Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes

Abstract

Abstract. Turbulent fluxes of latent and sensible heat are important physical processes that influence the energy and water budgets of the North American Great Lakes. These fluxes can be measured in situ using eddy covariance techniques and are regularly included as a component of lake–atmosphere models. To help ensure accurate projections of lake temperature, circulation, and regional meteorology, we validated the output of five algorithms used in three popular models to calculate surface heat fluxes: the Finite Volume Community Ocean Model (FVCOM, with three different options for heat flux algorithm), the Weather Research and Forecasting (WRF) model, and the Large Lake Thermodynamic Model. These models are used in research and operational environments and concentrate on different aspects of the Great Lakes' physical system. We isolated only the code for the heat flux algorithms from each model and drove them using meteorological data from four over-lake stations within the Great Lakes Evaporation Network (GLEN), where eddy covariance measurements were also made, enabling co-located comparison. All algorithms reasonably reproduced the seasonal cycle of the turbulent heat fluxes, but all of the algorithms except for the Coupled Ocean–Atmosphere Response Experiment (COARE) algorithm showed notable overestimation of the fluxes in fall and winter. Overall, COARE had the best agreement with eddy covariance measurements. The four algorithms other than COARE were altered by updating the parameterization of roughness length scales for air temperature and humidity to match those used in COARE, yielding improved agreement between modeled and observed sensible and latent heat fluxes.

Intra‐Annual and Interannual Dynamics of Evaporation Over Western Lake Erie

“…Year-round and long-term lake E measurements are fundamental for understanding these processes and to constrain the models of lake hydrologic cycles. However, such data sets rarely are available due to accession and logistic challenges posed by lake environments (Charusombat et al, 2018;McMahon et al, 2013). Most studies have relied on short-term campaigns restricted to the warm season, which may bias estimates of seasonal variation and annual sums of lake E. In deep lakes, turnover of the water column during the fall plays an important role in regulating the seasonal dynamics of E (Jensen, 2010).…”

A dataset of microclimate and radiation and energy fluxes from the Lake Taihu eddy flux network

“…where σ is the Stefan-Boltzmann constant; ε is emissivity; and L ↑ and L ↓ are upward and downward longwave radiation flux, respectively. We use a value of 0.97 for lake surface emissivity in this calculation (Deng et al, 2013;Wang et al, 2014). Figure 5 compares the annual mean air temperature, relative humidity and wind speed at the Taihu eddy flux sites with those at the four WMO weather stations (Wuxi, Liyang, Huzhou and Dongshan) around the lake (Fig.…”

“…Xiao et al (2013) improved the bulk parameterizations of heat, water and momentum fluxes for shallow lakes. Deng et al (2013) and Hu et al (2017) modified the Community Land Model (CLM) lake simulator (Subin et al, 2012) to improve its prediction of the lake evaporation. Wang et al (2017) and X.…”

Evaluating Operational Hydrodynamic Models for Real‐time Simulation of Evaporation From Large Lakes

“…First, in situ eddy-covariance stations needed to be deployed across the Great Lakes to (among other objectives) assess and refine the intrinsic flux algorithms in the models. To date, this step has been achieved; a small network of in situ eddy-covariance stations was deployed across the Great Lakes through an initiative launched by the International Joint Commission Spence et al, 2011) in the mid-to late-2000s, and the measurements have been used to validate the algorithms encoded in the operational models (Charusombat et al, 2018;Deacu et al, 2012;Fujisaki-Manome et al, 2017).…”

“…Verifying modeled evaporation is a challenge for any freshwater body, and it is particularly challenging for the Great Lakes given their vast surface areas, the intrinsic spatiotemporal variability of fluxes across those surfaces (Blanken et al, 2000), and the spatial coverage of the valuable (but relatively sparse) in situ flux monitoring network Spence et al, 2011Spence et al, , 2013. While the recent model evaluation studies utilizing this monitoring network (Charusombat et al, 2018;Fujisaki-Manome et al, 2017) indicate that the flux algorithms in FVCOM-COARE and FVCOM-SOLAR provide reasonable simulations of sensible and latent heat fluxes at discrete monitoring points, we know of no previous study that has explicitly verified lakewide simulations of evaporation for any configuration of FVCOM.…”