Rain fed maize is an important staple food crop for rural communities in Southern Africa as it plays a major role in ensuring food security and improving livelihoods. Rainfall consistency and intensity is an essential requirement for successful maize growing seasons. The variability of intra-seasonal rainfall characteristics such as onset, cessation and wet and dry spells threatens maize yields in Southern Africa. Previous studies have focused on the impacts of seasonal rainfall totals on maize yields. The aim of this study is to investigate the impacts of intra-seasonal rainfall variability on the maize growing season of Limpopo Province, South Africa from 1990 to 2014. A Self-Organizing Map (SOM) is used to identify and distinguish synoptic states and patterns that are conducive for growing maize in the province from those that are not. The SOM is trained using daily mean Geopotential height reanalysis data, composites for rainfall and moisture are then analysed to understand surface responses. CHIRPS daily rainfall data is used to analyse the variability of rainfall characteristics. The relationship between these rainfall characteristics and maize yield is evaluated to assess the impacts of variability on maize yields. The SOM shows that summer maize growing season is characterised by low pressure systems over the mainland which act as tropical sources of moisture and the formation of cloud-bands associated with Tropical Temperate Troughs. There is a trend in late rainfall onset and earlier cessation leading to a shift and shortening of the rainy season. The shifted and shortened rainy seasons are characterised by dry spells and high intensity rainfall events and are potentially more suitable for planting the shorter season maize cultivars. Regardless of these agrometeorological conditions being detrimental to yields, district level and provincially averaged maize yields show an overall increasing trend. This is a result of improved farming methods such as planting drought resistant short season yellow maize cultivars which can withstand dry spells.

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The freshwater input from the Arctic into the North Atlantic is an important component of the global climate system through its effects on deepwater formation. Part of this freshwater is transported through the Canadian Arctic Archipelago (CAA) via sea ice and low density surface water, where it is able to set up buoyancy boundary currents (BBCs). To infer the existence of freshwater transport via BBCs in the southwest CAA, data are examined from summer cruises conducted in 1995, 1999, and 2000. The hydrographic data are supplemented with traditional knowledge relevant to this study. The presence, predominantly on the south side of channels, of driftwood originating from the Mackenzie River confirms an eastward transport through the region. The hydrographic data also show that the southwest CAA is relatively fresh compared to surrounding regions, and that the sources of buoyancy forcing are large and from different origins. The presence of BBCs on both sides of the channels appears to be a frequent occurrence with, as shown in previous work, the fresher water being more often present on the south shore. Some data from the summer 2000 show a different feature with much fresher water on the north side. A subsequent strong wind event creates a complete reversal of this situation, setting up a strong cross-channel horizontal salinity gradient and an amplified BBC on the south shore. In this region, buoyancy and wind forcing act together to force an eastward freshwater transport in the southwest CAA. / Graduate

Convective snow bands develop commonly over the open water surface of lakes or seas when cold airgets advected from a continent. Enhanced heat and moisture fluxes from the comparatively warm waterbody trigger shallow convection and an unstable boundary layer builds up. Relatively strong wind canorganize this convection into wind-parallel quasi-stationary cloud bands with moving individual cells.Depending on various factors like the horizontal wind, the vertical shear or the shape of the coast, thosecloud bands can form of different strength and structure. When the air mass meets the coast orographicforcing causes horizontal convergence and vertical lifting intensifies the precipitation at the coast. If thewind direction stays constant for several days a single snow band would accumulate its precipitation ina very restricted region and cause locally a significant increase in snow depth. This process leads in thecold season repeatedly to severe precipitation events at the Swedish east coast. Large amounts of snowalong with strong wind speeds can cause serious problems for traffic and infrastructure.Two different cases of convective snow bands in the Baltic Sea area were selected to simulate theassociated atmospheric conditions with a total of five different model systems. The atmosphere climatemodel RCA has been used independently at default settings as well as with increased resolution on avertical and a horizontal scale and furthermore coupled either to the ice-ocean model NEMO or the wavemodel component WAM.Comparing all models the crucial parameters like wind, temperature, heat fluxes, and precipitationvary generally in a reasonable range. However, the model systems show systematical differences amongthemselves. The strongest 10 meter wind speeds can be observed for both RCA models with increasedresolution. The RCA-WAM simulation shows its wind enhancement during the snow band event witha time shift to the other models by several hours. The mean directional wind shear above the Gulf ofBothnia, the snow band’s region of origin, is for all models small. The warmest sea surface temperaturesare reached by the RCA-NEMO simulation, which as a result also stands out for its most intense heatfluxes in both sensible and latent heat. Both high resolution RCA models as well as RCA-NEMO givethe most remarkable local precipitation rates. The original RCA and RCA-WAM simulate significantlyless snowfall. Local comparison with SMHI station measurements show that the models represent thetrend of wind, temperature and precipitation evolution well. However, all models decelerate the air masstoo rapidly when meeting the coast. Moreover, it remains a challenge to simulate the exact time andlocation of the extreme precipitation.The coupling of the atmosphere model with the ice-ocean model as well as the increased resolution ofthe atmospheric component have been observed to show great improvements in the model performanceand are suggested for future research work to be used in combination with each other for the regionalmodelling of convective snow bands in the Baltic Sea area.

To date, no observation-based proxy for climate change has been successful in quantifying the feedbacks between clouds and climate. The most promising, yet demanding, avenue to gain confi dence in cloud–climate feedback estimates is to utilize observations and large-eddy simulations (LES) or cloud-resolving modeling (CRM) to improve cloud process parameterizations in large-scale models. Sustained and improved satellite observations are essential to evaluate large-scale models. A reanalysis of numerical prediction models with assimilation of cloud, aerosol, and precipitation observations would provide a valuable dataset for examining cloud interactions. The link between climate modeling and numerical weather prediction (NWP) may be exploited by evaluating how accurate cloud characteristics are represented by the parameterization schemes in NWP models. A systematic simplifi cation of large-scale models is an important avenue to isolate key processes linked to cloud–climate feedbacks and would guide the formulation of testable hypotheses for fi eld studies. Analyses of observation-derived correlations between cloud and aerosol properties in combination with modeling studies may allow aerosol–cloud interactions to be detected and quantifi ed. Reliable representations of cloud dynamic and physical processes in large-scale models are a prerequisite to assess aerosol indirect effects on a large scale with confi dence. To include aerosol indirect effects in a consistent manner, we recommend that a “radiative fl ux perturbation” approach be considered as a complement to radiative forcing.

This dissertation aims to better understand how various climate modeling approaches affect the fidelity of the North American Monsoon (NAM), as well as the sensitivity of the future state of the NAM under a global warming scenario. Here, we improved over current fully-coupled general circulation models (GCM), which struggle to fully resolve the controlling dynamics responsible for the development and maintenance of the NAM. To accomplish this, we dynamically downscaled a GCM with a regional climate model (RCM). The advantage here being a higher model resolution that improves the representation of processes on scales beyond that which GCMs can resolve. However, as all RCM applications are subject to the transference of biases inherent to the parent GCM, this study developed and evaluated a process to reduce these biases. Pertaining to both precipitation and the various controlling dynamics of the NAM, we found simulations driven by these bias-corrected forcing conditions performed moderately better across a 32-year historical climatology than simulations driven by the original GCM data. Current GCM consensus suggests future tropospheric warming associated with increased radiative forcing as greenhouse gas concentrations increase will suppress the NAM convective environment through greater atmospheric stability. This mechanism yields later onset dates and a generally drier season, but a slight increase to the intensity during July-August. After comparing downscaled simulations forced with original and corrected forcing conditions, we argue that the role of unresolved GCM surface features such as changes to the Gulf of California evaporation lead to a more convective environment. Even when downscaling the original GCM data with known biases, the inclusion of these surface features altered and in some cases reversed GCM trends throughout the southwest United States. This reversal towards a wetter NAM is further magnified in future bias-corrected simulations, which suggest (1) fewer average number of dry days by the end of the 21st century (2) onset occurring up to two to three weeks earlier than the historical average, and (3) more extreme daily precipitation values. However, consistent across each GCM and RCM model is the increase in inter-annual variability, suggesting greater susceptibility to drought conditions in the future.

Volvo Group Trucks Technology has found a need for a new way to present the battery status and electricity consumption of their on-board batteries in combustion engine trucks. Many battery related issues the drivers are facing could be prevented if a tool was developed that could assist with energy planning in an intuitive way. In many cases, the climate control system will constitute the bulk of the energy supplied by the battery. In addition, the climate system energy demand is dependent on both user settings and factors beyond the driver’s control. This work describes the process of developing a grey-box Simulink model able to predict the battery charge depletion rate based on signals already sampled by many Volvo truck versions. The resulting model is able to estimate the time remaining until the battery state of charge (SOC) is getting close to the crankability (starting engine) limit or risks causing battery damage. The settings of the climate system are shown to have great impact on the battery charge depletion rate. Predicting the time until the battery will reach a critical limit, and adjusting the climate system settings accordingly, can make the difference between the battery charge lasting overnight or not. A way to implement additional influences, such as sunlight, are discussed and recommendations are given.

To date, no observation-based proxy for climate change has been successful in quantifying the feedbacks between clouds and climate. The most promising, yet demanding, avenue to gain confi dence in cloud–climate feedback estimates is to utilize observations and large-eddy simulations (LES) or cloud-resolving modeling (CRM) to improve cloud process parameterizations in large-scale models. Sustained and improved satellite observations are essential to evaluate large-scale models. A reanalysis of numerical prediction models with assimilation of cloud, aerosol, and precipitation observations would provide a valuable dataset for examining cloud interactions. The link between climate modeling and numerical weather prediction (NWP) may be exploited by evaluating how accurate cloud characteristics are represented by the parameterization schemes in NWP models. A systematic simplifi cation of large-scale models is an important avenue to isolate key processes linked to cloud–climate feedbacks and would guide the formulation of testable hypotheses for fi eld studies. Analyses of observation-derived correlations between cloud and aerosol properties in combination with modeling studies may allow aerosol–cloud interactions to be detected and quantifi ed. Reliable representations of cloud dynamic and physical processes in large-scale models are a prerequisite to assess aerosol indirect effects on a large scale with confi dence. To include aerosol indirect effects in a consistent manner, we recommend that a “radiative fl ux perturbation” approach be considered as a complement to radiative forcing.

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.

Recent advancements in processing power, storage capabilities, and availability of data, has led to improvements in many applications through the use of machine learning. Using machine learning in control systems was first suggested in the 1990s, but is more recently being implemented. In this thesis, an embedded climate system, which is a type of control system, will be looked at. The ways in which machine learning can be used to replicate portions of the climate system is looked at. Deep Belief Networks are the machine learning models of choice. Firstly, the functionality of a PID controller is replicated using a Deep Belief Network. Then, the functionality of a more complex control path is replicated. The performance of the Deep Belief Networks are evaluated at how they compare to the original control portions, and the performance in hardware. It is found that the Deep Belief Network can quite accurately replicate the behaviour of a PID controller, whilst the performance is worse for the more complex control path. It was seen that the use of delays in input features gave better results than without. A climate system with a Deep Belief Network was also loaded onto hardware. The minimum requirements of memory usage and CPU usage were met. However, the CPU usage was greatly affected, and if this was to be used in practice, work should be done to decrease it. / Många applikationer har förbättras genom användningen av maskininlärning. Maskininlärning för reglersystem föreslogs redan på 1990-talet och har nu börjat tillämpas, eftersom processorkraft, lagringsmöjligheter och tillgänglighet till rådata ökat. I detta examensarbete användes ett inbäddat klimatsystem, som är en typ av reglersystem. Maskininlärningsmodellen Deep Belief Network användes för att undersöka hur delar av klimatsystemet skulle kunna återskapas. Först återskapades funktionaliteten hos en PID-regulator och sedan funktionaliteten av en mer komplex del av reglersystemet Prestandan hos nätverken utvärderades i jämförelse med prestandan i de ursprungliga kontrolldelarna och hårdvaran. Det visade sig att Deep Belief Network utmärkt kunde replikera PID-regulatorns beteende, medan prestandan var lägre för den komplexa delen av reglersystemet. Användningen av fördröjningar i indata till nätverken gav bättre resultat än utan. Ett klimatsystem med ett Deep Belief Network laddades också över på hårdvaran. Minimikrav för minnesanvändning och CPU- användning var uppfyllda, men CPU- användningen påverkades kraftigt. Detta gör, att om maskininlärning ska kunna användas i verkligheten, bör CPU-användningen minskas.