Conference Agenda

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Session Overview
Session 2A - Sea-ice thickness retrieval and validation #1
Tuesday, 21/Mar/2017:
8:30am - 10:10am

Session Chair: Thomas Armitage
Session Chair: Ron Kwok

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8:30am - 8:50am

Impact of Ice and Snow Properties on Freeboard Retrieval and Sea-Ice Thickness Calculation from ALS, ASIRAS and CryoSat-2

Jennifer King1, Henriette Skourup2, Sine M. Hvidegaard2, Gunnar Spreen3, Anja Rösel1, Sebastian Gerland1, Veit Helm4, Chris Polashenski5, Glen E. Liston6, J. Wilkinson7, M. Granskog1

1Norwegian Polar Institute, Norway; 2DTU Space, Denmark; 3University of Bremen, Germany; 4Alfred Wegener Institut, Bremerhaven, Germany; 5U.S. Army Cold Regions Research and Engineering Laboratory – Alaska, USA; 6Cooperative Institute for Research in the Atmosphere (CIRA), Colorado State University, USA; 7British Antarctic Survey

Current freeboard and associated sea-ice thickness retrievals from the SIRAL radar altimeter on the CryoSat-2 satellite rely on the premise that the return signal measured is from the ice surface. However, it has been indicated that where a thick, or wet, snow cover is present this return may arise from somewhere within the snowpack rather than from the snow-ice interface.
We present a case study in which co-located freeboard measurements from airborne laser scanner (ALS), the Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS) and CryoSat-2 are compared to ice thickness measurements from both helicopter-borne and ground based electromagnetic-sounding, and to point measurements of ice properties (ice thickness, density, and freeboard; and snow thickness and density). This case study was performed in the Arctic Ocean in April 2015 in the region north of Svalbard as part of a joint campaign between the N-ICE2015 expedition and the 2015 ICE-ARC airborne campaign.
This case study adds to a body of evidence that documents the complexity of sea-ice freeboard retrievals from radar altimetry. It particularly highlights that radar penetration of the snow on sea-ice can be low and variable even at temperatures as low as -15°C. Whilst this knowledge has far-reaching consequences for radar based sea-ice thickness and consequently total Arctic sea ice volume estimates, we can use this information to improve altimetry processing routines, reduce uncertainty assessments for freeboard retrievals, and thus increase the accuracy of derived ice thickness information.

8:50am - 9:10am

Novel Measurements of the Snow Depth Distribtuion on Sea Ice in Support of Polar Altimetry

Sinead L Farrell1, Jacqueline Richter-Menge2, Marissa Dattler1, Thomas Newman1

1University of Maryland, United States of America; 2USACE-ERDL-CRRELL, United States of America

The growth and retreat of the polar sea ice cover is influenced by the seasonal accumulation, redistribution and melt of snow on sea ice. Knowledge of the snow depth distribution is critical for understanding sea ice mass balance and thus the heat and energy budgets of the polar climate system. Snow loading on sea ice is also a key variable in the derivation of sea ice thickness from altimeter measurements collected over the polar oceans.

An ultra-wideband, frequency modulated-continuous-waveform airborne radar altimeter system, known as the snow radar, and flown onboard NASA’s Operation IceBridge mission, provides annual measurements of snow depth on Arctic sea ice. We describe recent advances in the processing techniques used to interpret airborne radar waveforms, to produce accurate and robust snow depth results across basin scales. We present the results of seven years of radar measurements collected over Arctic sea ice at the end of winter, just prior to melt. Our analysis provides the snow depth distribution on both seasonal and multi-year sea ice, allowing us to understand its relationship with the parent ice cover. We will discuss the outcome of a number of validation experiments where temporally and spatially coincident in situ measurements were gathered during many IceBridge over-flights. These data provide a means to improve our understanding of the impacts of instrument design and the geophysical environment on snow radar echograms, and the associated, derived snow depth.

Our results provide perspective on new airborne radar systems being developed and deployed for future sea ice investigations. They also provide new insights on snow loading and its inter-annual variability, which will inform algorithm development for current and future altimeter missions, including CryoSat-2, ICESat-2 and Sentinel-3.

9:10am - 9:30am

Deriving Snow Depth for Arctic Sea Ice Thickness Retrievals: Can We Trust Precipitation Estimates from Reanalyses?

Linette Nicole Boisvert1,2, Alek Petty1,2, Melinda Webster2, Thorsten Markus2

1Earth System Science Interdisciplinary Center (ESSIC), UMD; 2NASA Goddard Space Flight Center, Cryospheric Sciences Lab

Snow depth on sea ice is the biggest source of uncertainty in the retrieval of sea ice thickness from airborne and satellite altimeters (e.g. CryoSat-2 and IceSat-2). The seasonal and interannual variability of snow depth on sea ice varies due to precipitation, snowmelt, sublimation, and wind-driven redistribution. Precipitation, however, is one of the most uncertain variables in Arctic reanalyses and climate models due to the lack of in situ validation data [Walsh et al., 1998], which limits our confidence in the application of derived snow accumulation estimates in scientific investigations.

The decline in Arctic snow depth [Webster et al., 2014] has been linked to sea ice loss and changes in surface fluxes (e.g. evaporation/precipitation) and atmospheric circulation patterns, yet precipitation is projected to increase in the Arctic by the end of this century [Christensen et al., 2007; Overland et al., 2011; Singarayer et al., 2006; Deser et al., 2010; Bintanja and Selten, 2014] with peaks in autumn and winter. Bitanja and Selten [2014] showed that increased Arctic precipitation is strongly influenced by increased evaporation in fall and winter due to warming and loss of sea ice cover, and that transport from lower latitudes played a lesser role. Uncertainties in Arctic precipitation in climate models and reanalysis are large, and could be greatly improved with reliable remotely-sensed observations.

As part of the Precipitation, Accumulation and Snow Thickness in the Arctic (PASTA) project, we will use moisture fluxes, atmospheric moisture content and other atmospheric variables from satellite instruments to compare with reanalysis and modeled precipitation estimates. Specifically, moisture fluxes and atmospheric moisture content over the Arctic can be retrieved from NASA’s Aqua AIRS data [Boisvert et al., 2013; 2015a,b], passive microwave retrievals of snow depth and precipitable water estimates from NOAA AMSU-B data [Markus et al., 2006] will be synthesized to constrain uncertainties in precipitation fields from global climate models and reanalyses products. Improving our understanding of precipitation estimates in the Arctic offers a vital step forward in assessing the variability and uncertainty in snow accumulation and hence snow depth over Arctic sea ice.

9:30am - 9:50am

Consistent CryoSat-2/Envisat Waveform Interpretation Over Sea Ice

Stephan Paul1, Stefan Hendricks1, Eero Rinne2, Robert Ricker1

1Alfred Wegener Institute, Germany; 2Finish Meteorological Institute, Finland

CryoSat-2 showcased the potential of radar altimetry for sea-ice mass balance estimation over the last years. However, its precursor altimetry missions such as Envisats’ Radar Altimeter 2 (RA2) have not been used to the same extent and success. Combining these two data sets in order to acquire a decadal data set poses a challenging task, especially due to different foot-print sizes from either pulse-limited (2-10km, Envisat-RA2) or beam-sharpened (0.3 x 1.6 km, CryoSat-2) radar acquisitions. Based on most recent surface-type classification scheme and applied retrackers from ESA’s Sea Ice Climate Change Initiative Phase 1 (SICCI1) using the Envisat-RA2 Sensor Geophysical Data Record (SGDR), the resulting sea-ice freeboard showed rather large biases and a limited overall freeboard range compared to estimates from CryoSat-2 for both hemispheres. The SICCI1 surface-type classification is solely based on rather strict pulse-peakiness thresholds, resulting in only a very limited number of classified waveforms as either leads or sea ice. Therefore, Envisat SICCI1 freeboard estimates were unable to reproduce any regional features seen in CryoSat-2 freeboard estimates during the overlap period from November 2010 to March 2012. While the range limitations partly result from the much larger RA2 footprint, the use of inconsistent surface-type classifications and retrackers between the two different sensors is likely to further enhance these differences. In the here presented study, we implemented a common surface-type classification scheme for both sensors based on pulse peakiness, leading-edge width and sea-ice backscatter. This surface-type classification scheme was iteratively tuned to fit Cryosat-2’s lead and sea-ice fractions. Furthermore, in order to achieve a consistent retracking procedure, we adapted the Threshold First Maximum Retracker Algorithm to Envisat-RA2. Based on these changes and by utilizing a new surface-roughness correction, we are for the first time able to produce a consistent freeboard data set for the overlap period of Cryosat-2 and Envisat. This new data set features a spatial resolution of 25km x 25km and 50km x 50km for the Arctic and Antarctic, respectively.

9:50am - 10:10am

Validation Of CryoSat Sea Ice Thickness Retrievals

Christian Haas1,7,9, Steve Baker2, Justin Beckers1, Malcolm Davidson3, Sinead Farrell4, Rene Forsberg5, Sebastian Gerland6, Stefan Hendricks7, Sine Munk Hvidegaard5, Jennifer King6, Robert Ricker7,8, Henriette Skourup5, Gunnar Spreen9

1York University, Canada; 2Mullard Space Sciences Lab/University College London, UK; 3ESA ESTEC, NL; 4NOAA/University of Maryland, USA; 5Danish Technical University, DK; 6Norwegian Polar Institute, NO; 7Alfred Wegener Institute, DE; 8IFREMER, FR; 9University of Bremen, DE

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.

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