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Local Model-Data Symbiosis in Meteorology and Climate Science

Published online by Cambridge University Press:  01 January 2022

Wendy Parker
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Abstract

I introduce a distinction between general and local model-data symbiosis and offer three examples of local symbiosis in the fields of meteorology and climate science. Local model-data symbiosis refers to a beneficial, two-way interdependence between a particular model and data set or between closely related models and data sets. For each example presented here, I show how the symbiotic relationship works—what the interdependence consists in and how it is supposed to be beneficial—and I consider whether, alongside the benefits, there is some risk of problematic circularity.

Type
Models and Modeling
Information
Philosophy of Science , Volume 87 , Issue 5: PSA 2018 - Proceedings of the 2018 biennial meeting of the Philosophy of Science Association. Part II Symposia Papers , December 2020 , pp. 807 - 818
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

Thanks to my cosymposiasts and our audience at PSA 2018 for helpful questions, comments, and discussion. Some of this material is based on work supported by the US National Science Foundation under grant SES-1127710.

References

Bokulich, Alisa. 2018. “Using Models to Correct Data: Paleodiversity and the Fossil Record.” Synthese. https://doi.org/10.1007/s11229-018-1820-x.CrossRefGoogle Scholar
Bokulich, Alisa. 2020. “Toward a Taxonomy of the Model-Ladenness of Data.” Philosophy of Science, in this issue.CrossRefGoogle Scholar
Costa, Ana Christina, and Soares, Amilcar. 2009. “Homogenization of Climate Data: Review and New Perspectives Using Geostatistics.” Mathematical Geosciences 41 (3): 291–305.CrossRefGoogle Scholar
Cowtan, Kevin. 2015. “Homogenization of Temperature Data: An Assessment.” Unpublished manuscript, University of York. http://www-users.york.ac.uk/~kdc3/papers/homogenization2015/homog.pdf.Google Scholar
Edwards, Paul N. 1999. “Global Climate Science, Uncertainty and Politics: Data-Laden Models, Model-Filtered Data.” Science as Culture 8 (4): 437–72.CrossRefGoogle Scholar
Edwards, Paul N.. 2010. A Vast Machine: Computer Models, Climate Data and the Politics of Global Warming. Cambridge, MA: MIT Press.Google Scholar
Errico, Ronald M. 1997. “What Is an Adjoint Model?” Bulletin of the American Meteorological Society 78 (11): 2577–92.2.0.CO;2>CrossRefGoogle Scholar
Kalnay, Eugenia. 2003. Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge: Cambridge University Press.Google Scholar
Majumdar, Sharanya J. 2016. “A Review of Targeted Observations.” Bulletin of the American Meteorological Society 97 (12): 2287–303.CrossRefGoogle Scholar
Masson, David, and Knutti, Reto. 2011. “Climate Model Genealogy.” Geophysical Research Letters 38: L08703.CrossRefGoogle Scholar
Park, Young-Youn, Buizza, Roberto, and Leutbecher, Martin. 2008. “TIGGE: Preliminary Results on Comparing and Combining Ensembles.” Quarterly Journal of the Royal Meteorology Society 134:2029–50.CrossRefGoogle Scholar
Parker, Wendy S. 2017. “Computer Simulation, Measurement and Data Assimilation.” British Journal for the Philosophy of Science 68 (1): 273–304.CrossRefGoogle Scholar
Parker, Wendy S.. 2020. “Evaluating Data Journeys: Climategate, Synthetic Data and the Benchmarking of Methods for Climate Data Processing.” In Data Journeys in the Sciences, ed. Leonelli, Sabina and Tempini, Niccolo. Dordrecht: Springer.Google Scholar
Rennie, J. J., et al. 2014. “The International Surface Temperature Initiative Global Land Surface Databank: Monthly Temperature Data Release Description and Methods.” Geoscience Data Journal 1:75–102.CrossRefGoogle Scholar
Venema, Victor K. C., et al. 2012. “Benchmarking Homogenization Algorithms for Monthly Data.” Climate of the Past 8:89–115.CrossRefGoogle Scholar
Willett, Kate, et al. 2014. “A Framework for Benchmarking of Homogenisation Algorithm Performance on the Global Scale.” Geoscientific Instrumentation, Methods and Data Systems 3:187–200.CrossRefGoogle Scholar
Wu, Chun-Chieh, Chen, Jan-Huey, Lin, Po-Hsiung, and Chou, Kun-Hsuan. 2007. “Targeted Observations of Tropical Cyclone Movement Based on the Adjoint-Derived Sensitivity Steering Vector.” Journal of the Atmospheric Sciences 64:2611–26.CrossRefGoogle Scholar
Wu, Chun-Chieh, et al. 2009. “Intercomparison of Targeted Observation Guidance for Tropical Cyclones in the Northwestern Pacific.” Monthly Weather Review 137:2471–92.CrossRefGoogle Scholar