Pine Island Glacier and Thwaites Glacier in the Amundsen Sea Embayment are among the fastest changing outlet glaciers in West Antarctica with large consequences for global sea level. Yet, assessing how much and how fast both glaciers will weaken if these changes continue remains a major uncertainty as many of the processes that control their ice shelf weakening and grounding line retreat are not well understood. Here, we combine multisource satellite imagery with modeling to uncover the rapid development of damage areas in the shear zones of Pine Island and Thwaites ice shelves. These damage areas consist of highly crevassed areas and open fractures and are first signs that the shear zones of both ice shelves have structurally weakened over the past decade. Idealized model results reveal moreover that the damage initiates a feedback process where initial ice shelf weakening triggers the development of damage in their shear zones, which results in further speedup, shearing, and weakening, hence promoting additional damage development. This damage feedback potentially preconditions these ice shelves for disintegration and enhances grounding line retreat. The results of this study suggest that damage feedback processes are key to future ice shelf stability, grounding line retreat, and sea level contributions from Antarctica. Moreover, they underline the need for incorporating these feedback processes, which are currently not accounted for in most ice sheet models, to improve sea level rise projections.

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Statistically simulated time series of wave parameters are required for many coastal and offshore engineering applications, often at the resolution of approximately 1 h. Various studies have relied on autoregressive moving-average (ARMA) processes to simulate synthetic series of wave parameters in a Monte Carlo sense. However, accurately representing inter-series dependencies has remained a challenge. In particular, the relationship between wave height and period statistics is complex, due to the limiting steepness condition. Here, we present a new simulation method for joint time series of significant wave height, mean zero-crossing periods and a directional regime variable. The latter distinguishes between northern and southwestern waves. The method rests on several model components which include renewal processes, Fourier series with random coefficients, ARMA processes, copulas and regime-switching. A particular feature is a data-driven estimate for a wave height-dependent limiting wave steepness condition which is used to facilitate copula-based dependence modeling. The method was developed for and applied to a data set in the Southern North Sea. For this site, the method could simulate time series with realistic annual cycles and inter-annual variability. In the time series data, the bivariate distribution of significant wave height and mean zero-crossing period was well represented. An influence of the directional regime on the bivariate distribution could also be modeled. However, the influence was not as strong in simulated data as in observed data. Finally, simulated series captured duration and inter-arrival time of storm events well. Potential applications for output of the simulation method range from the assessment of risks to the planning and budgeting of operations in coastal and offshore environments.

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The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992-2017 (5 ± 46 billion tonnes per year) being the least certain.

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Part of Information Retrieval evaluation research is limited by the fact that we do not know the distributions of system effectiveness over the populations of topics and, by extension, their true mean scores. The workaround usually consists in resampling topics from an existing collection and approximating the statistics of interest with the observations made between random subsamples, as if one represented the population and the other a random sample. However, this methodology is clearly limited by the availability of data, the impossibility to control the properties of these data, and the fact that we do not really measure what we intend to. To overcome these limitations, we propose a method based on vine copulas for stochastic simulation of evaluation results where the true system distributions are known upfront. In the basic use case, it takes the scores from an existing collection to build a semi-parametric model representing the set of systems and the population of topics, which can then be used to make realistic simulations of the scores by the same systems but on random new topics. Our ability to simulate this kind of data not only removes the current limitations, but also offers new opportunities for research. As an example, we show the benefits of this approach in two sample applications replicating typical experiments found in the literature. We provide a full R package to simulate new data following the proposed method, which can also be used to fully reproduce the results in this paper.@en

SESAME (SEntinel-1 SAR companion Multistatic Explorer) is a passive SAR satellite mission proposed for the ESA Earth Explorer Program. SESAME comprises two receive-only C-band SAR satellites flying in close formation to build a single-pass SAR interferometer (SP-InSAR) using the active signal of the European Sentinel-1 satellite. The SESAME mission addresses applications in geoscience and climate research that require repeat measurements of high precision elevation data over land surfaces including ice covered areas and forests, exploiting the SP-InSAR and multistatic observation geometry of the satellite formation. The objectives, the measurement approach and geo-biophysical products of the mission are described.

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SIGNAL is an innovative earth exploration mission proposal with the main objective to estimate accurately and repeatedly topography and topographic changes associated with mass change or other dynamic effects on glaciers, ice caps and polar ice sheets. Elevation measurements are complemented with glacier velocity measurements, providing valuable additional information for a better understanding of the hydrology of glacierized basins and of the Arctic and Antarctic water cycle. SIGNAL is capable of monitoring all critical regions with a high spatial resolution and an adequate revisit time. This paper gives an overview about the actual mission design status and provides a brief description of the topography (DEM - digital elevation map) self-calibration strategy and the estimated global interferometric performance.@en