Graduation date: 2000

Thesis (Ph.D.). / Written for the Dept. of Atmospheric and Oceanic Sciences. Title from title page of PDF (viewed 2008/02/12). Includes bibliographical references.

Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2005. / Thesis Advisor(s): Patrick Harr. Includes bibliographical references (p. 91-92). Also available online.

Thesis (Ph.D.)--Memorial University of Newfoundland, 1999. / Bibliography: leaves 234-240.

Thesis (Ph. D.)--University of Washington, 1973. / Vita. Includes bibliographical references (leaves 155-157).

Thesis (Ph. D.)--Johns Hopkins University, 1996. / Vita. Includes bibliographical references (leaves 137-150).

Atmospheric CO₂ is projected to increase for the foreseeable future. The amount of CO₂ that remains in the atmosphere is regulated, in large part, by the ocean. As the long-term response to the changing atmospheric pCO₂ unfolds, the ocean sink will continue to be modified on seasonal to decadal timescales by climate variability and change. The magnitude of this variability is an active area of research. Accurately quantifying this variability is a challenge given the paucity of direct in-situ observations. In order calculate the global air-sea CO₂ sink, ocean pCO₂ needs to be known, or at least accurately estimated, at all locations at regular intervals. Two approaches to estimate air-sea CO₂ flux are, 1) from simulations of the Earth system and 2) data gap-filling mapping techniques. The goals of this thesis are to 1) rigorously quantify errors in a leading pCO₂ and ocean CO₂ sink mapping technique and 2) to evaluate the efficacy of adding Earth system model based estimates of ocean pCO₂ as a first guess into machine learning based mapping techniques. To meet the first goal, we use a suite of Large Ensemble model members as a testbed to evaluate a leading pCO₂ gap-filling approach (SOM-FFN). We find that the SOM-FFN performs well when sufficient data is available, but overestimates Southern Ocean decadal variability by about 39%. To meet our second goal, we incorporate Earth system model pCO₂ output into machine learning techniques either by adding the output as an additional feature or by post-processing the model output by learning the misfit (misfit=observation-model) and correcting for it. We find that blending model output and observations using machine learning marginally improves prediction accuracy. In addition, we discuss the potential of the learned misfits as a new model diagnostic tool, which can be used to visualize spatiotemporal pCO₂ estimates. Taken together, this study has significant implications in the development of carbon monitoring systems, in turn aiding policy making and improving our understanding of the evolution of the air-sea CO₂ sink.

No description available.

The relationships between teleconnection patterns the Pacific/North American (PNA) index, North Atlantic Oscillation (NAO) and the El Nino/Southern Oscillation (ENSO)—and lake-effect snowfall are examined. Bivariate and partial correlations are used over seasonal and semi-seasonal periods for stations within the Lake Erie snowbelt to link teleconnection phases to snowfall increases. Significant negative correlations were seen throughout the entire winter between NAO and snowfall. Relationships between PNA and ENSO on snowfall were less evident, with significant correlations during the mid-winter months between a positive PNA and snowfall and significant correlations during the late winter in the western zone between a negative ENSO and snowfall. / Department of Geography

Since an extratropical transition (ET) of a decaying tropical cyclone (TC) often results in a fast-moving, rapidly developing extratropical cyclone and amplification of synoptic-scale systems far downstream, proper forecasting of ET events is critical to forecast accuracy over large ocean regions. Past studies have linked forecast accuracy to the phasing of a decaying TC with favorable midlatitudes conditions. Because ET events are sensitive to the analyzed initial conditions, this phasing is examined using 11 member ensemble predictions available four times daily from the National Centers for Environmental Prediction, which were combined into a single 44 member ensemble based on a common forecast verification time. Recurring ET patterns within the 44 member ensemble were objectively identified using a combination of EOF and cluster analysis. Ensemble spread first appears near the point where the TC moves into the midlatitudes and then propagates downstream. Although ensemble spread in the forecast fields was large at extended forecast intervals, the ensemble spread, and the number of ET patterns identified in successive EPS predictions, decreased as the ET process became better defined. Within 48 hours of the ET event, the ensemble prediction system properly identified the ET pattern with a minimum ensemble spread. Similar to Klein et al. (2002), the shifts in the initial position of the TC and the subsequent dynamical coupling can explain differences between weak and strong ET reintensifications.