• Barnett, T. P., , and R. W. Preisendorfer, 1987: Origins and levels of monthly and seasonal forecast skill for North American surface temperature determined by canonical correlation analysis. Mon. Wea. Rev., 115, 18251850, doi:10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Berner, J., , F. J. Doblas-Reyes, , T. N. Palmer, , G. Shutts, , and A. Weisheimer, 2008: Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. Philos. Trans. Roy. Soc. London, 366A, 25612579, doi:10.1098/rsta.2008.0033.

    • Search Google Scholar
    • Export Citation
  • Berner, J., , G. J. Shutts, , M. Leutbecher, , and T. N. Palmer, 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603626, doi:10.1175/2008JAS2677.1.

    • Search Google Scholar
    • Export Citation
  • Berner, J., , T. Jung, , and T. N. Palmer, 2012: Systematic model error: The impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations. J. Climate, 25, 49464962, doi:10.1175/JCLI-D-11-00297.1.

    • Search Google Scholar
    • Export Citation
  • Berner, J., , K. R. Fossell, , S.-Y. Ha, , J. P. Hacker, , and C. Snyder, 2015: Increasing the skill of probabilistic forecasts: Understanding performance improvements from model-error representations. Mon. Wea. Rev., 143, 12951320, doi:10.1175/MWR-D-14-00091.1.

    • Search Google Scholar
    • Export Citation
  • Berner, J., and et al. , 2016: Stochastic parameterization: Toward a new view of weather and climate models. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-15-00268.1, in press.

    • Search Google Scholar
    • Export Citation
  • Bove, M. C., , J. B. Elsner, , C. W. Landsea, , X. Niu, , and J. J. O. Brien, 1998: Effect of El Niño on U.S. landfalling hurricanes, revisited. Bull. Amer. Meteor. Soc., 79, 24772482, doi:10.1175/1520-0477(1998)079<2477:EOENOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Braconnot, P., , F. Hourdin, , S. Bony, , J. Dufresne, , J. Grandpeix, , and O. Marti, 2007: Impact of different convective cloud schemes on the simulation of the tropical seasonal cycle in a coupled ocean–atmosphere model. Climate Dyn., 29, 501520, doi:10.1007/s00382-007-0244-y.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., , M. Miller, , and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 28872908, doi:10.1002/qj.49712556006.

    • Search Google Scholar
    • Export Citation
  • Chang, P., , L. Ji, , H. Li, , and M. Flügel, 1996: Chaotic dynamics versus stochastic processes in El Niño-Southern Oscillation in coupled ocean-atmosphere models. Physica D, 98, 301320, doi:10.1016/0167-2789(96)00116-9.

    • Search Google Scholar
    • Export Citation
  • Chen, D., and et al. , 2015: Strong influence of westerly wind bursts on El Niño diversity. Nat. Geosci., 8, 339345, doi:10.1038/ngeo2399.

    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., , S. E. Zebiak, , and M. A. Cane, 2001: Relative roles of elevated heating and surface temperature gradients in driving anomalous surface winds over tropical oceans. J. Atmos. Sci., 58, 13711394, doi:10.1175/1520-0469(2001)058<1371:RROEHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Christensen, H. M., , I. M. Moroz, , and T. N. Palmer, 2015: Simulating weather regimes: Impact of stochastic and perturbed parameter schemes in a simple atmospheric model. Climate Dyn., 44, 21952214, doi:10.1007/s00382-014-2239-9.

    • Search Google Scholar
    • Export Citation
  • Compo, G. P., and et al. , 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 128, doi:10.1002/qj.776.

    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., , S. C. Bates, , B. P. Briegleb, , S. R. Jayne, , M. Jochum, , W. G. Large, , S. Peacock, , and S. G. Yeager, 2012: The CCSM4 ocean component. J. Climate, 25, 13611389, doi:10.1175/JCLI-D-11-00091.1.

    • Search Google Scholar
    • Export Citation
  • Dawson, A., , and T. N. Palmer, 2015: Simulating weather regimes: impact of model resolution and stochastic parametrisation. Climate Dyn., 44, 21772193, doi:10.1007/s00382-014-2238-x.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Eisenman, I., , L. Yu, , and E. Tziperman, 2005: Westerly wind bursts: ENSO’s tail rather than the dog? J. Climate, 18, 52245238, doi:10.1175/JCLI3588.1.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and et al. , 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Flügel, M., , P. Chang, , and C. Penland, 2004: The role of stochastic forcing in modulating ENSO predictability. J. Climate, 17, 31253140, doi:10.1175/1520-0442(2004)017<3125:TROSFI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frankcombe, L. M., , H. A. Dijkstra, , and A. S. von der Heydt, 2010: The Atlantic multidecadal oscillation: A stochastic dynamical systems view. Stochastic Physics and Climate Modelling, T. N. Palmer and P. Williams, Eds., Cambridge University Press, 287–306.

  • Gent, P. R., and et al. , 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation
  • Grieger, B., , and M. Latif, 1994: Reconstruction of the El Niño attractor with neural networks. Climate Dyn., 10, 267276, doi:10.1007/BF00228027.

    • Search Google Scholar
    • Export Citation
  • Grinsted, A., , J. C. Moore, , and S. Jevrejeva, 2004: Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes Geophys., 11, 561566, doi:10.5194/npg-11-561-2004.

    • Search Google Scholar
    • Export Citation
  • Guilyardi, E., , A. Wittenberg, , A. Fedorov, , M. Collins, , C. Wang, , A. Capotondi, , G. J. van Oldenborgh, , and T. Stockdale, 2009: Understanding El Niño in ocean–atmosphere general circulation models: Progress and challenges. Bull. Amer. Meteor. Soc., 90, 325–340, doi:10.1175/2008BAMS2387.1.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1976: Stochastic climate models: Part I. Theory. Tellus, 28A, 473485, doi:10.1111/j.2153-3490.1976.tb00696.x.

  • Huffman, G. J., , R. F. Adler, , D. T. Bolvin, , and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Search Google Scholar
    • Export Citation
  • Hunke, E. C., , and W. H. Lipscomb, 2008: CICE: The Los Alamos Sea Ice Model user’s manual, version 4. Los Alamos National Laboratory Tech. Rep. LA-CC-06-012, 76 pp.

  • Jin, F.-F., , J. D. Neelin, , and M. Ghil, 1996: El Nino/Southern Oscillation and the annual cycle: Subharmonic frequency locking and aperiodicity. Physica D, 98, 442465, doi:10.1016/0167-2789(96)00111-X.

    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., , L. Lin, , A. Timmermann, , and J. Zhao, 2007: Ensemble-mean dynamics of the ENSO recharge oscillator under state-dependent stochastic forcing. Geophys. Res. Lett., 34, L03807, doi:10.1029/2006GL027372.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and et al. , 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, doi:10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation
  • Kleeman, R., , and A. M. Moore, 1997: A theory for the limitation of ENSO predictability due to stochastic atmospheric transients. J. Atmos. Sci., 54, 753767, doi:10.1175/1520-0469(1997)054<0753:ATFTLO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and et al. , 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model Earth Syst., 3, 2011MS000045, doi:10.1029/2011MS00045.

    • Search Google Scholar
    • Export Citation
  • Levine, A. F. Z., , and F.-F. Jin, 2010: Noise-induced instability in the ENSO recharge oscillator. J. Atmos. Sci., 67, 529542, doi:10.1175/2009JAS3213.1.

    • Search Google Scholar
    • Export Citation
  • Levine, A. F. Z., , and F.-F. Jin, 2015: A simple approach to quantifying the noise–ENSO interaction. Part I: Deducing the state-dependency of the windstress forcing using monthly mean data. Climate Dyn., doi:10.1007/s00382-015-2748-1, in press.

    • Search Google Scholar
    • Export Citation
  • Lin, J. W.-B., , and J. D. Neelin, 2000: Influence of a stochastic moist convective parametrization on tropical climate variability. Geophys. Res. Lett., 27, 36913694, doi:10.1029/2000GL011964.

    • Search Google Scholar
    • Export Citation
  • Lin, J. W.-B., , and J. D. Neelin, 2003: Towards stochastic deep convective parameterization in general circulation models. Geophys. Res. Lett., 30, 1162, doi:10.1029/2002GL016203.

    • Search Google Scholar
    • Export Citation
  • Mallat, S., 2008: A Wavelet Tour of Signal Processing. 3rd ed. Academic Press, 832 pp.

  • McWilliams, J., , and P. Gent, 1978: A coupled air and sea model for the tropical Pacific. J. Atmos. Sci., 35, 962989, doi:10.1175/1520-0469(1978)035<0962:ACAASM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., , and R. Kleeman, 1999: Stochastic forcing of ENSO by the intraseasonal oscillation. J. Climate, 12, 11991220, doi:10.1175/1520-0442(1999)012<1199:SFOEBT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Münnich, M., , M. A. Cane, , and S. E. Zebiak, 1991: A study of self-excited oscillations of the tropical ocean–atmosphere system. Part II: Nonlinear case. J. Atmos. Sci., 48, 12381248, doi:10.1175/1520-0469(1991)048<1238:ASOSEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , J. H. Richter, , and M. Jochum, 2008: The impact of convection on ENSO: From a delayed oscillator to a series of events. J. Climate, 21, 59045924, doi:10.1175/2008JCLI2244.1.

    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., 1990: A hybrid coupled general circulation model for El Niño studies. J. Atmos. Sci., 47, 674693, doi:10.1175/1520-0469(1990)047<0674:AHCGCM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., , D. S. Battisti, , A. C. Hirst, , F.-F. Jin, , Y. Wakata, , T. Yamagata, , and S. E. Zebiak, 1998: ENSO theory. J. Geophys. Res., 103, 14 26114 290, doi:10.1029/97JC03424.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., , R. Buizza, , F. Doblas-Reyes, , T. Jung, , M. Leutbecher, , G. J. Shutts, , M. Steinheimer, , and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. ECMWF Tech. Rep. 598, 44 pp. [Available online at http://www.ecmwf.int/sites/default/files/elibrary/2009/11577-stochastic-parametrization-and-model-uncertainty.pdf.]

  • Penland, C., 1996: A stochastic model of Indopacific sea surface temperature anomalies. Physica D, 98, 534558, doi:10.1016/0167-2789(96)00124-8.

    • Search Google Scholar
    • Export Citation
  • Penland, C., , and P. D. Sardeshmukh, 1995: The optimal growth of tropical sea surface temperature anomalies. J. Climate, 8, 19992024, doi:10.1175/1520-0442(1995)008<1999:TOGOTS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Phillips, A. S., , C. Deser, , and J. Fasullo, 2014: A new tool for evaluating modes of variability in climate models. Eos, Trans. Amer. Geophys. Union, 95, 453455, doi:10.1002/2014EO490002.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., , and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384, doi:10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., , D. E. Parker, , E. B. Horton, , C. K. Folland, , L. V. Alexander, , D. P. Rowell, , E. C. Kent, , and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., , and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115, 16061626, doi:10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., , and M. S. Halpert, 1996: Quantifying Southern Oscillation–precipitation relationships. J. Climate, 9, 10431059, doi:10.1175/1520-0442(1996)009<1043:QSOPR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Roulston, M. S., , and J. D. Neelin, 2000: The response of an ENSO model to climate noise, weather noise and intraseasonal forcing. Geophys. Res. Lett., 27, 37233726, doi:10.1029/2000GL011941.

    • Search Google Scholar
    • Export Citation
  • Sarachik, E. S., , and M. A. Cane, 2010: The El Niño–Southern Oscillation Phenomenon.Cambridge University Press, 384 pp.

  • Sardeshmukh, P. D., 2005: Issues in stochastic parameterization. Proc. Workshop on Representation of Sub-Grid Processes Using Stochastic-Dynamic Models, Shinfield Park, Reading, ECMWF, 5–12.

  • Sardeshmukh, P. D., , C. Penland, , and M. Newman, 2001: Rossby waves in a stochastically fluctuating medium. Stochastic Climate Models, P. Imkeller and J.-S. von Storch, Eds., Birkhaueser, 359–384.

  • Sardeshmukh, P. D., , C. Penland, , and M. Newman, 2003: Drifts induced by multiplicative red noise with application to climate. Europhys. Lett., 63, 498504, doi:10.1209/epl/i2003-00550-y.

    • Search Google Scholar
    • Export Citation
  • Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 30793102, doi:10.1256/qj.04.106.

    • Search Google Scholar
    • Export Citation
  • Stone, L., , P. I. Saparin, , H. Huppert, , and C. Price, 1998: El Niño chaos: The role of noise and stochastic resonance on the ENSO cycle. Geophys. Res. Lett., 25, 175178, doi:10.1029/97GL53639.

    • Search Google Scholar
    • Export Citation
  • Suarez, M. J., , and P. S. Schopf, 1988: A delayed action oscillator for ENSO. J. Atmos. Sci., 45, 32833287, doi:10.1175/1520-0469(1988)045<3283:ADAOFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sun, D.-Z., , Y. Yu, , and T. Zhang, 2009: Tropical water vapor and cloud feedbacks in climate models: A further assessment using coupled simulations. J. Climate, 22, 12871304, doi:10.1175/2008JCLI2267.1.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., , and J. Berner, 2008: A stochastic convective approach to account for model uncertainty due to unresolved humidity variability. J. Geophys. Res., 113, D18101, doi:10.1029/2007JD009284.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., , and L. Yu, 2007: Quantifying the dependence of westerly wind bursts on the large-scale tropical Pacific SST. J. Climate, 20, 27602768, doi:10.1175/JCLI4138a.1.

    • Search Google Scholar
    • Export Citation
  • Vose, R. S., and et al. , 2012: NOAA’s merged land–ocean surface temperature analysis. Bull. Amer. Meteor. Soc., 93, 16771685, doi:10.1175/BAMS-D-11-00241.1.

    • Search Google Scholar
    • Export Citation
  • Weisheimer, A., , S. Corti, , T. N. Palmer, , and F. Vitart, 2014: Addressing model error through atmospheric stochastic physical parametrizations: Impact on the coupled ECMWF seasonal forecasting system. Philos. Trans. Roy. Soc. London, 372A, 20130290, doi:10.1098/rsta.2013.0290.

    • Search Google Scholar
    • Export Citation
  • Williams, P. D., 2012: Climatic impacts of stochastic fluctuations in air–sea fluxes. Geophys. Res. Lett., 39, L10705, doi:10.1029/2012GL051813.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., , E. S. Sarachik, , and D. S. Battisti, 1999: Thermally forced surface winds on an equatorial beta plane. J. Atmos. Sci., 56, 20292037, doi:10.1175/1520-0469(1999)056<2029:TFSWOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., , and B. Kirtman, 2006: Origin of decadal El Niño–Southern Oscillation–like variability in a coupled general circulation model. J. Geophys. Res., 111, C01009, doi:10.1029/2005JC002985.

    • Search Google Scholar
    • Export Citation
  • Zebiak, S. E., , and M. A. Cane, 1987: A model El Niño–Southern Oscillation. Mon. Wea. Rev., 115, 22622278, doi:10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 136 136 30
PDF Downloads 141 141 22

Stochastic Parameterization and El Niño–Southern Oscillation

View More View Less
  • 1 Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
© Get Permissions
Restricted access

Abstract

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration.

Denotes Open Access content.

Corresponding author address: Hannah M. Christensen, Clarendon Laboratory, Atmospheric, Oceanic and Planetary Physics, Parks Road, Oxford OX1 3PU, United Kingdom. E-mail: h.m.christensen@atm.ox.ac.uk

Abstract

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical Pacific. However, the models in the ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have large deficiencies in ENSO amplitude, spatial structure, and temporal variability. The use of stochastic parameterizations as a technique to address these pervasive errors is considered. The multiplicative stochastically perturbed parameterization tendencies (SPPT) scheme is included in coupled integrations of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4). The SPPT scheme results in a significant improvement to the representation of ENSO in CAM4, improving the power spectrum and reducing the magnitude of ENSO toward that observed. To understand the observed impact, additive and multiplicative noise in a simple delayed oscillator (DO) model of ENSO is considered. Additive noise results in an increase in ENSO amplitude, but multiplicative noise can reduce the magnitude of ENSO, as was observed for SPPT in CAM4. In light of these results, two complementary mechanisms are proposed by which the improvement occurs in CAM. Comparison of the coupled runs with a set of atmosphere-only runs indicates that SPPT first improve the variability in the zonal winds through perturbing the convective heating tendencies, which improves the variability of ENSO. In addition, SPPT improve the distribution of westerly wind bursts (WWBs), important for initiation of El Niño events, by increasing the stochastic component of WWB and reducing the overly strong dependency on SST compared to the control integration.

Denotes Open Access content.

Corresponding author address: Hannah M. Christensen, Clarendon Laboratory, Atmospheric, Oceanic and Planetary Physics, Parks Road, Oxford OX1 3PU, United Kingdom. E-mail: h.m.christensen@atm.ox.ac.uk
Save