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High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm, and through sea level rise) and internal variability (variations in the storms intensity and location). We find that 1m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase of flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. A modelling framework connecting meteorological conditions to impacts provides relevant and accessible information that can directly be integrated into high impact event assessments. [ABSTRACT FROM AUTHOR]

While a global acceleration of sea-level rise (SLR) during the 20th century is now established, locally acceleration is more difficult to detect because additional processes play a role which sometimes mask the acceleration. Here we study the rate of SLR along the coast of the Netherlands from six tide gauge records, covering the period 1890–2000. We focus on the influence of the wind field and the nodal tide variations on the local sea-level trend. We use four generalised additive models, including different predictive variables, and a parametric bootstrap method to compute the sea-level trend. From the sea-level trend, we obtain the continuous evolution of the rate of SLR and its uncertainty over the observational period through differentiation. Accounting for the nodal cycle only or both the nodal cycle and the wind influence on sea level reduces the standard error in the estimation of the rate of SLR. Moreover, accounting for both the nodal and wind influence changes the estimated rate of SLR, unmasking an acceleration of SLR that started in the 1960s. Our best-fitting statistical model yields a rate of SLR of about 1.8 [1.4–2.3] mm/yr in 1900–1919 and 1.5 [1.1–1.8] mm/yr in 1940–1959 compared to 3.0 [2.4–3.5] mm/yr over 2000–2019. If, apart from tidal, wind effects and fluctuations, sea level would have increased at a constant rate, then the probability (the p-value) of finding a rate difference between 1940–1959 and 2000–2019 of at least our estimate is smaller than 1 %. Our findings can be interpreted as an unequivocal sign of the acceleration of current SLR along the Dutch coast since the 1960s. This aligns with global SLR observations and expectations based on a physical understanding of SLR related to global warming. A small but significant part of the long-term sea-level trend is due to wind forcing related to a strengthening and northward shift of the jet stream. Additionally, we detect a multidecadal mode of sea-level variability forced by the wind with an amplitude of around 1 cm. We argue that it is related to multi-decadal sea surface temperature variations in the North Atlantic, similar to the Atlantic Multidecadal Variability. [ABSTRACT FROM AUTHOR]

An analysis of the Indian Ocean circulation and the Agulhas Current retroflection is carried out using a primitive equation model with simplified coastline and flat bottom. Four configurations with 0.25° and 0.1° horizontal resolution and in barotropic and baroclinic cases are considered. The wind stress is taken as control parameter to increase the inertia of the currents. The volume transport of the Indonesian Throughflow, Mozambique Channel, and Agulhas Current are found to increase linearly with the wind stress strength, and three nonlinear retroflection regimes are found. A viscous and an inertial regime had already been docu-mented, but a new turbulent regime appears at large wind stress amplitude. In this turbulent regime, the volume of Agulhas leakage reaches a plateau because of strong mesoscale variability and, in contrast to the other regimes, does not depend on the wind stress magnitude. The physical mechanism causing the plateau is shown to be associated with the cross-jet exchange of Indian Ocean water and water from the Antarctic Circumpolar Current. In the turbulent regime, the permeability of the Agulhas Return Current to material transport increases and the Indian Ocean water available for the Agulhas leakage decreases. [ABSTRACT FROM AUTHOR]

Many coupled climate models (atmosphere/ocean/sea ice) show that as the Antarctic ice sheet looses mass, the freshwater input into the ocean increases stratification and subsurface temperature in contact with the ice shelves (Bronselaer et al. 2018). This could be a powerful positive feedback yet it is not included in sea level projections. One reason is that it has not been tested in climate models that include an ice sheet model. In fact, many different models are necessary to constrain the uncertainty associated with this feedback arising from (i) the magnitude of the subsurface ocean temperature rise for a given freshwater flux and (ii) the sensitivity of the ice sheet mass loss to temperature change.In this presentation I propose a simple method to quantify the impact of this feedback on 21st century sea level rise. I assess the relation between freshwater input and subsurface temperature from 5 climate models (EC-EARTH, GFDL, CESM, CSIRO Mk3L, GISS modelE-R). I then use a relation between subsurface temperature and Antarctic mass loss diagnosed from the SeaRise ice sheet models using Linear Response Functions (Levermann et al. 2014). This quantification of the freshwater positive feedback is then included in a probabilistic sea level rise projection model that includes other sea level contributors and their dependencies (Le Bars 2018). Initial results suggest that this feedback has small impact on sea level for a low emission scenario but for a large emission scenario it increases the skewness of the sea level probability distribution. In other words, low probability high end sea level projections become larger.Bronselaer, B., et al. (2018). Change in future climate due to Antarctic meltwater. Nature. http://doi.org/10.1038/s41586-018-0712-zLe Bars, D. (2018). Uncertainty in Sea Level Rise Projections Due to the Dependence Between Contributors. Earth's Future. http://doi.org/10.1029/2018EF000849Levermann, A., et al. (2014). Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models. Earth System Dynamics, 5(2), 271–293. http://doi.org/10.5194/esd-5-271-2014 [ABSTRACT FROM AUTHOR]