Abstract
Recent research suggests the widespread existence of the signal-to-noise paradox in seasonal-to-decadal climate predictions. The essence of the paradox is that the signal-to-noise ratio in models can be unrealistically small and models may make better predictions of the observations than they predict themselves. The paradox highlights a potentially serious issue with model predictions as previous studies may underestimate the limit of predictability. The focus of this paper is two-fold: the first objective is to re-examine decadal predictability from the lens of the signal-to-noise paradox in the context of CMIP5 models. We demonstrate that decadal predictability is generally underestimated in CMIP5 models possibly due to the existence of the signal-to-noise paradox. Models underestimate decadal predictability in regions where it is likely for the paradox to exist, especially over the Tropical Atlantic Ocean and Tropical Indian Ocean and eddy-rich regions, including the Gulf Stream, Kuroshio Current, and Southern Ocean. The second objective follows from the results of the first, attempting to determine if this underestimate of decadal predictability is, at least partially, due to missing ocean mesoscale processes and features in CMIP5 models. A suite of coupled model experiments is performed with eddying and eddy-parameterized ocean component. Compared with eddy-parameterized models, the paradox is less likely to exist in eddying models, particularly over eddy-rich regions. These also happen to be regions where increased decadal predictability is identified. We hypothesize that this enhanced predictability is due to the enhanced vertical connectivity in the ocean. The presence of mesoscale ocean features and associated vertical connectivity significantly influence decadal variability, predictability, and the signal-to-noise paradox.
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References
Allan R, Ansell T (2006) A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004. J Clim 19(22):5816–5842. https://doi.org/10.1175/JCLI3937.1
Boer GJ (2004) Long time-scale potential predictability in an ensemble of coupled climate models. Clim Dyn 23(1):29–44. https://doi.org/10.1007/s00382-004-0419-8
Bryan FO, Tomas R, Dennis JM, Chelton DB, Loeb NG, McClean JL (2010) Frontal scale air–sea interaction in high-resolution coupled climate models. J Clim 23(23):6277–6291. https://doi.org/10.1175/2010JCLI3665.1
Buckley MW, DelSole T, Lozier MS, Li L (2019) Predictability of North Atlantic Sea Surface Temperature and Upper-Ocean Heat Content. J Clim 32(10):3005–3023. https://doi.org/10.1175/JCLI-D-18-0509.1
Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin X, Brönnimann S (2011) The twentieth century reanalysis project. Q J R Meteorol Soc 137(654):1–28. https://doi.org/10.1002/qj.776
Ding R, Li J, Zheng F, Feng J, Liu D (2016) Estimating the limit of decadal-scale climate predictability using observational data. Clim Dyn 46(5–6):1563–1580. https://doi.org/10.1007/s00382-015-2662-6
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(15):5620–5628. https://doi.org/10.1002/2014GL061146
Ebisuzaki W (1997) A method to estimate the statistical significance of a correlation when the data are serially correlated. J Clim 10(9):2147–2153. https://doi.org/10.1175/1520-0442(1997)010%3c2147:AMTETS%3e2.0.CO;2
Foukal NP, Lozier MS (2018) Examining the origins of ocean heat content variability in the eastern North Atlantic subpolar gyre. Geophys Res Lett 45(20):11–275. https://doi.org/10.1029/2018GL079122
Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR et al (2011) The community climate system model version 4. J Clim 24(19):4973–4991. https://doi.org/10.1175/2011JCLI4083.1
Goddard L, Kumar A, Solomon A, Smith D, Boer G, Gonzalez P, Kirtman BP (2013) A verification framework for interannual-to-decadal predictions experiments. Clim Dyn 40(1–2):245–272. https://doi.org/10.1007/s00382-012-1481-2
Gonzalez PL, Goddard L (2016) Long-lead ENSO predictability from CMIP5 decadal hindcasts. Clim Dyn 46(9–10):3127–3147. https://doi.org/10.1007/s00382-015-2757-0
Guemas V, Corti S, García-Serrano J, Doblas-Reyes FJ, Balmaseda M, Magnusson L (2013) The Indian Ocean: the region of highest skill worldwide in decadal climate prediction. J Clim 26(3):726–739. https://doi.org/10.1175/JCLI-D-12-00049.1
Gupta AS, Jourdain NC, Brown JN, Monselesan D (2013) Climate drift in the CMIP5 models. J Clim 26(21):8597–8615. https://doi.org/10.1175/JCLI-D-12-00521.1
Hameed S, Wolfe CL, Chi L (2018) Impact of the Atlantic meridional mode on Gulf Stream North Wall position. J Clim 31(21):8875–8894. https://doi.org/10.1175/JCLI-D-18-0098.1
Harlaß J, Latif M, Park W (2018) Alleviating tropical Atlantic sector biases in the Kiel climate model by enhancing horizontal and vertical atmosphere model resolution: climatology and interannual variability. Clim Dyn 50(7–8):2605–2635. https://doi.org/10.1007/s00382-017-3760-4
He J, Kirtman B, Soden BJ, Vecchi GA, Zhang H, Winton M (2018) Impact of ocean eddy resolution on the sensitivity of precipitation to CO2 increase. Geophys Res Lett 45(14):7194–7203. https://doi.org/10.1029/2018GL078235
Hirahara S, Ishii M, Fukuda Y (2014) Centennial-scale sea surface temperature analysis and its uncertainty. J Clim. https://doi.org/10.1175/JCLI-D-12-00837.1
Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Zhang HM (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 30(20):8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1
Infanti JM, Kirtman BP (2019) A comparison of CCSM4 high-resolution and low-resolution predictions for south Florida and southeast United States drought. Clim Dyn 52(11):6877–6892. https://doi.org/10.1007/s00382-018-4553-0
IPCC (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. In: Pachauri (RK), Meyer (LA) (eds) Core writing team. IPCC, Geneva, Switzerland, 151 pp
Keenlyside NS, Latif M, Jungclaus J, Kornblueh L, Roeckner E (2008) Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453(7191):84. https://doi.org/10.1038/nature06921
Kim WM, Yeager S, Chang P, Danabasoglu G (2018) Low-frequency North Atlantic climate variability in the community earth system model large ensemble. J Clim 31(2):787–813. https://doi.org/10.1175/JCLI-D-17-0193.1
Kirtman BP, Schopf PS (1998) Decadal variability in ENSO predictability and prediction. J Clim 11(11):2804–2822. https://doi.org/10.1175/1520-0442(1998)011%3c2804:DVIEPA%3e2.0.CO;2
Kirtman BP, Pegion K, Kinter SM (2005) Internal atmospheric dynamics and tropical Indo-Pacific climate variability. J Atmos Sci 62(7):2220–2233. https://doi.org/10.1175/JAS3449.1
Kirtman BP, Bitz C, Bryan F, Collins W, Dennis J, Hearn N, Stan C (2012) Impact of ocean model resolution on CCSM climate simulations. Clim Dyn 39(6):1303–1328. https://doi.org/10.1007/s00382-012-1500-3
Kirtman B, Power SB, Adedoyin AJ, Boer GJ, Bojariu R, Camilloni I, Prather M (2013) Near-term climate change: projections and predictability. In: Stocker TF et al (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Kirtman BP, Min D, Infanti JM, Kinter JL III, Paolino DA, Zhang Q, Peng P (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95(4):585–601. https://doi.org/10.1175/BAMS-D-12-00050.1
Kirtman BP, Perlin N, Siqueira L (2017) Ocean eddies and climate predictability. Chaos 27(12):126902. https://doi.org/10.1063/1.4990034
Klavans JM, Clement AC, Cane MA (2019) Variable external forcing obscures the weak relationship between the NAO and North Atlantic Multidecadal SST variability. J Clim 32(13):3847–3864. https://doi.org/10.1175/JCLI-D-18-0409.1
Knight JR, Andrews MB, Smith DM, Arribas A, Colman AW, Dunstone NJ, Scaife AA (2014) Predictions of climate several years ahead using an improved decadal prediction system. J Clim 27(20):7550–7567. https://doi.org/10.1175/JCLI-D-14-00069.1
Kravtsov S (2012) An empirical model of decadal ENSO variability. Clim Dyn 39(9–10):2377–2391. https://doi.org/10.1007/s00382-012-1424-y
Kravtsov S (2020) Dynamics and predictability of hemispheric-scale multidecadal climate variability in an observationally constrained mechanistic model. J Clim. https://doi.org/10.1175/JCLI-D-19-0778.1
Kushnir Y, Scaife AA, Arritt R, Balsamo G, Boer G, Doblas-Reyes F, Matei D (2019) Towards operational predictions of the near-term climate. Nat Clim Change 9(2):94–101. https://doi.org/10.1038/s41558-018-0359-7
Latif M, Collins M, Pohlmann H, Keenlyside N (2006) A review of predictability studies of Atlantic sector climate on decadal time scales. J Clim 19(23):5971–5987. https://doi.org/10.1175/JCLI3945.1
Li J, Sun C, Jin FF (2013) NAO implicated as a predictor of Northern Hemisphere mean temperature multidecadal variability. Geophys Res Lett 40(20):5497–5502. https://doi.org/10.1002/2013GL057877
Marzocchi A, Hirschi JJM, Holliday NP, Cunningham SA, Blaker AT, Coward AC (2015) The North Atlantic subpolar circulation in an eddy-resolving global ocean model. J Mar Syst 142:126–143. https://doi.org/10.1016/j.jmarsys.2014.10.007
Meehl GA, Goddard L, Boer G, Burgman R, Branstator G, Cassou C, Karspeck A (2014) Decadal climate prediction: an update from the trenches. Bull Am Meteorol Soc 95(2):243–267. https://doi.org/10.1175/BAMS-D-12-00241.1
Merryfield WJ, Baehr J, Batté L, Becker EJ, Butler AH, Coelho CA, Ferranti L (2020) Current and emerging developments in subseasonal to decadal prediction. Bull Am Meteorol Soc. https://doi.org/10.1175/BAMS-D-19-0037.1
Minobe S, Kuwano-Yoshida A, Komori N, Xie SP, Small RJ (2008) Influence of the Gulf Stream on the troposphere. Nature 452(7184):206–209. https://doi.org/10.1038/nature06690
Murphy LN, Bellomo K, Cane M, Clement A (2017) The role of historical forcings in simulating the observed Atlantic multidecadal oscillation. Geophys Res Lett 44(5):2472–2480. https://doi.org/10.1002/2016GL071337
Newman M (2007) Interannual to decadal predictability of tropical and North Pacific sea surface temperatures. J Clim 20(11):2333–2356. https://doi.org/10.1175/JCLI4165.1
O’Reilly CH, Weisheimer A, Woollings T, Gray LJ, MacLeod D (2019) The importance of stratospheric initial conditions for winter North Atlantic Oscillation predictability and implications for the signal-to-noise paradox. Q J R Meteorol Soc 145(718):131–146. https://doi.org/10.1002/qj.3413
Patricola CM, Li M, Xu Z, Chang P, Saravanan R, Hsieh JS (2012) An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model. Clim Dyn 39(9–10):2443–2463. https://doi.org/10.1007/s00382-012-1320-5
Poli P, Hersbach H, Dee DP, Berrisford P, Simmons AJ, Vitart F, Trémolet Y (2016) ERA-20C: An atmospheric reanalysis of the twentieth century. J Clim 29(11):4083–4097. https://doi.org/10.1175/JCLI-D-15-0556.1
Rayner NAA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res Atmos. https://doi.org/10.1029/2002JD002670
Richter I (2015) Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. Wiley Interdiscip Rev Clim Change 6(3):345–358. https://doi.org/10.1002/wcc.338
Robson JI, Sutton RT, Smith DM (2012) Initialized decadal predictions of the rapid warming of the North Atlantic Ocean in the mid 1990s. Geophys Res Lett. https://doi.org/10.1002/2016GL070559
Robson J, Polo I, Hodson DL, Stevens DP, Shaffrey LC (2018) Decadal prediction of the North Atlantic subpolar gyre in the HiGEM high-resolution climate model. Clim Dyn 50(3–4):921–937. https://doi.org/10.1007/s00382-017-3649-2
Samanta D, Karnauskas KB, Goodkin NF, Coats S, Smerdon JE, Zhang L (2018) Coupled model biases breed spurious low-frequency variability in the tropical Pacific Ocean. Geophys Res Lett 45(19):10–609. https://doi.org/10.1029/2012GL053370
Scaife AA, Smith D (2018) A signal-to-noise paradox in climate science. npj Clim Atmos Sci 1(1):28. https://doi.org/10.1038/s41612-018-0038-4
Scaife AA, Arribas A, Blockley E, Brookshaw A, Clark RT, Dunstone N, Hermanson L (2014) Skillful long-range prediction of European and North American winters. Geophys Res Lett 41(7):2514–2519. https://doi.org/10.1002/2014GL059637
Scaife AA, Camp J, Comer R, Davis P, Dunstone N, Gordon M, Roberts M (2019) Does increased atmospheric resolution improve seasonal climate predictions? Atmos Sci Lett. https://doi.org/10.1002/asl.922
Shaffrey LC, Hodson D, Robson J, Stevens DP, Hawkins E, Polo I, Smith D (2017) Decadal predictions with the HiGEM high resolution global coupled climate model: description and basic evaluation. Clim Dyn 48(1–2):297–311. https://doi.org/10.1007/s00382-016-3075-x
Siegert S, Stephenson DB, Sansom PG, Scaife AA, Eade R, Arribas A (2016) A Bayesian framework for verification and recalibration of ensemble forecasts: how uncertain is NAO predictability? J Clim 29(3):995–1012. https://doi.org/10.1175/JCLI-D-15-0196.1
Siqueira L, Kirtman BP (2016) Atlantic near-term climate variability and the role of a resolved Gulf Stream. Geophys Res Lett 43(8):3964–3972. https://doi.org/10.1002/2016GL068694
Smith DM, Eade R, Scaife AA, Caron LP, Danabasoglu G, DelSole TM, Kharin V (2019) Robust skill of decadal climate predictions. npj Clim Atmos Sci 2(1):13. https://doi.org/10.1038/s41612-019-0071-y
Smith DM, Scaife AA, Eade R, Athanasiadis P, Bellucci A, Bethke I, Danabasoglu G (2020) North Atlantic climate far more predictable than models imply. Nature 583(7818):796–800. https://doi.org/10.1038/s41586-020-2525-0
Strommen K, Palmer TN (2019) Signal and noise in regime systems: a hypothesis on the predictability of the North Atlantic Oscillation. Q J R Meteorol Soc 145(718):147–163. https://doi.org/10.1002/qj.3414
Sun C, Li J, Jin FF (2015) A delayed oscillator model for the quasi-periodic multidecadal variability of the NAO. Clim Dyn 45(7–8):2083–2099. https://doi.org/10.1007/s00382-014-2459-z
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93(4):485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Ting M, Kushnir Y, Seager R, Li C (2009) Forced and internal twentieth-century SST trends in the North Atlantic. J Clim 22(6):1469–1481. https://doi.org/10.1175/2008JCLI2561.1
Wang G, Dommenget D, Frauen C (2015) An evaluation of the CMIP3 and CMIP5 simulations in their skill of simulating the spatial structure of SST variability. Clim Dyn 44(1–2):95–114. https://doi.org/10.1007/s00382-014-2154-0
Wittenberg AT, Rosati A, Delworth TL, Vecchi GA, Zeng F (2014) ENSO modulation: is it decadally predictable? J Clim 27(7):2667–2681. https://doi.org/10.1175/JCLI-D-13-00577.1
Wouters B, Hazeleger W, Drijfhout S, Van Oldenborgh GJ, Guemas V (2013) Multiyear predictability of the North Atlantic subpolar gyre. Geophys Res Lett 40(12):3080–3084. https://doi.org/10.1002/grl.50585
Xu Z, Chang P, Richter I, Tang G (2014) Diagnosing southeast tropical Atlantic SST and ocean circulation biases in the CMIP5 ensemble. Clim Dyn 43(11):3123–3145. https://doi.org/10.1007/s00382-014-2247-9
Yan X, Zhang R, Knutson TR (2018) Underestimated AMOC variability and implications for AMV and predictability in CMIP models. Geophys Res Lett 45(9):4319–4328. https://doi.org/10.1029/2018GL077378
Yeager SG, Robson JI (2017) Recent progress in understanding and predicting Atlantic decadal climate variability. Curr Clim Change Rep 3(2):112–127. https://doi.org/10.1007/s40641-017-0064-z
Zhang R (2017) On the persistence and coherence of subpolar sea surface temperature and salinity anomalies associated with the Atlantic multidecadal variability. Geophys Res Lett 44(15):7865–7875. https://doi.org/10.1002/2017GL074342
Zhang W, Kirtman B (2019a) Estimates of decadal climate predictability from an interactive ensemble model. Geophys Res Lett 46(6):3387–3397. https://doi.org/10.1029/2018GL081307
Zhang W, Kirtman B (2019b) Understanding the signal-to-noise paradox with a simple Markov model. Geophys Res Lett 46(22):13308–13317. https://doi.org/10.1029/2019GL085159
Zhang J, Zhang R (2015) On the evolution of Atlantic meridional overturning circulation fingerprint and implications for decadal predictability in the North Atlantic. Geophys Res Lett 42(13):5419–5426. https://doi.org/10.1002/2015GL064596
Zhang L, Delworth TL, Jia L (2017) Diagnosis of decadal predictability of Southern Ocean sea surface temperature in the GFDL CM2 1 model. J Climate 30(16):6309–6328. https://doi.org/10.1175/JCLI-D-16-0537.1
Acknowledgements
The research is supported by NOAA NA18OAR4310293, NA15OAR4320064, NSF OCE1419569, OCE1559151, and DOE DE-SC0019433. All the observations and CMIP5 model data used in this study are publicly available from the links and citations provided in the manuscript. CCSM4 model codes, experiments, and outputs were performed and archived at the University of Miami Center for Computational Science.
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Zhang, W., Kirtman, B., Siqueira, L. et al. Understanding the signal-to-noise paradox in decadal climate predictability from CMIP5 and an eddying global coupled model. Clim Dyn 56, 2895–2913 (2021). https://doi.org/10.1007/s00382-020-05621-8
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DOI: https://doi.org/10.1007/s00382-020-05621-8