• Balzer, K., 1998: Aktuelle Herausforderungen und erste Antworten. Wettervorhersage, K. Balzer, W. Enke, and W. Wehry, Eds., Springer-Verlag, 71–92.

  • Bauer, E., and C. Staabs, 1998: Statistical properties of global significant wave heights and their use for validation. J. Geophys. Res.,103, 1153–1166.

  • Chalikov, D., and V. K. Makin, 1991: Models of the wave boundary layer. Bound.-Layer Meteor.,56, 83–99.

  • Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc.,81, 639–640.

  • Chervin, R. M., and S. H. Schneider, 1976: On determining the statistical significance of climate experiments with general circulation models. J. Atmos. Sci.,33, 405–412.

  • Cubasch, U., 1985: The mean response of the ECMWF global model to the El Niño anomaly in extended range prediction experiments. Atmos.–Ocean,23, 46–66.

  • ——, and Coauthors, 1994: Monte Carlo climate change forecasts with a global coupled ocean–atmosphere model. Climate Dyn.,10, 1–19.

  • Davies, H. C., 1976: A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc.,102, 405–418.

  • Doyle, J., 1995: Coupled ocean wave–atmosphere mesoscale model simulations of cyclogenesis. Tellus,47A, 766–778.

  • Frankignoul, C., 1995: Statistical analysis of GCM output. Analysis of Climate Variability, H. von Storch and A. Navarra, Eds., Springer, 139–152.

  • Gibson, R., P. Kållberg, and S. Uppala, 1996: The ECMWF Re-Analysis (ERA) project. ECMWF Newsletter, Vol. 73, 7–17.

  • Janssen, P. A. E. M., 1989: Wave induced stress and the drag of of air flow over sea waves. J. Phys. Oceanogr.,19, 745–754.

  • ——, 1991: Quasi-linear theory of wind wave generation applied to wave forecasting. J. Phys. Oceanogr.,21, 1631–1642.

  • ——, and P. Viterbo, 1996: Ocean waves and the atmospheric climate. J. Climate,9, 1269–1287.

  • ——, J. D. Doyle, J. Bidlot, B. Hanssen, L. Isaksen, and P. Viterbo, 1997: The impact of ocean waves on the atmosphere. Proc. Seminar on Atmosphere–Surface Interaction, ECMWF, Reading, United Kingdom, 85–111.

  • Ji, Y., and A. D. Vernekar, 1997: Simulation of the Asian summer monsoon of 1987 and 1988 with a regional model nested in a global GCM. J. Climate,10, 1965–1979.

  • Källén, E., Ed., 1996: HIRLAM documentation manual, level 2–5. SMHI, 215 pp. [Available from SMHI, S-601 75 Norrköping, Sweden.].

  • Lionello, P., P. Malguzzi, and A. Buzzi, 1998: On the coupling between the atmospheric circulation and the ocean wave field: An idealized case. J. Phys. Oceaongr.,28, 161–177.

  • Makin, V. K., and V. N. Kudryavtsev, 1999: Coupled sea surface–atmospheric model, Part 1, wind over wave coupling. J. Geophys. Res.,104, 7613–7624.

  • ——, ——, and C. Mastenbroek, 1995: Drag of the sea surface. Bound.-Layer Meteor.,73, 159–182.

  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Part A. Quart. J. Roy. Meteor. Soc.,122, 73–119.

  • Rinke, A., and K. Dethloff, 2000: On the sensitivity of a regional Arctic climate model to inital and boundary conditions. Climate Res.,16, 101–113.

  • Sass, B. H., and J. H. Christensen, 1995: A simple framework for testing the quality of atmospheric limited area models. Mon. Wea. Rev.,123, 444–459.

  • Schär, C., D. Lüthi, U. Beyerle, and E. Heise, 1999: The soil–precipitation feedback: A process study with a regional climate model. J. Climate,12, 722–741.

  • Ulbrich, U., G. Bürger, D. Schriever, H. von Storch, S. L. Weber, and G. Schmitz, 1993: The effect of a regional increase in ocean surface roughness on the tropospheric circulation: A GCM experiment. Climate Dyn.,8, 277–285.

  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 494 pp.

  • ——, H. Langenberg, and F. Feser, 1999: Long-wave forcing for regional atmospheric modelling. GKSS Rep. 99/E/46, 29 pp. [Available from GKSS Forschungszentrum Geesthacht GmbH, Max-Planck-Str. 1, D-21502 Geesthacht, Germany.].

  • WAMDI Group, 1988: The WAM Model—A third generation ocean wave prediction model. J. Phys. Oceanogr.,18, 1776–1810.

  • Weber, S. L., 1994: Statistics of air–sea fluxes of momentum and mechanical energy in a coupled wave–atmosphere model. J. Phys. Oceaongr.,24, 1388–1398.

  • ——, H. von Storch, P. Viterbo, and L. Zambresky, 1993: Coupling an ocean wave model to an atmospheric general circulation model. Climate Dyn.,9, 63–69.

  • Weisse, R., and E. F. Alvarez, 1997. The European Coupled Atmosphere–Wave–Ocean Model ECAWOM. MPI-Rep. 238, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 34 pp.

  • Wu, J., 1982: Wind stress coefficient over sea surface from breeze to hurricane. J. Geophys. Res.,87, 9704–9706.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 105 30 3
PDF Downloads 28 9 2

Sensitivity of a Regional Atmospheric Model to a Sea State–Dependent Roughness and the Need for Ensemble Calculations

Ralf WeisseGKSS Forschungszentrum Geesthacht GmbH, Institut für Gewässerphysik, Geesthacht, Germany

Search for other papers by Ralf Weisse in
Current site
Google Scholar
PubMed
Close
,
Hauke HeyenGKSS Forschungszentrum Geesthacht GmbH, Institut für Gewässerphysik, Geesthacht, Germany

Search for other papers by Hauke Heyen in
Current site
Google Scholar
PubMed
Close
, and
Hans von StorchGKSS Forschungszentrum Geesthacht GmbH, Institut für Gewässerphysik, Geesthacht, Germany

Search for other papers by Hans von Storch in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The sensitivity of an atmospheric high-resolution limited area model to a sea state–dependent roughness is examined. Two sets of Monte Carlo experiments are compared. In the first set the sea state was explicitly accounted for in the computation of the sea surface roughness. In the second set the roughness was parameterized by the standard Charnock relation. On climatic timescales of months and longer, the differences between the two sets are small. On the daily timescale large deviations between individual realizations of the two ensembles in the order of several hectopascals are occasionally found suggesting a considerable impact of the sea state–dependent roughness on the atmospheric circulation. It is shown, however, that the comparison of individual realizations, a frequently used approach in regional sensitivity studies, can be misleading. It is found here that the largest differences between the two ensembles occurred simultaneously with high inherent model variability. In these situations an eventually existing impact of the sea state–dependent roughness on the atmospheric circulation could therefore not be discriminated from the background variability and the null hypothesis that both ensembles stem from the same population could not be rejected at given risk. At times at which the internal model variability was small a statistically significant impact of the sea state–dependent roughness on the atmospheric circulation was found. However, the impact was small and it is concluded that compared with the sea state–dependent parameterization used in this study the Charnock relation represents a reasonable parameterization in regional atmospheric climate models.

Corresponding author address: Ralf Weisse, GKSS-Forschungszentrum Geesthacht, Institut für Gewässerphysik, Max-Planck-Strasse 1, D-21502 Geesthacht, Germany.

Email: weisse@gkss.de

Abstract

The sensitivity of an atmospheric high-resolution limited area model to a sea state–dependent roughness is examined. Two sets of Monte Carlo experiments are compared. In the first set the sea state was explicitly accounted for in the computation of the sea surface roughness. In the second set the roughness was parameterized by the standard Charnock relation. On climatic timescales of months and longer, the differences between the two sets are small. On the daily timescale large deviations between individual realizations of the two ensembles in the order of several hectopascals are occasionally found suggesting a considerable impact of the sea state–dependent roughness on the atmospheric circulation. It is shown, however, that the comparison of individual realizations, a frequently used approach in regional sensitivity studies, can be misleading. It is found here that the largest differences between the two ensembles occurred simultaneously with high inherent model variability. In these situations an eventually existing impact of the sea state–dependent roughness on the atmospheric circulation could therefore not be discriminated from the background variability and the null hypothesis that both ensembles stem from the same population could not be rejected at given risk. At times at which the internal model variability was small a statistically significant impact of the sea state–dependent roughness on the atmospheric circulation was found. However, the impact was small and it is concluded that compared with the sea state–dependent parameterization used in this study the Charnock relation represents a reasonable parameterization in regional atmospheric climate models.

Corresponding author address: Ralf Weisse, GKSS-Forschungszentrum Geesthacht, Institut für Gewässerphysik, Max-Planck-Strasse 1, D-21502 Geesthacht, Germany.

Email: weisse@gkss.de

Save