scholarly journals Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

2018 ◽  
Vol 12 (11) ◽  
pp. 3671-3691 ◽  
Author(s):  
Jiping Xie ◽  
François Counillon ◽  
Laurent Bertino
Keyword(s):  
Sea Ice ◽  
Degrees Of Freedom ◽  
The Arctic ◽  
Ice Thickness ◽  
Large Set ◽  
Sea Ice Thickness ◽  
The North ◽  
Ice Drift ◽  
Marine Environment Monitoring ◽  
The Impact

Abstract. Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5 cm and the standard errors decrease from 53 to 38 cm (by 28 %) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24 % against the ice mass balance (IMB) buoy 2013F and by 12.5 % against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8 %–9 % in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.

scholarly journals Impact of assimilating a merged sea ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

2018 ◽  
Author(s):  
Jiping Xie ◽  
Francois Counillon ◽  
Larent Bertino
Keyword(s):  
Sea Ice ◽  
Degrees Of Freedom ◽  
The Arctic ◽  
Ice Thickness ◽  
Large Set ◽  
Sea Ice Thickness ◽  
Ice Drift ◽  
Accurate Forecast ◽  
Sea Ice Drift ◽  
The Impact

Abstract. Accurate forecast of Sea Ice Thickness (SIT) represents a major challenge for Arctic forecasting systems. The new CS2SMOS SIT product merges measurements from the CryoSat-2 and SMOS satellites and is available weekly during the winter months since October 2010. The impact of assimilating CS2SMOS is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Service (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted with and without assimilation of the previously weekly CS2SMOS for the period from 19th March 2014 to 31st March 2015. The SIT bias (too thin) is reduced from 16 cm to 5 cm and the RMSD decreases from 53 cm to 38 cm (reduction by 28 %) when compared to the simultaneous SIT from CS2SMOS. Furthermore, compared to independent SIT observations, the errors are reduced by 24 % against the Ice Mass Balance (IMB) buoy 2013F and by 11 % against SIT data from the IceBridge campaigns. When compared to sea ice drift derived from International Arctic Buoy Program (IABP) drifting buoys, we find that the assimilation of C2SMOS is beneficial in the sea ice pack areas, where the influence of SIT on the sea ice drift is strongest, with an error reduction of 0.2–0.3 km/day. Finally, we quantify the influence of C2SMOS compared to the other assimilated data by the number of Degrees of Freedom for Signal (DFS) and find that CS2SMOS is the main source of observations in the central Arctic and in the Kara Sea. These results suggest that C2SMOS observations should be included in Arctic reanalyses in order to improve the ice thickness and the ice drift, although some inconsistencies were found in the version of the data used.

scholarly journals Benefits of assimilating thin sea-ice thickness from SMOS-Ice into the TOPAZ system

2016 ◽  
Author(s):  
Jiping Xie ◽  
Francois Counillon ◽  
Laurent Bertino ◽  
Xiangshan Tian-Kunze ◽  
Lars Kaleschke
Keyword(s):  
Sea Ice ◽  
Degrees Of Freedom ◽  
Winter Season ◽  
The Arctic ◽  
Ice Thickness ◽  
Data Sets ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
The Impact

Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) Mission, and available in real-time at daily frequency during the winter season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ system. TOPAZ is a coupled ocean-sea ice forecast system that assimilates SST, altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the Ensemble Kalman Filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two paralleled runs of TOPAZ have been performed respectively in March and November 2014, with assimilation of thin sea ice thickness (thinner than 0.4 m) in addition to the standard ice and ocean observational data sets. It is found that the RMSD of thin sea-ice thickness is reduced by 11 % in March and 22 % in November suggesting that SMOS-Ice has a larger impact during the beginning of freezing season. There is a slight improvement of the ice concentration and no degradation of the ocean variables. The Degrees of Freedom for Signal (DFS) indicate that the SMOS-Ice contents important information (> 20 % of the impact of all observations) for some areas in the Arctic. The areas of largest impact are the Kara Sea, the Canadian archipelago, the Baffin Bay, the Beaufort Sea and the Greenland Sea. This study suggests that SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system as it improves the ice thickness and the ice concentration but does not degrade other quantities. Keywords: SMOS-Ice; EnKF; OSE; thin sea-ice thickness; DFS;

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

scholarly journals Suppression of Arctic sea ice growth by winter clouds and snowfall

2021 ◽  
Author(s):  
Won-il Lim ◽  
Hyo-Seok Park ◽  
Andrew Stewart ◽  
Kyong-Hwan Seo
Keyword(s):  
Sea Ice ◽  
Longwave Radiation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Downward Longwave Radiation ◽  
Arctic Sea ◽  
The Impact

Abstract The ongoing Arctic warming has been pronounced in winter and has been associated with an increase in downward longwave radiation. While previous studies have demonstrated that poleward moisture flux into the Arctic strengthens downward longwave radiation, less attention has been given to the impact of the accompanying increase in snowfall. Here, utilizing state-of-the art sea ice models, we show that typical winter snowfall anomalies of 1.0 cm, accompanied by positive downward longwave radiation anomalies of ~5 W m-2 can decrease sea ice thickness by around 5 cm in the following spring over the Eurasian Seas. This basin-wide ice thinning is followed by a shrinking of summer ice extent in extreme cases. In the winter of 2016–17, anomalously strong warm/moist air transport combined with ~2.5 cm increase in snowfall decreased spring ice thickness by ~10 cm and decreased the following summer sea ice extent by 5–30%. Projected future reductions in the thickness of Arctic sea ice and snow will amplify the impact of anomalous winter snowfall events on winter sea ice growth and seasonal sea ice thickness.

scholarly journals Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

2016 ◽  
Vol 10 (6) ◽  
pp. 2745-2761 ◽  
Author(s):  
Jiping Xie ◽  
François Counillon ◽  
Laurent Bertino ◽  
Xiangshan Tian-Kunze ◽  
Lars Kaleschke
Keyword(s):  
Sea Ice ◽  
Cold Season ◽  
The Arctic ◽  
Ice Thickness ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Ice Edge ◽  
Anomaly Analysis ◽  
The Impact

Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. This product is available in near-real time, at daily frequency, during the cold season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ coupled ocean and sea ice forecasting system, which is the Arctic component of the Copernicus marine environment monitoring services. The TOPAZ system assimilates sea surface temperature (SST), altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the ensemble Kalman filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two parallel Observing System Experiments (OSE) have been performed in March and November 2014, in which the thicknesses from SMOS-Ice (thinner than 0.4 m) are assimilated in addition to the standard observational data sets. It is found that the root mean square difference (RMSD) of thin sea ice thickness is reduced by 11 % in March and 22 % in November compared to the daily thin ice thicknesses of SMOS-Ice, which suggests that SMOS-Ice has a larger impact during the beginning of the cold season. Validation against independent observations of ice thickness from buoys and ice draft from moorings indicates that there are no degradations in the pack ice but there are some improvements near the ice edge close to where the SMOS-Ice has been assimilated. Assimilation of SMOS-Ice yields a slight improvement for ice concentration and degrades neither SST nor sea level anomaly. Analysis of the degrees of freedom for signal (DFS) indicates that the SMOS-Ice has a comparatively small impact but it has a significant contribution in constraining the system (> 20 % of the impact of all ice and ocean observations) near the ice edge. The areas of largest impact are the Kara Sea, Canadian Archipelago, Baffin Bay, Beaufort Sea and Greenland Sea. This study suggests that the SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system.

Farewell to IceBridge: 10 years of polar sea ice remote sensing

2020 ◽  
Author(s):  
Linette Boisvert ◽  
Joseph MacGregor ◽  
Brooke Medley ◽  
Nathan Kurtz ◽  
Ron Kwok ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Ice Drift ◽  
Laser Altimeters ◽  
Sea Ice Drift ◽  
To Come ◽  
The Antarctic

<p>NASA’s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by ‘bridging the gap’ between NASA’s ICESat (2003–2009) and ICESat-2 (launched September 2018) satellite missions. OIB’s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community’s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA’s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane’s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.</p><p> </p>

Sea Ice in the Greenland Polynya in 2018 - A Study with CryoSat-2 and SMOS

2020 ◽  
Author(s):  
Weixin Zhu ◽  
Lu Zhou ◽  
Shiming Xu
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Climate System ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The North ◽  
Ice Surface ◽  
Snow Conditions

<p><strong>Abstract</strong></p><p>Arctic sea ice is a critical component in the global climate system. It affects the climate system by radiating incident heat back into space and regulating ocean-atmosphere heat and momentum. Satellite altimetry such as CryoSat-2 serves as the primary approach for observing sea ice thickness. Nevertheless, the thickness retrieval with CryoSat-2 mainly depends on the height of the ice surface above the sea level, which leads to significant uncertainties over thin ice regimes. The sea ice at the north of Greenland is considered one of the oldest and thickest in the Arctic. However, during late February - early March 2018, a polynya formed north to Greenland due to extra strong southern winds. We focus on the retrieval of sea ice thickness and snow conditions with CryoSat-2 and SMOS during the formation of the polynya. Specifically, we investigate the uncertainty of CryoSat-2 and carry out inter- comparison of sea ice thickness retrieval with SMOS and CryoSat-2/SMOS synergy. Besides, further discussion of retrieval with CryoSat-2 is provided for such scenarios where the mélange of thick ice and newly formed thin ice is present.</p>

scholarly journals Arctic sea ice drift-strength feedback modelled by NEMO-LIM3.6

2017 ◽  
Author(s):  
David Docquier ◽  
François Massonnet ◽  
Neil F. Tandon ◽  
Olivier Lecomte ◽  
Thierry Fichefet
Keyword(s):  
Sea Ice ◽  
Positive Feedback ◽  
Seasonal Cycle ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Ice Strength

Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. The resulting increased sea ice export is thought to further lower sea ice concentration and thickness. We use the global ocean-sea ice NEMO-LIM3.6 model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite and buoy observations, as well as reanalysis data over the period from 1979 to 2013 to study this positive feedback for the first time in such detail. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. Although the seasonal cycle of sea ice drift speed is reasonably well reproduced by the model, the recent positive trend in drift speed is weaker than observations in summer. NEMO-LIM3.6 is able to capture the relationships between sea ice drift speed, concentration and thickness in terms of seasonal cycle, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Sensitivity experiments are carried out by varying the initial ice strength and show that higher values of ice strength lead to lower sea ice thickness. We demonstrate that higher ice strength results in a more uniform sea ice thickness distribution, leading to lower heat conduction fluxes, which provide lower ice production, and thus lower ice thickness. This shows that the positive feedback between sea ice drift speed and strength is more than just dynamic, more complex than originally thought and that other processes are at play. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the interactions between sea ice drift speed and strength.

scholarly journals Influence of initial stratification, wind and sea ice on the modelled oceanic circulation in Nares Strait, northwest Greenland

2018 ◽  
Author(s):  
Lovisa Waldrop Bergman ◽  
Céline Heuzé
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Global Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Oceanic Circulation ◽  
Regional Modelling ◽  
Sea Ice Thickness ◽  
Nares Strait ◽  
The North

Abstract. Nares Strait in northwest Greenland is one of the main gateways for oceanic freshwater and heat exchanges between the Arctic and the North Atlantic. With a changing Arctic climate, understanding the processes that govern the oceanic circulation in Arctic straits has become crucial and urgent, but this cannot be done with current geographically and temporally sparse in-situ observations only. High resolution regional modelling is thus required, but costly. We here report on one-year sensitivity experiments performed with the coupled ice-ocean regional model MITgcm to determine the relative importance of wind forcing, initial stratification and sea ice thickness on the accuracy of the modelled oceanic circulation in Nares Strait. We find that the modelled basin's circulation is mainly driven by density gradients in the upper oceanic layer, making accurate initial fields of temperature and salinity essential for a realistic oceanic circulation. The influence of the wind and sea ice thickness is less important, potentially making such high resolution fields not necessary for accurate strait modelling, provided these results are valid for other sea ice models as well. Comparison with ship-based measurements collected in summer 2015 reveals the experiments to be too cold at the surface, probably because of a not-dynamic-enough sea ice cover. Although the modelled freshwater is rather accurate, large efforts need to be put into observing the ocean and the sources of freshwater continuously throughout the year to produce realistic and efficient model simulations of the Arctic Straits, key players in the entire Arctic system and global climate.

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