Windstau rekonstruiert als Differenz zwischen Messdaten und astronomisch determinierten Anteil - Harmonisches Verfahren (T-Tide, Matlab). Dangendorf, Müller-Navarra, Jensen, Schenk, Wahl, Weisse (revised): North Sea Storminess from a novel storm surge record since AD 1843, Journal of Climate.

The coupling between the REMO atmosphere model and the MPI-OM ocean model was carried out using the OASIS coupler. OASIS is used for variables exchange and for coupling time synchronization only. A conservative bilinear interpolation routine is integrated in the MPIOM model to interpolate fields between REMO and MPIOM model grids. The HD model makes its own interpolation for REMO fields. REMO calculates heat, freshwater and momentum fluxes for each grid box and receives in turn SST, sea ice thickness and compactness from the ocean model. The atmosphere-ocean coupling frequency is set to 2 hours and the calculation of the river routing is every 24 hours.

Regional climate model COSMO-CLM developed by DWD used for dynamical downscaling of observed storm events and potential storm surge events in future GHG scenarios (see www.clm-community.eu for a detailed model description). COSMO-CLM driven by reanalysis data: The model was used for dynamical downscaling of wind for the most extreme observed historic storm surge events. As this study concentrated on the atmospheric influences we choosed storm surges with the highest wind surge values. The model data was used to compare wind speed estimates over the German Bight region with the storm surge atlas from the station Cuxhaven (provided by G. Gönnert, LSBG Hamburg). COSMO-CLM driven by ECHAM5/MPI-OM model data: To estimate possible future changes of storm surge risk due to increased GHG concentrations, the model was used to downscale events (detected in GCM model data) with high effective wind speeds (wind projected on the direction 295°) over the German Bight region. A comparison between the most intense events detected in GCM data under recent climate conditions (20C) and under possible future climate conditions (A1B) was used to estimate changes in storm surge intensity and frequency under future climate conditions. For most potential storm surge events (driven by GCM data) and for observed storm surge events (driven by ERA-Interim reanalysis data) each event is simulated by a five member mini ensemble. These members differ due to their model domain, which is shifted by 8 grid boxes to north, south, east and west. The results of this ensemble can be used to estimate the uncertainty of the storm surge event.

Hydrographic: This is the first version (1.0) of a hydrographic climatology for the region 47 to 65 °N, 15 °W to 15 °E. It contains monthly and yearly mean temperature and salinity data at 179 depth levels on a 0.25° x 0.5° latitude-longitude grid in the period 1890 to 2011. For the calculation of the means all available temperature and salinity data from water sample, CTD, and float measurements in this period were selected from the World Ocean Database 2009, the ICES (International Council for the Exploration of the Sea) data base, and the BSH data base, rejecting double stations. The derived data base consisted of about 735,000 stations. The original profiles of temperature and salinity were interpolated on 179 depth levels. There was no interpolation if the gap between two measurements in the original profile was too large. The data were then sorted in 0.25° x 0.5° latitude-longitude boxes. Atmospheric: This is the first version (1.0) of a meteorological climatology for the region 47 to 65 °N, 15 °W to 15 °E. It contains monthly mean air temperature, air pressure, dew point, relative humidity and windspeed data on a 1° x 1° latitude-longitude grid in the period 1950 to 2010. For the calculation of the means all available data from voluntary ship observations and buoys measurements in this period provided quality controlled by the DWD (Deutscher Wetterdienst) were taken. Data citations: Hydrographic: Manfred Bersch, Viktor Gouretski, Remon Sadikni (2013): The hydrographic climatology of the North Sea and surrounding regions, Centre for Earth System Research and Sustainability (CEN), University of Hamburg Atmospheric: Remon Sadikni, Manfred Bersch, Annika Jahnke-Bornemann (2013): The meteorolgical climatology of the North Sea and surrounding regions, Centre for Earth System Research and Sustainability (CEN), University of Hamburg

KLIWAS Climatology of Sea Surface Temperature and Ocean Colour Fronts in the North Sea. This is the first version (1.0) of a climatology for Sea Surface Temperature and Ocean Colour Fronts for the region: 47 to 63 °N, 16 °W to 15 °E (Standard-Grid AATSR, MERIS and MODIS) 48 to 63 °N, 11 °W to 17 °E (Standard-Grid AVHRR) It contains the multi-annual, multi-seasonal, and multi-monthly means of the gradient magnitude and of the gradient vector (magnitude and direction) for a frontal zone as well as the number of observations, the number of observations of frontal zone over a defined time interval and the probability of a front observation. The development of an algorithm which automatically detects frontal positions and gradients from satellite data was driven by the need to establish a climatology for oceanographic fronts in the North. GRADHIST is a new algorithm for the detection and mapping of oceanic fronts, which is based on a combination and refinement of the gradient algorithm of Canny (1986) and the histogram algorithm of Cayula and Cornillon (1992). GRADHIST preserves the main principles of both algorithms, improves the quality of front detection and can be applied to various ocean parameters as well as to different sensors. GRADHIST has been validated using both synthetic and real data and applied to sea surface temperature and ocean colour parameters retrieved from satellite data. After the algorithm has been validated and tested the satellite data have been processed to compute the gradient magnitude and gradient direction. Based on these two parameters the temporal statistic has been derived which include the calculation of a set of statistical measures for SST and Ocean Colour front products.

Das dreidimensionale, hydrostatische, regionale Klimamodell REMO (Regional Modell) ist ein atmosphärisches Zirkulationsmodell, das die relevanten physikalischen Prozesse dynamisch berechnet. Hierdurch werden insbesondere nicht-lineare Zusammenhänge berücksichtigt. Subskalige, das heißt nicht vom Modell auflösbare physikalische Prozesse wie z. B. Konvektionsbildung, werden durch sogenannte physikalische Parametrisierungen berechnet. REMO ist aus dem Europa-Modell des Deutschen Wetterdienstes (DWD) hervorgegangen. Die prognostischen Variablen des Modells sind die horizontalen Windkomponenten, der Bodendruck, die Temperatur, die spezifische Feuchte sowie der Flüssigwassergehalt. Es kann alternativ mit den physikalischen Parametrisierungen des Europa-Modells des DWD und mit denen des globalen Klimamodells ECHAM4 betrieben werden. Für die hier vorliegenden Untersuchungen wurde REMO mit den physikalischen Parametrisierungen aus ECHAM4 verwendet, da diese auf Klimasimulationen abgestimmt sind und so außerdem eine möglichst große Konsistenz mit dem antreibenden Globalmodell erreicht wird. Literatur: JACOB, D., R. PODZUN, 1997: Sensitivity studies with the regional climate model REMO. Meteorology and Atmospheric Physics, 63 (1-2), 119-129. MAJEWSKI, D., G. DOMS, W. EDELMANN, M. GERTZ, T. HANISCH, E. HEISE, A. LINK, P. PROHL, U. SCHAETTLER, B. RITTER, 1995: Dokumentation des EM/DM-Systems. Abteilung Forschung, Deutscher Wetterdienst.

Spektrales Wellenmodel der 3. Generation, Windfelder dienen als Antrieb zur Berechnung der Wellenspektren. Für Simulationen der Nordsee wird zuerst eine Simulation des Nordost-Atlantiks mit grober Auflösung (0.75° x 0.5° lon x lat) durchgeführt. Ergebnisse dieser Simulation dienen als Randwerte für die höher aufgelöste Simulation der Nordsee (0.075° x 0.05° lon x lat). Literatur: Komen, GJ, Cavaleri L, Donelan M, Hasselmann K, Hasselmann S, and Janssen PAEM (1994): Dynamics and Modelling of Ocean Waves. Cambridge University Press, 532pp. WASA-Group (1998) Changing waves and storms in the Northeast Atlantic? Bull Am Meteorol Soc 79:741¿760 Weisse R, Günther H (2007): Wave climate and long-term changes for the southern North Sea obtained from a high- resolution hindcast 1958¿2002. Ocean Dyn 57:161¿172. doi:10.1007/s10236-006-0094-x Grabemann I, Weisse R (2008): Climate change impact on extreme wave conditions in the North Sea: an ensemble study. Ocean Dyn 58:199¿212. doi:10.1007/s10236-008-0141-x Groll N, Grabemann I and Gaslikova L (2013): North Sea wave conditions: an analysis of four transient future climate realizations. doi: 10.1007/s10236-013-0666-5

Analyse Extremer Wasserstände: Jahresperzentile der Scheitelwasserstände in der Deutschen Bucht an 13 Pegeln. Dangendorf, Müller-Navarra, Jensen, Schenk, Wahl, Weisse (revised): North Sea storminess from a novel storm surge record since AD 1843, Journal of Climate

RCA4-NEMO is the regional, coupled climate model at the SMHI. It consists of the RCA4 atmosphere model in a model domain covering the East Atlantic and Europe and the NEMO setup for the North Sea and Baltic Sea. RCA4-NEMO is a fully coupled atmosphere-ice-ocean model, where the different components are coupled every 3 hours using the Oasis3 coupler. The coupler does exchange the surface temperatures of open water and sea ice together with the ice fraction and ice albedo to the atmosphere model. From the atmosphere the ocean-ice model receives the momentum fluxes and pressure at the surface, the shortwave and non-solar heat fluxes and the freshwater fluxes due to the evaporation - precipitation. The freshwater fluxes due to the runoff are routed back into the ocean model using CaMa-Flood, coupled to the system with the same coupling frequency. Alternatively river discharge may be prescribed using external sources like data or hydrological models. Literature: Evaluation of the SMHI coupled atmosphere-ice-ocean model RCA4_NEMO; C. Dieterich, S. Schimanke, S. Wang, G. Väli, Y. Liu, R. Hordoir, L. Axell, A. Höglund, H.E.M Meier; SMHI-Report, RO 47, 2013, ISSN 0283-1112 BaltiX: A 3D Ocean Modelling Configuration for Baltic & North Sea Exchange Analysis; R. Hordoir, B. W. An, J. Haapala, C. Dieterich, S. Schimanke, A. Höglund and H.E.M. Meier; SMHI-Report, Oceanography 115, 2013, ISSN 0283-7714 The OASIS3 coupler: a European climate modelling community software; S. Valcke; Geoscientific Model Development Discussions, Vol. 5, 2012, 2139-2178, doi:10.5194/gmdd-5-2139-2012 A physically based description of floodplain inundation dynamics in a global river routing model; Dai Yamazaki, Shinjiro Kanae, Hyungjun Kim and Taikan Oki; Water Resource Research, Vol. 47, W04501, 2011, doi:10.1029/2010WR009726