The Arctic sea-ice-scape is rapidly transforming. Increasing light penetration will initiate earlier seasonal primary production. This earlier growing season may be accompanied by an increase in ice algae and phytoplankton biomass, augmenting the emission of dimethylsulfide and capture of carbon dioxide. Secondary production may also increase on the shelves, although the loss of sea ice exacerbates the demise of sea-ice fauna, endemic fish and megafauna. Sea-ice loss may also deliver more methane to the atmosphere, but warmer ice may release fewer halogens, resulting in fewer ozone depletion events. The net changes in carbon drawdown are still highly uncertain. Despite large uncertainties in these assessments, we expect disruptive changes that warrant intensified long-term observations and modelling efforts.

Over the polar oceans, near-surface atmospheric transport of momentum is strongly influenced by sea-ice surface topography. The latter is analyzed on the basis of laser altimeter data obtained during airborne campaigns between 1995 and 2011 over more than 10,000 km of flight distance in different regions of the Arctic Ocean. Spectra of height and spacing between topographic features averaged over 10 km flight sections show that typical values are 0.45 m for the mean height and about 20 m for the mean spacing. Nevertheless, the variability is high and the spatial variability is stronger than the temporal one. The total topography spectrum is divided into a range with small obstacles (between 0.2 m and 0.8 m height) and large obstacles ( 0.8 m). Results show that large pressure ridges represent the dominant topographic feature only along the coast of Greenland. In the Central Arctic, the concentration of large ridges decreased over the years, accompanied by an increase of small obstacles concentration and this might be related to decreasing multiyear ice. The application of a topography-dependent parameterization of neutral atmospheric drag coefficients reflects the large variability in the sea-ice topography and reveals characteristic differences between the regions. Based on the analysis of the two spectral ranges, we find that the consideration of only large pressure ridges is not enough to characterize the roughness degree of an ice field, and the values of drag coefficients are in most regions strongly influenced by small obstacles.

Historical sea ice core chlorophyll‐a (Chla) data are used to describe the seasonal, regional, and vertical distribution of ice algal biomass in Antarctic landfast sea ice. The analyses are based on the Antarctic Fast Ice Algae Chlorophyll‐a data set, a compilation of currently available sea ice Chla data from landfast sea ice cores collected at circum‐Antarctic nearshore locations between 1970 and 2015. Ice cores were typically sampled from thermodynamically grown first‐year ice and have thin snow depths (mean = 0.052 ± 0.097 m). The data set comprises 888 ice cores, including 404 full vertical profile cores. Integrated ice algal Chla biomass (range: <0.1–219.9 mg/m2, median = 4.4 mg/m2, interquartile range = 9.9 mg/m2) peaks in late spring and shows elevated levels in autumn. The seasonal Chla development is consistent with the current understanding of physical drivers of ice algal biomass, including the seasonal cycle of irradiance and surface temperatures driving landfast sea ice growth and melt. Landfast ice regions with reported platelet ice formation show maximum ice algal biomass. Ice algal communities in the lowermost third of the ice cores dominate integrated Chla concentrations during most of the year, but internal and surface communities are important, particularly in winter. Through comparison of biomass estimates based on different sea ice sampling strategies, that is, analysis of full cores versus bottom‐ice section sampling, we identify biases in common sampling approaches and provide recommendations for future survey programs: for example, the need to sample fast ice over its entire thickness and to measure auxiliary physicochemical parameters.

Light transmission through sea ice is a critical process for energy partitioning at the polar atmosphere‐ice‐ocean boundary. Transmission of sunlight strongly impacts sea ice melting by absorption, as well as heat deposition, and primary productivity in the upper ocean. While earlier observations relied on a limited number of point observations, the recent years have seen an increase in spatially distributed light measurements underneath sea ice using remotely operated vehicles covering a wide range of ice conditions. These measurements allow us to reconstruct the seasonal evolution of the spatial variability in light transmission. Here we present measurements of sea ice light transmittance distributions from 6 years of Arctic under‐ice remotely operated vehicle operations. The data set covers the entire melt period of Central Arctic sea ice. Data are combined into a pseudo time series describing the seasonal evolution of the spatial variability of sea ice optical properties from spring to autumn freezeup. In spring, snowmelt increases light transmission continuously, until a secondary mode originating from translucent melt ponds appears in the histograms of light transmittance. This secondary mode persists long into autumn, before snowfall reduces overall light levels again. Comparison to several autonomous time series measurements from single locations confirms the detected general patterns of the seasonal evolution of light transmittance variability. This also includes characteristic spectral features caused by biological processes at the ice underside. The results allow for the evaluation of three different light transmittance parameterizations, implying that light transmission in current ice‐ocean models may not be accurately represented on large scales throughout all seasons while ice thickness alone is a poor predictor of light transmittance.

There is mounting evidence that multiyear ice (MYI) is a unique component of the Arctic Ocean and may play a more important ecological role than previously assumed. This study improves our understanding of the potential of MYI as a suitable habitat for sea ice algae on a pan-Arctic scale. We sampled sea ice cores from MYI and first-year sea ice (FYI) within the Lincoln Sea during four consecutive spring seasons. This included four MYI hummocks with a mean chl a biomass of 2.0 mg/m 2 , a value significantly higher than FYI and MYI refrozen ponds. Our results support the hypothesis that MYI hummocks can host substantial ice-algal biomass and represent a reliable ice-algal habitat due to the (quasi-) permanent lowsnow surface of these features. We identified an ice-algal habitat threshold value for calculated light transmittance of 0.014%. Ice classes and coverage of suitable ice-algal habitat were determined from snow and ice surveys. These ice classes and associated coverage of suitable habitat were applied to pan-Arctic CryoSat-2 snow and ice thickness data products. This habitat classification accounted for the variability of the snow and ice properties and showed an areal coverage of suitable icealgal habitat within the MYI-covered region of 0.54 million km 2 (8.5% of total ice area). This is 27 times greater than the areal coverage of 0.02 million km 2 (0.3% of total ice area) determined using the conventional block-model classification, which assigns single-parameter values to each grid cell and does not account for subgrid cell variability. This emphasizes the importance of accounting for variable snow and ice conditions in all sea ice studies. Furthermore, our results indicate the loss of MYI will also mean the loss of reliable ice-algal habitat during spring when food is sparse and many organisms depend on ice-algae. K E Y W O R D SCryoSat-2, habitat classification, hockey stick regression, light transmittance, piecewise regression, sea ice algae, sea ice bio-optics, the Last Ice AreaThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.