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Connections between simulations and observation in climate computer modeling. Scientist’s practices and “bottom-up epistemology” lessons
Authors:Hélène Guillemot
Institution:1. Aarhus University, Department of Science Studies, C.F.MøllersAlle’ 8, AarhusC, Denmark;2. Aarhus University, Department of Science Studies, C.F.MøllersAlle’ 8, AarhusC, Denmark;1. Earth and Life Institute, Université catholique de Louvain, Croix du Sud 4, L7.07.04, 1348 Louvain-la-Neuve, Belgium;2. RSPB Centre for Conservation Science, Royal Society for the Protection of Birds, The Lodge, Sandy, Bedfordshire SG19 2DL, United Kingdom;3. Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany;4. Institute for Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, 14469 Potsdam, Germany;5. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany;1. Systems Analysis Laboratory, Aalto University, P.O. Box 11100, 00076 Aalto, Finland;2. Water & Development Research Group, Aalto University, P.O. Box 15200, 00076 Aalto, Finland;1. Centro Conservazione Biodiversità (CCB), Dipartimento di Scienze della Vita e dell’Ambiente, Università degli Studi di Cagliari, Viale S. Ignazio da Laconi 13, I-09123, Cagliari, Italy;2. Hortus Botanicus Karalitanus (HBK), Università degli Studi di Cagliari, Viale Sant’Ignazio da Laconi 9-11, 09123 Cagliari, Italy;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, 7500 AA Enschede, The Netherlands;2. UFZ - Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, D-04318 Leipzig, Germany;3. Natural Environment Research Council, Centre for Ecology and Hydrology, Bush Estate, Penicuik, EH26 0QB, United Kingdom;4. Dynamic Macroecology, Landscape Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zurcherstrasse 111, 8903 Birmensdorf, Switzerland;5. Department of Environmental Systems Science, Swiss Federal Institute of Technology, ETH, 8092 Zurich, Switzerland;6. Department of Mechanical Engineering, School of Engineering, The University of Melbourne, Melbourne, Australia;7. Melbourne Academy for Sustainability and Society (MASS), Melbourne Sustainable Society Institute (MSSI), The University of Melbourne, Melbourne, Australia
Abstract:Climate modeling is closely tied, through its institutions and practices, to observations from satellites and to the field sciences. The validity, quality and scientific credibility of models are based on interaction between models and observation data. In the case of numerical modeling of climate and climate change, validation is not solely a scientific interest: the legitimacy of computer modeling, as a tool of knowledge, has been called into question in order to deny the reality of any anthropogenic climate change; model validations thereby bring political issues into play as well. There is no systematic protocol of validation: one never validates a model in general, but the capacity of a model to account for a defined climatic phenomenon or characteristic. From practices observed in the two research centers developing and using a climate model in France, this paper reviews different ways in which the researchers establish links between models and empirical data (which are not reduced to the latter validating the former) and convince themselves that their models are valid. The analysis of validation practices—relating to parametrization, modes of variability, climatic phenomena, etc.—allows us to highlight some elements of the epistemology of modeling.
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