Abstract
Near-term climate predictions — which operate on annual to decadal timescales — offer benefits for climate adaptation and resilience, and are thus important for society. Although skilful near-term predictions are now possible, particularly when coupled models are initialized from the current climate state (most importantly from the ocean), several scientific challenges remain, including gaps in understanding and modelling the underlying physical mechanisms. This Perspective discusses how these challenges can be overcome, outlining concrete steps towards the provision of operational near-term climate predictions. Progress in this endeavour will bridge the gap between current seasonal forecasts and century-scale climate change projections, allowing a seamless climate service delivery chain to be established.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).
Meehl, G. A., Boer, G. J., Covey, C., Latif, M. & Stouffer, R. J. The Coupled Model Intercomparison Project (CMIP). Bull. Am. Meteorol. Soc. 81, 313–318 (2000).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Hawkins, E. & Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dynam. 37, 407–418 (2011).
Murphy, J. M. et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768–772 (2004).
Smith, D. M. et al. Improved surface temperature prediction for the coming decade from a global climate model. Science 317, 796–799 (2007).
Smith, D. M. et al. Real-time multi-model decadal climate predictions. Clim. Dynam. 41, 2875–2888 (2013). This paper provides an experimental prediction of the upcoming decade. Detailed global maps of predicted temperature change are shown, as well as time series of global mean temperature and other climate indices. It also demonstrates the impact of forecast initialization with correct concurrent information.
Meehl, G. A. et al. Decadal prediction: can it be skillful? Bull. Am. Meteorol. Soc. 90, 1467–1485 (2009). This paper articulates the need for decadal predictions as a bridge between seasonal prediction and long-term climate change projections. It discusses which phenomena contribute to forecast skill, what the remaining scientific issues (at the time of writing) are and how forecasts should be evaluated.
Meehl, G. A. et al. Decadal climate prediction: an update from the trenches. Bull. Am. Meteorol. Soc. 95, 243–267 (2014).
Goddard, L. From science to service. Science 353, 1366–1367 (2016).
Hewitt, C. et al. Climate observations, climate modelling and climate services. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-17–0012.1 (2017).
Graham, R. J. et al. Long-range forecasting and the Global Framework for Climate Services. Clim. Res. 47, 47–55 (2011). This paper describes the infrastructure established by the World Meteorological Organization and the definition of operational standards to promote and support distribution of seasonal-to-interannual climate predictions. The paper also urges the development of decadal prediction capacity.
A European Research and Innovation Roadmap for Climate Services (European Commission, Directorate-General for Research and Innovation, 2015).
Bellucci, A. et al. Advancements in decadal climate predictability: the role of nonoceanic drivers. Rev. Geophys. 53, 165–202 (2015).
Kirtman, B. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 953–1028 (IPCC, Cambridge Univ. Press, 2013). This chapter of IPCC AR4 describes in detail the process of decadal prediction and presents the results of testing the concept within the framework of CMIP5.
Gray, L. J. et al. Solar influences on climate. Rev. Geophys. https://doi.org/10.1029/2009RG000282 (2010).
Thiéblemont, R., Matthes, K., Omrani, N.-E., Kodera, K. & Hansen, F. Solar forcing synchronizes decadal North Atlantic climate variability. Nat. Commun. 6, 8268 (2015).
Timmreck, C., Pohlmann, H., Illing, S. & Kadow, C. The impact of stratospheric volcanic aerosol on decadal‐scale climate predictions. Geophys. Res. Lett. 43, 834–842 (2016).
Zanchettin, D. et al. The Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP): experimental design and forcing input data for CMIP6. Geosci. Model Dev. 9, 2701–2719 (2016).
Scaife, A. A. et al. A mechanism for lagged North Atlantic climate response to solar variability. Geophys. Res. Lett. 40, 434–439 (2013).
Dunstone, N. et al. Skilful predictions of the winter North Atlantic Oscillation one year ahead. Nat. Geosci. 9, 809–814 (2016).
Zanchettin, D. Aerosol and Solar Irradiance Effects on Decadal Climate Variability and Predictability. Current Clim. Change Rep. 3, 150–162 (2017).
Cassou, C. et al. Decadal climate variability and predictability: challenges and opportunities. Bull. Am. Meteorol. Soc. 99, 479–490 (2018).
Latif, M. & Keenlyside, N. S. A perspective on decadal climate variability and predictability. Deep Sea Res. Pt II 58, 1880–1894 (2011). This review paper describes the key phenomena associated with decadal and multidecadal variability that is internal to the climate system and their underlying mechanisms and predictability. It pays special attention to the climate variability associated with the Atlantic Meridional Overturning Circulation.
Knight, J. R., Folland, C. K. & Scaife, A. A. Climate impacts of the Atlantic Multidecadal Oscillation. Geophys. Res. Lett. 33, L17706 (2006).
Ting, M., Kushnir, Y., Seager, R. & Li, C. Forced and internal twentieth-century SST trends in the North Atlantic. J. Clim. 22, 1469–1481 (2009).
Zhang, R. & Delworth, T. L. Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys. Res. Lett. 33, L17712 (2006).
Hátún, H. et al. Large bio-geographical shifts in the north-eastern Atlantic Ocean: from the subpolar gyre, via plankton, to blue whiting and pilot whales. Prog. Oceanogr. 80, 149–162 (2009).
Hátún, H. et al. An inflated subpolar gyre blows life toward the northeastern Atlantic. Prog. Oceanogr. 147, 49–66 (2016).
Zhang, L. & Wang, C. Multidecadal North Atlantic sea surface temperature and Atlantic meridional overturning circulation variability in CMIP5 historical simulations. J. Geophys. Res. Oceans 118, 5772–5791 (2013).
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).
Sheen, K. et al. Skilful prediction of Sahel summer rainfall on inter-annual and multi-year timescales. Nat. Commun. 8, 14966 (2017).
Yeager, S. & Robson, J. Recent progress in understanding and predicting Atlantic decadal climate variability. Curr. Clim. Change Rep. 3, 112–127 (2017). This is a recent evaluation of the feasibility of coupled model-based predictions of the decadal variability of North Atlantic SSTs. A discussion of the link between the surface phenomenon and variation in the Atlantic Meridional Overturning Circulation is included.
Knight, J. R., Allan, R. J., Folland, C. K., Vellinga, M. & Mann, M. E. A signature of persistent natural thermohaline circulation cycles in observed climate. Geophys. Res. Lett. 32, L20708 (2005).
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).
Newman, M. et al. The Pacific Decadal Oscillation, revisited. J. Clim. 29, 4399–4427 (2016).
Dong, B. & Dai, A. The influence of the Interdecadal Pacific Oscillation on temperature and precipitation over the globe. Clim. Dynam. 45, 2667–2681 (2015).
Kosaka, Y. & Xie, S.-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013). This is a pioneering paper that demonstrates that the long-term cooling of the eastern equatorial Pacific that began at the end of the twentieth century explains the slowdown in the rate of planetary warming that occurred in the following decade and a half.
Meehl, G. A., Hu, A., Santer, B. D. & Xie, S.-P. Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends. Nat. Clim. Change 6, 1005–1008 (2016).
Han, W. et al. Indian Ocean decadal variability: a review. Bull. Am. Meteorol. Soc. 95, 1679–1703 (2014).
Boer, G., Kharin, V. & Merryfield, W. Decadal predictability and forecast skill. Clim. Dynam. 41, 1817–1833 (2013).
Doblas-Reyes, F. et al. Initialized near-term regional climate change prediction. Nat. Commun. 4, 1715 (2013).This paper provides a thorough evaluation of initialized decadal predictions skill, based on multi-model hindcasts, performed every five years between 1960 and 2005. It finds that globally, most of the forecast skill is attributable to the known external forcing in the past. Initial conditions can also provide skill in some parts of the world ocean.
Matei, D. et al. Two tales of initializing decadal climate prediction experiments with the ECHAM5/MPI-OM model. J. Clim. 25, 8502–8523 (2012).
Müller, W. A. et al. Forecast skill of multi‐year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys. Res. Lett. 39, L22707 (2012).
Bellucci, A. et al. An assessment of a multi-model ensemble of decadal climate predictions. Clim. Dynam. 44, 2787–2806 (2015).
Smith, D. M. et al. Skilful multi-year predictions of Atlantic hurricane frequency. Nat. Geosci. 3, 846–849 (2010).
Eade, R., Hamilton, E., Smith, D. M., Graham, R. J. & Scaife, A. A. Forecasting the number of extreme daily events out to a decade ahead. J. Geophys. Res. Atmos. 117, D21110 (2012).This paper assesses the predictability of daily temperature and precipitation extremes over various timescales (up to a decade), using a state-of-the-art decadal prediction system. When assessing extreme temperature predictions for the season ahead, the study finds modest, but significant, skill over Europe and North America. However, when predictions of extremes over time intervals of 5 to 10 years are examined, the forecast skill is found to increase due to reduced noise associated with the use of longer data records.
Caron, L.-P. et al. How skilful are the multi-annual forecasts of Atlantic hurricane activity? Bull. Am. Meteorol. Soc. https://doi.org/10.1175/bams-d-17-0025.1 (2017).
Suckling, E. B., van Oldenborgh, G. J., Eden, J. M. & Hawkins, E. An empirical model for probabilistic decadal prediction: global attribution and regional hindcasts. Clim. Dynam. 48, 3115–3138 (2017).
Robson, J., Sutton, R. & Smith, D. Initialized decadal predictions of the rapid warming of the North Atlantic Ocean in the mid 1990s. Geophys. Res. Lett. 39, L19713 (2012).
Hermanson, L. et al. Forecast cooling of the Atlantic subpolar gyre and associated impacts. Geophys. Res. Lett. 41, 5167–5174 (2014).
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, 228–232 (2012).
Murphy, L. N., Bellomo, K., Cane, M. & Clement, A. The role of historical forcings in simulating the observed Atlantic multidecadal oscillation. Geophys. Res. Lett. 44, 2472–2480 (2017).
Zhang, R. et al. Have aerosols caused the observed Atlantic Multidecadal Variability? J. Atmos. Sci. 70, 1135–1144 (2013).
Otterå, O. H., Bentsen, M., Drange, H. & Suo, L. External forcing as a metronome for Atlantic multidecadal variability. Nat. Geosci. 3, 688–694 (2010).
Ding, H., Greatbatch, R. J., Latif, M., Park, W. & Gerdes, R. Hindcast of the 1976/77 and 1998/99 climate shifts in the Pacific. J. Clim. 26, 7650–7661 (2013).
Meehl, G. A., Hu, A. & Teng, H. Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation. Nat. Commun. 7, 11718 (2016).
Power, S., Haylock, M., Colman, R. & Wang, X. The predictability of interdecadal changes in ENSO activity and ENSO teleconnections. J. Clim. 19, 4755–4771 (2006).
Power, S. & Colman, R. Multi-year predictability in a coupled general circulation model. Clim. Dynam. 26, 247–272 (2006).
Smith, D. M. et al. Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nat. Clim. Change 6, 936–940 (2016).
Xie, S. P., Okumura, Y., Miyama, T. & Timmermann, A. Influences of Atlantic climate change on the tropical Pacific via the Central American Isthmus. J. Clim. 21, 3914–3928 (2008).
Kucharski, F. et al. Atlantic forcing of Pacific decadal variability. Clim. Dynam. 46, 2337–2351 (2016).
Chikamoto, Y., Mochizuki, T., Timmermann, A., Kimoto, M. & Watanabe, M. Potential tropical Atlantic impacts on Pacific decadal climate trends. Geophys. Res. Lett. 43, 7143–7151 (2016).
Li, X., Xie, S.-P., Gille, S. T. & Yoo, C. Atlantic-induced pan-tropical climate change over the past three decades. Nat. Clim. Change 43, 7143–7151 (2016).
Chikamoto, Y. et al. Skilful multi-year predictions of tropical trans-basin climate variability. Nat. Commun. 6, 6869 (2015).
Tokinaga, H., Xie, S.-P. & Mukougawa, H. Early 20th-century Arctic warming intensified by Pacific and Atlantic multidecadal variability. Proc. Natl Acad. Sci. USA 114, 6227–6232 (2017).
Boer, G. J. et al. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geosci. Model Dev. 9, 3751–3777 (2016).This paper describes the upcoming study of decadal prediction under CMIP6. This study will contribute to the issuance of the upcoming Global Annual to Decadal Climate Update by the WCRP GC-NTCP.
Gillett, N. P. et al. The Detection and Attribution Model Intercomparison Project (DAMIP v1. 0) contribution to CMIP6. Geosci. Model Dev. 9, 3685–3697 (2016).
Matthes, K. et al. Solar forcing for CMIP6 (v3. 2). Geosci. Model Dev. 10, 2247–2302 (2017).
Collins, M. et al. Challenges and opportunities for improved understanding of regional climate dynamics. Nat. Clim. Change 8, 101–108 (2018).
Eade, R. et al. Do seasonal‐to‐decadal climate predictions underestimate the predictability of the real world? Geophys. Res. Lett. 41, 5620–5628 (2014).
Power, S., Delage, F., Wang, G., Smith, I. & Kociuba, G. Apparent limitations in the ability of CMIP5 climate models to simulate recent multi-decadal change in surface temperature: implications for global temperature projections. Clim. Dynam. 49, 53–69 (2017).
Cheung, A. H. et al. Comparison of low-frequency internal climate variability in CMIP5 models and observations. J. Clim. 30, 4763–4776 (2017).
Wang, C., Zhang, L., Lee, S.-K., Wu, L. & Mechoso, C. R. A global perspective on CMIP5 climate model biases. Nat. Clim. Change 4, 201–205 (2014).
Pohlmann, H., Kröger, J., Greatbatch, R. J. & Müller, W. A. Initialization shock in decadal hindcasts due to errors in wind stress over the tropical Pacific. Clim. Dynam. 49, 2685–2693 (2017).
Sanchez-Gomez, E., Cassou, C., Ruprich-Robert, Y., Fernandez, E. & Terray, L. Drift dynamics in a coupled model initialized for decadal forecasts. Clim. Dynam. 46, 1819–1840 (2016).
Brune, S., Düsterhus, A., Pohlmann, H., Müller, W. A. & Baehr, J. Time dependency of the prediction skill for the North Atlantic subpolar gyre in initialized decadal hindcasts. Clim. Dynam. 51, 1947–1970 (2017).
Kröger, J., Müller, W. A. & von Storch, J.-S. Impact of different ocean reanalyses on decadal climate prediction. Clim. Dynam. 39, 795–810 (2012).
Kröger, J. et al. Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic. Clim. Dynam. 51, 2593–2608 (2017).
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).
Fučkar, N. S., Volpi, D., Guemas, V. & Doblas‐Reyes, F. J. A posteriori adjustment of near‐term climate predictions: accounting for the drift dependence on the initial conditions. Geophys. Res. Lett. 41, 5200–5207 (2014).
Smith, D. M., Eade, R. & Pohlmann, H. A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Clim. Dynam. 41, 3325–3338 (2013).
Stammer, D. et al. In OceanObs 09: Sustained Ocean Observations and Information for Society (eds Hall, J. et al.) 979–989 (European Space Agency, 2010).
Balmaseda, M. et al. The ocean reanalyses intercomparison project (ORA-IP). J. Oper. Oceanogr. 8, s80–s97 (2015).
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K. & Janssen, P. A coupled data assimilation system for climate reanalysis. Q. J. R. Meteorol. Soc. 142, 65–78 (2016).
Penny, S. G. & Hamill, T. M. Coupled data assimilation for integrated earth system analysis and prediction. Bull. Am. Meteorol. Soc. 98, ES169–ES172 (2017).
Budescu, D. V., Por, H.-H. & Broomell, S. B. Effective communication of uncertainty in the IPCC reports. Climatic Change 113, 181–200 (2012).
Corner, A., Lewandowsky, S., Phillips, M. & Roberts, O. The Uncertainty Handbook (University of Bristol, Bristol, 2015).
Spiegelhalter, D. Risk and uncertainty communication. Ann. Rev. Stat. Appl. 4, 31–60 (2017).
Buontempo, C. et al. What have we learnt from EUPORIAS climate service prototypes? Clim. Services 9, 21–32 (2018).
Marotzke, J. et al. MiKlip: a national research project on decadal climate prediction. Bull. Am. Meteorol. Soc. 97, 2379–2394 (2016).
Implementation Plan of the Global Framework for Climate Services (GFCS, 2014).
Hansen, J. W. Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agric. Syst. 74, 309–330 (2002).
Palin, E. J. et al. Skillful seasonal forecasts of winter disruption to the UK transport system. J. Appl. Meteorol. Climatol. 55, 325–344 (2016).
Clark, R. T., Bett, P. E., Thornton, H. E. & Scaife, A. A. Skilful seasonal predictions for the European energy industry. Environ. Res. Lett. 12, ARTN 024002 (2017).
Wood, A. W. & Lettenmaier, D. P. A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bull. Am. Meteorol. Soc. 87, 1699–1712 (2006).
National Flood Resilience Review (DEFRA, 2016); https://go.nature.com/2BiyExQ
Thompson, V. et al. High risk of unprecedented UK rainfall in the current climate. Nat. Commun. 8, 107 (2017).
Acknowledgements
The authors form the scientific steering group of the WCRP GC-NTCP. The GC-NTCP is one of the international initiatives promoting and advancing science and standards for the coordinated provision of near-term climate predictions at global scale. T.O.K. was supported by the CSIRO Decadal Forecasting Project (https://research.csiro.au/dfp). S.P. is supported by the National Environmental Science Program’s Earth Systems and Climate Change Hub. D.M. and W.A.M. were supported by the BMBF projects RACE II (D.M., grant no. FKZ:03F0729D) and MiKlip II (W.A.M., grant no. FKZ: 01LP1519A). The work of K.M. was partly supported by the BMBF within the nationally funded project ROMIC–SOLIC (grant no. 01LG1219) as well as within the frame of the WCRP/SPARC SOLARIS-HEPPA activity. A.A.S. and D.S. were supported by the Joint DECC/Defra Met Office Hadley Centre Climate under grant no. GA01101. E.H. was supported by the UK National Centre for Atmospheric Science and the SMURPHS project (grant no. NE/N006054/1). F.D.R. was supported by the H2020 EUCP (grant no. GA 776613) project.
Author information
Authors and Affiliations
Contributions
Y.K. and A.A.S. wrote the paper with input from all other authors. M.T. provided editing, drafting and factual support.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kushnir, Y., Scaife, A.A., Arritt, R. et al. Towards operational predictions of the near-term climate. Nature Clim Change 9, 94–101 (2019). https://doi.org/10.1038/s41558-018-0359-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41558-018-0359-7
This article is cited by
-
Wide range of possible trajectories of North Atlantic climate in a warming world
Nature Communications (2024)
-
Near-term temperature extremes in Iran using the decadal climate prediction project (DCPP)
Stochastic Environmental Research and Risk Assessment (2024)
-
Uncertainty assessment of future climate change using bias-corrected high-resolution multi-regional climate model datasets over East Asia
Climate Dynamics (2024)
-
Dampened predictable decadal North Atlantic climate fluctuations due to ice melting
Nature Geoscience (2023)
-
CAS FGOALS-f3-L Model Datasets for CMIP6 DCPP Experiment
Advances in Atmospheric Sciences (2023)