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On 4-6 December 2015, the storm "Desmond" caused very heavy rainfall in northern England and Scotland, which led to widespread flooding. Here we provide an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing northern England and southern Scotland using data and methods available immediately after the event occurred. The analysis is based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agree that the effect of climate change is positive, making precipitation events like this about 40 % more likely, with a provisional 2.5-97.5 % confidence interval of 5-80 %. [ABSTRACT FROM AUTHOR]

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere-atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]