By combining the high-order accuracy of spectral expansions with the locality and geometric flexibility of finite elements, spectral elements are an attractive option for the next generation of numerical climate models. Crucial to their construction is the configuration of nodes in an element — casual placement leads to polynomial fits exhibiting Runge phenomena manifested by wild spatial oscillations. I provide highorder triangular elements suitable for incorporation into existing spectral element codes. Constructed from a variety of measures of optimality, these nodes possess the best interpolation error norms discovered to date. Motivated by the need to accurately determine these error norms, I present an optimization method suitable for finding extrema in a triangle. It marries a branch and bound algorithm to a quadtree smoothing scheme. The resulting scheme is both robust and efficient, promising general applicability. In order to qualitatively evaluate these nodal distributions, I introduce the concept of a Lagrangian Voronoi tessellation. This partitioning of the triangle illustrates the regions over which each node dominates. I argue that distant and disconnected regions are undesirable as they exhibit a non-physical influence. Finally, I have discovered a link between point distributions in the simplex and on the hypersphere. Through a simple transformation, a distance metric is defined permitting the construction of Voronoi diagrams and the calculation of mesh norms.

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.

This thesis looks at the practice of climate modelling at the urban scale in relation to projections of future climate. It responds to the question of how climate models perform in a policy context, and how these models are translated in order to have agency at the urban scale. It considers the means and circumstances through which models are constructed to selectively represent urban realities and potential realities in order to explore and reshape the built environment in response to a changing climate. This thesis is concerned with an interdisciplinary area of research and practice, while at the same time it is based on methodologies originating in science and technology studies which were later applied to architecture and planning, geography, and urban studies. Fieldwork consisted of participant-observation and interviews with three groups of practitioners: firstly, climate impacts modellers forming part of the Adaptation and Resilience in a Changing Climate (ARCC) programme; secondly, planners and adaptation policymakers in the cities of Manchester and London; and thirdly, boundary organisations such as the UK Climate Impacts Programme (UKCIP). Project and climate policy material pertinent to these projects and the case study cities were also analysed in tandem. Of particular interest was the common space shared to researchers and stakeholders where modelling results were explained, contextualised, and interrogated for policy-relevant results. This took the form of stakeholder meetings in which the limits of the models in relation to policy demands could be articulated and mediated. In considering the agency of models in relation to uncertainties, it was found that although generated in a context of applied science, models had a limited effect on policy. As such, the salience of urban climatic risk-based assessment for urban planning is restrained, because it presupposes a quantitative understanding of climate impacts that is only slowly forming due to societal and governmental pressures. This can be related both to the nature of models as sites of exploration and experimentation, and to the distribution of expertise in the climate adaptation community. Although both the research and policy communities operate partly in a common space, models and their associated tools operate at a level of sophistication that policy-makers have difficulty comprehending and integrating into planning policy beyond the level of simple guidance and messages. Adaptation in practice is constrained by a limited understanding of climate uncertainties and urban climatology, evident through the present emphasis on catch-all solutions like green infrastructure and win-win solutions rather than the empowerment of actors and a corresponding distribution of adequate resources. An analysis is provided on the means by which models and maps can shape climate adaptation at scales relevant for cities, based on considerations of how models gain agency through forms of encoded expertise like maps and the types of interaction between science and policy that they imply.

Title: Analysis of Climate Model Outputs Author: RNDr. Zuzana Chládová E-mail: zuzana.chladova@gmail.com Department: Department of Meteorology and Environment Protection, Faculty of Mathematics and Physics, Charles University in Prague Supervisor: RNDr. Aleš Raidl, Ph.D. Supervisor's e-mail address: ales.raidl@mff.cuni.cz Consultant: doc. RNDr. Jaroslava Kalvová, CSc. Regional climate models are currently the most important tools regularly used for downscaling outputs of global climate models. This analysis compares control and future runs of the global climate models HadCM3, ECHAM5/OPYC3 and ARPÉGE/OPA and the regional climate models RCAO, RCA3, HIRHAM4, HIRHAM5 and ALADIN- CLIMATE/CZ with observed data and CRU data for the Czech Republic. In the period 1961-1990, the global climate models underestimated the air temperature in comparison with corresponding virtual time series representing real data; mean annual courses and variance of the temperature, on the other hand, were simulated satisfactorily. The results of the regional climate models showed overestimation of the model temperature in winter season, while in other seasons the model temperatures corresponded better with real values and the results of simulation were generally more accurate in comparison with global climate models. Concerning...

Climate science or climatology is the scientific study of the earth’s climate, where climate is the term representing weather conditions averaged over a period of time. Climate models are mathematical models used to quantitatively describe, simulate and study the interactions among the components of the climate system -atmosphere, ocean, land and sea-ice. CCSM (Community Climate System Model) is a state-of-the-art climate model, and a long-running coupled multicomponent parallel application involving component models for simulating the components of the climate system. Each of the component models is a large-scale parallel application, and the parallel components exchange climate data through a specialized component called coupler. Typical multi-century climate simulations using CCSM take several weeks or months to execute on most parallel systems. In this thesis, we study the applicability of a computational grid for effective execution of long-running coupled multi-component climate applications like CCSM. Initial studies of the application characteristics led us to develop a dynamic component extension strategy for temporal inter-component load-balancing. By means of experiments on different parallel platforms with different number of processors, we showed that using our strategy can lead to about 15% reduction and savings of several days in execution times of CCSM for 1000-year simulation runs. Our initial studies also indicated that unlike typical grid applications, CCSM has limits on scalability to very large number of processors and hence cannot directly benefit from the large number of processors on a computational grid. However, its long-running nature and the limits of execution imposed on jobs on most multi-user batch queueing systems, led us to investigate the benefits of its execution on a grid of batch systems. The idea is that multiple batch queues can improve the processor availability rate with respect to the application thereby possibly improving its effective throughput. We explored this idea in detail with simulation studies involving various system and application characteristics, and execution models. By conducting large number of simulations with different workload characteristics and queuing policies of the systems, processor allocations to components of the application, distributions of the components to the batch systems and inter-cluster bandwidths, we showed that multiple batch executions lead to upto 55% average increase in throughput over single batch executions for long-running CCSM. Having convinced ourselves of possible advantages in performance, we then ventured to construct an application-level middleware framework. Our framework supports long duration execution of multi-component applications spanning multiple submissions to queues on multiple batch systems. It coordinates the distribution, execution, rescheduling, migration and restart of the application components across resources on different sites. It also addresses challenges including execution time limits for jobs, and differences in job-startup times corresponding to different components. Further, within the framework, we developed robust rescheduling policies that decide when and where to reschedule the components to the available resources based on the application execution characteristics and queue dynamics. Our grid middleware framework resulted in multi-site executions that provided larger application throughput than single-site executions, typically performed by climate scientists, and also removed the bottlenecks associated with a single system execution. We used this framework for long-running executions of CCSM to study the effect of increased black carbon aerosols and dust aerosols on the Indian monsoons. Black Carbon aerosols are essentially of anthropogenic origin and occur due to improper burning of fossil fuels, and dust is a naturally occurring aerosol. The concentrations of both these aerosols is high over the Indian region. We study the impact of these aerosols on precipitation and sea surface temperature (SST) through multi-decadal simulations conducted with our grid-enabled climate system model. Our observations indicated that increasing the concentrations of aerosols leads to an increase in precipitation in the central and eastern parts of India, and a decrease in SST over most of Indian ocean.

The representation of physical processes by a climate model depends on its structure, numerical schemes, physical parameterizations and resolution, with initial conditions and future emission scenarios further affecting the output. The extent to which climate models agree is therefore of great interest, often with greater confidence in robust results across models. This has led to climate model output being analysed as ensembles rather than in isolation, and quantifying the sources of variation across these ensembles is the aim of many recent studies. Statistical attempts to do this include the use of variants of the mixed-effects analysis of variance or covariance (mixed-effects ANOVA/ANCOVA). This work usually focuses on identifying variation in a variable of interest that is due to differences in model structure, carbon emissions scenario, etc. Quantifying such variation is important in determining where models agree or disagree, but further statistical approaches can be used to diagnose the reasons behind the agreements and disagreements by representing the physical processes within the climate models. A process-based approach is presented that uses simulation with statistical models to perform a global sensitivity analysis and quantify the sources of variation in multi-model ensembles. This approach is a general framework that can be used with any generalised linear mixed model (GLMM), which makes it applicable to use with statistical models designed to represent (sometimes complex) physical relationships within different climate models. The method decomposes the variation in the response variable into variation due to 1) temporal variation in the driving variables, 2) variation across ensemble members in the distributions of the driving variables, 3) variation across ensemble members in the relationship between the response and the driving variables, and 4) variation unexplained by the driving variables. The method is used to quantify the extent to which, and diagnose why, precipitation varies across and within the members of two different climate model ensembles on various different spatial and temporal scales. Change in temperature in response to increased CO2 is related to change in global-mean annual-mean precipitation in a multi-model ensemble of general circulation models (GCMs). A total of 46% of the variation in the change in precipitation in the ensemble is found to be due to the differences between the GCMs, largely because the distribution of the changes in temperature varies greatly across different GCMs. The total variation in the annual-mean change in precipitation that is due to the differences between the GCMs depends on the area over which the precipitation is averaged, and can be as high as 63%. The second climate model ensemble is a perturbed physics ensemble using a regional climate model (RCM). This ensemble is used for three different applications. Firstly, by using lapse rate, saturation specific humidity and relative humidity as drivers of daily-total summer convective precipitation at the grid-point level over southern Britain, up to 8% of the variation in the convective precipitation is found to be due to the uncertainty in RCM parameters. This is largely because given atmospheric conditions lead to different rates of precipitation in different ensemble members. This could not be detected by analysing only the variation across the ensemble members in mean precipitation rate (precipitation bias). Secondly, summer-total precipitation at the grid-point level over the British Isles is used to show how the values of the RCM parameters can be incorporated into a GLMM to quantify the variation in precipitation due to perturbing each individual RCM parameter. Substantial spatial variation is found in the effect on precipitation of perturbing different RCM parameters. Thirdly, the method is extended to focus on extreme events, and the simulation of extreme winter pentad (five-day mean) precipitation events averaged over the British Isles is found to be robust to the uncertainty in RCM parameters.

Large explosive volcanic eruptions inject SO2 into the stratosphere where it is oxidised to sulphate aerosols which reflect sunlight. This causes a reduction in global temperature and precipitation lasting a few years. Here the robust features of this precipitation response are investigated, using superposed epoch analysis that combines results from multiple eruptions. The precipitation response is first analysed using the climate model HadCM3 compared to two gauge based land precipitation datasets. The analysis is then extended to a large suite of state-of-the art climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). This is the first multi-model study focusing on the precipitation response to volcanoes. The large ensemble allows analysis of a short satellite based dataset which includes ocean coverage. Finally the response of major world rivers to eruptions is examined using historical records. Whilst previous studies focus on the response of just a few rivers or global discharge to single eruptions, here the response of 50 major world rivers is averaged across multiple eruptions. Results are applicable in predicting the precipitation response to future eruptions and to geoengineering schemes that seek to counteract global warming through reducing incoming solar radiation. The main model-simulated features of the precipitation response include a significant global drying over both land and ocean, which is dominated by the wet tropical regions, whilst the dry tropical ocean regions get significantly wetter following eruptions. Monsoon rainfall decreases, whilst in response to individual eruptions the Intertropical Convergence Zone shifts away from the hemisphere with the greater concentration of volcanic aerosols. The ocean precipitation response is longer lived than that over land and correlates with near surface air temperature, whilst the land response correlates with aerosol optical depth and a reduction in land-ocean temperature gradient Many of these modelled features are also seen in observational data, including the decrease in global mean and wet tropical regions precipitation over land and the increase of precipitation over dry tropical ocean regions, all of which are significant in the boreal cold season. The land precipitation response features were robust to choice of dataset. Removing the influence of the El Nino Southern Oscillation (ENSO) reduces the magnitude of the volcanic response, as several recent eruptions coincided with El Nino events. However, results generally remain significant after subtraction of ENSO, at least in the cold season. Over ocean, observed results only match model expectations in the cold season, whilst data are noisy in the warm season. Results are too noisy in both seasons to confirm whether a long ocean precipitation response occurs. Spatial patterns of precipitation response agree well between observational datasets, including a decrease in precipitation over most monsoon regions. A positive North Atlantic Oscillation-like precipitation response can be seen in all datasets in boreal winter, but this is not captured by the models. A detection analysis is performed that builds on previous detection studies by focusing specifically on the influence of volcanoes. The influence of volcanism on precipitation is detectable using all three observational datasets in boreal winter, including for the first time in a dataset with ocean coverage, and marginally detectable in summer. However, the models underestimate the size of the winter response, with the discrepancy originating in the wet tropics. Finally, the number of major rivers that undergo a significant change in discharge following eruptions is slightly higher than expected by chance, including decreased flow in the Amazon, Congo, Nile, Orange, Ob and Yenisey. This proportion increases when only large or less humanly influenced basins are considered. Results are clearer when neighbouring basins are combined that undergo the same sign of CMIP5 simulated precipitation response. In this way a significant reduction in flow is detected for northern South American, central African and less robustly for high-latitude Asian rivers, along with a significant increase for southern South American and SW North American rivers, as expected from the model simulated precipitation response.

<p>This dissertation, titled “Uncertainty Discourse: Climate Models, Gender, and Environmental Literature in the Anthropocene,” takes a feminist approach to sustainability through the lens of climate science and English-language environmental fiction. I diagnose the appearance of what I call a discourse of uncertainty, which describes new constitutions of thought and social organization emerging in response to the structural uncertainties that characterize climate change. I root this discourse in the scientific practice of climate modeling, by which scientists calculate the probability, or degrees of uncertainty, of future weather scenarios. Though climate models inform socio-political preparations for a climate-changed future, their utility has gone unheeded in the humanities. I fill this gap by placing scientific and literary depictions of uncertainty into conversation to explore their epistemological and ethical implications for a climate-changing future through issues such as gender and representation, politics and sustainability, and knowledge and time. I not only trace how uncertainty is manifested in contemporary environmental literature, such as Ian McEwan’s <i>Solar</i> (2010) and Barbara Kingsolver’s <i>Flight Behavior </i>(2012), but also consider the drama of South Asian women playwrights alongside the works of feminist scholars, philosophers, and activists.</p>

Sclerochronology was conducted on the pedicle valves of four Sakmarian-age brachiopods (Squamaria moorei) to constrain climate model predictions of temperature seasonality along western equatorial Pangaea (WEP). The brachiopods are from a Lower Permian section within the Robledo Mountains, NM, and they reveal seasonal trends of δ18O and temperature for that interval that suggest global warming and moderation of seasonality. Elemental and SEM analyses verified the specimens were well preserved. δ18O profiles show a relatively rapid and consistent two-year growth rate corresponding to the organism's juvenile stage, followed by a slower, seasonal growth rate corresponding to the organism's sexually mature stage typical of most organismal growth. Their initial two-year cycles show consistent, high-amplitude profiles that captured virtually complete records of annual δ18O values, and these profiles were used for seasonality interpretations. The specimen from the stratigraphically lowest layer shows δ18O values varying from -4.26 to -2.17 minimum winter temperatures (MWT) and maximum summer temperatures (MST) of 25.2C and 35.7C, respectively; and a seasonal temperature variation ΔTs of 10.0C. The overlying horizon yielded two specimens showing δ18O values ranging from a minimum of -4.54 to a maximum of -2.79; MWT ranging from 28.2 to 29.6C; MST ranging from 34.9 to 37.2C; and ΔTs ranging from 6.7 to 7.6C. The uppermost layer yielded a specimen that shows δ18O values ranging from -4.49 to -3.03; MWT of 31.3C; MST of 37.0C; and ΔTs of 5.7C. The specimens show overall high seasonality for an equatorial regime, but the general trend shows increasing winter temperatures and a moderation of seasonality. The data supports climate-model predictions for the Permian of more equable temperatures, higher winter temperatures, and decreased seasonality. Three numerical climate models of Permian temperatures were evaluated against the brachiopod data, and their model predictions for ΔTs along WEP range from as high as 10C to as low as C. The models were supported somewhat by the independently derived temperature proxy data measured in this study.

A census of 24 coupled (CMIP) and 13 uncoupled (AMIP) models from the Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (AR4) were compared with observations and reanalysis to show varied ability of the models to simulate Caribbean precipitation and mechanisms related to precipitation in the region. Not only were errors seen in the annual mean, with CMIP models underestimating both rainfall and sea surface temperature (SST) and AMIP models overestimating rainfall, the annual cycle was also incorrect. Large overestimates of precipitation at all SSTs (and particularly above 28 degrees C) and at vertical circulations less than -10 hPa/day (the deep convective regime) were inherent in the atmospheric models with models using spectral type convective parameterizations performing best. In coupled models, however, errors in the frequency of occurrence of SSTs (the distribution is cold biased) and deep convective vertical circulations (reduced frequency) lead to an underestimation of Caribbean mean precipitation. On daily timescales, the models were shown to produce too frequent light rainfall amounts (especially less than 1 mm/day) and dry extremes and too few heavy rainfall amounts and wet extremes. The simulation of the mid-summer drought (MSD) proved a challenge for the models, despite their ability to produce a Caribbean low-level jet (CLLJ) in the correct location. Errors in the CLLJ, such as too strong magnitude and weak semi-annual cycle, were worse in the CMIP models and were attributed to problems with the location and seasonal evolution of the North Atlantic subtropical high (NASH) in both CMIP and AMIP models. Despite these discrepancies between models and observations, the ability of the models to simulate the correlation between the CLLJ and precipitation varied based on season and region, with the connection with United States precipitation particularly problematic in the AMIP simulations. An observational study of intraseasonal precipitation in the Caribbean showed an explicit connection between the Madden-Julian oscillation (MJO) and Caribbean precipitation for the first time. Precipitation anomalies up to 50 percent above (below) the annual mean are observed in phases 1 and 2 (5 and 6) of the MJO and are related to changes in the CLLJ, that is also modulated by the MJO. Considerable progress has been made on identifying both problems and successes in the simulation of Caribbean climate in general circulation models, but many areas still require investigation.