• Energy Research

In 2014, Central England experienced its warmest year in a record extending back to 1659. Using both state-of-the-art climate models and empirical techniques, our analysis shows a substantial and significant increase in the likelihood of record-breaking warm years, such as 2014, due to human influences on climate. With 90% confidence we find that anthropogenic forcings on the climate have increased the chances of record warm years in Central England by at least 13-fold. This study points to a large influence of human activities on extreme warm years despite the small region of study and the variable climate of Central England. Our analysis shows that climate change is clearly visible on the local-scale in this case.

AbstractExtreme event attribution studies attempt to quantify the role of human influences in observed weather and climate extremes. These studies are of broad scientific and public interest, although quantitative results (e.g., that a specific event was made a specific number of times more likely because of anthropogenic forcings) can be difficult to communicate accurately to a variety of audiences and difficult for audiences to interpret. Here, we focus on how results of these studies can be effectively communicated using standardized language and propose, for the first time, a set of calibrated terms to describe event attribution results. Using these terms and an accompanying visual guide, results are presented in terms of likelihood of event changes and the associated uncertainties. This standardized language will allow clearer communication and interpretation of probabilities by the public and stakeholders.

AbstractGiven the Paris Agreement it is imperative there is greater understanding of the consequences of limiting global warming to the target 1.5° and 2°C levels above preindustrial conditions. It is challenging to quantify changes across a small increment of global warming, so a pattern-scaling approach may be considered. Here we investigate the validity of such an approach by comprehensively examining how well local temperatures and warming trends in a 1.5°C world predict local temperatures at global warming of 2°C. Ensembles of transient coupled climate simulations from multiple models under different scenarios were compared and individual model responses were analyzed. For many places, the multimodel forced response of seasonal-average temperatures is approximately linear with global warming between 1.5° and 2°C. However, individual model results vary and large contributions from nonlinear changes in unforced variability or the forced response cannot be ruled out. In some regions, such as East Asia, models simulate substantially greater warming than is expected from linear scaling. Examining East Asia during boreal summer, we find that increased warming in the simulated 2°C world relative to scaling up from 1.5°C is related to reduced anthropogenic aerosol emissions. Our findings suggest that, where forcings other than those due to greenhouse gas emissions change, the warming experienced in a 1.5°C world is a poor predictor for local climate at 2°C of global warming. In addition to the analysis of the linearity in the forced climate change signal, we find that natural variability remains a substantial contribution to uncertainty at these low-warming targets.

AbstractWe thank the Comment's authors for their considered critique of our paper. We respond to their main criticisms and hope that this discussion motivates further consideration of communication strategies for event attribution analyses.

Various human heat stress indices have been developed to relate atmospheric measures of extreme heat to human health impacts, but the usefulness of different indices across various health impacts and in different populations is poorly understood. This paper determines which heat stress indices best fit hospital admissions for sets of cardiovascular, respiratory, and renal diseases across five Australian cities. We hypothesized that the best indices would be largely dependent on location. We fit parent models to these counts in the summers (November-March) between 2001 and 2013 using negative binomial regression. We then added 15 heat stress indices to these models, ranking their goodness of fit using the Akaike information criterion. Admissions for each health outcome were nearly always higher in hot or humid conditions. Contrary to our hypothesis that location would determine the best-fitting heat stress index, we found that the best indices were related largely by health outcome of interest, rather than location as hypothesized. In particular, heatwave and temperature indices had the best fit to cardiovascular admissions, humidity indices had the best fit to respiratory admissions, and combined heat-humidity indices had the best fit to renal admissions. With a few exceptions, the results were similar across all five cities. The best-fitting heat stress indices appear to be useful across several Australian cities with differing climates, but they may have varying usefulness depending on the outcome of interest. These findings suggest that future research on heat and health impacts, and in particular hospital demand modeling, could better reflect reality if it avoided "all-cause" health outcomes and used heat stress indices appropriate to specific diseases and disease groups.

AbstractDiurnal temperature range (DTR) is a useful index of climatic change in addition to mean temperature changes. Observational records indicate that DTR has decreased over the last 50 yr because of differential changes in minimum and maximum temperatures. However, modeled changes in DTR in previous climate model simulations of this period are smaller than those observed, primarily because of an overestimate of changes in maximum temperatures. This present study examines DTR trends using the latest generation of global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and utilizes the novel CMIP5 detection and attribution experimental design of variously forced historical simulations (natural-only, greenhouse gas–only, and all anthropogenic and natural forcings). Comparison of observed and modeled changes in DTR over the period of 1951–2005 again reveals that global DTR trends are lower in model simulations than observed across the 27-member multimodel ensemble analyzed here. Modeled DTR trends are similar for both experiments incorporating all forcings and for the historical experiment with greenhouse gases only, while no DTR trend is discernible in the naturally forced historical experiment. The persistent underestimate of DTR changes in this latest multimodel evaluation appears to be related to ubiquitous model deficiencies in cloud cover and land surface processes that impact the accurate simulation of regional minimum or maximum temperatures changes observed during this period. Different model processes are likely responsible for subdued simulated DTR trends over the various analyzed regions.

Abstract. Water availability is fundamental to societies and ecosystems, but our understanding of variations in hydroclimate (including extreme events, flooding, and decadal periods of drought) is limited because of a paucity of modern instrumental observations that are distributed unevenly across the globe and only span parts of the 20th and 21st centuries. Such data coverage is insufficient for characterizing hydroclimate and its associated dynamics because of its multidecadal to centennial variability and highly regionalized spatial signature. High-resolution (seasonal to decadal) hydroclimatic proxies that span all or parts of the Common Era (CE) and paleoclimate simulations from climate models are therefore important tools for augmenting our understanding of hydroclimate variability. In particular, the comparison of the two sources of information is critical for addressing the uncertainties and limitations of both while enriching each of their interpretations. We review the principal proxy data available for hydroclimatic reconstructions over the CE and highlight the contemporary understanding of how these proxies are interpreted as hydroclimate indicators. We also review the available last-millennium simulations from fully coupled climate models and discuss several outstanding challenges associated with simulating hydroclimate variability and change over the CE. A specific review of simulated hydroclimatic changes forced by volcanic events is provided, as is a discussion of expected improvements in estimated radiative forcings, models, and their implementation in the future. Our review of hydroclimatic proxies and last-millennium model simulations is used as the basis for articulating a variety of considerations and best practices for how to perform proxy–model comparisons of CE hydroclimate. This discussion provides a framework for how best to evaluate hydroclimate variability and its associated dynamics using these comparisons and how they can better inform interpretations of both proxy data and model simulations. We subsequently explore means of using proxy–model comparisons to better constrain and characterize future hydroclimate risks. This is explored specifically in the context of several examples that demonstrate how proxy–model comparisons can be used to quantitatively constrain future hydroclimatic risks as estimated from climate model projections.