CryoSat User Workshop 5

Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the nominal data set typically exceeds 1-2 months due to the time required to determine precise satellite orbits. We use a new rapid-delivery CryoSat-2 data set based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT). We show that this NRT sea ice thickness product is of comparable accuracy to the nominal CryoSat-2 data release, because the satellite orbit is not a critical factor in determining sea ice freeboard. It also provides measurements of Arctic sea ice thickness within 2 days of acquisition by the satellite, and a measurement is delivered, on average, within 15 km of each location in the Arctic every 2 days. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

ESA's CryoSat-2 level 2 SARIn ice altimetric products are obtained by retracking the first peak in the level 1b waveforms. The first peak is associated with the point of closest approach (POCA). McMillan et al. (2013) suggest that there seem to be useful geophysical information beyond this point. The objective of this study was to exploit this information. In doing so, we designed and implemented a subwaveform processing scheme to retrack also the peaks at and beyond the POCA and used it to derive elevation changes over the West Greenland ice sheet. We will show that our SARIn level 2 data processor provides about twice as many elevation estimates compared to the number available in ESA's level-2 SARIn product. We will also show a validation of our new product that includes a comparison of the elevations to existing digital elevation models (DEM) such as the ICESat DEM (DiMarzio et al., 2008). The elevation change trends were estimated using the procedure described by Wouters et al. (2015). We will show that although we do not observe a significant difference between our product and ESA's level-2 ice product, we obtain a much better sampling of the elevation change signal.

DiMarzio, J., Brenner, A., Schutz, R., Shuman, C. A., & Zwally, H. J. (2007). GLAS/ICESat 1 km laser altimetry digital elevation model of Greenland. Digital media, National Snow and Ice Data Center, Boulder, CO.

McMillan, M., Corr, H., Shepherd, A., Ridout, A., Laxon, S., & Cullen, R. (2013). Three‐dimensional mapping by CryoSat‐2 of subglacial lake volume changes. Geophysical Research Letters, 40(16), 4321-4327.

Wouters, B., Martin-Español, A., Helm, V., Flament, T., van Wessem, J. M., Ligtenberg, S. R. M., van den Broeke, M. R., & Bamber, J. L. (2015). Dynamic thinning of glaciers on the Southern Antarctic Peninsula. Science, 348(6237), 899-903.

We compare spaceborne radar altimeter data collected by the Cryosat-2 SIRAL instrument to data collected onboard Polar Suplly and Research VesseV S.A. Agulhas II during her supply run to the Antarctic base SANAE IV in Antarctic summer 2013-2014. The latter includes data from strain gauges mounted on the ship’s hull, measuring the response of the hull induced by the forces from the sea and ice the ship is navigating through. The ice-induced load on the ship hull data is a very good measure for the harshness of ice conditions from the viewpoint of winter navigation. This is the first time such ship data is compared to satellite altimeter waveforms.

We compare the ship hull load data to several waveform parameters as well as sea ice freeboard estimates. We show that areas where the largest ship hull deflections occurred coincided with areas of SIRAL echoes that exhibit larger leading edge widths and stack standard deviations than the ice covered areas where hull distortions were smaller. In other words, areas where S.A. Agulhas II encountered the largest impacts from the sea ice are recognisable from the altimeter data.

We show that spaceborne altimeter data, even with its sparse coverage, can provide valuable information for the needs of ice navigation especially in the areas of low synthetic aperture radar image cover – such as the Southern Ocean. The application of satellite RA data for ice navigation is of importance when planning future European radar altimeter missions.

Since April 2010 the CryoSat-2 mission with its innovative Synthetic-Aperture Radar Altimeter has produced sea ice thickness data of unprecedented accuracy and regional coverage. However, sea ice thicknesses retrievals require numerous processing steps to convert the altimeter’s range measurements first to ice freeboard and then to thickness. Each of these steps can introduce uncertainties in the resulting thickness estimate, thus requiring careful validation. Various studies have used a range of coincident submarine and airborne ice thickness observations for validation of CryoSat thickness retrievals at spatial and temporal scales of ≥10000 km² and ≥1 month, respectively. These show correlation coefficients of 0.6-0.8 and rms errors of 0.3-0.6 m between CryoSat and other thickness retrievals.

Here we first review the CryoSat sea ice thickness retrieval error budget and then show results from the ESA supported CryoSat Sea Ice Validation project: CryoVal-SI. CryoVal-SI used coincident airborne freeboard and thickness retrievals from ESA’s CryoVex and NASA IceBridge validation campaigns to validate CryoSat freeboard and thickness retrievals at the CryoSat footprint and orbit scale. Such comparisons allow better identification of the main error sources along the CryoSat orbits although they are limited by a lack of complete across-track footprint coverage. Airborne observations include ice freeboard derived from laser altimetry and ESA’s Airborne Synthetic Aperture Radar Altimeter System (ASIRAS) as well as total ice thickness obtained from electromagnetic sounding.

Results show that the main potential sources of error in freeboard retrievals are due to: 1) interpolation of sea surface height anomalies in areas of small lead coverage, in regions where errors remain in geoid or mean sea surface models; and 2) unknown radar penetration into the snow layer. Conversion of freeboard to thickness is mostly affected by uncertainties in the snow thickness distribution and the densities of snow and ice. Several research groups who have created ice thickness products have suggested different solutions to address these errors and uncertainties.

Results of the direct comparison of airborne and CryoSat data at the footprint scale show very low correlation coefficients and large rms errors. These are due to the instrument and speckle noise of CryoSat retrievals and the insufficient airborne coverage of across-track sea ice variability within the CryoSat footprint. Results improve with longer along-track averaging intervals resolving regional thickness gradients.

Several recent studies have shown the capability of sea-ice thickness retrieval from CryoSat-2 radar altimeter data. From the satellite range measurements we calculate the freeboard, which is the height of the sea ice above the water level. Therefore leads, cracks in the ice, are carefully detected to retrieve the instantaneous sea surface height. By assuming hydrostatic equilibrium and bulk densities for snow, ice and water, freeboard can then be converted into sea ice thickness. Since the statistical uncertainty of a thickness retrieval of a single observation is in the order of 30 cm and to obtain sufficient orbit coverage over the Arctic Ocean, it is necessary to calculate monthly means with a grid resolution of 25 km or larger. Therefore, recent studies mainly focused on averaging measurements monthly over a larger area. However, due to the high along track resolution of 300 m we obtain a sufficient number of measurements within a 25 km grid cell to calculate probability density functions of sea ice thickness and fractions of indicated leads.

Airborne validation campaigns (ESA’s CryoVEx, NASA’s IceBridge, PAMARCMIP) are carried out over Arctic sea ice in spring and allow to validate and calibrate the CryoSat-2 freeboard and thickness measurements. These airborne surveys include coincident laser and radar altimetry measurements, as well as electromagnetic induction sounding, which enables to measure sea ice thickness directly.

We present grid cell freeboard and thickness distributions which are compared to retrievals from airborne validation campaigns. A log-normal function is fitted to the grid cell thickness distribution to retrieve monthly modal thickness maps from CryoSat-2. Furthermore the width of the distribution bears information about potential ice deformation on hemispheric scale. Moreover we show monthly maps of indicated lead fractions for the CryoSat-2 era. Such products add new valuable information to the monthly means and therefore help to understand and characterize changes in the Arctic sea ice cover.