Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative

Paul, Stephan; Hendricks, Stefan; Ricker, Robert; Kern, Stefan; Rinne, Eero

doi:10.5194/tc-12-2437-2018

Cited by 70 publications

(112 citation statements)

References 44 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…Getting a more strict PP threshold would lead to areas with very low data coverage, not being able to get a region wide SLA record relaying on data and not only extrapolation. An other option could be to use more advanced classification schemes and machine learning to get a better control of the leads in the sea-ice cover similar to [63][64][65].…”

Section: The Sla Recordmentioning

confidence: 99%

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

Rose

Andersen

Passaro³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

In recent years, there has been a large focus on the Arctic due to the rapid changes of the region. Arctic sea level determination is challenging due to the seasonal to permanent sea-ice cover, lack of regional coverage of satellites, satellite instruments ability to measure ice, insufficient geophysical models, residual orbit errors, challenging retracking of satellite altimeter data. We present the European Space Agency (ESA) Climate Change Initiative (CCI) Technical University of Denmark (DTU)/Technischen Universität München (TUM) sea level anomaly (SLA) record based on radar satellite altimetry data in the Arctic Ocean from the European Remote Sensing satellite number 1 (ERS-1) (1991) to CryoSat-2 (2018). We use updated geophysical corrections and a combination of altimeter data: Reprocessing of Altimeter Product for ERS (REAPER) (ERS-1), ALES+ retracker (ERS-2, Envisat), combination of Radar Altimetry Database System (RADS) and DTUs in-house retracker LARS (CryoSat-2). Furthermore, this study focuses on the transition between conventional and Synthetic Aperture Radar (SAR) altimeter data to make a smooth time series regarding the measurement method. We find a sea level rise of 1.54 mm/year from September 1991 to September 2018 with a 95% confidence interval from 1.16 to 1.81 mm/year. ERS-1 data is troublesome and when ignoring this satellite the SLA trend becomes 2.22 mm/year with a 95% confidence interval within 1.67-2.54 mm/year. Evaluating the SLA trends in 5 year intervals show a clear steepening of the SLA trend around 2004. The sea level anomaly record is validated against tide gauges and show good results. Additionally, the time series is split and evaluated in space and time.sheet mass losses. Outlet glaciers are losing mass more rapidly [6,7], contributing to the sea level rise, changing the oceans freshwater flux, and influencing the ocean thermohaline circulation [8].The polar oceans are often not included in the global sea level estimations and can be seen as white spots on the global sea level maps. This is because of the challenging polar sea level determination due to; the seasonal to permanent sea-ice cover, the lack of regional coverage of satellites, satellite instruments ability to measure ice, insufficient geophysical models, residual orbit errors and retracking of satellite altimeter data.The sea-ice cover is in constant change. The sea-ice extent is the largest in March and the smallest in September. The Norwegian and Barents Sea are only seasonally covered by sea-ice while the central part up to the Canadian Archipelago and the North coast of Greenland are permanently ice covered (see Figure 1 for an Arctic Ocean overview). The older ice is pushed against these parts, and additionally, the Canadian Archipelago and the land-fast ice areas are also the part with the fewest leads and consequently the most inaccurate sea level determination [9].

show abstract

Section: The Sla Recordmentioning

confidence: 99%

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

Rose

Andersen

Passaro³

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Here we focus on combing sea ice thickness estimates from Envisat and CryoSat-2 using data from their common mission period of November 2010 to March 2012. correction for Envisat data that is applied to the whole mission, based on the relationship between Envisat waveform shape and Envisat and CryoSat-2 freeboard difference over their common mission period. Another approach uses a consistent surface type classification scheme for both missions and an adaptive threshold retracker for Envisat sea ice waveforms to obtain the smallest difference in freeboard compared with CryoSat-2 [Paul et al, 2018]. However, no physically based solution for correcting the intermission sea ice freeboard and subsequent thickness bias currently exists.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

2019

View full text Add to dashboard Cite

Multidecadal observations of sea ice thickness, in addition to those available for extent, are key to understanding long-term variations and trends in the amount of Arctic sea ice. The European Space Agency 's Envisat (2002's Envisat ( -2010 and CryoSat-2 (2010-present) satellite radar altimeter missions provide a continuous 17-year dataset with the potential to estimate sea ice thickness. However, the satellite footprints are not equal in area and so different distributions of floes and leads are sampled by each mission. Here, we compare lead and floe sampling from Envisat and CryoSat-2 to investigate the impact of geometric sampling differences on Arctic sea ice thickness estimates. We find that Envisat preferentially samples wider, thicker sea ice floes, and that floes in less consolidated ice regions are effectively thickened by off-nadir ranging to leads. Consequently, Envisat sea ice thicknesses that are an average of 80 cm higher than CryoSat-2 over first-year ice and 23 cm higher over multiyear ice. By considering the along-track distances between lead and floe measurements, we are able to develop a sea ice thickness correction that is based on Envisat's inability to resolve discrete surfaces relative to CryoSat-2. This is a novel, physically based approach to addressing the bias between the satellites and reduces the average thickness difference to negligible values over first-year and multiyear ice. Finally, we evaluate our new bias-corrected Envisat sea ice thickness product using independent airborne, moored-buoy and submarine data. The European Space Agency's Envisat and CryoSat-2 satellites have the potential to produce a continuous record of Arctic sea ice thickness since 2002, but this is complicated by the fact that the satellites do not sample the sea ice surface in the same way. We find that Envisat is only able to sample larger, thicker sea ice relative to CryoSat-2, because of its poorer resolution. In this paper we account for these differences in sampling to combine Arctic sea ice thickness estimates from two the satellite missions. Applying a sea ice thickness bias correction to Envisat data reduces the ice thickness difference between Envisat and CryoSat-2 from an average of 53.0 to 0.5 cm So far, only empirical solutions have been proposed to reconcile Arctic sea ice freeboard retrievals from Envisat with those from CryoSat-2. For example, Guerreiro et al.[2017] developed a radar freeboard

show abstract

“…Snow on sea ice is a major component of the marine cryosphere. It reduces the absorption of solar radiation, it reduces the atmosphere-ocean heat exchange, it insulates the sea ice against RA-2 and CryoSat-2 [8]. Note however that the approach of Rostosky et al [31] is developed for the Arctic, while the focus of the present paper is the Antarctic.…”

Section: Introductionmentioning

confidence: 89%

An Attempt to Improve Snow Depth Retrieval Using Satellite Microwave Radiometry for Rough Antarctic Sea Ice

Kern

Özsoy

2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Snow depth on sea ice is a major constituent of the marine cryosphere. It is a key parameter for the derivation of sea-ice thickness from satellite altimetry. One way to retrieve the basin-scale snow depth on sea ice is by satellite microwave radiometry. There is evidence from measurements and inter-comparison studies that current retrievals likely under-estimate the snow depth over deformed, rough sea ice. We follow up on an earlier study, where satellite passive microwave data were combined with information on the sea-ice topography from the satellite laser altimeter on board the Ice, Cloud and land Elevation Satellite (ICESat) in a hybrid approach. Such topography information is spatiotemporally limited because of ICESat’s operation mode. In this paper, we aim to derive a proxy for this topography information from satellite microwave radiometry. For this purpose, we co-locate parameters describing the sea-ice deformation taken from visual ship-based observations and the surface elevation standard deviation derived from ICESat laser altimetry with the microwave brightness temperatures (TB) measured via the Advanced Microwave Scanning Radiometer aboard Earth Observation Satellite (AMSR-E) and aboard Global Change Observation Mission-Water 1 (GCOM-W1) (AMSR2). We find that the TB polarization ratio at 6.9 GHz and the TB gradient ratio between 10.7 GHz (horizontal polarization) and 6.9 GHz (vertical polarization), might be suited as such a proxy. Using this proxy, we modify the above-mentioned hybrid approach and compute the snow depths on sea ice from the AMSR-E and AMSR2 data. We compare our snow depths with those of the commonly used approach, the hybrid approach, with the ship-based observations for the years 2002 through 2015 and with the measurements made by drifting buoys for the period of 2014 through 2018. We find a convincing overall agreement with the hybrid approach and some improvement over the common approach. However, our approach is sensitive to the presence of thin ice—here, the retrieved snow depths are too large; and our approach performs sub-optimally over old ice—here, the retrieved snow depths are too small. More investigations and, in particular, more evaluations are required to optimize our approach so that the snow depths retrieved for the combined AMSR-E/AMSR2 period could serve as a data set for sea-ice thickness retrieval based on satellite altimetry.

show abstract

Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative

Cited by 70 publications

References 44 publications

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

An Attempt to Improve Snow Depth Retrieval Using Satellite Microwave Radiometry for Rough Antarctic Sea Ice

Contact Info

Product

Resources

About