Knowledge of contemporaneous snow depth on Arctic sea ice is important both to constrain the regional climatology and to improve the accuracy of satellite altimeter estimates of sea ice thickness. We assess new data available from the NASA Operation IceBridge snow radar instrument and derive snow depth estimates across the western Arctic ice pack using a novel methodology based on wavelet techniques that define the primary reflecting surfaces within the snow pack. We assign uncertainty to the snow depth estimates based upon both the radar system parameters and sea ice topographic variability. The accuracy of the airborne snow depth estimates are examined via comparison with coincident measurements gathered in situ across a range of ice types in the Beaufort Sea. We discuss the effect of surface morphology on the derivation, and consequently the accuracy, of airborne snow depth estimates. We find that snow depths derived from the airborne snow radar using the wavelet-based technique are accurate to 1 cm over level ice. Over rougher surfaces including multiyear and ridged ice, the radar system is impacted by ice surface morphology. Across basin scales, we find the snow-radar-derived snow depth on first-year ice is at least approximate to 60% of the value reported in the snow climatology for the Beaufort Sea, Canada Basin, and parts of the central Arctic, since these regions were previously dominated by multiyear ice during the measurement period of the climatology. Snow on multiyear ice is more consistent with the climatology.

Only in the past eight years has the feasibility of using satellite-borne altimeters to estimate sea ice freeboard and thickness been demonstrated, and these estimates still have uncertainties primarily associated with limited knowledge of snow loading on sea ice. Because accurate estimates of Arctic-wide sea ice thickness and volume are fundamental inputs to global climate models, validation of satellite-derived thickness estimates using independent data is required. A detailed assessment of freeboard retrieved by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud, and land Elevation Satellite has been carried out using high-resolution laser altimetry from the National Aeronautics and Space Administration's Airborne Topographic Mapper (ATM), the Delay-Doppler radar altimeter, and digital photography collected along a 300-km segment of sea ice in the Canada Basin. Exploiting the repeat coverage of the aircraft flight line, a correction was applied to GLAS footprint geolocations to adjust for sea ice drift that occurred during the time between satellite and aircraft acquisitions. Comparisons of GLAS and ATM measurements over sea ice show excellent agreement (about a 0.00-m mean) with no apparent bias between data sets. Freeboard estimates were examined using data from GLAS and ATM independently, employing measurements over refrozen leads to estimate local sea surface heights (SSHs). The results demonstrate the sensitivity of freeboard and thickness calculations to an accurate estimation of local SSH. Snow depth derived by differencing laser and radar data was combined with the freeboard estimates to yield a mean sea ice thickness of ~ 5.5 m over a 250-km subsection of the flight track.

Precise mappings of sea surface topography, slope, and gravity of the Arctic Ocean are derived from altimeter data collected by Envisat and ICESat. Both altimeters measured instantaneous sea surface height at leads in the sea ice. To reduce contamination by ice‐freeboard signal and tracker noise in Envisat height data, a retracking of the waveform data was performed. Analogous reprocessing of ICESat data was also done. Arctic mean sea surfaces (MSSs) were computed from Envisat data spanning 2002–2008 and ICESat data spanning 2003–2009. Farrell et al. (2012) used these “ICEn” MSSs to estimate mean dynamic topography (MDT). These same Envisat and ICESat data are used, in sea‐surface‐slope form, to compute the ARCtic Satellite‐only (ARCS‐2) altimetric marine gravity field. ARCS‐2 extends north to 86°N and uses GRACE/GOCE gravity data (GOCO02S) for its long‐wavelength (>260 km) components. Use of Envisat data improves the spatial resolution over that of existing Arctic marine gravity fields in many areas. ARCS‐2's spatial resolution aids in tracing tectonic fabric—e.g., extinct plate boundaries—over broad areas of the Arctic basin whose tectonic origin remains a mystery. ARCS‐2's precision is validated using NASA 2010/2011 Operation IceBridge (OIB) airborne gravimetry. ARCS‐2 and OIB gravity along with ICEn‐MSS results are employed to locate short‐wavelength errors approaching 1 m in current Arctic marine geoids (EGM2008). Precise OIB airborne gravity corroborates that such errors in current geoid/gravity models are widespread in Arctic areas lacking accurate surface gravity data. These geoid errors limit the spatial resolution at which MDT can be mapped.

We present a first assessment of airborne laser and radar altimeter data over snow-covered sea ice, gathered during the National Aeronautics and Space Administration Operation IceBridge Mission. We describe a new technique designed to process radar echograms fromthe University of Kansas snow radar to estimate snow depth. We combine IceBridge laser altimetry with radar-derived snow depths to determine sea ice thickness. Results are validated through comparison with direct measurements of snow and ice thickness collected in situ at the Danish GreenArc 2009 sea ice camp located on fast ice north of Greenland. The IceBridge instrument suite provides accurate measurements of snow and ice thickness, particularly over level ice. Mean IceBridge snow and ice thickness agree with in situ measurements to within similar to 0.01 and similar to 0.05 m, respectively, while modal snow and ice thickness estimates agree to within 0.02 and 0.10 m, respectively. IceBridge snow depths were correlated with in situ measurements (R = 0.7, for an averaging length of 55 m). The uncertainty associated with the derived IceBridge sea ice thickness estimates is 0.40 m. The results demonstrate the retrieval of both first-year and multiyear ice thickness from IceBridge data. The airborne data were however compromised in heavily ridged ice where snow depth, and hence ice thickness, could not be measured. Techniques developed as part of this study will be used for routine processing of IceBridge retrievals over Arctic sea ice. The limitations of the GreenArc study are discussed, and recommendations for future validation of airborne measurements via field activities are provided.

The geopotential coefficients of a number of recent and past models have been examined with the aim of calibrating their formal (or published) errors, principally by direct comparison with the same coefficients of a precise and wholly independent satellite-only reference field determined (in 2010) predominantly from over 7 years of μ/s low–low GRACE inter-satellite range rate observations (ITG-GRACE2010S). In all of these comparisons the reference field used, over specified spectral ranges, has much smaller reported errors than the ones to be calibrated. In particular we find that a recently published field (in 2010) using gravity gradient and position data from ESA’s GOCE satellite, GO_CONS_GCF_2_TIM_R1, has formal errors which are significantly optimistic for the lowest degrees (n) but with increasing realism where the gradiometer gains influence over the position information (15 < n < 120). Other GOCE models do not afford an unbiased error calibration. Validating error calibration with the independent reference model, of two past comprehensive fields (containing both satellite and surface data), confirms that one of them (JGM3, published in 1994, complete to n = 70) reported generally realistic formal errors while another (EGM96, published in 1998, complete to n = 360, tested only for n < 150) had significantly optimistic ones for most n < 100 but with better realism when affected only by the surface information.

ICESat and Envisat altimetry data provide measurements of the instantaneous sea surface height (SSH) across the Arctic Ocean, using lead and open water elevation within the sea ice pack. First, these data were used to derive two independent mean sea surface (MSS) models by stacking and averaging along‐track SSH profiles gathered between 2003 and 2009. The ICESat and Envisat MSS data were combined to construct the high‐resolution ICEn MSS. Second, we estimate the 5.5‐year mean dynamic topography (MDT) of the Arctic Ocean by differencing the ICEn MSS with the new GOCO02S geoid model, derived from GRACE and GOCE gravity. Using these satellite‐only data we map the major features of Arctic Ocean dynamical height that are consistent with in situ observations, including the topographical highs and lows of the Beaufort and Greenland Gyres, respectively. Smaller‐scale MDT structures remain largely unresolved due to uncertainties in the geoid at short wavelengths.

The past few years have seen the emergence of satellite altimetry as valuable tool for taking quantitative sea ice monitoring beyond the traditional surface extent measurements and into estimates of sea ice thickness and volume, parameters that arc fundamental to improved understanding of polar dynamics and climate modeling. Several studies have now demonstrated the use of both microwave (ERS, Envisat/RA-2) and laser (ICESat/GLAS) satellite altimeters for determining sea ice thickness. The complexity of polar environments, however, continues to make sea ice thickness determination a complicated remote sensing task and validation studies remain essential for successful monitoring of sea ice hy satellites. One such validation effort, the Arctic Aircraft Altimeter (AAA) campaign of2006. included underflights of Envisat and ICESat north of the Canadian Archipelago using NASA's P-3 aircraft. This campaign compared Envisat and ICESat sea ice elevation measurements with high-resolution airborne elevation measurements, revealing the impact of refrozen leads on radar altimetry and ice drift on laser altimetry. Continuing this research and validation effort, the Canada Basin Sea Ice Thickness (CBSIT) experiment was completed in April 2009. CBSIT was conducted by NOAA. and NASA as part of NASA's Operation Ice Bridge, a gap-filling mission intended to supplement sea and land ice monitoring until the launch of NASA's ICESat-2 mission. CBIST was flown on the NASA P-3, which was equipped with a scanning laser altimeter, a Ku-band snow radar, and un updated nadir looking photo-imaging system. The CB5IT campaign consisted of two flights: an under flight of Envisat along a 1000 km track similar to that flown in 2006, and a flight through the Nares Strait up to the Lincoln Sea that included an overflight of the Danish GreenArc Ice Camp off the coast of northern Greenland. We present an examination of data collected during this campaign, comparing airborne laser altimeter measurements with (1) Envisat RA-2 returns retracked optimally for sea ice and (2) in situ measurements of sea ice thickness and snow depth gathered from ice camp surveys. Particular attention is given to lead identification and classification using the continuous photo-imaging system along the Envisat underflight as well as the performance of the snow radar over the ice camp survey lines.

Using data from the first Earth‐orbiting laser altimeter, the Geoscience Laser Altimeter System (GLAS), onboard the Ice, Cloud and land Elevation Satellite (ICESat), we analyze sea ice freeboard in the Arctic Ocean. We describe a new method for sea surface height retrieval, which relies on an algorithm that discriminates laser pulse reflections originating over leads or thin ice. The lead detection algorithm is based on surface reflectivity and analysis of parameters associated with the shape of reflected waveforms. Using knowledge of the local sea surface height and sea ice elevation, we estimate sea ice freeboard and present, for the first time, a time series of Arctic freeboard spanning 5 years between March 2003 and 2008. While the autumn (October–November) and winter (February–March) data illustrate the seasonal and interannual variations in freeboard, following the September 2007 record minimum sea ice extent, the autumn 2007 and winter 2008 spatially averaged freeboards are below the seasonal means at −4.5 cm and −6.8 cm, respectively. Over the observation period, mean freeboard has declined at a rate of ∼−1.8 cm/a during the autumn period and ∼−1.6 cm/a during the winter period, in the region bounded by the northern limit of ICESat coverage at 86°N. Because of the short 5‐year observation period, it is unclear whether these results represent a long‐term, downward trend in Arctic freeboard or are part of a natural variability. Furthermore, since the variability of snow thickness is included in the ICESat freeboard signal, a decrease in the freeboard cannot wholly be attributed to a decrease in sea ice thickness.

Sea ice thickness is a crucial, but very undersampled cryospheric parameter of fundamental importance for climate modeling. Advances in satellite altimetry have enabled the measurement of sea ice freeboard using satellite microwave altimeters. Unfortunately, validation of these new techniques has suffered from a lack of ground truth measurements. Therefore, an airborne campaign was carried out in March 2006 using laser altimetry and photo imagery to validate sea ice elevation measurements derived from the Envisat/RA-2 microwave altimeter.We present a comparative analysis of Envisat/RA-2 sea ice elevation processing with collocated airborne measurements collected north of the Canadian Archipelago. Consistent overall relationships between block-averaged airborne laser and Envisat elevations are found, over both leads and floes, along the full 1300 km aircraft track. The fine resolution of the airborne laser altimeter data is exploited to evaluate elevation variability within the RA-2 ground footprint. Our analysis shows good agreement between RA-2 derived sea ice elevations and those measured by airborne laser altimetry, particularly over refrozen leads where the overall mean difference is about I cm. Notwithstanding this small 1 cm mean difference. we identify a larger elevation uncertainty (of order 10 cm) associated with the uncertain location of dominant radar targets within the particular RA-2 footprint. Sources of measurement uncertainty or ambiguity are identified, and include snow accumulation, tracking noise, and the limited coverage of airborne measurements. Published by Elsevier Inc.

Bathymetric charts are essential for modeling oceanic processes, yet, in remote areas, direct measurements of seafloor depth are often scarce. It is possible to augment sparse depth soundings with dense satellite-derived gravity data to provide additional bathymetric detail in regions devoid of sounding data. We demonstrate this method by using marine gravity derived from the European Remote Sensing (ERS-1) satellite altimeter, combined with depth soundings, to form a bathymetric prediction of the Amundsen Sea, West Antarctica. We estimate the root mean square error of depth estimates at unsurveyed locations in our solution to be similar to 120 m. We use a Monte Carlo method to assess the value of gravity as a bathymetric predictor in sparsely surveyed regions by comparing our solution to predictions formed from depth soundings alone. When less than similar to 11% of 10-km grid cells contain depth soundings, inclusion of gravity data improves the depth accuracy of the solution by up to 17%, as compared to a minimum curvature surface interpolation of the depth soundings alone. When depth data are sparse, our gravity-derived prediction reveals additional short-wavelength bathymetric features, such as troughs on the continental shelf, which are not resolved by interpolations of the depth soundings alone.