scholarly journals Utilizing the ASPeCt sea ice thickness data set to evaluate a global coupled sea ice–ocean model

2004 ◽  
Vol 109 (C7) ◽  
Author(s):  
Ralph Timmermann
Keyword(s):  
Sea Ice ◽  
Ocean Model ◽  
Ice Thickness ◽  
Data Set ◽  
Sea Ice Thickness

scholarly journals Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter

2014 ◽  
Vol 119 (10) ◽  
pp. 6680-6692 ◽  
Author(s):  
Qinghua Yang ◽  
Svetlana N. Losa ◽  
Martin Losch ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Model ◽  
Ice Thickness ◽  
Sea Ice Thickness

scholarly journals Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations

2020 ◽  
Vol 14 (7) ◽  
pp. 2189-2203
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Stefan Hendricks ◽  
Jens Hoelemann ◽  
Markus A. Janout ◽  
...  
Keyword(s):  
Sea Ice ◽  
Source Region ◽  
European Space Agency ◽  
The Arctic ◽  
Ice Thickness ◽  
Laptev Sea ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness ◽  
The Laptev Sea

Abstract. The gridded sea ice thickness (SIT) climate data record (CDR) produced by the European Space Agency (ESA) Sea Ice Climate Change Initiative Phase 2 (CCI-2) is the longest available, Arctic-wide SIT record covering the period from 2002 to 2017. SIT data are based on radar altimetry measurements of sea ice freeboard from the Environmental Satellite (ENVISAT) and CryoSat-2 (CS2). The CCI-2 SIT has previously been validated with in situ observations from drilling, airborne remote sensing, electromagnetic (EM) measurements and upward-looking sonars (ULSs) from multiple ice-covered regions of the Arctic. Here we present the Laptev Sea CCI-2 SIT record from 2002 to 2017 and use newly acquired ULS and upward-looking acoustic Doppler current profiler (ADCP) sea ice draft (VAL) data for validation of the gridded CCI-2 and additional satellite SIT products. The ULS and ADCP time series provide the first long-term satellite SIT validation data set from this important source region of sea ice in the Transpolar Drift. The comparison of VAL sea ice draft data with gridded monthly mean and orbit trajectory CCI-2 data, as well as merged CryoSat-2–SMOS (CS2SMOS) sea ice draft, shows that the agreement between the satellite and VAL draft data strongly depends on the thickness of the sampled ice. Rather than providing mean sea ice draft, the considered satellite products provide modal sea ice draft in the Laptev Sea. Ice drafts thinner than 0.7 m are overestimated, while drafts thicker than approximately 1.3 m are increasingly underestimated by all satellite products investigated for this study. The tendency of the satellite SIT products to better agree with modal sea ice draft and underestimate thicker ice needs to be considered for all past and future investigations into SIT changes in this important region. The performance of the CCI-2 SIT CDR is considered stable over time; however, observed trends in gridded CCI-2 SIT are strongly influenced by the uncertainties of ENVISAT and CS2 and the comparably short investigation period.

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.

scholarly journals On retrieving sea ice freeboard from ICESat laser altimeter

2016 ◽  
Vol 10 (5) ◽  
pp. 2329-2346 ◽  
Author(s):  
Kirill Khvorostovsky ◽  
Pierre Rampal
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Sea Surface ◽  
The Arctic ◽  
Ice Thickness ◽  
Laser Altimeter ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Along Track ◽  
Multiyear Ice

Abstract. Sea ice freeboard derived from satellite altimetry is the basis for the estimation of sea ice thickness using the assumption of hydrostatic equilibrium. High accuracy of altimeter measurements and freeboard retrieval procedure are, therefore, required. As of today, two approaches for estimating the freeboard using laser altimeter measurements from Ice, Cloud, and land Elevation Satellite (ICESat), referred to as tie points (TP) and lowest-level elevation (LLE) methods, have been developed and applied in different studies. We reproduced these methods for the ICESat observation periods (2003–2008) in order to assess and analyse the sources of differences found in the retrieved freeboard and corresponding thickness estimates of the Arctic sea ice as produced by the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). Three main factors are found to affect the freeboard differences when applying these methods: (a) the approach used for calculation of the local sea surface references in leads (TP or LLE methods), (b) the along-track averaging scales used for this calculation, and (c) the corrections for lead width relative to the ICESat footprint and for snow depth accumulated in refrozen leads. The LLE method with 100 km averaging scale, as used to produce the GSFC data set, and the LLE method with a shorter averaging scale of 25 km both give larger freeboard estimates comparing to those derived by applying the TP method with 25 km averaging scale as used for the JPL product. Two factors, (a) and (b), contribute to the freeboard differences in approximately equal proportions, and their combined effect is, on average, about 6–7 cm. The effect of using different methods varies spatially: the LLE method tends to give lower freeboards (by up to 15 cm) over the thick multiyear ice and higher freeboards (by up to 10 cm) over first-year ice and the thin part of multiyear ice; the higher freeboards dominate. We show that the freeboard underestimation over most of these thinner parts of sea ice can be reduced to less than 2 cm when using the improved TP method proposed in this paper. The corrections for snow depth in leads and lead width, (c), are applied only for the JPL product and increase the freeboard estimates by about 7 cm on average. Thus, different approaches to calculating sea surface references and different along-track averaging scales from one side and the freeboard corrections as applied when producing the JPL data set from the other side roughly compensate each other with respect to freeboard estimation. Therefore, one may conclude that the difference in the mean sea ice thickness between the JPL and GSFC data sets reported in previous studies should be attributed mostly to different parameters used in the freeboard-to-thickness conversion.

scholarly journals Sea ice export through the Fram Strait derived from a combined model and satellite data set

2019 ◽  
Vol 13 (12) ◽  
pp. 3209-3224 ◽  
Author(s):  
Chao Min ◽  
Longjiang Mu ◽  
Qinghua Yang ◽  
Robert Ricker ◽  
Qian Shi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Combined Model ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Ice Volume

Abstract. Sea ice volume export through the Fram Strait plays an important role in the Arctic freshwater and energy redistribution. The combined model and satellite sea ice thickness (CMST) data set assimilates CryoSat-2 and soil moisture and ocean salinity (SMOS) thickness products together with satellite sea ice concentration. The CMST data set closes the gap of stand-alone satellite-derived sea ice thickness in summer and therefore allows us to estimate sea ice volume export during the melt season. In this study, we first validate the CMST data set using field observations, and then we estimate the continuous seasonal and interannual variations in Arctic sea ice volume flux through the Fram Strait from September 2010 to December 2016. The results show that seasonal and interannual sea ice volume export vary from about -240(±40) to -970(±60) km3 and -1970(±290) to -2490(±280) km3, respectively. The sea ice volume export reaches its maximum in spring and about one-third of the yearly total volume export occurs in the melt season. The minimum monthly sea ice export is −11 km3 in August 2015, and the maximum (−442 km3) appears in March 2011. The seasonal relative frequencies of sea ice thickness and drift suggest that the Fram Strait outlet in summer is dominated by sea ice that is thicker than 2 m with relatively slow seasonal mean drift of about 3 km d−1.

scholarly journals Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: Comparison to mooring observations

2020 ◽  
Author(s):  
Hans Jakob Belter ◽  
Thomas Krumpen ◽  
Stefan Hendricks ◽  
Jens A. Hoelemann ◽  
Markus A. Janout ◽  
...  
Keyword(s):  
Sea Ice ◽  
Source Region ◽  
European Space Agency ◽  
The Arctic ◽  
Ice Thickness ◽  
Laptev Sea ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness ◽  
The Laptev Sea

Abstract. The gridded sea ice thickness (SIT) climate data record (CDR) produced by the European Space Agency (ESA) Sea Ice Climate Change Initiative Phase 2 (CCI-2) is the longest available, Arctic-wide SIT record covering the period from 2002 to 2017. SIT data is based on radar altimetry measurements of sea ice freeboard from the Environmental Satellite (ENVISAT) and CryoSat-2 (CS2). The CCI-2 SIT has previously been validated with in situ observations from drilling, airborne electromagnetic (EM) measurements and Upward-Looking Sonars (ULS) from multiple ice-covered regions of the Arctic. Here we present the Laptev Sea CCI-2 SIT record from 2002 to 2017 and use newly acquired ULS and upward-looking Acoustic Doppler Current Profiler (ADCP) sea ice draft data (VAL) for validation of the gridded CCI-2 and additional satellite SIT products. The ULS and ADCP time series provide the first long-term satellite SIT validation data set from this important source region of sea ice in the Transpolar Drift. The comparison of VAL sea ice draft data with gridded monthly mean and orbit trajectory CCI-2 data, as well as merged CryoSat-2/SMOS (CS2SMOS) sea ice draft shows that the agreement between the satellite and VAL draft data strongly depends on the thickness of the sampled ice. Rather than providing mean sea ice draft the considered satellite products provide modal sea ice draft in the Laptev Sea. Ice thinner than the modal draft is overestimated, while thicker ice is increasingly underestimated by all satellite products investigated for this study. This tendency of the satellite SIT products to better agree with modal sea ice draft and underestimate thicker ice needs to be considered for all past and future investigations into SIT changes in this important region. The performance of the CCI-2 SIT CDR is considered stable over time, however, observed trends in gridded CCI-2 SIT are strongly influenced by the uncertainties of ENVISAT and CS2 and the comparably short investigation period.

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;

scholarly journals SMOS derived sea ice thickness: algorithm baseline, product specifications and initial verification

2013 ◽  
Vol 7 (6) ◽  
pp. 5735-5792 ◽  
Author(s):  
X. Tian-Kunze ◽  
L. Kaleschke ◽  
N. Maaß ◽  
M. Mäkynen ◽  
N. Serra ◽  
...  
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Remote Sensing Data ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Ice Thickness ◽  
Transfer Model ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness

Abstract. Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.

scholarly journals Sea ice export through the Fram Strait derived from a combined model and satellite data set

2019 ◽  
Author(s):  
Chao Min ◽  
Longjiang Mu ◽  
Qinghua Yang ◽  
Robert Ricker ◽  
Qian Shi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Combined Model ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Drift Frequency

Abstract. Sea ice volume export through the Fram Strait plays an important role on the Arctic freshwater and energy redistribution. The combined model and satellite thickness (CMST) data set assimilates CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) thickness products together with satellite sea ice concentration. The CMST data set closes the gap of stand-alone satellite-derived sea ice thickness in summer and therefore allows us to estimate sea ice volume export during the melt season. In this study, we first validate the CMST data set using field observations, and then estimate the continuous seasonal and interannual variations of Arctic sea ice volume flux through the Fram Strait from September 2010 to December 2016. The results show that seasonal and interannual sea ice volume export vary from −244 (±43) to −973 (±59) km3 and −1974 (±291) to −2491 (±280) km3, respectively. The sea ice volume export reaches its maximum in spring and the mean amount of the melt season ice volume export accounts about one third of the yearly total amount. The minimum monthly sea ice export is −11 km3 in August 2015 and the maximum (−442 km3) appears in March 2011. Seasonal variations of sea ice thickness and drift frequency distributions infer that the thicker ice accompanied with slower ice motion is easier to appear when there is sea ice exporting through the Fram Strait outlet in summer.

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