Abstract
Abstract. Extreme weather events are generally associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. On the other hand, the thermodynamic aspects of climate change are already largely apparent from observations and are far more certain since they are anchored in agreed-upon physical understanding. The storyline method of extreme-event attribution, which has been gaining traction in recent years, quantitatively estimates the magnitude of thermodynamic aspects of climate change, given the dynamical conditions. There are different ways of imposing the dynamical conditions. Here we present and evaluate a method where the dynamical conditions are enforced through global spectral nudging towards reanalysis data of the large-scale vorticity and divergence in the free atmosphere, leaving the lower atmosphere free to respond. We simulate the historical extreme weather event twice: first in the world as we know it, with the events occurring on a background of a changing climate, and second in a “counterfactual” world, where the background is held fixed over the past century. We describe the methodology in detail and present results for the European 2003 heatwave and the Russian 2010 heatwave as a proof of concept. These show that the conditional attribution can be performed with a high signal-to-noise ratio on daily timescales and at local spatial scales. Our methodology is thus potentially highly useful for realistic stress testing of resilience strategies for climate impacts when coupled to an impact model.
Highlights
There is increasing interest in understanding and quantifying the impact of climate change on individual extreme weather and climate events
We have presented a detailed description and assessment of a global spectrally nudged storyline methodology to quantify the role of known thermodynamic aspects of climate change in specific extreme weather events
The particular dynamical conditions leading to the event are taken as given, i.e. are regarded as random, and the attribution is highly conditional
Summary
There is increasing interest in understanding and quantifying the impact of climate change on individual extreme weather and climate events. The first is that every extreme event is unique, but the construction of a general event class blurs the connection to the actual event and makes it difficult to link the event attribution to climate impacts. The second limitation is that extreme events are generally associated with extreme dynamical conditions, and there is little understanding, let alone agreement, on how those dynamical conditions might respond to climate change (Hoskins and Woollings, 2015; Shepherd, 2014). This represents an uncertainty in the probabilistic estimates that is difficult to quantify
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