This proposal is based on a fundamentally important and previously unexpected change to our understanding of the marine inorganic carbon cycle. It follows our recent revelation that calcium carbonates excreted by fish make a significant contribution but our current estimates vary over more than a 10-fold range (3 to 45 % of global marine carbonate). BACKGROUND: As humans burn more fossil fuels, atmospheric concentrations of the 'greenhouse gas' carbon dioxide (CO2) rise contributing to climate change. Atmospheric CO2 is in balance with CO2 dissolved in the oceans, in something referred to as the marine-atmospheric carbon cycle. Whatever happens to CO2 in the oceans, will ultimately have an effect on CO2 in the atmosphere, and hence can influence global climate. When CO2 dissolves in seawater, it forms bicarbonate ions. An important part of the marine-atmospheric carbon cycle is the reaction of this bicarbonate with seawater calcium to produce a solid precipitate of white calcium carbonate (the mineral found in limestone). These precipitates are very dense, and sink to the ocean bottom in a continuous 'rain' of white crystals. The rate at which they form and sink (or re-dissolve) is important in the marine carbon cycle. The majority of calcium carbonate is generated by marine life that promotes this reaction to make a hard protective 'shell'. The most famous organisms involved in this 'biogenic' calcification are corals. However, those thought to produce the most are actually microscopic phytoplankton called coccolithophores that live in the open ocean. Dense skeletons of these and other microscopic organisms are normally considered to be the only important source of marine carbonates. Scientists collect samples in deep ocean traps, to measure this carbonate 'rainfall' for use in computer models of the carbon cycle. We have recently discovered that marine fish also produce substantial amounts of precipitated calcium carbonate, but for a very different purpose. They produce it in their intestines, by drinking large volumes of seawater and actively promoting the reaction of seawater calcium with bicarbonate ions that are produced by their own metabolism. Fish then excrete the precipitated calcium carbonate into the surrounding seawater, where it probably mixes with all the better known (planktonic) sources. In fact, some tropical fish will excrete calcium carbonate equivalent to its own dry body weight every year! We have conservatively estimated that the contribution of fish may be up to 45% of the total global carbonate production. This novel discovery suggests that fish also contribute to the marine carbon cycle, but scientists who model this cycle have never previously taken this into account. Indeed, the unusual chemistry of fish carbonates (which are more soluble than carbonate from more traditional sources), may explain a phenomenon that has puzzled oceanographers for decades - the rapid dissolution of 'apparently insoluble' carbonates in the upper layers of the ocean. Our research is a multi-disciplinary project that for the first time aims to precisely model how much calcium carbonate is produced by marine fish under different environmental conditions and determine its fate within in our oceans. This will also help with predictions about how carbonate excretion by marine fish will be affected by future environmental changes, such as temperature and CO2. We predict that fish will become even more important in this regard in the future, whereas marine plankton will become less important. Thus a precise understanding of this fish contribution to the global marine carbon cycle is both a novel and environmentally important topic.

North west Europe has a relatively mild climate in part because of heat pulled north through the Atlantic by the overturning. There is a risk that global warming will cause this circulation to rapidly decrease with consequences involving not only colder winters for Europe but also changes in sea level and precipitation. This project will carry out a risk assessment of rapid changes of the Atlantic overturning. We will use two models of the climate system, HADCM3, the Hadley Centre model used in the IPCC AR4, and CHIME, a global climate model developed at the National Oceanography Centre, Southampton. This has the same atmospheric model as HADCM3 but has a very different structure to the ocean component. Making use of the resources of climateprediction.net we will run a large ensemble of both models to assess the uncertainties in the system. We will then use modern Bayesian statistical techniques to combine model output, data and expert opinion in our risk assessment. An assessment of the utility of the data from the RAPID-WATCH arrays is an important aim of the project.

Society is becoming increasingly aware of climate change and its consequences for us. Examples of likely impacts are changes in food production, increases in mortality rates due to heat waves, and changes in our marine environment. Despite such emerging knowledge, precise predictions of future climate are (and will remain) unattainable owing to the fundamental chaotic nature of the climate system and to imperfections in our understanding, our climate simulation models and our observations of the climate system. This situation limits our ability to take effective adaptation actions. However, effective adaptation is still possible, particularly if we assess the level of precision associated with predictions, and thus quantify the risk posed by climate change. Coupled with assessments of the limitations on our knowledge, this approach can be a powerful tool for informing decision makers. Clearly, then, the quantification of uncertainty in the prediction of climate and its impacts is a critical issue. Considerable thought has gone into this issue with regard to climate change research, although a consensus on the best methods is yet to emerge. Climate impacts research, on the other hand, has focussed primarily on a different set of problems: what are the mechanisms through which climate change is likely to affect for example, agriculture and health, and what are the non-climatic influences that also need to be accounted for? Thus the research base for climate impacts is sound, but tends to be less thorough in its quantification of uncertainty than the physical climate change research that supports it. As a result, statements regarding the impacts of climate change often take a less sophisticated approach to risk and uncertainty. The logical next stage for climate impacts research is therefore to learn from the methods used for climate change predictions. Since climate and its impacts both exist within a broader earth system, with many interrelated components, this next stage is not a simple transfer of technology. Rather, it means taking an 'end-to-end' integrated look at climate and its impacts, and assessing risk and uncertainty across whole systems. These systems include not only physical and biological mechanisms, but also the decisions taken by users of climate information. The climate impacts chosen in EQUIP have been chosen to cover this spectrum from end to end. As well as aiding impacts research, end-to-end analyses are also the logical next stage for climate change research, since it is through impacts that society experiences climate change. The project focuses primarily on the next few decades, since this is a timescale of relevance for societies adapting to climate change. It is also a timescale at which our projections of greenhouse gas emissions are relatively well constrained, thus uncertainty is smaller than for, say, the end of the century. Work on longer timescales will also be carried out in order to gain a greater understanding of uncertainty. EQUIP research will build on work to date on the mechanisms and processes that lead to climate change and its impacts, since it is this understanding that forms the basis of predictive power. This knowledge is in the form of observations and experiments (e.g. experiments on crops have demonstrated that even brief episodes of high temperatures near the flowering of the crop can seriously reduce yield) and also simulation models. It is through effective use and combination of climate science and impacts science, and the models used by each community, that we will be able to quantify uncertainty, assess risk, and thus equip society to deal with climate change.

The Earth's climate sensitivity / how much it warms as greenhouses gases increase, is arguably the most important 'unknown' in predictions of climate change. Models give a range of approximately 1.5 - 4.5 K for the increase in equilibrium global mean temperature expected when carbon dioxide is doubled. Recently scientists have attempted to use combinations of observations and models to constrain this range / but if anything the range has increased. Uncertainties, mainly in the cloud feedback but also in other feedbacks such as water vapour and ice, account for these large differences between the climate models. These climate feedbacks act to either amplify or reduce the initial effects of the climate change mechanism. Water vapour is the largest positive feedback and acting alone is believed to increase by an amount which roughly doubles the effectiveness of the initial greenhouse gas perturbation. Prime objective: - To evaluate the four main feedback terms in the climate system using observed varaibles. The feedbacks evaluated will be 1) water vapour, 2) clouds (specifically cloud amount, cloud height and cloud optical depth), 3) lapse-rate and 4) surface albedo. A variety of global-scale observations will be combined from many sources and these will be incorporated into offline radiative transfer calculations to gauge the role of these feedbacks in modifying the global energy balance. Uncertainty assessment: - Both the proposed methodology and other more conventional methodologies of calculating climate feedbacks will be assessed in climate model simulations from project partners at the Hadley Centre. These feedback calculations with their model output will be of direct benefit to the Centre who to date have not calculated these feedback terms within their model. These model and data comparisons will be used to: test and assess assumptions used in the proposed methodology, and to quantify realistic uncertainties for each of the feedback terms. - A parallel energy budget calculation by project partners at the NASA Goddard Institute for Space Studies (GISS) will also be used to gauge uncertainty estimates from our analyses. Secondary objectives: - The second aim of the project employs similar methodologies to those of the prime aim to analyse feedbacks on both shorter timescales and on regional scales, and will also analyse feedbacks for different regimes. This work will be used to design diagnostic tests of feedback mechanisms in climate models. Here we will make use of the regime analysis of feedbacks already undertaken by the Hadley Centre. - The third aim of the study is to test the linear model of climate feedbacks: here we will use two different methodologies to evaluate the linear and non linear components of these feeback terms, testing assumptions of non-linearity. Additional output: - We will produce a synthetic dataset of the top-of-atmosphere fluxes, which we will make available to the wider community for their own model evaluation exercises. In summary the project will attempt to quantify some of the largest 'unknowns' in our predictions of global climate change. It will also develop diagnostic tests for feedback analysis in climate models. Overall it will lead to better and more trustworthy climate model predictions, which would not only be of great benefit to the climate modelling community, it would also benefit policy makers who need to rely on the accuracy of such climate model predictions.

Climate change is one of the most important challenges facing societies in the coming century but there are important gaps in our understanding of how climate change might affect local and regional scale hydrology. In particular, we do not know how European rainfall patterns might change. Observations of rainfall suggest that there have been increases in northern and central Europe, especially in winter, and also increases in rainfall intensity. These changes are consistent with atmospheric physics which indicate that warmer air can hold more moisture. We use climate models to examine how climate might change in the future and these suggest more frequent and intense heavy rainfall even in regions experiencing lower rainfall totals. This may cause an increase in the risk of flooding of the sort witnessed over the last decade across the UK and Europe. Although climate model ability to simulate observed processes has improved in recent years, there are still biases in their outputs due to uncertainties in the levels of future greenhouse gas emissions, due to the large-scale resolution of climate models compared to many natural processes and due to natural variations in the climate. There is also a lack of climate model simulations on the small scale needed to model some of the heaviest rainfall events, in particular summer storms. This research advances the study of extreme climate events by looking at the causes of climate model biases in the simulation of extreme rainfall, particularly with regards to heavy summer storms. We will first identify the historical characteristics of heavy rainfall using observed storms and, after we have identified the atmospheric causes for these events, we will try to provide physically-based explanations for any detected trends. Climate models represent physical processes in different ways and this can have an important influence on the simulation of heavy rainfall. We will assess which of these affect the simulation of heavy rainfall by comparing different model simulations with observations. Weather forecasting and climate models will also be run at a 1.5km resolution to see if such models are able to tell us more about how heavy rainfall events such as thunderstorms might change in the future. This research will provide new estimates of future changes to heavy rainfall and examine the atmospheric mechanisms responsible for such changes. This information will tell us which aspects of heavy rainfall and relevant processes are simulated well by models and which projections for the future we should use in informing any adaptation to climate change. Those that are not will be identified and this research will provide guidance on improvements that are needed in the next generation of climate models as well as weather forecasting models. As we use many different climate models, we can also produce estimates of how uncertain we are about future changes in extreme rainfall and flood risk. The summer 2007 floods cost the UK over £3 billion and the UK Government has announced increased annual budgets for flood risk management that will reach £800 million by 2010 but when and should this investment be prioritised. The Pitt Review in 2008 suggested that more information is needed for 'urgent and fundamental changes in the way the country is adapting to the likelihood of more frequent and intense periods of heavy rainfall'. We need to know how heavy rainfall and flood risks may change in the future, particularly for surface water flooding which is very poorly understood. The information provided by this research is vital for agencies responsible for future flood risk planning and management such as the Environment Agency, DEFRA and the Emergency Services and crucial for updating the climate change allowances used in flood risk management.