Abstract
In contrast to the temporal evolution of forecast ensemble mean (signal) and spread (noise) in an ensemble of seasonal forecasts, the spatial patterns of signal and noise components for sea surface temperature (SST) predictions have not been analyzed. In this work, we examine the leading patterns of signal and noise components of SST forecasts by the National Centers for Environmental Prediction Climate Forecast System version 2. It is noted that the leading empirical orthogonal function pattern of SST is similar between the signal and the noise with maximum loading in the central and eastern tropical Pacific associated with El Niño–Southern Oscillation (ENSO) variability. The similarity implies that while some members of the forecasts predict a stronger (weaker) ENSO than others, the dominant pattern of SST anomalies from all members still resembles the ENSO SST pattern. This reflects the notion that for each forecast ensemble member, the evolution of ENSO is governed by the similar air–sea coupled interactions, the strength of which, however, differs due to unpredictable noise. On the other hand, the leading mode of the signal and the noise are found temporally independent. Thus, it is concluded that although the largest variability in the signal and the noise is spatially collocated, their temporal evolution is independent.
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References
Behringer DW (2007) The Global Ocean Data Assimilation System (GODAS) at NCEP. In: Preprints, 11th symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface, San Antonio, TX, Amer. Meteor. Soc., 3.3. http://ams.confex.com/ams/87ANNUAL/techprogram/paper_119541.htm. Accessed 16 Jan 2007
Bellenger H, Guilyardi E, Leloup J, Lengaigne M, Vialard J (2014) ENSO representation in climate models: from CMIP3 to CMIP5. Clim Dyn 42(7–8):1999–2018. https://doi.org/10.1007/s00382-013-1783-z
Eade R, Smith D, Scaife A, Wallace E, Dunstone N, Hermanson L, Robinson N (2014) Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? Geophys Res Lett 41:5620–5628. https://doi.org/10.1002/2014GL061146
Frauen C, Dommenget D (2012) Influences of the tropical Indian and Atlantic Oceans on the predictability of ENSO. Geophys Res Lett 39:L02706. https://doi.org/10.1029/2011GL050520
Glantz MH (2000) Currents of change: impacts of El Niño and La Niña on climate and society. Cambridge University Press, Cambridge, p 266
Graham R et al (2011) Long-range forecasting and global framework for climate services. Clim Res 47:47–55. https://doi.org/10.3354/cr00963
Hu Z-Z, Kumar A, Huang B, Wang W, Zhu J, Wen C (2013) Prediction skill of monthly SST in the North Atlantic Ocean in NCEP climate forecast system version 2. Clim Dyn 40(11–12):2745–2756. https://doi.org/10.1007/s00382-012-1431-z
Hu Z-Z, Kumar A, Huang B, Zhu J, Guan Y (2014) Prediction skill of North Pacific variability in NCEP climate forecast system version 2: impact of ENSO and beyond. J Clim 27(11):4263–4272. https://doi.org/10.1175/JCLI-D-13-00633.1
Hu Z-Z, Kumar A, Zhu J, Peng P, Huang B (2019) On the challenge for ENSO cycle prediction: an example from NCEP climate forecast system version 2. J Clim 32(1):183–194. https://doi.org/10.1175/JCLI-D-18-0285.1
Hu Z-Z, Kumar A, Jha B, Huang B (2020) How much of monthly mean precipitation variability over global land is associated with SST anomalies? Clim Dyn 54(1–2):701–712. https://doi.org/10.1007/s00382-019-05023-5
Jha B, Kumar A (2009) A comparative analysis of change in the first and second moment of the PDF of seasonal means with ENSO SSTs. J Clim 22:1412–1423. https://doi.org/10.1175/2008JCLI2495.1
Jha B, Kumar A, Hu Z-Z (2019) An update on the estimate of predictability of seasonal mean atmospheric variability using North American Multi-Model Ensemble. Clim Dyn 53(12):7397–7409. https://doi.org/10.1007/s00382-016-3217-1
Johnson SJ et al (2019) SEAS5: the new ECMWF seasonal forecast system. Geosci Model Dev 12(3):1087–1117. https://doi.org/10.5194/gmd-12-1087-2019
Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteorol Soc 77:437–471
Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643. https://doi.org/10.1175/BAMS-83-11-1631
Kirtman BP et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601
Kumar A, Hoerling MP (1998) Annual cycle of Pacific/North American seasonal predictability associated with different phases of ENSO. J Clim 11:3295–3308. https://doi.org/10.1175/1520-0442(1998)011%3c3295:ACOPNA%3e2.0.CO;2
Kumar A, Hoerling MP (2000) Analysis of a conceptual model of seasonal climate variability and implications for seasonal prediction. Bull Am Meteorol Soc 81:255–264. https://doi.org/10.1175/1520-0477(2000)081%3c0255:AOACMO%3e2.3.CO;2
Kumar A, Hu Z-Z (2014) How variable is the uncertainty in ENSO sea surface temperature prediction? J Clim 27(7):2779–2788. https://doi.org/10.1175/JCLI-D-13-00576.1
Kumar A, Barnston AG, Peng P, Hoerling MP, Goddard L (2000) Changes in the spread of the variability of the seasonal mean atmospheric states associated with ENSO. J Clim 13:3139–3151. https://doi.org/10.1175/1520-0442(2000)013%3c3139:CITSOT%3e2.0.CO;2
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. https://doi.org/10.1175/MWR-D-11-00335.1
Kumar A, Hu Z-Z, Jha B, Peng P (2017) Estimating ENSO predictability: based on multi-model hindcasts. Clim Dyn 48(1–2):39–51. https://doi.org/10.1007/s00382-016-3060-4
Larson SM, Kirtman BP (2015) Revisiting ENSO coupled instability theory and SST error growth in a fully coupled model. J Clim 28:4724–4742. https://doi.org/10.1175/JCLI-D-14-00731.1
Larson SM, Kirtman BP (2017) Drivers of coupled model ENSO error dynamics and the spring predictability barrier. Clim Dyn 48:3631–3644. https://doi.org/10.1007/s00382-016-3290-5
Li X, Hu Z-Z, Huang B (2019) Contributions of atmosphere-ocean interaction and low-frequency variation to intensity of strong El Niño events since 1979. J Clim 32(5):1381–1394. https://doi.org/10.1175/JCLI-D-18-0209.1
Lloyd J, Guilyardi E, Weller H, Slingo J (2009) The role of atmosphere feedbacks during ENSO in the CMIP3 models. Atmos Sci Lett 10:170–176. https://doi.org/10.1175/JCLI-D-11-00178.1
MacLachlan C et al (2015) Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q J R Meteorol Soc 141:1072–1084. https://doi.org/10.1002/qj.2396
Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Q J R Meteorol Soc 122:73–119
National Research Council (2010) Assessment of Intraseasonal to Interannual Climate Prediction and Predictability. The National Academies Press, Washington, DC (ISBN-10: 0-309-15183-X)
O’Lenic EA, Unger DA, Halpert MS, Pelman KS (2008) Developments in operational long-range climate prediction at CPC. Weather Forecast 23:496–515. https://doi.org/10.1175/2007WAF2007042.1
Peng P, Kumar A (2005) A large ensemble analysis of the influence of tropical SSTs on seasonal atmospheric variability. J Clim 15:1068–1085
Peng P, Kumar A, Wang W (2011) An analysis of seasonal predictability in coupled model forecasts. Clim Dyn 36:419–430
Peng P, Kumar A, Halpert MS, Barnston AG (2012) An analysis of CPC’s operational 0.5-month lead seasonal outlooks. Weather Forecast 27:898–917. https://doi.org/10.1175/WAF-D-11-00143.1
Peng P, Kumar A, Jha B (2014) Climate mean, variability and dominant patterns of the Northern Hemisphere wintertime mean atmospheric circulation in the NCEP CFSv2. Clim Dyn 42:2783–2799. https://doi.org/10.1007/s00382-014-2116-6
Rasmusson EM, Carpenter TH (1982) Variations in tropic is surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon Weather Rev 110:354–384
Ropelewski CF, Halpert M (1987) Global and regional scale precipitation patterns associated with the El Niño–Southern Oscillation. Mon Weather Rev 115:1606–1626. https://doi.org/10.1175/1520-0493(1987)115%3c1606:GARSPP%3e2.0.CO;2
Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057. https://doi.org/10.1175/2010BAMS3001.1
Saha S et al (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1
Scaife AA, Smith D (2018) A signal-to-noise paradox in climate science. NPJ Clim Atmos Sci 1:28. https://doi.org/10.1038/s41612-018-0038-4
Tang Y, Kleeman R, Moore AM (2005) Reliability of ENSO dynamical predictions. J Atmos Sci 62:1770–1791. https://doi.org/10.1175/JAS3445.1
Tang Y, Lin H, Moore AM (2008) Measuring the potential predictability of ensemble climate predictions. J Geophys Res. https://doi.org/10.1029/2007JD008804
Tompkins AM et al (2017) The climate-system historical forecast project: providing open access to seasonal forecast ensembles from centers around the globe. Bull Am Meteorol Soc 98:2293–2301. https://doi.org/10.1175/BAMS-D-16-0209.1
Wu R, Kirtman BP, Pegion K (2006) Local air–sea relationship in observations and model simulations. J Clim 19:4914–4932. https://doi.org/10.1175/JCLI3904.1
Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558
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(11–12):2511–2539. https://doi.org/10.1007/s00382-010-0954-4
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(15):5358–5378. https://doi.org/10.1175/JCLI-D-12-00600.1
Acknowledgements
The authors appreciate the constructive comments from Dr. V. Krishnamurthy and two anonymous reviewers which help us to improve the paper significantly. The scientific results and conclusions, as well as any view or opinions expressed herein, are those of the authors and do not necessarily reflect the views of NWS, NOAA, or the Department of Commerce.
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Hu, ZZ., Kumar, A. & Zhu, J. Dominant modes of ensemble mean signal and noise in seasonal forecasts of SST. Clim Dyn 56, 1251–1264 (2021). https://doi.org/10.1007/s00382-020-05531-9
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DOI: https://doi.org/10.1007/s00382-020-05531-9