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Scientific and Human Errors in a Snow Model Intercomparison

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  • 1 School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom
  • 2 Institut de Géosciences de l—Environnement, Université Grenoble Alpes, CNRS, Grenoble, Franceh
  • 3 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
  • 4 Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
  • 5 CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 6 Institut de Géosciences de l—Environnement, Université Grenoble Alpes, CNRS, Grenoble, France
  • 7 Met Office Hadley Centre, Exeter, United Kingdom
  • 8 Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France
  • 9 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
  • 10 Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
  • 11 Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
  • 12 Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  • 13 WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 14 Institute of Water Problems, Russian Academy of Sciences, Moscow, Russia
  • 15 Institut für Küstenforschung, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
  • 16 CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory, Australia
  • 17 Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  • 18 Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d—Etudes de la Neige, Grenoble, France
  • 19 Department of Geography, University of Innsbruck, Innsbruck, Austria
  • 20 Institute of Water Problems, Russian Academy of Sciences, Moscow, Russia
  • 21 Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  • 22 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
  • 23 Centre for Hydrology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  • 24 Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 25 A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia
  • 26 Cooperative Institute for Research in Environmental Science, and NOAA/Earth System Research Laboratory, Boulder, Colorado
  • 27 Department of Geography, University of Innsbruck, Innsbruck, Austria
  • 28 Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado
  • 29 Institute of Geography, Russian Academy of Sciences, Moscow, Russia
  • 30 Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, and WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 31 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
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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.

Supplemental material: https://doi.org/10.1175/BAMS-D-19-0329.2

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Cecile B. Menard, cmenard@ed.ac.uk

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.

Supplemental material: https://doi.org/10.1175/BAMS-D-19-0329.2

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Cecile B. Menard, cmenard@ed.ac.uk

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