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.

Abstract. Extreme weather events are 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.

The 2011/2012 summer drought in Southeastern South America (SESA) was a short but devastating event. What would this event have looked like under pre‐industrial conditions, or in a +2 degC world? We find that climate change causes the region to be at a higher risk of drought. However, we found no large‐scale changes in the half‐month water budgets. We show that the climate change induced positive precipitation trend in the region outweighs the increased temperatures and potential evapotranspiration during the 2011/2012 drought.

This study analyzes changes in extratropical windstorms over the North Atlantic during the last decades. We assessed and compared North Atlantic winter storm activity in a comprehensive approach from three different data sources: modern reanalysis data sets, a dynamically downscaled high-resolution global atmospheric climate simulation, and observations. The multi-decadal observations comprise both a storm index derived from geostrophic wind speed triangles and an observational record of low pressure systems counted from weather analyses. Both observational data sets have neither been compared to the most recent reanalyses nor to the downscaled global climate simulation with respect to North Atlantic winter storms before.The similarity of the geostrophic wind speed storm index to reanalyzed high wind speed percentiles and storm numbers confirms its suitability to describe storm frequencies and intensities for multi-decadal time scales. The results show that high wind speeds, storm numbers, and spatial storm track distributions are generally alike in high-resolution reanalyses and downscaled data sets and they reveal an increasing similarity to observations over time. Strong decadal and multi-decadal variability emerged in high wind speed percentiles and storm frequency, but no long-term changes for the last decades were detected.

<p>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.</p>