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Measurements of the atmospheric boundary layer (ABL) structure were performed for three years (October 2017–August 2020) at the Russian observatory “Ice Base Cape Baranova” (79.280° N, 101.620° E) using SODAR (Sound Detection And Ranging). These measurements were part of the YOPP (Year of Polar Prediction) project “Boundary layer measurements in the high Arctic” (CATS_BL) within the scope of a joint German–Russian project. In addition to SODAR-derived vertical profiles of wind speed and direction, a suite of complementary measurements at the observatory was available. ABL measurements were used for verification of the regional climate model COSMO-CLM (CCLM) with a 5 km resolution for 2017–2020. The CCLM was run with nesting in ERA5 data in a forecast mode for the measurement period. SODAR measurements were mostly limited to wind speeds <12 m/s since the signal was often lost for higher winds. The SODAR data showed a topographical channeling effect for the wind field in the lowest 100 m and some low-level jets (LLJs). The verification of the CCLM with near-surface data of the observatory showed good agreement for the wind and a negative bias for the 2 m temperature. The comparison with SODAR data showed a positive bias for the wind speed of about 1 m/s below 100 m, which increased to 1.5 m/s for higher levels. In contrast to the SODAR data, the CCLM data showed the frequent presence of LLJs associated with the topographic channeling in Shokalsky Strait. Although SODAR wind profiles are limited in range and have a lot of gaps, they represent a valuable data set for model verification. However, a full picture of the ABL structure and the climatology of channeling events could be obtained only with the model data. The climatological evaluation showed that the wind field at Cape Baranova was not only influenced by direct topographic channeling under conditions of southerly winds through the Shokalsky Strait but also by channeling through a mountain gap for westerly winds. LLJs were detected in 37% of all profiles and most LLJs were associated with channeling, particularly LLJs with a jet speed ≥ 15 m/s (which were 29% of all LLJs). The analysis of the simulated 10 m wind field showed that the 99%-tile of the wind speed reached 18 m/s and clearly showed a dipole structure of channeled wind at both exits of Shokalsky Strait. The climatology of channeling events showed that this dipole structure was caused by the frequent occurrence of channeling at both exits. Channeling events lasting at least 12 h occurred on about 62 days per year at both exits of Shokalsky Strait.
A model-based temperature adjustment scheme for wintertime sea-ice production retrievals from MODIS
(2022)
Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.
We use a novel sea-ice lead climatology for the winters of 2002/03 to 2020/21 based on satellite observations with 1 km2 spatial resolution to identify predominant patterns in Arctic wintertime sea-ice leads. The causes for the observed spatial and temporal variabilities are investigated using ocean surface current velocities and eddy kinetic energies from an ocean model (Finite Element Sea Ice–Ice-Shelf–Ocean Model, FESOM) and winds from a regional climate model (CCLM) and ERA5 reanalysis, respectively. The presented investigation provides evidence for an influence of ocean bathymetry and associated currents on the mechanic weakening of sea ice and the accompanying occurrence of sea-ice leads with their characteristic spatial patterns. While the driving mechanisms for this observation are not yet understood in detail, the presented results can contribute to opening new hypotheses on ocean–sea-ice interactions. The individual contribution of ocean and atmosphere to regional lead dynamics is complex, and a deeper insight requires detailed mechanistic investigations in combination with considerations of coastal geometries. While the ocean influence on lead dynamics seems to act on a rather long-term scale (seasonal to interannual), the influence of wind appears to trigger sea-ice lead dynamics on shorter timescales of weeks to months and is largely controlled by individual events causing increased divergence. No significant pan-Arctic trends in wintertime leads can be observed.
Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.