Thomas W. K. Armitage

Using the Interferometric Capabilities of the ESA CryoSat-2 Mission to Improve the Accuracy of Sea Ice Freeboard Retrievals

A significant source of error in the retrieval of sea ice freeboard from pulse-limited radar altimeters arises when scattering from off-nadir leads dominates the power echo causing the onboard tracker to “snag” and overestimate the two-way travel time. This range overestimate translates into an ocean elevation underestimate relative to the ice surface and an overestimate of the sea ice freeboard. We demonstrate using interferometric CryoSat-2 data that it is possible to infer the across-track angle of return to off-nadir leads and their location in the CryoSat-2 footprint and hence correct for the associated range error for the first time. It is found that specular scattering from leads can dominate the radar echo some 1530 m off nadir. Over the region studied, the mean ocean elevation bias is closely associated with the “pulse peakiness” (PP) parameter used for identifying specular waveforms. Considering only the most specular waveforms, the elevation bias was measured to be -1.21 ±0.93 cm. However, lowering the PP threshold includes lower power waveforms originating from higher off-nadir angles, and the elevation bias becomes -4.06 ±1.66 cm. Unaccounted for, these biases represent an ~ 10-40-cm overestimate in ice thickness. Despite the relatively large error on the mean bias, correcting for off-nadir ranging contributes only a small amount to the elevation uncertainty when combined with range and orbit determination uncertainties. We found that making use of CryoSat-2s interferometric mode over sea ice ultimately decreases the uncertainty on the area-averaged ocean elevation by allowing the inclusion of more waveforms in the analysis.

A high‐resolution record of Greenland mass balance

We map recent Greenland Ice Sheet elevation change at high spatial (5 km) and temporal (monthly) resolution using CryoSat-2 altimetry. After correcting for the impact of changing snowpack properties associated with unprecedented surface melting in 2012, we find good agreement (3 cm/yr bias) with airborne measurements. With the aid of regional climate and firn modeling, we compute high spatial and temporal resolution records of Greenland mass evolution, which correlate (R = 0.96) with monthly satellite gravimetry and reveal glacier dynamic imbalance. During 2011–2014, Greenland mass loss averaged 269 ± 51 Gt/yr. Atmospherically driven losses were widespread, with surface melt variability driving large fluctuations in the annual mass deficit. Terminus regions of five dynamically thinning glaciers, which constitute less than 1% of Greenland’s area, contributed more than 12% of the net ice loss. This high-resolution record demonstrates that mass deficits extending over small spatial and temporal scales have made a relatively large contribution to recent ice sheet imbalance.

Arctic sea ice freeboard from AltiKa and comparison with CryoSat‐2 and Operation IceBridge

Satellite radar altimeters have improved our knowledge of Arctic sea ice thickness over the past decade. The main sources of uncertainty in sea ice thickness retrievals are associated with inadequate knowledge of the snow layer depth and the radar interaction with the snow pack. Here we adapt a method of deriving sea ice freeboard from CryoSat-2 to data from the AltiKa Ka band radar altimeter over the 2013–14 Arctic sea ice growth season. AltiKa measures basin-averaged freeboards between 4.4 cm and 6.9 cm larger than CryoSat-2 in October and March, respectively. Using airborne laser and radar measurements from spring 2013 and 2014, we estimate the effective scattering horizon for each sensor. While CryoSat-2 echoes penetrate to the ice surface over first-year ice and penetrate the majority (82 ± 3%) of the snow layer over multiyear ice, AltiKa echoes are scattered from roughly the midpoint (46 ± 5%) of the snow layer over both ice types.

Meteorological Origin of the Static Crossover Pattern Present in Low-Resolution-Mode CryoSat-2 Data Over Central Antarctica

The most effective way of determining the rate of elevation change of the Earths large ice sheets using radar altimeters is to examine the difference in the elevation measured on ascending and descending orbits. This crossover difference has a static and time-varying component, and by isolating the time-varying part, one can construct a time series of the ice sheet elevation change. The static component of the crossover difference arises as a result of an anisotropic dependence of the extinction coefficient on the angle between the radar polarization and wind-induced features of the firn. Here, the static crossover difference observed by CryoSat-2 over the Antarctic ice sheet is examined, and a simple model is developed to explain the observed pattern. There is an excellent agreement between the modeled results and the observations, calling into question the results of previous studies of the same phenomenon with different radar altimeters.

An Assessment of State‐of‐the‐Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

State-of-the-art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along-track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer-term, multi-sensor oceanographic time-series of sea level change in the Arctic. This study focuses on an assessment of five state-of-the-art Arctic MSS models (UCL13/04, DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, inter-satellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat-2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03-0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03-0.06 m.

Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards

Snow depth on sea ice remains one of the largest uncertainties in sea ice thickness retrievals from satellite altimetry. Here we outline an approach for deriving snow depth that can be applied to any coincident freeboard measurements after calibration with independent observations of snow and ice freeboard. Freeboard estimates from CryoSat-2 (Ku-band) and AltiKa (Ka-band) are calibrated against data from NASA’s Operation IceBridge (OIB) to align AltiKa to the snow surface and CryoSat-2 to the 5 ice/snow interface. Snow depth is found as the difference between the two calibrated freeboards, with a correction added for the slower speed of light propagation through snow. We perform an initial evaluation of our derived snow depth product against OIB snow depth data by excluding successive years of OIB data from the analysis. We find a root-mean-square deviation of 7.7, 5.3, 5.9 and 6.7 cm between our snow thickness product and OIB data from the springs of 2013, 2014, 2015 and 2016 respectively. We further demonstrate the applicability of the method to ICESat and Envisat, offering promising potential for 10 the application to CryoSat-2 and ICESat-2, when ICESat-2 is launched in 2018.