Dynamic Ocean Topography of the Greenland Sea: A comparison between satellite altimetry and ocean modeling

Abstract. The dynamic ocean topography (DOT) in the polar seas can be described by satellite altimetry sea surface height observations combined with geoid information and by ocean models. The altimetry observations are characterized by an irregular sampling and seasonal sea-ice coverage complicating reliable DOT estimations. Models display various spatio-temporal resolutions, but are limited to their computational and mathematical context and introduced forcing models. In the present paper, ALES+ retracked altimetry ranges and derived along-track DOT heights of ESA's Envisat and water heights of the Finite Element Sea-ice Ocean Model (FESOM) are compared to investigate similarities and discrepancies. The study period covers the years 2003–2009. An assessment analysis regarding seasonal DOT variabilities shows good accordance and confirms the most dominant impact of the annual signal in both datasets. A comparison based on estimated regional annual signal components shows 2–3 times stronger amplitudes of the observations but good agreement of the phase. Reducing both datasets by constant offsets and the annual signal reveals small regional residuals and highly correlated DOT time series (correlation coefficient at least 0.67). The highest correlations can be found in areas that are ice-free and affected by ocean currents. However, differences are visible in sea-ice covered shelf regions. Furthermore, remaining constant artificial elevations in the observational data can be addressed to an insufficient representation of the used geoid. In general, the comparison results in good accordance between simulated and altimetry based description of the DOT in the Greenland Sea. Furthermore, the investigation shows that combining both datasets and exploiting the advantages of along-track altimetry observations and those of homogeneous modeled DOT representations leads to a deeper comprehension of the Arctic Ocean's DOT.

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Dynamic ocean topography of the northern Nordic seas: a comparison between satellite altimetry and ocean modeling

The Cryosphere ◽

10.5194/tc-13-611-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 611-626 ◽

Cited By ~ 5

Author(s):

Felix L. Müller ◽

Claudia Wekerle ◽

Denise Dettmering ◽

Marcello Passaro ◽

Wolfgang Bosch ◽

...

Keyword(s):

Sea Ice ◽

Satellite Altimetry ◽

Ocean Model ◽

Irregular Sampling ◽

Ocean Modeling ◽

Nordic Seas ◽

Comparison Results ◽

Ocean Topography ◽

Annual Signal ◽

Good Agreement

Abstract. The dynamic ocean topography (DOT) of the polar seas can be described by satellite altimetry sea surface height observations combined with geoid information as well as by ocean models. The altimetry observations are characterized by an irregular sampling and seasonal sea ice coverage complicating reliable DOT estimations. Models display various spatiotemporal resolutions but are limited to their computational and mathematical context and introduced forcing models. In the present paper, ALES+ retracked altimetry ranges and derived along-track DOT heights of ESA's Envisat and water heights of the Finite Element Sea Ice-Ocean Model (FESOM) are compared to investigate similarities and discrepancies. The goal of the present paper is to identify to what extent pattern and variability of the northern Nordic seas derived from measurements and model agree with each other, respectively. The study period covers the years 2003–2009. An assessment analysis regarding seasonal DOT variabilities shows good agreement and confirms the dominant impact of the annual signal in both datasets. A comparison based on estimated regional annual signal components shows 2–3 times stronger amplitudes of the observations but good agreement of the phase. Reducing both datasets by constant offsets and the annual signal reveals small regional residuals and highly correlated DOT time series (Pearson linear correlation coefficient of at least 0.67). The highest correlations can be found in areas that are ice-free and affected by ocean currents. However, differences are visible in sea-ice-covered shelf regions. Furthermore, remaining constant artificial elevations in the observational data can be attributed to an insufficient representation of the used geoid. In general, the comparison results in good agreement between simulated and altimetry-based descriptions of the DOT in the northern Nordic seas.

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Multi-Peak Retracking of CryoSat-2 SARIn Waveforms: Potential for Sea Ice Applications

10.5194/egusphere-egu2020-19534 ◽

2020 ◽

Author(s):

Alessandro Di Bella ◽

Ron Kwok ◽

Thomas Armitage ◽

Henriette Skourup ◽

René Forsberg

Keyword(s):

Sea Ice ◽

Satellite Altimetry ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Phase Information ◽

Sea Ice Thickness ◽

Processing Scheme ◽

Thickness Estimation ◽

Along Track

For the last 25+ years, satellite altimetry has proven to be a powerful tool to estimate sea ice thickness from space, by measuring directly the sea ice freeboard. Nevertheless, available thickness estimates from satellite altimetry are affected by a relatively high uncertainty, with the largest contributions originating from the poor knowledge of both the Arctic snow cover and the sea surface height (SSH) in ice-covered regions. The ESA&#8217;s CryoSat-2 (CS2) radar altimetry mission is the first mission carrying on board an altimeter instrument able to operate in Synthetic Aperture Radar Interferometric (SARIn) mode. Previous studies showed how the phase information available in the SARIn mode can be used to reduce the random uncertainty of the SSH in ice-covered regions [1] and, consequently, the average uncertainty of along-track freeboard retrievals [2].This work shows that it is possible to extract even more information from level 1b SARIn data. In fact, while it is not possible to perform full swath processing [3] over sea ice, the contribution from sea ice reflections originating close to the satellite nadir is successfully separated from the specular returns from off-nadir leads for some SARIn waveforms. We find that retracking multiple peaks, in combination with the respective phase information, enables to obtain more than one valid height estimate from single SARIn waveforms over sea ice. The resulting larger amount of freeboard estimates, together with the more precise SSH, is found to contribute to an average reduction of the gridded random and total sea ice thickness uncertainties of ~40% and ~25%, respectively, compared to a regular SAR processing scheme. This study also investigates how the CS2 SARIn phase information can aid thickness estimation in coastal areas, using ESA Sentinel-1 SAR images and airborne data from NASA Operation IceBridge campaigns as a mean of validation.The more precise and, potentially, more accurate freeboard retrievals, as well as the potential for coastal freeboard and thickness estimation shown in this work, support the design of future satellite altimetry missions, e.g. Sentinel-9, operating in SARIn mode over the entire Arctic Ocean.&#160;References[1] Armitage, T. W. K., & Davidson, M. W. J. (2014). Using the interferometric capabilities of the ESA CryoSat-2 mission to improve the accuracy of sea ice freeboard retrievals. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 529&#8211;536. http://doi.org/10.1109/TGRS.2013.2242082[2] Di Bella, A., Skourup, H., Bouffard, J., & Parrinello, T. (2018). Uncertainty reduction of Arctic sea ice freeboard from CryoSat-2 interferometric mode. Advances in Space Research, 62(6), 1251&#8211;1264. http://doi.org/10.1016/j.asr.2018.03.018[3] Gray, L., Burgess, D., Copland, L., Cullen, R., Galin, N., Hawley, R., & Helm, V. (2013). Interferometric swath processing of Cryosat data for glacial ice topography. Cryosphere, 7 (6), 1857&#8211;1867.

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

10.5194/egusphere-egu21-12240 ◽

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

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.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.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.

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Sea ice volume variability and water temperature in the Greenland Sea

The Cryosphere ◽

10.5194/tc-14-477-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 477-495 ◽

Cited By ~ 2

Author(s):

Valeria Selyuzhenok ◽

Igor Bashmachnikov ◽

Robert Ricker ◽

Anna Vesman ◽

Leonid Bobylev

Keyword(s):

Sea Ice ◽

North Atlantic ◽

Heat Content ◽

The Arctic ◽

Fram Strait ◽

Greenland Sea ◽

Nordic Seas ◽

Ice Volume ◽

Long Term Variations

Abstract. This study explores a link between the long-term variations in the integral sea ice volume (SIV) in the Greenland Sea and oceanic processes. Using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, 1979–2016), we show that the increasing sea ice volume flux through Fram Strait goes in parallel with a decrease in SIV in the Greenland Sea. The overall SIV loss in the Greenland Sea is 113 km3 per decade, while the total SIV import through Fram Strait increases by 115 km3 per decade. An analysis of the ocean temperature and the mixed-layer depth (MLD) over the climatic mean area of the winter marginal sea ice zone (MIZ) revealed a doubling of the amount of the upper-ocean heat content available for the sea ice melt from 1993 to 2016. This increase alone can explain the SIV loss in the Greenland Sea over the 24-year study period, even when accounting for the increasing SIV flux from the Arctic. The increase in the oceanic heat content is found to be linked to an increase in temperature of the Atlantic Water along the main currents of the Nordic Seas, following an increase in the oceanic heat flux from the subtropical North Atlantic. We argue that the predominantly positive winter North Atlantic Oscillation (NAO) index during the 4 most recent decades, together with an intensification of the deep convection in the Greenland Sea, is responsible for the intensification of the cyclonic circulation pattern in the Nordic Seas, which results in the observed long-term variations in the SIV.

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Relationships linking satellite-retrieved ocean color data with atmospheric components in the Arctic

10.5194/egusphere-egu2020-19377 ◽

2020 ◽

Author(s):

Marjan Marbouti ◽

Sehyun Jang ◽

Silvia Becagli ◽

Tuomo Nieminen ◽

Gabriel Navarro ◽

...

Keyword(s):

Sea Ice ◽

Barents Sea ◽

Ocean Color ◽

Particle Formation ◽

The Arctic ◽

Ice Melting ◽

New Particle Formation ◽

Greenland Sea ◽

Chl A ◽

Color Data

We examined the relationships linking in-situ measurements of gas-phase methanesulfonic acid (MSA), sulfuric acid (SA), iodic acid (HIO3), Highly Oxidized Organic Molecules (HOM) and aerosol size-distributions with satellite-derived chlorophyll (Chl-a) and oceanic primary production (PP). Atmospheric data were collected at Ny-&#197;lesund site during spring-summer 2017 (30th March-4th August). We compared ocean color data from Barents Sea and Greenland Sea with concentrations of low-volatile vapours and new particle formation. The aim is to understand the main factors controlling the concentrations of atmospheric components in the Arctic in different ocean domains and seasons. Early phytoplanktonic bloom starting in April at the marginal ice zone caused Chl-a and PP in the Barents Sea to be higher than in the Greenland Sea during spring, whereas the pattern was opposite in summer. We found the correlation between ocean color data (Chl-a and PP) and MSA decreasing from spring to summer in Barents Sea and increasing in Greenland Sea. This establishes relationship between sea ice melting and phytoplanktonic bloom, which starts by sea ice melting. Similar pattern was observed for SA. Also HIO3 in both ocean domains correlated with Chl-a and PP during spring time. Greenland Sea was more active than Barents Sea. These results suggest that marine phytoplankton metabolism is an important source of MSA and SA, as expected, but also a source of HIO3 precursors (such as I2). HOMs had low correlation with ocean color parameters in comparison to other atmospheric vapours in this study both in spring and summer. The plausible explanation for low correlation is that the primary source of Volatile Organic Compounds (VOC) &#8211; precursors of HOM &#8211; is the soil of Svalbard archipelago rather than ocean. During spring, nucleation mode particles were found to correlate with Chl-a at Barents Sea and with PP at Greenland Sea. This means that biogenic productivity has a strong impact on new particle formation in spring although small particles are not related to biogenic parameters in summer.

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Ocean Surface Currents in the northern Nordic Seas from a combination of multi-mission satellite altimetry and numerical modeling

10.5194/egusphere-egu2020-4412 ◽

2020 ◽

Author(s):

Felix L. Müller ◽

Denise Dettmering ◽

Claudia Wekerle ◽

Christian Schwatke ◽

Marcello Passaro ◽

...

Keyword(s):

Sea Ice ◽

Satellite Altimetry ◽

Barents Sea ◽

Ocean Surface ◽

Ocean Model ◽

Main Step ◽

Surface Currents ◽

Data Set ◽

Ocean Surface Currents ◽

Ocean Topography

Satellite altimetry is an important part of the Global Geodetic Observing System providing precise information on sea level on different spatial and temporal scales. Moreover, satellite altimetry-derived dynamic ocean topography heights enable the computation of ocean surface currents by applying the well-known geostrophic equations. However, in polar regions, altimetry observations are affected by seasonally changing sea-ice cover leading to a fragmentary data sampling.In order to overcome this problem, an ocean model is used to fill in data gaps. The aim is to obtain a homogeneous ocean topography representation that enables consistent investigations of ocean surface current changes. For that purpose, the global Finite Element Sea-ice Ocean Model (FESOM) is used. It is based on an unstructured grid and provides daily water elevations with high spatial resolution.The combination is done based on a Principal Component Analysis (PCA) after reducing both quantities by their constant and seasonal signals. In the main step, the most dominant spatial patterns of the modeled water heights as provided by the PCA are linked with the temporal variability of the estimated dynamic ocean topography elevations from altimetry. At the end, the seasonal signal as well as the absolute reference from altimetry is added back to the data set.This contribution describes the combination process as well as the generated final product: a daily, more than 17 years covering dataset of geostrophic ocean currents. The combination is done for the marine regions Greenland Sea, Barents Sea and the Fram Strait and includes sea surface height observations of the ESA altimeter satellites ERS-2 and Envisat. In order to evaluate the combination results, independent surface drifter observations, corrected for a-geostrophic velocity components, are used.

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

10.5194/tc-2021-127 ◽

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.

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On retrieving sea ice freeboard from ICESat laser altimeter

The Cryosphere ◽

10.5194/tc-10-2329-2016 ◽

2016 ◽

Vol 10 (5) ◽

pp. 2329-2346 ◽

Cited By ~ 6

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.

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Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements

10.5194/tc-2020-27 ◽

2020 ◽

Author(s):

Hoyeon Shi ◽

Byung-Ju Sohn ◽

Gorm Dybkjær ◽

Rasmus Tage Tonboe ◽

Sang-Moo Lee

Keyword(s):

Sea Ice ◽

Snow Depth ◽

The Arctic ◽

Ice Thickness ◽

Interface Temperature ◽

Depth Information ◽

Simultaneous Estimation ◽

Snow Layer ◽

Sea Ice Thickness ◽

Comparison Results

Abstract. A method of simultaneously estimating snow depth and sea ice thickness using satellite-based freeboard measurements over the Arctic Ocean during winter was proposed. The ratio of snow depth to ice thickness (referred to as α) was defined and used in constraining the conversion from the freeboard to ice thickness in satellite altimetry. Then, α was empirically determined using the ratio of temperature difference of the snow layer to the difference of the ice layer, to allow the determination of α from satellite-derived snow surface temperature and snow–ice interface temperature. The proposed method was validated against NASA's Operation IceBridge measurements, and comparison results indicated that the algorithm adequately retrieves snow depth and ice thickness simultaneously: retrieved ice thickness was found to be better than the current satellite retrieval methods relying on the use of snow depth climatology as input, in terms of mean bias and RMSE. The application of the proposed method to CryoSat-2 ice freeboard measurements yields similar results. In conclusion, the developed α-based method has the capacity to derive ice thickness and snow depth, without relying on the snow depth information as input to the buoyancy equation for converting freeboard to ice thickness.

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A means-corrected estimate for the Arctic sea-ice volume in 1990–2019

10.5194/egusphere-egu21-3840 ◽

2021 ◽

Author(s):

Petteri Uotila ◽

Joula Siponen ◽

Eero Rinne ◽

Steffen Tietsche

Keyword(s):

Time Series ◽

Sea Ice ◽

Satellite Altimetry ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Volume ◽

Basin Scale ◽

Arctic Sea

Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS).The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002&#8211;2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4&#8211;1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990&#8211;2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.

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