ALBERT

Description: Spectra of wind, kinetic energy, and temperature are investigated for a dataset of 10 years of regional climate simulations for mid-Europe. The nonhydrostatic Consortium for Small-Scale Modeling (COSMO) model in Climate Mode [COSMO-CLM (CCLM)] climate model is used in a hindcast mode for 1991–2000. The three-step nesting chain starts with a CCLM run with 18-km resolution covering all of Europe nested in ERA-40 reanalyses and then a run with a resolution of 4.4 km is performed for mid-Europe. Finally, the 1.3-km run focuses on the region of mid-Germany and Luxembourg. In the present study, only results for the 4.4- and 1.3-km runs are shown. Different methods based on the Fourier and cosine transformations for the computation of the spectra are evaluated. The kinetic energy spectra show the expected slope in the mesoscale (up to the effective resolution), while the spectrum of the vertical wind shows a zero-slope behavior. The spectra of the horizontal wind components and temperature compare well to the observations. The effective model resolution was found to be about 7–10 (5–7) times the horizontal grid spacing for the one-dimensional (two-dimensional) spectral methods. A comparison between the different model resolutions shows a benefit of the 1.3-km simulations for the boundary layer for horizontal scales up to 25 km. The multiyear time-scale simulations allow for a climatological study of the seasonal cycle. The kinetic energy spectrum is found to have the largest values in summer.

Description: The nonhydrostatic regional climate model CCLM was used for a long-term hindcast run (2002–2016) for the Weddell Sea region with resolutions of 15 and 5 km and two different turbulence parametrizations. CCLM was nested in ERA-Interim data and used in forecast mode (suite of consecutive 30 h long simulations with 6 h spin-up). We prescribed the sea ice concentration from satellite data and used a thermodynamic sea ice model. The performance of the model was evaluated in terms of temperature and wind using data from Antarctic stations, automatic weather stations (AWSs), an operational forecast model and reanalyses data, and lidar wind profiles. For the reference run we found a warm bias for the near-surface temperature over the Antarctic Plateau. This bias was removed in the second run by adjusting the turbulence parametrization, which results in a more realistic representation of the surface inversion over the plateau but resulted in a negative bias for some coastal regions. A comparison with measurements over the sea ice of the Weddell Sea by three AWS buoys for 1 year showed small biases for temperature around ±1 K and for wind speed of 1 m s−1. Comparisons of radio soundings showed a model bias around 0 and a RMSE of 1–2 K for temperature and 3–4 m s−1 for wind speed. The comparison of CCLM simulations at resolutions down to 1 km with wind data from Doppler lidar measurements during December 2015 and January 2016 yielded almost no bias in wind speed and a RMSE of ca. 2 m s−1. Overall CCLM shows a good representation of temperature and wind for the Weddell Sea region. Based on these encouraging results, CCLM at high resolution will be used for the investigation of the regional climate in the Antarctic and atmosphere–ice–ocean interactions processes in a forthcoming study.

Description: In the present study a non-motion-stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December 2015–January 2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented. The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-to-noise ratio (SNR) threshold is presented, which can be calculated for individual measurement set-ups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship. The performance of the lidar measurements in comparison with radio soundings generally shows small root mean square deviation (bias) for wind speed of around 1 m s−1 (0.1 m s−1) and for wind direction of around 10∘ (1∘). The post-processing of the non-motion-stabilized data shows a comparably high quality to studies with motion-stabilized systems. Two case studies show that a flexible change in SNR threshold can be beneficial for special situations. Further the studies reveal that short-lived low-level jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motion-stabilized Doppler lidar can be operated successfully on an icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.

Description: Profiles of wind speed and direction at high spatial and temporal resolution are fundamental meteorological quantities for studies of the atmospheric boundary layer. Ship-based Doppler lidar measurements can contribute to fill the data gap over oceans particularly in polar regions. In the present study a non-motion stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December–January 2015/2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented. The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-to-noise ratio (SNR) threshold is presented, which can be calculated for individual measurement setups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship. The performance of the lidar measurements in comparison with radio soundings shows generally small RMSD (bias) for wind speed of around 1ms−1 (0.1ms−1) and for wind direction of around 12° (6°). The postprocessing of the non-motion stabilized data shows a comparable good quality as studies with motion stabilized systems. Two case studies show that a flexible change of SNR can be beneficial for special situations. Further the studies reveal that short-lived Low-Level Jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motion stabilized Doppler lidar can be operated successfully on an icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.

Description: The non-hydrostatic regional climate model CCLM was used for a long-term hindcast run (2002–2016) for the Weddell Sea region with resolutions of 15 and 5 km and two different turbulence parametrizations. CCLM was nested in ERA-Interim data. We prescribed sea-ice concentration from satellite data, and used a thermodynamic sea-ice model. The performance of the model was evaluated in terms of temperature and wind using data from Antarctic stations, AWS over land and sea ice, operational forecast model and reanalyses data, and lidar wind profiles. For the reference run we found a warm bias for the near-surface temperature over the Antarctic plateau. This bias was removed in the second run by adjusting the turbulence parametrization, which results in a more realistic representation of the surface inversion over the plateau. Differences in other regions were small. A comparison with measurements over the sea ice of the Weddell Sea by three AWS buoys for one year showed small biases for temperature around 1 K and for wind speed of 1 m s−1. Comparisons of radio soundings showed a model bias around zero and a RMSE of 1–2 K for temperature and of 3–4 m s−1 for wind speed. The comparison of CCLM simulations at resolutions down to 1 km with wind data from Doppler Lidar measurements during December 2015 and January 2016 yielded almost no bias in wind speed and RMSE of ca. 2 m s−1. Overall CCLM shows a good representation of temperature and wind for the Weddell Sea region. These results encourage for further studies using CCLM data for the regional climate in the Antarctic at high resolutions and the study of atmosphere-ice-ocean interactions processes.

Description: In the present study a non-motion-stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December 2015–January 2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented. The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-to-noise ratio (SNR) threshold is presented, which can be calculated for individual measurement set-ups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship. The performance of the lidar measurements in comparison with radio soundings generally shows small root mean square deviation (bias) for wind speed of around 1 m s−1 (0.1 m s−1) and for wind direction of around 10∘ (1∘). The post-processing of the non-motion-stabilized data shows a comparably high quality to studies with motion-stabilized systems. Two case studies show that a flexible change in SNR threshold can be beneficial for special situations. Further the studies reveal that short-lived low-level jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motion-stabilized Doppler lidar can be operated successfully on an icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.

Description: 〈jats:p〉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.〈/jats:p〉

Description: 〈jats:p〉Abstract. Sea ice formation dominates surface salt forcing in the southern Weddell Sea. Brine rejected in the process of sea ice production results in the production of High Salinity Shelf Water (HSSW) that feeds the global overturning circulation and fuels the basal melt of the adjacent ice shelf. The strongest sea ice production rates are found in coastal polynyas, where steady offshore winds promote divergent ice movement during the freezing season. We used the Finite Element Sea ice–ice shelf–Ocean Model (FESOM) forced by output from the regional atmospheric model COSMO-CLM (CCLM) with 14 km horizontal resolution to investigate the role of polynyas for the surface freshwater flux of the southern Weddell Sea (2002–2017). The presence of stationary icescape features (i.e., fast-ice areas and grounded icebergs) can influence the formation of polynyas and, therefore, impact sea ice production. The representation of the icescape in our model is included by prescribing the position, shape and temporal evolution of a largely immobile ice mélange formed between the Filchner–Ronne Ice Shelf (FRIS) and a major grounded iceberg based on satellite data. We find that 70 % of the ice produced on the continental shelf of the southern Weddell Sea is exported from the region. While coastal polynyas cover 2 % of the continental shelf area, sea ice production within the coastal polynyas accounts for 17 % of the overall annual sea ice production (1509 km3). The largest contributions come from the Ronne Ice Shelf and Brunt Ice Shelf polynyas and polynyas associated with the ice mélange. Furthermore, we investigate the sensitivity of the polynya-based ice production to the (i) representation of the icescape and (ii) regional atmospheric forcing. Although large-scale atmospheric fields determine the sea ice production outside polynyas, both the treatment of the icescape and the regional atmospheric forcing are important for the regional patterns of sea ice production in polynyas. The representation of the ice mélange is crucial for the simulation of polynyas westward/eastward of it, which are otherwise suppressed/overestimated. Compared to using ERA-Interim reanalysis as an atmospheric forcing data set, using CCLM output reduces polynya-based ice production over the eastern continental shelf due to weaker offshore winds, yielding a more realistic polynya representation. Our results show that the location and not just the strength of the sea ice production in polynyas is a relevant parameter in setting the properties of the HSSW produced on the continental shelf, which in turn affects the basal melting of the Filchner–Ronne Ice Shelf. 〈/jats:p〉

Description: This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [6], averaging time [8 sec], chosen SNR threshold [-20 dB].

Description: This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [6], averaging time [8 sec], chosen SNR threshold [-20 dB].