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Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions

Published online by Cambridge University Press:  01 January 2022

Casey Helgeson ,
Vivek Srikrishnan ,
Klaus Keller  and
Nancy Tuana
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Abstract

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science.

Type
Research Article
Information
Philosophy of Science , Volume 88 , Issue 2 , April 2021 , pp. 213 - 233
Copyright
Copyright © The Philosophy of Science Association

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References

Addor, N., and Melsen, L.. 2019. “Legacy, Rather than Adequacy, Drives the Selection of Hydrological Models.” Water Resources Research 55 (1): 378–90.CrossRefGoogle Scholar
Akaike, H. 1973. “Information Theory and an Extension of the Maximum Likelihood Principle.” In Proceedings of the 2nd International Symposium on Information Theory, ed. Petrov, B. N. and Csaki, F., 267–81. Budapest: Akademiai Kiado.Google Scholar
Baker, A. 2016. “Simplicity.” In Stanford Encyclopedia of Philosophy, ed. Zalta, Edward N.. Stanford, CA: Stanford University. https://plato.stanford.edu/archives/win2016/entries/simplicity/.Google Scholar
Bakker, A. M., Applegate, P. J., and Keller, K.. 2016. “A Simple, Physically Motivated Model of Sea-Level Contributions from the Greenland Ice Sheet in Response to Temperature Changes.” Environmental Modelling and Software 83:27–35.CrossRefGoogle Scholar
Bakker, A. M., Louchard, D., and Keller, K.. 2017. “Sources and Implications of Deep Uncertainties Surrounding Sea-Level Projections.” Climatic Change 140 (3–4): 339–47.CrossRefGoogle Scholar
Bakker, A. M., Wong, T. E., Ruckert, K. L., and Keller, K.. 2017. “Sea-Level Projections Representing the Deeply Uncertain Contribution of the West Antarctic Ice Sheet.” Scientific Reports 7 (1): 3880.CrossRefGoogle ScholarPubMed
Bankes, S. 1993. “Exploratory Modeling for Policy Analysis.” Operations Research 41 (3): 435–49.CrossRefGoogle Scholar
Beisbart, C., and Saam, J. J., eds. 2019. Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Dordrecht: Springer.CrossRefGoogle Scholar
Bessette, D. L., Mayer, L. A., Cwik, B., Vezér, M., Keller, K., Lempert, R. J., and Tuana, N.. 2017. “Building a Values-Informed Mental Model for New Orleans Climate Risk Management.” Risk Analysis 37 (10): 1993–2004.CrossRefGoogle ScholarPubMed
Betz, G. 2013. “In Defence of the Value Free Ideal.” European Journal for Philosophy of Science 3 (2): 207–20.CrossRefGoogle Scholar
Bray, D., and von Storch, H.. 2009. “‘Prediction’ or ‘Projection’? The Nomenclature of Climate Science.” Science Communication 30 (4): 534–43.CrossRefGoogle Scholar
Brynjarsdóttir, J., and O’Hagan, A.. 2014. “Learning about Physical Parameters: The Importance of Model Discrepancy.” Inverse Problems 30 (11): 114007.CrossRefGoogle Scholar
Butler, M. P., Reed, P. M., Fisher-Vanden, K., Keller, K., and Wagener, T.. 2014. “Inaction and Climate Stabilization Uncertainties Lead to Severe Economic Risks.” Climatic Change 127 (3–4): 463–74.CrossRefGoogle Scholar
Carlsen, H., Lempert, R., Wikman-Svahn, P., and Schweizer, V.. 2016. “Choosing Small Sets of Policy-Relevant Scenarios by Combining Vulnerability and Diversity Approaches.” Environmental Modelling and Software 84:155–64.CrossRefGoogle Scholar
Clatterbuck, H. 2015. “Chimpanzee Mindreading and the Value of Parsimonious Mental Models.” Mind and Language 30 (4): 414–36.CrossRefGoogle Scholar
CPRAL (Coastal Protection and Restoration Authority of Louisiana). 2017. “Louisiana’s Comprehensive Master Plan for a Sustainable Coast.” Technical report, CPRAL, Baton Rouge.Google Scholar
Daron, J. D., and Stainforth, D. A.. 2013. “On Predicting Climate under Climate Change.” Environmental Research Letters 8 (3): 034021.CrossRefGoogle Scholar
DeConto, R. M., and Pollard, D.. 2016. “Contribution of Antarctica to Past and Future Sea-Level Rise.” Nature 531 (7596): 591–97.CrossRefGoogle ScholarPubMed
Deser, C., Phillips, A. S., Alexander, M. A., and Smoliak, B. V.. 2014. “Projecting North American Climate over the Next 50 Years: Uncertainty due to Internal Variability.” Journal of Climate 27 (6): 2271–96.CrossRefGoogle Scholar
Dessai, S., and Hulme, M.. 2004. “Does Climate Adaptation Policy Need Probabilities?” Climate Policy 4 (2): 107–28.CrossRefGoogle Scholar
Douglas, H. 2009. Science, Policy, and the Value-Free Ideal. Pittsburgh: University of Pittsburgh Press.CrossRefGoogle Scholar
Douglas, H. 2013. “The Value of Cognitive Values.” Philosophy of Science 80 (5): 796–806.CrossRefGoogle Scholar
Draper, D. 1995. “Assessment and Propagation of Model Uncertainty.” Journal of the Royal Statistical Society B 57 (1): 45–70.Google Scholar
Edwards, N. R., Cameron, D., and Rougier, J.. 2011. “Precalibrating an Intermediate Complexity Climate Model.” Climate Dynamics 37 (7): 1469–82.CrossRefGoogle Scholar
Edwards, P. N. 2001. “Representing the Global Atmosphere: Computer Models, Data, and Knowledge about Climate Change.” In Changing the Atmosphere: Expert Knowledge and Environmental Governance, ed. Miller, C. A. and Edwards, P. N.. Cambridge, MA: MIT Press.Google Scholar
Elliott, K. C. 2017. A Tapestry of Values: An Introduction to Values in Science. Oxford: Oxford University Press.CrossRefGoogle Scholar
Elliott, K. C., and McKaughan, D. J.. 2014. “Nonepistemic Values and the Multiple Goals of Science.” Philosophy of Science 81 (1): 1–21.CrossRefGoogle Scholar
Forster, M., and Sober, E.. 1994. “How to Tell When Simpler, More Unified, or Less Ad Hoc Theories Will Provide More Accurate Predictions.” British Journal for the Philosophy of Science 45 (1): 1–35.CrossRefGoogle Scholar
Frigg, R., and Hoefer, C.. 2015. “The Best Humean System for Statistical Mechanics.” Erkenntnis 80 (3): 551–74.CrossRefGoogle Scholar
Frigg, R., and Reiss, J.. 2009. “The Philosophy of Simulation: Hot New Issues or Same Old Stew?” Synthese 169 (3): 593–613.CrossRefGoogle Scholar
Frigg, R., Smith, L. A., and Stainforth, D. A.. 2013. “The Myopia of Imperfect Climate Models: The Case of UKCP09.” Philosophy of Science 80 (5): 886–97.CrossRefGoogle Scholar
Frigg, R., Smith, L. A., and Stainforth, D. A.. 2015. “An Assessment of the Foundational Assumptions in High-Resolution Climate Projections: The Case of UKCP09.” Synthese 192 (12): 3979–4008.CrossRefGoogle Scholar
Grüne-Yanoff, T., and Weirich, P.. 2010. “The Philosophy and Epistemology of Simulation: A Review.” Simulation and Gaming 41 (1): 20–50.CrossRefGoogle Scholar
Haasnoot, M., Deursen, W. Van, Guillaume, J. H., Kwakkel, J. H., Beek, E. van, and Middelkoop, H.. 2014. “Fit for Purpose? Building and Evaluating a Fast, Integrated Model for Exploring Water Policy Pathways.” Environmental Modelling and Software 60:99–120.CrossRefGoogle Scholar
Harman, G., and Kulkarni, S.. 2007. Reliable Reasoning: Induction and Statistical Learning Theory. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Hoegh-Guldberg, O., et al. 2018. “Impacts of 1.5°C Global Warming on Natural and Human Systems.” In Global Warming of 1.5°C: An IPCC Special Report on the Impacts of Global Warming of 1.5°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Povertyj, ed. Masson-Delmotte, V., et al. Geneva: Intergovernmental Panel on Climate Change.Google Scholar
Hourdin, F., et al. 2017. “The Art and Science of Climate Model Tuning.” Bulletin of the American Meteorological Society 98 (3): 589–602.CrossRefGoogle Scholar
Hurrell, J. W., et al. 2013. “The Community Earth System Model: A Framework for Collaborative Research.” Bulletin of the American Meteorological Society 94 (9): 1339–60.CrossRefGoogle Scholar
Intemann, K. 2015. “Distinguishing between Legitimate and Illegitimate Values in Climate Modeling.” European Journal for Philosophy of Science 5 (2): 217–32.CrossRefGoogle Scholar
Jakeman, A. J., Letcher, R. A., and Norton, J. P.. 2006. “Ten Iterative Steps in Development and Evaluation of Environmental Models.” Environmental Modelling and Software 21 (5): 602–14.CrossRefGoogle Scholar
Jebeile, J. 2017. “Computer Simulation, Experiment, and Novelty.” International Studies in the Philosophy of Science 31 (4): 379–95.CrossRefGoogle Scholar
Kaplan, S., and Garrick, B. J.. 1981. “On the Quantitative Definition of Risk.” Risk Analysis 1 (1): 11–27.CrossRefGoogle Scholar
Keller, K., and Nicholas, R.. 2015. “Improving Climate Projections to Better Inform Climate Risk Management.” In The Oxford Handbook of the Macroeconomics of Global Warming, 9–18. New York: Oxford University Press.Google Scholar
Kelly, K. T. 2004. “Justification as Truth-Finding Efficiency: How Ockham’s Razor Works.” Minds and Machines 14 (4): 485–505.CrossRefGoogle Scholar
Kelly, K. T. 2007. “A New Solution to the Puzzle of Simplicity.” Philosophy of Science 74 (5): 561–73.CrossRefGoogle Scholar
Kelly, K. T., and Mayo-Wilson, C.. 2010. “Ockham Efficiency Theorem for Stochastic Empirical Methods.” Journal of Philosophical Logic 39 (6): 679–712.CrossRefGoogle Scholar
Kennedy, M. C., and O’Hagan, A.. 2001. “Bayesian Calibration of Computer Models.” Journal of the Royal Statistical Society B 63 (3): 425–64.Google Scholar
Lee, B. S., Huran, M., Fuller, R., Pollard, D., and Keller, K.. 2020. “A Fast Particle-Based Approach for Calibrating a 3-D Model of the Antarctic Ice Sheet.” Annals of Applied Statistics 14 (2): 605–34.CrossRefGoogle Scholar
Lempert, R. J., et al. 2013. “Making Good Decisions without Predictions.” Technical report, Rand, Santa Monica, CA.Google Scholar
Lenaerts, J. T., Vizcaino, M., Fyke, J., Kampenhout, L. Van, and van den Broeke, M. R.. 2016. “Present-Day and Future Antarctic Ice Sheet Climate and Surface Mass Balance in the Community Earth System Model.” Climate Dynamics 47 (5–6): 1367–81.CrossRefGoogle Scholar
Lipscomb, W. 2017. “Steps toward Modeling Marine Ice Sheets in the Community Earth System Model.” Technical Report LA-UR-17-21665, Los Alamos National Laboratory.Google Scholar
Lipscomb, W. 2018. “Ice Sheet Modeling and Sea Level Rise.” Lecture, NCAR CESM Sea Level Session, January 10.Google Scholar
Lipscomb, W., and Sacks, W.. 2013. “The CESM Land Ice Model Documentation and User’s Guide.” Technical report, National Center for Atmospheric Research.Google Scholar
Lloyd, E. A. 2010. “Confirmation and Robustness of Climate Models.” Philosophy of Science 77 (5): 971–84.CrossRefGoogle Scholar
Lloyd, E. A. 2015. “Model Robustness as a Confirmatory Virtue: The Case of Climate Science.” Studies in History and Philosophy of Science A 49:58–68.CrossRefGoogle ScholarPubMed
Lloyd, E. A., and Winsberg, E., eds. 2018. Climate Modelling: Philosophical and Conceptual Issues. Dordrecht: Springer.CrossRefGoogle Scholar
MacCracken, M. 2001. “Prediction versus Projection-Forecast versus Possibility.” WeatherZine, no. 26, February. https://sciencepolicy.colorado.edu/zine/archives/1-29/26/guest.html.Google Scholar
McGuffie, K., and Henderson-Sellers, A.. 2001. “Forty Years of Numerical Climate Modelling.” International Journal of Climatology 21 (9): 1067–109.CrossRefGoogle Scholar
Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J. F., Stouffer, R. J., and Taylor, K. E.. 2007. “The WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research.” Bulletin of the American Meteorological Society 88 (9): 1383–94.CrossRefGoogle Scholar
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.. 1953. “Equation of State Calculations by Fast Computing Machines.” Journal of Chemical Physics 21 (6): 1087–92.CrossRefGoogle Scholar
Oreskes, N., Stainforth, D. A., and Smith, L. A.. 2010. “Adaptation to Global Warming: Do Climate Models Tell Us What We Need to Know?” Philosophy of Science 77 (5): 1012–28.CrossRefGoogle Scholar
Parke, E. C. 2014. “Experiments, Simulations, and Epistemic Privilege.” Philosophy of Science 81 (4): 516–36.CrossRefGoogle Scholar
Parker, W. 2014. “Values and Uncertainties in Climate Prediction, Revisited.” Studies in History and Philosophy of Science A 46:24–30.CrossRefGoogle ScholarPubMed
Parker, W. S. 2009. “Confirmation and Adequacy-for-Purpose in Climate Modelling.” Proceedings of the Aristotelian Society Supplementary Volume 83 (1): 233–49.CrossRefGoogle Scholar
Parker, W. S. 2010. “Predicting Weather and Climate: Uncertainty, Ensembles and Probability.” Studies in History and Philosophy of Science B 41 (3): 263–72.Google Scholar
Parker, W. S. 2011. “When Climate Models Agree: The Significance of Robust Model Predictions.” Philosophy of Science 78 (4): 579–600.CrossRefGoogle Scholar
Parker, W. S. 2013. “Ensemble Modeling, Uncertainty and Robust Predictions.” Wiley Interdisciplinary Reviews: Climate Change 4 (3): 213–23.Google Scholar
Petersen, A. C. 2012. Simulating Nature: A Philosophical Study of Computer-Simulation Uncertainties and Their Role in Climate Science and Policy Advice. Boca Raton, FL: CRC.CrossRefGoogle Scholar
Pollard, D., and DeConto, R.. 2012. “Description of a Hybrid Ice Sheet-Shelf Model, and Application to Antarctica.” Geoscientific Model Development 5 (5): 1273–95.CrossRefGoogle Scholar
Pollard, D., DeConto, R. M., and Alley, R. B.. 2015. “Potential Antarctic Ice Sheet Retreat Driven by Hydrofracturing and Ice Cliff Failure.” Earth and Planetary Science Letters 412:112–21.CrossRefGoogle Scholar
Reilly, J., Stone, P. H., Forest, C. E., Webster, M. D., Jacoby, H. D., and Prinn, R. G.. 2001. “Uncertainty and Climate Change Assessments.” Science 293 (5529): 430–33.CrossRefGoogle ScholarPubMed
Robert, C. P., and Casella, G.. 1999. Monte Carlo Statistical Methods, chap. 7. Dordrecht: Springer.CrossRefGoogle Scholar
Rougier, J., and Crucifix, M.. 2018. “Uncertainty in Climate Science and Climate Policy.” In Climate Modelling: Philosophical and Conceptual Issues, ed. Lloyd, E. A. and Winsberg, E., 361–80. Dordrecht: Springer.Google Scholar
Ruckert, K. L., Shaffer, G., Pollard, D., Guan, Y., Wong, T. E., Forest, C. E., and Keller, K.. 2017. “Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration.” PLoS ONE 12 (1): e0170052.CrossRefGoogle Scholar
Scheller, R. M., Domingo, J. B., Sturtevant, B. R., Williams, J. S., Rudy, A., Gustafson, E. J., and Mladenoff, D. J.. 2007. “Design, Development, and Application of LANDIS-II, a Spatial Landscape Simulation Model with Flexible Temporal and Spatial Resolution.” Ecological Modelling 201 (3–4): 409–19.CrossRefGoogle Scholar
Schwartz, P. 1996. The Art of the Long View: Planning in an Uncertain World. New York: Currency-Doubleday.Google Scholar
Shaffer, G. 2014. “Formulation, Calibration and Validation of the DAIS Model (version 1): A Simple Antarctic Ice Sheet Model Sensitive to Variations of Sea Level and Ocean Subsurface Temperature.” Geoscientific Model Development 7 (4): 1803–18.CrossRefGoogle Scholar
Smith, L. A., and Petersen, A. C.. 2014. “Variations on Reliability: Connecting Climate Predictions to Climate Policy.” In Error and Uncertainty in Scientific Practice, ed. Boumans, M., Hon, G., and Petersen, A. C., 137–56. London: Pickering & Chatto.Google Scholar
Smith, L. A., and Stern, N.. 2011. “Uncertainty in Science and Its Role in Climate Policy.” Philosophical Transactions of the Royal Society A 369 (1956): 4818–41.Google ScholarPubMed
Smith, M. J., Palmer, P. I., Purves, D. W., Vanderwel, M. C., Lyutsarev, V., Calderhead, B., Joppa, L. N., Bishop, C. M., and Emmott, S.. 2014. “Changing How Earth System Modeling Is Done to Provide More Useful Information for Decision Making, Science, and Society.” Bulletin of the American Meteorological Society 95 (9): 1453–64.CrossRefGoogle Scholar
Sober, E. 1988. Reconstructing the Past: Parsimony, Evolution, and Inference. Cambridge, MA: MIT Press.Google Scholar
Sober, E. 2009. “Parsimony and Models of Animal Minds.” In The Philosophy of Animal Minds, ed. Lurz, R. W., 237–57. Cambridge: Cambridge University Press.Google Scholar
Sober, E. 2015. Ockham’s Razors. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Sobol, I. M. 2001. “Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates.” Mathematics and Computers in Simulation 55 (1–3): 271–80.Google Scholar
Sriver, R. L., Forest, C. E., and Keller, K.. 2015. “Effects of Initial Conditions Uncertainty on Regional Climate Variability: An Analysis Using a Low-Resolution CESM Ensemble.” Geophysical Research Letters 42 (13): 5468–76.CrossRefGoogle Scholar
Sriver, R. L., Urban, N. M., Olson, R., and Keller, K.. 2012. “Toward a Physically Plausible Upper Bound of Sea-Level Rise Projections.” Climatic Change 115 (3–4): 893–902.CrossRefGoogle Scholar
Steel, D. 2016a. “Accepting an Epistemically Inferior Alternative? A Comment on Elliott and McKaughan.” Philosophy of Science 83 (4): 606–12.CrossRefGoogle Scholar
Steel, D. 2016b. “Climate Change and Second-Order Uncertainty: Defending a Generalized, Normative, and Structural Argument from Inductive Risk.” Perspectives on Science 24 (6): 696–721.CrossRefGoogle Scholar
Steele, K. 2012. “The Scientist qua Policy Advisor Makes Value Judgments.” Philosophy of Science 79 (5): 893–904.CrossRefGoogle Scholar
Steele, K., and Werndl, C.. 2016. “The Diversity of Model Tuning Practices in Climate Science.” Philosophy of Science 83 (5): 1133–44.CrossRefGoogle Scholar
Thompson, E., Frigg, R., and Helgeson, C.. 2016. “Expert Judgement for Climate Change Adaptation.” Philosophy of Science 83 (5): 1110–21.CrossRefGoogle Scholar
Tuana, N. 2013. “Embedding Philosophers in the Practices of Science: Bringing Humanities to the Sciences.” Synthese 190 (11): 1955–73.CrossRefGoogle Scholar
Tuana, N. 2017. “Understanding Coupled Ethical-Epistemic Issues Relevant to Climate Modeling and Decision Support Science.” In Scientific Integrity and Ethics in the Geosciences, ed. Gundersen, L. C., 157–73. Hoboken, NJ: Wiley.Google Scholar
UCAR (University Corporation for Atmospheric Research). 2016. “CESM 1.2 Timing Table.” Technical report, UCAR. http://www.cesm.ucar.edu/models/cesm1.2/timing/.Google Scholar
UCAR (University Corporation for Atmospheric Research). 2019. “University Allocations.” Technical report, UCAR. https://www2.cisl.ucar.edu/user-support/allocations/university-allocations.Google Scholar
van Ravenzwaaij, D., Cassey, P., and Brown, S. D.. 2018. “A Simple Introduction to Markov Chain Monte-Carlo Sampling.” Psychonomic Bulletin and Review 25 (1): 143–54.CrossRefGoogle ScholarPubMed
van Vuuren, D. P., et al. 2011. “The Representative Concentration Pathways: An Overview.” Climatic Change 109 (1–2): 5.CrossRefGoogle Scholar
Vapnik, V. 1998. Statistical Learning Theory. New York: Wiley.Google Scholar
Vezér, M., Bakker, A., Keller, K., and Tuana, N.. 2018. “Epistemic and Ethical Trade-Offs in Decision Analytical Modelling.” Climatic Change 147 (1): 1–10.CrossRefGoogle Scholar
Vezér, M. A. 2016. “Computer Models and the Evidence of Anthropogenic Climate Change: An Epistemology of Variety-of-Evidence Inferences and Robustness Analysis.” Studies in History and Philosophy of Science A 56:95–102.CrossRefGoogle ScholarPubMed
Weaver, C. P., Lempert, R. J., Brown, C., Hall, J. A., Revell, D., and Sarewitz, D.. 2013. “Improving the Contribution of Climate Model Information to Decision Making: The Value and Demands of Robust Decision Frameworks.” Wiley Interdisciplinary Reviews: Climate Change 4 (1): 39–60.Google Scholar
Weisberg, M. 2013. Simulation and Similarity: Using Models to Understand the World. Oxford: Oxford University Press.CrossRefGoogle Scholar
Winsberg, E. 2010. Science in the Age of Computer Simulation. Chicago: University of Chicago Press.CrossRefGoogle Scholar
Winsberg, E. 2012. “Values and Uncertainties in the Predictions of Global Climate Models.” Kennedy Institute of Ethics Journal 22 (2): 111–37.CrossRefGoogle ScholarPubMed
Winsberg, E. 2018. “Computer Simulations in Science.” In Stanford Encyclopedia of Philosophy, ed. Zalta, Edward N.. Stanford, CA: Stanford University. https://plato.stanford.edu/archives/sum2018/entries/simulations-science/.Google Scholar
Wong, T. E., Bakker, A. M., and Keller, K.. 2017. “Impacts of Antarctic Fast Dynamics on Sea-Level Projections and Coastal Flood Defense.” Climatic Change 144 (2): 347–64.CrossRefGoogle Scholar
Wong, T. E., Bakker, A. M., Ruckert, K., Applegate, P., Slangen, A., and Keller, K.. 2017. “BRICK v0.2, a Simple, Accessible, and Transparent Model Framework for Climate and Regional Sea-Level Projections.” Geoscientific Model Development 10 (7): 2741–60.CrossRefGoogle Scholar
Wong, T. E., and Keller, K.. 2017. “Deep Uncertainty Surrounding Coastal Flood Risk Projections: A Case Study for New Orleans.” Earth’s Future 5 (10): 1015–26.CrossRefGoogle Scholar