Toward understanding predictability of climate: a linear stochastic modeling approach

This dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes...

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Main Author: Wang, Faming
Other Authors: Chang, Ping, Stossel, Achim, Panetta, Richard L., Reid, Robert O.
Format: Doctoral or Postdoctoral Thesis
Language:English
Published: Texas A&M University 2004
Subjects:
Online Access:https://hdl.handle.net/1969.1/1147
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spelling fttexasamuniv:oai:oaktrust.library.tamu.edu:1969.1/1147 2023-07-16T03:59:57+02:00 Toward understanding predictability of climate: a linear stochastic modeling approach Wang, Faming Chang, Ping Stossel, Achim Panetta, Richard L. Reid, Robert O. 2004-11-15 1424274 bytes 244073 bytes electronic application/pdf text/plain born digital https://hdl.handle.net/1969.1/1147 en_US eng Texas A&M University https://hdl.handle.net/1969.1/1147 Predictability Stochastic climate model Model reduction Ocean Advection Tropical Atlantic Variability Nonnormality Thesis Electronic Dissertation text 2004 fttexasamuniv 2023-06-27T22:15:10Z This dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere. First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information of deterministic dynamics and noise forcing, various predictability measures are defined and new predictability-analysis tools are introduced. For the sake of computational efficiency, we also discuss the formulation of a low-order model within the context of four reduction methods: modal, EOF, most predictable pattern, and balanced truncation. Subsequently, predictabilities of two specific physical systems are investigated within such framework. The first is a mixed layer model of SST with focus on the effect of oceanic advection.Analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain averaged error variance. However, a Predictable Component Analysis (PrCA) shows that advection can play a role in redistributing the predictable variance. This analytical result is further tested in a more realistic two-dimensional North Atlantic model with observed mean currents. The second is a linear coupled model of tropical Atlantic atmosphere-ocean system. Eigen-analysis reveals that the system has two types of coupled modes: a decadal meridional mode and an interannual equatorial mode. The meridional mode, which manifests itself as a dipole pattern in SST, is controlled by thermodynamic feedback between wind, latent heat flux, and SST, and modified by ocean heat transport. The equatorial mode, which manifests itself as an SST anomaly in the eastern equatorial basin, is dominated by dynamic ... Doctoral or Postdoctoral Thesis North Atlantic Texas A&M University Digital Repository
institution Open Polar
collection Texas A&M University Digital Repository
op_collection_id fttexasamuniv
language English
topic Predictability
Stochastic climate model
Model reduction
Ocean Advection
Tropical Atlantic Variability
Nonnormality
spellingShingle Predictability
Stochastic climate model
Model reduction
Ocean Advection
Tropical Atlantic Variability
Nonnormality
Wang, Faming
Toward understanding predictability of climate: a linear stochastic modeling approach
topic_facet Predictability
Stochastic climate model
Model reduction
Ocean Advection
Tropical Atlantic Variability
Nonnormality
description This dissertation discusses the predictability of the atmosphere-ocean climate system on interannual and decadal timescales. We investigate the extent to which the atmospheric internal variability (weather noise) can cause climate prediction to lose skill; and we also look for the oceanic processes that contribute to the climate predictability via interaction with the atmosphere. First, we develop a framework for assessing the predictability of a linear stochastic system. Based on the information of deterministic dynamics and noise forcing, various predictability measures are defined and new predictability-analysis tools are introduced. For the sake of computational efficiency, we also discuss the formulation of a low-order model within the context of four reduction methods: modal, EOF, most predictable pattern, and balanced truncation. Subsequently, predictabilities of two specific physical systems are investigated within such framework. The first is a mixed layer model of SST with focus on the effect of oceanic advection.Analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain averaged error variance. However, a Predictable Component Analysis (PrCA) shows that advection can play a role in redistributing the predictable variance. This analytical result is further tested in a more realistic two-dimensional North Atlantic model with observed mean currents. The second is a linear coupled model of tropical Atlantic atmosphere-ocean system. Eigen-analysis reveals that the system has two types of coupled modes: a decadal meridional mode and an interannual equatorial mode. The meridional mode, which manifests itself as a dipole pattern in SST, is controlled by thermodynamic feedback between wind, latent heat flux, and SST, and modified by ocean heat transport. The equatorial mode, which manifests itself as an SST anomaly in the eastern equatorial basin, is dominated by dynamic ...
author2 Chang, Ping
Stossel, Achim
Panetta, Richard L.
Reid, Robert O.
format Doctoral or Postdoctoral Thesis
author Wang, Faming
author_facet Wang, Faming
author_sort Wang, Faming
title Toward understanding predictability of climate: a linear stochastic modeling approach
title_short Toward understanding predictability of climate: a linear stochastic modeling approach
title_full Toward understanding predictability of climate: a linear stochastic modeling approach
title_fullStr Toward understanding predictability of climate: a linear stochastic modeling approach
title_full_unstemmed Toward understanding predictability of climate: a linear stochastic modeling approach
title_sort toward understanding predictability of climate: a linear stochastic modeling approach
publisher Texas A&M University
publishDate 2004
url https://hdl.handle.net/1969.1/1147
genre North Atlantic
genre_facet North Atlantic
op_relation https://hdl.handle.net/1969.1/1147
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