Description: Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150–200 km in space and 100–300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

Description: The extended multiple linear regression (eMLR) technique is used to determine changes in anthropogenic carbon in the intermediate layers of the Eurasian Basin based on occupations from four cruises between 1996 and 2015. The results show a significant increase in basin‐wide anthropogenic carbon storage in the Nansen Basin (0.44‐0.73 ± 0.14 mol C m−2 yr−1) and the Amundsen Basin (0.63‐1.04 ± 0.09 mol C m−2 yr−1). Over the last two decades, inferred changes in ocean acidification (0.020‐0.055 pH units) and calcium carbonate desaturation (0.05‐0.18 units) are pronounced and rapid. These results, together with results from carbonate‐dynamic box model simulations and 129I tracer distribution simulations, suggest that the accumulation of anthropogenic carbon in the intermediate layers of the Eurasian Basin are consistent with increasing concentrations of anthropogenic carbon in source waters of Atlantic origin entering the Arctic Ocean followed by interior transport. The dissimilar distributions of anthropogenic carbon in the interior Nansen and Amundsen Basins are likely due to differences in the lateral ventilation of the intermediate layers by the return flows and ramifications of the boundary current along the topographic boundaries in the Eurasian Basin.

Description: Improvement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for a coupled sea ice– oceanmodel based on a genetic algorithm. To take the interrelation of dynamic and thermodynamicmodel parameters into account, the system is set up to optimize 15 model parameters simultaneously. The optimization is minimizing a cost function composed of the model–observation misfit of three sea ice quantities (concentration, drift, and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990–2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013–16). The similarity of the final values of the cost function and the resulting sea ice fields from a set of 11 independent optimizations suggest that the obtained sea ice fields are close to the best possible achievable by the current model setup, which allows us to identify limitations of the model formulation. The optimized parameters are applied for a simulation with a higher-resolution model to examine a portability of the parameters. The result shows good portability, while at the same time, it shows the importance of the oceanic conditions for the portability.

Description: We developed an objective parameter optimization system for a coupled sea ice-ocean model based on a genetic algorithm. The system is set up to optimize 15 model parameters simultaneously by minimizing a cost function composed of the model-observation misfit of three sea ice quantities (concentration, drift and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990 - 2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013 - 2016). We also examined the uniqueness of the optimal parameter sets by independent optimization experiments. Regardless of the striking similarity of the values of the cost function and optimized sea ice fields, the corresponding optimal parameters exhibit a large spread, showing the existence of multiple optimal solutions. The result shows that the utilized sea ice observations, even though covering more than two decades, cannot constrain the process parameters towards an unique solution. A correlation analysis shows that the optimal parameters are inter-related and covariant. A principal component analysis reveals that the first three (six) principal components explain 70% (90%) of the total variance of the optimal parameter sets, indicating a contraction of the parameter space.

Description: The uniqueness of optimal parameter sets of an Arctic sea ice simulation is investigated. A set of parameter optimization experiments is performed using an automatic parameter optimization system, which simultaneously optimizes 15 dynamic and thermodynamic process parameters. The system employs a stochastic approach (genetic algorithm) to find the global minimum of a cost function. The cost function is defined by the model–observation misfit and observational uncertainties of three sea ice properties (concentration, thickness, drift) covering the entire Arctic Ocean over more than two decades. A total of 11 independent optimizations are carried out to examine the uniqueness of the minimum of the cost function and the associated optimal parameter sets. All 11 optimizations asymptotically reduce the value of the cost functions toward an apparent global minimum and provide strikingly similar sea ice fields. The corresponding optimal parameters, however, exhibit a large spread, showing the existence of multiple optimal solutions. The result shows that the utilized sea ice observations, even though covering more than two decades, cannot constrain the process parameters toward a unique solution. A correlation analysis shows that the optimal parameters are interrelated and covariant. A principal component analysis reveals that the first three (six) principal components explain 70% (90%) of the total variance of the optimal parameter sets, indicating a contraction of the parameter space. Analysis of the associated ocean fields exhibits a large spread of these fields over the 11 optimized parameter sets, suggesting an importance of ocean properties to achieve a dynamically consistent view of the coupled sea ice–ocean system.

Description: In September 2019, the research icebreaker Polarstern started the largest multidisciplinary Arctic expedition to date, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to an ice floe for a whole year, thus including the winter season, the declared goal of the expedition is to better understand and quantify relevant processes within the atmosphere–ice–ocean system that impact the sea ice mass and energy budget, ultimately leading to much improved climate models. Satellite observations, atmospheric reanalysis data, and readings from a nearby meteorological station indicate that the interplay of high ice export in late winter and exceptionally high air temperatures resulted in the longest ice-free summer period since reliable instrumental records began. We show, using a Lagrangian tracking tool and a thermodynamic sea ice model, that the MOSAiC floe carrying the Central Observatory (CO) formed in a polynya event north of the New Siberian Islands at the beginning of December 2018. The results further indicate that sea ice in the vicinity of the CO (〈40 km distance) was younger and 36 % thinner than the surrounding ice with potential consequences for ice dynamics and momentum and heat transfer between ocean and atmosphere. Sea ice surveys carried out on various reference floes in autumn 2019 verify this gradient in ice thickness, and sediments discovered in ice cores (so-called dirty sea ice) around the CO confirm contact with shallow waters in an early phase of growth, consistent with the tracking analysis. Since less and less ice from the Siberian shelves survives its first summer (Krumpen et al., 2019), the MOSAiC experiment provides the unique opportunity to study the role of sea ice as a transport medium for gases, macronutrients, iron, organic matter, sediments and pollutants from shelf areas to the central Arctic Ocean and beyond. Compared to data for the past 26 years, the sea ice encountered at the end of September 2019 can already be classified as exceptionally thin, and further predicted changes towards a seasonally ice-free ocean will likely cut off the long-range transport of ice-rafted materials by the Transpolar Drift in the future. A reduced long-range transport of sea ice would have strong implications for the redistribution of biogeochemical matter in the central Arctic Ocean, with consequences for the balance of climate-relevant trace gases, primary production and biodiversity in the Arctic Ocean.

Description: 129I measurements on samples collected during GEOTRACES oceanographic missions in the Arctic Ocean in 2015 have provided the first detailed, synoptic 129I sections across the Eurasian, Canada and Makarov Basins. During the 1990s, increased discharges of 129I from European nuclear fuel reprocessing plants produced a large, tracer spike whose passage through the Arctic Ocean has been followed by 129I time series measurements over the past 25 years. Elevated 129I levels measured over the Lomonosov and Alpha-Mendeleyev Ridges in 2015 were associated with tracer labeled, Atlantic-origin water bathymetrically steered by the ridge systems through the central Arctic while lower 129I levels were evident in the more poorly ventilated basin interiors. 129I levels of 200-400 x 107 at/l measured in intermediate waters in 2015 had increased by a factor of 10 compared to results from the same locations in 1994-1996 owing to the circulation of the 1990s, 129I input spike mainly associated with enhanced discharges from the La Hague nuclear fuel reprocessing plant. Comparisons of the patterns of 129I distributions between the mid-1990s and 2015 delineate large scale circulation changes that occurred during the shift from a positive Arctic Oscillation and a cyclonic circulation regime in the mid-1990s to anticyclonic circulation in 2015. The latter is characterized by a broadened Beaufort Gyre in the upper ocean, a weakened boundary current and partial mid-depth, AW flow reversal in the southern Canada Basin. Tracer 129I simulations using the applied circulation model, NAOSIM agree with both historical 129I results and recent GEOTRACES data sets, thereby lending context and credibility to the interpretation of large scale changes in arctic circulation and their relationship to shifts in climate indices revealed by tracer 129I distributions. This paper reports measurements and simulation results for 129I for the 1990s and 2015, and interprets them in the context of ocean circulation responses to changing atmospheric forcing regimes.

Description: When the air is very cold, water at the surface of the ocean freezes, forming sea ice. Parts of the Arctic Ocean are covered by sea ice during the entire year. Often, snow falls onto the sea ice. Despite the cold, many plants and animals can live in the Arctic Ocean, some in the water, and some even in the sea ice. Particularly, algae can live in small bubbles in the sea ice. Like other plants, algae need energy to grow. This energy comes from food and sunlight. But how can the sunlight reach these little algae living inside the sea ice? From the sun, the light must pass through the atmosphere, the snow, and finally the sea ice itself. In this article, we describe how ice algae can live in this special environment and we explain what influences how much light reaches the algae to make them grow.

Description: Arctic sea ice is shifting from a year-round to a seasonal sea ice cover. This substantial transformation, via a reduction in Arctic sea ice extent and a thinning of its thickness, influences the amount of light entering the upper ocean. This in turn impacts under-ice algal growth and associated ecosystem dynamics. Field campaigns have provided valuable insights as to how snow and ice properties impact light penetration at fixed locations in the Arctic, but to understand the spatial variability in the under-ice light field there is a need to scale up to the pan-Arctic level. Combining information from satellites with state-of-the-art parameterizations is one means to achieve this. This study combines satellite and modeled data products to map under-ice light on a monthly time-scale from 2011 through 2018. Key limitations pertain to the availability of satellite-derived sea ice thickness, which for radar altimetry, is only available during the sea ice growth season. We clearly show that year-to-year variability in snow depth, along with the fraction of thin ice, plays a key role in how much light enters the Arctic Ocean. This is particularly significant in April, which in some regions, coincides with the beginning of the under-ice algal bloom, whereas we find that ice thickness is the main driver of under-ice light availability at the end of the melt season in October. The extension to the melt season due to a warmer Arctic means that snow accumulation has reduced, which is leading to positive trends in light transmission through snow. This, combined with a thinner ice cover, should lead to increased under-ice PAR also in the summer months.