Peatlands store ~30% of global soil organic carbon (SOC) and are frequently carbon dioxide (CO2) sinks, while also being sources of methane (CH4) due to anaerobic decomposition under waterlogged soil conditions. Hence, the role of peatlands in the radiative forcing of the Earth’s atmospheric system and their impact on the global climate system is complex. This study presents the first long-term direct flux measurements of land-atmosphere CO2 and CH4 exchange at a temperate lowland fen peatland in East Anglia, UK. The dynamics and magnitude of CO2, H2O, CH4 and energy fluxes were quantified using the eddy covariance (EC) technique at two sites: a former-arable regenerating site (Baker’s Fen, BF) and a semi-natural fen (Sedge Fen, SF) at Wicken Fen NNR. This allowed investigation and comparison of ecosystem responses to climate variability and restoration. EC measurements at BF covered three annual cycles (2013 - 2015), and at SF two and a half cycles (August 2013 - December 2015). BF acted as a net CO2 source in all years, emitting 161.03±12.51, 83.61±11.53 and 98.39±13.31 g CO2-C m-2yr-1 in 2013, 2014 and 2015, respectively; it was a net CH4 source of 6.067±0.096 g CH4-C m-2yr-1 in 2013 and 2.009±0.087 g CH4-C m-2yr-1 in 2015, and of 2.845±0.103 g CH4-C m-2 (8th April - 31st December 2014). The annual carbon balance for BF was lower than average carbon losses from arable fens, indicating that restoration can achieve net carbon emissions reduction. SF was also a net CO2 source of 297.59±9.16 g CO2-C m-2 (1st August - 31st December 2013), and a large net CO2 sink of -356.86±49.13 g CO2-C m-2yr-1 in 2014 and of -243.78±15.25 g CO2-C m-2yr-1 in 2015. Large inter-annual variability in CO2 exchange at SF indicates sensitivity to climatic conditions, and highlights the need to maintain an appropriate water level height to prevent or reduce soil carbon losses to the atmosphere as CO2.

The study of past climates provides a unique opportunity to test our understanding of the Earth system and our confidence in climate models. The nature of this subject requires a fine balance between complexity and efficiency. While comprehensive models can capture the system’s behaviour more realistically, fast but less accurate models are capable of integrating on the long timescale associated with Palaeoclimatology. In this thesis, a statistical approach is proposed to address the limitation of our simple atmospheric module in simulating glacial climates by incorporating a statistical surrogate of a general circulation model of the atmosphere into our Earth system modelling framework, GENIE. To utilise the available model spectrum of different complexities, a multi-level Gaussian Process (GP) emulation technique is proposed to established the link between a computationally expensive atmospheric model, PLASIM (Planet Simulator), and a cheaper model, EMBM (energy-moisture balance model). The method is first demonstrated by emulating a scalar summary quantity. A dimensional reduction technique is then introduced, allowing the high-dimensional model outputs to be emulated as functions of high-dimensional boundary forcing inputs. Even though the two atmospheric models chosen are structurally unrelated, GP emulators of PLASIM atmospheric variables are successfully constructed using EMBM as a fast approximation. With the extra information gained from the cheap model, the emulators of PLASIM’s 2-D surface output fields, are built at a reduced computational cost. The emulated quantities are validated against simulated values, showing that the ensemble-wide behaviour of the spatial fields is well captured. Finally, the emulator of PLASIM’s wind field is incorporated into GENIE, providing an interactive statistical wind field which responds to changes in the bound- ary condition described by the ocean module. While exhibiting certain limitation due to the structural bias in PLASIM’s wind, the new hybrid model introduces additional variations to the over-diffusive spatial outputs of EMBM without incurring a substantial computational cost.

Probabilistic event attribution (PEA) presents a method of quantifying the change in risk of extreme weather events, both in magnitude and probability of occurrence, due to an- thropogenic climate change. Studies have so far covered numerous different extreme events in different regions (Stott et al., 2016). One method of PEA relies on computing large ensembles of climate models for two different climate scenarios, one which represents the observed climate forcings, and another the climate forcings with anthropogenic greenhouse gas contributions removed. Other PEA studies using different methodologies have also been conducted. Until now there has been no formal validation of the climate models used in PEA studies. This thesis presents two ways of validating the models and applies these methods to a very large ensemble of a climate model simulating the observed climate for the period 1960 to 2010. The premise is, that for an attribution statement to be meaningful, the model should be able to accurately represent relevant weather statistics over a range of climate scenarios. In order to compare to observations, the period 1960-2010 is used as the range of scenarios. Conceived as an e-Science project, this thesis presents technical development of method- ologies in the detection and attribution of extreme weather events to climate change. The first method identifies and tracks storm-like features in meteorological data. A novel set of algorithms transforms the meteorological data to a hierarchical equal-area triangular grid, identifies storm-like feature points in the data and grows the points into objects. These objects are tracked as they evolve over time by a hybrid prediction-optimisation routine, which minimises a cost function to find a locally optimal set of feature tracks. Applying the algorithms to the large ensemble of climate models, and also to the ERA-Interim data, shows that the model can successfully capture the track length, persistence and position of low-pressure systems over Europe. However, the depression depth of the systems is not as well represented. The second method applies a forecast verification technique to the distributions of climate variables. Comparing the temperature, precipitation, mean sea level pressure (MSLP) and winds between the large ensemble and ERA-40 and ERA-Interim shows that the model can accurately represent temperature, precipitation and windspeed variables over Europe, after a bias correction has been applied. However, for low MSLP, there are irreparable biases in the low tails of the distribution. Overall, it is shown that using a large ensemble of climate models is a valid method of investigating the change in risk of extreme weather events due to climate change, as long as the variable to be attributed is carefully chosen.

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The natural environment is being altered by anthropogenic activity at an unprecedented rate. The effects of climate change and damage caused by other human activities show little sign of decelerating. The ubiquity of the forecast changes means that all species are likely to face alterations in their environment and consequently may have to migrate or adapt to avoid extinction. Yet, as species respond they may initiate additional ecological changes, creating further selection pressures on species they interact with. Research has shown that ecological and evolutionary dynamics such as these occur on the same time-scale and produce dynamics that cannot be explained if evolution is ignored. Thus, to understand and predict how species will respond to environmental change, it is important to consider both ecology and evolution and the feedback between them. In this thesis I investigate the ecological and evolutionary responses of groups of species to a number of environmental changes using experimental evolution with naturally co-occurring bacteria and mathematical modelling. After reviewing and introducing the field in Chapter 1, in Chapter 2 I investigate the effect of species diversity on evolution to a novel environment. In Chapters 3 and 4 I report on the results of a field experiment in which I manipulated immigration into diverse microbial communities while exposing them to experimental warming. Specifically, in Chapter 3 I investigate how warming and immigration affect local adaptation and also whether the local adaptation of the community is predictable from that of component species and in Chapter 4 I discuss the impact of warming on predictability. Finally, in Chapter 5 I use mathematical modelling to explore how species' evolutionary responses to stress affect their interspecific interactions. My research places particular emphasis on the importance of species interactions to evolution and, furthermore, how these changes in biotic interactions affect ecosystem functioning.

Uncertainty in model simulations arises due to the construction of the model (structural uncertainty), the representation of sub-grid scale processes (parameter uncertainty) or the input of model boundary conditions. Perturbed physics ensembles (PPEs) produce an ensemble of simulations using a single climate model. A PPE produces different representations of climate by altering the tuning of parameterisations representing processes occurring on sub-grid scales, such as clouds and radiation. A PPE has been produced to investigate model parameter and boundary condition uncertainty for the mid-Pliocene Warm Period (3.264 to 3.025 Ma BP). Through the use of a PPE, 14 versions (13 perturbed members and the Standard version) of the UK Met Office atmosphere-ocean general circulation model HadCM3 were created. The full ensemble was re-run to assess the impact of simultaneously changing physical boundary conditions for orography, ice sheets and vegetation in combination with perturbed physics. Finally the effect of the potential range in reconstructed mid-Pliocene CO2 was investigated through a sub-ensemble of the PPE. Using data-model comparisons (DMCs), the ensemble members with higher than the Standard values of Charney sensitivity were better able to simulate the magnitude of high latitude mid-Pliocene warming. The strongest performing ensemble members for the DMCs displayed Charney sensitivities of 4.54°C, 4.62°C and 5.40°C, above the upper bound of the IPCC likely range (1.5 to 4.5°C). However, these warmer members with higher Charney sensitivities weakened the data-model comparison in the tropics. Ensemble members with lower than Standard values of Charney sensitivity, close to the lower bound of the IPCC likely range, better resolved temperature reconstructions in the tropics, but were unable to resolve high latitude warming. It is evident that the PPE is able to achieve the magnitude of mPWP warming but not the spatial distribution of the warming. The investigation into boundary condition uncertainty using the PPE reveals that the PRISM3D physical boundary conditions lead to improved simulations of the mPWP climate than the PRISM2 boundary conditions. For the range of atmospheric CO2 concentrations, the results from the sub-ensemble indicate that lower values of CO2 lead to reduced performance of the PPE members compared to the palaeo-data. The conclusion is that concentrations of CO2 below 350 ppmv for the mPWP would make simulating high latitude climates very difficult for climate models.

Between mid-2006 and late-2010 the UK experienced a parabola of spectacular protests relating to climate change, ranging from the occupation of airport taxiways through to the blockade of coal power stations. Mobilizing thousands of people, this ‘radical climate movement’ was distinguished from a popular concern with climate change by its general commitment to direct action, widely-held anti-capitalist and antiauthoritarian beliefs, and a stated focus of tackling the ‘root causes’ of climate change. Written from within this ‘radical climate movement’, this thesis is an investigation into the praxis of the movement, exploring the extent to which participants contributed to the emergence of a ‘radical’ knowledge of climate change, and thus assessing the appropriateness (and effectiveness) of the movement’s methodologies. Driven by an internal debate regarding the movement’s tendency to depart from its radical political roots, the theoretical core of this thesis draws upon the concept of the ‘post-political condition’, a condition of the liberal consciousness that forecloses the very possibility of a political praxis on the climate. It is contended that a specific postpolitical discourse of ‘dangerous climate change’ emerged in the late-1980s which, defined by an apocalyptic discourse that placed a ‘carbon fetishism’ at the core of its rationale, evacuated the space for political discourse in favour of a general humanitarian effort to forestall “the greatest danger we’ve ever faced”. It is suggested that despite the efforts of many to confront the problem, the UK’s ‘radical climate movement’ broadly failed to escape this liberal discourse. The research thus turns to the international mobilizations around the COP15 in 2009, concluding that the emergence of a discourse of ‘climate justice’ was a partial attempt to overcome this post-political discourse. From the World People’s Conference on Climate Change and the Rights of Mother Earth (CMPCC) in Bolivia, to the continued organization of the Climate Justice Action (CJA) network, it is suggested that ‘climate justice’ diverged according to two separate discourses - one around ‘climate debt’ and another around anti-capitalist critique. It is finally concluded that a true politicization necessitates celebrating the death of the environmental movement, instead placing our socialreproduction at the core of any claim to an ecological politics.

This project explores the uncertainty factors in drought planning for a water resource zone in Sussex. Nine planning options from the 2009 Sussex Water Resource Management Plan were assessed using four climate products: the 2009 UK Climate Projections Change Factors, the Spatial Coherent Projections, the 11 runs of the HadRM3 regional climate model and their subsequent downscaling by the Future Flows Project. The varying drought statistics from these four climate products reflect post-processing uncertainty - the uncertainty stemming from the process of converting original climate model outputs into products of different formats, variables and temporal/spatial scales. Overall, the study has integrated a cascade analysis of climate uncertainty, climate post-processing uncertainty, hydrological uncertainty, water resource model uncertainty and demand uncertainty on water resource planning. The study combines Robust Optimisation, Decision-Scaling and Robust Decision Making into Robust Decision Analysis, a decision making framework for dynamic adaptation pathways in response to different levels of uncertainty and risk averseness. Post-processing uncertainty is the dominate uncertainty until 2030s; 2050s is then dominated by demand and socio-economic uncertainty. The most severe droughts within the Spatial Coherent Projections and the 2009 UK Climate Projection products are variations of the 1975-1976 and the 1988-1989 droughts, two of the worst historic droughts currently used as the design events for drought planning in Sussex. The system appears to be robust to variations of these past droughts. Yet, under different sequences of droughts from the HadRM3 and Future Flows products, the system demonstrated frequent supply failures in the 2050s, unless water demand is maintained at the 2007 level or lower. While operational costs in the 2030s are generally within the region of 4 to 5 million GBP per year, those in the 2050s Market Forces jumped to the region of 5 to 15 million GBP per year and with supply deficit from 0 to 1100 Ml/year. When demand grows by 35% from the 2007 baseline level, universal metering becomes a key option. Despite climate post-processing uncertainty, the main hotspots of water deficits remains similar across the climate products and are driven by network bottlenecks and the continually high dependence of the system on water sources a round the Hardham area. The study also indicates that inter-regional transfers might not be as reliable as assumed.

Marine cloud brightening (MCB) geoengineering has been proposed as a means of ameliorating anthropogenic climate change. High concentrations of nanometre-sized aerosols would be emitted from seagoing vessels, with the intention of increasing the albedo of low-lying marine stratocumulus clouds (MSc) via indirect aerosol effects. Realistic estimates of the potential effectiveness of MCB are needed to inform policy-making on climate change. However, in spite of increasing model complexity and developments in representing MCB, the relatively coarse resolution of global-scale models prevents implementation details from being captured. This work identified three previously unrepresented implementation details, and examined their importance in achieving realistic estimates of MCB effectiveness. For this, the Weather Research and Forecasting model incorporating aerosol processes (WRF/Chem) was used, allowing clouds to be resolved over a kilometre-scale domain. Firstly, for a weakly precipitating cloud regime, cloud brightening was found to be sensitive to the timing of MCB aerosol emissions. The largest cloud albedo increase occurred for early morning emissions, with little change occurring for daytime emissions. Timing was less important for the non-precipitating regime where cloud albedo perturbations were generally smaller owing to the absence of a large second indirect (or ‘cloud lifetime’) effect. Secondly, near-surface evaporative cooling resulting from the more realistic simulation of MCB emissions as wet droplets rather than the previously assumed dry aerosols reduced aerosol plume heights by up to 30% (40 m), reducing cloud albedo increases by up to one-third. Finally, aerosol coagulation within the MCB aerosol plume (simulated at sub-metre resolution) resulted in number concentration decreases of up to 50%, consequentially reducing the cloud albedo increases by approximately half for the non-precipitating regime. These findings suggest that in omitting these details, global-scale model estimates of MCB effectiveness may be exaggerated. The inclusion of these details in global-scale MCB modelling could therefore materially improve the realism of future effectiveness estimates.