CSC2602
Topics in Analysis and Computation in Continuous Models:
Computational Models of Climate Change

Winter 2013

Note:

About the Course

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.

Assessment will be via participation in class discussion and a term paper addressing one or more of the computational challenges involved in climate modelling.

Prerequisites: None. This is an introductory graduate-level course aimed at students with a general computer science background. No prior knowledge of climate science or numerical methods is needed.

Computer Science breadth credit: This course is in methodology group 2 - Analysis and Computation in Continuous Models; and Research Area 15 (Interdisciplinary Studies in Computer Science).

Topics

Draft set of weekly topics, with some suggested readings (note: recommendations for specific readings each week will evolve!)

(1) History of climate and weather modelling. Early climate science. Quick overview of range of current models. Overview of what we knew about climate change before computational modeling was possible.
Here's the Timeline of Climate Modeling that I presented in class.
Background readings (FYI):
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.
Platzman, G. W. (1979). The ENIAC Computations of 1950: Gateway to Numerical Weather Prediction. Bulletin of the American Meteorological Society, 60, 302-312.
(2) Calculating the weather. Bjerknes' equations. ENIAC runs. What does a modern dynamical core do? [Includes basic introduction to thermodynamics of atmosphere and ocean]
Required Readings (read before this class!):
(A) Charney JG, Arakawa A, Baker J, et al. Carbon Dioxide and Climate: A Scientific Assessment. 1979.
(B) 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.
Notes from class:
newPif's slides from the discussion on the Charney Report
(3) Chaos and complexity science. Key ideas: forcings, feedbacks, dynamic equilibrium, tipping points, regime shifts, systems thinking. Planetary boundaries. Potential for runaway feedbacks.
Required Readings (read before this class!):
(A) Rind, D. (1999). Complexity and Climate. Science, 284(5411), 105-107.
(B) 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.
(C) 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.
More background:
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.
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.
(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.
Required Readings (read before this class!):
(A) Müller, P. (2010). Constructing climate knowledge with computer models. Wiley Interdisciplinary Reviews: Climate Change.
(B) Weber, S. L. (2010). The utility of Earth system Models of Intermediate Complexity (EMICs). Wiley Interdisciplinary Reviews: Climate Change, (April).
More background:
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.
(5) Earth System Modeling and the Computational Limits. Using models to study interactions in the earth system. Overview of key systems (carbon cycle, hydrology, ice dynamics, biogeochemistry). Where the complexity comes from, and what choices you can make to overcome it
Required Readings (read before this class!):
(A) 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.
(B) 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.
More background:
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.

Reading Week!

(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.
Required Readings (read before this class!):
(A) 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.
(B) 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.
(C) See also, this blog post on a conference session on model evaluation methodology.
More background:
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.
(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.
Required Readings (read before this class!):
(A) 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.
(B) Reichler, T., & Kim, J. (2008). How Well Do Coupled Models Simulate Today's Climate? Bulletin of the American Meteorological Society, 89(3), 303-311.
More background:
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.
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?
Required Readings (read before this class!):
(A) Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1107.
(B) Otto, F. E. L., Massey, N., Van Oldenborgh, G. J., Jones, R. G., & Allen, M. R. (2012). Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophysical Research Letters, 39(4), 1-5.
More background:
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).
Hargreaves, J. C. (2010). Skill and uncertainty in climate models. Wiley Interdisciplinary Reviews: Climate Change, 1.
(10) Current Research Challenges. Ensembles, Probabilistic modelling, Unified weather and climate modeling; Petascale datasets; Reusable couplers and software frameworks.
Required Readings (read before this class!):
Shukla, J., Hagedorn, R., Hoskins, B., Kinter, J., Marotzke, J., Miller, M., Palmer, T. N., et al. (2009). Revolution in Climate Prediction is Both Necessary and Possible. Bull. Amer. Meteorol. Soc, 90, 175178.
...and then pick one of the following papers to read, ready to summarize to the class:
(a) 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.
(b) Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., & Shelly, A. (2012). Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey. Bulletin of the American Meteorological Society, 93(12), 1865-1877.
(c) Kinter, J. L., Cash, B., Achuthavarier, D., Adams, J., Altshuler, E., Dirmeyer, P., Doty, B., et al. (2013). Revolutionizing Climate Modeling with Project Athena: A Multi-Institutional, International Collaboration. Bulletin of the American Meteorological Society, 94(2), 231-245.
(d) Palmer, T. N. (2012). Towards the probabilistic Earth-system simulator: a vision for the future of climate and weather prediction. Quarterly Journal of the Royal Meteorological Society, 138(665), 841-861. (see also my blog post on a talk by Tim Palmer about this work).
(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.
Required Readings (read before this class!):
(A) 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.
(B) 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.
More background:
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2011). A Summary of the CMIP5 Experiment Design.
Solomon, S., Plattner, G.-K., Knutti, R., & Friedlingstein, P. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 1704–9.
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, and what they don't tell us. The disconnect between our understanding of climate and our policy choices. Plus, we could talk about the politics of doubt - i.e. why different stakeholders have an interest in sowing doubt about climate change.
Required Readings (read before this class!):
(A) 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. (see James' discussion notes)
(B) Sterman, J. D., & Sweeney, L. B. (2002). Cloudy skies: assessing public understanding of global warming. System Dynamics Review, 18(2), 207-240. (see Bahar's discussion notes)
(C) Oreskes, N., & Conway, E. M. (2013). The Collapse of Western Civilization: A View from the Future. Daedalus, 142(1), 40–58.
More background:
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.
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.