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Global climate change poses significant societal and political challenges. The rapid increase in the near-surface air temperatures and the drastic retreat of the Arctic sea ice during summer are not well represented by climate models. The data sets introduced here intend to help improving the current understanding of the ongoing Arctic climate changes. In particular, this study considers observations from 24 helicopter flights (June–September 2020) and 5 flights with the helicopter-towed probe HELiPOD (May–July 2020) during MOSAiC. Distributions of various surface types (white ice/snow, bright melt ponds, dark melt ponds, open water, and bare ice) were determined using fisheye camera images. They were related to collocated broadband irradiance measurements to analyse the temporal and spatial changes of the surface albedo. Multiple linear regression was applied to assign the measured areal albedo to the corresponding surface-types. The resulting surface-type fractions, the albedo data and respective upward and downward broadband solar irradiances of several flights throughout the melting and refreezing season are provided. [ABSTRACT FROM AUTHOR]

Two parameterisation schemes for the turbulent surface fluxes and drag coefficients over the Arctic marginal sea-ice zone (MIZ) are (further) developed, and their results are compared with each other. Although the schemes are based on different principles (flux averaging and parameter averaging), the resulting drag coefficients differ only slightly in the case of neutral and stable stratification. For unstable stratification and sea-ice conditions being typical for the north-eastern Fram Strait, the drag coefficients resulting from the parameter-averaging concept are 5–10% larger than those of the flux-averaging concept. At a sea-ice concentration of 45%, the parameter-averaging method overestimates the heat fluxes by a factor of 1.2. An inclusion in the schemes of form drag caused by floe edges and ridges has a much larger effect on the drag coefficient, and on the momentum fluxes, than the choice between the parameter-averaging or flux-averaging methods. Based on sensitivity studies with the flux-averaging scheme, a simple formula for the effective drag coefficient above the Arctic MIZ is derived. It reduces the computational costs of the more complex parameterisations and could also be used in larger scale models. With this simple formula, the effective drag coefficient can be calculated as a function of the sea-ice concentration and skin drag coefficients for water and ice floes. The results obtained with this parameterisation differ only slightly from those using the more complex schemes. Finally, it is shown that in the MIZ, drag coefficients for sea-ice models may differ significantly from the effective drag coefficients used in atmospheric models. [ABSTRACT FROM AUTHOR]

Three of the latest global reanalysis products, the regional climate model RACMO and the operational weather forecast system AMPS were evaluated against vertical profiles obtained from radio soundings on the Antarctic plateau. Measurements at Kohnen Station (75°S, 0°E, 2892 m a.s.l.), which represent independent in situ data, were performed in January 2006, December 2013 and January 2014 four times a day. JRA-55 had marginally the most accurate temperature profiles, MERRA-2 had the best relative humidity profiles and ERA-interim had the smallest errors in wind speed profiles. Biases and root mean-square errors vary considerably with altitude in the troposphere and in the tropopause region. Surface or elevated inversions in the lowermost 1000 m of the atmosphere are captured quite differently. ERA-interim always shows a surface inversion with thickness and temperature increase that hardly vary in the course of a day. Overall, the frequency of surface and elevated inversions is better captured at 00 UTC and 06 UTC compared to 12 UTC and 18 UTC in JRA-55 and MERRA-2 as well as in the two regional models, which indicates that processes in the boundary layer, which lead to an erosion of a surface inversion are still poorly represented in the models. All reanalyses and regional models are able to simulate a low-level jet. However, ERA-interim, RACMO and AMPS underestimate the frequency at all four observation times. [ABSTRACT FROM AUTHOR]