Abstract
Physical sciences can be grouped in two categories: the experimental sciences (chemistry and physics) based on the simplest form of evidence.
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References
Arnold, C. P., & Dey, C. H. (1986). Observing-systems simulation experiments: Past present and the future. Bulletin of the American Meteorological Society, 67, 687–695.
Cooke, R., Golub, A., Wielicki, B. A., Young, D. F., Mlynczak, M. G., & Baize, R. R. (2016). Using the social cost of carbon to value earth observing systems. Climate Policy, 17, 1–16.
Cooke, R., Wielicki, B. A., Young, D. F., & Mlynczak, M. G. (2014). Value of information for climate observing systems. Environment Systems and Decisions, 34, 98–109.
Frigg, R., Thomson, E., & Werndl, C. (2015). Philosophy of climate science Part 1: Observing climate change. Philosophy Compass, 10(12), 953–964.
Goody, R. M., & Visconti, G. (2013). Climate prediction: An evidence-based perspective. Rendiconti Lincei: SCIENZE FISICHE E NATURALI. doi:10.1007/s12210-013-0228-2. Reproduced with permission of Springer.
Guttorp, P. (2014). Statistics and climate. Annual Review of Statistics and Its Application, 1, 7–101. Reproduced with permission of Annual Review \(\copyright \) by Annual Reviews.
Harries, J. E., Brindley, H. E., Sagoo, P. J., & Bantges, R. J. (2001). Increases in greenhouse forcing inferred from the outgoing longwave radiation spectra of the Earth in 1970 and 1997. Nature, 410, 355–357. Reprinted by permission from Macmillan Publishers Ltd.
Massonnet, F., Bellprat, O., Guemas, V., & Doblas-Reyes, F. J. (2016). Using climate modekls to estimate the quality of global observational data sets. Science, 354, 452–455.
Sanderson, B.M. & Knutti, R. (2012). On the interpretation of constrained climate model ensembles. Geophysical Research Letters, 39, L16708. doi:10.1029/2012GL052665.
Siegert, S., Stephenson, D. B., Sansom, P. G., Scaife, A. A., Eade, R., & Arriba, A. (2016). A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability? Journal of Climate, 28, 995–1012.
Taylor, K. E., Stouffer, R. J., & Meehl, C. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93, 485–98.
Tredger, E. (2009). On the evaluation of uncertainties in climate models. Ph.D. thesis, London School of Economics.
Wielicki, B. A., et al. (2013). Achieving climate change absolute accuracy in orbit. Bulletin of the American Meteorological Society, 94, 1519–40.
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Visconti, G. (2018). Experimental Data and Climate. In: Problems, Philosophy and Politics of Climate Science. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-319-65669-4_5
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DOI: https://doi.org/10.1007/978-3-319-65669-4_5
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