Adam, O., T. Schneider, F. Brient, and T. Bischoff, 2016: Relation of the double-ITCZ bias to the atmospheric energy budget in climate models. Geophys. Res. Lett., 43(14), 7670−7677, https://doi.org/10.1002/2016GL069465. |
Adam, O., T. Schneider, and F. Brient, 2017: Regional and seasonal variations of the double-ITCZ bias in CMIP5 models. Climate Dyn., 51, 101−117, https://doi.org/10.1007/s00382-017-3909-1. |
Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224−231, https://doi.org/10.1038/nature01092. |
Andrews, T., and Coauthors, 2018: Accounting for changing temperature patterns increases historical estimates of climate sensitivity. Geophys. Res. Lett., 45(16), 8490−8499, https://doi.org/10.1029/2018GL078887. |
Betts, A. K., and Harshvardhan, 1987: Thermodynamic constraint on the cloud liquid water feedback in climate models. J. Geophys. Res., 92, 8483−8485, https://doi.org/10.1029/JD092iD07p08483. |
Boé, J., A. Hall, and X. Qu, 2009: September sea-ice cover in the Arctic Ocean projected to vanish by 2100. Nature Geoscience, 2(5), 341−343, https://doi.org/10.1038/ngeo467. |
Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19(15), 3445−3482, https://doi.org/10.1175/JCLI3819.1. |
Bony, S., G. Bellon, D. Klocke, S. Sherwood, S. Fermepin, and S. Denvil, 2013: Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nature Geoscience, 6(6), 447−451, https://doi.org/10.1038/ngeo1799. |
Bony, S., B. Stevens, D. Coppin, T. Becker, K. A. Reed, A. Voigt, and B. Medeiros, 2016: Thermodynamic control of anvil cloud amount. Proceedings of the National Academy of Sciences of the United States of America, 113(32), 8927−8932, https://doi.org/10.1073/pnas.1601472113. |
Borodina, A., E. M. Fischer, and R. Knutti, 2017: Models are likely to underestimate increase in heavy rainfall in the extratropical regions with high rainfall intensity. Geophys. Res. Lett., 44(14), 7401−7409, https://doi.org/10.1002/2017GL074530. |
Bracegirdle, T. J., and D. B. Stephenson, 2013: On the robustness of emergent constraints used in multimodel climate change projections of arctic warming. J. Climate, 26(2), 669−678, https://doi.org/10.1175/JCLI-D-12-00537.1. |
Brient, F., and S. Bony, 2013: Interpretation of the positive low-cloud feedback predicted by a climate model under global warming. Climate Dyn., 40(9-10), 2415−2431, https://doi.org/10.1007/s00382-011-1279-7. |
Brient, F., and T. Schneider, 2016: Constraints on climate sensitivity from space-based measurements of low-cloud reflection. J. Climate, 29(16), 5821−5835, https://doi.org/10.1175/JCLI-D-15-0897.1. |
Brient, F., T. Schneider, Z. H. Tan, S. Bony, X. Qu, and A. Hall, 2016: Shallowness of tropical low clouds as a predictor of climate models’ response to warming. Climate Dyn., 47, 433−449, https://doi.org/10.1007/s00382-015-2846-0. |
Burnham, K. P., and D. R. Anderson, 2003: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. Springer. |
Caldwell, P. M., C. S. Bretherton, M. D. Zelinka, S. A. Klein, B. D. Santer, and B. M. Sanderson, 2014: Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett., 41(5), 1803−1808, https://doi.org/10.1002/2014GL059205. |
Caldwell, P. M., M. D. Zelinka, and S. A. Klein, 2018: Evaluating emergent constraints on equilibrium climate sensitivity. J. Climate, 31(10), 3921−3942, https://doi.org/10.1175/JCLI-D-17-0631.1. |
Ceppi, P., and J. M. Gregory, 2017: Relationship of tropospheric stability to climate sensitivity and earth’s observed radiation budget. Proceedings of the National Academy of Sciences of the United States of America, 114(50), 13126−13131, https://doi.org/10.1073/pnas.1714308114. |
Ceppi, P., F. Brient, M. D. Zelinka, and D. L. Hartmann, 2017: Cloud feedback mechanisms and their representation in global climate models. WIREs Climate Change, 8(4), e465, https://doi.org/10.1002/wcc.465. |
Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res., 95, 16601−16615, https://doi.org/10.1029/JD095iD10p16601. |
Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101, 12791−12794, https://doi.org/10.1029/96JD00822. |
Charney, J. G., and Coauthors, 1979: Carbon Dioxide and Climate: A Scientific Assessment. The National Academies Press, 33 pp. |
Christensen, J. H., K. K. Kanikicharla, G. Marshall, and J. Turner, 2013: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T.F. Stocker et al., Eds., Cambridge University Press. |
Covey, C., and Coauthors, 2000: The seasonal cycle in coupled ocean-atmosphere general circulation models. Climate Dyn., 16(10-11), 775−787, https://doi.org/10.1007/s003820000081. |
Cox, P. M., D. Pearson, B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494(7437), 341−344, https://doi.org/10.1038/nature11882. |
Cox, P. M., C. Huntingford, and M. S. Williamson, 2018: Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature, 553(7688), 319−322, https://doi.org/10.1038/nature25450. |
DeAngelis, A. M., X. Qu, M. D. Zelinka, and A. Hall, 2015: An observational radiative constraint on hydrologic cycle intensification. Nature, 528(7581), 249−253, https://doi.org/10.1038/nature15770. |
Dee, D. P., and Coauthors, 2011: The ERA-interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteorol. Soc., 137(656), 553−597, https://doi.org/10.1002/qj.828. |
Donat, M. G., A. J. Pitman, and O. Angélil, 2018: Understanding and reducing future uncertainty in midlatitude daily heat extremes via land surface feedback constraints. Geophys. Res. Lett., 45(19), 10627−10636, https://doi.org/10.1029/2018GL079128. |
Douville, H., and M. Plazzotta, 2017: Midlatitude summer drying: An underestimated threat in CMIP5 models? Geophys. Res. Lett., 44(19), 9967−9975, https://doi.org/10.1002/2017GL075353. |
Dufresne, J.-L., and S. Bony, 2008: An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models. J. Climate, 21(19), 5135−5144, https://doi.org/10.1175/2008JCLI2239.1. |
Eyring, V., and Coauthors, 2019: Taking climate model evaluation to the next level. Nat. Clim. Change, 9(2), 102−110, https://doi.org/10.1038/s41558-018-0355-y. |
Fasullo, J. T., and K. E. Trenberth, 2012: A less cloudy future: The role of subtropical subsidence in climate sensitivity. Science, 338(6108), 792−794, https://doi.org/10.1126/science.1227465. |
Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds., Cambridge University Press, 741−866. |
Găinuşă-Bogdan, A., P. Braconnot, and J. Servonnat, 2015: Using an ensemble data set of turbulent air-sea fluxes to evaluate the IPSL climate model in tropical regions. J. Geophys. Res., 120(10), 4483−4505, https://doi.org/10.1002/2014JD022985. |
Gao, Y., J. Lu, and L. R. Leung, 2016: Uncertainties in projecting future changes in atmospheric rivers and their impacts on heavy precipitation over Europe. J. Climate, 29(18), 6711−6726, https://doi.org/10.1175/JCLI-D-16-0088.1. |
Geoffroy, O., S. C. Sherwood, and D. Fuchs, 2017: On the role of the stratiform cloud scheme in the inter-model spread of cloud feedback. Journal of Advances in Modeling Earth Systems, 9(1), 423−437, https://doi.org/10.1002/2016MS000846. |
Gordon, N. D., and S. A. Klein, 2014: Low-cloud optical depth feedback in climate models. J. Geophys. Res., 119(10), 6052−6065, https://doi.org/10.1002/2013JD021052. |
Gregory, J. M., and Coauthors, 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31(3), L03205, https://doi.org/10.1029/2003GL018747. |
Hall, A., and S. Manabe, 1999: The role of water vapor feedback in unperturbed climate variability and global warming. J. Climate, 12, 2327−2346, https://doi.org/10.1175/1520-0442(1999)012<2327:TROWVF>2.0.CO;2. |
Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127. |
Hall, A., P. Cox, C. Huntingford, and S. Klein, 2019: Progressing emergent constraints on future climate change. Nat. Clim. Change, 9(4), 269−278, https://doi.org/10.1038/s41558-019-0436-6. |
Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the last glacial maximum constrain climate sensitivity? Geophys. Res. Lett., 39(24), L24702, https://doi.org/10.1029/2012GL053872. |
Harrison, S. P., P. J. Bartlein, K. Izumi, G. Li, J. Annan, J. Hargreaves, P. Braconnot, and M. Kageyama, 2015: Evaluation of CMIP5 palaeo-simulations to improve climate projections. Nat. Clim. Change, 5(8), 735−743, https://doi.org/10.1038/nclimate2649. |
Hartmann, D. L., and K. Larson, 2002: An important constraint on tropical cloud-climate feedback. Geophys. Res. Lett., 29, 12−1, https://doi.org/10.1029/2002GL015835. |
Hoffman, F. M., and Coauthors, 2014: Causes and implications of persistent atmospheric carbon dioxide biases in earth system models. J. Geophys. Res., 119(2), 141−162, https://doi.org/10.1002/2013JG002381. |
Huber, M., I. Mahlstein, M. Wild, J. Fasullo, and R. Knutti, 2011: Constraints on climate sensitivity from radiation patterns in climate models. J. Climate, 24(4), 1034−1052, https://doi.org/10.1175/2010JCLI3403.1. |
Hwang, Y.-T., and D. M. W. Frierson, 2013: Link between the double-intertropical convergence zone problem and cloud biases over the southern ocean. Proceedings of the National Academy of Sciences of the United States of America, 110(13), 4935−4940, https://doi.org/10.1073/pnas.1213302110. |
Kamae, Y., H. Shiogama, M. Watanabe, T. Ogura, T. Yokohata, and M. Kimoto, 2016: Lower-tropospheric mixing as a constraint on cloud feedback in a multiparameter multiphysics ensemble. J. Climate, 29(17), 6259−6275, https://doi.org/10.1175/JCLI-D-16-0042.1. |
Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the poleward shift of the austral jet stream in the CMIP3 integrations linked to biases in 20th century climatology. Geophys. Res. Lett., 37(9), L09708, https://doi.org/10.1029/2010GL042873. |
Klein, S. A., and A. Hall, 2015: Emergent constraints for cloud feedbacks. Current Climate Change Reports, 1(4), 276−287, https://doi.org/10.1007/s40641-015-0027-1. |
Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett., 40(6), 1194−1199, https://doi.org/10.1002/grl.50256. |
Kwiatkowski, L., L. Bopp, O. Aumont, P. Ciais, P. M. Cox, C. Laufkötter, Y. Li, and R. Séférian, 2017: Emergent constraints on projections of declining primary production in the tropical oceans. Nat. Clim. Change, 7(5), 355−358, https://doi.org/10.1038/nclimate3265. |
Li, G., S.-P. Xie, C. He, and Z. S. Chen, 2017: Western pacific emergent constraint lowers projected increase in Indian summer monsoon rainfall. Nat. Clim. Change, 7(10), 708−712, https://doi.org/10.1038/nclimate3387. |
Lin, Y. L., W. H. Dong, M. H. Zhang, Y. Y. Xie, W. Xue, J. B. Huang, and Y. Luo, 2017: Causes of model dry and warm bias over central U. S. and impact on climate projections. Nature Communications, 8(1), 881, https://doi.org/10.1038/s41467-017-01040-2. |
Lipat, B. R., G. Tselioudis, K. M. Grise, and L. M. Polvani, 2017: CMIP5 models’ shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent. Geophys. Res. Lett., 44(11), 5739−5748, https://doi.org/10.1002/2017GL073151. |
Masson, D., and R. Knutti, 2011: Climate model genealogy. Geophys. Res. Lett., 38(8), L08703, https://doi.org/10.1029/2011GL046864. |
Massonnet, F., T. Fichefet, H. Goosse, C. M. Bitz, G. Philippon-Berthier, M. M. Holland, and P.-Y. Barriat, 2012: Constraining projections of summer arctic sea ice. The Cryosphere, 6(6), 1383−1394, https://doi.org/10.5194/tc-6-1383-2012. |
McCoy, D. T., D. L. Hartmann, M. D. Zelinka, P. Ceppi, and D. P. Grosvenor, 2015: Mixed-phase cloud physics and southern ocean cloud feedback in climate models. J. Geophys. Res., 120(18), 9539−9554, https://doi.org/10.1002/2015JD023603. |
Meehl, G. A., G. J. Boer, C. J. Covey, M. Latif, and R. J. Stouffer, 2000: The coupled model intercomparison project (CMIP). Bull. Amer. Meteorol. Soc., 81, 313−318, https://doi.org/10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2. |
Mitchell, J. F. B., C. A. Senior, and W. J. Ingram, 1989: CO2 and climate: A missing feedback? Nature, 341(6238), 132−134, https://doi.org/10.1038/341132a0. |
Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117(D8), D08101, https://doi.org/10.1029/2011JD017187. |
Myers, T. A., and J. R. Norris, 2013: Observational evidence that enhanced subsidence reduces subtropical marine boundary layer cloudiness. J. Climate, 26(19), 7507−7524, https://doi.org/10.1175/JCLI-D-12-00736.1. |
Myers, T. A., and J. R. Norris, 2015: On the relationships between subtropical clouds and meteorology in observations and CMIP3 and CMIP5 models. J. Climate, 28(8), 2945−2967, https://doi.org/10.1175/JCLI-D-14-00475.1. |
O’Gorman, P. A., 2012: Sensitivity of tropical precipitation extremes to climate change. Nature Geoscience, 5(10), 697−700, https://doi.org/10.1038/ngeo1568. |
O’Gorman, P. A., and T. Schneider, 2008: The hydrological cycle over a wide range of climates simulated with an idealized GCM. J. Climate, 21(15), 3815−3832, https://doi.org/10.1175/2007JCLI2065.1. |
Plazzotta, M., R. Séférian, H. Douville, B. Kravitz, and J. Tjiputra, 2018: Land surface cooling induced by sulfate geoengineering constrained by major volcanic eruptions. Geophys, Res, Lett., 45, 5663−5671, https://doi.org/10.1029/2018GL077583. |
Qu, X., and A. Hall, 2014: On the persistent spread in snow-albedo feedback. Climate Dyn., 42(1−2), 69−81, https://doi.org/10.1007/s00382-013-1774-0. |
Qu, X., A. Hall, S. A. Klein, and P. M. Caldwell, 2014: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dyn., 42, 2603−2626, https://doi.org/10.1007/s00382-013-1945-z. |
Qu, X., A. Hall, S. A. Klein, and A. M. DeAngelis, 2015: Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. Geophys. Res. Lett., 42(18), 7767−7775, https://doi.org/10.1002/2015GL065627. |
Qu, X., A. Hall, A. M. DeAngelis, M. D. Zelinka, S. A. Klein, H. Su, B. J. Tian, and C. X. Zhai, 2018: On the emergent constraints of climate sensitivity. J. Climate, 31(2), 863−875, https://doi.org/10.1175/JCLI-D-17-0482.1. |
Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteorol. Soc., 80, 2261−2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2. |
Sanderson, B. M., R. Knutti, and P. Caldwell, 2015: A representative democracy to reduce interdependency in a multimodel ensemble. J. Climate, 28(13), 5171−5194, https://doi.org/10.1175/JCLI-D-14-00362.1. |
Schmidt, G. A., and Coauthors, 2013: Using palaeo-climate comparisons to constrain future projections in CMIP5. Climate of the Past, 10(1), 221−250, https://doi.org/10.5194/cp-10-221-2014. |
Schneider, T., 2018: Statistical inference with emergent constraints. [Available from https://climate-dynamics.org/statistical-inference-with-emergent-constraints/.] |
Seneviratne, S. I., M. G. Donat, A. J. Pitman, R. Knutti, and R. L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477−483, https://doi.org/10.1038/nature16542. |
Sherwood, S. C., S. Bony, and J.-L. Dufresne, 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505(7481), 37−42, https://doi.org/10.1038/nature12829. |
Siler, N., S. Po-Chedley, and C. S. Bretherton, 2018: Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds. Climate Dyn., 50(3-4), 1209−1220, https://doi.org/10.1007/s00382-017-3673-2. |
Simpson, I. R., and L. M. Polvani, 2016: Revisiting the relationship between jet position, forced response, and annular mode variability in the southern midlatitudes. Geophys. Res. Lett., 43(6), 2896−2903, https://doi.org/10.1002/2016GL067989. |
Stocker, T. F., and Coauthors, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, 1585 pp. |
Su, H., J. H. Jiang, C. X. Zhai, T. J. Shen, J. D. Neelin, G. L. Stephens, and Y. L. Yung, 2014: Weakening and strengthening structures in the hadley circulation change under global warming and implications for cloud response and climate sensitivity. J. Geophys. Res., 119(10), 5787−5805, https://doi.org/10.1002/2014JD021642. |
Tan, I., T. Storelvmo, and M. D. Zelinka, 2016: Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science, 352(6282), 224−227, https://doi.org/10.1126/science.aad5300. |
Thackeray, C. W., X. Qu, and A. Hall, 2018: Why do models produce spread in snow albedo feedback? Geophys. Res. Lett., 45(12), 6223−6231, https://doi.org/10.1029/2018GL078493. |
Tian, B. J., 2015: Spread of model climate sensitivity linked to double-intertropical convergence zone bias. Geophys. Res. Lett., 42(10), 4133−4141, https://doi.org/10.1002/2015GL064119. |
Trenberth, K. E., and A. G. Dai, 2007: Effects of mount pinatubo volcanic eruption on the hydrological cycle as an analog of geoengineering. Geophys. Res. Lett., 34(15), L15702, https://doi.org/10.1029/2007GL030524. |
Trenberth, K. E., and J. T. Fasullo, 2010: Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J. Climate, 23(2), 440−454, https://doi.org/10.1175/2009JCLI3152.1. |
Volodin, E. M., 2008: Relation between temperature sensitivity to doubled carbon dioxide and the distribution of clouds in current climate models. Izvestiya, Atmospheric and Oceanic Physics, 44(3), 288−299, https://doi.org/10.1134/S0001433808030043. |
Wagman, B. M., and C. S. Jackson, 2018: A test of emergent constraints on cloud feedback and climate sensitivity using a calibrated single-model ensemble. J. Climate, 31(18), 7515−7532, https://doi.org/10.1175/JCLI-D-17-0682.1. |
Wang, J., N. Zeng, Y. M. Liu, and Q. Bao, 2014: To what extent can interannual CO2 variability constrain carbon cycle sensitivity to climate change in CMIP5 earth system models? Geophys. Res. Lett., 41(10), 3535−3544, https://doi.org/10.1002/2014GL060004. |
Watanabe, M., Y. Kamae, H. Shiogama, A. M. DeAngelis, and K. Suzuki, 2018: Low clouds link equilibrium climate sensitivity to hydrological sensitivity. Nat. Clim. Change, 8(10), 901−906, https://doi.org/10.1038/s41558-018-0272-0. |
Webb, M. J., and Coauthors, 2015: The impact of parametrized convection on cloud feedback. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2054), 20140414, https://doi.org/10.1098/rsta.2014.0414. |
Wenzel, S., P. M. Cox, V. Eyring, and P. Friedlingstein, 2014: Emergent constraints on climate-carbon cycle feedbacks in the CMIP5 earth system models. J. Geophys. Res., 119(5), 794−807, https://doi.org/10.1002/2013JG002591. |
Wenzel, S., P. M. Cox, V. Eyring, and P. Friedlingstein, 2016: Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature, 538(7626), 499−501, https://doi.org/10.1038/nature19772. |
Winker, D. W., and Coauthors, 2010: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteorol. Soc., 91(9), 1211−1229, https://doi.org/10.1175/2010BAMS3009.1. |
Winkler, A. J., R. B. Myneni, G. A. Alexandrov, and V. Brovkin, 2019: Earth system models underestimate carbon fixation by plants in the high latitudes. Nature Communications, 10(1), 885, https://doi.org/10.1038/s41467-019-08633-z. |
Zelinka, M. D., S. A. Klein, K. E. Taylor, T. Andrews, M. J. Webb, J. M. Gregory, and P. M. Forster, 2013: Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. J. Climate, 26(14), 5007−5027, https://doi.org/10.1175/JCLI-D-12-00555.1. |
Zhai, C. X., J. H. Jiang, and H. Su, 2015: Long-term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity. Geophys. Res. Lett., 42(20), 8729−8737, https://doi.org/10.1002/2015GL065911. |
Zhou, C., M. D. Zelinka, A. E. Dessler, and S. A. Klein, 2015: The relationship between interannual and long-term cloud feedbacks. Geophys. Res. Lett., 42(23), 10463−10469, https://doi.org/10.1002/2015GL066698. |
Zhou, C., M. D. Zelinka, and S. A. Klein, 2016: Impact of decadal cloud variations on the earth’s energy budget. Nature Geoscience, 9(12), 871−874, https://doi.org/10.1038/ngeo2828. |