Search

This paper proposes a new approach to calculate aeroelastic stability of operating wind turbines using system identification, and explores how an efficient numerical experiment can be designed. The data-driven method is based on time series including induced motion of the blades, which can stem from any fidelity simulation. While this study utilises HAWC2, the findings enable the usage of e.g. computational fluid dynamics in a fluid-structure-interaction simulation. Different forcing sequences are suitable to excite the blade, but especially the doublet and chirp show favourable characteristics and results for an application of no more than five seconds. System identification, the Multivariable Output-Error State sPace (MOESP) algorithm in particular, is used to find an algebraic equivalent of the simulated turbine. Using the output-only version, about 20 seconds of simulated time are sufficient to identify the system such that the predicted response matches the input with an R2-score of > 99.8 %. A comparison with HAWCStab2 shows frequency differences of < 0.5% across the wind speed range, matching mode shapes but some bigger deviations in aeroelastic damping.

In this work we present multi-fidelity steady-state aeroelastic framework that leverages the state-of-the-art simulation tool HAWC2 for the structural model, and a variety of aerodynamic models, comprising of the low fidelity blade element momentum (BEM) method, the medium fidelity blade element vortex cylinder (BEVC) method and the coupled near wake and vortex cylinder method, and finally the high-fidelity CFD solver EllipSys3D. The aeroelastic framework is part of AESOpt, an aerostructural framework for design of wind turbine blades. The different aerodynamic models are applied to compute the aeroelastic steady state of the newly designed IEA 22 MW Reference Wind Turbine. The results show a very good agreement between the medium- and high-fidelity aerodynamic models with a maximum of 2.7% difference between the high-fidelity aeroelastic response and that of the lower fidelities.

This reports presents comparison of the steady state HAWC2 [1, 2, 3] simulation results and the HAWCStab2 computations of the DTU 10 MW reference turbine [4], as well as a comparison of structural blade-only frequencies and damping ratios. It serves as a simple verification study of the HAWCStab2 [5, 6, 7] computations. The steady state comparison is shown in Section 2. For a fair comparison, the following simplifications of the DTU 10 MW reference turbine are made: • no gravity; • shaft tilt angle is set to zero, since HAWCStab2 assumes the inflow is perpendicular to the rotor plane; • uniform aligned inflow conditions (no turbulence, shear, veer or yaw); • tower and shaft flexibility are not considered to assure the shaft remains perfectly aligned with the wind inflow vector (horizontal). Furthermore, each main-body in the HAWC2 model must contain as many bodies as elements, to be equivalent to the co-rotational formulation used by HAWCStab2. There are four test cases considered in the steady state comparison: • Case 1: no blade flexibility, and the aerodynamic modelling reduced to strip theory: no induction and no tip correction, labelled as “no induction” or “without induction”. • Case 2: no blade flexibility in conjunction with BEM induction model and Prandtl tip correction (labelled as “induction+tip”). • Case 3: flexible blades in conjunction with “induction+tip”. Includes power and thrust curve comparison with HAWCStab2 2.15. • Case 4: flexible blades in conjunction with “induction+tip”, 15 degrees coning. Includes power and thrust curve comparison with HAWCStab2 2.15. Both HAWC2 and HAWCStab2 have the ability to use different aerodynamic models. For the “induction+tip” model, the rotor induced velocities are calculated with Blade Element Momentum theory, and the presence of the tip vortex is accounted for by the Prandtl tip loss model. For not completely planar rotors, for example

Vortex-induced vibrations (VIVs) of wind turbine blades are a phenomenon that cannot be predicted sufficiently accurate by low fidelity methods. High fidelity methods, such as computational fluid dynamics (CFD) coupled to a structural solver come at a high computational cost. Most studies of the phenomenon have so far focused on analysis of single blades. For these cases, the so-called forced-motion method of prescribing the blade motion according to structural modeshapes has proven effective. In this article, VIVs of the IEA 10 MW reference turbine is studied in a fluid–structure interaction setup, where the structural dynamics of the full turbine are represented in the structural solver and the full rotor aerodynamics are simulated in the CFD solver. Due to the high computational cost this study is limited to only a single case with the rotor in bunny configuration, 80 degrees pitch and 90 degrees yaw. Also the purely structural power dissipation of the full turbine is studied to enable full rotor forced-motion in future work. A blade tip amplitude of roughly 17 meters is reached in the full turbine simulations, which is a significantly larger limit cycle amplitude than what would be reached in a corresponding isolated single blade simulation. The analysis also shows that forced-motion simulations combining three single blades vibrating according to the structural modeshape would be feasible. It is further observed, that the power injection or extraction close to the blade tip may change drastically at very large amplitudes compared to lower amplitudes. Keywords

This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/.

This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022, submited).

Surrogate models are vital for offshore wind farm optimisation, or digital twin for rapid execution. Reliable surrogates are difficult to make, especially for large wind farms and for fatigue loads. One limitation is the high resolution of turbulence needed for fatigue calculations and the vast size of a wind farm. We present code optimisations for implementing the Mann model for farm-sized flows, which are infeasible otherwise. Additionally, we present a case study of a full load surrogate for the Lillgrund offshore wind farm using large turbulence box generation in aeroelastic wind farm simulations using HAWC2Farm. Various mappings between the wind field in front of a turbine and the corresponding turbine structural fatigue loads are presented. The best-performing surrogate model, using a proper orthogonal decomposition of the input space with an artificial neural network, is able to significantly reduce the error in fatigue load estimates by as much as a factor of 5 compared to conventional methods. The presented recommendations are crucial to generating reliable wind farm load surrogates for wind farm optimisation purposes.

Stall-Induced Vibrations (SIV) are an important design consideration for wind turbine blade design, especially for large, modern, wind turbines with highly flexible blades. Their severity depends on both the inflow and the structural characteristics of the blades. Studying SIV has a high computational cost, because it requires high-fidelity aeroelastic simulations, and potentially a large number of input variables. In an effort to reduce the computational cost of the domain exploration, in this work we have adopted a Surrogate-Based Optimization (SBO) framework. This way, the combination of input variables that lead to SIV can be explored with the minimum number of aeroelastic simulations. The proposed SBO framework can use any type of surrogate model, and leverages Delaunay triangulation to iteratively select samples to refine the surrogate model. The occurrence and severity of SIV on the IEA 10MW turbine is studied in a five variables space consisting of: wind speed, yaw angle, vertical wind shear, wind veer, and atmospheric temperature. A well-trained surrogate model is developed and used to predict the damping ratio of the first blade edgewise mode in the entire inflow space at a reduced computational cost. Sensitivity analysis of the predicted damping ratio shows that yaw angle is the most influential variable, while temperature is the least influential variable in terms of inflow conditions that can lead to the occurrence of SIV. Inflow conditions with a moderate yaw angle (around 10–25 deg), high wind speeds, and moderate to high negative veer are found to lead to severe SIV. This study should serve as a guiding tool to decide the scope of the more computationally expensive simulations such as high-fidelity CFD-based aeroelastic simulations which can provide a more accurate description of SIV.

In this study, we examine the effect of transient mantle creep on the prediction of glacial isostatic adjustment (GIA) signals. Specifically, we compare predictions of relative sea level (RSL) change from GIA from a set of Earth models in which transient creep parameters are varied in a simple Burgers model to a reference case with a Maxwell viscoelastic rheology. The model predictions are evaluated in two ways: first, relative to each other to quantify the effect of parameter variation, and second, for their ability to reproduce well-constrained sea level records from selected locations. Both the resolution and geographic location of the RSL observations determine whether the data can distinguish between model cases. Model predictions are most sensitive to the inclusion of transient mantle deformation in regions that are near-field and peripheral relative to former ice sheets. This sensitivity appears particularly true along the North American west coast in the region of the former Cordilleran Ice Sheet, which experienced rapid sea-level fall following deglaciation between 14 and 12 kyr BP. Relative to the Maxwell case, Burgers models better reproduce this rapid phase of regional postglacial sea-level fall. As well, computed goodness-of-fit values in this region show a clear preference for models where transient deformation is present in the whole or lower mantle, and for models where the rigidity of the Kelvin element is weakened relative to the rigidity of the Maxwell element. In contrast, model predictions of relative sea-level change in the far-field show weak sensitivity to the inclusion of transient deformation.

A devastating tsunami struck Palu Bay in the wake of the 28 September 2018 Mw = 7.5 Palu earthquake (Sulawesi, Indonesia). With a predominantly strike-slip mechanism, the question remains whether this unexpected tsunami was generated by the earthquake itself, or rather by earthquake-induced landslides. In this study we examine the tsunami potential of the co-seismic deformation. To this end, we present a novel geodetic data set of Global Positioning System and multiple Synthetic Aperture Radar-derived displacement fields to estimate a 3D co-seismic surface deformation field. The data reveal a number of fault bends, conforming to our interpretation of the tectonic setting as a transtensional basin. Using a Bayesian framework, we provide robust finite fault solutions of the co-seismic slip distribution, incorporating several scenarios of tectonically feasible fault orientations below the bay. These finite fault scenarios involve large co-seismic uplift (>2 m) below the bay due to thrusting on a restraining fault bend that connects the offshore continuation of two parallel onshore fault segments. With the co-seismic displacement estimates as input we simulate a number of tsunami cases. For most locations for which video-derived tsunami waveforms are available our models provide a qualitative fit to leading wave arrival times and polarity. The modeled tsunamis explain most of the observed runup. We conclude that co-seismic deformation was the main driver behind the tsunami that followed the Palu earthquake. Our unique geodetic data set constrains vertical motions of the sea floor, and sheds new light on the tsunamigenesis of strike-slip faults in transtensional basins.

The uncertainty afflicting modal parameter estimates stems from e.g., the finite data length, unknown, or partly measured inputs and the choice of the identification algorithm. Quantification of the related errors with the statistical Delta method is a recent tool, useful in many modern modal analysis applications e.g., damage diagnosis, reliability analysis, model calibration. In this paper, the Delta method-based uncertainty quantification methodology is validated for obtaining the uncertainty of the modal parameter and the modal indicator estimates in the context of several well-known subspace identification algorithms. The focus of this study is to validate the quality of each Delta method-based approximation with respect to the experimental Monte Carlo distributions of parameter estimates using a statistical distance measure. On top of that, the accuracy in obtaining the related confidence intervals is empirically assessed. The case study is based on data obtained from an extensive experimental campaign of a large scale wind turbine blade tested in a laboratory environment. The results confirm that the Delta method is, on average, adequate to characterize the distribution of the considered estimates solely based on the quantities obtained from one data set, validating the use of this statistical framework for uncertainty quantification in practice.

Stall Induced Vibrations (SIV) are an aeroelastic instability that might happen when large portions of the wind turbine blade operate in moderate stall, which leads to high internal loads that can damage the structure. Exploring the inflow space that cause SIV has a high computational cost, which quickly increases with the number of input variables, since it involves high-fidelity aeroelastic simulations. In this work, a surrogate model is used to effectively explore the behavior of SIV in a five variable inflow space consisting of wind speed, yaw angle, vertical wind shear, wind veer, and atmospheric temperature. The surrogate model, trained from the results of a few aeroelastic simulations, predicts the severity of SIV for a given inflow condition. The surrogate model is chosen by comparing the performance of a few common model types, and Artificial Neural Networks (ANN) are found to provide a good balance between accuracy and computational time. Using the ANN, the occurrence and severity of SIV in the inflow space is studied. The analysis shows that for the turbine studied, yaw angle is the most influential variable, while temperature is the least influential variable in terms of conditions that lead to occurrence of SIV. Inflow conditions with a moderate yaw angle (around 10-25 deg), high wind speeds, and high negative veer are found to be the most critical in terms of severity of SIV. The current work is expected to serve as a helping tool to decide the focus of computationally expensive simulations such as high fidelity CFD based aeroelastic simulations.

A parked wind turbine can be subjected to large yaw angles, which causes vortex shedding. Under some particular inflow conditions, the interaction between the shed vorticity and the blade structure could lead to Vortex-Induced Vibrations (VIV). VIV are an instability where the response grows until it reaches a limit cycle oscillation. In this paper, we will focus on this last part of the response. We will see that the shedding makes the aeroelastic system linear time-periodic, and study its stability with Floquet theory. We will use high-fidelity simulations of different inflow conditions, and quantify the stability of the system using a model-free approach.

Joint effects of the dynamic sea-level rise projected changes in the large-scale atmosphere/ocean circulation, and wave climate on hurricane-induced extreme water levels in the Caribbean region are assessed. We use the 2D-depth integrated ADCIRC + SWAN wave-ocean model, baroclinically coupled to an ocean-eddying version of the Community Earth System Model, to compare impacts of the September 2017 hurricanes with projected impacts of similar hypothetical tropical storms occurring in the future. The model predicts only minor changes in the hurricane-induced extreme water levels for those Caribbean islands which were severely devastated by the 2017 tropical storms (Irma and Maria). That is, provided that the hurricane intensity remains at the present-day level, the global mean sea-level rise is the main future coastal flood risk factor.

Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, two challenges on illumination estimation were conducted. The main advantage of testing a method on a challenge over testing it on some of the known datasets is the fact that the ground‐truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The First illumination estimation challenge (IEC#1) had only a single task, global illumination estimation. The second illumination estimation challenge (IEC#2) was enriched with two additional tracks that encompassed indoor and two‐illuminant illumination estimation. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest‐like markup for the images from the Cube++ dataset. This article focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the first and second challenge that can be useful for similar future developments.

We present a method for illuminant estimation that exploits a generative adversarial network architecture to generate a spatially-varying illuminant map. This map is then transformed by consensus into a global illuminant estimation, in the form of a single RGB triplet. To this end, different consensus strategies are designed and compared in this paper. The best solution won second place in the 2nd International Illumination Estimation Challenge, specifically for the indoor track.

In this paper we recreate, and improve, the binary classification method for particles proposed in Roe et al. (2005) paper "Boosted decision trees as an alternative to artificial neural networks for particle identification". Such particles are tau neutrinos, which we will refer to as background, and electronic neutrinos: the signal we are interested in. In the original paper the preferred algorithm is a Boosted decision tree. This is due to its low effort tuning and good overall performance at the time. Our choice for implementation is a deep neural network, faster and more promising in performance. We will show how, using modern techniques, we are able to improve on the original result, both in accuracy and in training time., Comment: 9 pages, 6 figures. The code can be found at https://github.com/Denis-Stanev/DNN-vs-BDT . The data file can be found here: https://archive.ics.uci.edu/ml/datasets/MiniBooNE+particle+identification

Sea-level rise poses severe threats to coastal and low-lying regions around the world, by exacerbating coastal erosion and flooding. Adequate sea-level projections over the next decades are important for both decision making and for the development of successful adaptation strategies in these coastal and low-lying regions to climate change. Ocean components of climate models used in the most recent sea-level projections do not explicitly resolve ocean mesoscale processes. Only a few effects of these mesoscale processes are represented in these models, which leads to errors in the simulated properties of the ocean circulation that affect sea-level projections. Using the Caribbean Sea as an example region, we demonstrate a strong dependence of future sea-level change on ocean model resolution in simulations with a global climate model. The results indicate that, at least for the Caribbean Sea, adequate regional projections of sea-level change can only be obtained with ocean models which capture mesoscale processes.

Wind turbines are subject to stochastic cyclic loadings which reduce their fatigue life. In this paper, we rely on a Mass Resonance Exciter (MRE) to apply fatigue loadings to a blade. The MRE, high-pressure tubes with hydraulic oil and sensors, substantially alter the distribution of mass, stiffness and structural damping. We will investigate how the test setup has modified the blade, thus verifying the validity of the approach. The blade has been mounted on a block at the Large Scale Facility of DTU Wind Energy Department, and instrumented with accelerometers, to provide the modal properties in the clean configuration. Later, an MRE tuned to excite the first flapwise mode has been added, and the blade has been excited for two million cycles in the flapwise direction. After that, the MRE has been tuned to the first edgewise mode and the blade has been excited again for two million cycles. At regular intervals, the test has been stopped, to assess the effect of the test setup, as well as the impact of the damage on the modal properties. In each case, the blade modes have been excited using the hammer and pull and release methods. The accelerations along the blade have been processed with system identification algorithms, to provide the modal properties. The test setup has not significantly modified the frequencies of the first flapwise and edgewise modes, while it has altered the frequencies of higher-order modes.

Minimizing the cost of energy of a wind farm is a difficult task, which involves reducing the wake effects while satisfying several constraints. Due to its multidisciplinary nature, this problem is usually solved through numerical optimisers. TOPFARM is one of these tools, and in this paper, we have added to it a constraint on the fatigue loads. The efficiency of the implementation is guaranteed by an extensive use of gradients and load surrogate models. The paper is concluded by showing some case studies.

Wind turbines can exhibit flutter-like edgewise instabilities past a critical rotor speed. These edgewise instabilities are dominated by an edge-torsion coupling due to flapwise blade deflection. This paper experimentally validates the predicted stability limit of a 7 MW machine. State of the art simulation software is used to compare against recorded test data and characterize the observed flutter mechanism. The critical rotor speed at which the edgewise instabilities occur at in field measurements can be predicted by time domain simulations and stability analysis with very good agreement.

Sea-level rise poses severe threats to coastal and low-lying regions around the world, by exacerbating coastal erosion and flooding. Adequate sea-level projections over the next decades are important for both decision making and for the development of successful adaptation strategies in these coastal and low-lying regions to climate change. Ocean components of climate models used in the most recent sea-level projections do not explicitly resolve ocean mesoscale processes. Only a few effects of these mesoscale processes are represented in these models, which leads to errors in the simulated properties of the ocean circulation that affect sea-level projections. Using the Caribbean Sea as an example region, we demonstrate a strong dependence of future sea-level change on ocean model resolution in simulations with a global climate model. The results indicate that, at least for the Caribbean Sea, adequate regional projections of sea-level change can only be obtained with ocean models which capture mesoscale processes., info:eu-repo/semantics/published

info:eu-repo/semantics/published

Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, the 2nd Illumination estimation challenge~(IEC\#2) was conducted. The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The challenge had several tracks: general, indoor, and two-illuminant with each of them focusing on different parameters of the scenes. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest-like markup for the images from the Cube+ dataset that was used in IEC\#1. This paper focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the 1st and 2nd challenge that can be useful for similar future developments.

Tropical beaches provide coastal flood protection, income from tourism, and habitat for flagship species. They urgently need protection from erosion, which is being exacerbated by changing climate and coastal development. Traditional coastal engineering solutions are expensive, provide unstable temporary solutions, and often disrupt natural sediment transport. Instead, natural foreshore stabilization and nourishment may provide a sustainable and resilient long-term solution. Field flume and ecosystem process measurements, along with data from the literature, show that sediment stabilization by seagrass in combination with sediment-producing calcifying algae in the foreshore form an effective mechanism for maintaining tropical beaches worldwide. The long-term efficacy of this type of nature-based beach management is shown at a large scale by comparing vegetated and unvegetated coastal profiles. We argue that preserving and restoring vegetated beach foreshore ecosystems offers a viable, self-sustaining alternative to traditional engineering solutions, increasing the resilience of coastal areas to climate change.

info:eu-repo/semantics/published

The mesoscale variability in the Caribbean Sea is dominated by anticyclonic eddies that are formed in the eastern part of the basin. These anticyclones intensify on their path westward while they pass the coastal upwelling region along the Venezuelan and Colombian coast. In this study, we used a regional model to show that this westward intensification of Caribbean anticyclones is steered by the advection of cold upwelling filaments. Following the thermal wind balance, the increased horizontal density gradients result in an increase in the vertical shear of the anticyclones and in their westward intensification. To assess the impact of variations in upwelling on the anticyclones, several simulations were performed in which the northward Ekman transport (and thus the upwelling strength) is altered. As expected, stronger (weaker) upwelling is associated with stronger (weaker) offshore cooling and a stronger (weaker) westward intensification of the anticyclones. Moreover, the simulations with weaker upwelling show farther advection of the Amazon and Orinoco River plumes into the basin. As a result, in these simulations the horizontal density gradients were predominantly set by horizontal salinity gradients. The importance of the horizontal density gradients driven by temperature, which are associated with the upwelling, increased with increasing upwelling strength. The results of this study highlight that both upwelling and the advection of the river plumes affect the life cycle of mesoscale eddies in the Caribbean Sea., info:eu-repo/semantics/published

Rising sea levels due to climate change can have severe consequences for coastal populations and ecosystems all around the world. Understanding and projecting sea-level rise is especially important for low-lying countries such as the Netherlands. It is of specific interest for vulnerable ecological and morphodynamic regions, such as the Wadden Sea UNESCO World Heritage region. Here we provide an overview of sea-level projections for the 21st century for the Wadden Sea region and a condensed review of the scientific data, understanding and uncertainties underpinning the projections. The sea-level projections are formulated in the framework of the geological history of the Wadden Sea region and are based on the regional sea-level projections published in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). These IPCC AR5 projections are compared against updates derived from more recent literature and evaluated for the Wadden Sea region. The projections are further put into perspective by including interannual variability based on long-Term tide-gauge records from observing stations at Den Helder and Delfzijl. We consider three climate scenarios, following the Representative Concentration Pathways (RCPs), as defined in IPCC AR5: The RCP2.6 scenario assumes that greenhouse gas (GHG) emissions decline after 2020; the RCP4.5 scenario assumes that GHG emissions peak at 2040 and decline thereafter; and the RCP8.5 scenario represents a continued rise of GHG emissions throughout the 21st century. For RCP8.5, we also evaluate several scenarios from recent literature where the mass loss in Antarctica accelerates at rates exceeding those presented in IPCC AR5. For the Dutch Wadden Sea, the IPCC AR5-based projected sea-level rise is 0.07±0.06m for the RCP4.5 scenario for the period 2018-30 (uncertainties representing 5-95%), with the RCP2.6 and RCP8.5 scenarios projecting 0.01m less and more, respectively. The projected rates of sea-level ch

Different sea level reconstructions show a spread in sea level rise over the last six decades and it is not yet certain whether the sum of contributors explains the reconstructed rise. Possible causes for this spread are, among others, vertical land motion at tide-gauge locations and the sparse sampling of the spatially variable ocean. To assess these open questions, reconstructed sea level and the role of the contributors are investigated on a local, basin, and global scale. High-latitude seas are excluded. Tide-gauge records are combined with observations of vertical land motion, independent estimates of ice-mass loss, terrestrial water storage, and barotropic atmospheric forcing in a self-consistent framework to reconstruct sea level changes on basin and global scales, which are compared to the estimated sum of contributing processes. For the first time, it is shown that for most basins the reconstructed sea level trend and acceleration can be explained by the sum of contributors, as well as a large part of the decadal variability. The sparsely sampled South Atlantic Ocean forms an exception. The global-mean sea level reconstruction shows a trend of 1.5 ± 0.2 mm yr−1 over 1958–2014 (1σ), compared to 1.3 ± 0.1 mm yr−1 for the sum of contributors. Over the same period, the reconstruction shows a positive acceleration of 0.07 ± 0.02 mm yr−2, which is also in agreement with the sum of contributors, which shows an acceleration of 0.07 ± 0.01 mm yr−2. Since 1993, both reconstructed sea level and the sum of contributors show good agreement with altimetry estimates.

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g.,acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled "Regional Sea Level and Coastal Impacts", an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3mm yr(-1) and acceleration of 0.1 mm yr(-2) over 1993-present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present period. We also study t

Rising sea levels due to climate change can have severe consequences for coastal populations and ecosystems all around the world. Understanding and projecting sea-level rise is especially important for low-lying countries such as the Netherlands. It is of specific interest for vulnerable ecological and morphodynamic regions, such as the Wadden Sea UNESCO World Heritage region. Here we provide an overview of sea-level projections for the 21st century for the Wadden Sea region and a condensed review of the scientific data, understanding and uncertainties underpinning the projections. The sea-level projections are formulated in the framework of the geological history of the Wadden Sea region and are based on the regional sea-level projections published in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). These IPCC AR5 projections are compared against updates derived from more recent literature and evaluated for the Wadden Sea region. The projections are further put into perspective by including interannual variability based on long-term tide-gauge records from observing stations at Den Helder and Delfzijl. We consider three climate scenarios, following the Representative Concentration Pathways (RCPs), as defined in IPCC AR5: the RCP2.6 scenario assumes that greenhouse gas (GHG) emissions decline after 2020; the RCP4.5 scenario assumes that GHG emissions peak at 2040 and decline thereafter; and the RCP8.5 scenario represents a continued rise of GHG emissions throughout the 21st century. For RCP8.5, we also evaluate several scenarios from recent literature where the mass loss in Antarctica accelerates at rates exceeding those presented in IPCC AR5. For the Dutch Wadden Sea, the IPCC AR5-based projected sea-level rise is 0.07±0.06m for the RCP4.5 scenario for the period 2018–30 (uncertainties representing 5–95%), with the RCP2.6 and RCP8.5 scenarios projecting 0.01m less and more, respectively. The projected rates of sea-level chang

Rising sea levels due to climate change can have severe consequences for coastal populations and ecosystems all around the world. Understanding and projecting sea-level rise is especially important for low-lying countries such as the Netherlands. It is of specific interest for vulnerable ecological and morphodynamic regions, such as the Wadden Sea UNESCO World Heritage region. Here we provide an overview of sea-level projections for the 21st century for the Wadden Sea region and a condensed review of the scientific data, understanding and uncertainties underpinning the projections. The sea-level projections are formulated in the framework of the geological history of the Wadden Sea region and are based on the regional sea-level projections published in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). These IPCC AR5 projections are compared against updates derived from more recent literature and evaluated for the Wadden Sea region. The projections are further put into perspective by including interannual variability based on long-Term tide-gauge records from observing stations at Den Helder and Delfzijl. We consider three climate scenarios, following the Representative Concentration Pathways (RCPs), as defined in IPCC AR5: The RCP2.6 scenario assumes that greenhouse gas (GHG) emissions decline after 2020; the RCP4.5 scenario assumes that GHG emissions peak at 2040 and decline thereafter; and the RCP8.5 scenario represents a continued rise of GHG emissions throughout the 21st century. For RCP8.5, we also evaluate several scenarios from recent literature where the mass loss in Antarctica accelerates at rates exceeding those presented in IPCC AR5. For the Dutch Wadden Sea, the IPCC AR5-based projected sea-level rise is 0.07±0.06m for the RCP4.5 scenario for the period 2018-30 (uncertainties representing 5-95%), with the RCP2.6 and RCP8.5 scenarios projecting 0.01m less and more, respectively. The projected rates of sea-level ch

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g.,acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled "Regional Sea Level and Coastal Impacts", an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3mm yr(-1) and acceleration of 0.1 mm yr(-2) over 1993-present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present period. We also study t

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g.,acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled "Regional Sea Level and Coastal Impacts", an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3mm yr(-1) and acceleration of 0.1 mm yr(-2) over 1993-present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present period. We also study t