Climate models are software tools that simulate the climate system and require evaluation to assess their skill, guide their development, and assist in selecting model simulations from among the many different ones available. There are a variety of methods and approaches that can be used to evaluate models. But there is no one best method and many possible and valid approaches exist. Models contain inherent uncertainties which complicate their evaluation, and include limitations in the knowledge of climate process dynamics and structural errors in constructing the models. Similar to the multiplicity of methods for the evaluation of model simulations, there also exist many possible approaches to addressing these sources of uncertainty. The challenge with uncertainty, is the difficulty in disaggregating it from the underlying element of legitimate chaotic behaviour in complex systems. In response, this dissertation is primarily one of methodological development to contribute to new ways of addressing the model evaluation challenge. The work defines and demonstrates a new evaluation method which complements the existing toolset. Specifically, the method defines a model performance metric that focuses on the extent to which a model is able to simulate global modes of climate variability (modes, e.g.: ENSO) evident in the observed climate data. Modes are one aspect of the climate that can be evaluated and are fundamental to model skill. Therefore their credible simulation is a necessary (but not sufficient) condition to ensuring that models are producing the right result (appropriate variability on the range of spatial and temporal scales) for the right reason. By ranking models by this metric of their skill in capturing fundamental global modes, poorly performing model simulations can be identified for potential exclusion (discounted). This metric therefore serves as a potential method to assist in the management of uncertainty when assessing multi-model data. The method develops a novel application of Independent Component Analysis (ICA). ICA is used to find representations of modes in a record of the present day climate (represented by reanalysis data), and then their degree of manifestation in global models is assessed. Recognising the large volume of model data (highly autocorrelated in space and time) the technique includes a data reduction technique to facilitate the evaluation of multiple model simulations. The technique also includes a novel measure of variance to differentiate it from a similar technique (Principal Component Analysis), and offers an approach to improve the consistency of results (signals) when using an unmixing matrix initialized with random values. As reanalysis data is itself a model product (constrained by observations), the performance metric is tested for its strength in discriminating modes by using two different reanalysis datasets and a dataset containing only Gaussian noise. The metric is found to perform predictably, and clearly demonstrates the ability to discriminate signal from noise when using geopotential height (GHT, 700mb and 500mb) and near surface air temperature data (TAS). The dependency of model performance on the variable measured by any metric can be a problem for model evaluation, as it introduces the choice of which variable should be measured to assess model performance. The ICA-based metric is found to be slightly less sensitive to a change in model rank between GHT (700mb) and TAS, compared to a similar novel variance metric (Fourier Distance) and a mean climate metric (bias). The ICA application is also found to produce plausible representations of modes (static maps), while a direct association to known modes is left for future work due to inherit complexities. The plausibility, consistency, and rank sensitivity of the novel application of ICA, suggests it has value in assisting the evaluation of multi-model datasets and the ensemble members for any one model.

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Simple one-dimensional heat balance equations have been used to understand climate concepts since the 1960s, when a class of models was developed known as energy balance models (EBMs). EBMs use the growth or loss of polar surface ice as a climatic feedback, giving rise to surprisingly complex non-linear behaviours. One aspect of EBMs that has been relatively poorly examined is the effects of feedbacks caused by the other two phases of water in Earth’s climate other than ice: water clouds and water vapour. Cloud and water vapour play a critical role in the energy balance of Earth’s climate, and yet are some of the least well understood elements of the global climate system. This thesis explores the behaviour and interrelationships of climatic feedbacks caused by water in all three phases as it exists in the climate: surface ice caps, water vapour, and liquid water clouds. A two-layered EBM was modified with parameterizations of water vapour and liquid water clouds in order to conduct experiments. Three variants of the model were produced, each with progressively more water feedbacks than the last: a 1 phase model (with only surface ice feedback), a 2 phase model (with surface ice and water vapour) and a 3 phase model (with surface ice, water vapour, and cloud). The models were found to give generally realistic results, but with an underestimation of water vapour density, which in turn reduced the generated cloud fraction in the 3 phase model. Thus, the impacts of these extra feedbacks were likely to be underestimated in the analysis in general. The sensitivity of the model to several prognostic variables was studied by observing the changes in the model to a range of each variable. The 3 phase model was less sensitive to changes to the solar constant, S0, which measures incoming solar radiation, than the 1 phase model. This was probably caused by cloud reflecting and absorbing some radiation from the sun that would have otherwise reached the surface, changing the ratio of atmospheric heat transport to surface heat transport from 2.4953 for the 1 phase model to 2.0626 for the 3 phase model. Changing surface and ice albedo values resulted in changes in the model’s stability. The model was found to be insensitive to changes in surface humidity that drives the amount of water vapour the system has available, due to underestimation of water vapour in the model. The stability of the model was examined, and the 1 phase model was found to respond faster to changes in S0 than the 3 phase model. The model was tested for hysteresis, which was confirmed for all three model variants. The 1 phase model showed less stability then the 3 phase model as S0 was increased, but both models were similarly stable as S0 was decreased.

The thesis consists of three projects. Each of these projects is a diagnostic study of the interaction between strong convective events and the background atmosphere. In all projects, we use a satellite rainfall dataset to identify strong rain events. We then use radiosonde soundings to generate composite anomaly patterns of meteorological variables about the strong rain events. In Project 1, we examine temperature, relative humidity, and divergence anomalies about strong convective events in the Western Tropical Pacific. A low-level convergence coupled to a midlevel divergence develops prior to peak rainfall. A midlevel convergence coupled to a low-level divergence develops after peak rainfall. Strong surface cold pools develop in response to high rainfall. Observations were compared to models and reanalyses. In general, models and reanalyses do not fully represent the timing, strength, and altitude of the mid-level convergence and divergence features. The surface cold anomaly is also underestimated in models. These discrepancies suggest that the mesoscale downward transport of mid-level air into the boundary layer in models may be too weak. In Project 2, we investigate the impact of convection on the background distribution of a chemical tracer (ozone). Negative ozone anomalies and higher frequency of midlevel cloud tops occur in a layer between 3 and 8 km prior to peak rainfall. Negative ozone anomalies in the upper troposphere develop in response to high rainfall. Chemistry transport model simulations also exhibit negative ozone anomalies at upper and midlevels. However, the ozone anomalies in the model are symmetric about peak rainfall and are more persistent than observations. In Project 3, we identify regional variations in the interaction between convection and the background atmosphere. In all four regions, deep convection imposes cooling in the lower and warming in the upper troposphere. In mid-latitudes, convection is associated with stronger anomalies in surface pressure, geopotential height, and CAPE. Over land, a low-level warm anomaly develops prior to peak rainfall and the surface cold pool that develops during peak rainfall is more persistent. The PV generated prior to peak rainfall, is advected towards the surface after peak rainfall and may play a role in hurricane genesis.

Includes bibliographical references / Changes in precipitation extremes are projected by many global climate models as a response to greenhouse gas increases, and such changes will have significant environmental and social impacts. These impacts are a function of exposure and vulnerability. Hence there is critical need to understand the nature of weather and climate extremes. Results from an ensemble of regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project are used to investigate projected changes in extreme precipitation characteristics over southern Africa for the middle (2036-2065) and late century (2069-2098) under the representative concentration pathway 4.5 (RCP4.5) and 8.5 (RCP8.5). Two approaches are followed to identify and analyze extreme precipitation events. First, indices for extreme events, which capture moderate extreme events, are calculated on the basis of model data and are compared with indices from two observational gridded datasets at annual basis. The second approach is based on extreme value theory. Here, the Generalized Extreme Value distribution (GEV) is fitted to annual maxima precipitation by a L-moments method. The 20-year return values are analyzed for present and future climate conditions. The physical drivers of the projected change are evaluated by examining the models ability to simulate circulation patterns over the regions with the aid of Self-Organizing Maps (SOM).

The tracks of westward-propagating synoptic disturbances across the Intra-Americas Sea (IAS) and far-eastern Pacific, known as easterly waves or tropical depression (TD) waves, are an important feature of the region's climate. They are associated with heavy rainfall events, seed the majority of tropical cyclones, and contribute to the mean rainfall across the region. This study examines the ability of current climate models (CMIP5) to simulate TD-wave activity and associated environmental factors across the IAS and far-eastern Pacific as compared to reanalysis. Model projections for the future are then compared with the historical model experiment to investigate the southward shift in CMIP5 track density and the environmental factors that may contribute to it. While historical biases in TD-wave track-density patterns are well correlated with model biases in sea surface temperature and midlevel moisture, the projected southward shift of the TD track density by the end of the twenty-first century in CMIP5 models is best correlated with changes in deep wind shear and midlevel moisture. In addition, the genesis potential index is found to be a good indicator of both present and future regions of high TD-wave track density for the models in this region. This last result may be useful for understanding the more complex relationship between tropical cyclones and this index in models found in other studies.

Multiple global climate models suggest that the Canadian Prairies will experience temperature increases due to climate warming. This could influence pasture and grazing production. Three climate scenarios CGCM2 A21, CSIROMk2 B11 and HadCM3 A21 were used to predict daily weather data to 2099 and incorporated into the GrassGro decision support tool to project pastoral production during 30-year increments, 2010 to 2099. Simulations were compared with the World Meterological Organization baseline years, 1961-1990 at two sites (Saskatoon and Melfort) and two soil textures (loam topsoil / loam subsoil and sandy-loam / sandy-clay-loam). Two tame grasses [crested wheatgrass (CWG; <i>Agropyron cristatum</i>) and hybrid bromegrass (HBG; <i>Bromus inermis x Bromus riparius</i>) and a mixed native pasture (<i>Festuca hallii; Elymus lanceolatus; Pascopyrum smithii; Nassella viridula</i>) were studied at each location.<p> Soil moisture was greater for loam/loam than sandy-loam/sandy-clay-loam resulting in more plant available moisture in all climate scenarios at both locations. However, plant available moisture alone was unable to explain changes in pasture dry matter (DM) production. The results projected from CGCM2 A21 were more favorable to plant and livestock production than those of CSIROMk2 B11 and HadCM3 A21. CGCM2 A21 simulated increases in mean DM production of HBG at both locations during spring each 30-yr period (P<0.05) but an overall decline (P<0.05) in mean average daily gain (ADG) of steers at Melfort, whereas at Saskatoon there was an increase in ADG (P<0.05). CWG decreased in DM production at Melfort during summer and increased at Saskatoon with CGCM2 A21 but there was an overall decrease in ADG of steers during each 30-yr period relative to baseline. It was concluded that HBG was better able to stabilize production under various future climatic conditions than CWG. There was a shift in species dominance from <i>Festuca hallii</i> to <i>Elymus lanceolatus</i> in the mixed native pasture at both locations associated with the increase in summer temperatures. This suggests that various grass species may respond differently to climate change.These results indicate that climate change will cause significant changes in soil moisture, productivity and quality of tame pastures, liveweight of grazing cattle and species composition of native pasture.

Multiple global climate models suggest that the Canadian Prairies will experience temperature increases due to climate warming. This could influence pasture and grazing production. Three climate scenarios CGCM2 A21, CSIROMk2 B11 and HadCM3 A21 were used to predict daily weather data to 2099 and incorporated into the GrassGro decision support tool to project pastoral production during 30-year increments, 2010 to 2099. Simulations were compared with the World Meterological Organization baseline years, 1961-1990 at two sites (Saskatoon and Melfort) and two soil textures (loam topsoil / loam subsoil and sandy-loam / sandy-clay-loam). Two tame grasses [crested wheatgrass (CWG; <i>Agropyron cristatum</i>) and hybrid bromegrass (HBG; <i>Bromus inermis x Bromus riparius</i>) and a mixed native pasture (<i>Festuca hallii; Elymus lanceolatus; Pascopyrum smithii; Nassella viridula</i>) were studied at each location.<p> Soil moisture was greater for loam/loam than sandy-loam/sandy-clay-loam resulting in more plant available moisture in all climate scenarios at both locations. However, plant available moisture alone was unable to explain changes in pasture dry matter (DM) production. The results projected from CGCM2 A21 were more favorable to plant and livestock production than those of CSIROMk2 B11 and HadCM3 A21. CGCM2 A21 simulated increases in mean DM production of HBG at both locations during spring each 30-yr period (P<0.05) but an overall decline (P<0.05) in mean average daily gain (ADG) of steers at Melfort, whereas at Saskatoon there was an increase in ADG (P<0.05). CWG decreased in DM production at Melfort during summer and increased at Saskatoon with CGCM2 A21 but there was an overall decrease in ADG of steers during each 30-yr period relative to baseline. It was concluded that HBG was better able to stabilize production under various future climatic conditions than CWG. There was a shift in species dominance from <i>Festuca hallii</i> to <i>Elymus lanceolatus</i> in the mixed native pasture at both locations associated with the increase in summer temperatures. This suggests that various grass species may respond differently to climate change.These results indicate that climate change will cause significant changes in soil moisture, productivity and quality of tame pastures, liveweight of grazing cattle and species composition of native pasture.

This thesis examines the importance of energy and momentum consistency in subgrid-scale parameterization for climate models. It is divided into two parts according to the two aspects of the problem that are investigated, namely the importance of momentum conservation alone and the consistency between energy and momentum conservation. The first part addresses the importance of momentum conservation alone. Using a zonally-symmetric model, it is shown that violating momentum conservation in the parameterization of gravity wave drag leads to large errors and non-robustness of the response to an imposed radiative perturbation in the middle atmosphere. Using the Canadian Middle Atmosphere Model, a three-dimensional climate model, it is shown that violating momentum conservation, by allowing gravity wave momentum flux to escape through the model lid, leads to large errors in the mean climate when the model lid is placed at 10 hPa. When the model lid is placed at 0.001 hPa the errors due to nonconservation are minimal. When the 10 hPa climate is perturbed by idealized ozone depletion in the southern hemisphere, nonconservation is found to significantly alter the polar temperature and surface responses. Overall, momentum conservation ensures a better agreement between the 10 hPa and the 0.001 hPa climates. The second part addresses the self-consistency of energy and momentum conservation. Using Hamiltonian geophysical fluid dynamics, pseudoenergy and pseudomomentum wave-activity conservation laws are derived for the subgrid-scale dynamics. Noether’s theorem is used to derive a relationship between the wave-activity fluxes, which represents a generalization of the first Eliassen-Palm theorem. Using multiple scale asymptotics a theoretical framework for subgrid-scale parameterization is built which consistently conserves both energy and momentum and respects the second law of thermodynamics. The framework couples a hydrostatic resolved-scale flow to a non-hydrostatic subgrid-scale flow. The transfers of energy and momentum between the two scales are understood using the subgrid-scale wave-activity conservation laws, whose relationships with the resolved-scale dynamics represent generalized non-acceleration theorems. The derived relationship between the wave-activity fluxes — which represents a generalization of the second Eliassen-Palm theorem — is key to ensuring consistency between energy and momentum conservation. The framework includes a consistent formulation of heating and entropy production due to kinetic energy dissipation.

Climate change is an undeniable global event. Widely believed to be caused by human activities that release greenhouse gases, this change in climate will occur over a very short period of time when it is compared to natural climate variations. The effect the change will have on environments is somewhat unknown and any predictions are best guess scenarios. In British Columbia the mountain pine beetle has destroyed millions of pine trees but the infestation is not yet done. This may be one of the early symptoms of global warming on the British Columbia environment. Climate change will warm temperatures in BC and increase the annual precipitation. These warmer temperatures allow for longer growing seasons and in many cases, more favorable growing conditions. Studies on Sitka Spruce have found increases in volume growth by as much as 20% and growth in the boreal has been found to increase with higher CO2 levels and warmer temperatures. Climate change does not have to mean beetle epidemics and dying ecosystems. With proactive management British Columbia may actually find benefits in a warming climate.