• Energy Research

An attribution study has been performed to investigate the degree to which the unusually cold European winter of 2009/10 was modified by anthropogenic climate change. Two different methods have been included for the attribution: one based on large HadGEM3-A ensembles and one based on a statistical surrogate method. Both methods are evaluated by comparing simulated winter temperature means, trends, standard deviations, skewness, return periods, and 5% quantiles with observations. While the surrogate method performs well, HadGEM3-A in general underestimates the trend in winter by a factor of ⅔. It has a mean cold bias dominated by the mountainous regions and also underestimates the cold 5% quantile in many regions of Europe. Both methods show that the probability of experiencing a winter as cold as 2009/10 has been reduced by approximately a factor of 2 because of anthropogenic changes. The method based on HadGEM3-A ensembles gives somewhat larger changes than the surrogate method because of differences in the definition of the unperturbed climate. The results are based on two diagnostics: the coldest day in winter and the largest continuous area with temperatures colder than twice the local standard deviation. The results are not sensitive to the choice of bias correction except in the mountainous regions. Previous results regarding the behavior of the measures of the changed probability have been extended. The counterintuitive behavior for heavy-tailed distributions is found to hold for a range of measures and for events that become more rare in a changed climate.

In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, this study investigates whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from climateprediction.net (a regional version of HadAM3P). These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation across most of the model ensembles, although the magnitude and confidence intervals of the decrease depend on the ensemble used, with more certainty in the atmosphere-only model ensembles. An increase in probability of high precipitation in HadGEM3-A using the observed trend in SSTs for natural simulations highlights the need to ensure that estimates of natural SSTs are consistent with observed trends in order for results to be robust. Further work is needed to establish how anthropogenic forcings are affecting the rainfall processes in these simulations in order to better understand the differences in the overall effect.

AbstractThe last few years have seen an explosion of interest in extreme event attribution, the science of estimating the influence of human activities or other factors on the probability and other characteristics of an observed extreme weather or climate event. This is driven by public interest, but also has practical applications in decision-making after the event and for raising awareness of current and future climate change impacts. The World Weather Attribution (WWA) collaboration has over the last 5 years developed a methodology to answer these questions in a scientifically rigorous way in the immediate wake of the event when the information is most in demand. This methodology has been developed in the practice of investigating the role of climate change in two dozen extreme events world-wide. In this paper, we highlight the lessons learned through this experience. The methodology itself is documented in a more extensive companion paper. It covers all steps in the attribution process: the event choice and definition, collecting and assessing observations and estimating probability and trends from these, climate model evaluation, estimating modelled hazard trends and their significance, synthesis of the attribution of the hazard, assessment of trends in vulnerability and exposure, and communication. Here, we discuss how each of these steps entails choices that may affect the results, the common problems that can occur and how robust conclusions can (or cannot) be derived from the analysis. Some of these developments also apply to other attribution methodologies and indeed to other problems in climate science.

Southern Africa and Southern South America have experienced recent extremes in dry and wet rainy seasons which have caused severe socio-economic damages. Selected past extreme events are here studied, to estimate how human activity has changed the risk of the occurrence of such events, by applying an event attribution approach (Stott et al., 2004)comprising global climate models of Coupled Model Intercomparison Project 5 (CMIP5). Our assessment shows that models' representation of mean precipitation variability over Southern South America is not adequate to make a robust attribution statement about seasonal rainfall extremes in this region. Over Southern Africa, we show that unusually dry austral summers as occurred during 2002/2003 have become more likely, whereas unusually wet austral summers like that of 1999/2000 have become less likely due to anthropogenic climate change. There is some tentative evidence that the risk of extreme high 5-day precipitation totals (as observed in 1999/2000) have increased in the region. These results are consistent with CMIP5 models projecting a general drying trend over SAF during December–January–February (DJF) but also an increase in atmospheric moisture availability to feed heavy rainfall events when they do occur. Bootstrapping the confidence intervals of the fraction of attributable risk has demonstrated estimates of attributable risk are very uncertain, if the events are very rare. The study highlights some of the challenges in making an event attribution study for precipitation using seasonal precipitation and extreme 5-day precipitation totals and considering natural drivers such as ENSO in coupled ocean–atmosphere models.

AbstractIn March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.