Great Lakes Observing & Forecasting Systems
Evaluation of the evaporation and heat flux algorithms for the Great Lakes based on the eddy covariance measurements
Overview and Objectives
This project will enhance the forecast capability for the Great Lakes by evaluating and improving the current technique to estimate sensible and latent heat fluxes based on the comparison with direct flux measurements. These fluxes have never been inter-compared mainly because there has been no direct flux measurement to be referenced. Another important aspect is that the year-round station data allow us to evaluate winter evaporation and heat fluxes; the former is great unknown in terms of impacts on water levels and the latter is critical to predicting ice production.
GLERL has participated in the efforts of the Great Lakes evaporation network, where the direct flux measurements are continuously conducted at offshore stations using the eddy covariance technique. The efforts have been made by U.S. and Canadian investigators to understand the cause of water level change (Lenters et al. 2013). Since the first year-round station at Stannard Rock Light that started in June of 2008 (Blanken et al. 2011), additional sites have followed across the Great Lakes including Granite Island (Van Cleave et al. 2014), White Shoal Light, Spectacle Reef, and Long Point.
Given the record of these direct flux measurements to present, an important outcome of this evaporation network is the input to the assessment of the existing bulk flux algorithms that are used in Great Lake forecast models.
NOAA-GLERL Great Lakes Evaporation Model, which estimates lake-wide average evaporation and provides the inputs to GLERL’s hydrologic forecast, uses a lumped-parameter surface flux and heat-storage model. It uses air temperature, wind speed, humidity, precipitation and cloud cover averaged over area (Croley 1989). This model is applied on a one-dimensional basis in a vertical direction and represents the lake-wide average evaporation. In the current Great Lakes Coastal Forecasting System (GLCFS), where the three-dimensional hydrodynamic variables are simulated, surface sensible and latent heat fluxes are calculated using bulk formulae with stability-dependent heat transfer coefficients in the Princeton Ocean Model (POM) framework. In the next generation GLCFS, whose physical core is based on the unstructured-grid Finite Volume Community Ocean Model (FVCOM), three different options are available: 1) TOGA COARE-Met flux (COARE, hereafter) algorithm version 2.6, 2) an iterative method developed for the Great Lakes, and 3) an iterative method included in the ice module by Kauffman and Large (2002). Atmospheric models that are often used to provide external forcing to GLCFS have different algorithms for surface flux calculations (e.g. MM5, Eta).
Blanken, P.D., C. Spence, N. Hedstrom, and J. Lenters (2011), Evaporation from Lake Superior: 1. Physical controls and processes. Journal of Great Lakes Research, 37(4), pp. 707-716.
Croley, T.E.II. (1989), Lumped modeling of Laurentian Great Lakes evaporation, heat storage, and energy fluxes for forecasting and simulation. NOAA Technical Memorandum ERL GLERL-70, Great Lakes Environmental Research Laboratory, Ann Arbor, MI (PB89-185540/XAB) 48 pp. (1989).
Kauffman, B.G. and W.G. Large (2002), The CCSM coupler, version 5.0.1. Technical note, National Center for Atmospheric Research, August 2002. http://www.ccsm.ucar.edu/models/.
Lenters, J.D., J.B. Anderson, P.D. Blanken, C. Spence, and A.E. Suyker (2013), Assesing the Impacts of Climate Variability and Change on Great Lakes Evaporation, In: 2011 Project Reports. D Brown, D. Bidwell, and L. Briley, eds. Available http://glisaclimate.org/media/CLISA_Lake_Evaporation.pdf
Van Cleave, K., J.D. Lenters, J. Wang, and E.M. Verhamme (2014), A regime shift in Lake Superior ice cover, evaporation, and water temperature following the warm El Nino winter of 1997-1998, Limnol Oceanogr, 59(6), pp. 1889-1898.