Improving lake mixing process simulations in the Community Land Model by using <i>K</i> 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 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.

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 transfer and vertical temperature profiles. However, the current vertical mixing scheme in CLM assumes that mixing is driven primarily by wind, and it produces large biases in thermal process simulations. We improved the CLM lake model by using KPP, where vertical mixing was driven by winds and surface thermal forcing, the latter representing the net heat flux in the lake boundary layer. We chose an Arctic Alaskan lake to evaluate this improved lake model. Results demonstrated that KPP could reproduce the observed lake mixing and significantly improved lake temperature simulations when compared to the original mixing scheme in CLM. Our newly improved model better represents the transition between stratification and turnover due to surface thermal forcing combined with high winds. This improved lake model has great potential for reliable physical lake process predictions and better ecosystem services.