Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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- 170 - Modelling Sea Level Variability in Different Climates of the Baltic Sea H.E. Markus Meier, Barry Broman and Erik Kjellström Swedish Meteorological and Hydrological Institute, Rossby Centre, SE-60176 Norrköping, Sweden, E-mail: markus.meier@smhi.se 1. Introduction Within the Baltic Sea Region INTERREG IIIB project `Sea level change affecting the spatial development in the Baltic Sea region' (SEAREG, 2002-2005) sea levels in past and future climate were investigated based upon 6-hourly regional model results. The main factors affecting the longterm mean sea level in the Baltic Sea are the land-uplift, the eustatic sea level rise, and the water balance of the Baltic Sea. The land uplift (or the glacio-hydro-isostatic effect) is the Earth's response to the past changes in ice and water loads. Relative to the mean sea level a maximum uplift of 9.0 mm yr -1 in the Bay of Bothnia is found from long records of observations (Ekman, 1996). The mean sea level is not stationary in time but rising relative to the geoid. This eustatic sea level rise is estimated to be 1-2 mm yr -1 during the 20 th century (Church et al., 2001). In global atmosphere-ocean general circulation models (AOGCMs), at least a third of the 20 th century anthropogenic eustatic sea level rise is caused by thermal expansion, which has a geographically non-uniform signal in sea level change (Church et al., 2001). Other factors are the recent melting of glaciers and ice caps in Greenland and Antarctica and the long-term development of ice sheets. The largest uncertainty of the model results is in the terrestrial storage terms. The global average sea level is projected to rise from 1990 to 2100 in the range between 0.09 and 0.88 m (Church et al., 2001). Thereby it is assumed that the West Antarctic ice sheet, containing ice to rise the global average sea level by 6 m, is stable. The third factor affecting the long-term mean sea level is the water balance of the Baltic Sea, which is closely related to the sea level pressure (SLP) patterns over the North Atlantic. The dominant pattern over Northern Europe in winter is the North Atlantic Oscillation (NAO). The sea level variability on time scales longer than 1 yr correlates significantly with the NAO index. In greenhouse gas scenarios of some global models an increase of the NAO index was found giving rise to an increased winter mean wind speed. Consequently, an increase of the winter mean sea level in the Baltic Sea should be expected. 2. Method For the future climate the Rossby Centre Atmosphere Ocean model RCAO was used to perform a set of 30 yr long time slice experiments (Räisänen et al., 2004). For each of the two driving global models HadAM3H and ECHAM4/OPYC3, one control run (1961-1990) and two scenario runs (2071-2100) based upon the SRES emission scenarios A2 and B2 were conducted. To estimate the impact of the uncertainties of the global and regional model results and of the emission scenarios of anthropogenic greenhouse gases we calculated in this study three sea level scenarios. Firstly, a `worst case' scenario is estimated using the regional model results with the largest monthly mean sea level increase together with the upper limit for the global average sea level rise of 0.88 m. Secondly, an ensemble average is calculated from the four regional scenarios assuming a global average sea level rise of 0.48 m which is the central value for all scenarios (not only A2 and B2) presented by Church et al. (2001). Thirdly, a `best case' scenario is estimated using the regional model with the smallest (i.e. no) monthly mean sea level change together with the lower limit for the global average sea level rise of 0.09 m. We do not imply that the ensemble average scenario is the best estimate. The three scenarios were selected just to illustrate the range of uncertainty. For the calculation of the sea level variability of the 20 th century we have performed hindcast simulations with RCO using reconstructed atmospheric forcing fields for 1903-1998 (Kauker and Meier, 2003; Meier and Kauker, 2003). For further details of the model strategy and validation the reader is referred to Meier et al. (2004). 3. Results In our `best case' scenario the future winter mean sea surface height (SSH) in the Baltic Sea is lower compared to the annual mean SSH of the control climate except in the regions with subsidence close to the German and Polish coasts (Fig.1). In this scenario the overall land uplift is larger than the assumed global average sea level rise of only 9 cm. The calculated SSH increase in the southern Baltic is very small. In the ensemble average the future mean SSH is increasing in the southern Baltic, Baltic proper and Gulf of Finland and decreasing in the Bay of Bothnia and Bothnian Sea. The largest increase is found in the southern Baltic and in the eastern Gulf of Finland. In our `worst case' scenario a future mean SSH increase is found in the entire model domain. The projected winter mean sea level changes for 2071-2100 are generally larger than the biases of the control simulations. In principial, we followed the same strategy to calculate scenarios for the winter mean 99% quantiles of the sea level. However, as the annual cycles of the winter mean 99% quantiles of the sea level in both control simulations are biased, we applied the so-called ∆-change approach to calculate projections of future extremes for 2071-2100. The changes of the winter mean 99% quantiles between scenario and control simulations were added to the winter mean 99% quantiles in the hindcast simulation relative to the mean sea level during 1961-1990. Further, the rise of the global average sea level calculated from GCM scenarios and land uplift were considered. For practical purposes one might be interested in estimating extreme events which are even more rare than the winter mean 99% quantiles of the sea level. However, due to the short time slice of 30 yr we cannot calculate statistically significant changes for them. Therefore, we assume in the following that the variability of extremes recorded during the 20 th century will not change in future. As the simulated changes of the extremes relative to the monthly mean sea levels are small compared to the height of observed extremes during 1903-1998 the omission of this possible contribution is justified. We applied again the ∆-change approach using now results of the hindcast simulation for 1903-1998 and the changes of the monthly mean sea level between the scenario and control simulations to estimate the probability for the sea level exceeding certain levels at eigth selected stations. In present climate a probability of at least 0.01% to exceed a sea level of 160 cm above the mean sea level is found only
at the stations Pärnu (Estonia) and St.Petersburg (Russia). In Greifswald (Germany), Stockholm (Sweden), Gdansk (Poland), and Helsinki (Finland) such a risk is calculated only for the `worst case' scenario and in Pärnu and Hamina (Finland) also for the ensemble average scenario. In St.Petersburg is the probability to exceed 160 cm in all three scenarios higher than 0.01%. Figure 1. Climatological winter (December through February) mean SSH (in cm) relative to the annual mean SSH for 1961-1990. Upper panel: best case scenario with a sea level rise of 9 cm. Middel panel: ensemble average with a sea level rise of 48 cm. Lower panel: worst case scenario with a sea level rise of 88 cm. Contour interval: 10 cm. 4. Conclusions 1) The results of the RCO hindcast experiment for 1903- 1998 are quite close to observations. The climate of the past century for the mean SSH, the mean annual cycle, and storm - 171 - surges is reliable simulated emphasizing the high quality of the reconstructed surface wind and SLP fields. The biases of the simulated mean sea levels are mainly associated to uncertainties of the wind fields rather than to uncertainties of the freshwater inflow. The statistics of storm surges in the Baltic proper and in the gulfs are simulated correctly. 2) In the two control simulations of RCAO with boundary data from two different global models the mean SSH is simulated well but the mean annual cycle is biased and sea level extremes are significantly underestimated in the entire model domain. Thus a straight forward application of the scenarios to project future storm surges is impossible. State-of-the-art scenarios are based upon the ∆-change approach. 3) There is no agreement between our sea level scenarios due to the large range of projected eustatic sea level rises. In addition, it is unclear if the regional wind (including the volume balance) will change or not giving rise to a large degree of uncertainty of the scenarios. Therefore, it is impossible to quantify the risk for coastal areas from sea level rise and storm surges in future climate. 4) Extremes may increase stronger than monthly mean sea levels. In the ECHAM4/OPYC3 scenarios the stability decreases where the ice is melting so that the storminess is increasing locally. Such regional features will be resolved only if a regional climate model for the downscaling of global change as simulated with GCMs is used. References Church, J.A., J.M. Gregory, P. Huybrechts, M. Kuhn, K. Lambeck, M.T. Nhuan, D. Qin, P.L. Woodworth, Changes in sea level, in: Climate change 2001: The scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change, edited by J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, C.A. Johnson, chap.11, pp. 640-693, Cambridge University Press, Cambridge, 2001 Ekman, M., A consistent map of the postglacial uplift of Fennoscandia, Terra Nova, 8, 158-165, 1996 Kauker, F., H.E.M. Meier, Modeling decadal variability of the Baltic Sea: 1. Reconstructing atmospheric surface data for the period 1902-1998, J. Geophys. Res., 108(C8), 3267, 2003 Meier, H.E.M., F. Kauker, Modeling decadal variability of the Baltic Sea: 2. Role of freshwater inflow and largescale atmospheric circulation for salinity. J. Geophys. Res., 108(C11), 3368, 2003a Meier, H.E.M., B. Broman, E. Kjellström, Simulated sea level in past and future climate of the Baltic Sea, Clim. Res., submitted, 2004 Räisänen, J., U. Hansson, A. Ullerstig, R. Döscher, L.P. Graham, C. Jones, H.E.M. Meier, P. Samuelsson, U. Willén, European climate in the late 21st century: regional simulations with two driving global models and two forcing scenarios. Clim. Dyn., 22, 13-31, 2004
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Fourth Stu
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Preface The 4 th Study</str
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- I - Table of Abstracts Title Auth
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- III - Sensitivity in Calculation
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- V - Parameter Estimation of the S
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- VII - The Realism of the ECHAM5.2
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Adam, W. K. .......................
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- 1 - Activities of the GEWEX Hydro
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eference site archive (Cabauw, Neth
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- 5 - Remote Sensing of Atmospheric
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minute averages around the satellit
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- 9 - Precipitation Type Statistics
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- 11 - Assimilation of New Land Sur
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Multichannel Microwave Radiometer f
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output has been processed in an equ
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height dependence of the Z-R-relati
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- 19 - CERES and Surface Radiation
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- 21 - Coastal Wind Mapping from Sa
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- 23 - Clouds and Water Vapor over
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- 25 - Broadband Cloud Albedo from
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- 27 - Observation of Clouds and Wa
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shows a ground-track of the vessel
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- 31 - Determination and Comparison
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- 33 - Spatial Variability of Snow
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- 35 - EVA-GRIPS: Regional Evaporat
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- 37 - LITFASS-2003 - A Land Surfac
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-39- Calibrated Surface Temperature
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- 41 - The Marine Boundary Layer -
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- 43 - Characteristics of the Atmos
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Figure 2. Surface stress from two d
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During the experiment the water was
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While the number of smallest drops
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SCANDIA will not be supported any l
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- 53 - Relationships Between Precip
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- 55 - Analysis of the Role of Atmo
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- 57 - Meteorological Peculiarities
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Baltic Sea Inflow Events Jan Piechu
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- 61 - The Different Baltic Inflows
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- 63 - Observations of Turbulent Ki
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- 65 - The Influence of Synoptic Si
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-67- Improved Method for the Determ
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-69- The Helicopter-Borne Turbulenc
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- 71 - CEOP Reference Site Data fro
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- 73 - The BALTEX Hydrological Data
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- 75 - Hydrological and Hydrochemic
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-77- Sea-level Monitoring at MARNET
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1 0.8 0.6 0.4 0.2 GO(CLD-M) ERA40 S
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Figure 2. Monhly mean values of lat
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In the case of an ideal fit, the co
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- 85 - Influence of Atmospheric For
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The largest inter-annual variabilit
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References Andrejev, O, Sokolov, A.
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On the other hand, if the stratific
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Cyberska 1989, Cyberska and Krzymin
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- 95 - Continental-Scale Water-Bala
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- 97 - ICTS (Inter-CSE Transferabil
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The subsurface flow calculation is
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functions only data with higher qua
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- 103 - Modelling the Impact of Ine
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- 105 - Objective Calibration of th
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- 107 - Validation of Boundary Laye
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- 109 - Simulated Dynamical Process
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spreads along isobaths (fig.2). As
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than the precipitation pattern of J
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Figure 2. Long-term variability in
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obvious from temperature charts. Ho
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Page 189 and 190: 6. Results There are many possible
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Page 199 and 200: - 183 - Generating Synthetic Daily
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Fourth Study Conference on BALTEX Scala Cinema Gudhjem

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