• Energy Research

Abstract Climate change and increased coastal urbanization are causing low-lying coastlines to become increasingly susceptible to the threat of extreme water levels and coastal flooding. Robust decision-making on adaptation in the coastal zone, based on reliable ocean-modelling tools, is therefore crucially contingent on accurate assessments of current and future storm surge hazards. This accuracy relies considerably on the quality of the wind forcing used in the ocean models. In this paper, we use a high-resolution, regional 3D ocean model (HBM) covering the North Sea and Baltic Sea to simulate extreme water levels during three extreme storm surge events with different dynamics and patterns, in order to assess their impacts along Denmark’s coastlines, which are of varying levels of complexity. We demonstrate that the model is able to reproduce the observed extreme high-water levels accurately, indicating that the system is well suited for producing simulations of present and future projections of extreme storm surges with high resulting impacts and damage potentials. Additionally, we quantify the level at which acknowledged deficiencies in the otherwise most suitable atmospheric forcing data set influence the results of the storm surge simulations. We found that reducing the temporal resolution of the forcing data – that is, replacing two out of every six time stamps with linearly interpolated values – is preferable to using the original forcing data set when recurring noise is present in these time stamps. As a result, for given storm surge events, and depending on the stage reached in the storm’s evolution, mean absolute errors can be reduced by 4.5 cm. This emphasizes the importance of considering such model fluctuations when coupling high-resolution atmosphere and ocean models.

Climate change will affect the coastline of the Baltic Sea through changes in sea level, storm surges and waves. In Denmark, a large part of the responsibility for climate adaptation lies with the local municipalities. The purpose of this study was to map the user needs for coastal climate change information of five municipalities in the Danish south western Baltic Sea and the Danish Coastal Authority in a cost-efficient way and to transform the mapping into local climate indicators. An interview template was customized to form the basis for telephone interviews of key stakeholders and systematic gathering of the results. The interest for the interviews was high, and response from the interviewed persons on the use of the template was very positive. During the interviews, it was clear that the municipalities have access to extensive information on the population and infrastructure, as well as detailed geographical information. The main interests were in very high quality storm surge warnings and present day and future extreme sea level and wave heights. This should be based on modeling of past storm surges and future changes, taking observations, and historical records into account. There was a big need for more detailed information than presently available, and for common scenarios, which will help the collaboration between municipalities. Within this study, the user requirements were used to define targeted climate indicators. Within the C3S CODEC project, the indicators will be provided for the municipalities, based on a downscaling of European scale storm surge, and wave simulations to local scale.

In the future, shifts in wind storms across the North and Baltic Seas are highly unpredictable, challenging the projection of wave conditions for managing coastal hazards. Moreover, regional sea level rise (SLR), with very large uncertainty, complicates the situation for stakeholders seeking recommendations for climate adaptation plans. The purpose of this study is to examine the change of the storm surge and wind wave components of the water level due to climate change in a low tidal range Køge Bay near the entrance of the Baltic Sea. Under a high greenhouse gas emission scenario RCP8.5, we employed a regional climate model (HIRHAM) forced wave model (WAM) and focused on the wave model results during the “storm surge conditions” (exceeding 20 years storm surge events) and “stormy conditions” (exceeding 90th percentile of wave heights). We find that the change in both wave height and period in the future is negligible under “stormy conditions”. Nevertheless, under “storm surge conditions” when considering SLR, the simulated wave height is projected to double in the near future (mid-century) under RCP 8.5, and the wave period may also increase by about 1.5 seconds. This is because some high significant wave height events in the future are associated with the storm surge events when considering SLR. The findings suggest that the combined effects of mean sea level rise, storm surge and waves are likely to increase the risk to a bay with geography and exposure comparable to Køge Bay. As a result, the future plan for climate engineering protection should place a premium on the additional wave energy protection associated with storm surges.

The risk of compound events describes potential weather and climate events in which the combination of multiple drivers and hazards consolidate, resulting in extreme socio-economic impacts. Compound events affecting exposed societies can therefore be deemed a crucial security risk. Designing appropriate preparation proves difficult, as compound events are rarely documented. This paper explores the understanding and practices of climate risk management related to compound events in specific Danish municipalities vulnerable to flood hazards (i.e., Odense, Hvidovre, and Vejle). These practices illuminate that different understandings of compound events steer risk attitudes and consequently decisions regarding the use of different policy instruments. Through expert interviews supported by policy documents, we found that the municipalities understand compound events as either a condition or situation and develop precautionary strategies to some extent. Depending on their respective geographical surroundings, they observe compound events either as no clear trend (Odense), a trend to be critically watched (Hvidovre), or already as a partial reality (Vejle). They perceive flood drivers and their combinations as major physical risks to which they adopt different tailor-made solutions. By choosing a bottom-up approach focusing on local governance structures, it demonstrated that the mismatch between responsibility and capacity and the ongoing separation of services related to climatic risks in the Danish municipality context need to be critically considered. The findings highlight that the complex challenge of compound events cannot be solved by one (scientific) discipline alone. Thus, the study advocates a broader inclusion of scientific practices and increased emphasis on local focus within compound event research to foster creative thinking, better preparation, and subsequently more effective management of their risks.

The world’s coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. By the end of the century (2071–2100), it is projected that the 1 in 10-year water levels will have increased 0.34 m on average for RCP4.5, while some locations may experience increases of up to 0.5 m. The change in return levels is largely driven by SLR, although at some locations changes in storms surges and interaction with tides amplify the impact of SLR with changes up to 0.2 m. By presenting an application of the CoDEC dataset to the city of Copenhagen, we demonstrate how climate impact indicators derived from simulation can contribute to an understanding of climate impact on a local scale. Moreover, the CoDEC output locations are designed to be used as boundary conditions for regional models, and we envisage that they will be used for dynamic downscaling.

The scripts to produce sea level time series in Klimaatlas v2022a. Purpose To produce the sea level time series for Danish Klimaatlas sea level index calculation. The sea level time series processed from IPCC AR6. The electronic data of SLR are through the NASA/IPCC Sea Level Projections Tool (https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool). The sea level projections in AR6 are produced by a package named the Frameworks for Assessing Changes To Sea-level (FACTS). The original data includes the median value (50%) and the likely range (upper 83% and lower 17%) and very likely range (upper 95% and lower 5% limits). Scenarios The SSPs are now included into the latest cycle of climate modeling, known as the Coupled Model Intercomparison Project version 6 (CMIP6), for the IPCC AR6. Five scenarios will be used for standard runs in CMIP6 for a worldwide agreement, with the designation of individual scenarios consisting of the name of the fundamental pathway followed by two numbers indicating the extra radiative forcing attained by 2100. SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 are their designations. The low-confidence and high-impact scenario There is a small but significant risk of rapid sea level rise outside the likely estimates, which is mirrored in relatively high numbers for the upper percentiles for global-sea level because of uncertainty of ice-sheet-processes. IPCC AR6 now divides uncertainty into two types, normal uncertainty and deep uncertainty. Likely range projections do not include those ice-sheet-related processes whose quantification is highly uncertain or that are characterized by deep uncertainty. For evaluating this uncertainty, IPCC employ both a poll of experts and a smaller, more specific organized expert opinion. AR6 now incorporates this sort of expert opinion to evaluate risks that cannot be properly modelled but yet cannot be disregarded.crucial to comprehend what the risks are. Klimaatlas will provide a guideline for those who are intereted in this low-confidence scenario.

Rapid urbanisation along the coasts of the world in recent decades has increased their vulnerability to storm surges, especially in response to mean sea level rise. The unique geographical and social conditions of Copenhagen, a major European coastal city, have prompted urban expansion along Køge Bay to the south of the city. However, this new urbanisation area is confronted with the common obstacle of developing a coastal defence strategy, i.e., the lack of long-term observational data required to determine a reliable storm surge protection level. This study aims to address this issue by developing a framework that integrates historical records of extreme storm surge events into coastal defence strategies, using Copenhagen as a case study. 'Statistical Modelling and Forecasting' is one of the steps in our proposed four-step framework solution. Using Bayesian statistical methods, we fitted the historical storm surge data to appropriate probability distributions. This enabled us to generate probabilistic forecasts of storm surge magnitudes, providing insight into the likelihood of future events and their potential impacts on the coastal area. Bayesian MCMC methods offer a powerful framework for incorporating uncertainty and expert knowledge into extreme value analysis. By utilising prior distributions and combining them with the likelihood function, these methods enable the estimation of posterior distributions of model parameters. This is particularly advantageous when dealing with limited data, as expert opinions and historical knowledge can be effectively integrated. We outline the application of the aforementioned extreme value analysis techniques and Bayesian MCMC methods within our four-step framework to integrate historical storm surge events into coastal defence strategies.