Skip to main content

Advertisement

Log in

A spurious warming trend in the NMME equatorial Pacific SST hindcasts

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Using seasonal hindcasts of six different models participating in the North American Multimodel Ensemble project, the trend of the predicted sea surface temperature (SST) in the tropical Pacific for 1982–2014 at each lead month and its temporal evolution with respect to the lead month are investigated for all individual models. Since the coupled models are initialized with the observed ocean, atmosphere, land states from observation-based reanalysis, some of them using their own data assimilation process, one would expect that the observed SST trend is reasonably well captured in their seasonal predictions. However, although the observed SST features a weak-cooling trend for the 33-year period with La Niña-like spatial pattern in the tropical central-eastern Pacific all year round, it is demonstrated that all models having a time-dependent realistic concentration of greenhouse gases (GHG) display a warming trend in the equatorial Pacific that amplifies as the lead-time increases. In addition, these models’ behaviors are nearly independent of the starting month of the hindcasts although the growth rates of the trend vary with the lead month. This key characteristic of the forecasted SST trend in the equatorial Pacific is also identified in the NCAR CCSM3 hindcasts that have the GHG concentration for a fixed year. This suggests that a global warming forcing may not play a significant role in generating the spurious warming trend of the coupled models’ SST hindcasts in the tropical Pacific. This model SST trend in the tropical central-eastern Pacific, which is opposite to the observed one, causes a developing El Niño-like warming bias in the forecasted SST with its peak in boreal winter. Its implications for seasonal prediction are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Note that an error in amplitude is also calibrated based on the ratio of the model’s hindcast standard deviation to the standard deviation of observed SST (1982–2010) (see Niño3.4 plume amplitude correction at http://www.cpc.ncep.noaa.gov/products/NMME/current/plume.descr.html).

  2. Data sets of the other three models (the NCAR CESM, the CMC1 CanCM3 and CanCM4) are not fully available for 1982–2014. As far as we know, the NCAR CESM hindcasts are available up to December 2010 and its real-time forecasts start from July 2016 (http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/overview.html). As for the two Canadian models (CanCM3 and CanCM4), we do not have monthly SST data of either the hindcasts or the real-time forecasts from January to November in 2011.

  3. Due to the SST data discontinuity of the NCEP CFSv2 hindcasts initialized in January, the blue curve in Fig. 7d denotes an averaged Niño3.4 SST for 1982–2010.

References

  • Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart J Roy Meteor Soc 139:1132–1161. doi:10.1002/qj.2063

    Article  Google Scholar 

  • Becker E, Van den Dool H, Zhang Q (2014) Predictability and forecast skill in NMME. J Clim 27(15):5891–5906. doi:10.1175/JCLI-D-13-00597.1

    Article  Google Scholar 

  • Delworth TL et al (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19:643–674

    Article  Google Scholar 

  • Doblas-Reyes FJ, Hagedorn R, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—II. Calibration and combination. Tellus 57 A:234–252. doi:10.1111/j.1600-0870.2005.00104.x

    Article  Google Scholar 

  • England MH, McGregor S, Spence P, Meehl GA, Timmermann A, Cai W, Gupta AS, McPhaden MJ, Purich A, Santoso A (2014) Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat Clim Change. doi:10.1038/nclimate2106

    Article  Google Scholar 

  • Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting—I. Basic concept. Tellus 57 A:219–233. doi:10.1111/j.1600-0870.2005.00103.x

    Article  Google Scholar 

  • Huang B, L’Heureux M, Lawrimore J, Liu C, Zhang HM, Banzon V, Hu ZZ, Kumar A (2013) Why did large differences arise in the sea surface temperature datasets across the tropical Pacific during 2012? J Atmos Ocean Tech 30(12):2944–2953. doi:10.1175/JTECH-D-13-00034.1

    Article  Google Scholar 

  • Huang B, L’Heureux M, Hu Z-Z, Zhang H-M (2016) Ranking the strongest ENSOs while incorporating SST uncertainty. Geophys Res Lett 43(17):9165–9172. doi:10.1002/2016GL070888

    Article  Google Scholar 

  • Huang B, Shin CS, Shukla J, Marx L, Balmaseda M, Halder S, Dirmeyer PA, Kinter JL III (2017) Reforecasting the ENSO events in the past fifty-seven years (1958–2014). J Clim. doi:10.1175/JCLI-D-16-0642.1

    Article  Google Scholar 

  • Kirtman BP, Min D (2009) Multimodel ensemble ENSO prediction with CCSM and CFS. Mon Weather Rev 137:2908–2930. doi:10.1175/2009MWR2672.1

    Article  Google Scholar 

  • Kirtman BP, Min Du, Infanti JM, Kinter JL III, Paolino DA, Zhang Q, van den Dool H, Saha S, Pena Mendez M, Becker E, Peng P, Tripp P, Huang J, DeWitt DG, Tippett MK, Barnston AG, Li S, Rosati A, Schubert SD, Rienecker M, Suarez M, Li ZE, Marshak J, Lim Y-K, Tribbia J, Pegion K, Merryfield WJ, Denis B, Wood EF (2014) The North American multi-model ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95(4):585–601. doi:10.1175/BAMS-D-12-00050.1

    Article  Google Scholar 

  • Kosaka Y, Xie SP (2013) Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501:403–407

    Article  Google Scholar 

  • Kumar A, Chen M, Zhang L, Wang W, Xue Y, Wen C, Marx L, Huang B (2012) An analysis of the nonstationarity in the bias of sea surface temperature forecasts for the NCEP climate forecast system (CFS) version 2. Mon Weather Rev 140:3003–3016. doi:10.1175/MWR-D-11-00335.1

    Article  Google Scholar 

  • L’Heureux M, Collins D, Hu Z-Z (2013) Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Niño-Southern Oscillation. Clim Dyn 40(5–6):1223–1236. doi:10.1007/s00382-012-1331-2

    Article  Google Scholar 

  • Lawrence DM, Oleson KW, Flanner MG, Fletcher CG, Lawrence PJ, Levis S, Swenson SC, Bonan GB (2012) The CCSM4 land simulation, 1850–2005: assessment of surface climate and new capabilities. J Clim 25(7):2240–2260

    Article  Google Scholar 

  • McPhaden MJ, Zhang D (2004) Pacific Ocean circulation rebounds. Geophys Res Lett 31:L18301. doi:10.1029/2004GL020727

    Article  Google Scholar 

  • McPhaden MJ, Lee T, McClurg D (2011) El Niño and its relationship to changing background conditions in the tropical Pacific. Geophys Res Lett 38:L15709. doi:10.1029/2011GL048275

    Article  Google Scholar 

  • Palmer TN et al (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteor Soc 85:853–872. doi:10.1175/BAMS-85-6-853

    Article  Google Scholar 

  • Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W, 2002: An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625. doi:10.1175/1520-0442(2002)015,1609:AIISAS.2.0.CO;2

    Article  Google Scholar 

  • Saha S et al (2010) The NCEP climate forecast system re-analysis. Bull Am Meteor Soc 91:1015–1057. doi:10.1175/2010BAMS3001.1

    Article  Google Scholar 

  • Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou Y-T, Chuang H-Y, Iredell M, Ek M, Meng J, Yang R, Peña Mendez M, van den Dool H, Zhang Q, Wang W, Chen M, Becker E (2014) The NCEP climate forecast system version 2. J Clim 27(6):2185–2208. doi:10.1175/JCLI-D-12-00823.1

    Article  Google Scholar 

  • Smith DM et al (2013) Real-time multi-model decadal climate predictions. Clim Dyn 41:2875–2888. doi:10.1007/s00382-012-1600-0

    Article  Google Scholar 

  • Vecchi GA, Delworth T, Gudgel R, Kapnick S, Rosati A, Wittenberg A, Zeng F, Anderson W, Balaji V, Dixon K, Jia L, Kim H-S, Krishnamurthy L, Msadek R, Stern WF, Underwood SD, Villarini G, Yang X, Zhang S (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27(21):7994–8016. doi:10.1175/JCLI-D-14-00158.1

    Article  Google Scholar 

  • Vernieres G, Rienecker MM, Kovach R, Keppenne CL (2012) The GEOS-iODAS: description and evaluation. In: GEOS5 technical report NASA/TM-2012-104606, vol 30. 61 pp. http://gmao.gsfc.nasa.gov/pubs/docs/Vernieres589.pdf

  • Xue Y, Huang B, Hu Z-Z, Kumar A, Wen C, Behringer D, Nadiga S (2011) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn 37:2511–2539. doi:10.1007/s00382-010-0954-4

    Article  Google Scholar 

  • Xue Y, Chen M, Kumar A, Hu Z-Z, Wang W (2013) Prediction skill and bias of tropical Pacific sea surface temperatures in the NCEP climate forecast system version 2. J Clim 26:5358–5378. doi:10.1175/JCLI-D-12-00600.1

    Article  Google Scholar 

  • Zhang S, Harrison MJ, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135(10):3541–3564. doi:10.1175/MWR3466.1

    Article  Google Scholar 

  • Zhang L, Kumar A, Wang W (2012) Influence of changes in observations on precipitation: a case study for the climate forecast system reanalysis (CFSR). J Geophys Res 117:D08105. doi:10.1029/2011JD017347

    Article  Google Scholar 

  • Zheng Z, Hu Z-Z, L’Heureux M (2016) Predictable components of ENSO evolution in real-time multimodel predictions. Sci Rep 6:35909. doi:10.1038/srep35909

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by grants from NSF (AGS-1338427), NOAA (NA14OAR4310160), and NASA (NNX14AM19G), and a grant from the Indian Institute of Tropical Meteorology and the Ministry of Earth Sciences, Government of India (MM/SERP/COLA-GMU_USA/2013/INT-2/002). We acknowledge NOAA MAPP, NSF, NASA, and the DOE that support the NMME-Phase II system, and we thank the climate modeling groups (Environment Canada, NASA, NCAR, NOAA/GFDL, NOAA/NCEP, and University of Miami) for producing and making available their model output. NOAA/NCEP, NOAA/CTB, and NOAA/CPO jointly provided coordinating support and led development of the NMME-Phase II system. We also acknowledge the Extreme Science and Engineering Discovery Environment (XSEDE) for providing the computational resources for the reforecast project. Finally, we thank the editor and five anonymous reviewers for their constructive comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chul-Su Shin.

Additional information

This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions. This special issue is coordinated by Annarita Mariotti (NOAA), Heather Archambault (NOAA), Jin Huang (NOAA), Ben Kirtman (University of Miami) and Gabriele Villarini (University of Iowa).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shin, CS., Huang, B. A spurious warming trend in the NMME equatorial Pacific SST hindcasts. Clim Dyn 53, 7287–7303 (2019). https://doi.org/10.1007/s00382-017-3777-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-017-3777-8

Keywords

Navigation