Exploring the decision-making process in model development: focus on the Arctic snowpack

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

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Publication Details

Journal
˜The œcryosphere
Published
2024-10-09
DOI
https://doi.org/10.5194/tc-18-4671-2024
Citations
1
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
0.38

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article

Exploring the decision-making process in model development: focus on the Arctic snowpack

Melody J. Sandells, Richard Essery, Heidrun Matthes, Martin Schneebeli et al.
1 citations
˜The œcryosphere
Cryospheric studies and observations
0.38
article

Exploring the decision-making process in model development: focus on the Arctic snowpack

Melody J. Sandells, Richard Essery, Heidrun Matthes, Martin Schneebeli, Marie Dumont, Glen E. Liston, Gianpaolo Balsamo, Gerhard Krinner, Nick Rutter, Florent Dominé, Sirpa Rasmus, Dorothée Ehrich, Cécile B. Ménard, Bruce C. Forbes, David M. Lawrence, Annett Bartsch, Sari Stark, Ioanna Merkouriadi, Chris Derksen
article en
1 citations

Abstract

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

˜The œcryosphereVol. 18(10)
NSF National Center for Atmospheric Research (US), Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE), Finnish Meteorological Institute (FI), Centre National de la Recherche Scientifique (FR), University of Helsinki (FI), Environment and Climate Change Canada (CA), European Centre for Medium-Range Weather Forecasts (GB), Université Fédérale de Toulouse Midi-Pyrénées (FR), Makivik Corporation (CA), Northumbria University (GB), Météo-France (FR), Institut des Géosciences de l'Environnement (FR), Landesklinikum Korneuburg (AT), Université Laval (CA), Université Grenoble Alpes (FR), Colorado State University (US), University of Lapland (FI), University of Edinburgh (GB)
European Commission, Horizon 2020
Openalex Percentile: Top 40%
Cryospheric studies and observations
0.38
DOIOpen Access PDFOpenAlex
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