The Estimation of the North American Great Lakes Turbulent Fluxes Using Satellite Remote Sensing and MERRA Reanalysis Data

Abstract

Abstract:This study provides the first technique to investigate the turbulent fluxes over the Great Lakes from July 2001 to December 2014 using a combination of data from satellite remote sensing, reanalysis data sets, and direct measurements. Turbulent fluxes including latent heat flux (Q E ) and sensible heat flux (Q H ) were estimated using the bulk aerodynamic approach, then compared with the direct eddy covariance measurements from the rooftop of three lighthouses-Stannard Rock Lighthouse (SR) in Lake Superior, White Shoal Lighthouse (WS) in Lake Michigan, and Spectacle Reef Lighthouse (SP) in Lake Huron. The relationship between modeled and measured Q E and Q H were in a good statistical agreement, for Q E , R 2 varied from 0.41 (WS), 0.74 (SR), and 0.87 (SP) with RMSE of 5.68, 6.93, and 4.67 W·m −2 , respectively, while Q H , R 2 ranged from 0.002 (WS), 0.8030 (SP) and 0.94 (SR) with RMSE of 6.97, 4.39 and 4.90 W·m −2 respectively. Both monthly mean Q E and Q H were highest in January for all lakes except Lake Ontario, which was highest in early December. The turbulent fluxes then sharply drop in March and are negligible during June and July. The evaporation processes continue again in August.

Geographic and temporal variations in turbulent heat loss from lakes: A global analysis across 45 lakes

“…The reduced seasonality of the lake heat content (the difference between minimum and maximum heat content) toward the equator demonstrates that heating and cooling are more separated by season at higher latitudes, resulting in a greater amplitude of the heat budget. In deep and large temperate lakes, such as Tahoe and Taupo, the turbulent energy fluxes are greatest during autumn and winter, as a result of the large heat capacity that causes their surface waters to cool more slowly during winter than the ambient surface air, as has been reported in other studies focusing on large, deep North American lakes (Blanken et al ; Moukomla and Blanken ). These results indicate that the season in which the turbulent surface energy fluxes from lakes interact most strongly with the overlying atmosphere (and also affect internal lake mixing processes) can vary considerably among lakes.…”

“…These turbulent fluxes have been investigated in lakes around the world for many years (Dutton and Bryson 1962;Lofgren and Zhu 2000;MacIntyre et al 2002;Momii and Ito 2008), but our study is the first, to our knowledge, to investigate and compare these fluxes across a range of climatic zones and lake attributes. In addition, many earlier studies that have calculated surface heat fluxes from lakes have used remotely sensed water temperature in combination with land-based meteorological measurements (Derecki 1981;Croley 1989;Lofgren and Zhu 2000) or reanalysis data (Moukomla and Blanken 2017), which can lead to erroneous estimates of air-water interactions. Studies that have calculated heat fluxes using in situ temperature and meteorology data have dealt primarily with single lakes (Laird and Kristovich 2002;MacIntyre et al 2002;Lenters et al 2005;Verburg and Antenucci 2010;Lorenzzetti et al 2015;Dias and Vissotto 2017), or a number of lakes from a confined region (Woolway et al 2015b).…”

Improving surface heat flux estimation for a large lake through model optimization and two‐point calibration: The case of Lake Geneva

“…For Q an , several formulas (e.g., Brutsaert ; Satterlund ; Crawford and Duchon ) with a relatively wide range of values for the corresponding coefficients ( see , e.g., Supporting Information Table S5,) were reported. Studies over large lakes already indicated a significant spatial variability of turbulent heat fluxes, Q ev and Q co , in particular the latent heat flux, compared to the radiative terms (e.g., Lofgren and Zhu ; Verburg and Antenucci ; Moukomla and Blanken ). There is a similarity between the formulas for the Q ev and Q co estimation that is attributed to the physical analogy between processes controlling humidity and air temperature.…”

“…Such quasi one‐dimensional (1D) estimates are then considered representative for the whole lake. Although the single‐location approach might be suitable for small water bodies, spatial variability of SurHF due to variable meteorological conditions can be important for large lakes (e.g., Lofgren and Zhu ; Xue et al ; Moukomla and Blanken ). Data from multiple locations permit the investigation of SurHF spatial variability, and the availability of such data is growing (e.g., Rimmer et al ; Verburg and Antenucci ; Spence et al ).…”

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

“…The datasets available for 5 validation of ocean dynamical models, for example, include satellite-based surface water temperatures (Reynolds et al, 2007), sea surface height (Lambin et al, 2010), and, when available, in situ measurements of sensible and latent heat fluxes (Edson et al, 1998). Dynamical and thermodynamic models for large lakes are often verified using similar measurements (Chu et al, 2011;Croley, 1989aCroley, , 1989bMoukomla and Blanken, 2017;Xiao et al, 2016;Xue et al, 2016). 10 However the spatiotemporal resolution of in situ measurements for these variables in lakes is comparatively sparse (Gronewold and Stow, 2014), particularly for latent and sensible heat fluxes.…”