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Title:Quasi-periodic oscillations in observed and simulated temperatures, and implications for the future
Author(s):Lindner, Daniela
Director of Research:Schlesinger, Michael E.
Doctoral Committee Chair(s):Schlesinger, Michael E.
Doctoral Committee Member(s):Riemer, Nicole; Walsh, John E.; Wuebbles, Donald J.
Department / Program:Atmospheric Sciences
Discipline:Atmospheric Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Climate Change
Global Warming
Internal Variability
Natural Variability
Atlantic Multidecadal Oscillation (AMO)
Greenhouse-Gas Emissions
Global Climate Models
Coupled Model Intercomparison Project 5 (CMIP5)
Model Assessment
Simulated Variability
Abstract:Earth's climate system displays various modes of variability ranging from intra-seasonal to inter-decadal time scales, which hamper our ability to detect the human-caused warming signal in the climate record. Despite 44 years of climate modeling and much improvement being made, the spatial and temporal characteristics of the major modes of natural variability (i.e., AMO, ENSO, PDO, NAO) are still not simulated accurately. Furthermore, we find that some modes of low-frequency variability have not been yet identified, due to the shortness of the instrumental record and the resulting signal-to-noise detection problem. This dissertation summarizes our effort to create an up-to-date and comprehensive database of natural variability, as it is: (a) observed in nature and (b) simulated by state-of-the art global climate models (GCMs). We employ a non-parametric Singular Spectrum Analysis (SSA) approach that enables the study of non-periodic – so-called “quasi-periodic” oscillations (QPOs). SSA can resolve QPOs that have periods up to half the length of the record, something which is impossible using traditional Fourier analysis. All four long-term atmosphere-ocean near-surface instrumental temperature records updated through 2010 have been used for the observational study, while we also take advantage of the newly released data from the Coupled Model Intercomparison Project 5 (CMIP5) for the modeling study, thereby guaranteeing the currency of this work Six significant signals with periods of 61 years (O1), 21 years (O2), 9 years (O3), 5 years (O4), 4 years (O5) and 2 years (S1) are identified in the global temperature record. The regional analysis reveals that O1 is a pan-oceanic phenomenon and proposed to be due small variations in the thermohaline circulation in the respective ocean basins. It is the predominant cause of the observed Mid 20th Century Cooling period. The global distribution of O2 and O3 suggests an external forcing of the signals, but no support for a solar or lunar origin is found. We conclude that the interannual signals O4, O5 and S1 are not necessarily due to the El Niño Southern Oscillation (ENSO). Altogether, the observed modes of variability combine such that they contributed to a cooling of 0.13°C to 0.27°C over the past decade, thereby counteracting the human made warming and partially explaining the often noted slowdown of the global warming rate over the past decade. They are estimated to continue to do so over the next 30-40 years and the ‘pause’ in global warming is expected to persist until around 2020. Mistaking this perceived break as a disproof of global warming would be a tremendous error, and we strongly advise the global community to start implementing plans to phase out GHGs over the course of the 21st century Combining the modeling and observation studies yields an up-to date assessment of the CMIP5 models ability to reproduce the observed natural variability. Positive to note is that many models do simulate accurately one or more observed mode of variability, particularly the interannual signals O4 and O5. On the other hand, there is not a single model, or a set of models, that performs particularly well across all modes of variability. Less than one third of the GCMs are able to reproduce the first three most dominant and very regular QPOs, O1-O3. We conclude that much improvement has still to be made before we can claim that the current state-of-the-art GCMs simulate natural variability accurately, and recommend the use of the databases and assessments provided in this dissertation to do so.
Issue Date:2013-08-22
Rights Information:Copyright 2013 Daniela Lindner
Date Available in IDEALS:2013-08-22
Date Deposited:2013-08

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