Assessing Memory in Convection Schemes Using Idealized Tests Article Swipe
Yi-Ling Hwong
,
Maxime Colin
,
Philipp Aglas-Leitner
,
Caroline Muller
,
Steven C. Sherwood
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/essoar.168056812.21996560/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.22541/essoar.168056812.21996560/v1
Two assumptions commonly applied in convection schemes—the diagnostic and quasi-equilibrium assumptions—imply that convective activity (e.g., convective precipitation) is controlled only by the large-scale (macrostate) environment at the time. In contrast, numerical experiments indicate a “memory” or dependence of convection also on its own previous activity whereby subgrid-scale (microstate) structures boost but are also boosted by convection. In this study we investigated this memory by comparing single-column model behavior in two idealized tests previously executed by a cloud-resolving model (CRM). Conventional convection schemes that employ the diagnostic assumption fail to reproduce the CRM behavior. The memory-capable org and LMDZ cold pool schemes partially capture the behavior, but fail to fully exhibit the strong reinforcing feedbacks implied by the CRM. Analysis of this failure suggests that it is because the CRM supports a linear (or superlinear) dependence of the subgrid structure growth rate on the precipitation rate, while the org scheme assumes a sublinear dependence. Among varying versions of the org scheme, the growth rate of the org variable representing subgrid structure is strongly associated with memory strength. These results demonstrate the importance of parameterizing convective memory, and the ability of idealized tests to reveal shortcomings of convection schemes and constrain model structural assumptions.
Related Topics
Concepts
Convection
Sublinear function
Statistical physics
Convection cell
Mechanics
Physics
Mathematics
Combined forced and natural convection
Natural convection
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.22541/essoar.168056812.21996560/v1
- https://www.authorea.com/doi/pdf/10.22541/essoar.168056812.21996560
- OA Status
- gold
- Cited By
- 1
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362512500
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4362512500Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22541/essoar.168056812.21996560/v1Digital Object Identifier
- Title
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Assessing Memory in Convection Schemes Using Idealized TestsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-04Full publication date if available
- Authors
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Yi-Ling Hwong, Maxime Colin, Philipp Aglas-Leitner, Caroline Muller, Steven C. SherwoodList of authors in order
- Landing page
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https://doi.org/10.22541/essoar.168056812.21996560/v1Publisher landing page
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https://www.authorea.com/doi/pdf/10.22541/essoar.168056812.21996560Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.authorea.com/doi/pdf/10.22541/essoar.168056812.21996560Direct OA link when available
- Concepts
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Convection, Sublinear function, Statistical physics, Convection cell, Mechanics, Physics, Mathematics, Combined forced and natural convection, Natural convection, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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