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Evaluating and Improving Modeled Turbulent Heat Fluxes Across the North American Great Lakes

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Evaluating and Improving Modeled Turbulent Heat Fluxes Across the North American Great Lakes Hydrol. Earth Syst. Sci., 22, 5559–5578, 2018 https://doi.org/10.5194/hess-22-5559-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes Umarporn Charusombat1, Ayumi Fujisaki-Manome2,3, Andrew D. Gronewold1, Brent M. Lofgren1, Eric J. Anderson1, Peter D. Blanken4, Christopher Spence5, John D. Lenters6, Chuliang Xiao2, Lindsay E. Fitzpatrick2, and Gregory Cutrell7 1NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, Michigan 48108, USA 2University of Michigan, Cooperative Institute for Great Lakes Research, Ann Arbor, Michigan 48108, USA 3University of Michigan, Climate & Space Sciences and Engineering Department, Ann Arbor, Michigan 48109, USA 4University of Colorado, Department of Geography, Boulder, Colorado 80309, USA 5Environment and Climate Change Canada, Saskatoon, Saskatchewan, S7N 5C5, Canada 6University of Wisconsin-Madison, Center for Limnology, Boulder Junction, Wisconsin 54512, USA 7LimnoTech, Ann Arbor, Michigan 48108, USA Correspondence: Ayumi Fujisaki-Manome ([email protected]) Received: 12 December 2017 – Discussion started: 10 January 2018 Revised: 7 September 2018 – Accepted: 21 September 2018 – Published: 26 October 2018 Abstract. Turbulent fluxes of latent and sensible heat are surements. The four algorithms other than COARE were al- important physical processes that influence the energy and tered by updating the parameterization of roughness length water budgets of the North American Great Lakes. These scales for air temperature and humidity to match those used fluxes can be measured in situ using eddy covariance tech- in COARE, yielding improved agreement between modeled niques and are regularly included as a component of lake– and observed sensible and latent heat fluxes. atmosphere models. To help ensure accurate projections of lake temperature, circulation, and regional meteorology, we validated the output of five algorithms used in three popu- lar models to calculate surface heat fluxes: the Finite Vol- 1 Introduction ume Community Ocean Model (FVCOM, with three differ- ent options for heat flux algorithm), the Weather Research Simulating physical processes within and across large bod- and Forecasting (WRF) model, and the Large Lake Thermo- ies of freshwater are typically achieved using oceanographic- dynamic Model. These models are used in research and op- scale models representing heat and mass exchange below, erational environments and concentrate on different aspects above, and across the air–water interface. The verification of the Great Lakes’ physical system. We isolated only the and skill assessment of these models are limited, however, by code for the heat flux algorithms from each model and drove the quality and spatial extent of observations and data. The them using meteorological data from four over-lake stations datasets available for validation of ocean dynamical models within the Great Lakes Evaporation Network (GLEN), where include, for example, satellite-based surface water tempera- eddy covariance measurements were also made, enabling co- tures (Reynolds et al., 2007), sea surface height (Lambin et located comparison. All algorithms reasonably reproduced al., 2010), and when available, in situ measurements of sensi- the seasonal cycle of the turbulent heat fluxes, but all of ble and latent heat fluxes (Edson et al., 1998). Dynamical and the algorithms except for the Coupled Ocean–Atmosphere thermodynamic models for large lakes are often verified us- Response Experiment (COARE) algorithm showed notable ing similar measurements (Chu et al., 2011; Croley, 1989a, b; overestimation of the fluxes in fall and winter. Overall, Moukomla and Blanken, 2017; Xiao et al., 2016; Xue et al., COARE had the best agreement with eddy covariance mea- 2017). However, the spatiotemporal resolution of in situ mea- surements for these variables in lakes is comparatively sparse Published by Copernicus Publications on behalf of the European Geosciences Union. 5560 U. Charusombat et al.: Evaluating and improving modeled turbulent heat fluxes across the Great Lakes (Gronewold and Stow, 2014), particularly for latent and sen- To address this gap in the development and testing of phys- sible heat fluxes. ically based lake–atmosphere exchange models for use on On the Laurentian Great Lakes (hereafter referred to as the the Great Lakes, we employ data from a network of rela- Great Lakes), sensible and latent heat fluxes play an impor- tively novel year-round offshore eddy-covariance flux mea- tant role in the seasonal and interannual variability of critical surements collected over the past decade at lighthouse-based physical processes, including spring and fall lake evapora- towers. Specific foci in this study are to determine (1) the tion (Spence et al., 2013), the onset, retreat, and spatial ex- capability of the flux algorithms in reproducing inter-annual, tent of winter ice cover (Clites et al., 2014; Van Cleave et al., seasonal, and daily latent and sensible heat fluxes, (2) how 2014), and air mass modification, including processes such much variability occurs in the simulated latent and sensible as lake-effect snow (Fujisaki-Manome et al., 2017; Wright et heat fluxes from using different flux algorithms with com- al., 2013). These phenomena can, in turn, impact lake water mon forcing data (e.g., meteorology and water surface tem- levels (Gronewold et al., 2013; Lenters, 2001), atmospheric perature), and (3) the source of such variability and simula- and lake circulation patterns (Beletsky et al., 2006), and the tion errors. In particular, we address how different parame- fate and transport of watershed-borne pollutants (Michalak et terizations of roughness length scales affect simulations of al., 2013). For decades, dynamical and thermodynamic mod- turbulent latent and sensible heat fluxes over the water’s sur- els of the Great Lakes simulating these processes have done face in the Great Lakes. so with minimal observations. The Finite Volume Community Ocean Model (FVCOM), for example, is a widely used hydrodynamic ocean model 2 Methods that has been found to provide accurate real-time nowcasts We begin by describing the measured meteorology and tur- and forecasts of hydrodynamic conditions across the Great bulent heat flux data used in this study, followed by the flux Lakes, including currents, water temperature, and water level algorithms within the larger modeling framework, and lastly fluctuations with relatively fine spatiotemporal scales (An- the intercomparison methods used to evaluate the perfor- derson et al., 2015; Anderson and Schwab, 2013; Bai et al., mance of the flux algorithms. We selected the time period of 2013; Xue et al., 2017). FVCOM is currently being devel- January 2012–December 2014; this 3-year period is ideally oped, tested, and deployed across all of the Great Lakes as suited for our study, since it allows for a comparison between part of an ongoing update to the National Oceanic and At- two anomalously warm winters (2012 and 2013) and one un- mospheric Administration’s (NOAA’s) Great Lakes Opera- usually cold winter (2014; Clites et al., 2014). tional Forecasting System (GLOFS). To date, however, there has been no direct verification of the turbulent heat flux algo- 2.1 Data rithms intrinsic to FVCOM; this is an important step, in light of the fact that FVCOM flux algorithms were developed pri- Meteorological and turbulent heat flux data were collected marily for the open ocean and, until now, have been assumed from four offshore, lighthouse-based monitoring platforms to provide reasonable turbulent heat flux simulations across (Fig. 1): Stannard Rock (Lake Superior), White Shoal (Lake broad freshwater surfaces as well. Michigan), Spectacle Reef (Lake Huron), and Long Point The Large Lake Thermodynamic Model (LLTM) is a con- (Lake Erie). These observations were collected as part of a ventional lumped conceptual lake model (Croley, 1989a, b; broader collection of fixed and mobile-based platforms col- Croley et al., 2002; Hunter et al., 2015). It is employed in lectively referred to as the Great Lakes Evaporation Network seasonal operational water supply and water level forecast- (GLEN; Lenters et al., 2013). The National Data Buoy Cen- ing by water resource and hydropower management authori- ter (NDBC) refers to these installations as stations STDM4, ties (Gronewold et al., 2011) and is used as a basis for long- WSLM4, and SRLM4 at Stannard Rock, White Shoal, and term historical monthly average evaporation records (Hunter Spectacle Reef, respectively. et al., 2015). It has historically been calibrated and verified With the exception of Long Point, a footprint analysis in- using observed ice cover and surface water temperatures, but dicates that each station is located a sufficient distance from not using turbulent heat fluxes. Among more complex lake– shore, so there is no influence of the land surface on the tur- atmosphere model systems, the Weather Research and Fore- bulent flux measurements (Blanken et al., 2011). Long Point, casting (WRF) system is increasingly used in applications on however, is located at the tip of a narrow, 40 km peninsula the Great Lakes (Xiao et al., 2016; Xue et al., 2015). How- extending into Lake Erie. As a result, measured fluxes can be ever, a thorough assessment of predictive skill of turbulent influenced by the upwind land surface when the wind direc- heat fluxes over the Great Lakes has not been made with tion is from directions between
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