ESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance, in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2, which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over “virtual” sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new lognormal retracker produces sea ice freeboards that compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multiyear ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty.

The evolution of landfast sea ice melt pond coverage, surface topography, and mass balance was studied in the Canadian Arctic during May–June 2011 and 2012, using a terrestrial laser scanner, snow and sea ice sampling, and surface meteorological characterization. Initial melt pond formation was not limited to low-lying areas, rather ponds formed at almost all premelt elevations. The subsequent evolution of melt pond coverage varied considerably between the 2 years owing to four principle, temporally variable factors. First, the range in premelt topographic relief was 0.5 m greater in 2011 (rougher surface) than in 2012 (smoother surface), such that a seasonal maximum pond coverage of 60% and maximum hydraulic head of 204 mm were reached in 2011, versus 78% and 138 mm in 2012. A change in the meltwater balance (production minus drainage) caused the ponds to spread or recede over an area that was almost 90% larger in 2012 than in 2011. Second, modification of the premelt topography was observed during mid-June, due to preferential melting under certain drainage channels. Some of the lowest-lying premelt areas were subsequently elevated above these deepening channels and unexpectedly became drained later in the season. Third, ice interior temperatures remained 1–2°C colder later into June in 2012 than in 2011, even though the ice was 0.35 m thinner at melt onset, thereby delaying permeability increases in the ice that would allow vertical meltwater drainage to the ocean. Finally, surface melt was estimated to account for approximately 62% of the net radiative flux to the sea ice cover during the melt season.

Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emits Ku-band radar waves that are assumed in publicly available sea ice thickness products to penetrate overlying snow and scatter from the ice–snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time evolution of overlying snow density. Two conventional treatments introduce systematic underestimates of up to 15 cm into ice thickness estimates and up to 10 cm into thermodynamic growth rate estimates over multi-year ice in winter. Correcting these biases would impact a wide variety of model projections, calibrations, validations and reanalyses.

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 to 2021 as the balance of export, melt, and replenishment. Overall, MYI area declined at 72,500 km²/yr; however, a majority of the loss occurred during two stepwise reductions that interrupt an otherwise balanced budget and resulted in the northward contraction 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 to 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 of older MYI has significantly declined, transitioning the MYI pack toward 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.