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Salt mixing enables the transport of water between the inflow and outflow layers of estuarine circulation and therefore closes the circulation by driving a diahaline exchange flow. A recently derived universal law links the salt mixing inside an estuarine volume bounded by an isohaline surface to freshwater discharge: it states that on long-term average, the area-integrated mixing across the bounding isohaline is directly proportional to the freshwater discharge entering the estuary. However, even though numerous studies predict that periods of extreme discharge will become more frequent with climate change, the direct impact of such periods on estuarine mixing and circulation has yet to be investigated. Therefore, this numerical modeling study focuses on salinity mixing and diahaline exchange flows during a low-discharge and an extreme high-discharge period. To this end, we apply a realistic numerical setup of the Elbe estuary in northern Germany, using curvilinear coordinates that follow the navigational channel. This is the first time the direct relationship between diahaline exchange flow and salt mixing as well as the spatial distribution of the diahaline exchange flow is shown in a realistic tidal setup. The spatial distribution is highly correlated with the local mixing gradient for salinity, such that inflow occurs near the bottom at the upstream end of the isohaline. Meanwhile, outflow occurs near the surface at its downstream end. Last, increased vertical stratification occurs within the estuary during the high-discharge period, while estuarine-wide mixing strongly converges to the universal law for averaging periods of the discharge event time scale. Significance Statement: Inside estuaries, such as river mouths, terrestrial freshwater is mixed with salty ocean water. This is accompanied by an estuarine circulation with inflow of saltwater into the estuary and outflow of brackish water toward the ocean. Here, we aim to better understand how salt mixing and estuarine circulation in a tidal estuary react to periods of extreme freshwater discharge. We find that even during extremely high or low discharge, salt mixing follows the freshwater discharge on time scales as short as days, and that estuarine circulation patterns are largely explained by the local distribution of mixing. As extreme runoff events are likely to occur more often with climate change, these findings may help to understand the dynamics inside future estuaries. [ABSTRACT FROM AUTHOR]

Extreme sea level events, such as storm surges, pose a threat to coastlines around the globe. Many tide gauges have been measuring the sea level and recording these extreme events for decades, some for over a century. The data from these gauges often serve as the basis for evaluating the extreme sea level statistics, which are used to extrapolate sea levels that serve as design values for coastal protection. Hydrodynamic models often have difficulty in correctly reproducing extreme sea levels and, consequently, extreme sea level statistics and trends. In this study, we generate a 13-member hindcast ensemble for the non-tidal Baltic Sea from 1979 to 2018 using the coastal ocean model GETM (General Estuarine Transport Model). In order to cope with mean biases in maximum water levels in the simulations, we include both simulations with and those without wind-speed adjustments in the ensemble. We evaluate the uncertainties in the extreme value statistics and recent trends of annual maximum sea levels. Although the ensemble mean shows good agreement with observations regarding return levels and trends, we still find large variability and uncertainty within the ensemble (95 % confidence levels up to 60 cm for the 30-year return level). We argue that biases and uncertainties in the atmospheric reanalyses, e.g. variability in the representation of storms, translate directly into uncertainty within the ensemble. The translation of the variability of the 99th percentile wind speeds into the sea level elevation is in the order of the variability of the ensemble spread of the modelled maximum sea levels. Our results emphasise that 13 members are insufficient and that regionally large ensembles should be created to minimise uncertainties. This should improve the ability of the models to correctly reproduce the underlying extreme value statistics and thus provide robust estimates of climate change-induced changes in the future. [ABSTRACT FROM AUTHOR]

Heating rates induced by optically significant water constituents (OSCs), e.g. phytoplankton and coloured dissolved organic matter (CDOM), contribute to the seasonal modulation of thermal energy fluxes across the ocean–atmosphere interface in coastal and regional shelf seas. This is investigated in the western Baltic Sea, a marginal sea characterised by considerable inputs of freshwater carrying nutrients and CDOM and by complex bio-optical and hydrodynamic processes. Using a coupled bio-optical ocean model (ROMS–Bio-Optic), the inherent optical properties of different OSCs are modelled under varying environmental conditions, and the underwater light field is spectrally resolved in a dynamic ocean. We estimate the relative contribution of these OSCs to the divergence of the heat flux and heating rates and find that, while phytoplankton and CDOM both contribute to surface heating in summer, phytoplankton dominates the OSC contribution to heating in spring, and CDOM dominates the OSC contribution to heating in autumn. The study shows that seasonal and spatial changes in OSCs in the western Baltic Sea have a small but noticeable impact on radiative heating in surface waters and consequences for the exchange of energy fluxes across the air–sea interface and the distribution of heat within the water column. In the Pomeranian Bight, where riverine influx of CDOM is strongest, water-constituent-induced heating rates in surface waters in 2018 are estimated to be between 0.8 and 0.9 K m -1 d -1 in spring and summer, predominantly as a result of increased absorption by phytoplankton and CDOM. Further offshore, OSC-induced heating rates during the same periods are estimated to be between 0.4 and 0.8 K m -1 d -1. Warmer surface waters are balanced by cooler subsurface waters. Surface heat fluxes (latent, sensible and longwave) respond to warmer sea surface temperatures, with a small increase in heat loss to the atmosphere of 5 W m -2 during the period April to September. We find relatively good agreement between our modelled water constituent absorption and in situ and satellite observations. More rigorous co-located heating-rate calculations using an atmosphere–ocean radiative transfer model provide evidence of the suitability of the ROMS–Bio-Optic model for estimating heating rates. [ABSTRACT FROM AUTHOR]

Extreme sea level events, such as storm surges, pose a threat to coastlines around the globe. Many tide gauges have been measuring sea level and recording these extreme events for decades, some for over a century. The data from these gauges often serve as the basis for evaluating the extreme sea level statistics, which are used to extrapolate sea levels that serve as design values for coastal protection. Hydrodynamic models often have difficulty in correctly reproducing extreme sea levels and, consequently, extreme sea level statistics and trends. In this study, we generate a 13-member hind-cast ensemble for the non-tidal Baltic Sea from 1979 to 2018 using the coastal ocean model GETM (General Estuarine Transport Model). In order to cope with mean biases in maximum water levels in the simulations, we include both simulations with and without wind speed adjustments in the ensemble. We evaluate the uncertainties in the extreme value statistics and recent trends of annual maximum sea levels. Although the ensemble mean shows good agreement with observations regarding return levels and trends, we still find large variability and uncertainty within the ensemble (95 % confidence levels up to 60 cm for the 30-year return level). We argue that biases and uncertainties in the atmospheric reanalyses, e.g. variability in the representation of storms, translate directly into uncertainty within the ensemble. The translation of the variability of the 99th percentile wind speeds into the sea level elevation is in the order of the variability of the ensemble spread of the modelled maximum sea levels. Our results emphasise that 13 members are insufficient and that regionally large ensembles should be created to minimise uncertainties. This should improve the ability of the models to correctly reproduce the underlying extreme value statistics and thus provide robust estimates of climate change-induced changes in the future. [ABSTRACT FROM AUTHOR]

Hydrodynamic models are increasingly being used in recent years to map coastal floodplains on local to continental scales. On regional scales, however, high computational costs and the need for high-resolution data limit their application. Additionally, model validation constitutes a major concern, as in-situ data are hardly available or limited in spatial coverage to small parts of the study region. Here we address these challenges by developing a modelling framework, which couples a hydrodynamic coastal inundation model covering the German Baltic Sea coast with a hydrodynamic coastal ocean model of the western Baltic Sea, to produce high resolution (50 m) regional scale flood maps for the entire German Baltic Sea coast. Using a LiDAR derived digital elevation model with 1 m horizontal resolution, we derive and validate the elevation of dikes and natural flood barriers such as dunes. Using this model setup, we simulate a storm surge event from January 2019, a surge with a return period of 200 years and two sea-level rise scenarios for the year 2100 (200-year event plus 1 m and 1.5 m). We validate the simulated flood extents by comparing them to inundation maps derived from Sentinel-1 SAR satellite imagery, acquired between 1.5 and 3.5 hours after the peak of the 2019 surge, covering a large part of the study region. Our results confirm that the German Baltic Sea coast is exposed to coastal flooding, with flood extent varying between 118 km² and 1016 km² for the 2019 storm surge and a 200-year return water level plus 1.5 m of sea-level rise, respectively. Hotspots of coastal flooding are mostly located in the federal state of Mecklenburg Western Pomerania. Our results emphasise the importance of current plans to update coastal protection schemes along the German Baltic Sea coast over the course of the 21st century in order to prevent large-scale damage in the future. [ABSTRACT FROM AUTHOR]

Heating rates induced by optically significant water constituents (OSCs), e.g. phytoplankton and coloured dissolved organic matter (CDOM), contribute to the seasonal modulation of thermal energy fluxes across the ocean-atmosphere interface in coastal and regional shelf seas. This is investigated in the Western Baltic Sea, a marginal sea characterised by considerable inputs of freshwater carrying nutrients and CDOM, and complex bio-optical and hydrodynamic processes. Using a coupled bio-optical-ocean model (ROMS-Bio-Optic), the inherent optical properties of different OSCs are modelled under varying environmental conditions and the underwater light field is spectrally-resolved in a dynamic ocean. We estimate the relative contribution of these OSCs to the divergence of the heat flux and heating rates and find that phytoplankton dominates the OSC contribution to heating in spring and summer, while CDOM dominates in summer and autumn. The study shows that seasonal and spatial changes in OSCs in the Western Baltic Sea have a small but noticeable impact on radiative heating in surface waters and consequences for the exchange of energy fluxes across the air-sea interface and the distribution of heat within the water column. In the Pomeranian Bight, where riverine influx of CDOM is strongest, water constituent-induced heating rates in surface waters in 2018 are estimated to be between 0.8 and 0.9 K m-1 d-1 in spring and summer, predominantly as a result of increased absorption by phytoplankton and CDOM. Further offshore, OSC-induced heating rates during the same periods are estimated to be between 0.4 and 0.8 K m-1 d-1. Warmer surface waters are balanced by cooler subsurface waters. Surface heat fluxes (latent, sensible and longwave) respond to warmer sea surface temperatures with a small increase in heat loss to the atmosphere of 5 Wm-2 during the period April to September. We find relatively good agreement between our modelled water constituent absorption, and in situ and satellite observations. More rigorous co-located heating rate calculations using an atmosphere-ocean radiative transfer model provide evidence of the suitability of the ROMS-Bio-Optic model for estimating heating rates. [ABSTRACT FROM AUTHOR]