Logo: to the web site of Uppsala University

Extreme sea surface waves pose significant risks to coastal infrastructure, ecosystems, and human activities, not only in oceans but also in semi-enclosed basins like the Baltic Sea, where limited fetch and complex bathymetry can amplify local impacts. This study presents a novel machine learning framework to project significant wave height at local scales across the Baltic Sea from 1850 to 2100. Using Random Forest models trained on observed wave data and ERA5 atmospheric reanalysis, we simulate 3-hourly maximum Hs time series driven by four CMIP6 climate models under the SSP2–4.5 climate scenario. While SSP2-4.5 is the primary focus due to its alignment with current policy trajectories, the EC-Earth model was selected for detailed projection analysis as it demonstrated the highest skill in reproducing historical wave conditions and synoptic-scale atmospheric patterns. To explore scenario uncertainty, additional simulations using SSP1-2.6 and SSP3-7.0 were also included. Results indicate a basin-wide decline in the 95th percentile of Hs from the early 20th century until the middle of the 21st century, followed by a stagnation shift, with spatial variability across sub-basins. This change appears largely independent of the carbon emission scenario for the future, suggesting a dominant role of internal atmospheric variability and large-scale dynamics patterns, though the limited ensemble size and absence of formal trend significance testing warrant cautious interpretation. An independent analysis of Lamb Weather Types, based on ERA5-reconstructed wave time series, reveals that extreme wave events are most frequently associated with cyclonic and westerly synoptic patterns, with some site-specific differences. In contrast, calm meteorological conditions are negatively correlated with wave extremes. An inter-model comparison reveals substantial variability in projected extremes, underscoring the importance of ensemble approaches. The methodology is transferable to other regions, though its performance may vary with local atmospheric and oceanographic conditions. These findings provide valuable insights for coastal risk assessments and adaptation planning in a changing climate.

Extreme sea levels are a major global concern due to their potential to cause fatalities and significant economic losses in coastal areas. Consequently, accurate projections of these extremes for the coming century are crucial for effective coastal planning. While it is well established that relative sea level rise driven by ongoing climate change is a key factor influencing future extreme sea levels, changes in storm surges resulting from shifts in storm climatology may also play a critical role. In this study, we project future daily maximum storm tides (the combination of storm surge and tides) using a random forest machine learning approach for 59 stations around the Baltic Sea, based on atmospheric variables such as surface pressure, wind speed, and wind direction derived from climate datasets. The results suggest both positive and negative changes, with sub-regional variations, in 50-year storm tide return levels across the Baltic Sea when comparing the period of 2070–2099 to 1850–1879. Localized increases of up to 10 cm are projected along the west coast of Sweden and the northern Baltic Sea, while decreases of up to 6 cm are anticipated along the south coast of Sweden, the Gulf of Riga, and the mouth of the Gulf of Finland. Negligible levels of change are expected in other parts of the Baltic Sea. The variability in atmospheric drivers across the four climate models contributes to a high degree of uncertainty in future climate projections.

Swell waves, characterized by the long wavelength components generated by distant weather systems or storms, exert a significant influence on various air-sea interaction processes, thereby impacting weather and climate systems. Over recent decades, substantial progress has been achieved in comprehending the dynamics of swell waves and their implications for air-sea interactions. This paper presents a comprehensive review of advancements and key findings concerning surface swell waves and their interactions with the atmosphere. It encompasses a range of topics, including wave growth theory, the effects of swell waves on air-sea momentum, heat, and mass fluxes, as well as their influence on atmospheric turbulence and mixed layer processes. The most important characteristics of the swell impact (where it differs from wind sea conditions) are the wave-induced upward component of the surface stress leading to alteration of total surface stress, generation of a low-level wind maxima or changed wind profile and change of scale and behaviour of turbulence properties (turbulence kinetic energy and integral length scale). Furthermore, the paper explores the modelling of swell dissipation, the integration of swell influences in weather and climate models, and the broader climatic implications of surface swell waves. Despite notable advances in understanding swell processes, persistent knowledge gaps remain, underscoring the need for further research efforts, which are outlined in the paper.

Wind energy is becoming an essential part of the energy system in the Baltic Sea region (BSR). There has been a tremendous development of offshore wind energy in the early 21st century in this region, and the plan for further growth in the coming years is ambitious. The development and implementation of offshore wind energy is a complex process involving many physical and sociopolitical aspects. These aspects have their own characteristics in the BSR. Therefore, they have their unique impact and constraints on the regional development and implementation of the strategic energy technology (SET) plans. This includes implementing next-generation wind turbine technology, offshore wind farms and system integration, floating offshore wind and wind energy industrialization, wind energy operation, maintenance and installation, ecosystems, social impact and human capital agendas, and basic wind energy sciences.Climate change is an important issue to address in relation to future development. Among the questions that may arise are: How would climate change affect the wind resource, extreme wind, and several meteorological and oceanic variables relevant to the offshore wind energy sector? What does this effect imply for the development of offshore wind energy in the BSR?It is encouraging to acknowledge that there have been numerous relevant, good quality, pertinent studies on the subject of the BSR, and many more are ongoing. It is also inspiring to see that in the wind energy sector, there are already many technologies, methods, and tools that are sufficiently mature, and many of them, together with lessons learned through studies in other offshore regions, can be applied to support the urgent and extensive scale development of offshore wind in the BSR.

Coastal and riverine floods are major concerns worldwide as they can impact highly populated areas and result in significant economic losses. In a river mouth environment, interacting hydrological and oceanographical processes can enhance the severity of floods. The compound flood hazards from high sea levels and high river discharge are often estimated using copulas, among other methods. Here, we systematically investigate the influence of different data sources coming from observations and models as well as the choice of copula on extreme water level estimates. While we focus on the river mouth at the city of Halmstad (Sweden), the approach presented is easily transferable to other sites. Our results show that the choice of data sources can considerably impact the results up to 10 % and 15 % for the river time series and 3 % to 4.6 % for the sea level time series under the 5- and 30-year return periods, respectively. The choice of copula can also strongly influence the outcome of such analyses up to 13 % and 9.5 % for the 5-year and 30-year return periods. Each percentage refers to the normalized difference in return level results we can expect when choosing a certain copula or input dataset. The copulas found to statistically best fit our datasets are the Clayton, BB1, and Gaussian (once) ones. We also show that the compound occurrence of high sea levels and river runoff may lead to heightened flood risks as opposed to considering them independent processes and that, in the current study, this is dominated by the hydrological driver. Our findings contribute to framing existing studies, which typically only consider selected copulas and datasets, by demonstrating the importance of considering uncertainties.

To compare in situ and laboratory estimates of sea spray aerosol (SSA) production fluxes, we conducted two research campaigns in the vicinity of an eddy covariance (EC) flux tower on the island of ostergarnsholm in the Baltic Sea during May and August 2021. To accomplish this, we performed EC flux measurements for particles with diameters between 0.25 and 2.5 mu m simultaneously with laboratory measurements using a plunging jet sea spray simulation chamber containing local seawater sampled close to the footprint of the flux tower. We observed a log-linear relationship between wind speed and EC-derived SSA emission fluxes, a power-law relationship between significant wave height and EC-derived SSA emission fluxes, and a linear relationship between wave Reynolds number and EC-derived SSA emission fluxes, all of which are consistent with earlier studies. Although we observed a weak negative relationship between particle production in the sea spray simulation chamber and seawater chlorophyll-alpha concentration and a weak positive relationship with the concentration of fluorescent dissolved organic matter in seawater, we did not observe any significant impact of dissolved oxygen on particle production in the chamber.To obtain an estimate of the size-resolved emission spectrum for particles with dry diameters between 0.015 and 10 mu m, we combined the estimates of SSA particle production fluxes obtained using the EC measurements and the chamber measurements in three different ways: (1) using the traditional continuous whitecap method, (2) using air entrainment measurements, and (3) simply scaling the chamber data to the EC fluxes. In doing so, we observed that the magnitude of the EC-derived emission fluxes compared relatively well to the magnitude of the fluxes obtained using the chamber air entrainment method as well as the previous flux measurements of and the parameterizations of and . As a result of these measurements, we have derived a wind-speed-dependent and wave-state-dependent SSA parameterization for particles with dry diameters between 0.015 and 10 mu m for low-salinity waters such as the Baltic Sea, thus providing a more accurate estimation of SSA production fluxes.

Extreme sea levels may cause damage and the disruption of activities in coastal areas. Thus, predicting extreme sea levels is essential for coastal management. Statistical inference of robust return level estimates critically depends on the length and quality of the observed time series. Here, we compare two different methods for extending a very short (∼ 10-year) time series of tide gauge measurements using a longer time series from a neighbouring tide gauge: linear regression and random forest machine learning. Both methods are applied to stations located in the Kattegat Basin between Denmark and Sweden. Reasonable results are obtained using both techniques, with the machine learning method providing a better reconstruction of the observed extremes. By generating a set of stochastic time series reflecting uncertainty estimates from the machine learning model and subsequently estimating the corresponding return levels using extreme value theory, the spread in the return levels is found to agree with results derived by more physically based methods.

Non-idealized wind profiles frequently occur over the Baltic Sea and are important to take into consideration for offshore wind power, as they affect not only the power production but also the loads on the structure and the behavior of the wake behind the turbine. In this observational study, we classified non-idealized profiles as the following wind profiles having negative shear in at least one part of the lidar wind profile between 28 and 300 m: low-level jets (with a local wind maximum in the profile), profiles with a local minimum and negative profiles. Using observations spanning over 3 years, we show that these non-idealized profiles are common over the Baltic Sea in late spring and summer, with a peak of 40 % relative occurrence in May. Negative profiles (in the 28-300 m layer) mostly occurred during unstable conditions, in contrast to low-level jets that primarily occurred in stable stratification. There were indications that the strong shear zone of low-level jets could cause a relative suppression of the variance for large turbulent eddies compared to the peak of the velocity spectra, in the layer below the jet core. Swell conditions were found to be favorable for the occurrence of negative profiles and profiles with a local minimum, as the waves fed energy into the surface layer, resulting in an increase in the wind speed from below.

A natural hazard is a naturally occurring extreme event that has a negative effect on people and society or the environment. Natural hazards may have severe implications for human life and can potentially generate economic losses and damage ecosystems. A better understanding of their major causes, probability of occurrence, and consequences enables society to be better prepared to save human lives as well as to invest in adaptation options. Natural hazards related to climate change are identified as one of the Grand Challenges in the Baltic Sea region. Here, we summarize existing knowledge about extreme events in the Baltic Sea region with a focus on the past 200 years as well as on future climate scenarios. The events considered here are the major hydro-meteorological events in the region and include wind storms, extreme waves, high and low sea levels, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. We also address some ecological extremes and the implications of extreme events for society (phytoplankton blooms, forest fires, coastal flooding, offshore infrastructure, and shipping). Significant knowledge gaps are identified, including the response of large-scale atmospheric circulation to climate change and also concerning specific events, for example, the occurrence of marine heat waves and small-scale variability in precipitation. Suggestions for future research include the further development of high-resolution Earth system models and the potential use of methodologies for data analysis (statistical methods and machine learning). With respect to the expected impacts of climate change, changes are expected for sea level, extreme precipitation, heat waves and phytoplankton blooms (increase), and cold spells and severe ice winters (decrease). For some extremes (drying, river flooding, and extreme waves), the change depends on the area and time period studied.

Reducing uncertainties in the air–sea CO₂ flux calculations is one of the major challenges when addressing the oceanic contribution in the global carbon balance. In traditional models, the air–sea CO₂ flux is estimated using expressions of the gas transfer velocity as a function of wind speed. However, other mechanisms affecting the variability in the flux at local and regional scales are still poorly understood. The uncertainties associated with the flux estimates become particularly large in heterogeneous environments such as coastal and marginal seas. Here, we investigated the air–sea CO₂ exchange at a coastal site in the central Baltic Sea using nine years of eddy covariance measurements. Based on these observations we were able to capture the temporal variability of the air–sea CO₂ flux and other parameters relevant for the gas exchange. Our results show that a wind-based model with similar pattern to those developed for larger basins and open sea condition can, on average, be a good approximation for k. However, in order to reduce the uncertainty associated to these averages and produce reliable short-term k estimates, additional physical processes must be considered. Using a normalized gas transfer velocity, we identified conditions associated to enhanced exchange (large k values). During high and intermediate wind speeds (above 6–8 m s−1),conditions on both sides of the air–water interface were found to be relevant for the gas exchange. Our findings further suggest that at such relatively high wind speeds, sea spray is an efficient mechanisms for air–sea CO₂ exchange. During low wind speeds (<6 m s−1), water-side convection was found to be a relevant control mechanism. The effect of both sea spray and water-side convection on the gas exchange showed a clear seasonality with positive fluxes (winter conditions) being the most affected.