A procedure for model-assisted climate impact assessment is developed. The approach combines data from observations and atmospheric general circulation models (GCNs), and provides the basis for a potentially valuable means of using information derived from GCMs for climate impact assessments on local scales. The first component of this procedure is an extension of the 'climate inverse' method of Kim al. (1984). Daily mesoscale temperature and precipitation values are stochastically specifed on the basis of observational data representing the average over an area corresponding to a GCN grid element. Synthetic local data sets generated in this manner resemble the corresponding observations with respect to various spatial and temporal statistical measures. A method for extrapolation to grid-scale 'scenarios' of a changed climate on the basis of control and experimental integrations of a GCM, in conjunction with observational data, is also presented. The statistical characteristics of daily time series from each of these data sources are portrayed in terms of the parameters of a multivariate time-domain stochastic model. Significant differences between the model data sets are applied to the corresponding parameters derived from the observations, and synthetic data Bets representing the inferred changed climate are generated using Monte-Carlo simulations. The use of the procedure is illustrated in a case study. The potential climatic impacts of a doubling of atmospheric carbon dioxide concentrations on three important North American grain cropping regions is investigated using two 'physiological' crop models. Although the specific results must be interpreted with caution, they are moderately optimistic and demonstrate possible means by which agricultural production may adapt to climatic changes. / Graduation date: 1987

Digitized by Kansas Correctional Industries

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

The Lower Fraser Valley, straddling the USA-Canada border in western North America, often experiences episodes of elevated tropospheric ozone in summertime. The meteorology governing those episode days is characterized by a stagnant high pressure system, light wind, strong insolation and the occurrence of sea breezes. To shed some light on the mesometeorology of ozone episodes in the LFV, this study employed the Regional Atmospheric Modelling System from Colorado State University, a non hydrostatic, three-dimensional mesoscale modelling system to simulate the detailed structure of air flows over the valley for one specific episode day. Significant code modifications have been made to enhance the model's ability to represent surface energy fluxes and predict surface temperatures in the surface of complex terrain and land-use patterns. Evaluation of the model performance was made against an extensive set of observation son the episode day. Pollutant transport on the modelling day was explored with a Lagrangian particle dispersion model. Specifically, forward trajectories were calculated for particles released at various locations in the Lower Fraser Valley and at different times. A systematic qualitative and quantitative model evaluation with the statistical method of Willmott showed that the model could adequately simulate the observed sea breeze another interactive terrain-induced flows such as slope wind and channel flow. Trajectory studies indicated that pollutant recirculations occurred largely from sources originating in the northwest part of the valley where most emission sources are located, and ended in a region where the highest ozone concentrations were observed. Recirculations were due to pollutants traveling with the interactive flows of sea breeze and upslope winds, and later being captured and directed back to the valley by the down slope winds. Particles released in other part of the valley all travelled outside the valley, having a minimal contribution to the episode buildup.

The Lower Fraser Valley, straddling the USA-Canada border in western North America, often experiences episodes of elevated tropospheric ozone in summertime. The meteorology governing those episode days is characterized by a stagnant high pressure system, light wind, strong insolation and the occurrence of sea breezes. To shed some light on the mesometeorology of ozone episodes in the LFV, this study employed the Regional Atmospheric Modelling System from Colorado State University, a non hydrostatic, three-dimensional mesoscale modelling system to simulate the detailed structure of air flows over the valley for one specific episode day. Significant code modifications have been made to enhance the model's ability to represent surface energy fluxes and predict surface temperatures in the surface of complex terrain and land-use patterns. Evaluation of the model performance was made against an extensive set of observation son the episode day. Pollutant transport on the modelling day was explored with a Lagrangian particle dispersion model. Specifically, forward trajectories were calculated for particles released at various locations in the Lower Fraser Valley and at different times. A systematic qualitative and quantitative model evaluation with the statistical method of Willmott showed that the model could adequately simulate the observed sea breeze another interactive terrain-induced flows such as slope wind and channel flow. Trajectory studies indicated that pollutant recirculations occurred largely from sources originating in the northwest part of the valley where most emission sources are located, and ended in a region where the highest ozone concentrations were observed. Recirculations were due to pollutants traveling with the interactive flows of sea breeze and upslope winds, and later being captured and directed back to the valley by the down slope winds. Particles released in other part of the valley all travelled outside the valley, having a minimal contribution to the episode buildup. / Arts, Faculty of / Geography, Department of / Graduate

Land-atmosphere interaction includes complex feedbacks among radiative, hydrological, and ecological processes, and the understanding of it is hindered by many factors such as the heterogeneity of land surface properties, the chaotic nature of the atmosphere, and the lack of observational data. In this study, several different methods are used to investigate the land-atmosphere interaction processes and their relationship with climate variability. Firstly, a simple one-dimensional model is developed to simulate the dominant soil-vegetation-atmosphere interaction processes in the warm climate. Although the physical processes are described coarsely, the model can be more easily used to find some relationships which may be drown out or distorted by noise. The influence of land on climate variability mainly lies in it memory, which is greatly related with the atmospheric forcing, so this model is used to investigate the influence of different forcing strengths on land-atmosphere interaction and its difference at different land covers. The findings from the simple model can provide guidance for other studies. The second part of the study compares a lagged soil moisture-precipitation (S-P) correlation (soil moisture in current day and precipitation in future 30 days) in three atmospheric reanalysis products (ERA-40, NCEP/DOE reanalysis-2, and North American Regional Reanalysis (NARR)), Global Soil Wetness Project Phase 2 (GSWP-2) data, and NCAR CAM3 simulations. Different datasets and model simulations come to a similar negative-dominant S-P correlation pattern. This is different from the traditional view that the soil moisture should have positive influence on future precipitation. Further analysis shows that this correlation pattern is not caused by the soil moisture feedback but due to the combined effect of the precipitation oscillation and the memory of soil moisture. Theoretical analysis confirms the above results and finds that the precipitation time series with the strongest oscillation at 32-60 day period is most likely to induce a significantly negative S-P correlation, and regions with longer soil water retention time are more likely to have a significantly negative S-P correlation. This study illustrates that a lagged correlation does not always indicate a causal relation.

Monthly water balance models are important tools for hydrological impact assessment of climate change. Traditionally monthly models adopt a conceptual, lumped-parameter approach. Based on an extensive survey and review of existing monthly water balance models, six models with different conceptualization and structure, i.e., Thomthwaite-Mather, Belgium, Xinanjiang, Guo, WatBal and Schaake, were compared through calibration and validation using observed data of hydrology and climate of 1960-1988 in the Dongjiang basin. The model comparison offered insights for the development of a monthly distributed model which integrates the spatial variations of basin terrain and rainfall into runoff simulation. An innovative feature of the new model is that the spatial distribution of soil moisture capacity which is described as a parabolic curve in Xinanjiang model is represented by a cumulative frequency curve of index of relative difficulty of runoff generation based on the concept of topographic index in TOPMODEL. The calibration and validation results show that the developed model with only three parameters is suitable for monthly runoff simulation in the Dongjiang basin. / The developed model was applied to evaluate the changes in water availability in the Dongjiang basin under hypothetical climate change scenarios and those derived from projections of three General Circulation Models (GCM), i.e., CGCM1, CSIRO and ECHAM4. Sensitivity analyses based on hypothetical scenarios suggest that climatic change has greater effects on runoff than on soil moisture and greater effects on water availability in dry months than in wet months. The effects of precipitation changes on the amount of runoff and soil moisture can be characterized by a magnification factor whereas temperature increases alone produce negligible effects. Hydrological simulation with inputs of three GCM-generated scenarios indicates that annual and rain-season runoff will increase by 0.3°io to 13.9% and 7.6% to 12.0%, respectively, by the 2050s. Dry-season runoff will change between -23.2% and +26.4%. Average annual and dry-season soil moisture will decrease by 1.3% to 6.9% and 1.0% to 8.1%, respectively. Soil moisture will demonstrate little change in rain-season. Increase in annual runoff and reduction in annual soil moisture will be apparent over the whole basin, but there is relatively little consistency among the three GCM-generated scenarios as to the magnitudes of spatial change in runoff and soil moisture. Although these results are not definitive statements as to what will happen to runoff and soil moisture in the Dongjiang basin, they rather have significant implications for the study of response strategies of water supply and flood control to climate change. / Jiang Tao. / "July 2005." / Advisers: Chen Yongqin; Lam Kin-che. / Source: Dissertation Abstracts International, Volume: 67-01, Section: B, page: 0149. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 174-190). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / School code: 1307.

One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena.