CL3.2.8

Primer: The recent Pacific Northwest heatwave in June 2021 is widely considered a prime example of a record shattering low-likelihood extreme event, exceeding previous annual temperature maxima by large margins. The event intensity was generally perceived to be far beyond what was to be expected from historical data. It has been argued that the event would have been deemed essentially impossible, i.e. having an infinite return period, if estimated based on the historical record, even when taking the warming trend into account. This raises the question whether the non-stationary extreme value modelling approach, a widely used probabilistic framework applied to assess the likelihood of such extremes, yields systematically biased estimates determining the tail characteristics of the distribution.

Research objective: We here aim at understanding why the intensity of the event exceeds the upper bound of the estimated distribution when only using data up to the year before the event. We quantify the contribution of a multitude of factors for a generalized extreme value distribution GEV with a non-stationary parametrization to be too conservative in the characterisation of tail events, especially in the context of heatwaves. We analyse how physical properties of heat extremes materialise in statistical effects contributing to potential biases in the GEV parameter estimation, as well as some inherent deficiencies of the GEV in its application to heat extremes with limited sample size due to asymptotic properties.

Data & Methods: In order to test the respective hypotheses, we analyse climate model output of single model initial condition large ensembles (SMILEs), primarily an ensemble of 84 transient historical and RCP8.5 simulations performed with the Community Earth System Model CESM1.2. The results are further verified using additional CMIP6 models and ERA5 reanalysis.

Preliminary results and outlook: We find that non-stationary return period estimates tend to be systematically biased high when estimated on the historical records up to a year before a record-shattering event, which is a standard practice in applications of this framework. We here disentangle the reason responsible for potential biases in the estiamtes. We find that even in case of stationary extremes, the asymptotic nature of the GEV distribution applied to finite data favours an underestimation of the shape parameter, which has substantial effects on the characterisation of the tail, inducing biases in estimates of widely used tail measures (exceedance probabilities, return periods), and derivatives thereof (risk ratios, fraction of attributable risk). The conditional effects of non-stationary components like global warming on heatwave intensity are potentially further underestimated due to internal variability and noise in the covariates. In the light of these shortcomings, we provide evidence for an improvement of the GEV framework by learning from climate model output about the effect of further process variables (high pressure patterns and soil moisture deficiencies).

How to cite: Zeder, J., Sippel, S., and Fischer, E.: The challenges of assessing low-likelihood temperature extremes with empirical data of past events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9259, https://doi.org/10.5194/egusphere-egu22-9259, 2022.

Extreme heat conditions in the Pacific Northwest US and Southwestern Canada in summer 2021 were of unprecedented severity. Constituting a 5-sigma anomaly, the heatwave affected millions, likely led to thousands of excess deaths, and promoted wildfires that decreased air quality throughout the continent. Even as global warming causes an increase in the severity and frequency of heatwaves both locally and globally, this event’s magnitude went beyond what many would have considered plausible under current climate conditions. It is thus important to attribute such an exceptional event to specific physical drivers and assess its relation to climate change, to improve projection and prediction of future extreme heat events. A particularly pressing question is whether any changing variability of atmospheric dynamics or land-atmosphere interaction is implicated in amplifying current and future heat extremes. Using ERA5 reanalysis, we find that slow- and fast-moving components of the atmospheric circulation interacted to trigger extreme geopotential height anomalies during this event. We additionally identify anomalously low soil moisture levels as a critical event driver: we find that land-atmosphere feedbacks drove nonlinear amplification of its temperature anomaly by 40% (contributing 3K of the 10K peak regional-mean anomaly), catalyzed by multidecadal temperature and soil moisture trends. This is supported by a model experiment demonstrating that soil moisture interaction may increase the likelihood of the observed monthly-scale regional temperature anomaly by O(10)x. We estimate that over the four recent decades of gradual warming, the event’s temperature anomaly has become 10–100 times more likely, transforming from a ~10,000-year to a 100–1,000-year occurrence. Its likelihood continues to increase, roughly exponentially, and it is projected to recur ~20-yearly by 2060 based on continued warming at a constant rate. Our results therefore suggest an important role of atmospheric dynamics and nonlinear land-atmosphere interactions in driving this exceptional heat extreme, promoted by a long-term warming trend due to anthropogenic climate change that will continue to increase the likelihood of such extremes under continued emissions.

How to cite: Bartusek, S., Kornhuber, K., and Ting, M.: 2021 North American Heat Wave Fueled by Climate-Change-Driven Nonlinear Interactions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10405, https://doi.org/10.5194/egusphere-egu22-10405, 2022.

Here we explore the use of “counterfactual” weather forecasts, using perturbed initial condition runs of a state-of-the-art high-resolution coupled ocean-atmosphere-sea-ice ensemble NWP system, for the attribution of extreme weather events to anthropogenic climate change. We use the “record-shattering” heatwave experienced by Western North America during summer 2021 as a case study - though our forecast-based approach is applicable to other events.

Since we cannot make direct observations of a world without human influence on climate, all approaches to extreme event attribution involve some kind of modelling, either statistical or numerical. Both approaches struggle with the most extreme weather events, which are poorly represented in both observational records and the climate models normally used for attribution studies. Recognising the compromises involved, researchers have traditionally relied on comparing results from several different approaches to assess the robustness of conclusions. We argue that a better approach would be to use initialised numerical models that have demonstrated their ability to simulate the event in question through a successful forecast.

This work represents a continuation of a previous EGU talk and published study (https://meetingorganizer.copernicus.org/EGU21/EGU21-5731.html & https://doi.org/10.1073/pnas.2112087118), in which we used demonstrably successful weather forecasts to estimate the direct impact of increased CO₂ concentrations (one component, but not the entirety, of human influence) on the 2019 European winter heatwave. 

In the previous and current work we use the operational ECMWF ensemble prediction system. This state-of-the-art weather forecast system is run at a much higher resolution (Tco639 / 18km) than most climate model simulations - important as even small reductions in resolution often change the representation of extreme events in numerical models. Using a reliable forecast ensemble allows us to quantify the associated uncertainties in our attribution analyses.

We have built on this work with the aim of providing a more complete estimate of the human influence on an isolated extreme event. In addition to the reduction of CO₂ concentrations back to pre-industrial levels, we now also remove an estimate of the human influence on 3D ocean temperatures since the pre-industrial period from the initial state of the forecast model. These changes allow the model to provide a “counterfactual” picture of what an extreme event might have looked like if it had occurred before human influence on the climate.

Using this perturbed initial condition approach, we produce counterfactual forecasts of the Pacific Northwest heatwave at the end of June 2021. This event broke records throughout Western North America, including a new Canadian high temperature record of 49.6°C, shattering the previous record by almost 5°C. The heatwave was driven by a combination of meteorological factors, including an omega block and water vapour transport at the synoptic scale, and high solar irradiation and subsidence at the meso-scale (research into the drivers is ongoing). Crucially, the event was well-predicted by weather forecast models over a week in advance.

We estimate the human contribution to this exceptional heatwave by comparing our counterfactual forecasts to the operational forecasts that successfully predicted the event.

How to cite: Leach, N. J., Roberts, C., Palmer, T., Allen, M. R., and Weisheimer, A.: Towards forecast-based attribution of isolated extreme events: perturbed initial condition simulations of the Pacific Northwest heatwave, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5949, https://doi.org/10.5194/egusphere-egu22-5949, 2022.

How to cite: Thompson, V., Kennedy-Asser, A., Lo, E., Vosper, E., Mitchell, D., and Andrews, O.: Have there been previous heat extremes greater than the June 2021 western North America event? , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-796, https://doi.org/10.5194/egusphere-egu22-796, 2022.

Periods of prolonged extreme warm temperatures, or heatwaves, have been shown to have significant impacts on human health, in particular affecting the young and old disproportionately. Observations over the past century show that the severity, frequency and duration of these heatwaves are increasing as global temperatures rise, and model simulations suggest there will be further increases in these characteristics in the future. 

We use a range of CMIP6 ScenarioMIP future simulations to show how heatwave characteristics change both globally and regionally. We show how these changes differ depending on the Shared Socio-economic Pathway (SSP) taken, highlighting the sensitivity of heatwaves to both global and regional warming in each scenario. The work also explores the non-linear trend between warming and heatwave characteristics, and how they vary in different future scenarios. The results suggest that the pathway followed has significant influence on heatwave attributes, and that attempting to limit changes by a set measure cannot be done by simply restricting the level of future warming to an agreed, designated temperature, such as the “1.5C above pre-industrial” figure often used in policy.  

Finally, we present how this work is been utilised in the production of the Children’s Climate Risk Index (CCRI), which provides the first comprehensive view of children’s exposure and vulnerability to the impacts of climate change. We also aim to highlight how indices like this are being used to help prepare resources for future issues related to climate events.  

How to cite: Mollard, J., Henley, S. F., and Bollasina, M.: Heatwaves under different future climate scenarios and impacts on children, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12579, https://doi.org/10.5194/egusphere-egu22-12579, 2022.

Heat waves are among the most dangerous natural hazards worldwide. Central Europe has been affected by record-breaking heat waves in recent decades, especially in 2003, 2018 and 2019. Four frequently used indices are chosen in this study to diagnose heat waves in Europe based on both station data and ERA5 reanalysis: the Heat Wave Magnitude Index daily (HWMId), the Excess Heat Factor (EHF), the Wet Bulb Globe Temperature (WBGT) and the Universal Thermal Climate Index (UTCI). To improve the quantification of the events and comparability of the four indices, a normalisation is applied and the three metrics intensity, duration, spatial extent were combined by a cumulative intensity measure. The large-scale characteristics of the 1979 to 2019 European heat waves are analysed from a Lagrangian perspective, by daily tracking of contiguous heat wave areas. The events were ranked and visualized with bubble plots. The role of different meteorological input parameters like temperature, radiation, humidity and wind speed is explored to understand their contribution to the extremeness of heat waves and the variance in time series of the heat wave indices.

As expected, temperature explains the largest variance in all indices, but humidity is nearly as important in WBGT and wind speed plays a substantial role in UTCI. While the 2010 Russian heat wave is by far the most extreme event in duration and intensity in all indices, the 2018 heat wave was comparable in size for EHF, WBGT and UTCI. Interestingly, the well-known 2003 central European heat wave was only the fifth and tenth strongest in cumulative intensity in WBGT and UTCI, respectively. The June and July 2019 heat waves were very intense, but short-lived, thus not belonging to the top heat waves in Europe when duration and areal extent are taken into account. Overall, the proposed normalised indices and the multi-metric assessment of large-scale heat waves allow for a more robust description of their extremeness and will be helpful to assess heat waves worldwide and in CMIP6 climate projections.

Applying the normalization to the four indices and deriving the large-scale metrics of intensity, spatial extent and duration, as proposed in the present study, will facilitate trend studies using different sources of observations and models. As the combination of duration and intensity over large areas are responsible for the most severe health and economic impacts, interdisciplinary research (e.g. links to health effects) is recommended starting to better quantify the impacts of heat waves in a warming climate.

How to cite: Becker, F. N., Fink, A. H., Bissolli, P., and Pinto, J. G.: Towards a more comprehensive assessment of the intensity of European Heat Waves 1979-2019, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11726, https://doi.org/10.5194/egusphere-egu22-11726, 2022.

Meteorological conditions can affect crop development and yield in multiple and non-linear ways. Many studies have investigated the influence of climate change on crops by simulating crop responses to the most likely mean climatic projections in the future. However, this approach can potentially overlook changes in extreme-impact events, highly relevant for society, due to their low probability of occurrence and to potential different behaviour with respect to the mean conditions. One way of focusing on extreme-impact events is through the use of physical climate storylines. Storylines enable the construction of self-sustained and physically-plausible chain of events that recreate historical events from source to impact. In addition, storylines allow the exploration of future analogues of the historical events under different circumstances to account for externalities, such as climate change. In this experiment, we use physical climate storylines to reconstruct a historical extreme-impact event and to explore potential analogues of the same event under climate change influence. We develop two types of analogues, event-analogues and impact-analogues, and compare how the future manifestation of the historical event depends on the analogue definition. We use soybean production in the US as an example, with the year of 2012 being the historical extreme event. Based on a random forest model, we link the historical event to meteorological variables to identify the conditions associated with the failure event. To quantify the frequency of occurrence of the different analogues under climate change, we apply the trained random forest model to large ensembles of climate projections from the EC-Earth global climate model. We find that the 2012 failure event is linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. The analogues of the historical event greatly diverge: while event-analogues of the 2012 season are rare and not expected to increase, impact-analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. The results highlight the importance of considering the impact perspective when investigating future extreme crop yields.

How to cite: Moreno Dumont Goulart, H., van der Wiel, K., Folberth, C., Balkovic, J., and van den Hurk, B.: Analogues of a historical extreme-impact event and their implication for climate change risk assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-504, https://doi.org/10.5194/egusphere-egu22-504, 2022.

The UK has experienced recurring periods of hydrological droughts in the past and their frequency and severity are predicted to increase with climate change. However, quantifying the risks of extreme droughts is challenging given the short observational record, the multivariate nature of droughts and large internal variability of the climate system. We use EC-Earth time-slice large ensembles, which consist of 2000 years of data each for present day, 2°C and 3°C conditions, to drive the GR6J hydrological model at UK river catchments to obtain a large set of plausible droughts. Applying the UNSEEN (UNprecedented Simulation of Extreme Events using ENsembles) approach show an increasing chance of unprecedented dry summers with future warming and highlight the chance of an unprecedented drought with characteristics exceeding that of past severe droughts.

This study also aims to bridge the probabilistic UNSEEN approach with “bottom-up” storyline approaches. Physical climate storylines of preconditioned compound drought events are created by searching within the large ensemble for events resembling specific conditions that have led to past severe droughts and are relevant for water resources planning. This includes conditions such as 1) dry autumns followed by dry winters, 2) consecutive dry winters (both of which are relevant for slow-responding catchments), and 3) dry springs followed by dry summers (relevant for fast-responding catchments). The storylines can be used to understand the conditions leading to unprecedented droughts and the impacts of future droughts triggered by the same conditions. Unprecedented drought sequences and synthetic experiments conditioned on these storylines can be used to stress-test hydrological systems and inform decision-making.

How to cite: Chan, W., Shepherd, T., Facer-Childs, K., Darch, G., Arnell, N., and van der Wiel, K.: Current and future risks of unprecedented UK droughts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-704, https://doi.org/10.5194/egusphere-egu22-704, 2022.

Heavy precipitation events as the one in western Germany and the Benelux countries in July 2021 destroy the local infrastructure and numerous fatalities. Due to the lack of long homogenous climate data and methodological framework, it is uncertain how intense precipitation extremes could get. We address these questions by developing storylines of the rarest precipitation events. We here generate large samples of reinitialized heavy rainfall events starting from the most extreme events in an initial condition large ensemble for the near future, carried out with CESM2. In an approach referred to as ensemble boosting, we first reinitialize the most extreme 3-day precipitation events to estimate how anomalous they could get. We find that the most extreme precipitation events can be substantially exceeded in the boosted ensembles for different regions across the world. Second, we evaluate whether the model can reproduce analogues of the precipitation event in July 2021 and re-initialize these events to analyze how this event type could have evolved and whether it could have become even more intense. In doing so, the ensemble boosting method provides storylines of heavy rainfall development beyond the observational record, which can be used to generate worst-case scenarios and stress test the socioeconomic system.

How to cite: Gessner, C., Fischer, E. M., Beyerle, U., and Knutti, R.: Developing low-likelihood climate storylines for extreme precipitation using ensemble boosting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2228, https://doi.org/10.5194/egusphere-egu22-2228, 2022.

Multi-thousand member climate model simulations are highly valuable for showing characteristics of extreme weather events in historical and future climates. However, until now, studies using such a physically-based approach have been limited to using models with a resolution much coarser than the most modern systems. We have developed a global atmospheric model with ~60km resolution that can be run in the climateprediction.net distributed computing system to produce such large datasets. This resolution is finer than that of many current global climate models and sufficient for good simulation of extratropical synoptic features such as storms. It also allows many extratropical extreme weather events to be simulated without requiring regional downscaling. We will show that this model's simulation of extratropical winter weather is competitive with that in other state-of-the-art models. We will also present the first results generated by this system. One application has been the production of ~2000 member simulations based on sea surface temperatures in severe future winters produced in the UK Climate Projections 2018 dataset, generating large numbers of examples of plausible extreme wet and warm UK seasons. Another is showing the increasing spatial extent of precipitation extremes in the Northern Hemisphere extratropics. 

How to cite: Watson, P., Sparrow, S., Ingram, W., Wilson, S., Zappa, G., Bevacqua, E., Leach, N., Sexton, D., Jones, R., Drouard, M., Mitchell, D., Wallom, D., Woollings, T., and Allen, M.: Understanding extreme events with multi-thousand member high-resolution global atmospheric simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5900, https://doi.org/10.5194/egusphere-egu22-5900, 2022.

There are several tools for assessing potential future insurance flood losses in the UK, including catastrophe models which seek to generate an annualised view of flood risk losses. These catastrophe models include plausible high impact and low frequency flood events in their stochastic event sets. The addition of events which are generally considered implausible, or grey swan scenarios, is useful to increase understanding of how re/insurers will perform should our understanding of what is plausible be incorrect.

The Thames Barrier has high levels of redundancy by design and it is generally considered implausible that the barrier would completely fail to operate. We propose three increasingly extreme scenarios for flooding in London as a consequence of the Thames Barrier and other defences across London failing. In all scenarios we assume a 1 in 250-year water level from coastal flooding, well within the standard of protection offered by defences through the city.

The following defence failure scenarios are then modelled using a coupled 1D-2D model: 1) the Thames Barrier is open but the river defences remain intact with only overtopping occurring; 2) the Thames Barrier is open and defences are breached upstream of the barrier; and 3) a worst case scenario composite of several flood event scenarios, where for upstream reaches of the barrier, breach and overtopping occur with the barrier open and for downstream reaches, breach and overtopping occur with the barrier closed.

JBA’s catastrophe model for the UK probabilistically models loss from river, surface water and coastal flooding. The model comprises 2D hydraulic modelled hazard maps at 5 metre resolution, a stochastic event set of 106,424 events generated from extreme value statistical analysis, and detailed vulnerability data derived from the Multi-Coloured Manual. The catastrophe model includes an occurrence exceedance probability curve for insurable residential properties, providing the wider context for estimating the loss return period of the scenario events. We present the modelled losses and the estimated loss return periods for the grey swan scenarios and make available the model for re/insurers for stress testing. The loss return periods for the three scenarios are: 1/50, 1/358, and 1/8813.

How to cite: MacDonald, A. and Oldham, P.: Ruffling feathers: An appraisal of tail flood losses using grey swan scenarios in London, UK, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5606, https://doi.org/10.5194/egusphere-egu22-5606, 2022.

How can the extremity of an rainfall event be quantified? Extreme rainfall events are rarely homogeneous regarding rainfall intensities and the spatio-temporal distribution of rainfall can cause flooding on different scales. While small, mountainous catchments can react to short but high-intensity precipitation with flash floods, the same event can also trigger pluvial or fluvial floods on a spatially bigger scale with lower intensity precipitation, leading to compound flood events. Consequently, these cross-scale characteristics of extreme rainfall events are an important factor that should be considered regarding hydrological response or disaster management.

To quantify the extremity of rainfall events while considering the spatial and temporal distribution of rainfall, we introduce a new index, xWEI, based on the Weather Extremity Index (WEI). By using precipitation radar data with a high spatial and temporal resolution, we analyzed and evaluated extreme rainfall events in Germany and were able to show essential differences in the performance of the classical approach (WEI) and xWEI. 

This novel cross-scale index, in combination with modern high-resolution precipitation radar data, enables a better identification of extreme events and their characteristics and helps to link them to their impacts.

How to cite: Voit, P. and Heistermann, M.: xWEI – A novel cross-scale index for extreme precipitation events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1676, https://doi.org/10.5194/egusphere-egu22-1676, 2022.