Description: Simulations of ice-shelf basal melting in future climate scenarios from the IPCC’s Fourth Assessment Report (AR4) have revealed a large uncertainty and the potential of a rapidly increasing basal mass loss particularly for the large cold-water ice shelves in the Ross and Weddell Seas. The large spread in model results was traced back to uncertainties in the freshwater budget on the continental shelf, which is governed by sea-ice formation. Differences in sea-ice formation, in turn, were shown to follow the regional differences between the atmospheric heat fluxes imprinted by the climate models. A more recent suite of FESOM model experiments was performed with output from two members of the newer generation of climate models engaged in the IPCC’s Fifth Assessment Report (AR5). Comparing simulations forced with output from the AR5/CMIP5 models HadGem2 and MPI-ESM, we find that projected heat fluxes and thus sea-ice formation over the Southern Ocean continental shelves have converged to an ensemble with a much smaller spread than between the AR4 experiments. For most of the modeled ice shelves, a gradual but accelerating increase of basal melt rates during the 21st century is a robust feature. Both with HadGem2 and with MPI-ESM forcing, basal melt rates for the Filchner–Ronne Ice Shelf in FESOM increase by a factor of two by the end of the 21st century in the RCP85 scenario. For the smaller, warm-water ice shelves, inter-model differences in ice-shelf basal mass loss projections are still slightly larger than differences between the scenarios RCP45 and RCP85; compared with AR4 projections, however, the model-dependent spread has been strongly reduced. Current work aims at further reducing the uncertainties arising from atmospheric forcing by using output from the regional climate model RACMO. The effect of a varying cavity geometry and the response of the grounded ice are being adressed by coupling to the RIMBay ice shelf / ice sheet model.

Description: Simulations of ice shelf basal melting in future climate scenarios from the IPCC’s Fourth Assessment Report (AR4) have revealed a large uncertainty and the potential of a rapidly increasing basal mass loss particularly for the large cold-water ice shelves in the Ross and Weddell Seas. The large spread in model results was traced back to uncertainties in the freshwater budget on the continental shelf, which is governed by sea ice formation. Differences in sea ice formation, in turn, follow the regional differences between the atmospheric heat fluxes imprinted by the climate models. A more recent suite of BRIOS and FESOM model experiments was performed with output from two members of the newer generation of climate models enganged in the IPCC’s Fifth Assessment Report (AR5). Comparing simulations forced with output from the AR5/CMIP5 models HadGem2 and MPI-ESM, we find that uncertainties arising from inter-model differences in high latitudes have reduced considerably. Projected heat fluxes and thus sea ice formation over the Southern Ocean continental shelves have converged to an ensemble with a much smaller spread than between the AR4 experiments. For most of the ten larger ice shelves in Antarctica, a gradual (but accelerating) increase of basal melt rates during the 21st century is a robust feature throughout the various realisations. Both with HadGem2 and with MPI-ESM forcing, basal melt rates for Filchner-Ronne Ice Shelf in FESOM increase by a factor of two by the end of the 21st century in the RCP85 scenario. For the smaller, warm-water ice shelves, inter-model differences in ice shelf basal mass loss projections are still slightly larger than differences between the scenarios RCP45 and RCP85; compared to AR4 projections, however, the model-dependent spread has been strongly reduced.

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: 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.