Editorial Type:
Article
Article Type:
Research Article

Comparison of the SST-Forced Responses between Coupled and Uncoupled Climate Simulations

Hua Chen Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

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Edwin K. Schneider Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia

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Print Publication:
15 Jan 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00092.1
Page(s):
740–756
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Abstract

It is commonly assumed that a reasonable estimate of the SST-forced component of the observed atmospheric circulation is given by an atmospheric GCM (AGCM) forced with the observed SST. However, there are results that find different SST-forced responses from the observed, for example for the ENSO–monsoon relationship, and suggest that these differences are due to lack of coupling to the ocean rather than atmospheric model bias unrelated to coupling. Here, the coupling issue is isolated and examined through perfect model experiments. A coupled atmosphere–ocean GCM (CGCM) simulation and an AGCM simulation forced by the SST from the CGCM are compared to examine whether the SST-forced responses are the same. This question cannot be addressed directly, since the SST‐forced response of the CGCM is a priori unknown. Therefore, two indirect tests are applied, based on the assumption that the noise decorrelation time scale is short compared to a month.

The first test is to compare the time-lagged linear regressions of the atmospheric fields onto several SST indices (defined as the area-averaged SST anomalies in the tropics or extratropics), with SST leading the atmosphere by a month. The second test is to compare the time lagged linear covariances of several atmospheric indices (including two monsoon indices and a North Atlantic Oscillation index) and SST, with the SST leading the atmosphere by a month. Both tests find that the SST-forced responses are the same in the CGCM and SST-forced AGCM. These tests can be extended to compare the SST-forced responses between different AGCMs, CGCMs, and observations.

Current affiliation: Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.

Corresponding author address: Hua Chen, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing, 210044, China. E-mail: chenhua@nuist.edu.cn

Abstract

It is commonly assumed that a reasonable estimate of the SST-forced component of the observed atmospheric circulation is given by an atmospheric GCM (AGCM) forced with the observed SST. However, there are results that find different SST-forced responses from the observed, for example for the ENSO–monsoon relationship, and suggest that these differences are due to lack of coupling to the ocean rather than atmospheric model bias unrelated to coupling. Here, the coupling issue is isolated and examined through perfect model experiments. A coupled atmosphere–ocean GCM (CGCM) simulation and an AGCM simulation forced by the SST from the CGCM are compared to examine whether the SST-forced responses are the same. This question cannot be addressed directly, since the SST‐forced response of the CGCM is a priori unknown. Therefore, two indirect tests are applied, based on the assumption that the noise decorrelation time scale is short compared to a month.

The first test is to compare the time-lagged linear regressions of the atmospheric fields onto several SST indices (defined as the area-averaged SST anomalies in the tropics or extratropics), with SST leading the atmosphere by a month. The second test is to compare the time lagged linear covariances of several atmospheric indices (including two monsoon indices and a North Atlantic Oscillation index) and SST, with the SST leading the atmosphere by a month. Both tests find that the SST-forced responses are the same in the CGCM and SST-forced AGCM. These tests can be extended to compare the SST-forced responses between different AGCMs, CGCMs, and observations.

Current affiliation: Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.

Corresponding author address: Hua Chen, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing, 210044, China. E-mail: chenhua@nuist.edu.cn
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  • Barsugli, J. J., and D. S. Battisti, 1998: The basic effects of atmosphere–ocean thermal coupling on midlatitude variability. J. Atmos. Sci., 55, 477–493.

    • Search Google Scholar
    • Export Citation

  • Chen, H., E. K. Schneider, B. P. Kirtman, and I. Colfescu, 2013: Evaluation of weather noise and its role in climate model simulations. J. Climate, 26, 3766–3784.

    • Search Google Scholar
    • Export Citation

  • Colfescu, I., E. K. Schneider, and H. Chen, 2013: Consistency of 20th century sea level pressure trends as simulated by a coupled and uncoupled GCM. Geophys. Res. Lett.,40, 3276–3280, doi:10.1002/grl.50545.

  • Collins, W. D., and Coauthors, 2006a: The Community Climate System Model version 3 (CCSM3). J. Climate, 19, 2122–2143.

  • Collins, W. D., P. J. Rasch, B. A. Boville, J. J. Hack, J. R. McCaa, D. L. Williamson, and B. P. Briegleb, 2006b: The formulation and atmospheric simulation of the Community Atmosphere Model version 3 (CAM3). J. Climate, 19, 2144–2161.

    • Search Google Scholar
    • Export Citation

  • Compo, G. P., and P. D. Sardeshmukh, 2009: Oceanic influences on recent continental warming. Climate Dyn., 32, 333–342.

  • Copsey, D., R. Sutton, and J. R. Knight, 2006: Recent trends in sea level pressure in the Indian Ocean region. Geophys. Res. Lett.,33, L19712, doi:10.1029/2006GL027175.

  • Czaja, A., and C. Frankignoul, 1999: Influence of the North Atlantic SST on the atmospheric circulation. Geophys. Res. Lett., 26, 2969–2972.

    • Search Google Scholar
    • Export Citation

  • Fan, M., and E. K. Schneider, 2012: Observed decadal North Atlantic tripole SST variability. Part I: Weather noise forcing and coupled response. J. Atmos. Sci., 69, 35–50.

    • Search Google Scholar
    • Export Citation

  • Frankignoul, C., 1985: Sea surface temperature anomalies, planetary waves, and air–sea feedback in the middle latitudes. Rev. Geophys., 23, 357–390.

    • Search Google Scholar
    • Export Citation

  • Frankignoul, C., 1999: A cautionary note on the use of statistical atmospheric models in the middle latitudes: Comments on “Decadal variability in the North Pacific as simulated by a hybrid coupled model.” J. Climate, 12, 1871–1872.

    • Search Google Scholar
    • Export Citation

  • Frankignoul, C., and K. Hasselmann, 1977: Stochastic climate models, Part II, application to sea-surface temperature anomalies and thermocline variability. Tellus, 29, 289–305.

    • Search Google Scholar
    • Export Citation

  • Gallimore, R. G., 1995: Simulated ocean–atmosphere interaction in the North Pacific from a GCM coupled to a constant-depth mixed layer. J. Climate, 8, 1721–1737.

    • Search Google Scholar
    • Export Citation

  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, 29–55.

    • Search Google Scholar
    • Export Citation

  • Harzallah, A., and R. Sadourny, 1995: Internal versus SST-forced atmospheric variability as simulated by an atmospheric general circulation model. J. Climate, 8, 474–495.

    • Search Google Scholar
    • Export Citation

  • Hasselmann, K., 1976: Stochastic climate models, Part I. Theory. Tellus, 28, 473–485.

  • Hurrell, J., G. A. Meehl, D. Bader, T. L. Delworth, B. Kirtman, and B. Wielicki, 2009: A unified modeling approach to climate system prediction. Bull. Amer. Meteor. Soc., 90, 1819–1832.

    • Search Google Scholar
    • Export Citation

  • Kirtman, B. P., and J. Shukla, 2002: Interactive coupled ensemble: A new coupling strategy for CGCMs. Geophys. Res. Lett., 29, 1367–1370.

    • Search Google Scholar
    • Export Citation

  • Kirtman, B. P., D. M. Straus, D. Min, E. K. Schneider, and L. Siqueira, 2009: Toward linking weather and climate in the interactive ensemble NCAR climate model. Geophys. Res. Lett.,36, L13705, doi:10.1029/2009GL038389.

  • Kirtman, B. P., E. K. Schneider, D. M. Straus, D. Min, and R. Burgman, 2011: How weather impacts the forced climate response. Climate Dyn., 37, 2389–2416.

    • Search Google Scholar
    • Export Citation

  • Kumar, K., M. Hoerling, and B. Rajagopalan, 2005: Advancing dynamical prediction of Indian monsoon rainfall. Geophys. Res. Lett.,32, L08704, doi:10.1029/2004GL021979.

  • Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702–708.

    • Search Google Scholar
    • Export Citation

  • Majda, A. J., C. Franzke, and D. Crommelin, 2009: Normal forms for reduced stochastic climate models. Proc. Natl. Acad. Sci. USA, 106, 3649–3653.

    • Search Google Scholar
    • Export Citation

  • Meng, Q., M. Latif, W. Park, N. S. Keenlyside, V. A. Semenov, and T. Martin, 2012: Twentieth century Walker circulation change: Data analysis and model experiments. Climate Dyn., 38, 1757–1773.

    • Search Google Scholar
    • Export Citation

  • Newman, M., S.-I. Shin, and M. A. Alexander, 2011: Natural variation in ENSO flavors. Geophys. Res. Lett.,38, L14705, doi:10.1029/2011GL047658.

  • Penland, C., and P. D. Sardeshmukh, 1995: The optimal growth of tropical sea surface temperature anomalies. J. Climate, 8, 1999–2024.

  • Saravanan, R., 1998: Atmospheric low-frequency variability and its relationship to midlatitude SST variability: Studies using the NCAR Climate System Model. J. Climate, 11, 1386–1404.

    • Search Google Scholar
    • Export Citation

  • Schneider, E. K., and M. Fan, 2007: Weather noise forcing of surface climate variability. J. Atmos. Sci., 64, 3265–3280.

  • Schneider, E. K., and M. Fan, 2012: Observed decadal North Atlantic tripole SST variability. Part II: Diagnosis of mechanisms. J. Atmos. Sci., 69, 51–64.

    • Search Google Scholar
    • Export Citation

  • Sura, P., and P. D. Sardeshmukh, 2009: A global view of air–sea thermal coupling and related non-Gaussian SST variability. Atmos. Res., 94, 140–149.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., 1984: Signal versus noise in the Southern Oscillation. Mon. Wea. Rev., 112, 326–332.

  • Wang, B., Q. Ding, X. Fu, I. Kang, K. Jin, J. Shukla, and F. Doblas-Reyes, 2005: Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys. Res. Lett.,32, L15711, doi:10.1029/2005GL022734.

  • Weng, W., and J. D. Neelin, 1999: Analytical prototypes for ocean–atmosphere interaction at midlatitudes. J. Climate, 12, 2757–2774.

    • Search Google Scholar
    • Export Citation

  • Wu, R., and B. P. Kirtman, 2004: Impacts of the Indian Ocean on the Indian summer monsoon–ENSO relationship. J. Climate, 17, 3017–3054.

    • Search Google Scholar
    • Export Citation

  • Wu, R., B. P. Kirtman, and K. Pegion, 2006: Local air–sea relationship in observations and model simulations. J. Climate, 19, 4914–4932.

    • Search Google Scholar
    • Export Citation

  • Zebiak, S. E., and M. A. Cane, 1987: A model El Niño–Southern Oscillation. Mon. Wea. Rev., 115, 2262–2278.

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