Thesis advisor: Jeremy Shakun / As recently measured amounts of global atmospheric carbon dioxide concentrations have risen 40% from pre-Industrial levels and will likely reach double by mid-century, climate scientists have expressed concern over the future state of the climate system, and have attempted to gauge the consequences of such a large forcing. The principal parameter for climate scientists is equilibrium climate sensitivity, which is the change in temperature following a doubling of atmospheric CO2 concentrations. Current estimates of climate sensitivity span too expansive of a range to provide a clear understanding of the magnitude of temperature changes one can expect. Therefore, I conduct many individual multivariate analyses as a means of narrowing these ranges of sensitivity and to investigate geographical distributions of sensitivity, at the very least. To do so, I analyze four major climate forcings: greenhouse gas, atmospheric dust, ice volume, and insolation. Using several multiple linear regressions, I calculate the relative weighting of each forcing in driving the temperature signal in 47 local temperature proxy records. The paleoclimate proxy records chosen span glacial cycles over the past 800 kyr. These results provide insight into the geographical distributions of the relative influences of each of the forcings, while working to constrain the range of sensitivity estimates through the weighting of the greenhouse gas forcing. Separating out the individual climate inputs allows me to conclude what percentage of climate change was caused by CO2 in the past, and by implication how much warming might be expected due to GHG forcing in the future. / Thesis (BS) — Boston College, 2015. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Earth and Environmental Sciences.

Groundwater levels over northern Utah have undergone a declining trend since the 1960’s. This trend has made apparent the need to understand the relationship between climate and groundwater resources. Such necessary information is already in dire need in places such as California. At the close of 2013, California had experienced its driest year in recorded history, with severe drought continuing for the foreseeable future. Utah is the second driest state in the U.S., and therefore has been paying close attention to California’s current water crises. Water resource projections may prove to be one of the most vital pieces of information toward securing adequate water for those who are currently enduring such water shortages. In order to accomplish the initial research necessary for developing a fundable proposal, we requested support from the Utah State University Research Catalyst Grant to (a) evaluate a state-of-the-art climate model (its ability to assess groundwater) against statewide groundwater wells and operational groundwater models, (b) reduce climate model uncertainties, (c) conduct a study in the form of observational well site evaluations, and (d) develop strategies to effectively disseminate information on Utah’s future groundwater budget to water managers and policy makers. This research is now fully funded externally by the Bureau of Reclamation.

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&amp;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. / <p>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.</p>