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.

Description: Pacific Water (PW) enters the Arctic Ocean through Bering Strait and brings heat, fresh water and nutrients from the northern Bering Sea. The circulation of PW in the central Arctic Ocean is only partially understood due to the lack of observations. In this paper pathways of PW are investigated using simulations with six state-of-the art regional and global Ocean General Circulation Models (OGCMs). In the simulations PW is tracked by a passive tracer, released in Bering Strait. Simulated PW water spreads from the Bering Strait region in three major branches. One of them starts in the Barrow Canyon, bringing PW along continental slope of Alaska into the Canadian Straits and then into Baffin Bay. The other initiates in the vicinity of the Herald Canyon and transports PW along the continental slope of the East-Siberian Sea into the transpolar drift, and then through Fram Strait and the Greenland Sea. The third branch begins near the Herald Shoal and the central Chukchi shelf and brings PW waters into the Beaufort Gyre. Models suggest that the spread of PW through the Arctic Ocean depends on the atmospheric circulation. In the models the wind, acting via Ekman pumping, drives the seasonal and interannual variability of PW in the Canadian Basin of the Arctic Ocean. The wind effects the simulated PW pathways by changing vertical shear of the relative vorticity of the ocean flow in the Canada Basin. This article is protected by copyright. All rights reserved.

Description: The Columbia Climate Center, in partnership with World Wildlife Fund, Woods Hole Research Center, and Arctic 21, held a workshop titled A 5 C Arctic in a 2 C World on July 20 and 21, 2016. The workshop was co-sponsored by the International Arctic Research Center (University of Alaska Fairbanks), the Arctic Institute of North America (Canada), the MEOPAR Network (Marine Environmental Observation, Prediction, and Response), and the Future Ocean Excellence Cluster. The goal of the workshop was to advance thinking on the science and policy implications of the temperature change in the context of the 1.5 to 2 C warming expected for the globe, as dis- cussed during the 21st session of the Conference of the Parties of the United Nations Framework Convention on Climate Change at Paris in 2015. For the Arctic, such an increase means an antic- ipated increase of roughly 3.5 to 5 C. An international group of 41 experts shared perspectives on the regional and global impacts of an up to +5 C Arctic, examined the feasibility of actively lowering Arctic temperatures, and considered realistic timescales associated with such interventions. The group also discussed the science and the political and governance actions required for alternative Arctic futures.

Description: The recent thinning and shrinking of the Arctic sea ice cover has increased the interest in seasonal sea ice forecasts. Typical tools for such forecasts are numerical models of the coupled ocean sea ice system such as the North Atlantic/Arctic Ocean Sea Ice Model (NAOSIM). The model uses as input the initial state of the system and the atmospheric boundary condition over the forecasting period. This study investigates the potential of remotely sensed ice thickness observations in constraining the initial model state. For this purpose it employs a variational assimilation system around NAOSIM and the Alfred Wegener Institute's CryoSat-2 ice thickness product in conjunction with the University of Bremen's snow depth product and the OSI SAF ice concentration and sea surface temperature products. We investigate the skill of predictions of the summer ice conditions starting in March for three different years. Straightforward assimilation of the above combination of data streams results in slight improvements over some regions (especially in the Beaufort Sea) but degrades the over-all fit to independent observations. A considerable enhancement of forecast skill is demonstrated for a bias correction scheme for the CryoSat-2 ice thickness product that uses a spatially varying scaling factor.