In this study the natural variability of Southern Ocean water masses on interannual to centennial time scales is investigated using a long-term integration of the Commonwealth Scientic and Industrial Research Organisation (CSIRO) coupled climate model. We focus our attention on analysing the variability of Antarctic IntermediateWater (AAIW), Circumpolar DeepWater (CDW), and Antarctic Bottom Water (AABW). We present an analysis of the dominant modes of temperature and salinity (T - S) variability within these water masses. Climate signals are detected and analysed as they get transmitted into the interior from the water mass formation regions. Eastward propagating wavenumber-1, -2, and -3 signals are identied using a complex empirical orthogonal function (CEOF) analysis along the core of the AAIW layer. Variability in air-sea heat uxes and ice meltwater rates are shown by heat and salt budget analyses to control variability of Antarctic Surface Water where density surfaces associated with AAIW outcrop. The dominant mode in the CDW layer is found to exhibit an interbasin-scale of variability originating from the North Atlantic, and propagating southward into the Southern Ocean. Salinity dipole anomalies appear to propagate around the Atlantic meridional overturning circulation with the strengthening and weakening of North Atlantic Deep Water formation. In the AABW layer, T - S anomalies are shown to originate from the southwestern Weddell Sea, driven by salinity variations and convective overturning in the region. It is also demonstrated that the model exhibits spatial patterns of T - S variability for the most part consistent with limited observational record in the Southern Hemisphere. However, some observations of decadal T - S changes are found to be beyond that seen in the model in its unperturbed state. We further assess sea surface temperature (SST) variability modes in the Indian Ocean on interannual time scales in the CSIRO model and in reanalysis data. The emergence of a meridional SST dipole during years of southwest Western Australian rainfall extremes is shown to be connected to a large-scale mode of Indian Ocean climate variability. The evolution of the dipole is controlled by variations in atmospheric circulation driving anomalous latent heat uxes with wind-driven ocean transport moderating the impact of evaporation and setting the conditions favourable for the next generation phase of an opposite dipole.

In this study the natural variability of Southern Ocean water masses on interannual to centennial time scales is investigated using a long-term integration of the Commonwealth Scientic and Industrial Research Organisation (CSIRO) coupled climate model. We focus our attention on analysing the variability of Antarctic IntermediateWater (AAIW), Circumpolar DeepWater (CDW), and Antarctic Bottom Water (AABW). We present an analysis of the dominant modes of temperature and salinity (T - S) variability within these water masses. Climate signals are detected and analysed as they get transmitted into the interior from the water mass formation regions. Eastward propagating wavenumber-1, -2, and -3 signals are identied using a complex empirical orthogonal function (CEOF) analysis along the core of the AAIW layer. Variability in air-sea heat uxes and ice meltwater rates are shown by heat and salt budget analyses to control variability of Antarctic Surface Water where density surfaces associated with AAIW outcrop. The dominant mode in the CDW layer is found to exhibit an interbasin-scale of variability originating from the North Atlantic, and propagating southward into the Southern Ocean. Salinity dipole anomalies appear to propagate around the Atlantic meridional overturning circulation with the strengthening and weakening of North Atlantic Deep Water formation. In the AABW layer, T - S anomalies are shown to originate from the southwestern Weddell Sea, driven by salinity variations and convective overturning in the region. It is also demonstrated that the model exhibits spatial patterns of T - S variability for the most part consistent with limited observational record in the Southern Hemisphere. However, some observations of decadal T - S changes are found to be beyond that seen in the model in its unperturbed state. We further assess sea surface temperature (SST) variability modes in the Indian Ocean on interannual time scales in the CSIRO model and in reanalysis data. The emergence of a meridional SST dipole during years of southwest Western Australian rainfall extremes is shown to be connected to a large-scale mode of Indian Ocean climate variability. The evolution of the dipole is controlled by variations in atmospheric circulation driving anomalous latent heat uxes with wind-driven ocean transport moderating the impact of evaporation and setting the conditions favourable for the next generation phase of an opposite dipole.

In this study the natural variability of Southern Ocean water masses on interannual to centennial time scales is investigated using a long-term integration of the Commonwealth Scientic and Industrial Research Organisation (CSIRO) coupled climate model. We focus our attention on analysing the variability of Antarctic IntermediateWater (AAIW), Circumpolar DeepWater (CDW), and Antarctic Bottom Water (AABW). We present an analysis of the dominant modes of temperature and salinity (T - S) variability within these water masses. Climate signals are detected and analysed as they get transmitted into the interior from the water mass formation regions. Eastward propagating wavenumber-1, -2, and -3 signals are identied using a complex empirical orthogonal function (CEOF) analysis along the core of the AAIW layer. Variability in air-sea heat uxes and ice meltwater rates are shown by heat and salt budget analyses to control variability of Antarctic Surface Water where density surfaces associated with AAIW outcrop. The dominant mode in the CDW layer is found to exhibit an interbasin-scale of variability originating from the North Atlantic, and propagating southward into the Southern Ocean. Salinity dipole anomalies appear to propagate around the Atlantic meridional overturning circulation with the strengthening and weakening of North Atlantic Deep Water formation. In the AABW layer, T - S anomalies are shown to originate from the southwestern Weddell Sea, driven by salinity variations and convective overturning in the region. It is also demonstrated that the model exhibits spatial patterns of T - S variability for the most part consistent with limited observational record in the Southern Hemisphere. However, some observations of decadal T - S changes are found to be beyond that seen in the model in its unperturbed state. We further assess sea surface temperature (SST) variability modes in the Indian Ocean on interannual time scales in the CSIRO model and in reanalysis data. The emergence of a meridional SST dipole during years of southwest Western Australian rainfall extremes is shown to be connected to a large-scale mode of Indian Ocean climate variability. The evolution of the dipole is controlled by variations in atmospheric circulation driving anomalous latent heat uxes with wind-driven ocean transport moderating the impact of evaporation and setting the conditions favourable for the next generation phase of an opposite dipole.

The Antarctic climate has undergone complex changes over the last fifty years, driven largely by stratospheric ozone depletion. By the end of this century, under the current trajectory of anthropogenic emissions, the climate of Antarctica is projected to be significantly wetter, warmer and prone to the collapse of ice shelves and loss of sea ice cover. The overarching aim of this thesis is to increase our understanding of recent and projected Antarctic climate change and its drivers. We also investigate the potential global implications of these changes and show that the effects will not be limited to the southern high latitudes. In the first half, we investigate the drivers of Antarctic climate change over the observational period. Specifically, we study the influence of the stratosphere on the southern high latitude surface climate, through stratosphere-troposphere dynamic coupling as well as stratospheric ozone depletion. We examine the impact of these on the Amundsen Sea Low, a key circulation feature near West Antarctica. We demonstrate using reanalysis that stratospheric heat flux extremes are linked to high latitude tropospheric anomalies in the Amundsen Sea region. During extreme negative (positive) events there is a westward (eastward) shift of the Amundsen Sea Low, a warming (cooling) and increase (decrease) of geopotential height over the Amundsen and Bellingshausen Seas. We find that most CMIP5 models are not able to capture this relationship. Next, we demonstrate that, since 1965, stratospheric ozone depletion has acted to deepen the Amundsen Sea Low in austral summer by 1 hPa per decade. This result was consistent across two different comprehensive climate models, each with very different model physics and climate sensitivity. It must be noted that the ozone depletion signal on the Amundsen Sea Low is small compared to the internal climate variability in this region. Using ensembles of model integrations and analysing them over the full period of ozone depletion (which started a couple of decades before the satellite era) is necessary to detect a robust signal. In the second half, we investigate the effects of future Antarctic climate change, specifically the effects of projected sea ice loss over the coming century. Climate model simulations are used to isolate the effect of end-of-the-century Antarctic sea ice loss which is compared and contrasted with the effects of projected Arctic sea ice loss. We first study the effects of projected Antarctic sea ice loss used atmosphere-only simulations. As for the Arctic, results indicated that Antarctic sea ice loss will act to shift the tropospheric jet equatorward, an internal negative feedback to the poleward shift associated with increased greenhouse gases. Antarctic sea ice loss is shown to have an important effect throughout the year whereas Arctic sea ice loss will have more seasonally varying impacts. Building upon these results we the use the same climate model but in a fully coupled setup to study the effects of projected Antarctic sea ice loss on the climate system. We show that both Arctic and Antarctic sea ice loss will have important global effects, causing a ‘mini global warming’ signal. The tropical response to Antarctic sea ice loss is shown to be remarkably similar to that of Arctic sea ice loss, with enhanced warming in the Eastern Tropical Pacific and increased precipitation throughout much of the equatorial Pacific. These results highlight how intimately coupled the Antarctic climate is to the rest of the climate system.

Extreme temperatures, heat waves, heavy rainfall events, drought, and extreme air pollution events have adverse effects on human health, infrastructure, agriculture and economies. The frequency, magnitude and duration of these events are expected to change in the future in response to increasing greenhouse gases and decreasing aerosols, but future climate projections are uncertain. A significant portion of this uncertainty arises from uncertainty in the effects of aerosol forcing: to what extent were the effects from greenhouse gases masked by aerosol forcing over the historical observational period, and how much will decreases in aerosol forcing influence regional and global climate over the remainder of the 21st century? The observed frequency and intensity of extreme heat and precipitation events have increased in the U.S. over the latter half of the 20th century. Using aerosol only (AER) and greenhouse gas only (GHG) simulations from 1860 to 2005 in the GFDL CM3 chemistry-climate model, I parse apart the competing influences of aerosols and greenhouse gases on these extreme events. I find that small changes in extremes in the “all forcing” simulations reflect cancellations between the effects of increasing anthropogenic aerosols and greenhouse gases. In AER, extreme high temperatures and the number of days with temperatures above the 90th percentile decline over most of the U.S., while in GHG high temperature extremes increase over most of the U.S. The spatial response patterns in AER and GHG are significantly anti-correlated, suggesting a preferred regional mode of response that is largely independent of the type of forcing. Extreme precipitation over the eastern U.S. decreases in AER, particularly in winter, and increases over the eastern and central U.S. in GHG, particularly in spring. Over the 21st century under the RCP8.5 emissions scenario, the patterns of extreme temperature and precipitation change associated with greenhouse gas forcing dominate. The temperature response pattern in AER and GHG is characterized by strong responses over the western U.S. and weak or opposite signed responses over the southeast U.S., raising the question of whether the observed U.S. “warming hole” could have a forced component. To address this question, I systematically examine observed seasonal temperature trends over all time periods of at least 10 years during 1901-2015. In the northeast and southern U.S., significant summertime cooling occurs from the early 1950s to the mid 1970s, which I partially attribute to increasing anthropogenic aerosol emissions (median fraction of the observed temperature trends explained is 0.69 and 0.17, respectively). In winter, the northeast and southern U.S. cool significantly from the early 1950s to the early 1990s, which I attribute to long-term phase changes in the North Atlantic Oscillation and the Pacific Decadal Oscillation. Rather than being a single phenomenon stemming from a single cause, both the warming hole and its dominant drivers vary by season, region, and time period. Finally, I examine historical and projected future changes in atmospheric stagnation. Stagnation, which is characterized by weak winds and an absence of precipitation, is a meteorological contributor to heat waves, extreme pollution, and drought. Using CM3, I show that regional stagnation trends over the historical period (1860-2005) are driven by changes in anthropogenic aerosol emissions, rather than rising greenhouse gases. In the northeastern and central United States, aerosol-induced changes in surface and upper level winds produce significant decreases in the number of stagnant summer days, while decreasing precipitation in the southeast US increases the number of stagnant summer days. Outside of the U.S., significant drying over eastern China in response to rising aerosol emissions contributed to increased stagnation during 1860-2005. Additionally, this region was found to be particularly sensitive to changes in local aerosol emissions, indicating that decreasing Chinese emissions in efforts to improve air quality will also decrease stagnation. In Europe, I find a dipole response pattern during the historical period wherein stagnation decreases over southern Europe and increases over northern Europe in response to global increases in aerosol emissions. In the future, declining aerosol emissions will likely lead to a reversal of the historical stagnation trends, with increasing greenhouse gases again playing a secondary role. Aerosols have a significant effect on a number of societally important extreme events, including heat waves, intense rainfall events, drought, and stagnation. Further, uncertainty in the strength of aerosol masking of historical greenhouse gas forcing is a significant source of spread in future climate projections. Quantifying these aerosol effects is therefore critical for our ability to accurately project and prepare for future changes in extreme events.

Shaped by countless influences from the atmosphere, biosphere, hydrosphere, and anthroposphere acting across a wide spectrum of spatiotemporal scales, spatial variations in climate are ubiquitous. Meanwhile, the warming signal from anthropogenically elevated greenhouse-gas concentrations is emerging as an overriding determinant for more and more aspects of the climate system, extreme heat among them. In this dissertation, I explore the interaction of these two effects, and the implications of the patterns they create. A key finding is that rapid increases in extreme heat are already occurring, by some metrics having already doubled in the past 40 years, and further nonlinear increases are expected. Another is the strong dependence of extreme heat-humidity combinations on atmospheric moisture, creating subseasonal and interannual patterns dictated by the principal source of regional warm-season moisture — pre-monsoonal advection in some cases, local evapotranspiration in others. These relationships lead to the demonstrated potential for improvements in predictive power, on the basis of sea-surface temperatures and other canonical modes of large-scale climate variability. In contrast to this overall confidence in current temporal patterns and long-term projections, I show that extreme heat at small spatial scales is much more poorly characterized in gridded products, and that these biases are especially acute along coastlines. While summer daytime temperature differences between the shoreline of the Northeast U.S. and locations 60 km inland are often 5°C or more, I find that recent high-resolution downscaled Earth-system models typically represent no more than 25% of this difference. Across the globe, ERA-Interim reanalysis similarly underestimates extreme humid heat by >3°C, a highly significant margin given the large sensitivity of health and economic impacts to marginal changes in the most extreme conditions. I find that these biases propagate into projections, and their importance is also amplified by the large populations living in the affected areas. Rapid mean warming is pushing the climate system to more and more frequently include extreme heat-humidity combinations beyond that which the human species has likely ever experienced. Such conditions, which had not been previously reported in weather-station data, are described in detail and some of the associated characteristics examined. Several channels of analysis highlight that these events are driven primarily by rising sea-surface temperatures in shallow subtropical gulfs, and the subsequent impingement of marine air on the coastline. Given the severity of potential impacts on infrastructure and agriculture, and the size of the populations exposed, this result underscores that major research and adaptation efforts are needed to avoid calamitous outcomes from the emergence of extreme heat-humidity combinations too severe to tolerate in the absence of artificial cooling. This dissertation discusses strategies for advancing knowledge of extreme heat’s natural variations and its behavior under climate change, in order to design metrics, models, methodologies, and presentation types such that essential findings are translated into tangible action in the most effective way possible. Sustained and integrated efforts are necessary to transition to a climate-system management style encompassing more foresight than the effectively unplanned experiment which has been pursued so far, and which has already exacerbated extreme heat events so much.

Drought is a recurrent extreme climate event with tremendous hazard for every specter of natural environment and human lives. Drought analysis usually involves characterizing drought severity, duration and intensity. Similar to most of the hydrological problems, such characteristic variables are usually not independent. Copula, as a model of multivariate distribution, widely used in finance, actuarial analysis, has won increasingly popularity in hydrological study. Here, the study has two major focuses: (1) fit drought characteristics from Streamflow Drought Index (SDI) or Standardized Runoff Index (SRI) to appropriate copulas, then using fitted copulas to estimate conditional drought severity distribution and joint return periods for both historical time period 1920-2009 and future time period 2020-2090. SDI is calculated based on long term observed streamflow while SRI is based on simulated future runoff. Parameters estimation of marginal distribution and copulas are provided, with goodness fit measures as well; (2) investigate the effects of climate change on the frequency and severity of droughts. In order to quantify the impact, three drought indices have been proposed for this study to characterize the drought duration, severity and intensity changes under the climate change in Upper Klamath River Basin. Since drought can be defined as different types, such as meteorological drought, agricultural drought, hydrological drought and social economical drought, this study chooses Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Surface Water Supply Index (SWSI) to estimate the meteorological, agricultural and hydrological drought, respectively. Climate change effects come from three sources: the inherent reason, the human activity and the GCMs uncertainties. Therefore, the results show the long term drought condition by calculating yearly drought indices, and compared in three ways: First, compare drought characteristics of future time periods with base period; second, show the uncertainties of three greenhouse gas emission scenarios; third, present the uncertainties of six General Circulation Models (GCMs).

Radiative feedbacks associated with changes in water vapor, temperature, surface albedo and clouds remain a major source of uncertainty in our understanding of climate's response to anthropogenic forcing. In this dissertation climate model data is used to investigate variations in feedbacks that result from changing CO��� forcing and the time scales on which feedbacks operate, focusing on the applicability of one method in particular, the radiative kernel technique, to these problems. This computationally efficient technique uses a uniform, incremental change in feedback variables to infer top-of-atmosphere (TOA) radiative flux changes. The first chapters explore the suitability of the linear radiative kernel technique for large forcing scenarios. We show that kernels based on the present-day climate misestimate TOA flux changes for large perturbations, translating into biased feedback estimates. We address this issue by calculating additional kernels based on a large forcing climate state with eight times present day CO��� concentrations. Differences between these and the present-day kernels result from added absorption of radiation by CO��� and water vapor, and increased longwave emission due to higher temperatures. Combining present-day and 8xCO��� kernels leads to significant improvement in the approximation of TOA flux changes and accuracy of feedback estimates. While climate sensitivity remains constant with increasing CO��� forcing when the inaccurate present-day kernels are used, sensitivity increases significantly when new kernels are used. Comparison of feedbacks in climate models with observations is one way towards understanding the disagreement among models. However, climate change feedbacks operate on time scales that are too long to be evaluated from the observational record. Rather, short-term proxies for greenhouse-gas-driven warming are often used to compute feedbacks from observations. The third chapter of this dissertation examines links between the seasonal cycle and global warming using pattern correlations of spatial distribution of feedback variables and radiative flux changes. We find strong correlations between time scales for changes in surface temperature and climate variables, but not for TOA flux anomalies, reaffirming conclusions drawn in previous work. Finally, we investigate the fitness of the radiative kernel technique for evaluation of short-term feedbacks in a comparison with the more accurate, but more computationally expensive, partial radiative perturbations. / Graduation date: 2013

Ice sheets and mountain glaciers are critically important components of Earth'sclimate system due to societal and ecological risks associated with sea-level change, ocean freshening, ice-albedo feedback, glacial outburst floods, and freshwater availability. As Earth warms, increasing volumes of surface meltwater will access subglacial environments, potentially lubricating the base of the ice sheets and causing enhanced ice discharge into the ocean. Since subglacial water is effectively hidden beneath the ice, the primary ways to study subglacial hydrological systems are through mathematical modelling and interpreting indirect observations. Glaciers often host subglacial or ice-dammed lakes that respond to changes in subglacial water flow, thereby providing indirect information about the evolution of subglacial hydrological systems. While monitoring subaerial ice-dammed lakes is straightforward, the evolution of subglacial lakes must be inferred from the displacement of the overlying ice surface, posing additional challenges in modelling and interpretation. This dissertation addresses these challenges by developing and analyzing a series of mathematical models that focus on relating subglacial hydrology with observable quantities such as lake level or ice-surface elevation. The dissertation is divided into five chapters. Chapter 1 demonstrates how ageneralization of Nye's (1976) canonical model for subglacial water flow admits a wide class of solitary-wave solutions---localized regions of excess fluid that travel downstream with constant speed and permanent form---when melting at the ice-water interface is negligible. Solitary wave solutions are proven to exist for a wide range of material parameter values that are shown to influence the wave speed and wave profile. Melting at the ice-water interface is shown to cause growth and acceleration of the waves. To relate dynamics like these to observable quantities, Chapter 2 focuses on modelling water-volume oscillations in ice-dammed lakes during outburst flood cycles while accounting for the potential influence of neighboring lakes. Hydraulic connection between neighboring lakes is shown to produce a wide variety of new lake-level oscillations that depend primarily on the relative sizes and proximity of the lakes. In particular, the model produces lake-level time series that mirror ice-elevation changes above a well-known system of Antarctic subglacial lakes beneath the Whillans and Mercer ice streams even though the modelled ice-dammed lakes are not buried beneath the ice. The stability of lake systems with respect to variations in meltwater input is characterized by a transition from oscillatory to steady drainage at high water supply. To create a framework for extending these models of ice-dammed lakes to thesubglacial setting, variational methods for simulating the dynamics of subglacial lakes and subglacial shorelines are derived in Chapter 3. By realizing a direct analogy with the classical Signorini problem from elasticity theory, this chapter also furnishes a new, rigorous computational method for simulating the migration of oceanic subglacial shorelines, which are strongly tied to ice-sheet stability in response to climatic forcings. In Chapter 4, this newly developed model is used to highlight the challenge of accurately interpreting ice-surface elevation changes above subglacial lakes without relying on ice-flow models. The surface expression of subglacial lake activity is shown to depend strongly on the effects of viscous ice flow and basal drag, causing altimetry-derived estimates of subglacial lake size, water-volume change, and apparent highstand or lowstand timing to deviate considerably from their true values under many realistic conditions. To address this challenge, Chapter 5 introduces inverse methods for inferring time-varying subglacial lake activity or basal drag perturbations from altimetry data while accounting for the effects of viscous ice flow. Incorporating horizontal surface velocity data as additional constraints in the inversion is shown to facilitate reconstruction of multiple parameter fields or refinement of altimetry-based estimates. In sum, this dissertation constitutes several novel approaches to understanding ice-water interaction beneath glaciers while laying the foundation for future work seeking to elucidate the role of subglacial processes in the changing climate.

Accurately modeling climate and the impacts of climate change relies heavily on extensive observations. Soil moisture is a critical variable in this regard, as it influences energy partitioning, regulates the water cycle, directly affects vegetation dynamics, modulates terrestrial carbon sinks and sources, and overall plays a vital role in the land-atmosphere interactions and feedback. This work aims to improve the quality of available surface soil moisture data and its complementary dataset -- vegetation optical depth (since both are derived from the same satellite measurements). The datasets developed in the scope of this study fill the gap in the available observational data pool as unique, long-term, consistent datasets developed based on remote sensing data. These datasets were created with the help of machine learning tools, in particular, deep dense neural networks. The distinctive characteristics of the utilized approach include (1) decomposition of the signal into seasonal and residual parts and training a neural network to match the residuals; (2) applying a special transfer learning training scheme that allows adjusting the features of a trained neural network to a slightly different input that ultimately permits merging the non-compatible directly and disjoint satellite sources into a consistent dataset; (3) using an ensemble of neural networks to assess the data uncertainty. Upon development, the datasets were profoundly validated vs. in-situ soil moisture measurements for soil moisture and biomass and photosynthesis-related datasets for vegetation optical depth. The consistent and long-term nature of the created datasets allowed for the study of decadal trends in soil moisture and the potential drivers for its dynamics. Finally, this study presents two showcases of the datasets used for constraining models -- as data assimilated in a simple carbon cycle model and as an emergent constraint in an ensemble of global climate models. The vegetation optical depth dataset was used in a simple carbon cycle model and demonstrated how it can constrain unobserved respiration flux and carbon pools. In this project's scope, the role of information content, data quality, and local conditions is assessed. The soil moisture dataset is used to constrain global climate models' projections of future soil moisture change by constraining the past soil moisture change range. Altogether, this study proposes a robust methodology for merging data from different sources into a consistent long-term dataset (provided that at least a short overlap in data exists for transfer learning). The analysis of the soil moisture dataset reveals that the regions of drying and wetting dynamics exist globally and can be identified with statistically significant trends in soil moisture. The dynamics are studied seasonally, revealing the contradicting trends in soil moisture in some regions (for example, in Europe, wetting in spring and drying in summer) and persistent trends throughout the year for others (for example, drying in the Mediterranean). Similarly, the local drivers of the soil moisture change are established. The soil moisture change is mainly driven by variations in precipitation for dry regions and in temperature in wet regions with the rising role of vegetation dynamics, especially in high latitudes. Finally, the vegetation optical depth data has proven its high potential in constraining respiration flux and carbon pools, significantly improving the carbon cycle model predictions in the regions subjected to interannual variability in meteorological forcing conditions and vegetation response.