Search Results

You are looking at 1 - 3 of 3 items for :

  • Author or Editor: Andrew Ciavarella x
  • Refine by Access: All Content x
Clear All Modify Search
Nikolaos Christidis, Andrew Ciavarella, and Peter A. Stott


Attribution 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.

Full access
Shuangmei Ma, Tianjun Zhou, Dáithí A. Stone, Debbie Polson, Aiguo Dai, Peter A. Stott, Hans von Storch, Yun Qian, Claire Burke, Peili Wu, Liwei Zou, and Andrew Ciavarella


Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from 1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.

Full access
Bo Christiansen, Carmen Alvarez-Castro, Nikolaos Christidis, Andrew Ciavarella, Ioana Colfescu, Tim Cowan, Jonathan Eden, Mathias Hauser, Nils Hempelmann, Katharina Klehmet, Fraser Lott, Cathy Nangini, Geert Jan van Oldenborgh, René Orth, Peter Stott, Simon Tett, Robert Vautard, Laura Wilcox, and Pascal Yiou


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.

Open access