CryoSat: A mission to determine the fluctuations in Earth’s land and marine ice fields

Wingham, D. J.; Francis, C. R.; Baker, S. G.; Bouzinac, Catherine; Brockley, David; Cullen, R.; Chateau-Thierry, P. de; Laxon, Seymour W.; Mallow, U.; Mavrocordatos, C.; Phalippou, L.; Ratier, G.; Rey, L.; Rostan, F.; Viau, P.; Wallis, David

doi:10.1016/j.asr.2005.07.027

Cited by 539 publications

(564 citation statements)

References 40 publications

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“…The radar altimeter on board Envisat has a much coarser resolution and lower data coverage than the one on board CS-2. Due to the delay/Doppler processing, the CS-2 footprint corresponds to the size of a Doppler cell, which is approximately 300 × 1600 m (Wingham et al, 2006), while Envisat has a footprint of 2-10 km (Connor et al, 2009). Envisat waveforms from mixed surfaces are often discarded since they do not match the classification scheme.…”

Section: Discussionmentioning

confidence: 99%

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

et al. 2016

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Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on board Envisat and freeboard measurements from the SyntheticAperture Interferometric Radar Altimeter on board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are in reasonable agreement over the sea-ice growth season (May-October) and partly also beyond. In general, Envisat data show higher freeboards in the first-year ice zone while CryoSat-2 freeboards are higher in the multiyear ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regionally separated) Southern Ocean differs generally by not more than 3 cm (8 cm, with few exceptions) between Envisat and CryoSat-2, and the differences between modal freeboards lie generally within ±10 cm and often even below.

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Section: Discussionmentioning

confidence: 99%

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

et al. 2016

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“…The SIRAL altimeter is able to operate in three different modes: Low Resolution mode, SAR mode and SAR Interferometic (SARIn) mode [19]. Over rugged terrains (such as high Asia), SIRAL is operating in SARIn mode.…”

Section: Cryosat-2 Altimetry Datamentioning

confidence: 99%

“…Geophysical and atmospheric corrections are used to correct altimeter measurements from different perturbations due to the environment and ensure the highest precision output data. We used Level-2 SIRAL SARIn mode data over Tibetan lakes [19], which include each month of the period July 2010 to April 2014.…”

Section: Cryosat-2 Altimetry Datamentioning

confidence: 99%

Combined ICESat and CryoSat-2 Altimetry for Accessing Water Level Dynamics of Tibetan Lakes over 2003–2014

Song

Sheng

et al. 2015

Water

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Long-term observations of lake water level are essential to our understanding of the evolution of Tibetan lake system. CryoSat-2 radar altimetry data over the Tibetan Plateau (2010Plateau ( -2014) is used to extend lake level measurements from ICESat laser altimetry (2003-2009, P1). This study evaluates the performance of CryoSat-2 data by comparing with gauge-based water levels that are calibrated by ICESat-observed water level time series, and quantifies the uncertainty of water-level change rate estimates from satellite altimetry measurements. We completely investigate the 131 lakes that were observed by both ICESat and CryoSat-2. The mean change rate of water level for all of examined lakes in P2 (0.19 ± 0.03 m·year -1 ) is slightly lower than that (0.21 ± 0.02 m·year -1 ) observed in P1. The extended lake level time series also indicates that, in the past few years, lakes in the Northern Changtang (especially in Hol Xil) showed accelerated growth; and that the extensive lake level rises north to the Gangdise Mountains, during 2003Mountains, during -2009, were found dampened during the CryoSat-2 observation period. The spatio-temporal heterogeneity of precipitation observed from weather stations can be used to partly explain the observed temporal pattern of lake level changes over different sub-zones of the plateau. OPEN ACCESSWater 2015, 7 4686

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“…The principal challenges in deriving an accurate sea ice thickness using satellite altimetry are the discrimination of ice and open water, retracking radar waveforms to obtain height estimates, constructing sea surface height beneath the ice, and estimating the depth of the snow 20 cover. Ice thickness is retrieved from freeboard by processing CS2 Level 1B data, with a footprint of approximately 300 m by 1700 m (Wingham et al, 2006), and assuming snow density and snow depth from the Warren et al (1999) climatology (hereafter W99), modified for the distribution of multi-year versus first-year ice (see Laxon et al, 2013 andTilling et al, 2018 for data processing details).…”

Section: Ice Thickness Distribution From Cryosat-2mentioning

confidence: 99%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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CryoSat: A mission to determine the fluctuations in Earth’s land and marine ice fields

Cited by 539 publications

References 40 publications

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

Combined ICESat and CryoSat-2 Altimetry for Accessing Water Level Dynamics of Tibetan Lakes over 2003–2014

New insight from CryoSat-2 sea ice thickness for sea ice modelling

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