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...

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Published in:Hydrology and Earth System Sciences
Main Authors: Zhang, Qunhui, Jin, Jiming, Wang, Xiaochun, Budy, Phaedra, Barrett, Nick, Null, Sarah E.
Format: Text
Language:English
Published: 2019
Subjects:
Online Access:https://doi.org/10.5194/hess-23-4969-2019
https://www.hydrol-earth-syst-sci.net/23/4969/2019/
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spelling ftcopernicus:oai:publications.copernicus.org:hess77762 2023-05-15T15:04:03+02:00 Improving lake mixing process simulations in the Community Land Model by using K profile parameterization Zhang, Qunhui Jin, Jiming Wang, Xiaochun Budy, Phaedra Barrett, Nick Null, Sarah E. 2019-12-05 application/pdf https://doi.org/10.5194/hess-23-4969-2019 https://www.hydrol-earth-syst-sci.net/23/4969/2019/ eng eng doi:10.5194/hess-23-4969-2019 https://www.hydrol-earth-syst-sci.net/23/4969/2019/ eISSN: 1607-7938 Text 2019 ftcopernicus https://doi.org/10.5194/hess-23-4969-2019 2019-12-24T09:48:07Z 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. Text Arctic Alaska Copernicus Publications: E-Journals Arctic Hydrology and Earth System Sciences 23 12 4969 4982
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
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 Text
author Zhang, Qunhui
Jin, Jiming
Wang, Xiaochun
Budy, Phaedra
Barrett, Nick
Null, Sarah E.
spellingShingle Zhang, Qunhui
Jin, Jiming
Wang, Xiaochun
Budy, Phaedra
Barrett, Nick
Null, Sarah E.
Improving lake mixing process simulations in the Community Land Model by using K profile parameterization
author_facet Zhang, Qunhui
Jin, Jiming
Wang, Xiaochun
Budy, Phaedra
Barrett, Nick
Null, Sarah E.
author_sort Zhang, Qunhui
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
publishDate 2019
url https://doi.org/10.5194/hess-23-4969-2019
https://www.hydrol-earth-syst-sci.net/23/4969/2019/
geographic Arctic
geographic_facet Arctic
genre Arctic
Alaska
genre_facet Arctic
Alaska
op_source eISSN: 1607-7938
op_relation doi:10.5194/hess-23-4969-2019
https://www.hydrol-earth-syst-sci.net/23/4969/2019/
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
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