Green, M.,J.,A., Liljebladh, B., and A., Omstedt (2006). Physical oceanography and water exchange in the Northern Kvark Strait. Continental Shelf Research, 26, 721-732.
The Northern Kvark connects the very fresh Bothnian Bay to the slightly saltier Bothnian Sa in the northern Baltic Sea. Intense field experiments were made in the strait in May 2003 and October-December 2004 to describe the physical oceanography of the strait. Three different hydrographic regimes were identified and a first-order analyses were made on each regime, neglecting friction, rotation, and horizontal gradients. The strait is shown to be barotropically blocked about 45% of the time. The rest of the time there is vertical stratification, which can be described either by two homogenous layers or by linear density and velocity profiles. The flows in the two stratified regimes are shown to be hydraulically controlled.
BALTEX (2006): BALTEX Phase II 2003-2012. Science Framework and Implementation Strategy. International BALTEX Secr., Publication, No. 34, pp.92, GKSS, Geestacht, Germany
The Baltic Sea Science Conference 2007 is to be held at the University of Rostock, March 19-23 2007. Please visit the conference's homepage for more information at http://www.bssc2007.org.
5th Study Conference on BALTEX
On 4-8 of June 2007 the 5th study conference on BALTEX will be held on the island of Saaremaa, Estonia. Visit the conference homepage for more information.
Omstedt, A. and D., Hansson, (2006). The Baltic Sea ocean climate system memory andresponse to changes in the water and heat balance components. Continental Shelf Research 26, 236-251.
The Baltic Sea climate system is analysed based on observation and mathematical modelling, and steady state and transient-response characteristics are derived and analysed. Some sensitivity experiments are also run based on observed forcing extracted from various Baltic Sea regions representing a range extending from sub-arctic to marine climatic conditions. We show that two important time scales should be considered: one is associated with the water balance (salinity) and the e-folding time is approximately 33 years; the other is associated with the heat balance and the e-folding time is approximately 1 year. Modelling demonstrates that current ocean conditions, starting from known as well as arbitrary initial conditions, can be realistically simulated. Our modelling indicates that salinity is non-linearly dependent on and strongly sensitive to changes in freshwater inflow, in accordance with the findings of other studies. The annual maximum ice extent is strongly sensitive to change in, the winter air temperature over the Baltic Sea. Calculations indicate that the sea will become almost completely ice covered or ice free at Baltic Sea winter air temperatures of 6 and 2 1C, respectively. Changes in the Baltic Sea annual mean water temperature are closely related to the air temperature above the sea surface. However, during climate warming the water and air temperatures may differ due to changes in the surface heat balance components.
Presentation of the BACC-report
The results from the BACC/HELCOM assessment will be presented 22 and 23 May 2006 in Göteborg at the Conference Centre Wallenberg. Detailed information will be available on the BALTEX/BACC website (http://www.baltex-research.eu/BACC/).
Omstedt, A., Chen, Y. and K., Wesslander, (2005). A comparison between the ERA40 and the SMHI gridded meteorological databases as applied to Baltic Sea modelling. Nordic Hydrology, 36(4), 369-380. See also Supporting material under Products.
Two gridded meteorological data sets for the Baltic Sea region, both having 1° ´ 1° horizontal resolution, were compared and analysed for use in Baltic Sea modelling. The SMHI 1° ´ 1° data set covers surface parameters with a three-hour time resolution over the 1970–2004 period. The ERA40 data cover analysed and modelled parameters for several atmospheric layers with a six-hour time resolution over the 1957–2002 period. Meteorological variables considered in this analysis were air temperature, wind speed, total cloud cover, relative humidity, and precipitation. In considering Baltic Sea modelling, we examined maximum ice extent, water temperature, salinity, and net precipitation calculations. The two data sets are largely similar and can both be used in Baltic Sea modelling. However, their horizontal resolution is too coarse for resolving marine conditions over the Baltic Sea. This implies, for example, that the ERA40 original surface winds are too low for some Baltic Sea regions. The ERA40 precipitation values are also too low compared with those of the SMHI and other available data.
Rutgersson, A., Omstedt, A. and Y., Chen, 2005. Evaluation of the heat balance components over the Baltic Sea using four gridded meteorological data bases and direct observations. Nordic Hydrology, 36(4), 381-396. See also Supporting material under Products.
In this paper, which reports on part of the BALTEX project, various components of the heat balance over the Baltic Sea are calculated using a number of gridded meteorological databases. It is the heat exchange between the Baltic Sea surface and the atmosphere that is of interest. The databases have different origins, comprising synoptic data, data re-analysed with a 3D assimilation system, an ocean model forced with gridded synoptic data, ship data, and satellite data.We compared the databases, and found that the greatest variation between them is in the long- and short-wave radiation values. However, considerable upward long-wave radiation is followed by considerable downward short-wave radiation, so the total radiation component is partly compensated for in the total budget. The variation in the total heat transport in the databases therefore appears smaller (1.5 ± 3 Wm–2) as the average and one standard deviation. The turbulent heat fluxes estimated from satellite data have very low values; this can largely be explained by the method of calculating air temperature, which also produces an unrealistic stratification over the Baltic Sea. The ERA40 data were compared with measured values and there we found a certain land influence even in the centre of the Baltic Proper. The indicated turbulent heat fluxes were too large, mainly in the fall and winter, and the sensible heat flux was too large in a downward direction in spring and summer.
International BALTEX Secretariat Publication No. 31, October 2005: BALTEX PHASE I, 1993-2002, State of the Art Report. Editors: Daniela Jacob and Anders Omstedt.181 pages
The present report summaries the main achievements within the BALTEX phase I period. The report includes 15 chapters dealing with the atmosphere, the Baltic Sea, sea ice, land-atmosphere interaction, turbulent fluxes over the Baltic Sea, precipitation, runoff, satellite applications, weather radars, GPS for remote sensing of water vapour, data assimilation, cloud observations and modeling, coupled regional climate modelling. In a synthesis chapter the major achievements are listed.The BALTEX Phase I State of the art Report is now available electronically or as a report via the BALTEX website, see http://www.gkss.de/baltex.
Chen, D. and A. Omstedt (2005). Climate-induced variability of sea level in Stockholm: Influence of air temperature and atmospheric circulation. Advances in Atmospheric Sciences. 22(5),655-664.
This study is focused on climate-induced variation of sea level in Stockholm during 1873-1995. After the effect of the land uplift is removed, the residual is characterized and related to large-scale temperature and atmospheric circulation. The residual shows an overall upward trend, although this result depends on the uplift rate used. However, the seasonal distribution of the trend is uneven. There are even two months (June and August) that show a negative trend. The significant trend in August may be linked to fresh water input that is controlled by precipitation. The influence of the atmospheric conditions on the sea level is mainly manifested through zonal winds, vorticity and temperature. While the wind is important in the period January-May, the vorticity plays a main role during June and December. A successful linear multiple-regression model linking the climatic variables (zonal winds, vorticity and mean air temperature during the past two months) and the sea level was established for each month. An independent verification of the model shows that it has a considerable skill in simulating the variability.