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Czasopismo
2019 | No. 61 (3) | 300--307
Tytuł artykułu

“Noise” in climatologically driven ocean models with different grid resolution

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The internally generated variability in the climate system, which is unrelated to any external factors, can be conceptualized as “noise”. This noise is a constitutive element of high-dimensional nonlinear models of such systems. In a three-layer nested simulation, which is forced by climatological (periodic) atmospheric forcing and includes an (almost) global model, a West-Pacific model, and South China Sea (SCS) model, we demonstrate that such “noise” builds also ocean models. They generate variability by themselves without an external forcing. The “noise” generation intensifies with higher resolution, which favors macroturbulence.
Wydawca

Czasopismo
Rocznik
Strony
300--307
Opis fizyczny
Bibliogr. 15 poz., mapa, rys., tab., wykr.
Twórcy
  • College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
  • Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
  • College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China, hvonstorch@web.de
  • Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
autor
  • College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
autor
  • Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
Bibliografia
  • [1] Bleck, R., 2002. An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Model. 4, 55-88, http://dx.doi.org/10.1016/S1463-5003(01)00012-9.
  • [2] Chen, G., Hou, Y., Chu, X., 2011. Mesoscale eddies in the South China Sea: Mean properties, spatiotemporal variability, and impact on thermohaline structure. J. Geophys. Res. 116, C06018, http://dx.doi.org/10.1029/2010JC006716.
  • [3] Chervin, R. M., Schneider, S. H., 1976. On determining the statistical significance of climate experiments with general circulation models. J. Atmos. Sci. 33 (3), 405-412, http://dx.doi.org/10.1175/1520-0469(1976)033<0405:ODTSSO>2.0.CO;2.
  • [4] Faghmous, J. H., Frenger, I., Yao, Warmka, R., Lindell, A., Kumar, V., 2015. A daily global mesoscale ocean eddy dataset from satellite altimetry. Sci. Data 2, 150028, http://dx.doi.org/10.1038/sdata.2015.28.
  • [5] Hasselmann, K., 1976. Stochastic climate models Part I. Theory. Tellus 28 (6), 473-485, http://dx.doi.org/10.1111/j.2153-3490.1976.tb00696.x.
  • [6] Hasselmann, K., 1993. Optimal fingerprints for the detection of time-dependent climate change. J. Climate 6 (10), 1957-1971, http://dx.doi.org/10.1175/1520-0442(1993)006<1957:OFFTDO>2.0.CO;2.
  • [7] Large, W. G., McWilliams, J. C., Doney, S. C., 1994. Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev. Geophys. 32 (4), 363-403, http://dx.doi.org/10.1029/94RG01872.
  • [8] Leroux, S., Penduff, T., Bessières, L., Molines, J., Brankart, J., Sérazin, G., Barnier, B., Terray, L., 2018. Intrinsic and atmospherically forced variability of the AMOC: insights from a large-ensemble ocean hindcast. J. Climate 31 (3), 1183-1203, http://dx.doi.org/10.1175/JCLI-D-17-0168.1.
  • [9] Lorenz, E. N., 1956. Empirical orthogonal functions and statistical weather prediction. Sci. Rep. No. 1, Statistical Forecasting Project. M.I.T., Cambridge, MA, 48 pp.
  • [10] von Storch, H., Zwiers, F. W., 1999. Statistical Analysis in Climate Research. Cambridge Univ. Press, Cambridge, 293-305.
  • [11] von Storch, H., von Storch, J.-S., Müller, P., 2001. Noise in the climate system — ubiquitous, constitutive and concealing. In: Engquist, B., Schmid, W. (Eds.), Mathematics Unlimited — 2001 and Beyond. Part II. Springer Verlag, Berlin, Heidelberg, 1179-1194, http://dx.doi.org/10.1007/978-3-642-56478-9_62.
  • [12] Wang, Y., Fang, G., Wei, Z., Qiao, F., Chen, H., 2006. Interannual variation of the South China Sea circulation and its relation to El Niño, as seen from a variable grid global ocean model. J. Geophys. Res. 111, C11S14, http://dx.doi.org/10.1029/2005JC003269.
  • [13] Weisse, R., Heyen, H., von Storch, H., 2000. Sensitivity of a regional atmospheric model to a sea state-dependent roughness and the need for ensemble calculations. Mon. Weather Rev. 128 (10), 3631-3642, http://dx.doi.org/10.1175/1520-0493(2000)128<3631:SOARAM>2.0.CO;2.
  • [14] Zhang, M., von Storch, H., 2016. Towards downscaling oceanic hydrodynamics - Suitability of a high-resolution OGCM for describing regional ocean variability in the South China Sea. Oceanologia 59 (2), 166-176, http://dx.doi.org/10.1016/j.oceano.2017.01.001.
  • [15] Zhang, M., von Storch, H., 2018. Distribution features of travelling eddies in the South China Sea. Research Activities in Atmospheric and Oceanic Modelling. 2018 Blue Book, vol. 2. 2-31.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
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