The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections

Understanding Sea-Level Change Using Global and Regional Models

The sea level is changing around the world due to a combination of complex processes, such as changes in ocean density and circulation, the melt of ice sheets and glaciers, terrestrial water storage and vertical land motion. Projections of how much and how fast sea level will change are crucial information for adaptation planning. At the basis of most sea-level projections are global climate models, which can be used to simulate how different components of the Earth’s system, such as the ocean and the atmosphere, evolve as the greenhouse gas concentration in the atmosphere increases. However, differences between global climate models introduce uncertainties in sea-level projections. Additionally, due to their typically low grid resolution, such models poorly capture sea-level change in coastal regions in which small-scale bathymetric features and oceanic processes are important. Another uncertainty is natural sea-level variability, which can obscure long-term sea-level change in model simulations and observational records. In this thesis, the sea-level projections of two generations of global climate models (CMIP5 & CMIP6) are compared to understand how the increased climate sensitivity in CMIP6 affects sea-level projections. Additionally, regional ocean models are used to refine the simulations of two global climate models on the Northwestern European Shelf (dynamical downscaling) and to better understand the drivers of interannual sea-level variability in this region. Finally, global climate model simulations of future changes in the seasonal sea-level cycle on the Northwestern European Shelf are analyzed and explained using sensitivity tests performed with a regional climate model. Based on this research, this thesis concludes that embedding regional ocean models in sea-level science will help to improve the simulations of global climate models, to better understand the mechanisms behind sea-level change and variability and to provide stakeholders with the local sea-level information they need.Physical and Space Geodes

Supporting Data for Reversal of the Direction of Horizontal Velocities Induced by GIA as a Function of Mantle Viscosity

Supporting data for GRL article Reversal of the Direction of Horizontal Velocities Induced by GIA as a Function of Mantle Viscosity. An edited version of this paper was published by AGU. Copyright (2018), American Geophysical Union. The data contains the output of FEM and normal mode GIA models used to produce the figures in the article. Data consists of horizontal and vertical velocities on an axisymmetric Earth model as a function of colatitude

Trends and uncertainties of mass-driven sea-level change in the satellite altimetry era

Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, ocean mass change is caused by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides an analysis of regional patterns of contemporary mass redistribution associated with barystatic SLC since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself), temporal (related to the temporal variability in the time series) and spatial–structural (related to the spatial distribution of the mass change sources). Regional patterns (fingerprints) of barystatic SLC are computed from a range of estimates of the individual freshwater sources and used to analyze the different types of uncertainty. Combining all contributions, we find that regional sea-level trends range from −0.4 to 3.3 mm yr−1 for 2003–2016 and from −0.3 to 2.6 mm yr−1 for 1993–2016, considering the 5–95th percentile range across all grid points and depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.6 to 1.3 mm yr−1 for 2003–2016 and from 0.4 to 0.8 mm yr−1 for 1993–2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, responsible on average for 65 % of the total uncertainty, followed by the spatial–structural and intrinsic uncertainties, which contribute on average 16 % and 18 %, respectively. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for 35 %–60 % of the total uncertainty, depending on the region of interest. Another important contribution comes from the spatial–structural uncertainty from Antarctica and land water storage, which shows that different locations of mass change can lead to trend deviations larger than 20 %. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning.Physical and Space Geodes

Exploring Sources of Uncertainty in Steric Sea-Level Change Estimates

Recent studies disagree about the contribution of variations in temperature and salinity of the oceans—steric change—to the observed sea-level change. This article explores two sources of uncertainty to both global mean and regional steric sea-level trends. First, we analyze the influence of different temperature and salinity data sets on the estimated steric sea-level change. Next, we investigate the impact of different stochastic noise models on the estimation of trends and their uncertainties. By varying both the data sets and noise models, the global mean steric sea-level trend and uncertainty can vary from 0.69 to 2.40 and 0.02 to 1.56 mm/year, respectively, for 1993–2017. This range is even larger on regional scales, reaching up to 30 mm/year. Our results show that a first-order autoregressive model is the most appropriate choice to describe the residual behavior of the ensemble mean of all data sets for the global mean steric sea-level change over the last 25 years, which consequently leads to the most representative uncertainty. Using the ensemble mean and the first-order autoregressive noise model, we find a global mean steric sea-level change of 1.36 ± 0.10 mm/year for 1993–2017 and 1.08 ± 0.07 mm/year for 2005–2015. Regionally, a combination of different noise models is the best descriptor of the steric sea-level change and its uncertainty. The spatial coherence in the noise model preference indicates clusters that may be best suited to investigate the regional sea-level budget.Physical and Space Geodes

Total magnetic force on a ferrofluid droplet in microgravity

The formulation of the total force exerted by magnetic fields on ferrofluids has historically been a subject of intense debate and controversy. Although the theoretical foundations of this problem can now be considered to be well established, significant confusion still remains regarding the implementation of the associated expressions. However, the development of future applications in low-gravity environments is highly dependent on the correct modeling of this force. This paper presents a contextualized analysis of different proposed calculation procedures and validation in a space-like environment. Kinematic measurements of the movement of a ferrofluid droplet subjected to an inhomogeneous magnetic field in microgravity are compared with numerical predictions from a simplified physical model. Theoretical results are consistent with the assumptions of the model and show an excellent agreement with the experiment. The Kelvin force predictions are included in the discussion to exemplify how an incomplete modeling of the magnetic force leads to significant errors in the absence of gravity.</p

Dynamically downscaled data accompanying Improving sea-level change projections on the Northwestern European shelf using dynamical downscaling

This dataset accompanies the article Improving sea-level change projections for the Northwestern European Shelf using dynamical downscaling by Hermans et al. This dataset includes the data from the dynamical downscaled simulations for the Northwestern European Shelf that underlie part of the figures presented in the article. This data has been generated using the regional shelf seas model NEMO AMM7 CO6, forced by two CMIP5 models: HadGEM2-ES and MPI-ESM-LR

Dynamical downscaling of unforced interannual sea-level variability in the North-West European shelf seas

Variability of Sea-Surface Height (SSH) from ocean dynamic processes is an important component of sea-level change. In this study we dynamically downscale a present-day control simulation of a climate model to replicate sea-level variability in the Northwest European shelf seas. The simulation can reproduce many characteristics of sea-level variability exhibited in tide gauge and satellite altimeter observations. We examine the roles of lateral ocean boundary conditions and surface atmospheric forcings in determining the sea-level variability in the model interior using sensitivity experiments. Variability in the oceanic boundary conditions leads to uniform sea-level variations across the shelf. Atmospheric variability leads to spatial SSH variability with a greater mean amplitude. We separate the SSH variability into a uniform loading term (change in shelf volume with no change in distribution), and a spatial redistribution term (with no volume change). The shelf loading variance accounted for 80% of the shelf mean total variance, but this drops to ~ 60% around Scotland and in the southeast North Sea. We analyse our modelled variability to provide a useful context to coastal planners and managers. Our 200-year simulation allows the distribution of the unforced trends (over 4–21 year) of sea-level changes to be quantified. We found that the 95th percentile change over a 4-year period can lead to coastal sea-level changes of ~ 58 mm, which must be considered when using smooth sea level projections. We also found that simulated coastal SSH variations have long correlation length-scales, suggesting that observations of interannual sea-level variability from tide gauges are typically representative of &gt; 200 km of the adjacent coast. This helps guide the use of tide gauge variability estimates.Physical and Space Geodes

Improving sea-level projections on the Northwestern European shelf using dynamical downscaling

Changes in ocean properties and circulation lead to a spatially non-uniform pattern of ocean dynamic sea-level change (DSLC). The projections of ocean dynamic sea level presented in the IPCC AR5 were constructed with global climate models (GCMs) from the Coupled Model Intercomparison Project 5 (CMIP5). Since CMIP5 GCMs have a relatively coarse resolution and exclude tides and surges it is unclear whether they are suitable for providing DSLC projections in shallow coastal regions such as the Northwestern European Shelf (NWES). One approach to addressing these shortcomings is dynamical downscaling – i.e. using a high-resolution regional model forced with output from GCMs. Here we use the regional shelf seas model AMM7 to show that, depending on the driving CMIP5 GCM, dynamical downscaling can have a large impact on DSLC simulations in the NWES region. For a business-as-usual greenhouse gas concentration scenario, we find that downscaled simulations of twenty-first century DSLC can be up to 15.5 cm smaller than DSLC in the GCM simulations along the North Sea coastline owing to unresolved processes in the GCM. Furthermore, dynamical downscaling affects the simulated time of emergence of sea-level change (SLC) above sea-level variability, and can result in differences in the projected change of the amplitude of the seasonal cycle of sea level of over 0.3 mm/yr. We find that the difference between GCM and downscaled results is of similar magnitude to the uncertainty of CMIP5 ensembles used for previous DSLC projections. Our results support a role for dynamical downscaling in future regional sea-level projections to aid coastal decision makers.Physical and Space GeodesyEnvironmental Fluid MechanicsAstrodynamics & Space Mission