Analysis of climate sensitivity via high-dimensional principal component regression

References

• Abe, M., Shiogama, H., Nozawa, T., and Emori, S. 2011. “Estimation of Future Surface Temperature Changes Constrained Using the Future-present Correlated Modes in Inter-model Variability of CMIP3 Multimodel Simulations.” Journal of Geophysical Research 116: D18104. doi: 10.1029/2010JD015111.
   Web of Science ®Google Scholar
• Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Santer, B. D., and Klein, S. A., and Sanderson, B. M. 2014. “Statistical Significance of Climate Sensitivity Predictors Obtained by Data Mining.” Geophysical 41:1803–1808. doi: 10.1002/2014GL059205.
   Google Scholar
• Caldwell, P. M., Zelinka, M. D., and Klein, S. A. 2018. “Evaluating Emergent Constraints on Equilibrium Climate Sensitivity.” Journal Of Climate 31:3921–3942. doi: 10.1175/JCLI-D-17-0631.1.
   Web of Science ®Google Scholar
• Daniel, C. 1959. “Use of Half Normal Probability plots in Interpreting Factorial Two Level Experiments.” Technometrics 1:311–342. doi: 10.2307/1266715.
   Google Scholar
• Daniel, C. 1976. Applications of Statistics to Industrial Experimentation. New York: Wiley.
   Google Scholar
• Draper, N., and Smith, H. 1998. Applied Regression Analysis. 3rd ed. New York: Wiley.
   Google Scholar
• Christensen, R. 2011. Plane Answers to Complex Questions: The Theory of Linear Models. 4th ed. New York: Springer-Verlag.
   Google Scholar
• Fasullo, J. T., and Trenberth, K. E. 2012. “A Less Cloudy Future: The Role of Subtropical Subsidence in Climate Sensitivity.” Science 338:792–794. doi: 10.1126/science.1227465.
   PubMed Web of Science ®Google Scholar
• Harris, G. R., Sexton, D. M. H., Booth, B. B. B., Collins, M., Murphy, J. M., and Webb, M. J. 2006. “Frequency Distributions of Transient Regional Climate Change from Perturbed Physics Ensembles of General Circulation Model Simulations.” Climate Dynamics 27:357–375. doi: 10.1007/s00382-006-0142-8.
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., and Friedman, J. 2009. The Elements of Statistical Learning. 2nd ed. New York: Springer-Verlag.
   Google Scholar
• Hill, R. C., Fomby, T. B., and Johnson, S. R. 1977. “Component Selection Norms for Principal Components Regression.” Communications in Statistics—Theory and Methods 6:309–334. doi: 10.1080/03610927708827494.
   Web of Science ®Google Scholar
• Hocking, R. R., Speed F. M., and Lynn, M. J. 1976. “A Class of Biased Estimators in Linear Regression.” Technometrics 18:425–437. doi: 10.2307/1268658.
   Web of Science ®Google Scholar
• Hoerl, A. E., and Kennard, R. 1970. “Ridge Regression: Biased Estimation for Non-orthogonal Problems.” Technometrics 12:55–67. doi: 10.2307/1267351.
   Web of Science ®Google Scholar
• Huber, M., Mahlstein, I., Wild, M., Fasullo, J., and Knutti, R. 2011. “Constraints on Climate Sensitivity from Radiation Patterns in Climate Models.” Journal of Climate 24: 1034–1052. doi: 10.1175/2010JCLI3403.1.
   Web of Science ®Google Scholar
• Ingram, W. 2013. “Some Implications of a New Approach to the Water Vapour Feedback.” Climate Dynamics 40:925–933. doi: 10.1007/s00382-012-1456-3.
   Web of Science ®Google Scholar
• Jackson, C. S., Sen, M. K., Huerta, G., Deng, Y., and Bowman, K. P. 2008. “Error Reduction and Convergence in Climate Prediction.” Journal of Climate 21: 6698–6709. doi: 10.1175/2008JCLI2112.1.
   Web of Science ®Google Scholar
• Jackson, C. S., Sen, M. K., and Stoffa, P. L. 2004. “An Efficient Stochastic Bayesian Approach to Optimal Parameter and Uncertainty Estimation for Climate Model Predictions.” Journal of Climate 17: 2828–2841. doi: 10.1175/1520-0442(2004)017<2828:AESBAT>2.0.CO;2.
   Web of Science ®Google Scholar
• Johnson, R. A., and Wichern, D. W. 2007. Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River, NJ: Pearson Prentice-Hall.
   Google Scholar
• Jolliffe, I. T. 2002. Principal Component Analysis. 2nd ed. New York: Springer-Verlag.
   Google Scholar
• Jolliffe, I. T. 1982. “A Note on the Use of Principal Components in Regression.” Applied Statistics 31: 300–303. doi: 10.2307/2348005.
   Web of Science ®Google Scholar
• Klein, S. A., and Hall, A. 2015. Emergent Constraints for Cloud Feedbacks. Current Climate Change Reports 1:276–87. doi: 10.1007/s40641-015-0027-1.
   Web of Science ®Google Scholar
• Klocke, D., Pincus, R., and Quaas, J. 2011. “On Constraining Estimates of Climate Sensitivity with Present-Day Observations Through Model Weighting.” Journal of Climate 24: 6092–6099. doi: 10.1175/2011JCLI4193.1.
   Web of Science ®Google Scholar
• Kung, E. C., and Sharif, T. A. 1980. “Multi-Regression Forecasting of the Indian Summer Monsoon with Antecedent Patterns of the Large-Scale Circulation.” Nice, France: WMO Symposium on Probabilistic and Statistical Methods in Weather Forecasting, 295–302.
   Google Scholar
• McCallum, B. T. 1970. Artificial Orthogonalization in Regression Analysis. The Review of Economics and Statistics 52:110–113. doi: 10.2307/1927606.
   Web of Science ®Google Scholar
• Mandel, J. 1982. “Use of the Singular Value Decomposition in Regression Analysis.” The American Statistician 36:15–24. doi: 10.2307/2684086.
   Web of Science ®Google Scholar
• Marquardt, D. W. 1970. “Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation.” Technometrics 12:591–612. doi: 10.2307/1267205.
   Web of Science ®Google Scholar
• Masson, D., and Knutti, R. 2013. “Predictor Screening, Calibration, and Observational Constraints in Climate Model Ensembles: An Illustration Using Climate Sensitivity.” Journal of Climate, 26:887–898. doi: 10.1175/JCLI-D-11-00540.1.
   Web of Science ®Google Scholar
• Massy, F. M. 1965. “Principal Components Regression in Exploratory Statistical Research.” Journal of the American Statistical Association 60:234–256. doi: 10.2307/2283149.
   Web of Science ®Google Scholar
• Montgomery, D. C. 1996. Design and Analysis of Experiments. 4th ed. New York: Wiley.
   Google Scholar
• Nash, J. C., and Lefkovitch, L. P. 1976. “Principal Components and Regression by Singular Value Decomposition on a Small Computer.” Journal of the Royal Statistical Society. Series C (Applied Statistics) 25:210–216. doi: 10.2307/2347228.
   Google Scholar
• Navarra, A., and Simoncini, V. 2010. A Guide to Empirical Orthogonal Functions for Climate Data Analysis. Netherlands: Springer.
   Google Scholar
• Oman, S. D. 1978. “A Bayesian Comparison of Some Estimators Used in Linear Regression with Multicollinear Data.” Communications in Statistics—Theory and Methods 7:517–534. doi: 10.1080/03610927808827646.
   Web of Science ®Google Scholar
• Piani, C., Frame, D. J., Stainforth, D. A., and Allen, M. R. 2005. “Constraints on Climate Change from a Multi-thousand Member Ensemble of Simulations.” Geophysical Research Letters 32 (23):L23825. doi: 10.1029/2005GL024452.
   Web of Science ®Google Scholar
• Piani, C., Sanderson, B., Giorgi, F., Frame, D. J., Christensen, C., and Allen, M. R. 2007. “Regional Probabilistic Climate Forecasts from a Multithousand, Multimodel Ensemble of Simulations.” Journal of Geophysical Research 112(D24):D24108.
   Web of Science ®Google Scholar
• Sanderson, B. M. 2013. “On the Estimation of Systematic Error in Regression Based Predictions of Climate Sensitivity.” Climatic Change 118:757–770. doi: 10.1007/s10584-012-0671-6.
   Web of Science ®Google Scholar
• Sherwood, S. C., Bony, S., and Dufresne, J. L. 2014. “Spread in Model Climate Sensitivity Traced to Atmospheric Convective Mixing.” Nature 505:37–42. doi: 10.1038/nature12829.
   PubMed Web of Science ®Google Scholar
• Stephens, B. A., Jackson, C. S, Wagman, B. M. 2019. “Effect of Tropical Large-scale Condensation on Uncertainty in Modeled Projections of Rainfall.” Journal Of Climate 32:6571–6588. doi: 10.1175/JCLI-D-18-0833.1.
   Web of Science ®Google Scholar
• Villagran, A., Huerta, G., Jackson, C. S., and Sen, M. K. 2008. “Computational Methods for Parameter Estimation in Climate Models.” Bayesian Analysis 3:823–850. doi: 10.1214/08-BA331.
   Web of Science ®Google Scholar
• 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 44:288–299. doi: 10.1134/S0001433808030043.
   Web of Science ®Google Scholar
• Wagman, B. M., Jackson, C. S. 2018. “A Test of Emergent Constraints on Cloud Feedback and Climate Sensitivity Using a Calibrated Single-Model Ensemble.” Journal Of Climate 31: 7515–7532. doi: 10.1175/JCLI-D-17-0682.1.
   Web of Science ®Google Scholar
• West, M. 2003. “Bayesian Factor Regression Models in the Large p, Small n Paradigm.” Bayesian Statistics 7:723–732.
   Google Scholar
• Wilks, D. S. 2006. Statistical Methods in the Atmospheric Sciences. 2nd ed. Amsterdam: Elsevier.
   Google Scholar
• Yip, S., Ferro, C. A. T., Stephenson, D. B., and Hawkins, E. 2011. “A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions.” Journal Of Climate 24:4634–4643. doi: 10.1175/2011JCLI4085.1.
   Web of Science ®Google Scholar