Booth B. B. B.,N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484, 228-232, https://doi.org/10.1038/nature10946
Braverman A.,N. Cressie, and J. Teixeira, 2011: A likelihood-based comparison of temporal models for physical processes. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4, 247-258, https://doi.org/10.1002/sam.10113
Ericsson K.,1996: The Road to Expert Performance: Empirical Evidence from the Arts and Sciences,Sports, and Games. Lawrence Erlbaum Associates, 369 pp.
Gleckler P. J.,K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007JD008972
Herger N.,G. Abramowitz, R. Knutti, O. Angèlil K. Lehmann, and B. M. Sanderson, 2017: Selecting a climate model subset to optimise key ensemble properties. Earth System Dynamics, 9, 135-151, https://doi.org/10.5194/esd-9-135-2018
Hourdin, F., Coauthors, 2017: The art and science of climate model tuning. Bull. Amer. Meteor. Soc., 98, 589-602, https://doi.org/10.1175/BAMS-D-15-00135.1
Knutti R.,J. Sedlá\vcek, B. M. Sand erson, R. Lorenz, E. M. Fischer, and V. Eyring, 2017: A climate model projection weighting scheme accounting for performance and interdependence. Geophys. Res. Lett., 44, 1909-1918, https://doi.org/10.1002/2016GL072012
Min S. K.,A. Hense, 2006: A Bayesian approach to climate model evaluation and multi-model averaging with an application to global mean surface temperatures from IPCC AR4 coupled climate models. Geophys. Res. Lett., 33, L08708, https://doi.org/10.1029/2006GL025779
Nosedal-Sanchez A.,C. S. Jackson, and G. Huerta, 2016: A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields. Geoscientific Model Development, 9, 2407-2414, https://doi.org/10.5194/gmd-9-2407-2016
Qian, Y., Coauthors, 2015: Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5. Journal of Advances in Modeling Earth Systems, 7, 382-411, https://doi.org/10.1002/2014MS000354
Qian, Y., Coauthors, 2016: Uncertainty quantification in climate modeling and projection. Bull. Amer. Meteor. Soc., 97, 821-824, http://dx.doi.org/10.1175/BAMS-D-15-00297.1
Reichler T.,J. Kim, 2008: How well do coupled models simulate today's climate? Bull. Amer. Meteor. Soc., 89, 303-311, https://doi.org/10.1175/BAMS-89-3-303
Riffenburgh R. H.,P. A. Johnstone, 2009: Measuring agreement about ranked decision choices for a single subject. The International Journal of Biostatistics,5, https://doi.org/10.2202/1557-4679.1113
Seinfeld, J. H.,Coauthors, 2016: Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system. Proceedings of the National Academy of Sciences of the United States of America, 113, 5781-5790, https://doi.org/10.1073/pnas.151404311
Stevens B.,2013: Aerosols: Uncertain then,irrelevant now. Nature, 503, 47-48, https://doi.org/10.1038/503047a
Stier P.,2016: Limitations of passive remote sensing to constrain global cloud condensation nuclei. Atmospheric Chemistry and Physics, 16, 6595-6607, https://doi.org/10.5194/acp-16-6595-2016
Stocker, T. F.,Coauthors, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,1535 pp, https://doi.org/10.1017/CBO9781107415324
Suckling E. B.,L. A. Smith, 2013: An evaluation of decadal probability forecasts from state-of-the-art climate models. J. Climate, 26, 9334-9347, https://doi.org/10.1175/JCLI-D-12-00485.1
Yang, B., Coauthors, 2013: Uncertainty quantification and parameter tuning in the CAM5 Zhang-McFarlane convection scheme and impact of improved convection on the global circulation and climate. J. Geophys. Res., 118, 395-415, https://doi.org/10.1029/2012JD018213
Zhang T.,L. Li, Y. Lin, W. Xue, F. Xie, H. Xu, and X. Huang, 2015: An automatic and effective parameter optimization method for model tuning. Geoscientific Model Development, 8, 3579-3591, https://doi.org/10.5194/gmd-8-3579-2015