ein interessantes wenn auch herausforderndes Anwendungsgebiet moderner statistischer Verfahren. Computermodelle spielen bei der Analyse des Klimazustands eine zentrale Rolle. Obwohl diese deterministisch sind, liegt es nahe, die von den Modellen produzierten Daten mittels statistischer Methoden zu interpretieren. Dies liegt vor allen Dingen daran, dass das Klimasystem stochastischer Natur ist und eine große Anzahl an Freiheitsgraden sowie ein nicht-lineares oder chaotisches Verhalten auf unterschiedlichen Skalen aufweist. Die Analyse von Klimamodelldaten stellt eine große Herausforderung dar, zum einen aufgrund ihrer schieren Menge, zum anderen wegen komplexer Abhängigkeiten zwischen den meteorologischen Feldern, unterschiedlichen Modellen und den langen Zeitskalen, die eine Validierung erschweren etc. In dieser Dissertation werden statistische Methoden zur Analyse von Klimadaten entwickelt, um wichtige offene Fragen wie die Wahl der Abweichungsannahme bei der Aufbereitung der Daten zu klären. Abweichungen oder Fehler in Klimamodellen haben mannigfaltige Gründe: Numerische Ungenauigkeiten und Programmierfehler, fehlende Modellprozesse, Parametrisierungen von Prozessen, die auf Skalen kleiner als der Modellauflösung ablaufen und so weiter. Diese Abweichungen treten beim Vergleich von Modellen mit Beobachtungen für vergangene und gegenwärtige Zeiträume zu Tage. Demzufolge sind Klimamodelle nur
6 xii Zusammenfassung ein ungenaues Abbild der Realität. Aufgrund der Tatsache, dass keine Beobachtungen für die Zukunft vorhanden sind, müssen Annahmen über den zukünftigen Verlauf der Abweichungen getroffen werden. Diese Arbeit beleuchtet prominente Abweichungsannahmen und analysiert deren Gültigkeit in einem sich wandelnden Klima. Wir nutzen dazu 2m Temperaturen des EU ENSEMBLES Projektes, einer breitangelegten Initiative zur Beurteilung des europäischen Klimawandels. Wir untersuchen die antreibenden globalen Klimamodelle (GCMs) als auch deren räumlich-hochaufgelöste Entsprechungen (RCMs). Entgegen der gängigen Auffassung, dass Änderungen bei den Abweichungen vernachlässigbar klein relativ zum Klimaänderungssignal sind, finden wir signifikante Änderungen. Daher scheint es geboten, zeitlichveränderliche Abweichungen zuzulassen. Trotz der starken und vornehmlich linearen Beziehungen zwischen RCMs und ihren antreibenden GCMs, sind die RCM Abweichungen kleiner als die der Antreiber. Auf Basis dieser Resultate haben wir ein Bayesianisches hierarchisches Modell entwickelt, dass eine kohärente und ganzheitliche Analyse von heterogenen RCM-GCM Multimodell-Ensembles erlaubt. Alle relevanten Annahmen, auf denen das vorgestellte statistische Modell beruht, werden transparent dargelegt. Dabei zeigt sich, dass die von uns präsentierte Methode alternativen Ansätzen überlegen ist aufgrund ihrer flexiblen funktionalen Form. Diese ermöglicht eine Berücksichtigung der multidekadischen Variabilität und erlaubt eine allumfassende Analyse von Klimazeitreihen. Ausserdem können unsere Resultate einen wichtigen Beitrag für den Entwurf zukünftiger RCM-GCM Multimodell- Ensembles liefern: um die Vorhersagekraft zu maximieren bedarf es nämlich möglichst unabhänginger RCM-GCM Modellketten. Die Methoden, die in dieser Arbeit entwickelt wurden, sind weder auf bestimmte Datensätze noch klimatologische Variablen beschränkt, sondern generisch und transferierbar.
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