10:00 to 11:00 M Thompson & J Sieber ([Cambridge and Portsmouth Universities])Climate tipping as a noisy bifurcation: a predictive techniqueSession: Tipping points In the first half of this contribution (speaker JMTT) we review the bifurcations of dissipative dynamical systems. The co-dimension-one bifurcations, namely those which can be typically encountered under slowly evolving controls, can be classified as safe, explosive or dangerous. Focusing on the dangerous events, which could underlie climate tippings, we examine the precursors (in particular the slowing of transients) and the outcomes which can be indeterminate due to fractal basin boundaries. It is often known, from modelling studies, that a certain mode of climate tipping is governed by an underlying bifurcation. For the case of a so-called fold, a commonly encountered bifurcation (of the oceanic thermohaline circulation, for example), we estimate (speaker JS) how likely it is that the system escapes from its currently stable state due to noise before the tipping point is reached. Our analysis is based on simple normal forms, which makes it potentially useful whenever this type of tipping is identified (or suspected) in either climate models or measurements. Drawing on this, we suggest a scheme of analysis that determines the best stochastic fit to the existing data. This provides the evolution rate of the effective control parameter, the (parabolic) variation of the stability coefficient, the path itself and its tipping point. By assessing the actual effective level of noise in the available time series, we are then able to make probability estimates of the time of tipping. In this vein, we examine, first, the output of a computer simulation for the end of greenhouse Earth about 34 million years ago when the climate tipped from a tropical state into an icehouse state with ice caps. Second, we use the algorithms to give probabilistic tipping estimates for the end of the most recent glaciation of the Earth using actual archaeological ice-core data. INI 1 11:00 to 11:30 Morning CoffeeSession: Tipping points 11:30 to 12:30 Rate-dependent tipping points: the example of the compost-bomb instabilitySession: Tipping points This paper discusses rate-dependent tipping points related to a novel excitability type where a (globally) stable equilibrium exists for all different fixed settings of a system's parameter but catastrophic excitable bursts appear when the parameter is increased slowly, or ramped, from one setting to another. Such excitable systems form a singularly perturbed problem with at least two slow variables, and we focus on the case with locally folded critical manifold. Our analysis based on desingularisation relates the rate-dependent tipping point to a canard trajectory through a folded saddle and gives the general equation for the critical rate of ramping. The general analysis is motivated by a need to understand the response of peatlands to global warming. It is estimated that peatland soils contain 400 to 1000 billion tonnes of carbon, which is of the same order of magnitude of the carbon content of the atmosphere. Recent work suggests that biochemical heat release could destabilize peatland above some critical rate of global warming, leading to a catastrophic release of soil carbon into the atmosphere termed the compost bomb instability''. This instability is identified as a rate-dependent tipping point in the response of the climate system to anthropogenic forcing (atmospheric temperature ramping). INI 1 12:30 to 13:30 Lunch at Wolfson CourtSession: Tipping points 14:00 to 15:00 Delivering local-scale climate scenarios for impact assessmentsSession: Use of ensembles Process-based models, used in assessment of impact of climate change, require daily weather as one of their main inputs. The direct use of climate predictions from global or regional climate models could be problematic, because the coarse spatial resolution and large uncertainty in their output at a daily scale, particularly for precipitation. Output from a climate model requires application of various downscaling techniques, such as weather generator (WG). WG is a model which, after calibration of site parameters with observed weather, is capable of simulating synthetic daily weather that are statistically similar to observed. By altering the site parameters using changes in climate predicted from climate models, it is possible to generate daily weather for the future. A dataset, ELPIS, of local-scale daily climate scenarios for Europe has been developed. This dataset is based on 25 km grids of interpolated daily precipitation, minimum and maximum temperatures and radiation from the European Crop Growth Monitoring System (CGMS) meteorological dataset and climate predictions from the multi-model ensemble of 15 global climate models that were used in the IPCC 4th Assessment Report. The site parameters for the distributions of climatic variables have been estimated by the LARS-WG weather generator for nearly 12 000 grids in Europe for the period 1982–2008. The ability of LARS-WG to reproduce observed weather was assessed using statistical tests. This dataset was designed for use in conjunction with process-based impact models (e.g. crop simulation models) for the assessment of climate change impacts in Europe. A climate scenario generated by LARS-WG for a grid represents daily weather at a typical site from this grid that is used for agricultural production. This makes it different from the recently developed 25 km gridded dataset for Europe (E-OBS), which gives the best estimate of grid box averages to enable direct comparison with regional climate models. INI 1 15:00 to 15:30 Afternoon TeaSession: Use of ensembles 15:30 to 16:30 Biases and uncertainty in multi-model climate projectionsSession: Use of ensembles The ensemble approach has originally been derived in probabilistic medium-range weather forecasting, and is now broadly used in numerical weather prediction, seasonal forecasting and climate research on a wide range of time scales. Applications geared towards climate projections are usually based on a heterogeneous ensemble with typically a mere handful of ensemble members, stemming from different models in an only partly coordinated framework. An important feature of ensemble approaches in climate research is the inability to rigorously quantify climate model biases. While biases of climate models are monitored for the control period, the lack of long-term comprehensive observations (on the centennial time-scales considered) implies that it is difficult to decide how the model biases will change with the climate state. In contrast to other studies, we look not only at 20 or 30 year averages, but also at the interannual variability. This allows us to consider additive and multiplicative biases. In the talk, I will discuss two plausible assumptions about the extrapolation of additive biases, referred to as the constant bias'' and constant relation'' assumptions. The former is used implicitly in most studies of climate change. The latter asserts that over-/underestimation of the interannual variability in the control period leads also to over-/underestimation of climate change, and this assumption is closely related to the statistical post-processing of seasonal climate predictions. In addition we explicitly allow the additive and multiplicative model biases to change between control and scenario periods, resolving the resulting lack of identifiability by the use of informative priors. An analysis of of GCM/RCM simulations from the ENSEMBLES project shows that bias assumptions critically affect the results for several regions and seasons. INI 1 16:45 to 17:45 P Cox (University of Exeter)Model resolution versus ensemble size: optimizing the trade-off for finite computing resourcesSession: Use of ensembles INI 1 19:30 to 22:00 Conference Dinner at Jesus College