This dissertation aims to better understand how various climate modeling approaches affect the fidelity of the North American Monsoon (NAM), as well as the sensitivity of the future state of the NAM under a global warming scenario. Here, we improved over current fully-coupled general circulation models (GCM), which struggle to fully resolve the controlling dynamics responsible for the development and maintenance of the NAM. To accomplish this, we dynamically downscaled a GCM with a regional climate model (RCM). The advantage here being a higher model resolution that improves the representation of processes on scales beyond that which GCMs can resolve. However, as all RCM applications are subject to the transference of biases inherent to the parent GCM, this study developed and evaluated a process to reduce these biases. Pertaining to both precipitation and the various controlling dynamics of the NAM, we found simulations driven by these bias-corrected forcing conditions performed moderately better across a 32-year historical climatology than simulations driven by the original GCM data. Current GCM consensus suggests future tropospheric warming associated with increased radiative forcing as greenhouse gas concentrations increase will suppress the NAM convective environment through greater atmospheric stability. This mechanism yields later onset dates and a generally drier season, but a slight increase to the intensity during July-August. After comparing downscaled simulations forced with original and corrected forcing conditions, we argue that the role of unresolved GCM surface features such as changes to the Gulf of California evaporation lead to a more convective environment. Even when downscaling the original GCM data with known biases, the inclusion of these surface features altered and in some cases reversed GCM trends throughout the southwest United States. This reversal towards a wetter NAM is further magnified in future bias-corrected simulations, which suggest (1) fewer average number of dry days by the end of the 21st century (2) onset occurring up to two to three weeks earlier than the historical average, and (3) more extreme daily precipitation values. However, consistent across each GCM and RCM model is the increase in inter-annual variability, suggesting greater susceptibility to drought conditions in the future.

<p>This thesis consists of four papers dealing with estimation and inference for quantile regression of longitudinal data, with an emphasis on nonlinear models. </p><p>The first paper extends the idea of quantile regression estimation from the case of cross-sectional data with independent errors to the case of linear or nonlinear longitudinal data with dependent errors, using a weighted estimator. The performance of different weights is evaluated, and a comparison is also made with the corresponding mean regression estimator using the same weights. </p><p>The second paper examines the use of bootstrapping for bias correction and calculations of confidence intervals for parameters of the quantile regression estimator when longitudinal data are used. Different weights, bootstrap methods, and confidence interval methods are used.</p><p>The third paper is devoted to evaluating bootstrap methods for constructing hypothesis tests for parameters of the quantile regression estimator using longitudinal data. The focus is on testing the equality between two groups of one or all of the parameters in a regression model for some quantile using single or joint restrictions. The tests are evaluated regarding both their significance level and their power.</p><p>The fourth paper analyzes seven longitudinal data sets from different parts of the biostatistics area by quantile regression methods in order to demonstrate how new insights can emerge on the properties of longitudinal data from using quantile regression methods. The quantile regression estimates are also compared and contrasted with the least squares mean regression estimates for the same data set. In addition to looking at the estimates, confidence intervals and hypothesis testing procedures are examined.</p>

This thesis consists of four papers dealing with estimation and inference for quantile regression of longitudinal data, with an emphasis on nonlinear models. The first paper extends the idea of quantile regression estimation from the case of cross-sectional data with independent errors to the case of linear or nonlinear longitudinal data with dependent errors, using a weighted estimator. The performance of different weights is evaluated, and a comparison is also made with the corresponding mean regression estimator using the same weights. The second paper examines the use of bootstrapping for bias correction and calculations of confidence intervals for parameters of the quantile regression estimator when longitudinal data are used. Different weights, bootstrap methods, and confidence interval methods are used. The third paper is devoted to evaluating bootstrap methods for constructing hypothesis tests for parameters of the quantile regression estimator using longitudinal data. The focus is on testing the equality between two groups of one or all of the parameters in a regression model for some quantile using single or joint restrictions. The tests are evaluated regarding both their significance level and their power. The fourth paper analyzes seven longitudinal data sets from different parts of the biostatistics area by quantile regression methods in order to demonstrate how new insights can emerge on the properties of longitudinal data from using quantile regression methods. The quantile regression estimates are also compared and contrasted with the least squares mean regression estimates for the same data set. In addition to looking at the estimates, confidence intervals and hypothesis testing procedures are examined.

Consider a time series model which is stationary apart from a single shift in mean. If the time of a level shift is known, the least squares estimator of the magnitude of this level shift is a minimum variance unbiased estimator. If the time is unknown, however, this estimator is biased. Here, we first carry out extensive simulation studies to determine the relationship between the bias and three parameters of our time series model: the true magnitude of the level shift, the true time point and the autocorrelation of adjacent observations. Thereafter, we use two generalized additive models to generalize the simulation results. Finally, we examine to what extent the bias can be reduced by multiplying the least squares estimator with a shrinkage factor. Our results showed that the bias of the estimated magnitude of the level shift can be reduced when the level shift does not occur close to the beginning or end of the time series. However, it was not possible to simultaneously reduce the bias for all possible time points and magnitudes of the level shift.

Numerical models are powerful and widely used tools for environmental prediction; however, any model prediction contains errors due to imperfect model parameterizations, insufficient model resolution, numerical errors, imperfect initial and boundary conditions etc. A variety of approaches is applied to quantify, correct and minimize these errors including skill assessments, bias correction and formal data assimilation. All of these require observations and benefit from comprehensive data sets. In this thesis, two aspects related to the quantification and correction of errors in biological ocean models are addressed: (i) A new bias correction method for a biological ocean model is evaluated, and (ii) a novel approach for expanding the set of typically available phytoplankton observations is assessed. The bias correction method, referred to as frequency-dependent nudging, was proposed by Thompson et al. (Ocean Modelling, 2006, 13:109-125) and is used to nudge a model only in prescribed frequencies. A desirable feature of this method is that it can preserve high frequency variability that would be dampened with conventional nudging. The method is first applied to an idealized signal consisting of a seasonal cycle and high frequency variability. In this example, frequency-dependent nudging corrected for the imposed seasonal bias without affecting the high-frequency variability. The method is then applied to a non-linear, 1 dimensional (1D) biogeochemical ocean model. Results showed that application of frequency-dependent nudging leads to better biogeochemical estimates than conventional nudging. In order to expand the set of available phytoplankton observations, light measurements from sensors attached on grey seals where assessed to determine if they provide a useful proxy of phytoplankton biomass. A controlled experiment at Bedford Basin showed that attenuation coefficient estimates from light attenuation measurements from seal tags were found to correlate significantly with chlorophyll. On the Scotian Shelf, results of the assessment indicate that seal tags can uncover spatio-temporal patterns related to phytoplankton biomass; however, more research is needed to derive absolute biomass estimates in the region.

This thesis evaluates regional hydrology using output from the Canadian Regional Climate Model (CRCM 4.1) and examines changes in the hydrological processes over the Churchill River Basin (CRB) by employing the Variable Infiltration Capacity (VIC) hydrology model. The CRCM evaluation has been performed by combining the atmospheric and the terrestrial water budget components of the hydrological cycle. The North American Regional Reanalysis (NARR) data are used where direct observations are not available. The outcome of the evaluation reveals the potential of the CRCM for use in long-term hydrological studies. The CRCM atmospheric moisture fluxes and storage tendencies show reasonable agreement with the NARR. The long-term moisture flux over the CRB was found to be generally divergent during summer. A systematic bias is observed in the CRCM precipitation and temperature. A quantile-based mapping of the cumulative distribution function is applied for precipitation adjustments. The temperature correction only involves shifting and scaling to adjust mean and variance. The results indicate that the techniques employed for correction are useful for hydrological studies. Bias-correction is also applied to the CRCM future climate. The CRCM bias-corrected data is then used for hydrological modeling of the CRB. The VIC-simulated streamflow exhibits acceptable agreement with observations. The VIC model's internal variables such as snow and soil moisture indicate that the model is capable of simulating internal process variables adequately. The VIC-simulated snow and soil moisture shows the potential of use as an alternative dataset for hydrological studies. Streamflow along with precipitation and temperature are analyzed for trends. No statistically significant trend is observed in the daily precipitation series. Results suggest that an increase in temperature may reduce accumulation of snow during fall and winter. The flow regime may be in transition from a snowmelt dominated regime to a rainfall dominated regime. Results from future climate simulations of the A2 emission scenario indicate a projected increase of streamflow, while the snow depth and duration exhibit a decrease. Soil moisture response to future climate warming shows an overall increase with a greater likelihood of occurrences of higher soil moisture.

We review several nonparametric regression techniques and discuss their various strengths and weaknesses with an emphasis on derivative estimation and confidence band creation. We develop a generalized C(p) criterion for tuning parameter selection when interest lies in estimating one or more derivatives and the estimator is both linear in the observed responses and self-consistent. We propose a method for constructing simultaneous confidence bands for the mean response and one or more derivatives, where simultaneous now refers both to values of the covariate and to all derivatives under consideration. In addition we generalize the simultaneous confidence bands to account for heteroscedastic noise. Finally, we consider the characterization of nanoparticles and propose a method for identifying a proper subset of the covariate space that is most useful for characterization purposes.

This thesis evaluates regional hydrology using output from the Canadian Regional Climate Model (CRCM 4.1) and examines changes in the hydrological processes over the Churchill River Basin (CRB) by employing the Variable Infiltration Capacity (VIC) hydrology model. The CRCM evaluation has been performed by combining the atmospheric and the terrestrial water budget components of the hydrological cycle. The North American Regional Reanalysis (NARR) data are used where direct observations are not available. The outcome of the evaluation reveals the potential of the CRCM for use in long-term hydrological studies. The CRCM atmospheric moisture fluxes and storage tendencies show reasonable agreement with the NARR. The long-term moisture flux over the CRB was found to be generally divergent during summer. A systematic bias is observed in the CRCM precipitation and temperature. A quantile-based mapping of the cumulative distribution function is applied for precipitation adjustments. The temperature correction only involves shifting and scaling to adjust mean and variance. The results indicate that the techniques employed for correction are useful for hydrological studies. Bias-correction is also applied to the CRCM future climate. The CRCM bias-corrected data is then used for hydrological modeling of the CRB. The VIC-simulated streamflow exhibits acceptable agreement with observations. The VIC model's internal variables such as snow and soil moisture indicate that the model is capable of simulating internal process variables adequately. The VIC-simulated snow and soil moisture shows the potential of use as an alternative dataset for hydrological studies. Streamflow along with precipitation and temperature are analyzed for trends. No statistically significant trend is observed in the daily precipitation series. Results suggest that an increase in temperature may reduce accumulation of snow during fall and winter. The flow regime may be in transition from a snowmelt dominated regime to a rainfall dominated regime. Results from future climate simulations of the A2 emission scenario indicate a projected increase of streamflow, while the snow depth and duration exhibit a decrease. Soil moisture response to future climate warming shows an overall increase with a greater likelihood of occurrences of higher soil moisture.

Inductively coupled plasma mass spectrometry is a well-established technique for the measurement of isotope ratios. Double focussing mass analysers enable increased resolution to be applied to separate spectroscopic interferences, or the use of multi-collector detection techniques for high precision isotope ratio determinations. For the Central Science Laboratory (CSL), trace elements team, methods were developed for Zn and Fe isotope ratio measurements in acid digested faecal samples from a human nutritional study. For Zn, a novel high resolutionlmulticollector combination was employed; for Fe a single collector, high resolution method was used. In both cases, samples from the nutritional study known to contain the analytes in natural isotopic abundance were used to correct for the mass bias. Two independent methods for determining Zn and Fe isotope ratios were used to validate the measurement strategies. The team at CSL are also involved in the authentication of food products. Isotope ratio and elemental concentration data were used to determine the geographical origin of rice samples, and to distinguish between traditional and modem Basmati rice grown in India and Pakistan. NERC Isotope Geosciences Laboratory are primarily concerned with the achievable accuracy and precision of an isotope ratio measurement. Use of a mass bias correction expression appropriate to the ICP-MS instrument is essential for high quality isotope ratio measurements. Cd and Sn were used to study the variation of the mass bias in a double focussing ICP-MS system with time, absolute mass and mass difference. It was proposed that mass bias should be considered as a result of the change in the instrument response with mass, and not a fundamental parameter in its own right. A method for determination of the best mass bias correction model for an individual instrument, through examination of the instrument response function was developed.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23165号 / 工博第4809号 / 新制||工||1752(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 立川 康人, 准教授 市川 温, 教授 田中 茂信 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM