Coexchangeable Process Modeling for Uncertainty Quantification in Joint Climate Reconstruction

References

• Astfalck, L., Cripps, E., Gosling, J., and Milne, I. (2019), “Emulation of Vessel Motion Simulators for Computationally Efficient Uncertainty Quantification,” Ocean Engineering, 172, 726–736. DOI: 10.1016/j.oceaneng.2018.11.059.
   Web of Science ®Google Scholar
• Benz, V., Esper, O., Gersonde, R., Lamy, F., and Tiedemann, R. (2016), “Last Glacial Maximum Sea Surface Temperature and Sea-Ice Extent in the Pacific Sector of the Southern Ocean,” Quaternary Science Reviews, 146, 216–237. DOI: 10.1016/j.quascirev.2016.06.006.
   Web of Science ®Google Scholar
• Chandler, R. E. (2013), “Exploiting Strength, Discounting Weakness: Combining Information from Multiple Climate Simulators,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371, 20120388. DOI: 10.1098/rsta.2012.0388.
   PubMed Web of Science ®Google Scholar
• CLIMAP Project. (1981), Seasonal Reconstructions of the Earth’s Surface at the Last Glacial Maximum, Geological Society of America.
   Google Scholar
• Cressie, N., and Johannesson, G. (2008), “Fixed Rank Kriging for Very Large Spatial Data Sets,” Journal of the Royal Statistical Society, Series B, 70, 209–226. DOI: 10.1111/j.1467-9868.2007.00633.x.
   Google Scholar
• De Finetti, B. (1975), Theory of Probability: A Critical Introductory Treatment (Vol. 1), Chichester: Wiley.
   Google Scholar
• De Vernal, A., Eynaud, F., Henry, M., Hillaire-Marcel, C., Londeix, L., Mangin, S., Matthießen, J., Marret, F., Radi, T., Rochon, A., et al. (2005), “Reconstruction of Sea-Surface Conditions at Middle to High Latitudes of the Northern Hemisphere during the Last Glacial Maximum (LGM) based on Dinoflagellate Cyst Assemblages,” Quaternary Science Reviews, 24, 897–924. DOI: 10.1016/j.quascirev.2004.06.014.
   Web of Science ®Google Scholar
• Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M., et al. (2019), “Taking Climate Model Evaluation to the Next Level,” Nature Climate Change, 9, 102–110. DOI: 10.1038/s41558-018-0355-y.
   Web of Science ®Google Scholar
• Gersonde, R., Crosta, X., Abelmann, A., and Armand, L. (2005), “Sea-Surface Temperature and Sea Ice Distribution of the Southern Ocean at the EPILOG Last Glacial Maximum–a circum-Antarctic view based on Siliceous Microfossil Records,” Quaternary Science Reviews, 24, 869–896. DOI: 10.1016/j.quascirev.2004.07.015.
   Web of Science ®Google Scholar
• Goldstein, M. (1986), “Exchangeable Belief Structures,” Journal of the American Statistical Association, 81, 971–976. DOI: 10.1080/01621459.1986.10478360.
   Web of Science ®Google Scholar
• Goldstein, M., and Wooff, D. (2007), Bayes Linear Statistics: Theory and Methods (Vol. 716), Chichester: Wiley.
   Google Scholar
• Goldstein, M., and Wooff, D. A. (1998), “Adjusting Exchangeable Beliefs,” Biometrika, 85, 39–54. DOI: 10.1093/biomet/85.1.39.
   Web of Science ®Google Scholar
• Gregoire, L. J., Otto-Bliesner, B., Valdes, P. J., and Ivanovic, R. (2016), “Abrupt Bølling Warming and Ice Saddle Collapse Contributions to the Meltwater Pulse 1a Rapid Sea Level Rise,” Geophysical Research Letters, 43, 9130–9137. DOI: 10.1002/2016GL070356.
   PubMed Web of Science ®Google Scholar
• Gregoire, L. J., Payne, A. J., and Valdes, P. J. (2012), “Deglacial Rapid Sea Level Rises Caused by Ice-Sheet Saddle Collapses,” Nature, 487, 219–222. DOI: 10.1038/nature11257.
   PubMed Web of Science ®Google Scholar
• Gregory, J. M., George, S. E., and Smith, R. S. (2020), “Large and Irreversible Future Decline of the Greenland Ice Sheet,” The Cryosphere, 14, 4299–4322. DOI: 10.5194/tc-14-4299-2020.
   Web of Science ®Google Scholar
• Harrison, S. P., Bartlein, P., Izumi, K., Li, G., Annan, J., Hargreaves, J., Braconnot, P., and Kageyama, M. (2015), “Evaluation of CMIP5 Palaeo-Simulations to Improve Climate Projections,” Nature Climate Change, 5, 735–743. DOI: 10.1038/nclimate2649.
   Web of Science ®Google Scholar
• Hodges, J. S. (1998), “Some Algebra and Geometry for Hierarchical Models, Applied to Diagnostics,” Journal of the Royal Statistical Society, Series B, 60, 497–536. DOI: 10.1111/1467-9868.00137.
   Google Scholar
• Ivanovic, R., Gregoire, L., Kageyama, M., Roche, D., Valdes, P., Burke, A., Drummond, R., Peltier, W. R., and Tarasov, L. (2016), “Transient Climate Simulations of the Deglaciation 21-9 Thousand Years before Present; PMIP4 Core Experiment Design and Boundary Conditions,” Geoscientific Model Development, 9, 2563–2587. DOI: 10.5194/gmd-9-2563-2016.
   Web of Science ®Google Scholar
• Joughin, I., Alley, R. B., and Holland, D. M. (2012), “Ice-Sheet Response to Oceanic Forcing,” Science, 338, 1172–1176. DOI: 10.1126/science.1226481.
   PubMed Web of Science ®Google Scholar
• Kageyama, M., Albani, S., Braconnot, P., Harrison, S. P., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Marti, O., Peltier, W. R., Peterschmitt, J.-Y., et al. (2017), “The PMIP4 Contribution to CMIP6–Part 4: Scientific Objectives and Experimental Design of the PMIP4-CMIP6 Last Glacial Maximum Experiments and PMIP4 Sensitivity Experiments,” Geoscientific Model Development, 10, 4035–4055. DOI: 10.5194/gmd-10-4035-2017.
   Web of Science ®Google Scholar
• Kageyama, M., Harrison, S. P., Kapsch, M.-L., Lofverstrom, M., Lora, J. M., Mikolajewicz, U., Sherriff-Tadano, S., Vadsaria, T., Abe-Ouchi, A., Bouttes, N., et al. (2021), “The PMIP4 Last Glacial Maximum Experiments: Preliminary Results and Comparison with the PMIP3 Simulations,” Climate of the Past, 17, 1065–1089. DOI: 10.5194/cp-17-1065-2021.
   Web of Science ®Google Scholar
• Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C., and Weinelt, M. (2005), “Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO),” Quaternary Science Reviews, 24, 813–819. DOI: 10.1016/j.quascirev.2004.07.017.
   Web of Science ®Google Scholar
• Liu, X., and Guillas, S. (2017), “Dimension Reduction for Gaussian Process Emulation: An Application to the Influence of Bathymetry on Tsunami Heights,” SIAM/ASA Journal on Uncertainty Quantification, 5, 787–812. DOI: 10.1137/16M1090648.
   Web of Science ®Google Scholar
• Paul, A., Mulitza, S., Stein, R., and Werner, M. (2020), “A Global Climatology of the Ocean Surface during the Last Glacial Maximum Mapped on a Regular Grid (GLOMAP),” Climate of the Past Discussions, 17, 805–824. DOI: 10.5194/cp-17-805-2021.
   Google Scholar
• Ramsay, J. O. (1988), “Monotone Regression Splines in Action,” Statistical science, 3, 425–441. DOI: 10.1214/ss/1177012761.
   Google Scholar
• Rougier, J., Goldstein, M., and House, L. (2013), “Second-Order Exchangeability Analysis for Multimodel Ensembles,” Journal of the American Statistical Association, 108, 852–863. DOI: 10.1080/01621459.2013.802963.
   Web of Science ®Google Scholar
• Rougier, J., Sparks, R., Aspinall, W., and Mahony, S. (2022), “Estimating Tephra Fall Volume from Point-Referenced Thickness Measurements,” Geophysical Journal International, 230, 1699–1710. DOI: 10.1093/gji/ggac131.
   Web of Science ®Google Scholar
• Salter, J. M., Williamson, D. B., Gregoire, L. J., and Edwards, T. L. (2022), “Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation,” SIAM/ASA Journal on Uncertainty Quantification, 10, 717–744. DOI: 10.1137/21M1409135.
   Web of Science ®Google Scholar
• Salter, J. M., Williamson, D. B., Scinocca, J., and Kharin, V. (2019), “Uncertainty Quantification for Computer Models with Spatial Output Using Calibration-Optimal Bases,” Journal of the American Statistical Association, 114, 1800–1814. DOI: 10.1080/01621459.2018.1514306.
   Web of Science ®Google Scholar
• Sansom, P. G., Stephenson, D. B., and Bracegirdle, T. J. (2021), “On Constraining Projections of Future Climate Using Observations and Simulations from Multiple Climate Models,” Journal of the American Statistical Association, 116, 546–557. DOI: 10.1080/01621459.2020.1851696.
   Web of Science ®Google Scholar
• Sarnthein, M., Gersonde, R., Niebler, S., Pflaumann, U., Spielhagen, R., Thiede, J., Wefer, G., and Weinelt, M. (2003), “Overview of Glacial Atlantic Ocean Mapping (GLAMAP 2000),” Paleoceanography, 18, 1–6. DOI: 10.1029/2002PA000769.
   Google Scholar
• Smith, R. S., George, S., and Gregory, J. M. (2021), “FAMOUS version xotzt (FAMOUS-ice): A General Circulation Model (GCM) Capable of Energy-and Water-Conserving Coupling to an Ice Sheet Model,” Geoscientific Model Development, 14, 5769–5787. DOI: 10.5194/gmd-14-5769-2021.
   Web of Science ®Google Scholar
• Steiger, N. J., Hakim, G. J., Steig, E. J., Battisti, D. S., and Roe, G. H. (2014), “Assimilation of Time-Averaged Pseudoproxies for Climate Reconstruction,” Journal of Climate, 27, 426–441. DOI: 10.1175/JCLI-D-12-00693.1.
   Web of Science ®Google Scholar
• Tarasov, L., Dyke, A. S., Neal, R. M., and Peltier, W. R. (2012), “A Data-Calibrated Distribution of Deglacial Chronologies for the North American Ice Complex from Glaciological Modeling,” Earth and Planetary Science Letters, 315, 30–40. DOI: 10.1016/j.epsl.2011.09.010.
   Web of Science ®Google Scholar
• Tierney, J. E., Zhu, J., King, J., Malevich, S. B., Hakim, G. J., and Poulsen, C. J. (2020), “Glacial Cooling and Climate Sensitivity Revisited,” Nature, 584, 569–573. DOI: 10.1038/s41586-020-2617-x.
   PubMed Web of Science ®Google Scholar
• Whitaker, J. S., and Hamill, T. M. (2002), “Ensemble Data Assimilation Without Perturbed Observations,” Monthly weather review, 130, 1913–1924. DOI: 10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.
   Web of Science ®Google Scholar
• Williamson, D. B., and Sansom, P. G. (2019), “How Are Emergent Constraints Quantifying Uncertainty and What Do They Leave Behind?” Bulletin of the American Meteorological Society, 100, 2571–2588. DOI: 10.1175/BAMS-D-19-0131.1.
   Web of Science ®Google Scholar
• Xiao, X., Fahl, K., Müller, J., and Stein, R. (2015). “Sea-Ice Distribution in the Modern Arctic Ocean: Biomarker Records from Trans-Arctic Ocean Surface Sediments,” Geochimica et Cosmochimica Acta, 155, 16–29. DOI: 10.1016/j.gca.2015.01.029.
   Web of Science ®Google Scholar
• Zhang, B., and Cressie, N. (2020), “Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice,” Bayesian Analysis, 15, 605–631. DOI: 10.1214/20-BA1209.
   Web of Science ®Google Scholar