Attribution of extreme weather and climate‐related events

Abstract

Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380For further resources related to this article, please visit the WIREs website.

Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

“…As part of the ENSEMBLES project (van der Linden and Mitchell, 2009), this dataset was designed for the evaluation of daily precipitation across Europe in RCMs at similar resolutions to the AGCM simulations used here. This makes E-OBS the dataset of choice for our purposes and we refer to differences of model precipitation from E-OBS as model biases.…”

“…Yuan et al, 2015;Arnell and Gosling, 2016;Scoccimarro et al, 2016), and extreme event attribution (e.g. Stott et al, 2016;Schaller et al, 2016). Due to the wide range of applications, the required realism concerns all aspects of the precipitation distribution in space and time including the probability distribution function of the precipitation time series and its extremes.…”

“…Multidecadal GCM simulations can now be carried out at grid spacings of about 20 km (Jung et al, 2012;Mizuta et al, 2012;Wehner et al, 2014;Mizielinski et al, 2014;van Haren et al, 2015). This offers the possibility of using a single global physically consistent model in applications that until a few years ago were in the realm of RCM simulations (van der Linden and Mitchell, 2009). GCMs also remain important for providing initial and boundary conditions for RCMs (e.g.…”

Global Increase in Climate-Related Disasters

“…Second, changes in the climate variables affect the frequency of climate-related hazards (IPCC 2012). Third, the frequency of climaterelated hazards affects the risk of natural disasters (IPCC 2012, Stott et al 2012). …”

A 133-Year Record of Climate Change and Variability from Sheffield, England

“…In Section 3.4, we analyse the potential future evolution of two extreme indices, TXx and RX1day, from global climate models. Here, we analyse the three WP climate records discussed above by fitting the time series of TNn, TXx and RX1day to the Generalised Extreme Value distribution (details given in Supplementary A) with smoothed global average temperatures as a covariate [21][22][23][24]. Table 7 provides results for this analysis for return periods calculated for the climate at the time of the record and again in 2015 and the respective ratio between these for the WP record statistics and A posteriori determined "moderate" extreme values (a threshold of 75 mm, 32 • C and −8 • C for precipitation, TXx and TNn, respectively) that have an approximate return period of a decade.…”