Turbulent fluxes of latent and sensible heat are important physical processes that influence the energy and water budgets of the North American Great Lakes. Validation and improvement of bulk flux algorithms to simulate these turbulent heat fluxes are critical for accurate prediction of lake hydrodynamics, water levels, weather, and climate over the region. Here we consider five heat flux algorithms from three parent model systems; 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 Thermodynamics Model, which are used in research and operational environments and concentrate on different aspects of the Great Lakes’ physical system. The heat flux algorithms were isolated from each model and driven by meteorological data from four over-lake stations within the Great Lakes Evaporation Network (GLEN). The simulation results were then compared with eddy covariance flux measurements from the same GLEN sites. All algorithms reasonably reproduced the seasonal cycle of the turbulent heat fluxes while the original 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. Simulations with the four algorithms other than COARE were improved by updating the parameterization of roughness length scales for air temperature and humidity to match those used in COARE. Agreement between modeled and observed fluxes notably varied according to the geographic locations of the GLEN sites.