Improving lake mixing process simulations in the Community Land Model by using K profile parameterization

We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfe...

Full description

Bibliographic Details
Published in:Hydrology and Earth System Sciences
Main Authors: Q. Zhang, J. Jin, X. Wang, P. Budy, N. Barrett, S. E. Null
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2019
Subjects:
geo
Online Access:https://doi.org/10.5194/hess-23-4969-2019
https://www.hydrol-earth-syst-sci.net/23/4969/2019/hess-23-4969-2019.pdf
https://doaj.org/article/268af9164f6f4a77ba5266def5161124
id fttriple:oai:gotriple.eu:oai:doaj.org/article:268af9164f6f4a77ba5266def5161124
record_format openpolar
spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:268af9164f6f4a77ba5266def5161124 2023-05-15T15:04:38+02:00 Improving lake mixing process simulations in the Community Land Model by using K profile parameterization Q. Zhang J. Jin X. Wang P. Budy N. Barrett S. E. Null 2019-12-01 https://doi.org/10.5194/hess-23-4969-2019 https://www.hydrol-earth-syst-sci.net/23/4969/2019/hess-23-4969-2019.pdf https://doaj.org/article/268af9164f6f4a77ba5266def5161124 en eng Copernicus Publications doi:10.5194/hess-23-4969-2019 1027-5606 1607-7938 https://www.hydrol-earth-syst-sci.net/23/4969/2019/hess-23-4969-2019.pdf https://doaj.org/article/268af9164f6f4a77ba5266def5161124 undefined Hydrology and Earth System Sciences, Vol 23, Pp 4969-4982 (2019) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2019 fttriple https://doi.org/10.5194/hess-23-4969-2019 2023-01-22T17:49:34Z We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development, and in the lake interior water mixing is associated with internal wave activity and shear instability. We chose a lake in Arctic Alaska and a lake on the Tibetan Plateau to evaluate this improved lake model. Results demonstrated that CLM-KPP reproduced the observed lake mixing and significantly improved lake temperature simulations when compared to the original CLM. Our newly improved model better represents the transition between stratification and turnover. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services. Article in Journal/Newspaper Arctic Alaska Unknown Arctic Hydrology and Earth System Sciences 23 12 4969 4982
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic geo
envir
spellingShingle geo
envir
Q. Zhang
J. Jin
X. Wang
P. Budy
N. Barrett
S. E. Null
Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
topic_facet geo
envir
description We improved lake mixing process simulations by applying a vertical mixing scheme, K profile parameterization (KPP), in the Community Land Model (CLM) version 4.5, developed by the National Center for Atmospheric Research. Vertical mixing of the lake water column can significantly affect heat transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM requires an arbitrarily enlarged eddy diffusivity to enhance water mixing. The coupled CLM-KPP considers a boundary layer for eddy development, and in the lake interior water mixing is associated with internal wave activity and shear instability. We chose a lake in Arctic Alaska and a lake on the Tibetan Plateau to evaluate this improved lake model. Results demonstrated that CLM-KPP reproduced the observed lake mixing and significantly improved lake temperature simulations when compared to the original CLM. Our newly improved model better represents the transition between stratification and turnover. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.
format Article in Journal/Newspaper
author Q. Zhang
J. Jin
X. Wang
P. Budy
N. Barrett
S. E. Null
author_facet Q. Zhang
J. Jin
X. Wang
P. Budy
N. Barrett
S. E. Null
author_sort Q. Zhang
title Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
title_short Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
title_full Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
title_fullStr Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
title_full_unstemmed Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
title_sort improving lake mixing process simulations in the community land model by using k profile parameterization
publisher Copernicus Publications
publishDate 2019
url https://doi.org/10.5194/hess-23-4969-2019
https://www.hydrol-earth-syst-sci.net/23/4969/2019/hess-23-4969-2019.pdf
https://doaj.org/article/268af9164f6f4a77ba5266def5161124
geographic Arctic
geographic_facet Arctic
genre Arctic
Alaska
genre_facet Arctic
Alaska
op_source Hydrology and Earth System Sciences, Vol 23, Pp 4969-4982 (2019)
op_relation doi:10.5194/hess-23-4969-2019
1027-5606
1607-7938
https://www.hydrol-earth-syst-sci.net/23/4969/2019/hess-23-4969-2019.pdf
https://doaj.org/article/268af9164f6f4a77ba5266def5161124
op_rights undefined
op_doi https://doi.org/10.5194/hess-23-4969-2019
container_title Hydrology and Earth System Sciences
container_volume 23
container_issue 12
container_start_page 4969
op_container_end_page 4982
_version_ 1766336364064800768