1. Scientific and Human Errors in a Snow Model Intercomparison
- Author
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Matthieu Lafaysse, Richard Essery, Paul Bartlett, Vladimir Semenov, Ulrich Strasser, Aaron Boone, Xing Fang, Gabriele Arduini, Cécile B. Ménard, Yongjiu Dai, Claire Brutel-Vuilmet, Gerd Schädler, Thomas Marke, Eleanor J. Burke, Tatiana G. Smirnova, Dmitry Turkov, Emanuel Dutra, Stefan Hagemann, Vanessa Haverd, Sean Swenson, Hua Yuan, Charles Fierz, Hyungjun Kim, Yeugeniy M. Gusev, Masashi Niwano, Gerhard Krinner, Matthias Cuntz, Olga N. Nasonova, Tomoko Nitta, Bertrand Decharme, John W. Pomeroy, Nander Wever, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,0207 environmental engineering ,02 engineering and technology ,[SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology ,Snowpack ,Snow ,01 natural sciences ,[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology ,13. Climate action ,Climatology ,Environmental science ,[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology ,020701 environmental engineering ,0105 earth and related environmental sciences - Abstract
Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.
- Published
- 2021
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