Simulation of the ABL over the North Water polynya and comparison with aircraft data

Simulations for Greenland with focus on the wind regime are presented using the high-resolution non-hydrostatic model COSMO (Consortium for Small scale modeling). The simulations are performed at 15 km, 5.5 km and 1.3 km resolution for the time period of June 2010. The Nares Strait, including the North Water (NOW) polynya, in northwest Greenland was selected as focus of the simulations, since comprehensive measurements of the structure of the boundary layer are available from an aircraft study. The observations on four different days show a shallow stable boundary layer over the polynya and a pronounced low-level jet associated with the flow channeling in the Nares Strait, particularly at Smith Sound. The reproduction of the vertical patterns of wind and temperature by the simulations is realistic at all resolutions and best results are found for 5.5 km and 1.3 km resolution. A vertical displacement of the patterns and an overestimation of the temperature was found. The measured low-level inversion is not simulated well, but overall the vertical structures of the simulation and observation correlate highly. Thus, the model is well suited for simulations in particular for the situation of flow channeling in a topographically complex area. The analysis of the synoptic situations associated with channeled flow through the Nares Strait shows that the wind speed increases with higher pressure difference between the Lincoln Sea and Baffin Bay. Channeling effects lead to a prevailing flow direction towards Baffin Bay. A strong increase of the wind speed occurs at Smith Sound, where the flow also passes over mountains of the Greenland coast. The wind maximum is found downstream of Smith Sound, and typical low-level jets with wind speeds of around 20 m/s occur at a height of 100 m

Abschätzung der Meereisproduktion in der Laptev-See mit dem Ozean-Meereismodell NAOSIM

Fernerkundungsdaten haben eine kontinuierliche Abnahme des Meereises in den vergangenen 30 Jahren gezeigt, Klimamodelle prognostizieren eine anhaltende Abnahme für die Zukunft. Dies erfordert eine genauere Analyse der verursachenden Prozesse, der Trendentwicklung und der regionalen Variabilität. Dabei spielt die Laptev-See in der sibirischen Arktis eine bedeutende Rolle, da es hier, bedingt durch eine große Polynja-Aktivität, zur vermehrten Eisproduktion kommt. Zur näheren Untersuchung der verursachenden thermodynamischen und dynamischen Prozesse nutzen wir eine mit täglichen NCEP/NCAR-Daten angetriebene Simulation mit dem gekoppelten Ozean-Meereismodell NAOSIM (North Atlantic/Arctic Ocean-Sea Ice Model) von 1990-2008 mit 0.08° Auflösung. Aufgrund seiner realitätsnahen Wiedergabe des mittleren Jahresgangs und des negativen Trends der Eisbedeckung ist dieses Modell für die Auswertung gut geeignet. Die getrennte Analyse der thermodynamischen Eisproduktion bzw. Eisschmelze und der dynamischen Umverteilung für die gesamte Arktis bestätigt, dass im Bereich der Laptev-See die Eisproduktion im Mittel 850km3/a größer ist als die Eisschmelze. Dieses Eis wird von der Laptev-See in die zentrale Arktis exportiert. In der gesamten Arktis nimmt das Eisvolumen im Mittel um -450km3/a von 1990-2008 ab. usammenhänge zwischen der Eisproduktion der Laptev-See und des Eisvolumens der Arktis werden mittels einer Zeitreihenananlyse untersucht. Die Entstehungsgründe für Extremjahre (Bsp.: Minimum 2007, Maximum 1996) werden aufgezeigt und ihre regionalen Folgen in der Arktis diskutiert

Extreme Warming in the Kara Sea and Barents Sea during the Winter Period 2000–16

The regional climate model COSMOin Climate Limited-AreaMode (COSMO-CLM or CCLM) is used with a high resolution of 15km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 208C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice.Also, the 30-km version of theArctic SystemReanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 18C for the ocean and sea ice area. Thus,ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.58Cyr21 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 708N; for CCLM the warming amounts to an average of almost 58C for 2002/03–2011/12

A dream comes true: One month on the icebreaker Polarstern, measuring winds in the Arctic

Working alongside scientists of various disciplines, PhD student Svenja Kohnemann collected valuable data for her research in atmospheric sciences. PhD student Svenja Kohnemann had the adventure of a lifetime when she lived aboard the ice-breaking research vessel, Polarstern, for four weeks. Living and working in the company of scientists from an array of disciplines, she not only obtained essential results for her work in atmostpheric sciences, but also participated in others' studies of the Arctic world

Eiskalter Job : Trierer Forscherin in der Arktis

Mitten im Eismeer bei null Grad im Juni. Die Trierer Umweltforscherin Svenja Kohnemann war für ihre Doktorarbeit einen Monat lang im Polareis unterwegs. Für die junge Frau ein Traumjob

Expedition Arktis: Atmo­sphä­ri­sche Grenz­schicht­mes­sun­gen auf der Polar­stern­fahrt

Das Jahr neigt sich dem Ende ent­ge­gen. Svenja Koh­ne­mann, Dok­to­ran­din in der Umwelt­me­teo­ro­lo­gie im Fach­be­reich IV wird die­ses Jahr lange in Erin­ne­rung blei­ben. Die Arbeit an der Pro­mo­tion ermög­lichte ihr ein außer­ge­wöhn­li­ches Erleb­nis: vier Wochen For­schungs­auf­ent­halt auf dem Eis­bre­cher Polar­stern des Alfred-Wegener-Instituts (Bre­mer­ha­ven) in der Ark­tis. Ihr „Reisebericht“

A climatology of wintertime low-level jets in Nares Strait

Intense, southward low-level winds are common in Nares Strait, between Ellesmere Island and northern Greenland. The steep topography along Nares Strait leads to channelling effects, resulting in an along-strait flow. This research study presents a 30-year climatology of the flow regime from simulations of the COSMO-CLM climate model. The simulations are available for the winter periods (November–April) 1987/88 to 2016/17, and thus, cover a period long enough to give robust long-term characteristics of Nares Strait. The horizontal resolution of 15 km is high enough to represent the complex terrain and the meteorological conditions realistically. The 30-year climatology shows that LLJs associated with gap flows are a climatological feature of Nares Strait. The maximum of the mean 10-m wind speed is around 12 m s-1 and is located at the southern exit of Smith Sound. The wind speed is strongly related to the pressure gradient. Single events reach wind speeds of 40 m s-1 in the daily mean. The LLJs are associated with gap flows within the narrowest parts of the strait under stably stratified conditions, with the main LLJ occurring at 100–250 m height. With increasing mountain Froude number, the LLJ wind speed and height increase. The frequency of strong wind events (>20 m s-1 in the daily mean) for the 10 m wind shows a strong interannual variability with an average of 15 events per winter. Channelled winds have a strong impact on the formation of the North Water polynya

Wind and backscatter profiles measured by a wind lidar during POLARSTERN cruise PS106/1 (ARK-XXXI/1.1)

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]

Wind and backscatter profiles measured by a wind lidar during POLARSTERN cruise PS106/2 (ARK-XXXI/1.2)

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]

Wind and backscatter profiles measured by a wind lidar during POLARSTERN cruise PS85 (ARK-XXVIII/2)

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 [8], averaging time [1.5 sec], chosen SNR threshold [-17 dB]