Different ways of framing event attribution questions: The example of warm and wet winters in the United Kingdom similar to 2015/16

This is the final version. Available from the American Meteorological Society via the DOI in this recordAttribution analyses of extreme events estimate changes in the likelihood of their occurrence due to human climatic influences by comparing simulations with and without anthropogenic forcings. Classes of events are commonly considered that only share one or more key characteristics with the observed event. Here we test the sensitivity of attribution assessments to such event definition differences, using the warm and wet winter of 2015/16 in the United Kingdom as a case study. A large number of simulations from coupled models and an atmospheric model are employed. In the most basic case, warm and wet events are defined relative to climatological temperature and rainfall thresholds. Several other classes of events are investigated that, in addition to threshold exceedance, also account for the effect of observed sea surface temperature (SST) anomalies, the circulation flow, or modes of variability present during the reference event. Human influence is estimated to increase the likelihood of warm winters in the United Kingdom by a factor of 3 or more for events occurring under any atmospheric and oceanic conditions, but also for events with a similar circulation or oceanic state to 2015/16. The likelihood of wet winters is found to increase by at least a factor of 1.5 in the general case, but results from the atmospheric model, conditioned on observed SST anomalies, are more uncertain, indicating that decreases in the likelihood are also possible. The robustness of attribution assessments based on atmospheric models is highly dependent on the representation of SSTs without the effect of human influence.Joint BEIS/Defra Met Office Hadley Centre Climate Programm

The increasing likelihood of temperatures above 30 to 40 °C in the United Kingdom

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: The HadUK-Grid temperature data and station temperature data from the Met Office Integrated Data Archive System (MIDAS) that support the findings of this study are available from the CEDA Archive, http://archive.ceda.ac.uk. The CMIP5 simulated temperature data that support the findings of this study are available from the Earth System Grid Federation (ESGF) Archive, https://esgf.llnl.gov/.Code availability: IDL code used for the analysis is available upon request.As European heatwaves become more severe, summers in the United Kingdom (UK) are also getting warmer. The UK record temperature of 38.7 °C set in Cambridge in July 2019 prompts the question of whether exceeding 40 °C is now within reach. Here, we show how human influence is increasing the likelihood of exceeding 30, 35 and 40 °C locally. We utilise observations to relate local to UK mean extremes and apply the resulting relationships to climate model data in a risk-based attribution methodology. We find that temperatures above 35 °C are becoming increasingly common in the southeast, while by 2100 many areas in the north are likely to exceed 30 °C at least once per decade. Summers which see days above 40 °C somewhere in the UK have a return time of 100-300 years at present, but, without mitigating greenhouse gas emissions, this can decrease to 3.5 years by 2100.Met Office Hadley Centre Climate Programm

The effect of human land use change in the Hadley Centre attribution system

This is the final version. Available on open access from Wiley via the DOI in this recordAtmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. We have investigated the effects of land use on past climate change by means of a new 15-member ensemble of the HadGEM3-A-N216 model, usually used for event attribution studies. This ensemble runs from 1960 to 2013, and includes natural external climate forcings with the addition of human land use changes. It supports previously-existing ensembles, either with only natural forcings, or with all forcings (both anthropogenic and natural, including land use changes), in determining the contribution to the change in risk of extreme events made by land use change. We found a significant difference in near-surface air temperature trends over land, attributable to the effects of human land use. The main part of the signal derives from a relative cooling in Arctic regions which closely matches that of deforestation. This cooling appears to spread by polar amplification. A similar pattern of change is seen in latent heat flux trend, but significant rainfall change is almost entirely absent.Department for Business, Energy and Industrial Strategy, Met Office Hadley Centre Climate ProgrammeDepartment for Environment, Food and Rural AffairsEuropean CommissionUK‐China Research & Innovation Partnership Fund, Newton Fun

Designing Secure Ethereum Smart Contracts: A Finite State Machine Based Approach

The adoption of blockchain-based distributed computation platforms is growing fast. Some of these platforms, such as Ethereum, provide support for implementing smart contracts, which are envisioned to have novel applications in a broad range of areas, including finance and Internet-of-Things. However, a significant number of smart contracts deployed in practice suffer from security vulnerabilities, which enable malicious users to steal assets from a contract or to cause damage. Vulnerabilities present a serious issue since contracts may handle financial assets of considerable value, and contract bugs are non-fixable by design. To help developers create more secure smart contracts, we introduce FSolidM, a framework rooted in rigorous semantics for designing con- tracts as Finite State Machines (FSM). We present a tool for creating FSM on an easy-to-use graphical interface and for automatically generating Ethereum contracts. Further, we introduce a set of design patterns, which we implement as plugins that developers can easily add to their contracts to enhance security and functionality

Human Influence on Seasonal Precipitation in Europe

This is the final version. Available on open access from the American Meteorological Society via the DOI in this recordData availability statement. CRU TS4.03 gridded precipitation data are available from the CEDA archive (https://archive.ceda.ac.uk/). Data from different experiments with the CMIP6 models used in the study can be downloaded from nodes of the ESGF (https://esgf.llnl.gov/).The response of precipitation to global warming is manifest in the strengthening of the hydrological cycle but can be complex on regional scales. Fingerprinting analyses have so far detected the effect of human influence on regional changes of precipitation extremes. Here we examine changes in seasonal precipitation in Europe since the beginning of the twentieth century and use an ensemble of new climate models to assess the role of different climatic forcings, both natural and anthropogenic. We find that human influence gives rise to a characteristic pattern of contrasting trends, with drier seasons in the Mediterranean basin and wetter over the rest of the continent. The trends are stronger in winter and weaker in summer, when drying is more spatially widespread. The anthropogenic signal is dominated by the response to greenhouse gas emissions, but is also weakened, to some extent, by the opposite effect of anthropogenic aerosols. Using a formal fingerprinting attribution methodology, we show here for the first time that the effects of the total anthropogenic forcing, and also of its greenhouse gas component, can be detected in observed changes of winter precipitation. Greenhouse gas emissions are also found to drive an increase in precipitation variability in all seasons. Moreover, the models suggest that human influence alters characteristics of seasonal extremes, with the frequency of high precipitation extremes increasing everywhere except the Mediterranean basin, where low precipitation extremes become more common. Regional attribution information contributes to the scientific basis that can help European citizens build their climate resilience.Met Office Hadley Centre Climate Programm

Attribution of human-induced dynamical and thermodynamical contributions in extreme weather events

This is the final version. Available on open access from IOP Publishing via the DOI in this recordWe present a new method that allows a separation of the attribution of human influence in extreme events into changes in atmospheric flows and changes in other processes. Assuming two data sets of model simulations or observations representing a natural, or 'counter-factual' climate, and the actual, or 'factual' climate, we show how flow analogs used across data sets can provide quantitative estimates of each contribution to the changes in probabilities of extreme events. We apply this method to the extreme January precipitation amounts in Southern UK such as were observed in the winter of 2013/2014. Using large ensembles of an atmospheric model forced by factual and counterfactual sea surface temperatures, we demonstrate that about a third of the increase in January precipitation amounts can be attributed to changes in weather circulation patterns and two thirds of the increase to thermodynamic changes. This method can be generalized to many classes of events and regions and provides, in the above case study, similar results to those obtained in Schaller et al (2016 Nat. Clim. Change 6 627-34) who used a simple circulation index, describing only a local feature of the circulation, as in other methods using circulation indices (van Ulden and van Oldenborgh 2006 Atmos. Chem. Phys. 6 863-81).European Union FP7French Ministry of EcologyEuropean Research Council (ERC