Validation of Climate Models: An Essential Practice
This chapter describes a structure for climate model verification and validation. The construction of models from components and subcomponents is discussed, and the construction is related to verification and validation. In addition to quantitative measures of mean, bias, and variability, it is argued that physical consistency must be informed by correlative behavior that is related to underlying physical theory. The more qualitative attributes of validation are discussed. The consideration of these issues leads to the need for deliberative, expert evaluation as a part of the validation process. The narrative maintains a need for a written validation plan that describes the validation criteria and metrics and establishes the protocols for the essential deliberations. The validation plan, also, sets the foundations for independence, transparency, and objectivity. These values support both scientific methodology and integrity in the public forum.
KeywordsClimate Modeling Verification Validation Science Society Quantitative Qualitative Community
I thank the editors, Claus Beisbart and Nicole J. Saam, for the opportunity to contribute this chapter and for their efforts in putting together this volume. I thank Cecelia Deluca for reading an early version of the manuscript and many discussions on modeling infrastructure, verification and validation, and insights into modeling culture.
- Data Assimilation Office (DAO). (1996). Algorithm Theoretical Basis Document Version 1.01, Data Assimilation Office, Goddard Space Flight Center. Retrieved from https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd-dao.pdf.
- Dee, D. P. (1995). A pragmatic approach to model validation. In Quantitative skill assessment for coastal ocean models. American Geophysical Union (pp. 1–14).Google Scholar
- Edwards, P. N. (2010). A vast machine. Cambridge, MA, USA: The MIT Press.Google Scholar
- Farber, D. A. (2007). Climate models: A user’s guide. Berkeley, CA, USA, UC Berkeley Public Law Research Paper No. 1030607.Google Scholar
- Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., et al. (2013) Evaluation of climate models. In T. F. Stocker, D. Qin, G. -K. Plattner, M. Tignor, S. K. Allen, J. Boschung, et al. (Eds.) Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.Google Scholar
- Jablonowski, C., & Williamson, D. L. (2006). A baroclinic wave test case for dynamical cores of General Circulation Models: Model intercomparisons. NCAR Technical Note NCAR/TN-4691STR, National Center for Atmospheric Research, Boulder, CO (89 pp).Google Scholar
- Lloyd, E. A. (2012). The role of ‘complex’ empiricism in the debates about satellite data and climate models. Studies in History and Philosophy of Science, 43, 390–401.Google Scholar
- National Aeronautics and Space Administration (NASA). (2016). Independent Verification and Validation Framework. IVV 09-1, Version: P. Retrieved from https://www.nasa.gov/sites/default/files/atoms/files/ivv09-1-verp.doc.
- Norton, S. D., & Suppe, F. (2001). Why atmospheric modeling is good science. In C. A. Miller & P. N. Edwards (Eds.), Changing the atmosphere: Expert knowledge and environmental governance (pp. 67–105). Cambridge, MA, USA: The MIT Press.Google Scholar
- Oberkampf, W. L., & Trucano, T. G. (2002). Verification and validation in computational fluid dynamics, SAND2002 – 0529. Albuquerque, NM, USA: Sandia National Laboratories.Google Scholar
- Robock, A. (1983). El Chichón provides test of volcanoes’ influence on climate. National Weather Digest, 8, 40–45.Google Scholar
- Roesler, E. L., Posselt, D. J., & Rood, R. B. (2017). Using large eddy simulations to reveal the size, strength, and phase of updraft and downdraft cores of an Arctic mixed-phase stratocumulus cloud. Journal Geophysical Research, 122, 4378–4400.Google Scholar
- Rood, R. B. (2010). The role of the model in the data assimilation system. In W. Lahoz, B. Khattatov, & R. Menard (Eds.), Data assimilation: Making sense of observations (pp. 351–379). Berlin, Heidelberg: Springer. http://dx.doi.org/10.1007/978-3-540-74703-1_14.
- Shackley, S. (2001). Epistemic lifestyles in climate change modeling. In C. A. Miller & P. N. Edwards (Eds.), Changing the atmosphere: Expert knowledge and environmental governance (pp. 107–133). Cambridge, MA, USA: The MIT Press.Google Scholar