Multi-Frequency Satellite Approaches for Snow on Sea Ice: first results from the POLAR+ Snow on Sea Ice ESA project

2021 ◽  
Author(s):  
Michel Tsamados ◽  
Keyword(s):  
Sea Ice ◽  
Primary Objective ◽  
The Arctic ◽  
Ice Thickness ◽  
Laser Altimeter ◽  
Sea Ice Thickness ◽  
Model Studies ◽  
Polar Snow ◽  
The Impact ◽  
Snow Thickness

<p>Abstract: We propose new methods for multi-frequency snow thickness retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI). The primary objective of this project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent ‘golden era in polar altimetry’ with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent ‘operational‘ period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2). In addition to snow thickness, and as a secondary objective, we will explore other snow characteristics (snow density, snow metamorphism, scattering horizon, roughness, etc) and compare these results with in-situ, airborne and other snow on sea ice products including from model studies and reanalysis on drifting sea ice products. In preparation to future multi-frequency mission we will put an emphasis on uncertainty analysis of our snow product, the impact of the snow on the sea ice thickness retrieval, and on climate physics via model runs with snow initialisation and data assimilation. Finally, learning from past and present campaings (i.e. CryoVex, MOSAiC) we will propose methodologies for effective future snow and sea ice thickness airborne validation campaigns via innovative inverse modelling approaches and airborne retrackers.</p><p> </p>

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 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.

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 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 Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2

2018 ◽  
Vol 12 (8) ◽  
pp. 2789-2801 ◽  
Author(s):  
Ron Kwok ◽  
Sahra Kacimi
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Basal Layer ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Mean Differences ◽  
The Impact ◽  
Snow Thickness

Abstract. We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2.40±1.07 (2011) to 2.60±1.15 m (2014) and snow depth from 35.8±11.5 (2016) to 43.6±10.2 cm (2014), suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the western Weddell. The impact of undersampling the thin end of the snow depth distribution on the regional statistics, due to the resolution of the snow radar, is assessed. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2) sampled along the same transect are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow–ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thicknesses computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore generally higher; mean differences in sea ice thickness along a transect are up to ∼0.6 m higher (in 2014). This analysis is relevant to the use of differences between ICESat-2 and CS-2 freeboards to estimate snow depth for ice thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, with which the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding the limits of retrievals of Weddell Sea ice thickness from satellite altimeters.

scholarly journals Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

2016 ◽  
Vol 9 (6) ◽  
pp. 2239-2254 ◽  
Author(s):  
Yao Yao ◽  
Jianbin Huang ◽  
Yong Luo ◽  
Zongci Zhao
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Wrf Model ◽  
The Arctic ◽  
Wave Radiation ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Impact ◽  
Ice Model ◽  
Snow And Ice

Abstract. Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 °C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 °C and 20 W m−2, respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

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 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.

Improving sea-ice cover and SST forecasts by sea-ice thickness initialization

2020 ◽  
Author(s):  
Steffen Tietsche ◽  
Beena Balan Sarojini ◽  
Michael Mayer ◽  
Hao Zuo ◽  
Frederic Vitart ◽  
...  
Keyword(s):  
Sea Ice ◽  
Daily Basis ◽  
The Arctic ◽  
Ice Thickness ◽  
Ocean Reanalysis ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Arctic Ice ◽  
The Impact ◽  
Melting Season

<p>A substantial amount of subseasonal-to-seasonal sea-ice variability is potentially predictable, but improved model biases and initialization techniques are needed to realize this potential. Forecasts for other Earth System components can be expected to benefit from improved sea-ice forecasts as well, because the presence of sea ice drastically alters exchanges of heat and momentum between the atmosphere and the ocean. Here, we present the impact of initializing subseasonal forecasts with observed sea-ice thickness. The newly developed sea-ice thickness data set CS2SMOS that we use is derived from radar altimetry and L-band radiance satellite observations. It allows for the first time a spatially complete view of pan-Arctic ice thickness on a near-daily basis during the freezing season. The ingestion of this data into the ECMWF ocean reanalysis system improves subseasonal forecasts of the Arctic ice edge during the melting season by up to 10%. Sea-surface temperature forecasts at high latitudes are also significantly improved during the melting season, because an improved prediction of ice-free date allows an improved forecast of the amount of seasonal warming. These results illustrate the potential for improving subseasonal-to-seasonal predictions by initializing the sea-ice thickness.</p>

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