Models disagree on a significant number of responses to climate change, such as climate feedback, regional changes, or the strength of equilibrium climate sensitivity. Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections. In this paper, the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties, and a review of emergent constraints found in the last two decades. Potential links between highlighted predictors are explored, especially those targeting uncertainty reductions in climate sensitivity, cloud feedback, and changes of the hydrological cycle. Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed. This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship. This would also permit more efficient model evaluation and better targeted model development. In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected, which call for robust statistical inferences that allow cross-validation of more likely estimates.
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, 4314, 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/s00382017-3909-1.
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 ex-tratropical 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. Hunting-ford, 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.12.2.7465.
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–
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/10.1175/15200442(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. Meteor-ol. 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.
This work received funding from the Agence Nationale de la Recherche (ANR) [grant HIGH-TUNE ANR-16-CE01-0010]. I thank Tapio SCHNEIDER for the numerous discussions we had on this topic, and for sharing his thoughts on statistical inference. I also thank Ross DIXON for interesting discussions and for proofreading the manuscript. Finally, I thank the two anonymous reviewers for their insightful comments on the manuscript. Routines for the randomly generated relationship and the statistical inferences are available on the Github website (https://github.com/florentbrient/emergent_constraint/).
• Emergent constraints aim to reduce uncertainties in inter-model climate projections by relating them to observational predictors.
• Tens of constraints that provide best estimates for several climate change signals have already been found, with various level of credibility.
• Emergent constraints for equilibrium climate sensitivity so far suggest a slight shift towards high values, without narrowing the spread.
About this article
Cite this article
Brient, F. Reducing Uncertainties in Climate Projections with Emergent Constraints: Concepts, Examples and Prospects. Adv. Atmos. Sci. 37, 1–15 (2020). https://doi.org/10.1007/s00376-019-9140-8
- climate modeling
- emergent constraint
- climate change
- climate sensitivity