Reading List

This list is an introduction to how our group approaches climate risk analysis. It is organized as an argument rather than a taxonomy: each section answers a question raised by the one before it, so reading in order is worth more than reading the same papers scattered.

Everyone should read Start Here. How much the later sections matter depends on what you work on, but all of them describe something the group actually does.

These are also a good source of candidates for journal club – sign up in the journal-club repository.

Start Here

What climate risk management is, why it is a decision problem rather than a prediction problem, and what kind of modeling that implies.

Read Bankes first if you read nothing else. The distinction it draws between consolidative modeling – building a model you treat as a surrogate for the real system, then asking it what will happen – and exploratory modeling – using ensembles of plausible models to map what could happen and which of those possibilities matter – is the epistemic commitment underneath nearly everything else the group does. Most of the methodological choices in the rest of this list follow from taking the second view seriously.

  • Bankes, S. (1993). Exploratory Modeling for Policy Analysis. Operations Research, 41(3), 435–449. https://doi.org/10.1287/opre.41.3.435
  • Keller, K., Helgeson, C., & Srikrishnan, V. (2021). Climate Risk Management. Annual Review of Earth and Planetary Sciences, 49(1), 95–116. https://doi.org/10.1146/annurev-earth-080320-055847
  • Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169. https://doi.org/10.1007/BF01405730
  • Srikrishnan, V., Lafferty, D. C., Wong, T. E., Lamontagne, J. R., Quinn, J. D., Sharma, S., et al. (2022). Uncertainty Analysis in Multi-Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems. Earth’s Future, 10(8), e2021EF002644. https://doi.org/10.1029/2021EF002644
  • Reed, P. M., Hadjimichael, A., Moss, R. H., Brelsford, C., Burleyson, C. D., Cohen, S., et al. (2022). Multisector Dynamics: Advancing the Science of Complex Adaptive Human-Earth Systems. Earth’s Future, 10(3), e2021EF002621. https://doi.org/10.1029/2021EF002621
  • Pollack, A. B., Auermuller, L., Burleyson, C. D., Campbell, J., Condon, M., Cooper, C., et al. (2026). Unlocking the benefits of transparent and reusable science for climate risk management. Proceedings of the National Academy of Sciences, 123(3), e2422157123. https://doi.org/10.1073/pnas.2422157123

Models and Their Limits

If models are instruments for exploration rather than oracles, the question becomes what a model can legitimately be used to claim. These are about the gap between a model running and a model telling you something true.

Oreskes is the one people misremember. It does not argue that models are useless; it argues that “verification” and “validation” mean something much weaker for open systems than the words suggest. Worth keeping straight, since the same two words appear in this manual in their software-testing sense, where they mean something entirely different.

Quantifying Uncertainty

The methodological core: how to characterize what you do not know, propagate it through a model, and work out which unknowns actually matter.

The MSD uncertainty eBook is the practical companion to the rest of this section, and the place to start when you need to actually run a sensitivity analysis rather than read about one. It is a living document, so check it again rather than trusting what you read the first time.

Structural Uncertainty in Practice

The previous section gives you the theory of model discrepancy. This one is the group demonstrating, repeatedly and in unrelated systems, that how you structured the model usually matters more than what values you gave its parameters — and so an analysis that propagates only parametric uncertainty will understate risk, often in the tail, which is the part anyone cares about.

If there is one claim to take away from this list, it is this one. Notice that these three papers reach it in coastal flood hazard, in agent-based household decisions, and in riverine flood risk, using different methods each time.

  • Wong, T. E., Klufas, A., Srikrishnan, V., & Keller, K. (2018). Neglecting model structural uncertainty underestimates upper tails of flood hazard. Environmental Research Letters, 13(7), 074019. https://doi.org/10.1088/1748-9326/aacb3d
  • Yoon, J., Wan, H., Daniel, B., Srikrishnan, V., & Judi, D. (2023). Structural model choices regularly overshadow parametric uncertainty in agent-based simulations of household flood risk outcomes. Computers, Environment and Urban Systems, 103, 101979. https://doi.org/10.1016/j.compenvurbsys.2023.101979
  • Hosseini-Shakib, I., Alipour, A., Lee, B. S., Srikrishnan, V., Nicholas, R. E., Keller, K., & Sharma, S. (2024). What drives uncertainty surrounding riverine flood risks? Journal of Hydrology, 634, 131055. https://doi.org/10.1016/j.jhydrol.2024.131055

From Uncertainty to Decisions

Characterizing uncertainty is not the goal. These are about what you do once you have it: how to frame a decision problem, how the framing shapes the answer, and what a good strategy means when probabilities are not available.

  • Walker, W. E., Haasnoot, M., & Kwakkel, J. H. (2013). Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty. Sustainability, 5(3), 955–979. https://doi.org/10.3390/su5030955
  • Quinn, J. D., Reed, P. M., Giuliani, M., & Castelletti, A. (2017). Rival framings: A framework for discovering how problem formulation uncertainties shape risk management trade-offs in water resources systems. Water Resources Research, 53(8), 7208–7233. https://doi.org/10.1002/2017WR020524
  • Herman, J. D., Quinn, J. D., Steinschneider, S., Giuliani, M., & Fletcher, S. (2020). Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty. Water Resources Research, 56(2). https://doi.org/10.1029/2019WR025502
  • Oddo, P. C., Lee, B. S., Garner, G. G., Srikrishnan, V., Reed, P. M., Forest, C. E., & Keller, K. (2020). Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management. Risk Analysis, 40(1), 153–168. https://doi.org/10.1111/risa.12888
  • Anderies, J. M., Rodriguez, A. A., Janssen, M. A., & Cifdaloz, O. (2007). Panaceas, uncertainty, and the robust control framework in sustainability science. Proceedings of the National Academy of Sciences of the United States of America, 104(39), 15194–15199. https://doi.org/10.1073/pnas.0702655104

Values, People, and Risk

Every step above involves choices that the data does not settle: which outcomes to model, whose losses count, what counts as an acceptable risk. These are about treating those as choices — including the ones modelers make without noticing they are making them.

  • Kunreuther, H., Novemsky, N., & Kahneman, D. (2001). Making Low Probabilities Useful. Journal of Risk and Uncertainty, 23(2), 103–120. https://doi.org/10.1023/A:1011111601406
  • Wong-Parodi, G. (2020). When climate change adaptation becomes a “looming threat” to society: Exploring views and responses to California wildfires and public safety power shutoffs. Energy Research & Social Science, 70, 101757. https://doi.org/10.1016/j.erss.2020.101757
  • Mayer, L. A., Loa, K., Cwik, B., Tuana, N., Keller, K., Gonnerman, C., et al. (2017). Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models. Global Environmental Change: Human and Policy Dimensions, 42, 107–116. https://doi.org/10.1016/j.gloenvcha.2016.12.007
  • Bessette, D. L., Mayer, L. A., Cwik, B., Vezér, M., Keller, K., Lempert, R. J., & Tuana, N. (2017). Building a Values-Informed Mental Model for New Orleans Climate Risk Management. Risk Analysis, 37(10), 1993–2004. https://doi.org/10.1111/risa.12743