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Initialized Earth System prediction from subseasonal to decadal timescales

Nature Reviews Earth & Environment volume 2, pages 340–357 (2021)Cite this article

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Abstract

Initialized Earth System predictions are made by starting a numerical prediction model in a state as consistent as possible to observations and running it forward in time for up to 10 years. Skilful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, ranging from agriculture to water resource management to human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skilful predictions can be made: on S2S timescales, there has been some skill in predicting the Madden–Julian Oscillation and North Atlantic Oscillation; on S2I, in predicting the El Niño–Southern Oscillation; and on S2D, in predicting ocean and atmosphere variability in the North Atlantic region. However, challenges remain, and future work must prioritize reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could enhance predictive skill and, in turn, confidence. As numerical models progress towards Earth System models, initialized predictions are expanding to include prediction of sea ice, air pollution, and terrestrial and ocean biochemistry that can bring clear benefit to society and various stakeholders.

Key points

  • Initialization methods vary greatly across different prediction timescales, creating difficulties for seamless prediction.

  • Model error and drift limit predictability across all timescales. Although higher resolution models show promise in reducing these errors, improvements in physical parameterizations are needed to improve predictability.

  • The effects of land processes, interactions across various ocean basins and the role of stratospheric processes in predictability are not well understood.

  • Predictability on seasonal to decadal timescales is largely associated with predictability of the major modes of variability in the atmosphere and the ocean.

  • Evolution of Earth System models will lead to predictability of more societal-relevant variables spanning multiple parts of the Earth System.

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Fig. 1: Timescales and processes involved with initialized predictions.
Fig. 2: Influence of ensemble size and lead year ranges on predictive skill.
Fig. 3: Extending proxy observations of S2D variability back in time derived from corals.
Fig. 4: Impact of model drift on initialized predictions.
Fig. 5: Initialized S2S predictions of the MJO.
Fig. 6: S2D predictions and aspects of time-evolving globally averaged temperature.

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References

  1. Meehl, G. A. et al. Decadal prediction. Bull. Am. Meteorol. Soc. 90, 1467–1486 (2009).

    Google Scholar 

  2. Meehl, G. A., Hu, A., Arblaster, J. M., Fasullo, J. & Trenberth, K. E. Externally forced and internally generated decadal climate variability associated with the Interdecadal Pacific Oscillation. J. Clim. 26, 7298–7310 (2013).

    Google Scholar 

  3. Hawkins, E. & Sutton, R. (2009). The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).

    Google Scholar 

  4. Lehner, F. et al. Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst. Dynam. 11, 491–508 (2020).

    Google Scholar 

  5. Vitart, F. & Robertson, A. W. The Sub-Seasonal to Seasonal Prediction Project (S2S) and the prediction of extreme events. npj Clim. Atmos. Sci. 1, 3 (2018).

    Google Scholar 

  6. Pegion, K. et al. The Subseasonal Experiment (SubX). Bull. Amer. Meteorol. Soc. 100, 2043–2060 (2019).

    Google Scholar 

  7. Kirtman, B. P. et al. The North American multimodel ensemble: phase-1 seasonal to interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-12-00050.1 (2014).

    Article  Google Scholar 

  8. Boer, G. J. et al. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geosci. Model Dev. 9, 3751–3777 (2016).

    Google Scholar 

  9. Palmer, T. N., Doblas-Reyes, F. J., Weisheimer, A. & Rodwell, J. J. Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull. Amer. Meteorol. Soc. 89, 459–470 (2008).

    Google Scholar 

  10. Branstator, G. & Teng, H. Potential impact of initialization on decadal predictions as assessed for CMIP5 models. Geophy. Res. Lett. https://doi.org/10.1029/2012GL051974 (2012).

    Article  Google Scholar 

  11. Barnett, T. et al. On the prediction of the El Niño of 1986–1987. Science 241, 192–196 (1988).

    Google Scholar 

  12. Kushnir, Y. et al. Towards operational predictions of the near-term climate. Nat. Clim. Chang. https://doi.org/10.1038/s41558-018-0359-7 (2019).

    Article  Google Scholar 

  13. Lean, P. et al. Continuous data assimilation for global numerical weather prediction. Q J R Meteorol. Soc. https://doi.org/10.1002/qj.3917 (2020).

    Article  Google Scholar 

  14. Sandery, P. A., O’Kane, T. J., Kitsios, V. & Sakov, P. Climate model state estimation using variants of EnKF coupled data assimilation. Mon. Weather Rev. 148, 2411–2431 (2020).

    Google Scholar 

  15. Johnson, C., Hoskins, B. J. & Nichols, N. K. A singular vector perspective of 4D-Var: filtering and interpolation. Q J R Meteorol. Soc. 131, 1–19 (2005).

    Google Scholar 

  16. Magnusson, L., Nycander, J. & Kallen, E. Flow-dependent versus flow-independent initial perturbations for ensemble prediction. Tellus A 61, 194–209 (2009).

    Google Scholar 

  17. Infanti, J. M. & Kirtman, B. P. Prediction and predictability of land and atmosphere initialized CCSM4 climate forecasts over North America. J. Geophys. Res. Atmos. 121, 12,690–12,701 (2016b).

    Google Scholar 

  18. Trenary, L., DelSole, T., Tippett, M. K. & Pegion, K. A new method for determining the optimal lagged ensemble. J. Adv. Model Earth Syst. 9, 291–306 (2017).

    Google Scholar 

  19. Kirtman, B. P. & Min, D. Multi-model ensemble ENSO prediction with CCSM and CFS. Mon. Weather Rev. https://doi.org/10.1175/2009MWR2672.1 (2009).

    Article  Google Scholar 

  20. Yeager, S. G. et al. Predicting near-term changes in the earth system: a large ensemble of initialized decadal prediction simulations using the community earth system model. Bull. Am. Meteorol. Soc. 99, 1867–1886 (2018).

    Google Scholar 

  21. Smith, D. M. et al. Real-time multi-model decadal climate predictions. Clim. Dyn. 41, 2875–2888 (2013a).

    Google Scholar 

  22. MacLachlan, C. et al. Global Seasonal Forecast System version 5 (GloSea5): a high resolution seasonal forecast system. Q J R Meteorol Soc. https://doi.org/10.1002/qj.2396 (2014).

    Article  Google Scholar 

  23. Muñoz-Sabater et al. Assimilation of SMOS brightness temperatures in the ECMWF integrated forecasting system. Q J R Meteorol Soc. https://doi.org/10.1002/qj.3577 (2019).

    Article  Google Scholar 

  24. Drewitt, G., Berg, A. A., Merryfield, W. J. & Lee, W.-S. Effect of realistic soil moisture initialization on the Canadian CanCM3 seasonal forecast model. Atmos. Ocean 50, 466–474 (2012).

    Google Scholar 

  25. Polkova, I., Köhl, A. & Stammer Climate-mode initialization for decadal climate predictions. Clim. Dyn. 53, 7097–7111 (2019).

    Google Scholar 

  26. Smith, D. M., Eade, R. & Pohlmann, H. A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim. Dyn. 41, 3325–3338 (2013).

    Google Scholar 

  27. Volpi, D., Guemas, V. & Doblas-Reyes, F. J. Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state. Clim. Dyn. 49, 1181–1195 (2017).

    Google Scholar 

  28. Penny, S. G., et al. Coupled data assimilation for integrated earth system analysis and prediction: goals, challenges and recommendations. Technical report (World Meteorological Organisation, 2017).

  29. Williams, K. D. et al. The Met Office Global Coupled Model 2.0 (GC2) configuration. Geosci. Model Dev. 8, 1509–1524 (2015).

    Google Scholar 

  30. Becker, E. & Van Den Dool, H. Probabilistic seasonal forecasts in the North American multimodel ensemble: a baseline skill assessment. J. Clim. 29, 3015–3026 (2016).

    Google Scholar 

  31. Kadow, C. et al. Decadal climate predictions improved by ocean ensemble dispersion filtering. J. Adv. Model. Earth Syst. 9.2, 1138–1149 (2017).

    Google Scholar 

  32. Dobrynin, M. et al. Improved teleconnection-based dynamical seasonal predictions of boreal winter. Geophys. Res. Lett. 45, 3605–3614 (2018).

    Google Scholar 

  33. Smith, D. M. et al. North Atlantic climate far more predictable than models imply. Nature 583, 796–800 (2020).

    Google Scholar 

  34. Richter, J. H. et al. Subseasonal prediction with and without a well-represented stratosphere in CESM1. Weather and Forecasting, https://journals.ametsoc.org/view/journals/wefo/aop/WAF-D-20-0029.1/WAF-D-20-0029.1.xml (2020).

  35. Scaife, A. A. et al. Skillful long-range prediction of European and North American winters. Geophys. Res. Lett. 41, 2514–2519 (2014).

    Google Scholar 

  36. Smith, D. M. et al. Robust skill of decadal climate predictions. npj Clim. Atmos. Sci. 2, 13 (2019).

    Google Scholar 

  37. Athanasiadis, P. J. et al. Decadal predictability of North Atlantic blocking and the NAO. NPJ Clim. Atmos. Sci. 3, 20 (2020).

    Google Scholar 

  38. Nie, Y. et al. Stratospheric initial conditions provide seasonal predictability of the North Atlantic and Arctic oscillations. Env. Res. Lett. 14, 3 (2019).

    Google Scholar 

  39. Capotondi, A. et al. Understanding ENSO diversity. Bull. Am. Meteorol. Soc. 96, 921–938 (2015).

    Google Scholar 

  40. Cobb, K. M. et al. Highly variable El Niño–Southern Oscillation throughout the Holocene. Science 339, 67–70 (2013).

    Google Scholar 

  41. Capotondi, A. & Sardeshmukh, P. D. Is El Niño really changing? Geophys. Res. Lett. https://doi.org/10.1002/2017GL074515 (2017).

    Article  Google Scholar 

  42. Grothe, P. R. et al. Enhanced El Niño–Southern Oscillation variability in recent decades. Geophys. Res. Lett. https://doi.org/10.1029/2019GL083906 (2019).

    Article  Google Scholar 

  43. Deser, C., Phillips, A. S. & Alexander, M. A. Twentieth century tropical sea surface temperature trends revisited. Geophys. Res. Lett. 37, L10701 (2010).

    Google Scholar 

  44. Meehl, G. A., Arblaster, J. M., Fasullo, J., Hu, A. & Trenberth, K. E. Model-based evidence of deep ocean heat uptake during surface temperature hiatus periods. Nat. Clim. Change 1, 360–364 (2011).

    Google Scholar 

  45. Mann, M. E. & Emanuel, K. A. Atlantic hurricane trends linked to climate change. Eos 87, 233–241 (2006).

    Google Scholar 

  46. Mann, M. E., Steinman, B. A. & Miller, S. K. On forced temperature changes, internal variability and the AMO. (“Frontier” article). Geophys. Res. Lett. 41, 3211–3219 (2014).

    Google Scholar 

  47. Mann, M. E. et al. Predictability of the recent slowdown and subsequent recovery of large-scale surface warming using statistical methods. Geophys. Res. Lett. 43, 3459–3467 (2016).

    Google Scholar 

  48. Steinman, B. A., Frankcombe, L. M., Mann, M. E., Miller, S. K. & England, M. H. Response to comment on “Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures”. Science 350, 1326 (2015).

    Google Scholar 

  49. Roemmich, D. & Gilson, J. The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Prog. Oceanogr. 82, 81–100 (2009).

    Google Scholar 

  50. Roemmich, D. et al. On the future of Argo: a global, full-depth, multi-disciplinary array. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00439 (2019).

    Article  Google Scholar 

  51. Thompson, D. M., Cole, J. E., Shen, G. T., Tudhope, A. W. & Meehl, G. A. Early twentieth-century warming linked to tropical Pacific wind strength. Nat. Geosci. 8, 117–121 (2015).

    Google Scholar 

  52. Cook, E. R. et al. Megadroughts in North America: placing IPCC projections of hydroclimatic change in a long-term palaeoclimate context. J. Quat. Sci. 25, 48–61 (2010).

    Google Scholar 

  53. Emile-Geay, J., Cobb, K. M., Mann, M. E. & Wittenberg, A. T. Estimating central equatorial Pacific SST variability over the past millennium. Part II: reconstructions and implications. J. Clim. 26, 2329–2352 (2013).

    Google Scholar 

  54. Linsley, B. K., Wu, H. C., Dassié, E. P. & Schrag, D. P. Decadal changes in South Pacific sea surface temperatures and the relationship to the Pacific decadal oscillation and upper ocean heat content. Geophys. Res. Lett. 42, 2358–2366 (2015).

    Google Scholar 

  55. Buckley, B. M. et al. Interdecadal Pacific Oscillation reconstructed from trans-Pacific tree rings: 1350–2004 CE. Clim. Dyn. 53, 3181–3196 (2019).

    Google Scholar 

  56. Abram, N. J. et al. Palaeoclimate perspectives on the Indian Ocean dipole. Quat. Sci. Rev. https://doi.org/10.1016/j.quascirev.2020.106302 (2020).

    Article  Google Scholar 

  57. Sanchez, S. C., Charles, C. D., Carriquiry, J. D. & Villaescusa, J. A. Two centuries of coherent decadal climate variability across the Pacific North American region. Geophys. Res. Lett. 43, 9208–9216 (2016).

    Google Scholar 

  58. Abram, N. J. et al. Coupling of Indo-Pacific climate variability over the last millennium. Nature 579, 385–392 (2020).

    Google Scholar 

  59. Konecky, B., Dee, S. G. & Noone, D. WaxPSM: a forward model of leaf wax hydrogen isotope ratios to bridge proxy and model estimates of past climate. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2018JG004708 (2019).

    Article  Google Scholar 

  60. Neukom, R. et al. Consistent multi-decadal variability in global temperature reconstructions and simulations over the common era. Nat. Geosci. 12, 643 (2019).

    Google Scholar 

  61. McGregor, H. V. et al. Robust global ocean cooling trend for the pre-industrial common era. Nat. Geosci. 8, 671–677 (2015).

    Google Scholar 

  62. Tierney, J. E. et al. Tropical sea surface temperatures for the past four centuries reconstructed from coral archives. Paleoceanography 30, 226–252 (2015).

    Google Scholar 

  63. Goosse, H. et al. Reconstructing surface temperature changes over the past 600 years using climate model simulations with data assimilation. J. Geophys. Res. 115, D09108 (2010).

    Google Scholar 

  64. Hakim, G. J. et al. The last millennium climate reanalysis project: framework and first results. J. Geophys. Res. Atmos. 121, 6745–6764 (2016).

    Google Scholar 

  65. Steiger, N. J., Jason, E. S., Cook, E. R. & Cook, B. I. A reconstruction of global hydroclimate and dynamical variables over the common era. Sci. Data 5, 180086 (2018).

    Google Scholar 

  66. Evans, M. N., Tolwinski-Ward, S. E., Thompson, D. M. & Anchukaitis, K. J. Applications of proxy system modeling in high resolution paleoclimatology. Quat. Sci. Rev. 76, 16–28 (2013).

    Google Scholar 

  67. Dee, S. et al. PRYSM: an open-source framework for PRoxY system modeling, with applications to oxygen-isotope systems. J. Adv. Model. Earth Syst. 7, 1220–1247 (2015).

    Google Scholar 

  68. Becker, E., Dool, den, H. V. & Zhang, Q. Predictability and forecast skill in NMME. J. Clim. 27, 5891–5906 (2014).

    Google Scholar 

  69. Doblas-Reyes, F. J. et al. Initialized near-term regional climate change prediction. Nat. Commun. 4, 1715 (2013).

    Google Scholar 

  70. Meehl, G. A., Hu, A. & Teng, H. Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation. Nat. Commun. https://doi.org/10.1038/NCOMMS11718 (2016).

    Article  Google Scholar 

  71. Kharin, V. V., Boer, G. J., Merryfield, W. J., Scinocca, J. F. & Lee, W.-S. Statistical adjustment of decadal predictions in a changing climate. Geophys. Res. Lett. 39, L19705 (2012).

    Google Scholar 

  72. Scaife, A. A. & Smith, D. A signal-to-noise paradox in climate science. npj Clim. Atmos. Sci. 1, 28 (2018).

    Google Scholar 

  73. Sévellec, F. & Drijfhout, S. S. The signal-to-noise paradox for interannual surface atmospheric temperature predictions. Geophys. Res. Lett. 46, 9031–9041 (2019).

    Google Scholar 

  74. Zhang, W. & Kirtman,, B. Estimates of decadal climate predictability from an interactive ensemble model. Geophys. Res. Letts. 46, 3387–3397 (2019).

    Google Scholar 

  75. Weisheimer, A. et al. How confident are predictability estimates of the winter North Atlantic oscillation? Quart. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.3446 (2019).

    Article  Google Scholar 

  76. Barnston, A. G., Tippett, M. K., L’Heureux, M. L., Li, S. & DeWitt, D. G. Skill of real-time seasonal ENSO model predictions during 2002–11: is our capability increasing? Bull. Am. Meteorol. Soc. 93, 631–651 (2012).

    Google Scholar 

  77. Robertson, A. W. & Vitart, F. (eds) Sub-seasonal to Seasonal Prediction (Elsevier, 2018).

  78. Kim, H., Vitart, F. & Waliser, D. E. Prediction of the Madden–Julian Oscillation: a review. J. Clim. 31, 9425–9443 (2018).

    Google Scholar 

  79. Stan, C. et al. Review of tropical–extratropical teleconnections on intraseasonal time scales. Rev. Geophys. 55, 902–937 (2017).

    Google Scholar 

  80. Kim, H., Richter, J. H. & Martin, Z. Insignificant QBO–MJO prediction skill relationship in the SubX and S2S subseasonal reforecasts. J. Geophys. Res. Atmos. https://doi.org/10.1029/2019JD031416 (2019).

    Article  Google Scholar 

  81. Lim, E.-P., Hendon, H. H. & Thompson, D. W. J. Seasonal evolution of stratosphere–troposphere coupling in the southern hemisphere and implications for the predictability of surface climate. J. Geophys. Res. Atmos. 123, 1–15 (2018).

    Google Scholar 

  82. Zheng, C., Chang, E. K. M., Kim, H., Zhang, M. & Wang, W. Subseasonal to seasonal prediction of wintertime northern hemisphere extratropical cyclone activity by S2S and NMME models. J. Geophys. Res. Atmos. https://doi.org/10.1029/2019JD031252 (2019).

    Article  Google Scholar 

  83. DeFlorio, M. J. et al. Global evaluation of atmospheric river subseasonal prediction skill. Clim. Dyn. 52, 3039–3060 (2019).

    Google Scholar 

  84. Baggett, C. et al. Skillful subseasonal forecasts of weekly tornado and hail activity using the Madden–Julian Oscillation. J. Geophys. Res. Atmos. 123, 12,661–12,675 (2018).

    Google Scholar 

  85. Broennimann, S. Impact of El Niño–Southern Oscillation on European climate. Rev. Geophys. 45, RG3003 (2007).

    Google Scholar 

  86. Ambaum, P. & Hoskins, B. J. The NAO troposphere–stratosphere connection. J. Clim. 15, 1969–1978 (2002).

    Google Scholar 

  87. Kushnir, Y., Robinson, W. A., Chang, P. & Robertson, A. W. The physical basis for predicting Atlantic sector seasonal-to-interannual climate variability. J. Clim. 19, 5949–5970 (2006).

    Google Scholar 

  88. Riddle, E. E., Butler, A. H., Furtado, J. C., Cohen, J. L. & Kumar, A. CFSv2 ensemble prediction of the wintertime Arctic oscillation. Clim. Dyn. 41, 1099–1116 (2013).

    Google Scholar 

  89. Hendon, H. H., Thompson, D. W. J. & Wheeler, M. C. Australian rainfall and surface temperature variations associated with the southern hemisphere annular mode. J. Clim. 20, 2452–2467 (2007).

    Google Scholar 

  90. Marshall, A. G. et al. Intra-seasonal drivers of extreme heat over Australia in observations and POAMA-2. Clim. Dyn. 43, 1915–1937 (2014).

    Google Scholar 

  91. Seviour, W. J. M. et al. Skillful seasonal prediction of the southern annular mode and Antarctic ozone. J. Clim. 27, 7462–7474 (2014).

    Google Scholar 

  92. Lim, E.-P., Hendon, H. H. & Rashid, H. A. Seasonal predictability of the southern annular mode due to its association with ENSO. J. Clim. 26, 8037–8054 (2013).

    Google Scholar 

  93. Lim, E. et al. Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex. Nat. Geosci. 12, 896–901 (2019).

    Google Scholar 

  94. Mariotti, A. et al. Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-18-0326.1 (2020).

    Article  Google Scholar 

  95. Dirmeyer, P. A., Halder, S. & Bombardi, R. On the harvest of predictability from land states in a global forecast model. J. Geophys. Res. Atmos. 123, 13111–13127 (2018).

    Google Scholar 

  96. Koster, R. D. et al. Regions of strong coupling between soil moisture and precipitation. Science 305, 1138–1140 (2004).

    Google Scholar 

  97. Koster, R. D. et al. The second phase of the global land–atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J. Hydrometeor. 12, 805–822 (2011).

    Google Scholar 

  98. Seo, E. et al. Impact of soil moisture initialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events. Clim. Dyn. https://doi.org/10.1007/s00382-018-4221-4 (2018).

    Article  Google Scholar 

  99. Zampieri, L., Goessling, H. F. & Jung, T. Bright prospects for Arctic sea ice prediction on subseasonal time scales. Geophys. Res. Lett. 45, 9731–9738 (2018).

    Google Scholar 

  100. Zampieri, L., Goessling, H. F. & Jung, T. Predictability of Antarctic sea ice edge on subseasonal time scales. Geophys. Res. Lett. 46, 9719–9727 (2019).

    Google Scholar 

  101. Bushuk, M. et al. A mechanism for the Arctic sea ice spring predictability barrier. Geophys. Res. Lett. https://doi.org/10.1029/2020GL088335 (2020).

    Article  Google Scholar 

  102. Kimmritz, M. et al. Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic. JAMES https://doi.org/10.1029/2019MS001825 (2019).

    Article  Google Scholar 

  103. Ono, J., Komuro, Y. & Tatebe, H. Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model. Ann. Glaciol. 61, 97–105 (2020).

    Google Scholar 

  104. Liu, J. et al. Towards reliable Arctic sea ice prediction using multivariate data assimilation. Sci. Bull. 64, 63–72 (2019).

    Google Scholar 

  105. Jung, T. et al. Advancing polar prediction capabilities on daily to seasonal time scales. Bull. Am. Meteorol. Soc. 97, 1631–1647 (2016).

    Google Scholar 

  106. Jung, T., Kasper, M. A., Semmler, T. & Serrar, S. Arctic influence on subseasonal midlatitude prediction. Geophys. Res. Lett. 41, 3676–3680 (2014).

    Google Scholar 

  107. Baldwin, M. P. et al. Stratospheric memory and skill of extended-range weather forecasts. Science 301, 636–640 (2003).

    Google Scholar 

  108. Butler, A. H., Polvani, L. M. & Deser, C. Separating the stratospheric and tropospheric pathways of El Nino–Southern Oscillation teleconnections. Environ. Res. Lett https://doi.org/10.1088/1748-9326/9/2/024014 (2014).

    Article  Google Scholar 

  109. Sigmond, M. et al. Enhanced seasonal forecast skill following stratospheric sudden warmings. Nat. Geosci. 6, 98–102 (2013).

    Google Scholar 

  110. Scaife, A. A. et al. Seasonal winter forecasts and the stratosphere. Atmos. Sci. Lett https://doi.org/10.1002/asl.598 (2016).

    Article  Google Scholar 

  111. Anstey, J. A. & Shepherd, T. G. High-latitude influence of the quasi-biennial oscillation (Review article). Quart. J. Roy. Meteorol. Soc. 140, 1–21 (2014).

    Google Scholar 

  112. Garfinkel, C. I. & Hartmann, D. L. Influence of the quasi-biennial oscillation on the North Pacific and El Niño teleconnections. J. Geophys. Res. 115, D20116 (2010).

    Google Scholar 

  113. Wang, J., Kim, H. -M. & Chang, E. K. M. Interannual modulation of northern hemisphere winter storm tracks by the QBO. Geophys. Res. Lett. 45, 2786–2794 (2018).

    Google Scholar 

  114. Yoo, C. & Son, S.-W. Modulation of the boreal wintertime Madden–Julian Oscillation by the stratospheric quasi-biennial oscillation. Geophys. Res. Lett. 43, 1392–1398 (2016).

    Google Scholar 

  115. Son, S.-W., Lim, Y., Yoo, C., Hendon, H. H. & Kim, J. Stratospheric control of Madden–Julian Oscillation. J. Clim. 30, 1909–1922 (2017).

    Google Scholar 

  116. Lim, Y. et al. Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models. Clim. Dyn. https://doi.org/10.1007/s00382-019-04719-y (2019).

    Article  Google Scholar 

  117. Tompkins, A. M. et al. The climate-system historical forecast project: providing open access to seasonal forecast ensembles from centers around the globe. Bull. Am. Meteorol. Soc. 98(11), 2293–2301 (2017).

    Google Scholar 

  118. Acosta Navarro, J. C. et al. Link between autumnal Arctic sea ice and northern hemisphere winter forecast skill. Geophys. Res. Lett. 47, e2019GL086753 (2020).

    Google Scholar 

  119. Scaife, A. A. et al. Skill of tropical rainfall predictions in multiple seasonal forecast systems. Int. J. Climatol. https://doi.org/10.1002/joc.5855 (2018).

    Article  Google Scholar 

  120. Hu, Z. et al. How much of monthly mean precipitation variability over global land is associated with SST anomalies? Clim. Dyn. 54, 701–712 (2020).

    Google Scholar 

  121. Kirtman, B. P. et al, in Climate Science for Serving Society: Research, Modelling and Prediction Priorities (eds Asrar, G. R. & Hurrell, J. W.) 205–235 (Springer, 2013).

  122. Capotondi, A., Wittenberg, A. T., Kug, J.-S., Takahashi, K. & McPhaden, M. J., in El Niño Southern Oscillation in a Changing Climate (eds McPhaden, M., Santoso, A. & Cai, W.) 65–86 (AGU, 2020).

  123. Vimont, D. J., Alexander, M. A. & Newman, M. Optimal growth of central and east Pacific ENSO events. Geophys. Res. Lett. 41, 4027–4034 (2014).

    Google Scholar 

  124. Zhang, H., Clement, A. & DiNezio The south Pacific meridional mode: a mechanism for ENSO-like variability. J. Clim. 27, 769–783 (2014).

    Google Scholar 

  125. Larson, S. & Kirtman, B. P. The Pacific meridional mode as a trigger for ENSO in a high-resolution coupled model. Geophys. Res. Lett. https://doi.org/10.1002/grl.50571 (2013).

    Article  Google Scholar 

  126. Capotondi, A. & Sardeshmukh, P. D. Optimal precursors of different types of ENSO events. Geophys. Res. Lett. 42, 9952–9960 (2015).

    Google Scholar 

  127. Amaya, D. The Pacific meridional mode and ENSO: a review. Curr. Clim. Change Rep. https://doi.org/10.1007/s40641-019-00142-x (2019).

    Article  Google Scholar 

  128. Larson, S. M. & Kirtman, B. P. Assessing Pacific Meridional Mode forecasts and its role as an ENSO precursor and predictor in the North American multi-model ensemble. J. Clim. 27, 7018–7032 (2014).

    Google Scholar 

  129. Ren, H. F.-F., Jin, B. & & Tian, A. A. Scaife distinct persistence barriers in two types of ENSO. Geophys. Res. Lett. 43, 10,973–10,979 (2016).

    Google Scholar 

  130. Infanti, J. M. & Kirtman, B. P. North American rainfall and temperature prediction response to the diversity of ENSO. Clim. Dyn. https://doi.org/10.1007/s00382-015-2749-0 (2016).

    Article  Google Scholar 

  131. DiNezio, P. et al. A two-year forecast for a 60-80% chance of La Nina in 2017–2018. Geophys. Res. Lett. https://doi.org/10.1002/2017GL074904 (2017).

    Article  Google Scholar 

  132. Freund, M. B. et al. Higher frequency of central Pacific El Niño events in recent decades relative to past centuries. Nat. Geosci. 12, 450–455 (2019).

    Google Scholar 

  133. McPhaden, M. J. Genesis and evolution of the 1997–98 El Niño. Science 283, 950–954 (1999).

    Google Scholar 

  134. Capotondi, A., Sardeshmukh, P. D. & Ricciardulli, L. The nature of the stochastic wind forcing of ENSO. J. Clim. 31, 8081–8099 (2018).

    Google Scholar 

  135. Tan, X. et al. A study of the effects of westerly wind bursts on ENSO based on CESM. Clim. Dyn. 54, 885–899 (2020).

    Google Scholar 

  136. Lopez, H. & WWBs, B. P. K. ENSO predictability, the spring barrier and extreme events. J. Geophys. Res. Atmos. 119, 10,114–10,138 (2014).

    Google Scholar 

  137. Ren, H. L. et al. Seasonal predictability of winter ENSO types in operational dynamical model predictions. Clim. Dyn. 52, 3869–3890 (2019).

    Google Scholar 

  138. Chang, P. et al. Climate fluctuations of tropical coupled systems: the role of ocean dynamics. J. Clim. 19, 5122–5174 (2006).

    Google Scholar 

  139. Lübbecke, J. F. & McPhaden, M. J. Symmetry of the Atlantic Niño mode. Geophys. Res. Lett. 44, 965–973 (2017).

    Google Scholar 

  140. Richter, I. et al. On the link between mean state biases and prediction skill in the tropics: an atmospheric perspective. Clim. Dyn. 50, 3355–3374 (2018).

    Google Scholar 

  141. Stockdale, T. N., Balmaseda, M. A. & Vidard, A. Tropical Atlantic SST prediction with coupled ocean–atmosphere GCMs. J. Clim. 19, 6047–6061 (2006).

    Google Scholar 

  142. Ding, H. et al. The impact of mean state errors on equatorial Atlantic interannual variability in a climate model. J. Geophys. Res. Oceans 120, 1133–1151 (2015).

    Google Scholar 

  143. Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363 (1999).

    Google Scholar 

  144. Krishnamurthy, V. & Kirtman, B. P. Variability of the Indian Ocean: relation to monsoon and ENSO. Q. J. R. Meteorol. Soc. 129, 1623–1646 (2003).

    Google Scholar 

  145. Wu, R., Kirtman, B. P. & Krishnamurthy, V. An asymmetric mode of tropical Indian Ocean rainfall variability in boreal spring. J. Geophys. Res. Atmos. https://doi.org/10.1029/2007JD009316 (2008).

    Article  Google Scholar 

  146. Lu, B. et al. An extreme negative Indian Ocean dipole event in 2016: dynamics and predictability. Clim. Dyn. https://doi.org/10.1007/s00382-017-3908-2 (2017).

    Article  Google Scholar 

  147. Shinoda, T. & Han, W. Influence of Indian Ocean dipole on atmospheric subseasonal variability. J. Clim. 18, 3891–3909 (2005).

    Google Scholar 

  148. Dunstone, N. et al. Skilful predictions of the winter North Atlantic Oscillation one year ahead. Nat. Geosci. 9, 809–814 (2016).

    Google Scholar 

  149. Dunstone, N. et al. Skilful seasonal predictions of summer European rainfall. Geophys. Res. Lett. 45, 3246–3254 (2018).

    Google Scholar 

  150. Paolino, D. A., Kinter, J. L., Kirtman, B. P., Min, D. & Straus, D. M. The impact of land surface and atmospheric initialization on seasonal forecasts with CCSM. J. Clim. 25, 1007–1021 (2011).

    Google Scholar 

  151. Dirmeyer, P. A. The role of the land surface background state in climate predictability. J. Hydrometeorol. 4, 599–610 (2003).

    Google Scholar 

  152. Prodhomme, C., Doblas-Reyes, F., Bellprat, O. & Dutra, E. Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe. Clim. Dyn. 47, 919–935 (2016).

    Google Scholar 

  153. Ardilouze, C., Batté, L., Decharme, B. & Déqué, M. On the link between summer dry bias over the US Great Plains and seasonal temperature prediction skill in a dynamical forecast system. Weather Forecast. 34, 1161–1172 (2019).

    Google Scholar 

  154. Marshall, A. G. & Scaife, A. A. Improved predictability of stratospheric sudden warming events in an atmospheric general circulation model with enhanced stratospheric resolution. J. Geophys. Res. 115, D16114 (2010).

    Google Scholar 

  155. Boer, G. J. & Hamilton, K. QBO influence on extratropical predictive skill. Clim. Dyn. 31, 987–1000 (2008).

    Google Scholar 

  156. Marshall, A. G. & Scaife, A. A. Impact of the QBO on surface winter climate. J. Geophys. Res. 114, D18110 (2009).

    Google Scholar 

  157. Scaife, A. A. et al. Predictability of the Quasi-Biennial Oscillation and its northern winter teleconnection on seasonal to decadal timescales. Geophys. Res. Letts. 41, 1752–1758 (2014).

    Google Scholar 

  158. Doblas-Reyes, F. J., Hagedorn, R., Palmer, T. N. & Morcrette, J.-J. Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts. Geophys. Res. Lett. 33, L07708 (2006).

    Google Scholar 

  159. Solaraju-Murali, B., Caron, L.-P., González-Reviriego, N. & Doblas-Reyes, F. J. Multi-year prediction of European summer drought conditions for the agricultural sector. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ab5043 (2019).

    Article  Google Scholar 

  160. Cassou, C. et al. Decadal climate variability and predictability: challenges and opportunities. Bull. Am. Meteorol. Soc. 99, 479–490 (2018).

    Google Scholar 

  161. Liu, Z. & Di Lorenzo, E. Mechanisms and predictability of Pacific decadal variability. Curr. Clim. Chang. Rep. 4, 128–144 (2018).

    Google Scholar 

  162. Power, S., Casey, T., Folland, C., Colman, A. & Mehta, V. Inter-decadal modulation of the impact of ENSO on Australia. Clim. Dyn. 15, 319–324 (1999).

    Google Scholar 

  163. Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M. & Francis, R. C. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc. 78, 1069–1079 (1997).

    Google Scholar 

  164. Newman, M. et al. The Pacific Decadal Oscillation, revisited. J. Clim. 29, 4399–4427 (2016).

    Google Scholar 

  165. Chiang, J. C. H. & Vimont, D. J. Analogous Pacific and Atlantic Meridional Modes of tropical atmosphere–ocean variability. J. Clim. 17, 4143–4158 (2004).

    Google Scholar 

  166. Di Lorenzo, E. et al. North Pacific Gyre Oscillation links ocean climate and ecosystem change. GRL 35, L08607 (2008).

    Google Scholar 

  167. Han, W. et al. Indian Ocean decadal variability: a review. Bull. Am. Meteor. Soc. 95, 1679–1703 (2014).

    Google Scholar 

  168. Han, W. et al. Intensification of decadal and multi-decadal sea level variability in the western tropical Pacific during recent decades. Clim. Dyn. 43, 1357–1379 (2014).

    Google Scholar 

  169. Li, Y., Han, W., Wang, F., Zhang, L. & Duan, J. Vertical structure of the upper-Indian Ocean thermal variability. J. Clim. 33, 7233–7253 (2020).

    Google Scholar 

  170. Tozuka, T., Luo, J., Masson, S. & Yamagata, T. Decadal modulations of the Indian Ocean dipole in the SINTEX-F1 coupled GCM. J. Clim. 20, 2881–2894 (2007).

    Google Scholar 

  171. Feng, M. H. H. et al. Decadal increase in Ningaloo Niño since the late 1990s. Geophys. Res. Lett. 42, 104–112 (2015).

    Google Scholar 

  172. Ummenhofer, C. C., Biastoch, A. & Böning, C. W. Multi-decadal Indian Ocean variability linked to the Pacific and implications for preconditioning Indian Ocean Dipole events. J. Clim. 30, 1739–1751 (2017).

    Google Scholar 

  173. Lee, S.-K. et al. Pacific origin of the abrupt increase in Indian Ocean heat content during the warming hiatus. Nat. Geosci 8, 445–450 (2015).

    Google Scholar 

  174. Nieves, V., Willis, J. K. & Patzert, W. C. Recent hiatus caused by decadal shift in Indo-Pacific heating. Science 349, 532–535 (2015).

    Google Scholar 

  175. Jin, X. et al. Distinct mechanisms of decadal subsurface heat content variations in the eastern and western Indian Ocean modulated by tropical Pacific SST. J. Clim. 31, 7751–7769 (2018).

    Google Scholar 

  176. Annamalai, H., Potemra, J., Murtugudde, R. & McCreary, J. P. Effect of preconditioning on the extreme climate events in the tropical Indian Ocean. J. Clim. 18, 3450–3469 (2005).

    Google Scholar 

  177. Henley, B. J. et al. Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation. Environ. Res. Lett. 12, 044011 (2017).

    Google Scholar 

  178. Fasullo, J. T., Phillips, A. S. & Deser, C. Evaluation of leading modes of climate variability in the CMIP archives. J. Clim. https://doi.org/10.1175/JCLI-D-19-1024.1 (2020).

    Article  Google Scholar 

  179. Mann, M. E., Steinman, B. A. & Miller, S. K. Absence of internal multidecadal and interdecadal oscillations in climate model simulations. Nat. Commun. https://doi.org/10.1038/s41467-019-13823-w (2020).

    Article  Google Scholar 

  180. Kosaka, Y. & Xie, S.-P. The tropical Pacific as a key pacemaker of the variable rates of global warming. Nat. Geosci. https://doi.org/10.1038/NGEO2770 (2016).

    Article  Google Scholar 

  181. Tung, K.-K. & Chen,, X. Understanding the recent global surface warming slowdown: a review. Climate 6, 82 (2018).

    Google Scholar 

  182. England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Clim. Change 4, 222–227 (2014).

    Google Scholar 

  183. Meehl, G. A., Teng, H. & Arblaster, J. M. Climate model simulations of the observed early-2000s hiatus of global warming. Nat. Clim. Change 4, 898–902 (2014).

    Google Scholar 

  184. Fyfe, J. C. et al. Making sense of the early-2000s warming slowdown. Nat. Clim. Change 6, 224–228 (2016).

    Google Scholar 

  185. Xie, S.-P. & Kosaka,, Y. What caused the global surface warming hiatus of 1998–2013? Curr. Clim. Change Rep. 3, 128–140 (2017).

    Google Scholar 

  186. Seager, R. et al. Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nat. Clim. Change. 9, 517–522 (2019).

    Google Scholar 

  187. Chen, X. & Tung, K.-K. Varying planetary heat sink led to global-warming slowdown and acceleration. Science 345, 897–903 (2014).

    Google Scholar 

  188. Santer, B. D. et al. Observed multivariable signals of late 20th and early 21st century volcanic activity. Geophys. Res. Lett. 42, 500–509 (2015).

    Google Scholar 

  189. Smith, D. M. et al. Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nat. Clim. Chang. 6, 936 (2016).

    Google Scholar 

  190. Oudar, T., Kushner, P. J., Fyfe, J. & Sigmond, M. No impact of anthropogenic aerosols on early 21st century global temperature trends in a large initial-condition ensemble. Geophys. Res. Lett. 45, 9245–9252 (2018).

    Google Scholar 

  191. Guemas, V., Doblas-Reyes, F. J., Andreu-Burillo, I. & Asif, M. Retrospective prediction of the global warming slowdown in the past decade. Nat. Clim. Change 3, 649–653 (2013).

    Google Scholar 

  192. Bordbar, M. H. et al. Uncertainty in near-term global surface warming linked to tropical Pacific climate variability. Nat. Commun. 10, 1990 (2019).

    Google Scholar 

  193. Meehl, G. A., Chung, C. T. Y., Arblaster, J. M., Holland, M. M. & Bitz, C. M. Tropical decadal variability and the rate of Arctic sea ice retreat. Geophys. Res. Lett. https://doi.org/10.1029/2018GL079989 (2018).

    Article  Google Scholar 

  194. Meehl, G. A., Arblaster, J. M., Bitz, C., Chung, C. T. Y. & Teng, H. Antarctic sea ice expansion between 2000–2014 driven by tropical Pacific decadal climate variability. Nat. Geosci https://doi.org/10.1038/NGEO2751 (2016).

    Article  Google Scholar 

  195. Purich, A. et al. Tropical Pacific SST drivers of recent Antarctic sea ice trends. J. Clim. 29, 8931–8948 (2016).

    Google Scholar 

  196. Thoma, M., Greatbatch, R. J., Kadow, C. & Gerdes, R. Decadal hindcasts initialized using observed surface wind stress: evaluation and prediction out to 2024. Geophys. Res. Lett. 42, 6454–6461 (2015).

    Google Scholar 

  197. Meehl, G. A. et al. Recent sudden Antarctic sea ice retreat caused by connections to the tropics and sustained ocean changes around Antarctica. Nat. Commun. 10, 14 (2019).

    Google Scholar 

  198. Yin, J., Overpeck, J., Peyser, C. & Stouffer, R. Big jump of record warm global mean surface temperature in 2014–2016 related to unusually large oceanic heat releases. Geophys. Res. Lett. 45, 1069–1078 (2018).

    Google Scholar 

  199. Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T. & Bellouin, N. Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484, 228 (2012).

    Google Scholar 

  200. Watanabe, M. & Tatebe, H. Reconciling roles of sulphate aerosol forcing and internal variability in Atlantic multidecadal climate changes. Clim. Dyn. 53, 4651–4665 (2019).

    Google Scholar 

  201. Hermanson, L. et al. Robust multiyear climate impacts of volcanic eruptions in decadal prediction systems. J. Geophys. Res. Atmos. https://doi.org/10.1029/2019JD031739 (2020).

    Article  Google Scholar 

  202. Menary, M. B. & Scaife, A. A. Naturally forced multidecadal variability of the Atlantic meridional overturning circulation. Clim. Dyn. 42, 1347–1362 (2014).

    Google Scholar 

  203. Cai, W. et al. Pantropical climate interactions. Science 363, eaav4236 (2019).

    Google Scholar 

  204. Mechoso, R. (ed.) Interacting Climates of Ocean Basins: Observations, Mechanisms, Predictability, and Impacts (Cambridge Univ. Press, 2020).

  205. Meehl, G. A. et al. Atlantic and Pacific tropics connected by mutually interactive decadal-timescale processes. Nat. Geosci. https://doi.org/10.1038/s41561-020-00669-x (2020).

    Article  Google Scholar 

  206. Chikamoto, Y. et al. Skillful multi-year predictions of tropical trans-basin climate variability. Nat. Commun. 6, 6869 (2015).

    Google Scholar 

  207. Ruprich-Robert, Y. et al. Assessing the climate impacts of the observed Atlantic multidecadal variability using the GFDL CM2.1 and NCAR CESM1 global coupled models. J. Clim. 30, 2785–2810 (2017).

    Google Scholar 

  208. Levine, A. F. Z., McPhaden, M. J. & Frierson, D. M. W. The impact of the AMV on multidecadal ENSO variability. Geophys. Res. Lett. 44, 3877–3886 (2017).

    Google Scholar 

  209. Kumar, A., Bhaskar, J. & Wang, H. Attribution of SST variability in global oceans and the role of ENSO. Clim. Dyn. 43, 209–220 (2014).

    Google Scholar 

  210. Taschetto, A. S., Rodrigues, R. R., Meehl, G. A., McGregor, S. & England, M. H. How sensitive are the Pacific-North Atlantic teleconnections to the position and intensity of El Niño-related warming. Clim. Dyn. https://doi.org/10.1007/s00382-015-2679-x (2015).

    Article  Google Scholar 

  211. Han, W. et al. Decadal variability of Indian and Pacific Walker Cells: do they co-vary on decadal timescales? J. Clim. 30, 8447–8468 (2017).

    Google Scholar 

  212. Han, W. et al. Multi-decadal trend and decadal variability of the regional sea level over the Indian Ocean since the 1960s: roles of climate modes and external forcing. Climate 6, 51 (2018).

    Google Scholar 

  213. Deepa, J. S. et al. The tropical Indian Ocean decadal sea level response to the Pacific decadal oscillation forcing. Clim. Dyn. 52, 5045 (2019).

    Google Scholar 

  214. Zhang, R. et al. A review of the role of the Atlantic meridional overturning circulation in Atlantic multidecadal variability and associated climate impacts. Rev. Geophys. 57, 316–375 (2019).

    Google Scholar 

  215. Li, X., Xie, S.-P., Gille, S. T. & Yoo, C. Atlantic-induced pan-tropical climate change over the past three decades. Nat. Clim. Change https://doi.org/10.1038/NCLIMATE2840 (2015).

    Article  Google Scholar 

  216. Li, H., Ilyina, T., Müller, W. A. & Seinz, F. Decadal prediction of the North Atlantic CO2 uptake. Nat. Commun. 7, 11076 (2016).

    Google Scholar 

  217. Jin, D. & Kirtman, B. P. How the annual cycle affects the extratropical response to ENSO. J. Geophys. Res. 115, D06102 (2010).

    Google Scholar 

  218. Zhang, L., Han, W. & Sienz, F. Unraveling causes for the changing behavior of tropical Indian Ocean in the past few decades. J. Clim. 31, 2377–2388 (2018).

    Google Scholar 

  219. Thornton, H. et al. Skillful seasonal prediction of winter gas demand. Env. Res. Lett. 14, 024009 (2019).

    Google Scholar 

  220. Palin, E. J. et al. Skillful seasonal forecasts of winter disruption to the U.K. transport system. J. Appl. Meteor. Climatol. 55, 325–344 (2016).

    Google Scholar 

  221. Towler, E., Paimazumder, D. & Done, J. Toward application of decadal climate predictions. J. Appl. Meteorol. Climatol. 57, 555–568 (2018).

    Google Scholar 

  222. Vecchi, G. A. et al. On the seasonal forecasting of regional tropical cyclone activity. J. Clim. 27, 7994–8016 (2014).

    Google Scholar 

  223. Annan, J. D. et al. Parameter estimation in an atmospheric GCM using the ensemble Kalman filter. Nonlinear Processes Geophys. https://doi.org/10.5194/npg-12-363-2005 (2005).

    Article  Google Scholar 

  224. Düben, P. D., Hugh McNamara, H. & Palmer, T. N. The use of imprecise processing to improve accuracy in weather & climate prediction. J. Comput. Phys. 271, 2–18 (2014).

    Google Scholar 

  225. Palmer, T. N., Peter Düben, P. & McNamara, H. Stochastic modelling and energy-efficient computing for weather and climate prediction. Phil. Trans. Roy. Soc. A https://doi.org/10.1098/rsta.2014.0118 (2014).

    Article  Google Scholar 

  226. Ham, Y.-G., Kim, J.-H. & Luo, J.-J. Deep learning for multi-year ENSO forecasts. Nature https://doi.org/10.1038/s41586-019-1559-7 (2019).

    Article  Google Scholar 

  227. Zhang, S. et al. Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review. Clim. Dyn. 54, 5127–5144 (2020).

    Google Scholar 

  228. Karspeck, A. R. et al. A global coupled ensemble data assimilation system using the community earth system model and the data assimilation research testbed. Q. J. R. Meteorol. Soc. 144, 2404–2430 (2018).

    Google Scholar 

  229. Mulholland, D., Laloyaux, P., Haines, K. & Balmaseda, M. Origin and impact of initialization shocks in coupled atmosphere–ocean forecasts. Monthly Weather. Rev. 143, 4631–4644 (2015).

    Google Scholar 

  230. Saha, S. et al. The NCEP climate forecast system reanalysis. Bull. Am. Meteorol. Soc. 91, 1015–1057 (2010).

    Google Scholar 

  231. Laloyaux, P. et al. A coupled data assimilation system for climate reanalysis. Q. J. R. Meteorol. Soc. 142, 65–78 (2016).

    Google Scholar 

  232. Herman, R. J. et al. The effects of anthropogenic and volcanic aerosols and greenhouse gases on twentieth century Sahel precipitation. Sci. Rep. 10, 12203 (2020).

    Google Scholar 

  233. Schurer, A., Hegerl, G., Mann, M. E. & Tett, S. F. B. Separating forced from chaotic climate variability over the past millennium. J. Clim. 26, 6954–6973 (2013).

    Google Scholar 

  234. Ault, T. R. et al. The continuum of hydroclimate variability in western North America during the last millennium. J. Clim. 26, 5863–5878 (2013).

    Google Scholar 

  235. Laepple, T. & Huybers, P. Global and regional variability in marine surface temperatures. Geophys. Res. Lett. 41, 2528–2534 (2014).

    Google Scholar 

  236. Loope, G., Thompson, D. M., Cole, J. E. & Overpeck, J. Is there a low-frequency bias in multiproxy reconstructions of Pacific SST variability? Quat. Sci. Rev. 246, 106530 (2020).

    Google Scholar 

  237. Frankignoul, C., Muller, P. & Zorita, E. A simple model of the decadal response of the ocean to stochastic wind forcing. J. Phys. Oceanogr. 27, 1533–1546 (1997).

    Google Scholar 

  238. Capotondi, A., Alexander, M. A. & Deser, C. Why are there Rossby wave maxima in the Pacific at 10S and 13N? J. Phys. Oceanogr. 33, 1549–1563 (2003).

    Google Scholar 

  239. Chikamoto, Y., Timmermann, A., Widlansky, M. J., M. A., & L. Multi-year predictability of climate, drought, and wildfire in southwestern North America. Sci. Rep. https://www.ncbi.nlm.nih.gov/pubmed/28747719 (2017).

  240. Lovenduski, N. S., Bonan, G. B., Yeager, S. G., Lindsay, K. & Lombardozzi, D. L. High predictability of terrestrial carbon fluxes from an initialized decadal prediction system. Environ. Res. Lett. 14, 124074 (2019).

    Google Scholar 

  241. Sospedra-Alfonso, R., Merryfield, W. J. & Kharin, V. V. Representation of snow in the Canadian seasonal to interannual prediction system: part II. Potential predictability and hindcast skill. J. Hydrometeorol. 17, 2511–2535 (2016).

    Google Scholar 

  242. Kapnick, S. B. et al. Potential for western US seasonal snowpack prediction. Proc. Natl Acad. Sci. USA 115, 1180–1185 (2018).

    Google Scholar 

  243. Holbrook, N. J. et al. Keeping pace with marine heatwaves. Nat. Rev. Earth Environ. 1, 482–493 (2020).

    Google Scholar 

  244. Batté, L. et al. Summer predictions of Arctic sea ice edge in multi‑model seasonal re‑forecasts. Clim. Dyn. 54, 5013–5029 (2020).

    Google Scholar 

  245. Subramanian, A., Juricke, S., Dueben, P. & Palmer, T. A stochastic representation of subgrid uncertainty for dynamical core development. Bull. Am. Meteorol. Soc. 100, 1091–1101 (2019).

    Google Scholar 

  246. Penny, S. G. et al. Observational needs for improving ocean and coupled reanalysis, S2S prediction, and decadal prediction. Front. Mar. Sci https://doi.org/10.3389/fmars.2019.00391 (2019).

    Article  Google Scholar 

  247. Lofverstrom et al. An efficient ice-sheet/Earth System model spin-up procedure for CESM2.1 and CISM2.1: description, evaluation, and broader applicability. JAMES https://doi.org/10.1029/2019MS001984 (2020).

    Article  Google Scholar 

  248. Gettelman, A. et al. The Whole Atmosphere Community Climate Model version 6 (WACCM6). J. Geophys. Res. Atmos. https://doi.org/10.1029/2019JD030943 (2019).

    Article  Google Scholar 

  249. Tommasi, D. C. et al. Managing living marine resources in a dynamic environment: the role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49 (2017).

    Google Scholar 

  250. Stock, C. A. et al. Seasonal sea surface temperature anomaly prediction for coastal ecosystems. Prog. Oceanogr. 137, 219–236 (2015).

    Google Scholar 

  251. Liu, G. et al. Predicting heat stress to inform reef management: NOAA Coral Reef Watch’s 4-month coral bleaching outlook. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00057 (2018).

    Article  Google Scholar 

  252. Capotondi, A., Sardeshmukh, P. D., Di Lorenzo, E., Subramanian, A. & Miller, A. J. Predictability of US West Coast ocean temperatures is not solely due to ENSO. Sci. Rep. 9, 10993 (2019).

    Google Scholar 

  253. Wells, M. L. et al. Harmful algal blooms and climate change: learning from the past and present to forecast the future. Harmful Algae 49, 68–93 (2015).

    Google Scholar 

  254. Séférian, R. et al. Multiyear predictability of tropical marine productivity. Proc. Natl Acad. Sci. USA 111, 11646–11651 (2014).

    Google Scholar 

  255. Park, J.-Y., Stock, C. A., Dunne, J. P., Yang, X. & Rosati, A. Seasonal to multiannual marine ecosystem prediction with a global Earth System model. Science 365, 284–288 (2019).

    Google Scholar 

  256. Krumhardt, K. M. et al. Potential predictability of net primary production in the ocean. Glob. Biogeochem. Cycles 34, e2020GB006531 (2020).

    Google Scholar 

  257. Siedlecki, S. A. et al. Experiments with seasonal forecasts of ocean conditions for the northern region of the California Current upwelling system. Sci. Rep. 6, 1–18 (2016).

    Google Scholar 

  258. Brady, R. X., Lovenduski, N. S., Yeager, S. G., Long, M. C. & Lindsay, K. Skillful multiyear predictions of ocean acidification in the California Current System. Nat. Commun. 11, 2166 (2020).

    Google Scholar 

  259. Séférian, R., Berthet, S. & Chevallier, M. Assessing the decadal predictability of land and ocean carbon uptake. Geophys. Res. Lett. 45, 2455–2466 (2018).

    Google Scholar 

  260. Lovenduski, N. S., Yeager, S. G., Lindsay, K. & Long, M. C. Predicting near-term variability in ocean carbon uptake. Earth Syst. Dyn. 10, 45–57 (2019).

    Google Scholar 

  261. Li, H., Ilyina, T., Müller, W. A. & Landschützer, P. Predicting the variable ocean carbon sink. Sci. Adv. https://doi.org/10.1126/sciadv.aav6471 (2019).

    Article  Google Scholar 

  262. Bett, P. E. et al. Skillful seasonal prediction of key carbon cycle components: NPP and fire risk. Environ. Res. Commun. 2, 055002 (2020).

    Google Scholar 

  263. Park, J.-Y., Dunne, J. P. & Stock, C. A. Ocean chlorophyll as a precursor of ENSO: an earth system modeling study. Geophys. Res. Lett. https://doi.org/10.1002/2017GL076077 (2018).

    Article  Google Scholar 

  264. Capotondi, A. et al. Observational needs supporting marine ecosystem modeling and forecasting: from the global ocean to regional and coastal systems. Front. Mar. Sci. 6, 623 (2019).

    Google Scholar 

  265. Fennel, K. et al. Advancing marine biogeochemical and ecosystem reanalyses and forecasts as tools for monitoring and managing ecosystem health. Front. Mar. Sci. 6, 89 (2019).

    Google Scholar 

  266. Weisheimer, A. & Palmer, T. N. On the reliability of seasonal climate forecasts. J. R. Soc. Interface https://doi.org/10.1098/rsif.2013.1162 (2014).

    Article  Google Scholar 

  267. National Academies of Sciences, Engineering and Medicine. Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts 1–351 (National Academies Press, 2017).

  268. National Research Council. Assessment of Intraseasonal to Interannual Climate Prediction and Predictability 1–193 (National Academies Press, 2010).

  269. Mehta, V. Natural Decadal Climate Variability: Phenomena, Mechanisms, and Predictability 1-374 (CRC Press, 2020).

  270. GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies Dataset accessed 2021-02-25 at https://data.giss.nasa.gov/gistemp/ (2020).

  271. Lenssen, N. J. L. et al. Improvements in the GISTEMP Uncertainty Model. J. Geophys. Res. Atmos. 124, 6307–6326 (2019).

    Google Scholar 

Download references

Acknowledgements

The foundations of this Review emerged from a workshop held by the National Academies of Sciences, Engineering and Medicine in 2015 at Woods Hole, MA, USA, and the authors gratefully acknowledge support from A. Purcell and N. Huddleston. G.A.M., J.H.R. and N.R. were supported in part by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US Department of Energy’s Office of Biological & Environmental Research (BER) via National Science Foundation IA 1844590, and by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement No. 1852977. M.H.E. acknowledges support from the Australian Research Council (Grant CE170100023). H.T. was partly supported by DOE/BER RGMA HiLAT-RASM. M.E.M. was supported by a grant from the NSF Paleoclimate Program #1748097. F.D.-R. and M.G.D. were supported by the H2020 EUCP project under Grant agreement no. 776613, and M.G.D also by the Ramón y Cajal 2017 grant reference RYC-2017-22964. A.C. acknowledges support from the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office’s Modeling Analysis, Prediction and Projections (MAPP) Program and from the NOAA Climate Program Office’s Climate Variability and Predictability (CVP) Program. A.C.S. acknowledges support from the NOAA Climate Variability and Predictability Program (Award NA18OAR4310405) and NOAA-MAPP (NA17OAR4310106). N.S.L. is grateful for support from the NSF (OCE-1752724). D.T. acknowledges support from the NCAR Advanced Study Program and NSF (OCE-1931242). S.C.S was supported by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) Postdoctoral Fellowship. A.A.S. and D.S were supported by the Met Office Hadley Centre Climate Programme funded by the Department for Business, Energy & Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (Defra) and by the European Commission Horizon 2020 EUCP project (GA 776613).

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Authors and Affiliations

  1. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Gerald A. Meehl, Jadwiga H. Richter & Nan Rosenbloom

  2. Pacific Northwest National Laboratory, Richland, WA, USA

    Haiyan Teng

  3. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    Antonietta Capotondi

  4. NOAA Physical Sciences Laboratory, Boulder, CO, USA

    Antonietta Capotondi

  5. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Kim Cobb

  6. Department of Earth Sciences, Barcelona Supercomputing Center-Centro Nacional de Supercomputación, Barcelona, Spain

    Francisco Doblas-Reyes & Markus G. Donat

  7. Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain

    Francisco Doblas-Reyes

  8. Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

    Matthew H. England

  9. Canadian Centre for Climate Modeling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

    John C. Fyfe & William J. Merryfield

  10. Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA

    Weiqing Han, Nicole S. Lovenduski & Aneesh C. Subramanian

  11. School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA

    Hyemi Kim

  12. Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, FL, USA

    Ben P. Kirtman

  13. Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA

    Yochanan Kushnir

  14. Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

    Nicole S. Lovenduski

  15. Department of Meteorology & Atmospheric Science, Pennsylvania State University, State College, PA, USA

    Michael E. Mann

  16. Earth & Environmental Systems Institute, Pennsylvania State University, State College, PA, USA

    Michael E. Mann

  17. Image Processing Laboratory, University of Valencia, Valencia, Spain

    Veronica Nieves

  18. Department of Atmospheric Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA

    Kathy Pegion

  19. Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, WA, USA

    Sara C. Sanchez

  20. Hadley Centre, Exeter, UK

    Adam A. Scaife & Doug Smith

  21. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Adam A. Scaife

  22. Department of Atmospheric Science, Colorado State University, Ft. Collins, CO, USA

    Lantao Sun

  23. Department of Geosciences, University of Arizona, Tucson, AZ, USA

    Diane Thompson

  24. Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Caroline C. Ummenhofer

  25. ARC Centre of Excellence for Climate Extremes, Sydney, New South Wales, Australia

    Caroline C. Ummenhofer

  26. Scripps Institution of Oceanography, La Jolla, CA, USA

    Shang-Ping Xie

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Contributions

H.T. suggested the original concept. G.A.M. led the overall conceptual design and coordinated the writing. J.H.R. and H.T. made major contributions to the conceptual design and organization. J.H.R. generated Fig. 1a. H.T. generated Fig. 4. All authors discussed the concepts presented and contributed to the writing.

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Correspondence to Gerald A. Meehl.

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Nature Reviews Earth & Environment thanks Debra Hudson, Constantin Ardilouze, Terrence O’Kane and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

APCC climate predictions: https://www.apcc21.org/

Copernicus Climate Change Service: https://www.copernicus.eu/en

North American Multi-model Ensemble: https://www.cpc.ncep.noaa.gov/products/NMME/

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Meehl, G.A., Richter, J.H., Teng, H. et al. Initialized Earth System prediction from subseasonal to decadal timescales. Nat Rev Earth Environ 2, 340–357 (2021). https://doi.org/10.1038/s43017-021-00155-x

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