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Potential predictability of Northern America surface temperature in AGCMs and CGCMs

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Abstract

In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mode and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extracting prediction targets.

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Notes

  1. The SST prediction is made using either a persistent model or another forecast model (e.g., a statistical model).

References

  • Allen MR, Smith LA (1997) Optimal filtering in singular spectrumanalysis. Phys Lett 234:419–428

    Article  Google Scholar 

  • Barnett TP, Preisendorfer R (1987) Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon Weather Rev 115:1825–1850

    Article  Google Scholar 

  • Barnston AG, Smith TM (1996) Specification and prediction of global surface temperature and precipitation from global SST using CCA. J Clim 9:2660–2697

    Article  Google Scholar 

  • Brunsell NA, Wilson CJ (2013) Multiscale interactions between water and carbon fluxes and environmental variables in a central U.S. Grassland. Entropy 15:1324–1341. doi:10.3390/e15041324

    Article  Google Scholar 

  • DelSole T (2004) Predictability and information theory. Part I: measures of predictability. J Atmos Sci 61:2425–2440

    Article  Google Scholar 

  • DelSole T, Tippett MK (2007) Predictability: recent insights from information theory. Rev Geophys 45:RG4002. doi:10.1029/2006RG000202

    Article  Google Scholar 

  • Derome J, Brunet G, Plante A, Gagnon N, Boer GJ, Zwiers FW, Lambert S, Ritchie H (2001) Seasonal predictions based on two dynamical models. Atmos Ocean 39:485–501

    Article  Google Scholar 

  • Doblas-Reyes FJ et al (2009) Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts. doi:10.1002/qj.464

  • Duan W, Wei C (2012) The ‘spring predictability barrier’ for ENSO predictions and its possible mechanism: results from a fully coupled model. Int J Climatol. doi:10.1002/joc.3513

    Google Scholar 

  • Duan W, Liu X, Zhu K, Mu M (2009) Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events. J Geophys Res Ocean. doi:10.1029/2008JC004925

    Google Scholar 

  • Hoerling MP, Kumar A (2002) Atmospheric response patterns associated with tropical forcing. J Clim 15:2184–2203

    Article  Google Scholar 

  • Jha B, Kumar A (2009) A comparison of the atmospheric response to ENSO in coupled and uncoupled model simulations. Mon Weather Rev 137:479–487

    Article  Google Scholar 

  • Jia X, Lin H, Derome J (2010) Improving seasonal forecast skill of North American surface air temperature in fall using a postprocessing method. Mon Weather Rev 138:1843–1857

    Article  Google Scholar 

  • Kharin VV, Teng Q, Zwiers FW, Boer GJ, Derome J, Fontecilla JS (2009) Skill assessment of seasonal hindcasts from the Canadian Historical Forecast Project. Atmos Ocean 47:204–223

    Article  Google Scholar 

  • Kirtman BP (2003) The COLA anomaly coupled model: ensemble ENSO prediction. Mon Weather Rev 10:2324–2341

    Article  Google Scholar 

  • Kirtman BP, Min D (2009) Multimodel ensemble ENSO prediction with CCSM and CFS. Mon Weather Rev 137:2908–2930

    Article  Google Scholar 

  • Kleeman (2002) Measuring dynamical prediction utility using relative entropy. J Atmos Sci 59:2057–2072

    Article  Google Scholar 

  • Krishnamurti TN, Kishtawal CM, LaRow TE, Bachiochi DR, Zhang Z, Willford CE, Gadgil S, Surendran S (1999) Improved weather and seasonal climate prediction forecasts from multimodel superensemble. Science 285:1548–1550

    Article  Google Scholar 

  • Krishnamurti TN, Kishtawal CM, Zhang Z, LaRow T, Bachiochi D, Williford E, Gadgil S, Surendran S (2000) Multimodel ensemble forecasts for weather and seasonal climate. J Clim 13:4196–4216

    Article  Google Scholar 

  • Kug J-S, Kang I-S, Choi D-H (2008) Seasonal climate predictability with tier-one and tier-two prediction systems. Clim Dyn 31:403–416

    Article  Google Scholar 

  • Kumar A, Hoerling MP (1998) Annual cycle of pacific-North American seasonal predictability associated with different phases of ENSO. J Clim 11:3295–3308

    Article  Google Scholar 

  • Kumar A, Hoerling MP, Schubert SD, Suarez MS (2003) Variability and predictability of 200-mb seasonal mean height during summer and winter. J Geophys Res 108:4169. doi:10.1029/2002JD002728

    Article  Google Scholar 

  • Latif M, Barnett TP (1994) Causes of decadal climate variability over the north pacific and North America. Science 266:634–637

    Article  Google Scholar 

  • Lin H, Brunet G, Derome J (2008) Seasonal forecasts of Canadian winter precipitation by postprocessing GCM integrations. Mon Weather Rev 136:769–783

    Article  Google Scholar 

  • Livezey RE, Masutani M, Ji M (1996) SST-forced seasonal simulation and prediction skill for versions of the NCEP/MRF model. Bull Am Meteorol Soc 77:507–517

    Article  Google Scholar 

  • Luo J, Masson S, Behera S, Shingu S, Yamagata T (2005) Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J Clim 18:4474–4496

    Article  Google Scholar 

  • Misra V, Li H, Wu Z, DiNapoli S (2013) Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons. Clim Dyn. doi:10.1007/s00382-013-1812-y

    Google Scholar 

  • Palmer T et al (2004) Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER). Bull Am Meteorol Soc 85:853–872

    Article  Google Scholar 

  • Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2009) Toward seamless prediction: calibration of climate change projections using seasonal forecasts reply. Bull Am Meteorol Soc 90:1551–1554

    Article  Google Scholar 

  • Peng P, Kumar A, Wang W (2011) An analysis of seasonal predictability in coupled model forecasts. Clim Dyn. doi:10.1007/s00382-009-0711-8

  • Quan X, Hoerling M, Whitaker J, Bates G, Xu T (2006) Diagnosing sources of U.S. seasonal forecast skill. J Clim 19:3279–3293

    Article  Google Scholar 

  • Ritchie H (1991) Application of the semi-Lagrangian method to a multilevel spectral primitive equations model. Q J R Meteorol Soc 117:91–106

    Article  Google Scholar 

  • Rowell D (1998) Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J Clim 11:109–120

    Article  Google Scholar 

  • Schlosser CA, Kirtman BP (2005) Predictable skill and its association to sea-surface temperature variations in an ensemble climate simulation. J Geophys Res 110:D19107. doi:10.1029/2005JD005835

    Article  Google Scholar 

  • Schneider T, Griffies SM (1999) A conceptual framework for predictability studies. J Clim 12:3133–3155

    Article  Google Scholar 

  • Shabbar A, Barnston AG (1996) Skill of seasonal climate forecasts in Canada using canonical correlation analysis. Mon Weather Rev 124:2370–2385

    Article  Google Scholar 

  • Shukla J et al (1998) Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282:728–731

    Article  Google Scholar 

  • Shukla J et al (2000) Dynamical seasonal prediction. Bull Am Meteorol Soc 81:2593–2606

    Article  Google Scholar 

  • Sutton RT, Jewson SP, Rowell DP (2000) The elements of climate variability in the tropical Atlantic region. J Clim 13:3261–3284

    Article  Google Scholar 

  • Tang Y, Kleeman R, Moore A (2005) On the reliability of ENSO dynamical predictions. J Atmos Sci 62(6):1770–1791

  • Tang Y, Chen D, Yan X (2014) Potential predictability of Northern America surface temperature part I: information-based vs signal-to-noise based metrics. J Clim 27:1578–1599

    Article  Google Scholar 

  • Trenberth KE, Branstrator GW, Karoly D, Kumar A, Lau N-C, Ropelewski C (1998) Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J Geophys Res 103(C7):14 291–14 324

    Article  Google Scholar 

  • Vautard R, Plaut G, Wang R, Brunet G (1999) Seasonal prediction of North American surface air temperatures using space-time principal components. J Clim 12:380–394

    Article  Google Scholar 

  • Venzke S, Allen MR (1999) The atmospheric response over the North Atlantic to decadal changes in sea surface temperature. J Clim 12:2562–2584

    Article  Google Scholar 

  • Wang B et al (2009) Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim Dyn 33:93–117

    Article  Google Scholar 

  • Weisheimer A et al (2009) ENSEMBLES: a new multi-model ensemble for seasonal-to-annual predictions-skill and progress beyond DEMETER in forecasting tropical PacificSSTs. Geophys Res Lett 36(L21):711. doi:10.1029/2009GL040896

    Google Scholar 

  • Yan X, Tang Y (2012) An analysis of multi-model ensemble for seasonal climate predictions. Q J R Meteorol Soc. doi:10.1002/qj.2019

  • Yang D, Tang Y, Zhang Y, Yang X (2012) Information-based potential predictability of the Asian summer monsoon in a coupled model. J Geophys Res. doi:10.1029/2011JD016775

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Canada NSERC Discovery Grant, NSF of China (41276029), and the National Key Basic Research Program of China (Grant No. 2013CB430302).

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Author notes

  1. Xiaoqin Yan

    Present address: Department of Earth and Atmospheric Sciences, George Mason University, Fairfax, VA, USA

Authors and Affiliations

  1. Environmental Science and Engineering, University of Northern British Columbia, 3333 University Way, Prince George, BC, V2N 4Z9, Canada

    Youmin Tang & Xiaoqin Yan

  2. State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, China

    Youmin Tang & Dake Chen

Authors
  1. Youmin Tang
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  2. Dake Chen
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Correspondence to Youmin Tang.

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Tang, Y., Chen, D. & Yan, X. Potential predictability of Northern America surface temperature in AGCMs and CGCMs. Clim Dyn 45, 353–374 (2015). https://doi.org/10.1007/s00382-014-2335-x

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