Convective cold pools are important modulators of the onset and evolution of deep convection in the tropics. Cold pools are generated by downdrafts and can outlive the storms they originate from to spark new convection. However, most of our understanding of cold pool mechanics comes from high-resolution simulations and a relatively small number of in situ observational studies. This thesis brings novel observational approaches together with climate model data to understand the behavior of cold pools on a global scale and how a mesoscale weather behavior can be accounted for in a climate-scale simulation. First, we leverage a dataset derived from the Advanced Scatterometer (ASCAT) satellite instrument by Garg et al. (2020) to quantify seasonal variations in cold pool activity and their relationship to deep convection across tropical ocean basins. The dataset identifies gradient features (GFs) in the surface wind field, which have been shown to serve as reliable proxies for the boundaries of atmospheric cold pools. We examine the relationship between GFs and climatologies of precipitation, column relative humidity (CRH), and bulk vertical wind shear. We also collocate GFs with precipitation and CRH. High GF frequency, precipitation, and CRH coincide in many regions of the tropics, consistent with our understanding of the physical connections between precipitation and cold pool generation. On the other hand, climatological bulk wind shear is often low in convective regions, and there is a weak inverse correlation between GF frequency and bulk wind shear, while our prior expectation might have been that shear promotes cold pool formation. Compared to GF frequency, GF size shows a weaker relationship with the convective environment, with some of the largest GFs occurring at lower CRH values for a given rainfall rate. In a few exceptional regions and seasons, such as the Indian Ocean in northern hemisphere summer, the region of greatest precipitation does not coincide with the region of greatest GF frequency. These cases also have very high seasonal mean CRH, suggesting that in these regions cold pool formation is suppressed by reduced evaporation of precipitation. Following that, we apply the GF data set to the task of evaluating the realism of the cold pool parameterization in the GISS E3 earth model originally designed by Del Genio et al. (2015). We compare the GF data set to model results from six versions of the GISS model with perturbed parameters. Cold pools generated by the model have significantly different geographic distribution to satellite-observed GFs, particularly in critical convective regions. They also appear to be much less common than GFs, though they have a broadly similar dependence on column water vapor (CWV), especially in terms of size. Finally, we seek to understand the mechanics of the model cold pool parameterization on its own. A subset of high-time resolution model versions is used to deconstruct the behavior of the model parameterization at the scale of individual time steps. Our aim is to see what level of physical realism is associated with the emergent trends seen in the climatological statistics. We find that the model generates cold pool temperature and moisture depressions of similar magnitude to cold pools measured from ships, but tend to dissipate too quickly. Model cold pools also appear to spark increased precipitation, as they are designed to do, but that precipitation appears to come from the stratiform model parameterization, not the moist convection one. Together, these results provide a first opportunity to empirically evaluate a model parameterization originally developed using theory.

Thesis submitted to the Faculty of Science, Department of Geography and Environmental Studies, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the Degree of Doctor of Philosophy / The representation of contemporary southern African climate by a wide range of general circulation models used in climate studies is evaluated. In addition, projections of regional climate change by the models are interpreted in terms of their present climate performance. Projections of regional climate change by two different types of climate models are considered. First, projections of the equilibrium response to an instantaneous doubling of atmospheric carbon dioxide using atmospheric models linked to simple mixed-layer oceans are assessed. Second, projections of the transient response to gradually-increasing anthropogenic forcing by fully-coupled ocean-atmosphere general circulation models are considered. All of the mixed-layer models considered have been developed since 1990 and are more recent and generally higher-resolution versions of the models considered previously for southern Africa. The improved resolution and model physics result in a general improvement in the representation of several features of circulation around southern Africa. Specifically, these include the meridional pressure gradient, the zonal wind profile, the intensity and seasonal location of the circumpolar trough and the subtropical anticyclones, as well as planetary wave structure at 500 hPa. Atmospheric models forced by observed sea-surface temperatures simulate the large-scale circulation adjustments around southern Africa known to accompany periods of above- and below-average rainfall over the subcontinent. Fully-coupled models simulate the observed features of intra- and intra- annual variability in mean sea-level pressure, although the simulated variability is weaker than observed. Summer rainfall totals throughout southern Africa are overestimated by all of the models, although the pattern of rainfall seasonality over the subcontinent as a whole is well-reproduced. The inclusion of sulphate aerosols in addition to greenhouse gases does not result in a statistically significant improvement in the simulation of contemporary temperature variability over southern Africa. Warming projected by fully-coupled models is smaller than projections by mixed-layer models due to the fact that the transient response of the fully-coupled system and not an equilibrium response of an atmospheric model linked to a mixed-layer ocean is simulated. The inclusion of sulphate aerosols results in a reduction in the magnitude and rate of warming over southern Africa. Projected changes in the diurnal temperature range are seasonally-dependent, with increases in summer and autumn and decreases in winter. Simulated changes in mean sea-level pressure are small but similar in magnitude to observed anomalies associated with extended wet and dry spells over the subcontinent. No change in rainfall seasonality over southern Africa is expected. Nonetheless, little confidence exists in projected changes in total rainfall. While both types of model simulate a 10-15% decrease in summer rainfall on average, projected changes are smaller than the simulation errors and little inter-model consensus in terms of the sign of projected changes exists. No change in the location or intensity of anticyclonic circulation and divergence at 700 hPa in winter is expected. While fully-coupled models provide a more comprehensive treatment of the global climate system and the process of climate change, there is no evidence to conclude that current fully-coupled models should be used to the exclusion of mixed-layer models when developing regional climate change scenarios for southern Africa. / Andrew Chakane 2018

"The main objective of this study is to examine the influence of global SSTAs [sea surface temperature anomalies] on rainfall over eastern Africa (Fig. 1) using Florida State University T21 Global Spectral Model (FSUT21GSM) during the southern hemisphere summer of 1982 (wet year) and 1983 (dry year) (Fig. 2)"--Leaf 3. / Typescript. / "Summer Semester, 1991." / "Submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: T. N. Krishnamurti, Professor Directing Thesis. / Includes bibliographical references.

Weather and climate events are costly to society both financially and in terms of human health and well being. The costs associated with extreme climate events have motivated governments, NGOs, private investors, and insurance companies to use the data and tools at their disposal to estimate the past, present, and future hazards associated with a wide range of natural phenomena in an effort to develop mitigation and/or adaptation strategies. The nonstationary nature of climate risks requires the use of numerical climate models, often general circulation models (GCMs), to project future risk. The climate risk field, however, currently finds itself in a predicament because GCMs can be biased and do not provide a clear way to credibly estimate their uncertainty with respect to simulations of future surface climate conditions. In response to this predicament, I lay the groundwork for a set of GCM credibility assessments by identifying the large-scale drivers of surface climate events that evolve over a range of timescales ranging from daily to multi-decadal. I specifically focus on three types of climate events relevant to the water and energy sectors: 1) seasonal precipitation, which impacts drinking water supplies and agricultural productivity; 2) extreme precipitation and the costly associated riverine flooding; and 3) temperature, wind, and solar radiation fields that modulate both electricity demand and potential renewable electricity supply. In chapter I, I derive a set of atmospheric indices and investigate their efficacy to predict distributed seasonal precipitation throughout the conterminous United States. These indices can also be used to diagnose the impact of tropical sea surface temperature heating patterns on conterminous United States precipitation. This is particularly of interest in the aftermath of the unexpected precipitation patterns in the conterminous United States during the 2015-2016 El Niño event. I show that the set of atmospheric indices, which I derive from zonal winds over the conterminous United States and portions of the North Atlantic and Pacific oceans, can skillfully predict precipitation over most regions of the conterminous United States better than previously recognized mid-latitude atmospheric and tropical oceanic indices. This work contributes a set of intermediate atmospheric indices that can be used to assess the efficacy of forecasting and simulation climate models to capture signal that exists between tropical heating, mid-latitude circulation, and mid-latitude precipitation. In chapter II, I first show that the frequency of regional extreme precipitation events, which are predictive of riverine flooding, in the Ohio River Basin are poorly simulated by a GCM relative to historical precipitation observations. I then illustrate that the same GCM is much better able to simulate the statistical characteristics of a set of atmospheric field-derived indices that I show to be strongly related to the precipitation events of interest. Thus, I develop a statistical model that allows for the simulation of the precipitation events based on the GCM's atmospheric fields, which allows me to estimate future hazard based on credibly simulated GCM fields. Lastly, I validate the fully Bayesian statistical model against historical observations and use the statistical model to project the future frequency of the regional extreme precipitation events. I conclude that there is evidence of increasing regional riverine flood hazard in the Central US river basin out to the year 2100, but that there is high uncertainty regarding the magnitude of the trend. This work suggests that the identification of atmospheric circulation patterns that modulate the probability of extreme precipitation and riverine flood risk may improve flood hazard projections by allowing risk analysts to assess GCMs with respect to their ability to simulate relevant atmospheric patterns. In chapter III, I present the first comprehensive assessment of quasi-periodic decadal variations in wind and solar electricity potential and of covariability between heating and cooling electricity demand and potential wind and solar electricity production. I focus on six locations/regions in the conterminous United States that represent different climate zones and contain major load centers. The decadal variations are linked to quasi-oscillatory variations of the global climate system and lead to time-varying risks of meeting heating + cooling demand using wind/solar power. The quasi-cyclical patterns in renewable energy availability have significant ramifications for energy systems planning as we continue to increase our reliance on renewable, weather- and climate-dependent energy generation. This work suggests that certain modes of low frequency climate variability influence potential wind and solar energy supplies and are thus especially important for GCMs to credibly simulate. All of the investigations are designed to be broadly applicable throughout the mid-latitudes and are demonstrated with specific case studies in the conterminous United States. The dissertation sections represent three cases where statistical techniques can be used to understand surface climate and climate hazards. This understanding can ultimately help to mitigate and adapt to climate variabilities and secular changes, which impact society, by assisting in the development, improvement, and credibility assessment of GCMs capable of reliably projecting future climate hazards.

In this thesis I present the results of a comprehensive assessment of the Pacific-North American (PNA) teleconnection pattern in general circulation models (GCMs) and a regional climate model (RCM). The PNA teleconnection pattern is a quasi-stationary wave field over the North Pacific and North America that has long been recognized as a robust feature of Northern Hemisphere atmospheric circulation, and directly affects the interannual variability of North American temperature and precipitation. The teleconnection is evaluated under present (1950-2000) and future (2050-2100) climate in a coupled GCM (MPI/ECHAM5) and a high-resolution regional climate model (RegCM3). I further assess the PNA in 27 atmosphere-ocean GCMs and earth system models (ESMs) from the ongoing fifth phase of the Coupled Model Intercomparison Project (CMIP5). The National Centers for Environmental Prediction and Atmospheric Research (NCEP/NCAR) Reanalysis serves a quasi-observational baseline against which the models are evaluated. For each analysis, changes in the spatial and temporal patterns of the PNA spatial are assessed for both the present and future climates, and these changes are then related to changes in climate and surface hydrology in North America. Coupling the NCEP and ECHAM5 GCMs with RegCM3 is very successful in that the PNA is resolved in both models with little loss of information between the GCMs and RegCM3, thereby allowing an assessment of high-resolution climate with an inherent skill comparable to that of the global models. The value of the PNA index is generally independent of the method used to calculate it: three- and four-point modified linear pointwise calculations for both the RegCM3 and ECHAM5 model simulations produce very similar indices compared with each other, and compared with those extracted from a rotated principle component analysis (RPCA) which is also used to determine the PNA spatial pattern. The spatial pattern of the PNA teleconnection emerges as a leading mode of variability from the RPCA, although the strength of the teleconnections are consistently weaker than NCEP as defined by four main "centers of action". This discrepancy translates into the strength of the controls of the PNA on surface climate. Maps of the correlations between the GCM PNA indices and RCM surface climate variables are compared to the results from the NCEP/NCAR Reanalysis. I find that correlation patterns with temperature and precipitation are directly related to the positioning of the Aleutian low and Canadian high, the two main drivers of upper-atmospheric circulation in the PNA sector. The CMIP5 models vary significantly in their ability to simulate the quasi-observed features of the PNA teleconnections. The behavior of the models relative to NCEP is more definite than the trends within the models. Most models are unable to resolve the temporal variability of NCEP; however, on the other hand most of the models are able to capture the PNA as a low-frequency quasi-oscillation. Many of the models are unable to simulate the barotropic instability that initiates wave energy propagation through the 500-hPa geopotential height field, thereby leading to phase-locking and thus the positive and negative modes of PNA are indistinguishable. The behavior and the spatial patterns of the PNA throughout the 21st century are consistent with other projections of future climate change in that most models exhibit a lengthening of the eddy length scale and a poleward shift of the mid-latitude jet stream associated with polar amplification of greenhouse-gas driven global warming. Finally, my analyses underscore the robustness of multi-model means, suggesting that the cumulative results of multiple climate models outperform the results from individual models because ensemble means effectively cancel discrepancies and hereby expose only the most robust common features of the model runs. While ensembles provide better representation of the average climate, they potentially mask climate dynamics associated with inter-annual and longer time scales. Relying on ensemble means to limit model spread and uncertainties remains a necessity in using models to project future climate. / Graduation date: 2013

This study focuses on the importance of solute dissolution kinetics for cloud droplet formation. To comprehensively account for the kinetics, a numerical model of the process was developed. Simulations of cloud droplet growth were performed for solute diffusivity, droplet growth rates, dry particle and droplet diameters relevant for ambient conditions. Simulations suggest that high ambient supersaturations and a decrease in solute diffusivity are major contributors to significant decreases in effective solute surface concentrations. The numerical simulations were incorporated into Khler theory to assess the impact of dissolution kinetics on the droplet equilibrium vapor pressure. For CCN composed of partially soluble material, a significant increase was found in the equilibrium supersaturation of CCN.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology, 1980. / Microfiche copy available in Archives and Science. / Bibliography: leaves 147-151. / by Ross N. Hoffman. / Ph.D.

Dissertation submitted to the Faculty of Science, University of the Witwatersrand, for completion of the Degree of' Master of Science / The atmospheric response of the Colorado State University Regional Atmospheric Modelling System (RAMS) to sea-surface temperature anomaliesis investigated. A period of four days was chosen from 21 to 24 January 1981, where focus was placed on the development and dissipation of a tropical-temperate trough across Southern Africa. Previous experimenting this mesoscalenumerical model have detemined the kinematic, moisture, and thermodynamic nature of these synoptic features. The research in this dissertation focuses specifically on the sensitivity of the numerical model's simulated responses to positive sea-surface temperature anomalies. Three separate experiments were devised, in which positive anomalous temperatures were added to the ocean surface north of Madagascar (in the tropical Indian Ocean), at the region of the Agulhas Current retroflection, and along the tropical African west coast (in the Northern Benguela and Angola currents). The circulation aspects of each sensitivity test were investigated through the comparison of simulated variables such as vapour and cloud mixing ratios, temperature, streamlines and vertical velocity, with the same variables created by a control simulation. The results indicate that for the first sensitivity test, (the Madagascar anomaly), cyclogenesis was initiated over the area of modified sea temperatures which resulted in a marginal decrease in continental precipitation. The second sensitivity test (over the Agulhas retroflection) produced a much smaller simulated response to the addition of anomalously warm sea temperatures than the tropical Indian Ocean anomaly. Instability and precipitation values increased over the anomalously warm retroflection region, and were slowly transferred along the westerly wave perturbation and the South African east coast. The third sensitivity experiment showed a predominantly localised simulated increase in precipitation over Gabon and the Congo, with the slow southward progression of other simulated circulation differences taking place. The small perturbations in each of the simulated meteorological responses are consistent with the expected climate response to anomalously warm sea-surface temperatures in those areas. / AC 2018

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.

Changes in global climate conditions significantly impact ice sheet and glacier mass change leading to global mean sea level (GMSL) change. One of the largest present-day contributors to GMSL is the Greenland ice sheet (GrIS) and it will likely continue to be so in the future. To accurately predict future ice mass changes, it is crucial to understand the response of GrIS to a changing climate and to correctly represent this behavior in climate models. The GrIS’ contribution to GMSL can in large part be attributed to the loss of ice and snow mass from the ice sheet surface. The surface mass loss has accelerated in the past decades due to increased surface melting and runoff in response to atmospheric warming. Surface melting is strongly controlled by ice albedo, a complex and dynamic property of ice that regulates the amount of solar radiation that is absorbed or reflected by the surface. Absorbed solar radiation leads to heating and melting of the ice surface. However, we lack a comprehensive understanding of the physical processes controlling ice mass loss, including ice albedo. These processes are, therefore, often simplified or crudely parameterized in climate models and subsequently add to large uncertainties in sea level rise predictions. This uncertainty prevents effective mitigation of and adaptation to the effects of climate change and sea level rise. It is, therefore, essential to advance our understanding of these processes and their representation in climate models. In this dissertation, I describe improvements to our understanding of the behavior of the GrIS and pose improvements to climate modeling capabilities that can lead to a reduced uncertainty of sea level rise projections. In the first chapter, I put constraints on the past response of the GrIS to a changing climate. Understanding the response of the GrIS to times in the past when temperatures were as warm or warmer than today offers insights into its current and future response to climate change. The southwestern GrIS retreated inland beyond its current margin during the (at least regionally) warmer-than-present mid-Holocene, before it readvanced. To investigate the timing and magnitude of southwest GrIS retreat and readvance in response to Holocene warmth, we model the response of the solid Earth and local relative sea level (RSL) to past ice sheet change. I compare model predictions to observations of paleo and present-day RSL and present-day vertical land motion around Nuuk, Greenland. I find that the southwest GrIS minimum extent likely occurred between 5 and 3 ka and that the historical maximum extent was likely approached between 2 and 1 ka. Comparing this timing to local and regional records of temperature and ice-sheet change suggest that the evolution of the southwestern GrIS presented here was in-phase with the likely evolution of southwestern GrIS mass balance through the Holocene. In the second chapter, I assess the performance of a regional climate model in simulating the spatiotemporal variability of GrIS ice extent and ice albedo in the period 2000-2021. A large portion of runoff from the GrIS originates from exposure of the darker ice in the ablation zone when the overlying snow melts, where surface albedo plays a critical role in modulating the energy available for melting. Ice albedo is spatially and temporally variable and contingent on non-linear feedbacks and the presence of light-absorbing constituents. An assessment of models aiming at simulating albedo variability and associated impacts on meltwater production is crucial for improving our understanding of the processes governing these feedbacks and, in turn, surface mass loss from Greenland. Our findings suggest that the regional climate model Modèle Atmosphérique Régional (MAR) overestimates ice albedo on average by 22.8 % compared to the ice albedo observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). We also find that this ice albedo bias can lead to an underestimation of total meltwater production from the GrIS ice zone of 42.8 %. In the third chapter, I build upon the second chapter and present PIXAL, a physics-informed explainable machine learning architecture for Greenland ice albedo modeling. PIXAL is an Extreme Gradient Boosting (XGBoost) model and is trained on a suite of modeled topographic, atmospheric, radiative, and glaciologic variables from MAR to capture the complex and non-linear relationships with ice albedo observations from MODIS in the period 2000-2021. PIXAL outperforms MAR in modeling ice albedo on the southwestern GrIS. The performance metrics show that PIXAL achieves an R2 of 0.563, an SSIM of 0.620, an MSE of 0.005, and a MAPE of 14.699%, compared to MAR’s R2 of 0.062, SSIM of 0.112, MSE of 0.032, and MAPE of 46.202%. Explainable artificial intelligence (XAI) analysis reveals that topographic features, specifically ice sheet surface height and slope, are primary drivers of ice albedo. Near-surface air temperature and runoff further impact ice albedo. These findings highlight that understanding the complex physical processes underlying ice albedo variability is essential for accurate climate modeling and sea level rise predictions. PIXAL represents a crucial advancement in ice albedo modeling and paves the way for improved climate models that can more accurately estimate GrIS ice surface melting and its contribution to sea level rise. Overall, my results have implications for future ice sheet modeling studies targeting Greenland and provide a deeper understanding of the interactions between the climate and the cryosphere and thus of future ice sheet change.