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 Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office’s Forecast Ocean Assimilation Model (FOAM)

2021 ◽  
Author(s):  
Emma Kathleen Fiedler ◽  
Matthew Martin ◽  
Ed Blockley ◽  
Davi Mignac ◽  
Nicolas Fournier ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Sampling Error ◽  
Mean Difference ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Along Track ◽  
Assimilation Model

Abstract. The feasibility of assimilating SIT (sea ice thickness) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office’s global, coupled ocean-sea ice model, FOAM (Forecast Ocean Assimilation Model). The Arctic freeboard measurements are produced by CPOM (Centre for Polar Observation and Modelling) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally-averaged observations. The assimilation greatly improves the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics show improvements of 0.75 m mean difference and 0.41 m RMSD (root-mean-square difference) in the freeze-up period, and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period, for 2015–2017. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5-day SIT forecast. Validation of the SIT assimilation with independent BGEP (Beaufort Gyre Exploration Project) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with Air-EM (airborne electromagnetic induction) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, which may be evidence of noise in the SIT analysis, sampling error, or uncertainties in the modelled snow depth, the assimilated observations, or the validation observations themselves. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations over the summer due to the effect of meltponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months, due to previous SIT assimilation. This also leads to regional improvements in the July SIC (sea ice concentration) of 5 % RMSD in the European sector, due to slower melt of the thicker modelled sea ice.

scholarly journals On retrieving sea ice freeboard from ICESat laser altimeter

2016 ◽  
Author(s):  
Kirill Khvorostovsky ◽  
Pierre Rampal
Keyword(s):  
Sea Ice ◽  
Sea Level ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Laser Altimeter ◽  
Sea Ice Thickness ◽  
The Difference ◽  
Along Track

Abstract. Sea ice freeboard derived from satellite altimetry is the basis for 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 estimation of 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 in order to assess and analyze 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). For the ICEsat observation periods (2003–2008) it is found that when applying the same along-track averaging scales in the two methods to calculate the local sea level references the LLE method gives significantly lower (by up to 15 cm) sea ice freeboard estimates over thick multi-year ice areas, but significantly larger estimates (by 3–5 cm in average and locally up to about 10 cm) over thin first-year ice areas, as compared to the TP method. However, we show that the difference over first-year ice areas can be reduced to less than 2 cm when using the improved TP method proposed in this paper. About 4 cm of the difference in the JPL and GSFC freeboard estimates can be attributed to the different along-track averaging scales used to calculate the local sea level references. We show that the effect of applying corrections for lead width relative to the ICESat footprint, and for snow depth accumulated in refrozen leads (as it is done for the last release of the JPL product), is very large and increase freeboard estimates by about 7 cm. Thus, the different along-track averaging scales and approaches to calculate sea surface references, from one side, and the freeboard adjustments as applied in the TP method used to produce the JPL dataset, from the other side, are roughly compensating each other with respect to freeboard estimation. Therefore the difference in the mean sea ice thickness found between the JPL and GSFC datasets should be attributed to different parameters used in the freeboard-to-thickness conversion.

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>

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.

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>

Sea Ice Enhancements to Polar WRF*

2015 ◽  
Vol 143 (6) ◽  
pp. 2363-2385 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich ◽  
Lesheng Bai ◽  
Cecilia M. Bitz ◽  
Jordan G. Powers ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Surface Model ◽  
Sea Ice Thickness ◽  
Near Surface ◽  
The Arctic Ocean

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

scholarly journals Arctic Mission Benefit Analysis: Impact of Sea Ice Thickness, Freeboard, and Snow Depth Products on Sea Ice Forecast Performance

2017 ◽  
Author(s):  
Thomas Kaminski ◽  
Frank Kauker ◽  
Leif Toudal Pedersen ◽  
Michael Voßbeck ◽  
Helmuth Haak ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
High Accuracy ◽  
Added Value ◽  
The Arctic ◽  
Ice Thickness ◽  
Performance Gain ◽  
Benefit Analysis ◽  
Sea Ice Thickness ◽  
Forecast Performance

Abstract. Assimilation of remote sensing products of sea ice thickness (SIT) into sea ice-ocean models has been shown to improve the quality of sea ice forecasts. Open questions are whether the assimilation of rawer products such as radar freeboard (RFB) can achieve yet a better performance and what performance gain can be achieved by the joint assimilation with a snow depth product. The Arctic Mission Benefit Analysis (ArcMBA) system was developed to address this type of question. Using the quantitative network design (QND) approach, the system can evaluate, in a mathematically rigorous fashion, the observational constraints imposed by individual and groups of data products. We present assessments of the observation impact (added value) in terms of the uncertainty reduction in a four-week forecast of sea ice volume (SIV) and snow volume (SNV) for three regions along the Northern Sea Route by a coupled model of the sea ice-ocean system. The assessments cover seven satellite products, three real products and four hypothetical products. The real products are monthly SIT, sea ice freeboard (SIFB), and RFB, all derived from CryoSat-2 by the Alfred Wegener Institute. These are complemented by two hypothetical monthly laser freeboard (LFB) products (one with low accuracy and one with high accuracy), as well as two hypothetical monthly snow depth products (again one with low accuracy and one with high accuracy). On the basis of the per-pixel uncertainty ranges that are provided with the CryoSat-2 SIT, SIFB, and RFB products, the SIT achieves a much better performance for SIV than the SIFB product, while the performance of RFB is more similar to that of SIT. For SNV, the performance of SIT is only low, the performance of SIFB higher and the performance of RFB yet higher. A hypothetical LFB product with low accuracy (20 cm uncertainty) lies in performance between SIFB and RFB for both SIV and SNV. A reduction in the uncertainty of the LFB product to 2 cm yields a significant increase in performance. Combining either of the SIT/freeboard products with a hypothetical snow depth product achieves a significant performance increase. The uncertainty in the snow product matters: A higher accuracy product achieves an extra performance gain. The provision of spatial and temporal uncertainty correlations with the EO products would be beneficial not only for QND assessments, but also for assimilation of the products.

scholarly journals Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

2022 ◽  
Vol 16 (1) ◽  
pp. 61-85
Author(s):  
Emma K. Fiedler ◽  
Matthew J. Martin ◽  
Ed Blockley ◽  
Davi Mignac ◽  
Nicolas Fournier ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Mean Difference ◽  
Validation Dataset ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Forecast Validation ◽  
Along Track ◽  
Assimilation Model

Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

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.

Optimizing Observations of Sea Ice Thickness and Snow Depth in the Arctic

2013 ◽  
Author(s):  
Jacqueline A. Richter-Menge ◽  
Sinead L. Farrell
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness
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