Torge Martin

Seasonality and long‐term trend of Arctic Ocean surface stress in a model

A numerical ocean sea-ice model is used to demonstrate that Arctic sea ice retreat affects momentum transfer into the ocean. A thinner and thus weaker ice cover is more easily forced by the wind, which increases the momentum flux. In contrast, increasing open water reduces momentum transfer because the ice surface provides greater drag than the open water surface. We introduce the concept of optimal ice concentration: momentum transfer increases with increasing ice concentration up to a point, beyond which frictional losses by floe interaction damp the transfer. For a common ice internal stress formulation, a concentration of 80–90% yields optimal amplification of momentum flux into the ocean. We study the seasonality and long-term evolution of Arctic Ocean surface stress over the years 1979–2012. Spring and fall feature optimal ice conditions for momentum transfer, but only in fall is the wind forcing at its maximum, yielding a peak basin-mean ocean surface stress of ∼0.08 N/m2. Since 1979, the basin-wide annual mean ocean surface stress has been increasing by 0.004 N/m2/decade, and since 2000 by 0.006 N/m2/decade. In contrast, summertime ocean surface stress has been decreasing at −0.002 N/m2/decade. These trends are linked to the weakening of the ice cover in fall, winter and spring, and to an increase in open water fraction in summer, i.e., changes in momentum transfer rather than changes in wind forcing. In most areas, the number of days per year with optimal ice concentration is decreasing.

The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: A model study

The Arctic sea ice cover is thinning and retreating, causing changes in surface roughness that in turn modify the momentum flux from the atmosphere through the ice into the ocean. New model simulations comprising variable sea ice drag coefficients for both the air and water interface demonstrate that the heterogeneity in sea ice surface roughness significantly impacts the spatial distribution and trends of ocean surface stress during the last decades. Simulations with constant sea ice drag coefficients as used in most climate models show an increase in annual mean ocean surface stress (0.003 N/m2 per decade, 4.6%) due to the reduction of ice thickness leading to a weakening of the ice and accelerated ice drift. In contrast, with variable drag coefficients our simulations show annual mean ocean surface stress is declining at a rate of −0.002 N/m2 per decade (3.1%) over the period 1980–2013 because of a significant reduction in surface roughness associated with an increasingly thinner and younger sea ice cover. The effectiveness of sea ice in transferring momentum does not only depend on its resistive strength against the wind forcing but is also set by its top and bottom surface roughness varying with ice types and ice conditions. This reveals the need to account for sea ice surface roughness variations in climate simulations in order to correctly represent the implications of sea ice loss under global warming.

Retrieval of Antarctic sea-ice pressure ridge frequencies from ERS SAR imagery by means of in situ laser profiling and usage of a neural network

Application of a neural network to ERS-SAR images to retrieve pressure ridge spatial frequencies is presented. For an independent dataset, the rmserror between the retrieved and the true ridge frequency as determined by means of laser profiling was about 5 ridges per kilometre, or 30%. The network is trained with results from in situ laser profiling of ridge distributions and coincident SAR backscatter properties. The study focuses on summer data from the Bellingshausen, Amundsen and Weddell Seas in Antarctica, which were gathered in February 1994 and 1997. Pressure ridge frequencies varied from 3 to 30 ridges per kilometre between different regions, thus providing a wide range of training and test data for the algorithm development. From ERS-SAR images covering the area of the laser flights with a time difference of a few days at maximum, histograms of the backscatter coefficient sigma0 were extracted. Statistical parameters (e.g. mean, standard deviation, tail-to-mean ratio) were calculated from these distributions and compared with the results of the laser flights. Generally, the mean backscatter increases with a growing ridge frequency, and the signal range becomes narrower. However, these correlations are only poor, and improved results are obtained when the statistical parameters are combined to train the neural network.

Role of Ice Dynamics in the Sea Ice Mass Balance

Over the past decade, the Arctic Ocean and Beaufort Sea ice pack has been less extensive and thinner than has been observed during the previous 35 years [e.g., Wadhams and Davis, 2000; Tucker et al., 2001; Rothrock et al., 1999; Parkinson and Cavalieri, 2002; Comiso, 2002]. During the summers of 2007 and 2008, the ice extents for both the Beaufort Sea and the Northern Hemisphere were the lowest on record. Mechanisms causing recent sea ice change in the Pacific Arctic and the Beaufort Sea are under investigation on many fronts [e.g., Drobot and Maslanik, 2003; Shimada et al., 2006]; the mechanisms include increased ocean surface warming due to Pacific Ocean water inflow to the region and variability in meteorological and surface conditions. However, in most studies addressing these events, the impact of sea ice dynamics, specifically deformation, has not been measured in detail.

Impact of the ice strength formulation on the performance of a sea ice thickness distribution model in the Arctic

The impact of a subgrid-scale ice thickness distribution (ITD) and two standard ice strength formulations on simulated Arctic sea ice climate is investigated. To this end, different model configurations with and without an ITD were tuned by minimizing the weighted mean error between the simulated and observed sea ice concentration, thickness, and drift speed with an semiautomatic parameter optimization routine. The standard ITD and ice strength parameterization lead to larger errors when compared to the simple single-category model with an ice strength parameterization based on the mean ice thickness. Interestingly, the simpler ice strength formulation, which depends linearly on the mean ice thickness, also reduces the model-observation error when using an ITD. For the ice strength parameterization that makes use of the ITD, the effective ice strength depends strongly on the number of thickness categories, so that introducing more categories can lead to overall thicker ice that is more easily deformed.

Assessing climate impacts and risks of ocean albedo modification in the Arctic

The ice albedo feedback is one of the key factors of accelerated temperature increase in the high northern latitudes under global warming. This study assesses climate impacts and risks of idealized Arctic Ocean albedo modification (AOAM), a proposed climate engineering method, during transient cli- mate change simulations with varying representative concentration pathway (RCP) scenarios. We find no potential for reversing trends in all assessed Arctic climate metrics under increasing atmospheric CO2 con- centrations. AOAM only yields an initial offset during the first years after implementation. Nevertheless, sea ice loss can be delayed by 25(60) years in the RCP8.5(RCP4.5) scenario and the delayed thawing of perma- frost soils in the AOAM simulations prevents up to 40(32) Pg of carbon from being released by 2100. AOAM initially dampens the decline of the Atlantic Meridional Overturning and delays the onset of open ocean deep convection in the Nordic Seas under the RCP scenarios. Both these processes cause a subsurface warming signal in the AOAM simulations relative to the default RCP simulations with the potential to desta- bilize Arctic marine gas hydrates. Furthermore, in 2100, the RCP8.5 AOAM simulation diverts more from the 2005–2015 reference state in many climate metrics than the RCP4.5 simulation without AOAM. Considering the demonstrated risks, we conclude that concerning longer time scales, reductions in emissions remain the safest and most effective way to prevent severe changes in the Arctic.