scholarly journals Elevation Changes of the Antarctic Ice Sheet from Joint Envisat and CryoSat-2 Radar Altimetry

2020 ◽  
Vol 12 (22) ◽  
pp. 3746
Author(s):  
Baojun Zhang ◽  
Zemin Wang ◽  
Quanming Yang ◽  
Jingbin Liu ◽  
Jiachun An ◽  
...  
Keyword(s):  
Time Series ◽  
Ice Sheet ◽  
Surface Processes ◽  
Volume Loss ◽  
Volume Changes ◽  
Ice Dynamics ◽  
Radar Altimetry ◽  
Antarctic Ice Sheet ◽  
Elevation Changes ◽  
The Antarctic

The elevation changes of ice sheets have been recognized as an essential climate variable. Long-term time series of these changes are an important parameter to understand climate change, and the longest time-series of ice sheet elevation changes can be derived from combining multiple Ku-band satellite altimetry missions. However, unresolved intermission biases obscure the record. Here, we revise the mathematical model commonly used in the literature to simultaneously correct for intermission bias and ascending–descending bias to ensure the self-consistency and cohesion of the elevation time series across missions. This updated approach is applied to combine Envisat and CryoSat-2 radar altimetry in the period of 2002–2019. We tested this approach by validating it against airborne and satellite laser altimetry. Combining the detailed temporal and spatial evolution of elevation changes with firn densification-modeled volume changes due to surface processes, we found that the Amundsen Sea sector accounts for most of the total volume loss of the Antarctic Ice Sheet (AIS), mainly from ice dynamics. However, surface processes dominate the volume changes in the key regions, such as the Totten Glacier sector, Dronning Maud Land, Princess Elizabeth Land, and the Bellingshausen Sea sector. Overall, accelerated volume loss in the West Antarctic continues to outpace the gains observed in the East Antarctic. The total volume change during 2002–2019 for the AIS was −68.7 ± 8.1 km3/y, with an acceleration of −5.5 ± 0.9 km3/y2.

scholarly journals Four decades of Antarctic surface elevation changes from multi-mission satellite altimetry

2019 ◽  
Vol 13 (2) ◽  
pp. 427-449 ◽  
Author(s):  
Ludwig Schröder ◽  
Martin Horwath ◽  
Reinhard Dietrich ◽  
Veit Helm ◽  
Michiel R. van den Broeke ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Altimetry ◽  
Average Rate ◽  
Ice Sheet ◽  
Surface Elevation ◽  
Antarctic Ice Sheet ◽  
Mission Satellite ◽  
Elevation Changes ◽  
Dronning Maud Land ◽  
The Antarctic

Abstract. We developed a multi-mission satellite altimetry analysis over the Antarctic Ice Sheet which comprises Seasat, Geosat, ERS-1, ERS-2, Envisat, ICESat and CryoSat-2. After a consistent reprocessing and a stepwise calibration of the inter-mission offsets, we obtained monthly grids of multi-mission surface elevation change (SEC) with respect to the reference epoch 09/2010 (in the format of month/year) from 1978 to 2017. A validation with independent elevation changes from in situ and airborne observations as well as a comparison with a firn model proves that the different missions and observation modes have been successfully combined to a seamless multi-mission time series. For coastal East Antarctica, even Seasat and Geosat provide reliable information and, hence, allow for the analysis of four decades of elevation changes. The spatial and temporal resolution of our result allows for the identification of when and where significant changes in elevation occurred. These time series add detailed information to the evolution of surface elevation in such key regions as Pine Island Glacier, Totten Glacier, Dronning Maud Land or Lake Vostok. After applying a density mask, we calculated time series of mass changes and found that the Antarctic Ice Sheet north of 81.5∘ S was losing mass at an average rate of -85±16 Gt yr−1 between 1992 and 2017, which accelerated to -137±25 Gt yr−1 after 2010.

A new elevation change time series of the Antarctic Ice Sheet from Envisat and CryoSat-2 radar altimetry

2020 ◽  
Author(s):  
Baojun Zhang ◽  
Quanming Yang ◽  
Zemin Wang ◽  
Hong Geng ◽  
Jiachun An ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Altimetry ◽  
Ice Sheet ◽  
Least Square ◽  
Surface Elevation ◽  
The West ◽  
Antarctic Ice Sheet ◽  
West Antarctic ◽  
Elevation Changes ◽  
The Antarctic

<p>Satellite altimetry is an important data source for ice sheet change observation. The long-term time series of ice sheet changes can be obtained by combining satellite altimetry missions with similar sensor characteristics. Then, how to correct the inter-mission offsets becomes an important scientific issue. Review of previous studies, we found that the observations of satellite ascending and descending orbits also have an important influence on the estimation of inter-mission offsets. On this basis, have created a new least-square fitting mathematical model to estimate and correct the errors of ascending and descending orbits and inter-mission offsets by introducing the inter-mission offsets terms related to the observations of ascending and descending orbits. Utilizing this model, we developed a time series of monthly Antarctic ice sheet elevation changes of 5 km grid from May 2002 to April 2019. A validation with surface elevation from airborne observations and a comparison with surface elevation changes from ICESat proved that the proposed model can successfully estimate and correct the errors and be used to construct multi-mission surface elevation time series. Without a doubt, the temporal and spatial changes of Antarctic ice sheet elevation can be obtained from our monthly grid time series. From the time series, we find that over the period May 2002 to April 2019 the loss of ice and snow in the Antarctic ice sheet mainly occurred in the glaciers along the Amundsen coast in the West Antarctic and the Totten glacier in the East Antarctic, while the accumulation took place in Queen Maud of the East Antarctic. In May 2002, the Antarctic ice sheet experienced a volume loss of -71.4 ± 11.7 km<sup>3</sup>/yr, with an acceleration of –5.8 ± 1.2 km<sup>3</sup>/yr<sup>2</sup> over the period May 2002 to April 2019, including 45.0 ± 9.6 km<sup>3</sup>/yr and 0.1 ±1.0 km<sup>3</sup>/yr<sup>2</sup> for the East Antarctic ice sheet, -97.0 ± 4.4 km<sup>3</sup>/yr and -7.6 ±0.5 km<sup>3</sup>/yr<sup>2</sup> for the West Antarctic ice sheet and -19.5 ± 5.3 km<sup>3</sup>/yr and 1.7 ±0.5 km<sup>3</sup>/yr<sup>2</sup> for the Antarctic Peninsula ice sheet.</p>

scholarly journals Monitoring changes of the Antarctic Ice sheet by GRACE, ICESat and GNSS

2019 ◽  
Vol 49 (4) ◽  
pp. 403-424
Author(s):  
Fang Zou ◽  
Robert Tenzer ◽  
Samurdhika Rathnayake
Keyword(s):  
Mass Loss ◽  
Ice Sheet ◽  
The West ◽  
Antarctic Ice Sheet ◽  
West Antarctic Ice Sheet ◽  
Ice Volume ◽  
West Antarctic ◽  
Elevation Changes ◽  
A Minor ◽  
The Antarctic

Abstract In this study, we estimate the ice mass changes, the ice elevation changes and the vertical displacements in Antarctica based on analysis of multi-geodetic datasets that involve the satellite gravimetry (GRACE), the satellite altimetry (ICESat) and the global navigation satellite systems (GNSS). According to our estimates, the total mass change of the Antarctic ice sheet from GRACE data is −162.91 Gt/yr over the investigated period between April 2002 and June 2017. This value was obtained after applying the GIA correction of −98.12 Gt/yr derived from the ICE-5G model of the glacial iso-static adjustment. A more detailed analysis of mass balance changes for three individual drainage regions in Antarctica reveal that the mass loss of the West Antarctic ice sheet was at a rate of −143.11 Gt/yr. The mass loss of the Antarctic Peninsula ice sheet was at a rate of −24.31 Gt/yr. The mass of the East Antarctic ice sheet increased at a rate of 5.29 Gt/yr during the investigated period. When integrated over the entire Antarctic ice sheet, average rates of ice elevation changes over the period from March 2003 to October 2009 derived from ICESat data represent the loss of total ice volume of −155.6 km3.The most prominent features in ice volume changes in Antarctica are characterized by a strong dynamic thinning and ice mass loss in the Amundsen Sea Embayment that is part of the West Antarctic ice sheet. In contrast, coastal regions between Dronning Maud Land and Enderby Land exhibit a minor ice increase, while a minor ice mass loss is observed in Wilkes Land. The vertical load displacement rates estimated from GRACE and GPS data relatively closely agree with the GIA model derived based on the ice-load history and the viscosity profile. For most sites, the GRACE signal appears to be in phase and has the same amplitude as that obtained from the GPS vertical motions while other sites exhibit some substantial differences possibly attributed to thermo-elastic deformations associated with surface temperature.

scholarly journals Nonlinear response of the Antarctic Ice Sheet to late Quaternary sea level and climate forcing

2019 ◽  
Vol 13 (10) ◽  
pp. 2615-2631 ◽  
Author(s):  
Michelle Tigchelaar ◽  
Axel Timmermann ◽  
Tobias Friedrich ◽  
Malte Heinemann ◽  
David Pollard
Keyword(s):  
Sea Level ◽  
Late Quaternary ◽  
Ice Sheet ◽  
Volume Changes ◽  
Antarctic Ice Sheet ◽  
Glacial Ice ◽  
Ice Volume ◽  
Global Sea Level ◽  
The Individual ◽  
The Antarctic

Abstract. Antarctic ice volume has varied substantially during the late Quaternary, with reconstructions suggesting a glacial ice sheet extending to the continental shelf break and interglacial sea level highstands of several meters. Throughout this period, changes in the Antarctic Ice Sheet were driven by changes in atmospheric and oceanic conditions and global sea level; yet, so far modeling studies have not addressed which of these environmental forcings dominate and how they interact in the dynamical ice sheet response. Here, we force an Antarctic Ice Sheet model with global sea level reconstructions and transient, spatially explicit boundary conditions from a 408 ka climate model simulation, not only in concert with each other but, for the first time, also separately. We find that together these forcings drive glacial–interglacial ice volume changes of 12–14 ms.l.e., in line with reconstructions and previous modeling studies. None of the individual drivers – atmospheric temperature and precipitation, ocean temperatures, or sea level – single-handedly explains the full ice sheet response. In fact, the sum of the individual ice volume changes amounts to less than half of the full ice volume response, indicating the existence of strong nonlinearities and forcing synergy. Both sea level and atmospheric forcing are necessary to create full glacial ice sheet growth, whereas the contribution of ocean melt changes is found to be more a function of ice sheet geometry than climatic change. Our results highlight the importance of accurately representing the relative timing of forcings of past ice sheet simulations and underscore the need for developing coupled climate–ice sheet modeling frameworks that properly capture key feedbacks.

Climate parameters influencing satellite-based volume and elevation changes of the Antarctic ice sheet

2020 ◽  
Author(s):  
Athul Kaitheri ◽  
Anthony Mémin ◽  
Frédérique Rémy
Keyword(s):  
Mass Balance ◽  
Ice Sheet ◽  
Snow Accumulation ◽  
Amundsen Sea ◽  
Antarctic Ice Sheet ◽  
Changing Climate ◽  
Basin Scale ◽  
Elevation Changes ◽  
The Antarctic ◽  
Grace Mission

<p>Precisely quantifying the Antarctic Ice Sheet (AIS) mass balance remains a challenge as several processes compete at differing degrees in the basin-scale with regional variations. Understanding of changes in AIS has been largely based on observations from various altimetry missions and Gravity Recovery And Climate Experiment (GRACE) missions due to its scale and coverage. Analysis of linear trends in surface height variations of AIS since the early 1990s showed multiple variabilities in the rate of changes over the period of time. These observations are a reflection of various underlying ice sheet processes. Therefore understanding the processes that interact on the ice sheet is important to precisely determine the response of the ice sheet to a rapidly changing climate.</p><p>Changing climate constitutes variations in major short term processes including snow accumulation and surface melting. Variations in accumulation rate and temperature at the ice sheet surface cause changes in the firn compaction (FC) rate. Variations in the FC rate change the AIS thickness, that should be detected from altimetry, but do not change its mass, as observed by the GRACE mission. We focus our study on the seasonal and interannual changes in the elevation and mass of the AIS. We use surface elevation changes from Envisat data and gravity changes derived from the latest GRACE solutions between 10/2002 and 10/2010. As mass changes observed using the GRACE mission is strongly impacted by long term isostasy, as it involves mantle mass redistribution, we remove from all dataset an 8-year trend. We use weather variable historical data solutions including surface mass balance, temperature and wind velocities from the regional climate model RACMO2.3p2 as input to an FC model to estimate AIS elevation changes. We obtain a very good correlation between height change estimates from GRACE, Envisat and RACMO2.3p2 at several places such as along the coast of Dronning Maud Land, Wilkes land and Amundsen sea sector. Considerable differences in Oates and Mac Robertson regions, with a strong seasonal signal in Envisat estimates, reflect spatial variability in physical parameters of the surface of the AIS due to climate parameter changes such as winds.</p>

Automated mapping of Antarctic supraglacial lakes and streams using machine learning

2020 ◽  
Author(s):  
Mariel Dirscherl ◽  
Andreas Dietz ◽  
Celia Baumhoer ◽  
Christof Kneisel ◽  
Claudia Kuenzer
Keyword(s):  
Machine Learning ◽  
Mass Balance ◽  
Antarctic Peninsula ◽  
Ice Sheet ◽  
Ice Shelf ◽  
Ice Dynamics ◽  
Antarctic Ice Sheet ◽  
Supraglacial Lakes ◽  
The Antarctic ◽  
Sentinel 2

<p>Antarctica stores ~91 % of the global ice mass making it the biggest potential contributor to global sea-level-rise. With increased surface air temperatures during austral summer as well as in consequence of global climate change, the ice sheet is subject to surface melting resulting in the formation of supraglacial lakes in local surface depressions. Supraglacial meltwater features may impact Antarctic ice dynamics and mass balance through three main processes. First of all, it may cause enhanced ice thinning thus a potentially negative Antarctic Surface Mass Balance (SMB). Second, the temporary injection of meltwater to the glacier bed may cause transient ice speed accelerations and increased ice discharge. The last mechanism involves a process called hydrofracturing i.e. meltwater-induced ice shelf collapse caused by the downward propagation of surface meltwater into crevasses or fractures, as observed along large coastal sections of the northern Antarctic Peninsula. Despite the known impact of supraglacial meltwater features on ice dynamics and mass balance, the Antarctic surface hydrological network remains largely understudied with an automated method for supraglacial lake and stream detection still missing. Spaceborne remote sensing and data of the Sentinel missions in particular provide an excellent basis for the monitoring of the Antarctic surface hydrological network at unprecedented spatial and temporal coverage.</p><p>In this study, we employ state-of-the-art machine learning for automated supraglacial lake and stream mapping on basis of optical Sentinel-2 satellite data. With more detail, we use a total of 72 Sentinel-2 acquisitions distributed across the Antarctic Ice Sheet together with topographic information to train and test the selected machine learning algorithm. In general, our machine learning workflow is designed to discriminate between surface water, ice/snow, rock and shadow being further supported by several automated post-processing steps. In order to ensure the algorithm’s transferability in space and time, the acquisitions used for training the machine learning model are chosen to cover the full circle of the 2019 melt season and the data selected for testing the algorithm span the 2017 and 2018 melt seasons. Supraglacial lake predictions are presented for several regions of interest on the East and West Antarctic Ice Sheet as well as along the Antarctic Peninsula and are validated against randomly sampled points in the underlying Sentinel-2 RGB images. To highlight the performance of our model, we specifically focus on the example of the Amery Ice Shelf in East Antarctica, where we applied our algorithm on Sentinel-2 data in order to present the temporal evolution of maximum lake extent during three consecutive melt seasons (2017, 2018 and 2019).</p>

scholarly journals Changes in ice dynamics and mass balance of the Antarctic ice sheet

2006 ◽  
Vol 364 (1844) ◽  
pp. 1637-1655 ◽  
Author(s):  
Eric Rignot
Keyword(s):  
Mass Balance ◽  
Sea Level ◽  
Ice Sheet ◽  
East Antarctica ◽  
West Antarctica ◽  
Ice Shelf ◽  
Ice Dynamics ◽  
Antarctic Ice Sheet ◽  
Ice Shelves ◽  
The Antarctic

The concept that the Antarctic ice sheet changes with eternal slowness has been challenged by recent observations from satellites. Pronounced regional warming in the Antarctic Peninsula triggered ice shelf collapse, which led to a 10-fold increase in glacier flow and rapid ice sheet retreat. This chain of events illustrated the vulnerability of ice shelves to climate warming and their buffering role on the mass balance of Antarctica. In West Antarctica, the Pine Island Bay sector is draining far more ice into the ocean than is stored upstream from snow accumulation. This sector could raise sea level by 1 m and trigger widespread retreat of ice in West Antarctica. Pine Island Glacier accelerated 38% since 1975, and most of the speed up took place over the last decade. Its neighbour Thwaites Glacier is widening up and may double its width when its weakened eastern ice shelf breaks up. Widespread acceleration in this sector may be caused by glacier ungrounding from ice shelf melting by an ocean that has recently warmed by 0.3 °C. In contrast, glaciers buffered from oceanic change by large ice shelves have only small contributions to sea level. In East Antarctica, many glaciers are close to a state of mass balance, but sectors grounded well below sea level, such as Cook Ice Shelf, Ninnis/Mertz, Frost and Totten glaciers, are thinning and losing mass. Hence, East Antarctica is not immune to changes.

scholarly journals A new approach to estimate ice dynamic rates using satellite observations in East Antarctica

2016 ◽  
Author(s):  
Bianca Kallenberg ◽  
Paul Tregoning ◽  
Janosch F. Hoffmann ◽  
Rhys Hawkins ◽  
Anthony Purcell ◽  
...  
Keyword(s):  
Mass Balance ◽  
Satellite Altimetry ◽  
Ice Sheet ◽  
East Antarctica ◽  
Surface Mass Balance ◽  
Antarctic Ice Sheet ◽  
Surface Mass ◽  
Elevation Changes ◽  
Ice Sheet Mass Balance ◽  
The Antarctic

Abstract. Mass balance changes of the Antarctic ice sheet are of significant interest due to its sensitivity to climatic changes and its contribution to changes in global sea level. While regional climate models successfully estimate mass input due to snowfall, it remains difficult to estimate the amount of mass loss due to ice dynamic processes. It's often been assumed that changes in ice dynamic rates only need to be considered when assessing long term ice sheet mass balance; however, two decades of satellite altimetry observations reveal that the Antarctic ice sheet changes unexpectedly and much more dynamically than previously expected. Despite available estimates on ice dynamic rates obtained from radar altimetry, information about changes in ice dynamic rates are still limited, especially in East Antarctica. Without understanding ice dynamic rates it is not possible to properly assess changes in ice sheet mass balance, surface elevation or to develop ice sheet models. In this study we investigate the possibility of estimating ice dynamic rates by removing modelled rates of surface mass balance, firn compaction and bedrock uplift from satellite altimetry and gravity observations. With similar rates of ice discharge acquired from two different satellite missions we show that it is possible to obtain an approximation of ice dynamic rates by combining altimetry and gravity observations. Thus, surface elevation changes due to surface mass balance, firn compaction and ice dynamic rates can be modelled and correlate with observed elevation changes from satellite altimetry.

scholarly journals Present-day high-resolution ice velocity map of the Antarctic ice sheet

2018 ◽  
Author(s):  
Qiang Shen ◽  
Hansheng Wang ◽  
C. K. Shum ◽  
Liming Jiang ◽  
Hou Tse Hsu ◽  
...  
Keyword(s):  
Sea Level ◽  
Displacement Vector ◽  
Ice Sheet ◽  
Landsat 8 ◽  
Ice Streams ◽  
Coupled Models ◽  
Ice Dynamics ◽  
Antarctic Ice Sheet ◽  
Ice Velocity ◽  
The Antarctic

Abstract. Ice velocity constitutes a key parameter for estimating ice-sheet discharge rates and is crucial for improving coupled models of the Antarctic ice sheet to accurately predict its future fate and contribution to sea-level change. Here, we present a new Antarctic ice velocity map at a 100-m grid spacing inferred from Landsat 8 imagery data collected from December 2013 through March 2016 and robustly processed using the feature tracking method. These maps were assembled from over 73,000 displacement vector scenes inferred from over 32,800 optical images. Our maps cover nearly all the ice shelves, landfast ice, ice streams, and most of the ice sheet. The maps have an estimated uncertainty of less than 10 m yr-1 based on robust internal and external validations. These datasets will allow for a comprehensive continent-wide investigation of ice dynamics and mass balance combined with the existing and future ice velocity measurements and provide researchers access to better information for monitoring local changes in ice glaciers. Other uses of these datasets include control and calibration of ice-sheet modelling, developments in our understanding of Antarctic ice-sheet evolution, and improvements in the fidelity of projects investigating sea-level rise (https://doi.pangaea.de/10.1594/PANGAEA.895738).

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