Scientific and Human Errors in a Snow Model Intercomparison

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...

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Veröffentlicht in:	Bulletin of the American Meteorological Society Jg. 102; H. 1; S. E61 - E79
Hauptverfasser:	Menard, Cecile B., Essery, Richard, Krinner, Gerhard, Arduini, Gabriele, Bartlett, Paul, Boone, Aaron, Brutel-Vuilmet, Claire, Burke, Eleanor, Cuntz, Matthias, Dai, Yongjiu, Decharme, Bertrand, Dutra, Emanuel, Fang, Xing, Fierz, Charles, Gusev, Yeugeniy, Hagemann, Stefan, Haverd, Vanessa, Kim, Hyungjun, Lafaysse, Matthieu, Marke, Thomas, Nasonova, Olga, Nitta, Tomoko, Niwano, Masashi, Pomeroy, John, Schädler, Gerd, Semenov, Vladimir A., Smirnova, Tatiana, Strasser, Ulrich, Swenson, Sean, Turkov, Dmitry, Wever, Nander, Yuan, Hua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston American Meteorological Society 01.01.2021
Schlagworte:	Albedo Albedo (solar) Climatology Earth Sciences Energy budget Errors Evaluation Human error Hydrology Hypotheses Intercomparison Meteorology Modelling Properties Sciences of the Universe Simulation Snow Temperature United States > US
ISSN:	0003-0007, 1520-0477
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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.
AbstractList	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. Twenty-seven models participated in the Earth System Model - Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modelling. 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 modelling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parametrizations 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 behaviour 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 for15 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
Author	Gusev, Yeugeniy Cuntz, Matthias Wever, Nander Hagemann, Stefan Nasonova, Olga Decharme, Bertrand Arduini, Gabriele Niwano, Masashi Strasser, Ulrich Krinner, Gerhard Schädler, Gerd Bartlett, Paul Haverd, Vanessa Lafaysse, Matthieu Menard, Cecile B. Boone, Aaron Pomeroy, John Essery, Richard Turkov, Dmitry Kim, Hyungjun Marke, Thomas Dai, Yongjiu Fang, Xing Dutra, Emanuel Yuan, Hua Brutel-Vuilmet, Claire Fierz, Charles Burke, Eleanor Nitta, Tomoko Semenov, Vladimir A. Swenson, Sean Smirnova, Tatiana
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Copyright	2020 American Meteorological Society Copyright American Meteorological Society Jan 2021 Distributed under a Creative Commons Attribution 4.0 International License
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Snippet	Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling.... Twenty-seven models participated in the Earth System Model - Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow...
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SubjectTerms	Albedo Albedo (solar) Climatology Earth Sciences Energy budget Errors Evaluation Human error Hydrology Hypotheses Intercomparison Meteorology Modelling Properties Sciences of the Universe Simulation Snow Temperature
Title	Scientific and Human Errors in a Snow Model Intercomparison
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