This research explored the role of community-led initiatives in encouraging the uptake of more sustainable lifestyles within the social and physical context of remote rural Scotland. Participant observation with Arlen Eco Trust (AET) and Thriving Thornton (TT), two community-led sustainability initiatives funded by the Scottish Government’s Climate Challenge Fund (CCF), led to findings which challenge the common assumption that funding for community-led initiatives will be of net benefit at the local level. In line with the requirements of the CCF, both AET and TT define community in terms of geography. However, only a small minority of the members of the geographically-defined communities of Arlen and Thornton were found to be actively involved in the groups’ activities or objectives. Both Arlen and Thornton were observed to be segmented into multiple and diverse ‘communities within communities’ and, rather than representing ‘the community’, AET and TT can more accurately be understood as an example of sub-communities in themselves. This sub-division within the communities was found to be exacerbated by the fact that both the governance and management of AET and TT were observed to be undertaken primarily by individuals regarded as ‘incomers’ to Arlen and Thornton, which resulted in an ‘incomer’ identity being passed on to the group and its activities. Historic connotations with ‘incomers’ as disruptive to traditional ways of life were found to resonate with the suspicion and scepticism expressed by some ‘locals’ wary of ‘incomer’ groups that were actively trying to change local lifestyles. The groups’ ability to engage with the wider geographic community was also observed to be further weakened in several ways by the receipt of government grant funding. The short timescales and expected outputs associated with many funding schemes were found to be discordant with the long-term sustainability goals of the community groups studied, and participation in top-down funding programmes was found to reduce the time and resources available for ‘hands on’ community participation activities. Furthermore, the need for groups to adapt their ambitions and approach to align with top-down demands from funders is incongruent with the notion of a ‘community-led’ initiative. Together, these local conditions were found to have significant implications with respect to the impact and influence of AET and TT. The funding received by the groups was found to create pockets of social capital – rather than being distributed through the geographic community – which served to strengthen the group, but segment the wider population, implying that, rather than increasing local social sustainability, schemes such as the CCF may be undermining it. Overall, this thesis concludes that, whilst the CCF was observed to facilitate community as a means by which to reduce carbon emissions, ‘community’ was not being strengthened as a policy end. As such, it questions whether current mechanisms of central government funding for isolated, self-identified community-led groups to deliver finite, output-driven projects will inherently help to empower geographic communities to adopt more sustainable lifestyles.

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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.

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.

River discharge can affect ocean surface temperature by altering stratification within the oceanic mixed layer. A hitherto unexplored aspect of present climate is the feedback of river runoff onto climate. This thesis presents an investigation of the impact of global river runoff on oceans and climate using a fully coupled global climate model, Community Climate System Model (CCSM). Two model simulations for a period of 100 years have been carried out: 1) a reference run (CTRL) that incorporates all the features of a global coupled model with river runoff into the ocean embedded in it, and 2) a sensitivity run (NoRiv) in which the global river runoff into the ocean is blocked. Comparison of model climate devoid of fluvial discharge with the reference run reveals the significance of fluvial discharge in the present climate. By the end of 50 years of NoRiv experiment, salinity growth slows down and reaches a quasi-stable state. Regions close to river mouths exhibited maximum salinity rise that can potentially alter local density and stratification. On an average, denser and saltier waters in the NoRiv run annihilate barrier layer and form a deeper mixed layer, compared to CTRL run. Density gradient created by the modulation in salinity set forth anomalous currents and circulation across coastlines that carries coastal anomalies to open ocean, preventing local salinity buildup. Arctic Ocean, Bay of Bengal, northern high latitude Pacific and the Atlantic are the most affected regions in terms of changes in salinity and temperature. Model simulations demonstrate that major transformation in Arctic freshwater budget can have potential impact on northern Pacific and Atlantic climate. In the absence of runoff, global average sea surface temperature (SST) rise by about ~ 0.5oC, with major contribution from northern higher latitude oceans. In the Pacific, high latitude warming is related to deepening of mixed layer as well as the northward transport of low latitude warmer waters. Substantial cooling in the central equatorial Pacific (~1oC during winter) can alter large-scale ocean-atmosphere circulation, including El Niño-Southern Oscillation (ENSO). The reinforcement of Pacific and Atlantic western boundary currents aids the transport of warm saline water from low latitudes to higher latitudes. The results suggest that the river runoff can have potential impact on oceanic climate. Response of Indian summer monsoon rainfall to global continental runoff is also examined. In the NoRiv run, average summer monsoon rainfall over India increased by ~ 0.55 mm day−1. Consistent with the increase in annual average Indian monsoon rainfall, all other northern hemispheric monsoon systems showed an increase, while southern hemispheric monsoons weakened. Associated with enhanced monsoon, the periodicity of ENSO in the NoRiv run changes as a result of cooling tendency in the equatorial Pacific, a sign of consistent La Niña. Equatorial Pacific cooling, in spite of a global ocean warming trend, is found to be primarily because of the enhanced local easterly winds and resultant strong equatorial upwelling. Cold anomaly due to upwelling spread entire equatorial Pacific basin within a span of 50 years. The La Niña situation in the Pacific favored increased monsoon rainfall over Indian subcontinent. Another surprising result of this study is the strengthening of ENSO-monsoon relationship in the NoRiv run. This suggests that the river discharge can be considered as a dampening force in the ENSO-monsoon relationship. Northern hemisphere showed a clear warming in the NoRiv simulation compared to CTRL, the result of which is an enhanced trans-hemispheric gradient. Cross-equatorial winds triggered by this gradient blow from southern hemisphere and shift the Inter Tropical Convergence Zone (ITCZ) northward, increasing the precipitation in the northern hemisphere. The cooling in the eastern equatorial Indian Ocean and the warming in the west, reflected in the increase in number of positive Indian Ocean Dipole (IOD) events (9 positive and 5 negative IOD events in the last 50 years), also favored summer-time rainfall over India.

Indian summer monsoon (ISM) is an important component of the tropical climate system, known for its regular seasonality and abundance of rainfall over the country. The droughts and floods associated with the year-to-year variation of the average seasonal rainfall have devastating effect on people, agriculture and economy of this region. The demand for prediction of seasonal monsoon rainfall, therefore, is overwhelming. A number of attempts to predict the seasonal mean monsoon have been made over a century, but neither dynamical nor empirical models provide skillful forecasts of the extremes of the monsoon such as the unprecedented drought of 2002. This study investigates the problems and prospects of extended range monsoon prediction. An evaluation of the potential predictability of the ISM with the aid of an ensemble of Atmospheric General Circulation Model (AGCM) simulations indicates that the interannual variability (IAV) of ISM is contributed equally by the slow boundary forcing (‘externally’ forced variability) and the inherent climate noise (‘internal’ variability) in the atmosphere. Success in predicting the ISM would depend on our ability to extract the predictable signal from a background of noise of comparable amplitude. This would be possible only if the ‘external’ variability is separable from the ‘internal’ variability. A serious effort has been made to understand and isolate the sea surface temperature (SST) forced component of ISM variability that is not strongly influenced by the ‘internal’ variability. In addition, we have investigated to unravel the mechanism of generation of ‘internal’ IAV so that the method of isolating it from forced variability may be found. Since the primary forcing mechanism of the monsoon is the large-scale meridional gradient of deep tropospheric heat sources, large-scale changes in tropospheric temperature (TT) due to the boundary forcing can induce interannual variations of the timing and duration of the monsoon season. The concept of interannually varying monsoon season is introduced here, with the onset and withdrawal of monsoon definitions based on the reversal of meridional gradient of TT between north and south. This large scale definition of the monsoon season is representative of the planetary scale influence of the El Ni˜no Southern Oscillation (ENSO) on monsoon through the modification of TT and the cross equatorial pressure gradient over the ISM region. A sig- nificant relationship between ENSO and monsoon, that has remained steady over the decades, is discovered by which an El Ni˜no (La Ni˜na) delays (advances) the onset, advances (delays) the withdrawal and suppresses (enhances) the strength of the monsoon. The integral effect of the meridional gradient of TT from the onset to withdrawal proves to be a useful index of seasonal monsoon which isolates the boundary forced signal from the influence of internal variations that has remained steady even in the recent decades. However, consistent with the estimates of potential predictability, the boundary forced variability isolated with the above definitions explains only about 50% of the total interannual variability of ISM. Detailed diagnostics of the onset and withdrawal processes are performed to understand how the ENSO forcing modifies the onset and withdrawal, and thus the seasonal mean monsoon. It is found that during an El Ni˜no, the onset is delayed due to the enhanced adiabatic subsidence that inhibits vertical mixing of sensible heating from the warm landmass during pre-monsoon months, and the withdrawal is advanced due to the horizontal advective cooling. This link between ENSO and monsoon is realized through the advective processes associated with the stationary waves in the upper troposphere set up by the tropical ENSO heating. The remaining 50% of the monsoon IAV is governed by internal processes. To unravel the mechanism of the generation of internal IAV, we perform another set of AGCM simulations, forced with climatological monthly mean SSTs, to extract the pure internal IAV. We find that the spatial structure of the intraseasonal oscillations (ISOs) in these simulations has significant projection on the spatial structure of the seasonal mean and a common spatial mode governs both intraseasonal and interannual variability. Statistical average of ISO anomalies over the season (seasonal ISO bias) strengthens or weakens the seasonal mean. It is shown that interannual anomalies of seasonal mean are closely related to the seasonal mean of intraseasonal anomalies and explain about 50% of the IAV of the seasonal mean. The seasonal mean ISO bias arises partly due to the broadband nature of the ISO spectrum, allowing the intraseasonal time series to be aperiodic over the season and partly due to a non-linear process where the amplitude of ISO activity is proportional to the seasonal bias of ISO anomalies. The later relationship is a manifestation of the binomial character of the rainfall time series. The remaining part of IAV may arise due to the complex land-surface processes, scale interactions, etc. We also find that the ISOs over the ISM region are not significantly modulated by the Pacific and Indian Ocean SST variations. Thus, even with a perfect prediction of SST, only about 50% of the observed IAV of ISM could be predicted with the best model in forced mode. Even so, prediction of all India rainfall (AIR) representing the average conditions of the whole country and the season may not always serve the purposes of monsoon forecasting. One reason is the large inhomogeneities in the rainfall distribution during a normal seasonal monsoon. Agriculture and hydrological sector could benefit more if provided with regional scale forecasts of active/break spells 2-3 weeks ahead. Therefore, we advocate an alternative strategy to the seasonal prediction. Here, we present a method to estimate the potential predictability of active and break conditions from daily rainfall and circulation from observations for the recent 24 years. We discover that transitions from break to active conditions are much more chaotic than those from active to break, a fundamental property of the monsoon ISOs. The potential predictability limit of monsoon breaks (∼20 days) is significantly higher than that of the active conditions (∼10 days). An empirical real- time forecasting strategy to predict the sub-seasonal variations of monsoon up to 4 pentads (20 days) in advance is developed. The method is physically based, with the consideration that the large-scale spatial patterns and slow evolution of monsoon intraseasonal variations possess some similarity in their evolutions from one event to the other. This analog method is applied on NOAA outgoing longwave radiation (OLR) pentad mean data which is available on a near real time basis. The elimination of high frequency variability and the use of spatial and temporal analogs produces high and useful skill of predictions over the central and northern Indian region for a lead-time of 4-5 pentads. An important feature of this method is that, unlike other empirical methods to forecast monsoon ISOs, this uses minimal time filtering to avoid any possible end-point effects, and hence it has immense potential for real-time applications.