New Grad Course on Computational Models of Climate Change

This week, I start teaching a new grad course on computational models of climate change, aimed at computer science grad students with no prior background in climate science or meteorology. Here’s my brief blurb:

Detailed projections of future climate change are created using sophisticated computational models that simulate the physical dynamics of the atmosphere and oceans and their interaction with chemical and biological processes around the globe. These models have evolved over the last 60 years, along with scientists’ understanding of the climate system. This course provides an introduction to the computational techniques used in constructing global climate models, the engineering challenges in coupling and testing models of disparate earth system processes, and the scaling challenges involved in exploiting peta-scale computing architectures. The course will also provide a historical perspective on climate modelling, from the early ENIAC weather simulations created by von Neumann and Charney, through to today’s Earth System Models, and the role that these models play in the scientific assessments of the UN’s Intergovernmental Panel on Climate Change (IPCC). The course will also address the philosophical issues raised by the role of computational modelling in the discovery of scientific knowledge, the measurement of uncertainty, and a variety of techniques for model validation. Additional topics, based on interest, may include the use of multi-model ensembles for probabilistic forecasting, data assimilation techniques, and the use of models for re-analysis.

I’ve come up with a draft outline for the course, and some possible readings for each topic. Comments are very welcome:

1. History of climate and weather modelling. Early climate science. Quick tour of range of current models. Overview of what we knew about climate change before computational modeling was possible.
   • Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7), 3431-3444.
   • Weart, S. (2010). The development of general circulation models of climate. Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41(3), 208-217.
2. Calculating the weather. Bjerknes’ equations. ENIAC runs. What does a modern dynamical core do? [Includes basic introduction to thermodynamics of atmosphere and ocean]
   • Platzman, G. W. (1979). The ENIAC Computations of 1950: Gateway to Numerical Weather Prediction. Bulletin of the American Meteorological Society, 60, 302-312.
   • Staniforth, a, & Wood, N. (2008). Aspects of the dynamical core of a nonhydrostatic, deep-atmosphere, unified weather and climate-prediction model. Journal of Computational Physics, 227(7), 3445-3464.
3. Chaos and complexity science. Key ideas: forcings, feedbacks, dynamic equilibrium, tipping points, regime shifts, systems thinking. Planetary boundaries. Potential for runaway feedbacks. Resilience & sustainability. (way too many readings this week. Have to think about how to address this – maybe this is two weeks worth of material?)
   • Liepert, B. G. (2010). The physical concept of climate forcing. Wiley Interdisciplinary Reviews: Climate Change, 1(6), 786-802.
   • Manson, S. M. (2001). Simplifying complexity: a review of complexity theory. Geoforum, 32(3), 405-414.
   • Rind, D. (1999). Complexity and Climate. Science, 284(5411), 105-107.
   • Randall, D. A. (2011). The Evolution of Complexity In General Circulation Models. In L. Donner, W. Schubert, & R. Somerville (Eds.), The Development of Atmospheric General Circulation Models: Complexity, Synthesis, and Computation. Cambridge University Press.
   • Meadows, D. H. (2008). Chapter One: The Basics. Thinking In Systems: A Primer (pp. 11-34). Chelsea Green Publishing.
   • Randers, J. (2012). The Real Message of Limits to Growth: A Plea for Forward-Looking Global Policy, 2, 102-105.
   • Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E., Lenton, T. M., et al. (2009). Planetary boundaries: exploring the safe operating space for humanity. Ecology and Society, 14(2), 32.
   • Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008). Tipping elements in the Earth’s climate system. Proceedings of the National Academy of Sciences of the United States of America, 105(6), 1786-93.
4. Typology of climate Models. Basic energy balance models. Adding a layered atmosphere. 3-D models. Coupling in other earth systems. Exploring dynamics of the socio-economic system. Other types of model: EMICS; IAMS.
   • Müller, P. (2010). Constructing climate knowledge with computer models. Wiley Interdisciplinary Reviews: Climate Change.
   • Weart, S. (2012). Simple Models of Climate Change. The Discovery of Global Warming.
   • Gramelsberger, G. (2010). Conceiving processes in atmospheric models – General equations, subscale parameterizations, and “superparameterizations.” Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41(3), 233-241.
   • Weber, S. L. (2010). The utility of Earth system Models of Intermediate Complexity (EMICs). Wiley Interdisciplinary Reviews: Climate Change, (April).
5. Earth System Modeling. Using models to study interactions in the earth system. Overview of key systems (carbon cycle, hydrology, ice dynamics, biogeochemistry).
   • Dahan, A. (2010). Putting the Earth System in a numerical box? The evolution from climate modeling toward global change. Studies In History and Philosophy of Science Part B: Studies In History and Philosophy of Modern Physics, 41(3), 282-292.
   • Claussen, M. (2007). Climate system models – a brief introduction. Developments in Quaternary Science, 7, 495-497.
6. Overcoming computational limits. Choice of grid resolution; grid geometry, online versus offline; regional models; ensembles of simpler models; perturbed ensembles. The challenge of very long simulations (e.g. for studying paleoclimate).
   • Washington, W. M., Buja, L., & Craig, A. (2009). The computational future for climate and Earth system models: on the path to petaflop and beyond. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 367(1890), 833-46.
   • Slingo, J., Bates, K., Nikiforakis, N., Piggott, M., Roberts, M., Shaffrey, L., Stevens, I., et al. (2009). Developing the next-generation climate system models: challenges and achievements. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1890), 815-31.
7. Epistemic status of climate models. E.g. what does a future forecast actually mean? How are model runs interpreted? Relationship between model and theory. Reproducibility and open science.
   • Shackley, S. (2001). Epistemic Lifestyles in Climate Change Modeling. In P. N. Edwards (Ed.), Changing the Atmosphere: Expert Knowledge and Environmental Government (pp. 107-133). MIT Press.
   • Sterman, J. D., Jr, E. R., & Oreskes, N. (1994). The Meaning of Models. Science, 264(5157), 329-331.
   • Randall, D. A., & Wielicki, B. A. (1997). Measurement, Models, and Hypotheses in the Atmospheric Sciences. Bulletin of the American Meteorological Society, 78(3), 399-406.
   • Smith, L. a. (2002). What might we learn from climate forecasts? Proceedings of the National Academy of Sciences of the United States of America, 99 Suppl 1, 2487-92.
8. Assessing model skill – comparing models against observations, forecast validation, hindcasting. Validation of the entire modelling system. Problems of uncertainty in the data. Re-analysis, data assimilation. Model intercomparison projects.
   • Oreskes, N. (2001). Philosophical Issues in Model Assessment. Model validation: Perspectives in.
   • Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263(5147), 641.
   • Knutti, R. (2008). Should we believe model predictions of future climate change? Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 366(1885), 4647-64.
   • Reichler, T., & Kim, J. (2008). How Well Do Coupled Models Simulate Today’s Climate? Bulletin of the American Meteorological Society, 89(3), 303-311.
   • Shackley, S., Young, P., & Parkinson, S. (1998). Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools? Climatic Change, 38, 159-205.
9. Uncertainty. Three different types: initial state uncertainty, scenario uncertainty and structural uncertainty. How well are we doing? Assessing structural uncertainty in the models. How different are the models anyway?
   • Masson, D., & Knutti, R. (2011). Climate model genealogy. Geophysical Research Letters, 38(8), 1-4.
   • Pennell, C., & Reichler, T. (2011). On the Effective Number of Climate Models. Journal of Climate, 24(9), 2358-2367.
   • Murphy, J. M., Sexton, D. M. H., Barnett, D., & Jones, G. S. (2004). Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430(August 2004).
   • Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1107.
   • Hargreaves, J. C. (2010). Skill and uncertainty in climate models. Wiley Interdisciplinary Reviews: Climate Change, 1.
10. Current Research Challenges. Eg: Non-standard grids – e.g. non-rectangular, adaptive, etc; Probabilistic modelling – both fine grain (e.g. ECMWF work) and use of ensembles; Petascale datasets; Reusable couplers and software frameworks. (need some more readings on different research challenges for this topic)
    • Collins, M. (2007). Ensembles and probabilities: a new era in the prediction of climate change. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 365(1857), 1957-70.
    • Leutbecher, M., & Palmer, T. N. (2008). Ensemble forecasting. Journal of Computational Physics, 227(7), 3515-3539.
11. The future. Projecting future climates. Role of modelling in the IPCC assessments. What policymakers want versus what they get. Demands for actionable science and regional, decadal forecasting. The idea of climate services.
    • Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2011). A Summary of the CMIP5 Experiment Design.
    • Moss, R. H., Edmonds, J. A., Hibbard, K. a, Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., et al. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-56.
    • New, M., Liverman, D., Schroeder, H., Schroder, H., & Anderson, K. (2011). Four degrees and beyond: the potential for a global temperature increase of four degrees and its implications. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 369(1934), 6-19.
    • Agrawala, S., Broad, K., & Guston, D. H. (2001). Integrating Climate Forecasts and Societal Decision Making: Challenges to an Emergent Boundary Organization. Science, Technology & Human Values, 26(4), 454-477.
12. Knowledge and wisdom. What the models tell us. Climate ethics. The politics of doubt. The understanding gap. Disconnect between our understanding of climate and our policy choices.
    • Ramanathan, V., & Feng, Y. (2008). On avoiding dangerous anthropogenic interference with the climate system: Formidable challenges ahead. Proc. of the Nat. Acad. of Sciences, 105(38), 14245-14250.
    • Stainforth, D. a., Allen, M. R., Tredger, E. R., & Smith, L. a. (2007). Confidence, uncertainty and decision-support relevance in climate predictions. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 365(1857), 2145-61.
    • Randalls, S. (2010). History of the 2°C climate target. Wiley Interdisciplinary Reviews: Climate Change, 1(4), 598-605.
    • Hansen, J. E., Sato, M., Kharecha, P., Beerling, D. J., Berner, R., Masson-Delmotte, V., Pagani, M., et al. (2008). Target atmospheric CO2: Where should humanity aim? Open Atmospheric Science Journal, 2(15), 217-231.
    • Turner, G. M. (2012). On the Cusp of Global Collapse? Gaia, 21(2), 116-124.
    • Sterman, J. D., & Sweeney, L. B. (2002). Cloudy skies: assessing public understanding of global warming. System Dynamics Review, 18(2), 207-240.