ALBERT

Description: Sea ice drift, surface temperature, and barometric pressure were measured by the Compact Air-Launched Ice Beacon (CALIB) 2018C27 drifting on Arctic sea ice deployed by air-plane drop off during ASIMBO 2018. The time series describes the position and additional parameters of the buoy between 13 August 2018 and 30 January 2019 in sample intervals of 1 hour. The data set has been processed, including the removal of obvious inconsistencies (missing values).

Description: To counteract global warming, a geoengineering approach that aims at intervening in the Arctic ice-albedo feedback has been proposed (Desch et al., (2017)). A large number of wind-driven pumps shall spread seawater on the surface in winter to enhance ice growth, allowing more ice to survive the summer melt. We test this idea with a coupled climate model by modifying the surface exchange processes such that the physical effect of the pumps is simulated. This database contains a selection of fields from CMIP5 type RCP 8.5 ensemble climate projections with the AWI Climate Model (AWI-CM). The data are stored as netCDF and include the following variables: Monthly averaged time series of pan-Arctic sea ice extent and volume from 1850 to 2100. These are divided into a "Historical" simulation (1850 to 1999; 1 ensemble member), a "Control" simulation (2000 to 2100; 4 ensemble members) and a "Geoengineering" simulation (2020 to 2100; 4 ensemble members). Monthly averaged 2D fields of 2m temperature, total cloud cover, net solar radiation energy flux and total precipitation for the "Control", "Geoengineering" and "Extreme Geoengineering" simulations. The data are averaged over two periods: 2021 to 2060 and 2061 to 2100. Further details about the data can be found in the publication associated with the database. For practical reasons, the full climate model output is stored at DKRZ and will be made available only upon request to the authors. This dataset has been created with the financial support of the Federal Ministry of Education and Research of Germany in the framework of the research group Seamless Sea Ice Prediction (SSIP; grant 01LN1701A).

Description: This bibliography unites the individual data collected by different types of autonomous platforms deployed during MOSAiC in 2019/2020.

Description: A new climate model has been developed that employs a multi-resolution dynamical core for the sea ice-ocean component. In principle, the multi-resolution approach allows one to use enhanced horizontal resolution in dynamically active regions while keeping a coarse-resolution setup otherwise. The coupled model consists of the atmospheric model ECHAM6 and the finite element sea ice-ocean model (FESOM). In this study only moderate refinement of the unstructured ocean grid is applied and the resolution varies from about 25 km in the northern North Atlantic and in the tropics to about 150 km in parts of the open ocean; the results serve as a benchmark upon which future versions that exploit the potential of variable resolution can be built. Details of the formulation of the model are given and its performance in simulating observed aspects of the mean climate is described. Overall, it is found that ECHAM6–FESOM realistically simulates many aspects of the observed climate. More specifically it is found that ECHAM6–FESOM performs at least as well as some of the most sophisticated climate models participating in the fifth phase of the Coupled Model Intercomparison Project. ECHAM6–FESOM shares substantial shortcomings with other climate models when it comes to simulating the North Atlantic circulation.

Description: Sea ice deformation localizes along Linear Kinematic Features (LKFs) that are relevant for the air/ocean/sea-ice interaction and for shipping andmarine operations. At high resolution (〈 5km) viscous-plastic sea ice models start to resolve LKFs. Here, we study the short-range (up to 10 days) potential predictability of LKFs in Arctic sea ice using ensemble simulations of an ocean/sea-ice model with a grid point separation of 4.5 km. We analyze the sensitivity of predictability to idealized initial perturbations, mimicking the uncertainties in sea ice analyses, and to growing uncertainty of the atmospheric forcing caused by the chaotic nature of the atmosphere. The similarity between pairs of ensemble members is quantified by Pearson correlation and Modified Hausdorff Distance (MHD). In our perfect model experiments, the potential predictability of LKFs, based on the MHD, drops below 0.6 after 4 days in winter. We find that forcing uncertainty (due to limited atmospheric predictability) largely determines LKF predictability on the 10-day time scale, while uncertainties in the initial state impact the potential predictability only within the first 4 days.

Description: Sea ice formation is accompanied by the rejection of salt which in nature tends to be mixed vertically by the formation of convective plumes. Here we analyze the influence of a salt plume parameterization (SPP) in an atmosphere-sea ice-ocean model. Two 330 years long simulations have been conducted with the AWI Climate Model. In the reference simulation, the rejected salt in the Arctic Ocean is added to the upper-most ocean layer. This approach is commonly used in climate modelling. In another experiment, employing SPP, the rejected salt is vertically redistributed within the mixed layer based on a power law profile that mimics the penetration of salt plumes. We discuss the effects of this redistribution on the simulated mean state and on atmosphere-ocean linkages associated with the intensity of deep water formation. We find that the salt plume parametrization leads to simultaneous increase of sea ice (volume and concentration) and decrease of sea surface salinity in the Arctic. The SPP considerably alters the interplay between the atmosphere and the ocean in the Nordic Seas. The parameterization modifies the ocean ventilation; however, resulting changes in temperature and salinity largely compensate each other in terms of density so that the overturning circulation is not significantly affected.

Description: Sea ice dynamics determine the drift and deformation of sea ice. Nonlinear physics, usually expressed in a viscous‐plastic rheology, makes the sea ice momentum equations notoriously difficult to solve. At increasing sea ice model resolution the nonlinearities become stronger as linear kinematic features (leads) appear in the solutions. Even the standard elastic‐viscous‐plastic (EVP) solver for sea ice dynamics, which was introduced for computational efficiency, becomes computationally very expensive, when accurate solutions are required, because the numerical stability requires very short, and hence more, subcycling time steps at high resolution. Simple modifications to the EVP solver have been shown to remove the influence of the number of subcycles on the numerical stability. At low resolution appropriate solutions can be obtained with only partial convergence based on a significantly reduced number of subcycles as long as the numerical procedure is kept stable. This previous result is extended to high resolution where linear kinematic features start to appear. The computational cost can be strongly reduced in Arctic Ocean simulations with a grid spacing of 4.5 km by using modified and adaptive EVP versions because fewer subcycles are required to simulate sea ice fields with the same characteristics as with the standard EVP.

Description: The Year of Polar Prediction (YOPP) aims to close gaps in polar forecasting capacity. From mid-2017 to mid-2019, scientists and operational forecasting centers worldwide are working together to observe, model, and improve forecasts of the Arctic and Antarctic weather and climate systems. As a period of intensive observing, modelling, verification, user-engagement and education activities, this international effort initiated by the World Meteorological Organization (WMO) will enable a significant improvement in environmental prediction capabilities for the polar regions and beyond. Improved forecasts of weather and sea-ice conditions in polar regions are also expected to positively influence weather and longer-range predictions at lower latitudes. Routine observations such as radiosonde launches and buoy deployments have been enhanced during three Special Observing Periods (in the Arctic: 1 February – 31 March 2018 and 1 July – 30 September 2018, in the Antarctic: 16 November 2018 – 15 February 2019) during the Year of Polar Prediction. In a coordinated effort to observe the Arctic and Antarctic systems, scientists have implemented aircraft campaigns, ship campaigns, and installed new automatic weather stations to enable new insights into the processes governing the polar weather and climate, and to understand the related impacts on the global weather systems. Data collected during YOPP through the WMO’s Global Telecommunication System (GTS) are available for operational forecasting centres to feed into their weather and sea-ice forecasting systems in real-time. The International Coordination Office for Polar Prediction (ICO; hosted by the Alfred Wegener Institute in Germany) supports the Polar Prediction Project by supporting the planning and implementation of YOPP activities, ensuring international coordination between a variety of involved partners and divulgating progresses and events through an active communication strategy. Here, an overview of the main achievements accomplished during the three Special Observing Periods, and prospects for future evaluations and numerical experiments as well as the plans for the upcoming YOPP Consolidation Phase are provided.

Description: Sea ice models have become essential components of weather, climate and ocean models. The reliability of process studies, environmental forecasts and climate projections alike depend on a realistic representation of sea ice. Developing and evaluating sea ice models requires methods for both large scales and fine-scale geomorphological structures such as linear kinematic features (LKF). We introduce a Multiscale Directional Analysis (MDA) method that diagnoses distributions of LKF orientation and intersection angles. The MDA method is different from previous methods in that it (a) takes into account the width of LKFs instead of estimating the orientation of centerlines; (b) separates curve-like features from point-like features providing the opportunity to reach a unified definition of LKF in both numerical and observational fields; (c) estimates scale-dependent intersection angles.