Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection

Abstract A series of high‐resolution three‐dimensional simulations of the diurnal cycle of deep convection over land are performed using the new Met Office NERC cloud‐resolving model. This study features scattered convection. A memory function is defined to identify the effects of previous convectio...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	C. L. Daleu, R. S. Plant, S. J. Woolnough, A. J. Stirling, N. J. Harvey
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2020
Materias:	diurnal cycle of deep convection convective memory memory function Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/bfbcf326cb0d44049f22265aced26ff8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:bfbcf326cb0d44049f22265aced26ff8
record_format	dspace
spelling	oai:doaj.org-article:bfbcf326cb0d44049f22265aced26ff82021-11-15T14:20:27ZMemory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection1942-246610.1029/2019MS001897https://doaj.org/article/bfbcf326cb0d44049f22265aced26ff82020-08-01T00:00:00Zhttps://doi.org/10.1029/2019MS001897https://doaj.org/toc/1942-2466Abstract A series of high‐resolution three‐dimensional simulations of the diurnal cycle of deep convection over land are performed using the new Met Office NERC cloud‐resolving model. This study features scattered convection. A memory function is defined to identify the effects of previous convection in modifying current convection. It is based on the probability of finding rain at time t0 and at an earlier time t0−Δt compared to the expected probability given no memory. The memory is examined as a function of the lag time Δt. It is strongest at gray‐zone scales of 4–10 km, there is a change of behavior for spatial scales between 10 and 15 km, and it is reduced substantially for spatial scales larger than 25 km. At gray‐zone scales, there is a first phase of the memory function which represents the persistence of convection and it is maintained for about an hour. There is a second phase which represents the suppression of convection in regions which were raining 1 to 3 hr previously, and subsequently a third phase which represents a secondary enhancement of precipitation. The second and third phases of the memory function develop earlier for weaker forcing. When thermodynamic fluctuations resulting from the previous day are allowed to influence the development of convection on the next day, there are fewer rainfall events with relatively large sizes, which are more intense, and thus decay and recover more slowly, in comparison to the simulations where feedback from previous days is removed. Further sensitivity experiments reveal that convective memory attributed to these thermodynamic fluctuations resides in the lower troposphere.C. L. DaleuR. S. PlantS. J. WoolnoughA. J. StirlingN. J. HarveyAmerican Geophysical Union (AGU)articlediurnal cycle of deep convectionconvective memorymemory functionPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 12, Iss 8, Pp n/a-n/a (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	diurnal cycle of deep convection convective memory memory function Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	diurnal cycle of deep convection convective memory memory function Physical geography GB3-5030 Oceanography GC1-1581 C. L. Daleu R. S. Plant S. J. Woolnough A. J. Stirling N. J. Harvey Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection
description	Abstract A series of high‐resolution three‐dimensional simulations of the diurnal cycle of deep convection over land are performed using the new Met Office NERC cloud‐resolving model. This study features scattered convection. A memory function is defined to identify the effects of previous convection in modifying current convection. It is based on the probability of finding rain at time t0 and at an earlier time t0−Δt compared to the expected probability given no memory. The memory is examined as a function of the lag time Δt. It is strongest at gray‐zone scales of 4–10 km, there is a change of behavior for spatial scales between 10 and 15 km, and it is reduced substantially for spatial scales larger than 25 km. At gray‐zone scales, there is a first phase of the memory function which represents the persistence of convection and it is maintained for about an hour. There is a second phase which represents the suppression of convection in regions which were raining 1 to 3 hr previously, and subsequently a third phase which represents a secondary enhancement of precipitation. The second and third phases of the memory function develop earlier for weaker forcing. When thermodynamic fluctuations resulting from the previous day are allowed to influence the development of convection on the next day, there are fewer rainfall events with relatively large sizes, which are more intense, and thus decay and recover more slowly, in comparison to the simulations where feedback from previous days is removed. Further sensitivity experiments reveal that convective memory attributed to these thermodynamic fluctuations resides in the lower troposphere.
format	article
author	C. L. Daleu R. S. Plant S. J. Woolnough A. J. Stirling N. J. Harvey
author_facet	C. L. Daleu R. S. Plant S. J. Woolnough A. J. Stirling N. J. Harvey
author_sort	C. L. Daleu
title	Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection
title_short	Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection
title_full	Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection
title_fullStr	Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection
title_full_unstemmed	Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection
title_sort	memory properties in cloud‐resolving simulations of the diurnal cycle of deep convection
publisher	American Geophysical Union (AGU)
publishDate	2020
url	https://doaj.org/article/bfbcf326cb0d44049f22265aced26ff8
work_keys_str_mv	AT cldaleu memorypropertiesincloudresolvingsimulationsofthediurnalcycleofdeepconvection AT rsplant memorypropertiesincloudresolvingsimulationsofthediurnalcycleofdeepconvection AT sjwoolnough memorypropertiesincloudresolvingsimulationsofthediurnalcycleofdeepconvection AT ajstirling memorypropertiesincloudresolvingsimulationsofthediurnalcycleofdeepconvection AT njharvey memorypropertiesincloudresolvingsimulationsofthediurnalcycleofdeepconvection
_version_	1718428399618228224

Memory Properties in Cloud‐Resolving Simulations of the Diurnal Cycle of Deep Convection

Ejemplares similares