AbstractHeatwaves, droughts, floods, storms and other types of extreme weather events cause significant human suffering as well as material and economic damages. Discerning how climate change is influencing different extremes is a prerequisite to understand what we may expect in the future and how we can reduce loss of life and damage. A generally accepted method for attributing extreme weather events to climate change is the probabilistic approach, which is a statistical analysis of the unusual dynamical conditions that steer the extreme. It computes the probability of such an event in a world with and without climate change. However, the signal-to-noise ratio of the dynamical aspects of climate change appears to be small, which means that the results of the unconditional probabilistic approach are generally quite uncertain. The thermodynamic aspects of climate change, on the other hand, are readily apparent from observations and are far more certain since they are anchored in agreed-upon physical understanding. A novel conditional attribution approach, which is not based on probabilities, is the ‘storyline’ approach which quantitatively estimates the magnitude of thermodynamic aspects of climate change, taking the dynamical conditions as a given. Each storyline places a particular extreme event in different circumstances, e.g. a world without or with increased global warming, and quantifies the effect climate change has on the thermodynamic aspects of the event. The main goal of the work presented here is thus to obtain high-quality conditional climate change attribution of singular extreme weather events by developing a conditional storyline method.
The spectrally nudged event storylines presented here have globally enforced dynamical conditions by spectrally nudging the large-scale vorticity and divergence in the free atmosphere towards reanalysis data, leaving the lower atmosphere free to respond. Historical extreme weather events are then simulated in three storylines: 1) the factual storyline, which is the world as we know it with a changing climate, 2) the pre-industrial counterfactual storyline which is defined as an imagined modern world without climate change and 3) the plus 2 °C counterfactual storyline which is a world that might be, a world with 2 °C global warming compared to pre-industrial.
The results show a consistent increase in both global average temperature and precipitation due to climate change, which is in line with well established results using unconditional methods and indicates that nothing is lost when applying a conditional setup. Regional seasonal precipitation characteristics are changing, for example the Mexican monsoons of 2012 and 2014 became dryer and the Indian monsoons of 2011 and 2014 became wetter. Temperature extremes show robust results on small spatial and temporal scales. The 2003 European heatwave was on average 0.6 °C warmer due to climate change and the 2010 Russian heatwave was on average 2 °C warmer which is an amplified climate-change signal. The southeastern South American drought of 2011/2012 was at risk of intensification due to climate change, but was counter balanced by the general background wetting trend also due to climate change.
Spectrally nudged event storylines provide both a continuous and specific event attribution by enabling a robust separation of climate change from natural variability on small temporal and spatial scales. The drought example proves the method is capable of distinguishing between opposing climate signals on different time scales. The method is widely applicable as it is not limited to the technical setup presented here, which means a convection permitting model can be included to enable accurate attribution of local precipitation extremes. Moreover, the ensemble size required for robust results is small, reducing computational costs. The methodology has the great potential to be used for realistic stress testing of resilience strategies for climate impacts when coupled to an impact model. Furthermore, the spectrally nudged event storylines can be used for operationalising extreme event attribution, which until now has been difficult. In conclusion, the nudged global storyline method is an important step towards a holistic approach within the attribution of individual extreme events, which can quantify the role of both dynamical variability and known thermodynamic aspects of climate change, and the interplay between them.