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.

Spectral properties of global surface temperature and uncertainties of global climate sensitivity are explored in this work through the medium of Energy Balance Climate Models (EBCMs) and observational surface temperature data. In part I, a complete series of 2D time-dependent non-orthogonal eigenmodes of global surface temperature are analytically derived and their geographic patterns are presented. The amplitudes of these modes have temporal characteristics and present exponentially decaying patterns. Theoretically, if the energy balance model is forced by white noise forcing in time, the autocorrelation functions of the mode amplitudes should present the same exponentially decaying patterns. When observed surface temperature data are projected onto these theoretical modes, the autocorrelation time scales of the mode amplitudes exhibit similar exponential decaying patterns. These modes are believed to be useful for surface temperature studies and model intercomparison. In part II, an objective means of deriving the probability density function (PDF) of global climate sensitivity is investigated. The method constrains the PDF by its fit to the present climate in terms of surface temperature. We found that a wide range of parameter combinations, which corresponds to a broad range of the sensitivity, shows equally good fits to the present climate. It means that the uncertainties in global climate sensitivity are very difficult to eliminate if climate models are tuned to fit observations of surface temperature alone. The origin of the skewness of the PDF is found in very simple terms.

Spectral properties of global surface temperature and uncertainties of global climate sensitivity are explored in this work through the medium of Energy Balance Climate Models (EBCMs) and observational surface temperature data. In part I, a complete series of 2D time-dependent non-orthogonal eigenmodes of global surface temperature are analytically derived and their geographic patterns are presented. The amplitudes of these modes have temporal characteristics and present exponentially decaying patterns. Theoretically, if the energy balance model is forced by white noise forcing in time, the autocorrelation functions of the mode amplitudes should present the same exponentially decaying patterns. When observed surface temperature data are projected onto these theoretical modes, the autocorrelation time scales of the mode amplitudes exhibit similar exponential decaying patterns. These modes are believed to be useful for surface temperature studies and model intercomparison. In part II, an objective means of deriving the probability density function (PDF) of global climate sensitivity is investigated. The method constrains the PDF by its fit to the present climate in terms of surface temperature. We found that a wide range of parameter combinations, which corresponds to a broad range of the sensitivity, shows equally good fits to the present climate. It means that the uncertainties in global climate sensitivity are very difficult to eliminate if climate models are tuned to fit observations of surface temperature alone. The origin of the skewness of the PDF is found in very simple terms.

No description available.

No description available.

Significant challenges in water resources management arise because of the ever-increasing pressure on the world’s heavily exploited and limited water resources. These stressors include demographic growth, intensification of agriculture, climate variability, and climate change. These challenges to water resources are usually tackled using a top-down approach, which suffers from many limitations including the use of a limited set of climate change scenarios, the lack of methodology to rank these scenarios, and the lack of credibility, particularly on extremes. The bottom-up approach, the recently introduced approach, reverses the process by assessing vulnerabilities of water resources systems to variations in future climates and determining the prospects of such wide range of changes. While it solves some issues of the top-down approach, several issues remain unaddressed. The current project seeks to provide end-users and the research community with an improved version of the bottom-up framework for streamlining climate variability into water resources management decisions. The improvement issues that are tackled are a) the generation of a sufficient number of climate projections that provide better coverage of the risk space; b) a methodology to quantitatively estimate the plausibility of a future desired or undesired outcome and c) the optimization of the size of the projections pool to achieve the desired precision with the minimum time and computing resources. The results will hopefully help to cope with the present-day and future challenges induced mainly by climate. In the first part of the study, the adequacy of stochastically generated climate time series for water resources systems risk and performance assessment is investigated. A number of stochastic weather generators (SWGs) are first used to generate a large number of realizations (i.e. an ensemble of climate outputs) of precipitation and temperature time series. Each realization of the generated climate time series is then used individually as an input to a hydrological model to obtain streamflow time series. The usefulness of weather generators is evaluated by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space (CSS), and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space (RPIS). The performance of the weather generator is assessed using visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out using five different weather generators: two versions of WeaGETS, two versions of MulGETS and the k-nearest neighbor weather generator (knn). In the second part of the thesis, the impacts of climate change, on the other hand, was evaluated by generating a large number of representative climate projections. Large ensembles of future series are created by perturbing downscaled regional climate models’ outputs with a stochastic weather generator, then used as inputs to a hydrological model that was calibrated using observed data. Risk indices calculated with the simulated streamflow data are converted into probability distributions using Kernel Density Estimations. The results are dimensional joint probability distributions of risk-relevant indices that provide estimates of the likelihood of unwanted events under a given watershed configuration and management policy. The proposed approach offers a more complete vision of the impacts of climate change and opens the door to a more objective assessment of adaptation strategies. The third part of the thesis deals with the estimation of the optimal size of SWG realizations needed to calculate risk and performance indices. The number of realizations required to reach is investigated utilizing Relative Root Mean Square Error and Relative Error. While results indicate that a single realization is not enough to adequately represent a given stochastic weather generator, results generally indicate that there is no major benefit of generating more than 100 realizations as they are not notably different from results obtained using 1000 realizations. Adopting a smaller but carefully chosen number of realizations can significantly reduce the computational time and resources and therefore benefit a larger audience particularly where high-performance machines are not easily accessible. The application was done in one pilot watershed, the South Nation Watershed in Eastern Ontario, yet the methodology will be of interest for Canada and beyond. Overall, the results contribute to making the bottom-up more objective and less computationally intensive, hence more attractive to practitioners and researchers.

<p>The Earth's atmosphere is expected to warm in response to increasing atmospheric concentrations of greenhouse gases (GHG). The response of the Earth's complex and chaotic climate system to the GHG emissions is, however, difficult to assess. In this thesis, two issues of importance for the assessment of this response are studied. The first concerns the magnitude of the natural and anthropogenic emissions of CO₂. An atmospheric transport model is used, combined with inventories of anthropogenic CO₂ emissions and estimates of natural emissions, to compare modelled and observed variations in the concentration of CO₂ at an Arctic monitoring site. The anthropogenic and natural emissions are shown to exert approximately equal influence on Arctic CO₂variations during winter.</p><p>The primary focus of this thesis is the response of the climate system to the enhanced GHG forcing. It has been proposed that this response may project onto the leading modes of variability. In the present thesis, this hypothesis is tested against the alternative that the spatial patterns of variability change in response to the enhanced forcing. The response of the atmospheric circulation to the enhanced GHG forcing as simulated by a specific coupled global climate model (CGCM) is studied. The response projects strongly onto the leading modes of present-day variability. The spatial patterns of the leading modes are however changed in response to the enhanced GHG forcing. These changes in the spatial patterns are associated with a strengthening of the waveguide for barotropic Rossby waves in the Southern Hemisphere. The Northern Hemisphere waveguide is however unchanged.</p><p>The magnitude of the global mean responses to an enhanced GHG forcing as simulated by CGCMs vary. Moreover, the regional responses vary considerably among CGCMs. In this thesis, it is hypothesised that the inter-CGCM differences in the spatial patterns of the response to the enhanced GHG forcing are partially explained by inter-CGCM differences in zonal-mean properties of the atmospheric flow. In order to isolate the effect of these differences in the zonal-mean background state from the effects of other sensitivities, a simplified model with idealised forcing is employed. The model used is a global three-level quasi-geostrophic model. The sensitivity of the stationary wave pattern (SWP) to changes in the zonal-mean wind and tropopause height of similar magnitude as those found in response to the enhanced GHG forcing in CGCMs is investigated. The SWP in the simplified model shows a sensitivity of comparable magnitude to the analogous response in CGCMs. These results indicate that the CGCM-simulated response is sensitive to relatively small differences in the zonal-mean background state. To assess the uncertainties in the regional response to the enhanced forcing associated with this sensitivity, ensemble simulations of climate change are of great importance.</p>

The Earth's atmosphere is expected to warm in response to increasing atmospheric concentrations of greenhouse gases (GHG). The response of the Earth's complex and chaotic climate system to the GHG emissions is, however, difficult to assess. In this thesis, two issues of importance for the assessment of this response are studied. The first concerns the magnitude of the natural and anthropogenic emissions of CO2. An atmospheric transport model is used, combined with inventories of anthropogenic CO2 emissions and estimates of natural emissions, to compare modelled and observed variations in the concentration of CO2 at an Arctic monitoring site. The anthropogenic and natural emissions are shown to exert approximately equal influence on Arctic CO2 variations during winter. The primary focus of this thesis is the response of the climate system to the enhanced GHG forcing. It has been proposed that this response may project onto the leading modes of variability. In the present thesis, this hypothesis is tested against the alternative that the spatial patterns of variability change in response to the enhanced forcing. The response of the atmospheric circulation to the enhanced GHG forcing as simulated by a specific coupled global climate model (CGCM) is studied. The response projects strongly onto the leading modes of present-day variability. The spatial patterns of the leading modes are however changed in response to the enhanced GHG forcing. These changes in the spatial patterns are associated with a strengthening of the waveguide for barotropic Rossby waves in the Southern Hemisphere. The Northern Hemisphere waveguide is however unchanged. The magnitude of the global mean responses to an enhanced GHG forcing as simulated by CGCMs vary. Moreover, the regional responses vary considerably among CGCMs. In this thesis, it is hypothesised that the inter-CGCM differences in the spatial patterns of the response to the enhanced GHG forcing are partially explained by inter-CGCM differences in zonal-mean properties of the atmospheric flow. In order to isolate the effect of these differences in the zonal-mean background state from the effects of other sensitivities, a simplified model with idealised forcing is employed. The model used is a global three-level quasi-geostrophic model. The sensitivity of the stationary wave pattern (SWP) to changes in the zonal-mean wind and tropopause height of similar magnitude as those found in response to the enhanced GHG forcing in CGCMs is investigated. The SWP in the simplified model shows a sensitivity of comparable magnitude to the analogous response in CGCMs. These results indicate that the CGCM-simulated response is sensitive to relatively small differences in the zonal-mean background state. To assess the uncertainties in the regional response to the enhanced forcing associated with this sensitivity, ensemble simulations of climate change are of great importance.

xvi, 156 p. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / This dissertation examined a number of general vegetation-modeling issues, and the sensitivity of terrestrial net primary productivity (NPP), soil moisture and actual evapotranspiration (ET) to climatic variations in China. The specific issues addressed included: (1) the sensitivity of the performance of an equilibrium vegetation model to the choice of monthly-mean climatologies, observed validation data sets, and three map-comparison approaches; and (2) the limitations of existing map-comparison approaches in vegetation modeling; and the variation and climate sensitivity of (3) terrestrial NPP and (4) soil moisture and actual ET in China. To address the first issue, BIOME4 (Kaplan et al., 2002), a typical example of an equilibrium vegetation model, was used along with a set of 19 different monthly-mean climatologies, three validation data sets, and several map-comparison methods. To address the second issue, the "opposite and identity" (01) index (Tang, 2008) was developed for evaluating the correspondence of two simulation results. To examine the third issue, a set of historical NPP dynamics were derived from normalized-difference vegetation index data by modifying the CASA (Potter et al., 1999) approach and then were linked to the variation of temperature and precipitation to analyze the climatic effects on terrestrial NPP in China. To examine the fourth issue, a stand-alone water balance model, LH (LPJ-hydrology), was developed by modifying the LPJ dynamic global vegetation model (Sitch et al., 2003), and applying it to a China case study. The results of these analyses indicate that (1) the 30-year mean-climatology preceding the observed data produces the most accurate vegetation simulations; (2) the OI index is a useful tool to compare two simulation results or to evaluate simulation results against observed spatiotemporal data; (3) climate and land-use change jointly controlled NPP dynamics in the eastern monsoon zone of China. In contrast, NPP dynamics in the north-west and zone and in the Tibet Plateau frigid zone depended more on climatic variation; and (4) the spatial patterns of soil moisture and ET in China were correlated with the variation of temperature and precipitation. However, the strength of such relationship varies spatially. This dissertation includes my published and coauthored materials. / Adviser: Patrick J. Bartlein

Cette thèse porte sur l'agrégation de la convection dans le modèle de circulation générale LMDZ5A à l'équilibre radiatif-convectif (RCE). L'instabilité du RCE mise en évidence nous permet d'étudier les mécanismes d'initiation de l'agrégation et leur dépendance à la température de surface océanique (SST). A basse SST, l'agrégation résulte d'un couplage entre la circulation grande-échelle et les effets radiatifs des nuages bas. A haute SST, elle provient d'un couplage entre la circulation de grande-échelle et les flux turbulents à la surface. Le couplage de l'atmosphère avec une couche de mélange océanique rend l'initiation de l'agrégation moins dépendante de la SST et des mécanismes d'initiation, à l'exception des effets radiatifs des nuages hauts. L'impact de l'agrégation sur la sensibilité climatique et la température de surface est aussi analysé. En favorisant la formation de zones ciel clair sèches, l'agrégation refroidit fortement le système climatique. Toutefois, cet effet est limité par l'effet des changements de gradients de SST et de fraction de nuages bas qui tendent au contraire à faire augmenter la sensibilité climatique. Aux plus courtes échelles temporelles, en revanche, le couplage entre océan et agrégation de la convection est à l'origine d'une boucle de rétroaction stabilisatrice qui contrôle l'agrégation et renverse complètement son effet. Ainsi, l'effet de l'agrégation sur la sensibilité climatique est assez faible par rapport à ce que laissent penser les simulations où le couplage océan-atmosphère est absent. Ces résultats montrent l'importance de considérer le couplage océan-atmosphère dans l'étude du rôle de l'agrégation dans le climat. / This thesis focuses on the study of convective aggregation in LMDZ5A general circulation model, used in Radiative-Convective Equilibrium (RCE) configuration. The instability of the RCE allows us to look at the mechanisms controlling the initiation of convective aggregation and its dependence on sea surface temperatures (SST). At low SSTs, a coupling between the large-scale circulation and the radiative effects of low clouds is needed to trigger self-aggregation. At high SSTs, the coupling between the large-scale circulation and the surface fluxes controls this initiation. When the atmosphere is coupled to a slab ocean mixed layer, SST gradients facilitate the initiation of convective aggregation. Except for the high-cloud radiative effects, triggering mechanisms are less crucial. Convection also becomes less dependent on the SST.The impact of convective aggregation on the climate sensitivity and surface temperature is also analyzed. Convective aggregation is found to increase the area of dry clear-sky zones. Thus, it tends to cool the system very efficiently. However, the negative feedback associated with an increase in aggregation is generally balanced by offsetting changes in SST gradients and low clouds that tend to increase the climate sensitivity. In contrast, at shorter timescales, the coupling between ocean and convective aggregation also controls the strength of convective aggregation and overturn its effect. Thus the impact of convective aggregation may not be as strong as what can be inferred from experiments with uniform SSTs.These results emphasize the importance of considering ocean-atmosphere coupling when studying the role of aggregation in climate.