Fram Strait is the primary pathway for sea ice export from the Arctic Ocean, yet estimates of volume export are constrained by observations of ice thickness and drift. Using a new year-round CryoSat-2 ice thickness product we determine an average annual export of 1,712 ± 452 km^3 from 2011-2022. 15% of the Arctic Oceans sea ice volume is exported annually, while 3.2% of the volume lost during the melt season is exported. Comparing high- and low-resolution ice drift products reveals the latter underestimate export by 30%. Comparing volume export between 82°N and 79°N reveals a high melt rate of 1 cm d-1, reducing export by 53%. September sea ice volume declines by 286 km^3 for every 100 km^3 exported during summer, highlighting how export amplifies the ice-albedo feedback. Our estimates of volume export provide new insight into Fram Straits role as a sea ice sink and freshwater source.

Rapidly shrinking Arctic sea ice has had significant impacts on the Arctic Ocean and many outer Arctic regions. It is therefore urgently needed to reliably estimate Arctic sea-ice thickness (SIT) by combined use of available observation and numerical modeling. Here, for the first time, we assimilate the latest CryoSat-2 summer SIT data into a coupled ice-ocean model. In particular, an Incremental Analysis Update scheme is implemented to overcome the discontinuity brought by assimilating biweekly SIT and daily sea ice concentration data. Along with an improvement in sea ice volume, our SIT estimates have smaller errors than that without SIT assimilation in areas where the sea ice is roughest and experiences strong deformation, e.g., around the Fram Strait and Greenland. This study suggests that the newly-developed CryoSat-2 SIT product, when assimilated properly with our approach, has great potential for Arctic sea ice simulation and prediction.

Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from CryoSat-2 from 2011–2020, are assimilated into the GFDL ocean–sea ice model. The model’s SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to the early addition of SIC assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions.

The loss of multiyear sea ice (MYI) in the Arctic Ocean is a significant change that affects all facets of the Arctic environment. Using a lagrangian ice age product we examine MYI loss and quantify the annual MYI area budget from 1980-2021 as the balance of export, melt and replenishment. Overall, MYI area declined at 72,500 km^2/yr, however a majority of the loss occurred during two stepwise reductions that interrupt an otherwise balanced budget and resulted in northward contractions of the MYI pack. First, in 1989, a change in atmospheric forcing led to a +56% anomaly in MYI export through Fram Strait. The second occurred from 2006-2008 with anomalously high melt (+25%) and export (+23%) coupled with low replenishment (-8%). In terms of trends, melt has increased since 1989, particularly in the Beaufort Sea, export has decreased since 2008 due to reduced MYI coverage north of Fram Strait, and replenishment has increased over the full time series due to a negative feedback that promotes seasonal ice survival at higher latitudes exposed by MYI loss. However, retention to older MYI has significantly declined, transitioning the MYI pack towards younger MYI that is less resilient than previously anticipated and could soon elicit another stepwise reduction. We speculate that future MYI loss will be driven by increased melt and reduced replenishment, both of which are enhanced with continued warming and will one day render the Arctic Ocean free of MYI, a change that will coincide with a seasonally ice-free Arctic Ocean.

In the summer of 2020, ESA changed the orbit of CryoSat-2 to align periodically with NASA’s ICESat-2 mission, a campaign known as CRYO2ICE, which allows for near-coincident CryoSat-2 and ICESat-2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat-2 and ICESat-2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along-track snow depth observations can provide high-resolution snow depth distributions which are vital for air-ice-ocean heat and momentum transfer, understanding light transmission, and snow-ice-interactions. Generally, ICESat-2 is backscattered at a surface above the elevation of the CryoSat-2 signal. CRYO2ICE snow depths are thinner than the daily model- or passive-microwave-based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite-derived and model-based snow estimates show similar seasonal accumulation over FYI, but CRYO2ICE has limited seasonal accumulation over MYI which is linked to a slow increase in ICESat-2, and to some extent CryoSat-2, freeboards. We present a first estimation of along-track snow depth estimates with average uncertainty of 9 +/- 3 cm for 7-km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along-track dual-frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research. 

Operation Icebridge (OIB) 2016 level 1b Airborne Ku-band Synthetic Aperture Radar waveforms, concurrent with both OIB lidar data and Environment and Climate Change Canada in situ data, over snow on arctic sea ice, near Eureka, Canada. Footprint-scale (~16 m × 4.5 m) snow depths are estimated and compared to in situ measurements. Relative return powers and energies of the air-snow and snow-ice interface are estimated, as well as footprint-scale roughness and slope. Quasi-specular scattering (higher return powers/energies) are observed for smoother, flatter footprints. In addition to this, satellite data is discussed, including Cryosat-2 SARIn.

A growing number of studies are concluding that the resilience of the Arctic sea ice cover in a warming climate is essentially controlled by its thickness. Satellite radar and laser altimeters have allowed us to routinely monitor sea ice thickness across most of the Arctic Ocean for several decades. However, a key uncertainty remaining in the sea ice thickness retrieval is the error on the sea surface height (SSH) which is conventionally interpolated at ice floes from a limited number of lead observations along the altimeter’s orbital track. Here, we use an objective mapping approach to determine sea surface height from all proximal lead samples located on the orbital track and from adjacent tracks within a neighborhood of 10s of kilometers. The patterns of the SSH signal’s zonal, meridional, and temporal decorrelation length scales are obtained by analyzing the covariance of historic CryoSat-2 Arctic lead observations, which match the scales obtained from an equivalent analysis of high-resolution sea ice-ocean model fields. We use these length scales to determine an optimal SSH and error estimate for each sea ice floe location. By exploiting leads from adjacent tracks, we can increase the SSH precision estimated at orbital crossovers by a factor of three. In regions of high SSH uncertainty, biases in CryoSat-2 sea ice freeboard can be reduced by 25% with respect to coincident airborne validation data. The new method is not restricted to a particular sensor or mode, so it can be generalized to all present and historic polar altimetry missions.

Historically multiyear sea ice (MYI) covered a majority of the Arctic and circulated through the Beaufort Gyre for years. However, increased ice melt in the Beaufort Sea during the early-2000s was proposed to have severed this circulation. Constructing a regional MYI budget from 1997-2021 reveals that MYI import into the Beaufort Sea has increased year-round, yet less MYI now survives through summer and is transported onwards in the Gyre. Annual average MYI loss quadrupled over the study period and increased from ~7% to ~33% of annual Fram Strait MYI export, while the peak in 2018 (385,000 km^2) was similar to Fram Strait MYI export. An accelerating ice-albedo feedback coupled with dynamic conditioning towards younger thinner MYI is responsible for the increased MYI loss. MYI transport through the Beaufort Gyre has not been severed, but it has been reduced so severely to prevent it from being redistributed throughout the Arctic Ocean