Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. precipitation set a record (Fig. 3a). Sustained high precipitation amounts 60 during the whole winter led to this record, rather than a few very wet days, Human influence on climate in the 2014 Southern 61and none of the 5-day precipitation averages over the three winter months 62 was a record (Fig. 3b). Similarly, while Thames' daily peak river flows were 63 not exceptional, the 30-day peak flow was the second highest since 64 measurements began in 1883 ( Supplementary Fig. 10 to provide a conservative estimate of uncertainty. 106We consider January precipitation and SLP, with Southern England 107Precipitation (SEP) averaged over land grid points in 50º-52ºN, 6.5ºW-2ºE. 189In the large RCM ensemble, the best estimate for the overall change in risk of is an increase of 43%, with a range from no change to 164% increase 192 associated with uncertainty in the pattern of anthropogenic warming (Fig. 5d). rainfall that we simulate is less on timescales that dominate flooding in this 252 catchment, consistent with the mechanism being an increase in the frequency 253 of the zonal regime, and so, successions of strong but fast-moving storms. 254Outputs from CLASSIC are combined with information about the location of

In the summer 2010 Western Russia was hit by an extraordinary heat wave, with the region experiencing by far the warmest July since records began. Whether and to what extent this event is attributable to anthropogenic climate change is controversial. Dole et al. (2011) report the 2010 Russian heat wave was “mainly natural in origin” whereas Rahmstorf and Coumou (2011) write that with a probability of 80% “the 2010 July heat record would not have occurred” without the large‐scale climate warming since 1980, most of which has been attributed to the anthropogenic increase in greenhouse gas concentrations. The latter explicitly state that their results “contradict those of Dole et al. (2011).” Here we use the results from a large ensemble simulation experiment with an atmospheric general circulation model to show that there is no substantive contradiction between these two papers, in that the same event can be both mostly internally‐generated in terms of magnitude and mostly externally‐driven in terms of occurrence‐probability. The difference in conclusion between these two papers illustrates the importance of specifying precisely what question is being asked in addressing the issue of attribution of individual weather events to external drivers of climate.

Abstract. The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 • C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 • C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climatePublished by Copernicus Publications on behalf of the European Geosciences Union. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), the second two being estimates of a similar decade but under 1.5 and 2 • C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 • C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 • C scenario.

Demonstrating the effect that climate change is having on regional weather is a subject which occupies climate scientists, government policy makers and the media. After an extreme weather event occurs, the question is often posed, 'Was the event caused by anthropogenic climate change?' Recently, a new branch of climate science (known as attribution) has sought to quantify how much the risk of extreme events occurring has increased or decreased due to climate change. One method of attribution uses very large ensembles of climate models computed via volunteer distributed computing. A recent advancement is the ability to run both a global climate model and a higher resolution regional climate model on a volunteer's home computer. Such a set-up allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events. This article introduces a global climate model that has been developed to simulate the climatology of all major land regions with reasonable accuracy. This then provides the boundary conditions to a regional climate model (which uses the same formulation but at higher resolution) to ensure that it can produce realistic climate and weather over any region of choice. The development process is documented and a comparison to previous coupled climate models and atmosphere-only climate models is made. The system (known as weather@home) by which the global model is coupled to a regional climate model and run on volunteers' home computers is then detailed. Finally, a validation of the whole system is performed, with a particular emphasis on how accurately the distributions of daily mean temperature and daily mean precipitation are modelled in a particular application over Europe. This builds confidence in the applicability of the weather@home system for event attribution studies.

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properties controlling the twenty-first century response to sustained 31 anthropogenic greenhouse-gas forcing were not fully sampled, 32 partially owing to a correlation between climate sensitivity and 33 aerosol forcing 7,8 , a tendency to overestimate ocean heat uptake 11,12 34 and compensation between short-wave and long-wave feedbacks 9 . 35This complicates the interpretation of the ensemble spread as Fig. S1).

Abstract. Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of riskbased approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline , (ii) five nearfuture scenarios (2020-2049) and (iii) five far-future scenarios . The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period (> 3 months) and shorter-duration high precipitation (1-30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK.

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.classification and regression trees ͉ climate change ͉ distributed computing ͉ general circulation models ͉ sensitivity analysis S imulation with complex mechanistic spatial models is central to science from the level of molecules (1) via biological systems (2, 3) to global climate (4). The objective typically is a mechanistically based prediction of system-level behavior. However, both through incomplete knowledge of the system simulated and the approximations required to make such models tractable, the ''true'' or ''optimal'' values of some model parameters necessarily will be uncertain. A limiting factor in such simulations is the availability of computational resources. Thus, combinations of plausible parameter values rarely are tested, leaving the dependence of conclusions on the particular parameters chosen unknown.Observations of the modeled system are vital for model verification and analysis, e.g., turning model output into probabilistic predictions of real-world system behavior (5-7). However, typically, few observations are available relative to the complexity of the model. There also may be little true replicate data available. For instance, there can be only one observational time series for global climate. Thus, if the same observations are used to fit parameter values, there is a severe risk of overfitting, gaining limited verisimilitude at the cost of the mechanistic insight and predictive ability for which the model originally was designed.To avoid fitting problems, parameter estimates must be refined directly. In some biological systems, direct and simultaneous measurement of large numbers of system parameters (e.g., protein binding or catalytic constants) soon may be possible. I...