Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.
Subscribe to Journal
Get full journal access for 1 year
only $17.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
All data used in this study are publicly available. The CMIP5 simulations are available through PCMDI, the large ensembles are available at the MMLE Archive and the observational data are available through the respective institutions.
IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, 2007).
IPCC Climate Change 2013: The Physical Science Basis (Cambridge Univ. Press, 2013).
Wallace, J. M., Deser, C., Smoliak, B. V. & Phillips, A. S. in Climate Change: Multidecadal and Beyond (eds. Chang, C.-P. et al.) 1–29 (World Scientific, 2015).
Hall, A. Projecting regional change. Science 346, 1461–1462 (2014).
Xie, S. P. et al. Towards predictive understanding of regional climate change. Nat. Clim. Change 5, 921–930 (2015).
Stammer, D. et al. Science directions in a post COP21 world of transient climate change: enabling regional to local predictions in support of reliable climate information. Earths Future 6, 1498–1507 (2018).
Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Philos. T. R. Soc. A 365, 2053–2075 (2007).
Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).
Hawkins, E. & Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 37, 407–418 (2011).
Deser, C., Knutti, R., Solomon, S. & Phillips, A. S. Communication of the role of natural variability in future North American climate. Nat. Clim. Change 2, 775–779 (2012).
Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).
Kumar, D. & Ganguly, A. R. Intercomparison of model response and internal variability across climate model ensembles. Clim. Dyn. 51, 207–219 (2018).
Mankin, J. S., Viviroli, D., Singh, D., Hoekstra, A. Y. & Diffenbaugh, N. S. The potential for snow to supply human water demand in the present and future. Environ. Res. Lett. 10, 114016 (2015).
Hawkins, E., Smith, R. S., Gregory, J. M. & Stainforth, D. A. Irreducible uncertainty in near-term climate projections. Clim. Dyn. 46, 3807–3819 (2016).
Machete, R. L. & Smith, L. A. Demonstrating the value of larger ensembles in forecasting physical systems. Tellus A 68, 28393 (2016).
Bengtsson, L. & Hodges, K. I. Can an ensemble climate simulation be used to separate climate change signals from internal unforced variability? Clim. Dyn. 52, 3553–3573 (2019).
Selten, F. M., Branstator, G. W., Dijkstra, H. A. & Kliphuis, M. Tropical origins for recent and future Northern Hemisphere climate change. Geophys. Res. Lett. 31, 4–7 (2004).
Kay, J. E. et al. The community Earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-13-00255.1 (2014).
Otto, F. E. L. et al. Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environ. Res. Lett. 13, 12 (2018).
Fučkar, N. S. et al. On high precipitation in Mozambique, Zimbabwe and Zambia in February 2018. Bull. Am. Meteorol. Soc. 10, S47–S52 (2019).
US CLIVAR Multi-Model LE Archive (NCAR); http://www.cesm.ucar.edu/projects/community-projects/MMLEA/
Diffenbaugh, N. S., Swain, D. L. & Touma, D. Anthropogenic warming has increased drought risk in California. Proc. Natl Acad. Sci. USA 112, 3931–3936 (2015).
McKinley, G. A. et al. Timescales for detection of trends in the ocean carbon sink. Nature 530, 469–472 (2016).
Long, M. C., Deutsch, C. & Ito, T. Finding forced trends in oceanic oxygen. Global Biogeochem. Cycles 30, 381–397 (2016).
Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E. & Phillips, A. S. Quantifying the role of internal climate variability in future climate trends. J. Climate 28, 6443–6456 (2015).
Lehner, F., Deser, C. & Terray, L. Toward a new estimate of ‘time of emergence’ of anthropogenic warming: Insights from dynamical adjustment and a large initial-condition model ensemble. J. Climate 30, 7739–7756 (2017).
Dai, A. & Bloecker, C. E. Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Clim. Dyn. 52, 289–306 (2019).
Deser, C., Terray, L. & Phillips, A. S. Forced and internal components of winter air temperature trends over North America during the past 50 years: Mechanisms and implications. J. Climate 29, 2237–2258 (2016).
Sippel, S. et al. Uncovering the forced climate response from a single ensemble member using statistical learning. J. Climate https://doi.org/10.1175/JCLI-D-18-0405.1 (2019).
Swain, D. L., Langenbrunner, B., Neelin, J. D. & Hall, A. Increasing precipitation volatility in twenty-first-century California. Nat. Clim. Change 8, 427–433 (2018).
Bureau of Reclamation Climate Change Adaptation Strategy: 2016 Progress Report (U.S. Department of the Interior Bureau of Reclamation, 2016).
National Academies of Sciences, Engineering and Medicine Attribution of Extreme Weather Events in the Context of Climate Change (The National Academies Press, 2016).
Lehner, F., Deser, C. & Sanderson, B. M. Future risk of record-breaking summer temperatures and its mitigation. Clim. Change 146, 1–13 (2016).
Mitchell, D. et al. Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geosci. Model Dev. 10, 571–583 (2017).
Otto, F. E. L. et al. Climate change increases the probability of heavy rains in Northern England/Southern Scotland like those of storm Desmond—a real-time event attribution revisited. Environ. Res. Lett. 13, 2 (2018).
Ciavarella, A. et al. Upgrade of the HadGEM3-A based attribution system to high resolution and a new validation framework for probabilistic event attribution. Weather Clim. Extrem. 20, 9–32 (2018).
Lehner, F., Deser, C., Simpson, I. R. & Terray, L. Attributing the U. S. Southwest’s recent shift into drier conditions. Geophys. Res. Lett. 45, 6251–6261 (2018).
Seager, R. et al. Climate variability and change of mediterranean-type climates. J. Climate 32, 2887–2915 (2019).
Lehner, F. et al. The potential to reduce uncertainty in regional runoff projections from climate models. Nat. Clim. Change 9, 926–933 (2019).
Borodina, A., Fischer, E. M. & Knutti, R. Potential to constrain projections of hot temperature extremes. J. Climate 30, 9949–9964 (2017).
Massey, N. et al. Weather@home-development and validation of a very large ensemble modelling system for probabilistic event attribution. Q. J. R. Meteorol. Soc. 141, 1528–1545 (2015).
Mizuta, R. et al. Over 5,000 years of ensemble future climate simulations by 60-km global and 20-km regional atmospheric models. Bull. Am. Meteorol. Soc. 98, 1383–1398 (2017).
Pall, P. et al. Diagnosing conditional anthropogenic contributions to heavy Colorado rainfall in September 2013. Weather Clim. Extrem. 17, 1–6 (2017).
Merrifield, A. L. et al. Local and non-local land surface influence in European heatwave initial condition ensembles. Geophys. Res. Lett. 46, 14082–14092 (2019).
Leduc, M. et al. The ClimEx project: A 50-member ensemble of climate change projections at 12-km resolution over Europe and northeastern North America with the Canadian Regional Climate Model (CRCM5). J. Appl. Meteorol. Climatol. 58, 663–693 (2019).
McKinnon, K. & Deser, C. Internal variability and regional climate trends in an Observational Large Ensemble. J. Climate https://doi.org/10.1175/JCLI-D-17-0901.1 (2018).
Frankignoul, C., Gastineau, G. & Kwon, Y. O. Estimation of the SST response to anthropogenic and external forcing and its impact on the Atlantic multidecadal oscillation and the Pacific decadal oscillation. J. Climate 30, 9871–9895 (2017).
Wills, R. C., Schneider, T., Hartmann, D. L., Battisti, D. S. & Wallace, J. M. Disentangling global warming, multidecadal variability, and El Niño in Pacific temperatures. Geophys. Res. Lett. 45, 2487–2496 (2018).
Barnes, E. A., Hurrell, J. W. & Uphoff, I. E. Viewing forced climate patterns through an AI lens. Geophys. Res. Lett. 46, 13389–13398 (2019).
Wills, R. C., Battisti, D. S., Armour, K. C., Schneider, T. & Deser, C. Identifying forced climate responses in climate model ensembles and observations using pattern recognition methods. J. Climate (in the press).
Gould, S. J. Wonderful Life: The Burgess Shale and the Nature of History (W. W. Norton & Co., 1989).
Newman, M., Alexander, M. A. & Scott, J. D. An empirical model of tropical ocean dynamics. Clim. Dyn. 37, 1823–1841 (2011).
Newman, M., Shin, S. I. & Alexander, M. A. Natural variation in ENSO flavors. Geophys. Res. Lett. 38, L14705 (2011).
Newman, M. An empirical benchmark for decadal forecasts of global surface temperature anomalies. J. Clim. 26, 5260–5269 (2013).
McKinnon, K. A., Poppick, A., Dunn-Sigouin, E. & Deser, C. An ‘Observational Large Ensemble’ to compare observed and modeled temperature trend uncertainty due to internal variability. J. Climate https://doi.org/10.1175/JCLI-D-16-0905.1 (2017).
Link, R. et al. Fldgen v1.0: An emulator with internal variability and space-time correlation for Earth system models. Geosci. Model Dev. 12, 1477–1489 (2019).
Castruccio, S., Hu, Z., Sanderson, B., Karspeck, A. & Hammerling, D. Reproducing internal variability with few Ensemble runs. J. Climate https://doi.org/10.1175/JCLI-D-19-0280.1 (2019).
Beusch, L., Gudmundsson, L. & Seneviratne, S. I. Emulating Earth System Model temperatures: from global mean temperature trajectories to grid-point level realizations on land. Earth Syst. Dyn. Discuss. https://doi.org/10.5194/esd-2019-34-RC1 (2019).
Poppick, A., McInerney, D. J., Moyer, E. J. & Stein, M. L. Temperatures in transient climates: Improved methods for simulations with evolving temporal covariances. Ann. Appl. Stat. 10, 477–505 (2016).
Maher, N. et al. The Max Planck Institute Grand Ensemble – enabling the exploration of climate system variability. J. Adv. Model. Earth Syst. 11, 2050–2069 (2019).
Roberts, M. J. et al. The benefits of global high resolution for climate simulation process understanding and the enabling of stakeholder decisions at the regional scale. Bull. Am. Meteorol. Soc. 99, 2341–2359 (2018).
Freychet, N., Tett, S. F. B., Bollasina, M., Wang, K. C. & Hegerl, G. C. The local aerosol emission effect on surface shortwave radiation and temperatures. J. Adv. Model. Earth Syst. 11, 806–817 (2019).
Pendergrass, A. G. et al. Nonlinear response of extreme precipitation to warming in CESM1. Geophys. Res. Lett. 46, 10551–10560 (2019).
Aalbers, E. E., Lenderink, G., van Meijgaard, E. & van den Hurk, B. J. J. M. Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Clim. Dyn. 50, 4745–4766 (2018).
Gómez-Navarro, J. J. et al. Event selection for dynamical downscaling: a neural network approach for physically-constrained precipitation events. Clim. Dyn. https://doi.org/10.1007/s00382-019-04818-w (2019).
DiNezio, P. N., Deser, C., Okumura, Y. & Karspeck, A. Predictability of 2-year La Niña events in a coupled general circulation model. Clim. Dyn. 49, 4237–4261 (2017).
DiNezio, P. N. et al. A 2 year forecast for a 60–80% chance of La Niña in 2017–2018. Geophys. Res. Lett. 44, 11,624–11,635 (2017).
Lambert, F. H. et al. Interactions between perturbations to different Earth system components simulated by a fully-coupled climate model. Clim. Dyn. 41, 3055–3072 (2013).
Haarsma, R. J. et al. High resolution model intercomparison project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev. 9, 4185–4208 (2016).
Raff, D., Brekke, L., Werner, K., Wood, A. & White, K. Short-Term Water Management Decisions: User Needs for Improved Climate, Weather, and Hydrologic Information (NOAA, 2013).
Hogrefe, C. et al. Simulating changes in regional air pollution over the eastern United States due to changes in global and regional climate and emissions. J. Geophys. Res. D Atmos. 109, D22 (2004).
Garcia-Menendez, F., Monier, E. & Selin, N. E. The role of natural variability in projections of climate change impacts on U. S. ozone pollution. Geophys. Res. Lett. 44, 2911–2921 (2017).
Horton, D. E., Skinner, C. B., Singh, D. & Diffenbaugh, N. S. Occurrence and persistence of future atmospheric stagnation events. Nat. Clim. Change 4, 698–703 (2014).
Shen, L., Mickley, L. J. & Gilleland, E. Impact of increasing heat waves on U. S. ozone episodes in the 2050s: Results from a multimodel analysis using extreme value theory. Geophys. Res. Lett. 43, 4017–4025 (2016).
Yue, X., Mickley, L. J. & Logan, J. A. Projection of wildfire activity in southern California in the mid-twenty-first century. Clim. Dyn. 43, 1973–1991 (2013).
Mulholland, D. P., Haines, K., Sparrow, S. N. & Wallom, D. Climate model forecast biases assessed with a perturbed physics ensemble. Clim. Dyn. 49, 1729–1746 (2017).
Rodgers, K. B., Lin, J. & Frölicher, T. L. Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model. Biogeosciences 12, 3301–3320 (2015).
Schlunegger, S. et al. Emergence of anthropogenic signals in the ocean carbon cycle. Nat. Clim. Change 9, 719–725 (2019).
Lovenduski, N. S., McKinley, G. A., Fay, A. R., Lindsay, K. & Long, M. C. Partitioning uncertainty in ocean carbon uptake projections: Internal variability, emission scenario, and model structure. Global Biogeochem. Cycles 30, 1276–1287 (2016).
Frölicher, T. L., Rodgers, K. B., Stock, C. A. & Cheung, W. W. L. Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors. Global Biogeochem. Cycles 30, 1224–1243 (2016).
Krumhardt, K. M., Lovenduski, N. S., Long, M. C. & Lindsay, K. Avoidable impacts of ocean warming on marine primary production: insights from the CESM ensembles. Global Biogeochem. Cycles 31, 114–133 (2017).
Li, H. & Ilyina, T. Current and future decadal trends in the oceanic carbon uptake are dominated by internal variability. Geophys. Res. Lett. 45, 916–925 (2018).
Labe, Z., Ault, T. & Zurita-Milla, R. Identifying anomalously early spring onsets in the CESM large ensemble project. Clim. Dyn. 48, 3949–3966 (2017).
Fann, N. et al. The geographic distribution and economic value of climate change-related ozone health impacts in the United States in 2030. J. Air Waste Manag. Assoc. 65, 570–580 (2015).
Silva, R. A. et al. The effect of future ambient air pollution on human premature mortality to 2100 using output from the ACCMIP model ensemble. Atmos. Chem. Phys. 16, 9847–9862 (2016).
Rieder, H. E., Fiore, A. M., Horowitz, L. W. & Naik, V. Projecting policy-relevant metrics for high summertime ozone pollution events over the eastern United States due to climate and emission changes during the 21st century. J. Geophys. Res. 120, 784–800 (2015).
Dentener, F. et al. The global atmospheric environment for the next generation. Environ. Sci. Technol. 40, 3586–3594 (2006).
Schnell, J. L. et al. Effect of climate change on surface ozone over North America, Europe, and East Asia. Geophys. Res. Lett. 43, 3509–3518 (2016).
Barnes, E. A., Fiore, A. M. & Horowitz, L. W. Detection of trends in surface ozone in the presence of climate variability. J. Geophys. Res. 121, 6112–6129 (2016).
Saari, R. K., Mei, Y., Monier, E. & Garcia-Menendez, F. Effect of health-related uncertainty and natural variability on health impacts and cobenefits of climate policy. Environ. Sci. Technol. 53, 1098–1108 (2019).
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).
Rew, R. & Davis, G. NetCDF: an interface for scientific data access. IEEE Comput. Graph. Appl. 10, 76–82 (1990).
Kirchmeier-Young, M. C., Zwiers, F. W. & Gillett, N. P. Attribution of extreme events in Arctic Sea ice extent. J. Climate 30, 553–571 (2017).
Jeffrey, S. et al. Australia’s CMIP5 submission using the CSIRO-Mk3. 6 model. Aust. Meteorol. Ocean. 63, 1–13 (2013).
Sun, L., Alexander, M. & Deser, C. Evolution of the global coupled climate response to Arctic sea ice loss during 1990–2090 and its contribution to climate change. J. Climate 31, 7823–7843 (2018).
Kay, J. E. et al. The community earth system model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).
Hazeleger, W. et al. EC-Earth. Bull. Am. Meteorol. Soc. 91, 1357–1364 (2010).
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).
Trenberth, K. E. & Shea, D. J. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. 33, L12704 (2006).
Dai, A., Fyfe, J. C., Xie, S. P. & Dai, X. Decadal modulation of global surface temperature by internal climate variability. Nat. Clim. Change 5, 555–559 (2015).
We thank the US National Science Foundation, National Oceanic and Atmospheric Administration, National Aeronautics and Space Administration, and Department of Energy for sponsoring the activities of the US CLIVAR Working Group on Large Ensembles. We also gratefully acknowledge all of the modelling groups listed in Table 1 for making their Large Ensemble simulations available in the Multi-Model Large Ensemble data repository. We thank the three anonymous reviewers for their constructive comments and suggestions, and J. Mankin for inspirational discussions on Large Ensemble use. This Perspective also benefited from discussions that took place at the US CLIVAR Workshop on Large Ensembles held July 2019 in Boulder, CO, USA. Some of this material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (cooperative agreement no. 1852977).
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Ryan Abernathey, Jens Christensen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Deser, C., Lehner, F., Rodgers, K.B. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Chang. 10, 277–286 (2020). https://doi.org/10.1038/s41558-020-0731-2
Quantifying the role of internal variability in the temperature we expect to observe in the coming decades
Environmental Research Letters (2020)