Extreme weather events pose significant challenges to communities worldwide, necessitating proactive measures for mitigation and adaptation. We will present a case study of the extreme weather event Hans, which struck Norway with remarkable intensity in August 2023 and led to large-scale flooding and landslides.  Two characteristics set Hans apart from typical extreme rainfall events hitting Norway: 1) its unusual trajectory from the southeast, contrasting with the customary storms from the west, and 2) the large area of heavy rainfall, with return periods exceeding at least 100 years. The event caused heavy rainfall that lasted over several days, thus affecting a widespread area. 

Through accurate forecasting, successful communication of risks, and well-organized collaboration between key actors, significant societal costs were saved and no lives were lost. We will present the perspectives of the forecasters in the onset of Hans, and on the preparedness efforts undertaken by municipalities in response to the forecasted threat. We will also discuss the value of climate adaptation to such events in an area where extreme precipitation is one of the main direct physical climate risks, including the importance of understanding Hans in a communication and outreach perspective. 

The event itself is examined by its temporal evolution, spatial distribution, and associated meteorological phenomena. By analyzing observational data,  we can interpret the key atmospheric dynamics driving the extreme weather event, thereby enhancing understanding of its underlying mechanisms. To assess the event’s rarity, we will also present the associated return periods for the observed precipitation amounts.

The presentation will highlight our experience from the event, emphasizing the role of communicating forecasts and the forecasters' close collaboration with preparedness and response services, while also highlighting the significance of climate adaptation efforts in regions vulnerable to extreme precipitation.

How to cite: Ødemark, K., Verpe Dyrrdal, A., and Mjelstad, H.: When “Hans” struck Norway: Forecasting, communication and municipal preparedness during an extreme weather event , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-847, https://doi.org/10.5194/ems2024-847, 2024.

Between 2000 and 2019, natural hazards and extreme weather events claimed approximately 1.23 million lives, affected over 4 billion people, and resulted in economic losses of around US$ 2.97 trillion worldwide. With climate change increasing the frequency and severity of severe weather events globally, it is critically important to develop effective strategies for disaster risk communication and reduction. One such strategy is the provision of early warning systems (EWS), shown to significantly reduce mortality and economic losses. To develop and improve EWS, it is critical to understand how (a) the public perceive and respond to risk information and (b) the factors that promote and inhibit risk preparedness.

Since 2011, in the UK, the Met Office have been responsible for issuing impact-based weather warnings for rain, thunderstorms, wind, snow, lightning, ice, fog, with extreme heat added in 2021. When amber or red warnings are issued, post event surveys are regularly used to capture public awareness, understanding, perceptions and behavioural responses to the warning. This talk presents the results of analysis of data collected from 32 surveys between 2014 and 2022 (n= 16,276). Here we examine whether perceptions and behavioural responses significantly differ according to the timing of the event, type of event the warning was issued for (e.g., snow, wind, heat etc), warning colour and whether storm naming was used. We also consider whether socio-demographic characteristics such as age, gender and location (urban/rural) predict perceptions and responses. Our results have implications for the communication of impact based weather warnings, and provide insights into how these can be best utilised to promote protective action and preparedness. 

How to cite: Jenkins, S., Taylor, A., Summers, B., and Roberts, H.: Public Perceptions and Behaviour in Response to Weather Warnings: A UK Perspective, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-438, https://doi.org/10.5194/ems2024-438, 2024.

Severe weather poses significant threats to society, necessitating the development of effective forecasting and warning systems to mitigate their impacts. Integrating impact-based forecasts into warnings, with the premise of reducing people’s harm, currently consists in a paradigm change for many national weather services. Being at the intersection of probabilistic forecasts, uncertainty analysis and state-of-the art risk modelling, this new paradigm also entails significant challenges.

This contribution presents the ongoing efforts at MeteoSwiss to incorporate impacts into weather warnings. Through collaboration with stakeholders, including first responders and local authorities, MeteoSwiss is initiating a local pilot project focusing on heavy precipitation and deployment planning in the canton of Zurich. This impact case study, tailored to specific locations and use case, serves as a pilot to learn for further development and scaling up of impact-based warnings.

Furthermore, as a national-scale counterpart, we work on establishing a quasi-operational demonstrator for Switzerland, which integrates impact proxies (hazard-exposure) rather than full-fletched impact computations. We address the trade-offs inherent to developing impact-based warnings, including the need to balance data availability, specificity and utility across varying warning scales, and the need for seamless integration within an operational weather service.

This contribution discusses potentials and draw-backs of integrating many-dimensional impact considerations into weather warnings from the perspective of a national weather service, highlighting the importance of interdisciplinary collaboration and stakeholder co-development, and demonstrating the use of impact modelling tools in a (quasi-)operational context, with the ultimate aim to better cater to the diverse needs of society in the face of severe weather events.

How to cite: Mühlhofer, E. and Willemse, S.: From local impacts to country-wide warnings: progress and challenges of developing an operational impact-warning chain , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-652, https://doi.org/10.5194/ems2024-652, 2024.

The projected increase in heatwave intensity and frequency will have far-reaching consequences for the human and natural environment of Switzerland. Two particularly important consequences are heat-related excess mortality in the low-lying areas and heat-related acceleration of climate-change-induced alpine permafrost thawing in high-elevation areas. The latter will potentially have far-reaching impacts on alpine hazards, ecosystems, infrastructure, and tourism. In this interdisciplinary project, we assess the potential of using subseasonal heatwave predictions as a basis for early warning systems for the above-mentioned sectors in Switzerland. For the health sector, we show that the (observation-based) statistical relationship between temperature and mortality in combination with downscaled subseasonal temperature forecasts can be used to predict mortality attributable to heat. We demonstrate that for two densely populated areas of Switzerland (Cantons of Zurich and Geneva) and two past hot summers (2018 and 2022) this system is able to predict individual heat-related mortality peaks up to two weeks ahead and anticipate longer-lasting periods of heat-related excess mortality up to four weeks in advance. For the alpine sector, we show that individual summer heatwaves can play an important role in accelerating permafrost thawing, even though the process is driven by long-term climate change. We demonstrate this with idealized sensitivity experiments with the SNOWPACK model (a physical model that predicts the evolution of the snowpack and the ground temperature below). They indicate that both the duration of heatwaves as well as their timing within an individual summer are important for the intensity of the ground warming in permafrost regions. In summary, this project demonstrates a large potential for using subseasonal heatwave predictions for early warning systems for the health sector. For the alpine sector, it highlights the potential importance of individual heatwaves for permafrost thawing and raises the question if subseasonal heatwave predictions could support monitoring and early warning systems in high-elevation areas in some way.

How to cite: Büeler, D., Pyrina, M., Sharaborova, E., Sivaraj, S., Vicedo-Cabrera, A. M., Imamovic, A., Spirig, C., Lehning, M., and Domeisen, D. I. V.: Potential for subseasonal early warning systems for two heatwave-affected sectors of Switzerland: health and alpine permafrost, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-661, https://doi.org/10.5194/ems2024-661, 2024.

Hail is a severe meteorological hazard causing extensive damage to agriculture, buildings, and cars. Within the scClim hail research project, we prototype an application of the open-source risk assessment platform CLIMADA to quantify hail risk. This allows for the provision of impact-based forecasts in Switzerland based on operational radar data, crowdsourced hail reports, and ensemble weather forecasts with the HAILCAST diagnostic built into the operational COSMO model. Combining this with exposure and vulnerability information, we provide hail impact estimates for buildings and different crops in Switzerland. The platform is co-designed with relevant stakeholders from public and private institutions and is running in a pre-operational fashion during the 4-year research project. In addition to forecast products, the platform provides post-event assessments of hail impacts based on radar data and exposure information, which is being used by, e.g., building insurances to have an immediate first estimate for the extent of damages. However, radar-based damage estimates entail considerable uncertainties, which may be constrained locally by using crowdsourced hail reports.

Exchanges with stakeholders show that for bespoke preventive actions, such as sheltering cars and animals, or rolling up blinds to avoid hail damage, nowcasting information on expected hazard intensities is often sufficient. Nevertheless, for larger-scale actions such as a preterm crop harvest, preparation of temporary roof covers, or communication to the public, impact forecasts based on ensemble weather prediction may prove highly beneficial, depending on the forecast’s accuracy.

As a possible expansion, probabilistic hazard event sets were generated using kilometer-scale regional climate model simulations for Europe covering the present-day period from 2011 to 2021 and projecting a future climate using the pseudo global warming approach for a 3 K global warming level. The feasibility and utility of such an expansion are currently being evaluated, not least to assign return periods to predicted hail events.

How to cite: Schmid, T., Villiger, L., Raphael, P., and Bresch, D. N.: Opportunities and challenges of impact-based forecasts for hail damage in Switzerland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-52, https://doi.org/10.5194/ems2024-52, 2024.

Physical activity is one of the most important factors for human health, but around 28 % of the world's population does not meet the World Health Organisation's recommendations. Physical activity can be increased in everyday life, for example through exercise or walking, with weather being a facilitator or a barrier. Due to climate change, extreme weather events are becoming more frequent, highlighting the relevance of further research into the associations between weather and physical activity. The future increase in heat waves may become a problem, particularly for vulnerable groups. Current research shows that higher air temperatures, more daylight hours, no precipitation and low wind speeds are associated with more physical activity. The present study aims to investigate these associations at the within-subject level (i.e., differences within individuals over time) by further exploring individuals’ fitness level as a moderating factor.

The Days in Motion study examined 676 participants (aged 18-80) from 338 heterosexual couples living in Berlin (Germany) at a baseline week and a 12-month follow-up week between 2013 and 2015. This secondary analysis used data from a sub-sample of 228 participants who did not receive an intervention, provided longitudinal data, and did not report to be ill, injured or on vacation during the assessment period. Minutes of moderate-to-vigorous physical activity were measured using accelerometers. Hourly physical activity data were linked to meteorological variables (air temperature, relative humidity, precipitation, wind speed, sunshine duration and cloud cover) from a central weather station in Berlin. Physical fitness level was assessed objectively by the 2-km indoor walking test. Mixed effects models were used for the analysis, with the following data structure: hours nested within days, weeks, and persons. Between-subject (i.e., differences between individuals) and within-subject associations can be analysed in mixed effects models.

Significant between-subject associations occurred only for wind speed, with higher mean wind speed reducing physical activity. Positive significant within-subject associations with physical activity were found for air temperature and sunshine duration, whereas a significant negative association was found for precipitation and no association for cloud cover. However, the effects were moderated by the fitness index. In the future more data is needed during heat waves to be able to adequately analyse when physical activity might start to decrease again, or when physical activity in heat might have a detrimental effect on health.

How to cite: Tunn, S., Keller, J., Becker, N., Knoll, N., and Rust, H.: Exploring within-subject associations with mixed effects models: How does weather affect physical activity?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-966, https://doi.org/10.5194/ems2024-966, 2024.

In the fields of research concerning heatstroke, air pollution, and disasters, it is necessary to distinguish between indoor and outdoor populations in order to evaluate the benefit of different measures indoors and outdoors. However, there are few studies such distinctions have been taken into account because it is difficult to obtain the dataset of the distribution of indoor and outdoor populations.

This study predicts the ratio of indoor populations on a 500 m grid basis in Tokyo by machine learning approach. In this prediction, time-series location data, building area, building attributes and temperature is used as explanatory variables and the ratio of indoor populations is set as objective variables. In particular, it focuses on improving the accuracy of predictions for times when the ratio of indoor to outdoor populations stabilizes, which is critical for assessing risks such as heatstroke.

The machine learning model, employing Random Forest, demonstrated high predictive accuracy with an average error of 4%. Additionally, the model's performance improved during morning commute hours, lunch breaks, and evening return times. Also, the effects of various factors such as whether it is a holiday, convenience of transportation, and commercial activities are investigated in terms of the respective increases and decreases in indoor and outdoor populations.

While our methods have some limitations such as accuracy of location data, and the scarcity of data points, these results are expected to have broad social applications in the future, including risk assessments for heatstroke, air pollution, and disasters.

Future work includes establishing more robust validation methods, using more accurate time-series location data, and adding explanatory variables that better represent the characteristics of each grid. This would lead to even more accurate predictions.

How to cite: Suzuki, T., Kimura, T., Ohashi, Y., Takane, Y., Yamaguchi, K., and Ihara, T.: Indoor and Outdoor Population Prediction Using Location-Based Services Based on Machine Learning Approach, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-221, https://doi.org/10.5194/ems2024-221, 2024.

Context. Extreme weather events are affecting society in unprecedented ways. Weather and climate services, including early warnings, play a fundamental role in preparing for and responding to these events, ultimately reducing loss and damage from the weather. In 2024, the EEA published the European Climate Risk Assessment, identifying different types of extreme weather as some of the most important climate change risks. To prepare for and respond to the rising weather and climate risks, the WMO has promoted the move towards impact-based forecasts (WMO, 2015, 2021). Knowing what to expect in terms of impacts is much more actionable for people and enables decision-makers to take targeted, cost-effective, and informed decisions (Harrowsmith et al., 2020). For some time, the Royal Netherlands Meteorological Institute (KNMI) and its Early Warning Centre (EWC) have been working towards impact-based warnings.

Approach. Within the context of early warning services, KNMI and its EWC are experimenting with quantitative impact forecasting: "not what the weather will be, but what the weather will do."  Through collaboration with decision-makers and end-users, we apply data-driven methods to make quantitative forecasts of impact. We provide these quantitative forecasts as an ensemble, so that the stakeholders can base their decision on the expected impact and the uncertainty of the forecast. We apply a general machine learning framework to train a model on the observed weather and impact data for the Netherlands, the latter being provided by the stakeholders. Then, we use this model to derive quantitative two-week impact forecasts.

Results. We show results for three different fields: storm damage, human health, and wildfires. The storm damage results are aggregated to a 10 x 10 km spatial resolution and 24-hour time resolution, and we provide a two-week forecast of the expected number of daily storm damage emergency calls as received by the fire services at the combined emergency services dispatch centre. We are collaborating closely with the emergency service provider to communicate these quantitative impact forecasts in an efficient way. For human health (cardio-vascular and respiratory fatalities) as well as wildfires (nature fires and road-side fires), we are currently working on quantitative impact forecasts aggregated at the national and monthly levels. The spatial and temporal scale of forecasts will be further improved to the provincial and weekly levels. Lastly, we will highlight the current limitations and challenges of impact-based forecasts based on our practical experience, and further research needed in this area.

How to cite: de Baar, J. H. S., Pereira Marghidan, C., van der Schrier, G., and van den Besselaar, E. J. M.: Collaborative data-driven quantitative impact forecasting: applications in storm damage, human health and wildfires, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-671, https://doi.org/10.5194/ems2024-671, 2024.

Extreme weather conditions like wind gusts and heavy precipitation frequently lead to impacts like treefall or flooded roads. This in turn leads to a large number of emergency calls to fire brigades, which they have to deal with in addition to their routine operations. If suitable warning information about approaching hazardous weather is available in time, there is potential for preparatory actions like calling in additional staff or preparing equipment and vehicles. However, in order to properly prepare for an event, it is important to understand how a particular fire department is affected by different types of weather conditions.

Therefore, an event detection algorithm is applied to operation data provided by different fire brigades in Southern Germany. The algorithm is applied to time series of hourly numbers of weather-related emergency calls to detect time periods with high numbers of operations. Each of the identified time periods, which we refer to as events, is characterized by different parameters, like total number of operations, maximum hourly number of operations, duration and affected area. Furthermore, the meteorological conditions during the event are characterized using convective cell tracks, tracks of large-scale windstorms, radar-based precipitation amounts and wind gusts from reanalysis data. The 100 events with the highest number of operations are selected and their characteristics are compiled into an event catalog. To facilitate the analysis of the event catalog, an interactive dashboard is developed to visualize the mutual interactions between the different event characteristics.

It is shown that thunderstorms are responsible for most of the events, followed by windstorms and continuous rainfall. In general, events related to thunderstorms are short and intense and cover a smaller area, whereas events related to windstorms generally affect a larger area. Events related to continuous rainfall are shown to have the longest durations, leading to fire brigade operations for time periods of up to several days.

The event catalog is helpful to analyze past events, but can also be useful in the context of impact forecasting. In case of an approaching meteorological hazard, it allows to visualize the potential impacts on fire departments referring to previous events and their characteristics. Furthermore, the event catalog builds the foundation to the development models for the prediction of weather-related fire brigade operations in future work.

How to cite: Becker, N., Göber, M., Ulbrich, U., and Rust, H.: An event-based analysis of weather-related fire brigade operations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-481, https://doi.org/10.5194/ems2024-481, 2024.

Evidence suggests that high temperatures may trigger preterm birth (PTB), which is associated with a higher risk of infant mortality and morbidity during childhood and adult life. However, there is limited evidence on the role of sociodemographic factors on the vulnerability of pregnant women. In a multi-location setting, we aimed to assess 1) the effect of heat on PTB of different gestational ages (extreme, very, late, standard preterm births) and at-term births and 2) how mother and child characteristics (sex, ethnicity, parity, age, education, marital status, socioeconomic class) influence the association between heat and PTB.

The analysis included all singleton births born in the warm season (5 warmer months) in 243 cities in 13 countries between 1979-2019. A two-stage design was applied with conditional quasi-Poisson regression with distributed lag nonlinear models to estimate the association between daily mean temperature and PTB or at-term birth (lags 0-4 days) in each location and for each category of characteristic of the mother and child. Then, a random-effects multilevel metaanalytical model was applied to report overall effects and by country level. Extreme heat effects were measured as the percentage change (ch%, 95%CI) in the outcome when the mean temperature increased from 1% to the 95% percentile.

Heat was positively associated with all endpoints, except for extreme or very PTB whose risks were very imprecise, with larger risks for late-PTB (5%, 0.8-9.3) and smaller in at term births (2%, 1-3.1). Younger, Caucasian mothers and those with low socioeconomic class seemed to be more vulnerable to heat. Female fetal gender was associated with higher risk than males.

This is the largest multi-location study assessing vulnerability patterns of heat-related PTB. It emphasises the need to integrate evidence from vulnerability assessments in designing public health interventions to face climate change effects.

How to cite: Salvador, C., Iñiguez, C., Kim, Y., Lavigne, E., Orru, H., Ragettli, M. S., Royé, D., de’Donato, F., Leon Guo, Y.-L., Chang, H., Astrom, C., Konishi, S., Tobías, A., Agay-Shay, K., Scovronick, N., Singh, T., Valdes Ortega, N., and Vicedo-Cabrera, A. M. and the Multi-Country Multi-City Collaborative Research Network: Exploring vulnerability patterns in heat-related preterm birth: a multi-country multi-city analysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-938, https://doi.org/10.5194/ems2024-938, 2024.

Current climate projections illustrate a drastic increase in the likelihood of heatwaves throughout the century. At the same time, population scenarios for many developed countries indicate a coeval rise in elderly population fraction, in particular, in rural areas. Research demonstrates that failing to consider projected changes in population and demographics leads to a significant underestimation of health burdens [1]. Thus, in addition to the fundamental increase in healthcare demand due to the increased baseline morbidity of older individuals, there are also additional infrastructural requirements to prepare for weather and climate change-related consequences. Incorporating population changes into climate projections is therefore crucial for a comprehensive assessment of heat-related health impacts.

Here, we pursue a risk analysis for Germany by mid-century by combining health-relevant heat indicators with demographic scenarios and data on the healthcare provision situation at the county level. Climate indicators are based on latest results from statistically-downscaled CMIP6 climate projections for Germany [2]. Demographic changes are inferred from socioeconomic model simulation conducted for the latest Climate Impact and Vulnerability Analysis by the German Federal Environment Agency, available at the county level until 2045 [3]. Data from the German Federal Statistical Office are used to depict the spatial healthcare provision in terms of the number of spatially-explicit emergency department beds. For the risk analysis selected literature-based heat indicators with demonstrated health impacts are evaluated and the potential consequences for the German hospital emergency care system are discussed. In particular, the unequal spatial distribution of risks for the elderly population as well as the “double exposure” dilemma for certain areas facing large demographic changes and intensified heatwaves are identified.

Interpreting the spatial-temporal pattern of German regional exposure of hospitals to future heatwaves and demographic changes will help health care practitioners and political decision makers to develop strategies to reduce heat-related health risks both in the short and long term.

References

[1] Vanos, J. K., Baldwin, J. W., Jay, O., & Ebi, K. L. (2020). Simplicity lacks robustness when projecting heat-health outcomes in a changing climate. Nature Communications, 11(1), 6079. https://doi.org/10.1038/s41467-020-19994-1

[2] Kreienkamp, F., Lorenz, P., & Geiger, T. (2020). Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany. Atmosphere, 11(11), Article 11. https://doi.org/10.3390/atmos11111245

[3] Buth, M., Kahlenborn, W., Greiving, S., Fleischhauer, M., Zebisch, M., Schneiderbauer, S., & Schause, I. (2017). Leitfaden für Klimawirkungs- und Vulnerabilitätsanalysen. Umweltbundesamt.

How to cite: Geiger, T., Potzauf, M., Detring, I., Castino, F., and Kreienkamp, F.: The Dilemma of Coeval Climate and Demographic Changes: Heat Risk Analysis for the German Hospital Care System, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1018, https://doi.org/10.5194/ems2024-1018, 2024.

Adaptation to climate change requires a timely implementation of effective adaptation actions. A major effort in this context is devoted to extreme weather and climate events that are a major driver of climate change impacts. Planning and designing effective and efficient adaptation actions ideally requires the assessment of risk posed by impactful events. Complex and conceptual risk models already cohabit in the scientific literature in the form of a disorganised ecosystem of non-homogeneous models. This study explores the concept and mathematical formalism of intermediate complexity (IC) models for assessing climate risk and their significance in supporting climate change adaptation efforts. Existing climate risk models are classified into categories of different complexity and intermediate-complexity climate risk models identified and conceptualised to fill gaps in the hierarchy. The formulation of an intermediate-complexity model for flood risk under future climate conditions is introduced, drawing inspiration from established large-scale methodologies and utilizing freely available data. The simulations combine data of hazard, exposure and vulnerability provided by the European Copernicus Services and local datasets to perform high resolution (order 100 m) risk assessments. We discuss the application of IC climate risk models as a conceptual laboratory to develop the concept of parametrizations to take into account the effect of small scale (order tents of meters) engineering actions in a risk model with a coarser resolution (typically hundreds meters or kilometers). In this sense, they will mimic traditional parametrizations used to model subgrid process for geophysical flows in numerical climate models. The use of IC models to rank the impact of extreme events simulated with climate models with the aim to replace hazard-based metrics with impact-based metrics for the definition of an extreme events is discussed. While acknowledging that these models may not encompass all factors influencing risk, the authors argue that they are a crucial component of model hierarchies, capable of promptly addressing critical knowledge gaps and providing valuable insights to decision-makers.

How to cite: Ruggieri, P. and Di Sabatino, S.: On the need and applications of intermediate-complexity climate risk models , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1068, https://doi.org/10.5194/ems2024-1068, 2024.

Climate change, primarily through extreme weather events, significantly impacts various societal sectors such as health, industry and agriculture, and it is expected that these impacts will further increase in the coming decades. Despite the abundance of knowledge and data on climate change, translating this information into actionable insights for stakeholders preparing for climate change proves challenging. Communication breakdowns often arise between scientists and stakeholders due to differing terminology, understanding of climate science and expectations.

In this presentation, we aim to showcase recent Belgian initiatives focused on effectively communicating climate risks to stakeholders. One notable initiative, triggered by the ongoing CORDEX.be II project to provide high-resolution climate information for Belgium, involves recreating historical heatwaves at future global warming levels based on long-term climatological datasets. Subsequent discussions with diverse sectors, including health authorities, event organizers, and the energy sector, were tailored to each sector's specific needs. For instance, we used the operational Belgian heat and health plan thresholds to visualize the occurrence of warning and alarm phases across various levels of global warming. In this way we effectively concretize the implications of climate change for health stakeholders in a comprehensible form. As a second case, we will present our ongoing collaboration with cities to monitor urban greening projects within their public spaces. Through this collaboration, we gather quantitative data on heat stress at the microscale, e.g. under a tree canopy or next to a water element. This approach allows cities and citizens to grasp the effectiveness of these measures, assisting in the formulation of future plans. The purpose of the presentation is to share our insights and inspire colleagues facing comparable challenges when engaging with stakeholders.

How to cite: Caluwaerts, S., Top, S., Hellebosch, I., Vergauwen, T., Vandelanotte, K., Van Schaeybroeck, B., and Termonia, P.: Preparing society for extreme weather: insights from Belgian initiatives to create actionable information for stakeholders, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-816, https://doi.org/10.5194/ems2024-816, 2024.

Hydrometeorological extremes cause widespread socio-economic disruption and damage worldwide. Assessing the probability and impact of such events is critical for improving risk preparedness and emergency management. Ensemble climate simulations allow to extend the sample size of plausible weather extremes and estimate the probability of disruptive events that may be unprecedented in the historical record. This approach, commonly known as UNSEEN (UNprecedented Simulated Extremes using ENsembles) has demonstrated a comparative advantage over traditional approaches that rely solely on observations for risk assessment. In this work, we apply the UNSEEN methodology to estimate the probability of river flooding in European medium-size catchments, using ensemble simulations of seasonal forecasts provided by the European Flood Awareness System (EFAS). Simulated values of river streamflow during rare events can be used as input for risk models to yield estimates of the socio-economic loss resulting from unprecedented floods. This framework promotes the co-design of flood risk storylines that can assist stakeholders and policy-planners in the development of mitigation and adaptation strategies to manage disasters. 

How to cite: Bianco, E. and Ruggieri, P.: Assessing the risk of unprecedented flooding with event-based storylines , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-800, https://doi.org/10.5194/ems2024-800, 2024.

As part of the Climate Ready Barcelona project, a crucial aspect is the development of a Climate Vulnerability Index (CVI). This abstract highlights the creation of the climatological and meteorological foundation, at the microscale urban level, for the CVI models, which is a critical task within this project.

The CVI is visualized via an interactive interface, illustrating its geographical distribution in Barcelona under existing and potential climate change scenarios. The meteorological data sets used in this research offer a high-resolution grid, which facilitates a detailed examination of each building block. Furthermore, the system is capable of generating forecasts and alerts for imminent climate events, such as heatwaves or extreme nighttime temperatures.

The CVI and alerts are derived from an integration of diverse data types, including energy consumption, climate and weather data, socioeconomic factors, and building characteristics. The climate and meteorological base for the CVI models also involves the integration of diverse data types, which, in this case, only focuses on regional and European models. Both of these data are represented and analyzed using a knowledge graph, which encapsulates diverse urban environment concepts and aggregates data at various administrative levels.

A significant aspect of this work involves the construction of an ontology for structuring these highly heterogeneous datasets. This ontology forms the backbone of the knowledge graph, comprising a linked network of nodes encapsulating various concepts within the urban environment. These include data aggregated at different administrative levels and points of interest strategically distributed throughout the urban environment.

The connections established among the distinct node typologies facilitate the development of advanced geospatial data analytics modules. These modules enable the accurate estimation of a climate vulnerability index, providing valuable insights into urban heat risk dynamics and potential mitigation strategies.

Coupled with machine learning techniques, the knowledge graph predicts urban heat patterns, drawing insights from historical data and identifying underlying trends. This integration offers a platform for advanced analytical reasoning, simulation, and accurate forecasting, capturing the spatial and temporal components inherent in the data.

The resulting map highlights areas most susceptible to climate change impacts, providing invaluable information for policymakers and planners. This aids in the development of informed decisions to enhance the city's climate resilience and adaptation strategies. Furthermore, it provides Barcelona's municipal housing department with a detailed diagnosis of climate-vulnerable areas, crucial for energy renovation plans.

The Climate Ready Barcelona project has been funded by Local Governments for Sustainability (ICLEI) and the Google Foundation and demonstrates a commitment to proactive climate change readiness. The work presented here underscores the importance of creating a robust climatological and meteorological foundation to enhance the liability of urban resilience models at the building scale, a task that forms the core of this research.

How to cite: Comas Lázaro, A., Pariente, A., Mor, G., Broto, J. M., Sellart, M., and Cipriano, J.: Enhancing Climate Resilience Through Urban Microscale Weather Data Analysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-490, https://doi.org/10.5194/ems2024-490, 2024.

The proliferation of harmful algal blooms (HABs) is a significant environmental issue exacerbated by climate change. They have many detrimental impacts on public health, recreational activities, ecological equilibrium, animals, fisheries, microbiota, water quality, and the economy. HABs can be attributed to various factors, including water pollution resulting from agricultural activities, discharges from wastewater treatment plants, leakages from sewer systems, natural factors such as pH and light levels, and the impacts of climate change, such as increased water temperature, reduced water flows due to droughts, and water-related disasters like flooding and inundation. Although numerous causes of HABs are acknowledged, the mechanisms by which toxin-producing algae develop, as well as the crucial processes and components that contribute to their proliferation, remain unknown. This lack of understanding is primarily due to the unique dynamics of each lake's algal population and the nonlinear nature of the conditions that influence these dynamics. Modeling HABs in a changing climate is critical to meeting sustainable development targets for clean water and sanitation. Nevertheless, the absence of adequate and sufficient data on HABs poses a substantial obstacle for research endeavors. This study utilized the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, which is a toxin produced by algae, using dissolved oxygen as a variable for water quality and evaporation as a meteorological parameter. SINDy is a state-of-the-art approach that integrates sparse regression and machine learning methodologies to reconstruct the analytical representation of a dynamic system. Furthermore, a web platform was developed that utilizes models and is accessible through the web. This tool aims to promote and enhance environmental education, increase the public's awareness of these events, and generate more efficient solutions to them by using what-if scenarios. This web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. On an interactive and user-friendly platform, users may effortlessly share photographs of HABs in lakes, enabling others to monitor the lakes' status.

How to cite: Baydaroğlu, Ö., Yeşilköy, S., Linderman, M., and Demir, I.: Unveiling Harmful Algal Bloom Dynamics with Sparse Identification of Nonlinear Dynamics and the Model-based Web Platform for Lakes, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-290, https://doi.org/10.5194/ems2024-290, 2024.

Urban heat island (UHI) effect poses a significant human-induced challenge to urban communities, jeopardizing their livelihoods. Amplified by the upward global temperature trends, as a consequence of climate change, the effects of UHI have increasingly become severe. This manifests in crucial social (e.g., increased heat-related illnesses and mortality rates, particularly affecting vulnerable populations), economic (e.g. difficulties of some groups to access to cooling infrastructure for mitigating heat stress) and environmental implications (e.g., higher heat stress on vegetation, increase risk of species loss, increase of ozone pollutant levels). The current study examined the daytime and nighttime UHI effect in the region of Paris from 1990 to 2022. The study presents a novel method for assessing UHI using readily available data from the NCEP-NCAR Reanalysis 1 dataset (at almost 1.8 km spatial resolution) and low computational resources, enhancing the feasibility of UHI monitoring and analysis. By integrating daily circulation weather type (WT) classification with UHI analysis, this study elucidates the influence of weather patterns on UHI events.  Results showed that the most common WT were the anticyclonic (A) and the cyclonic (C) with almost 25% and 10% of occurrence respectively, combined with two additional directional types: the Southwestern (SW) and the Western (W) type which account for around 10% of the total cases for both WT. The anticyclonic type predominance is mainly due to the migration of the Azores anticyclone towards the area of study which is linked with higher urban heat island intensities either for maximum and minimum temperatures, especially during the autumn season. These anticyclonic situations were mainly associated with anomalous daytime and nighttime temperatures and accordingly a broader extension of Paris region affected by the UHI effects. It should be noted that a highest UHI effect was found during summer and especially noticeably for the nighttime. The findings of this study highlight the escalating severity of UHI impacts, exacerbated by global temperature trends due to climate change. Overall, this study highlights the urgent need for proactive measures to mitigate UHI effects in urban areas, encompassing policy interventions, infrastructure improvements, and community resilience strategies. By comprehensively understanding the dynamics of UHI and its interactions with broader climatic patterns, stakeholders can develop targeted interventions to safeguard urban communities against the escalating challenges posed by UHI in the context of climate change.

Keywords: Urban Heat Island assessment, climate risks, impacts, Lamb Weather Types, spatial analysis, Paris.

How to cite: Fernández-Duque, B., Pérez, I. A., García, M. Á., El Kenawy, A. M., and Vicente-Serrano, S. M.: Assessment of the Urban Heat Island Effect in Paris under different circulation weather types, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-724, https://doi.org/10.5194/ems2024-724, 2024.