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  • View in gallery
    Fig. 1.

    Elevation maps (m) for the (a) outer domain with 15-km grid spacing and (b) inner domain with 3-km grid spacing.

  • View in gallery
    Fig. 2.

    (left) Bias and (right) root-mean-square difference in (a),(b) 2-m air temperature (°C), (c),(d) precipitation (mm day−1), (e),(f) snowpack snow water equivalent (mm), (g),(h) downward surface shortwave radiation (W m−2), and (i),(j) 2-m specific humidity (g kg−1) across the Great Lakes region, over land, among 20 NU-WRF simulations for February 2015, compared to Daymet in (a) and (b) and NLDAS-2 in (c)–(j). Panels (g) and (h) closely match each other in magnitude, as all of the runs have a positive bias in solar radiation, which explains most of the RMSD.

  • View in gallery
    Fig. 3.

    (left) Temporal and (right) spatial correlations in (a),(b) 2-m air temperature, (c),(d) precipitation, (e),(f) snowpack snow water equivalent, (g),(h) downward surface shortwave radiation, and (i),(j) 2-m specific humidity across the Great Lakes region, over land, among 20 NUWRF simulations for February 2015, compared to Daymet in (a) and (b) and NLDAS-2 in (c)–(j). Correlation coefficients are saved to the hundredth decimal point, explaining why some runs appear to share the same exact correlation values.

  • View in gallery
    Fig. 4.

    (first column) Bias, (second column) temporal correlation, (third column) spatial correlation, and (fourth column) root-mean-square difference during November 2014–March 2015 in (a)–(d) 2-m air temperature (°C), (e)–(h) precipitation (mm day−1), (i)–(l) snowpack snow water equivalent (mm), (m)–(p) surface downward shortwave radiation (W m−2), and (q)–(t) 2-m specific humidity (g kg−1) between observations [Daymet for (a)–(d) and NLDAS-2 for (e)–(t)] and eight NU-WRF simulations over land in the Great Lakes region.

  • View in gallery
    Fig. 5.

    Distribution of daily (a) 2-m air temperature (°C) and (b) precipitation (mm) in space and time across overland portions of the Great Lakes region inner domain, during November 2014–March 2015, according to bin values on the x axis. Data sources include NLDAS-2, Daymet, and eight NU-WRF simulations. Percentage change in the frequency of different bins of (c) air temperature and (d) precipitation values due to nudging, varying LST, 1D lake model implementation, spatially varying bathymetry, and Morrison combination.

  • View in gallery
    Fig. 6.

    Mean 2-m air temperature (°C) in (a) Daymet and the (b) MorrL run for November 2014–March 2015. Mean bias in 2-m air temperature (°C) during the same time period for the (c) Nud, (d) NoNud, (e) NudVary, (f) NoNudVary, (g) Nud1D, (h) Nud1Ddep, (i) MorrNoL, and (j) MorrL runs. Mean effect on 2-m air temperature (°C) during November 2014–March 2015 from (k) nudging, (l) varying LST, (m) 1D lake model implementation, (n) spatially varying bathymetry, and (o) Morrison combination.

  • View in gallery
    Fig. 7.

    Mean precipitation (mm day−1) in (a) NLDAS2 and the (b) MorrL run for November 2014–March 2015. Mean bias in precipitation (mm day−1) during the same time period for the (c) Nud, (d) NoNud, (e) NudVary, (f) NoNudVary, (g) Nud1D, (h) Nud1Ddep, (i) MorrNoL, and (j) MorrL runs. Mean effect on precipitation (mm day−1) during November 2014–March 2015 from (k) nudging, (l) varying LST, (m) 1D lake model implementation, (n) spatially varying bathymetry, and (o) Morrison combination.

  • View in gallery
    Fig. 8.

    Mean liquid-equivalent snowpack (mm) in (a) NLDAS-2 and the (b) MorrL run for November 2014–March 2015. Mean bias in liquid-equivalent snowpack (mm) during the same time period for the (c) Nud, (d) NoNud, (e) NudVary, (f) NoNudVary, (g) Nud1D, (h) Nud1Ddep, (i) MorrNoL, and (j) MorrL runs, Mean effect on liquid-equivalent snowpack (mm) during November 2014–March 2015 from (k) nudging, (l) varying LST, (m) 1D lake model implementation, (n) spatially varying bathymetry, and (o) Morrison combination.

  • View in gallery
    Fig. 9.

    Mean surface downward shortwave radiation (W m−1) in (a) NLDAS2 and the (b) MorrL run for November 2014–March 2015. Mean bias in surface downward shortwave radiation (W m−1) during the same time period for the (c) Nud, (d) NoNud, (e) NudVary, (f) NoNudVary, (g) Nud1D, (h) Nud1Ddep, (i) MorrNoL, and (j) MorrL runs. Mean effect on surface downward shortwave radiation (W m−1) during November 2014–March 2015 from (k) nudging, (l) varying LST, (m) 1D lake model implementation, (n) spatially varying bathymetry, and (o) Morrison combination.

  • View in gallery
    Fig. 10.

    Mean 2-m specific humidity (g kg−1) in (a) NLDAS2 and the (b) MorrL run for November 2014–March 2015. Mean bias in 2-m specific humidity (g kg−1) during the same time period for the (c) Nud, (d) NoNud, (e) NudVary, (f) NoNudVary, (g) Nud1D, (h) Nud1Ddep, (i) MorrNoL, and (j) MorrL runs. Mean effect on 2-m specific humidity (g kg−1) during November 2014–March 2015 from (k) nudging, (l) varying LST, (m) 1D lake model implementation, (n) spatially varying bathymetry, and (o) Morrison combination.

  • View in gallery
    Fig. 11.

    Mean temporal correlation between MorrL-simulated and observed daily values of (a) surface pressure, (b) 10-m meridional wind component, (c) 10-m zonal wind component, (d) 2-m specific humidity, (e) 2-m air temperature, (f) snowpack snow water equivalent, (g) physical snow depth, (h) surface albedo, (i) precipitation, (j) surface downward shortwave radiation, (k) sensible heat flux, and (l) latent heat flux. One correlation is performed per calendar month during November 2014–March 2015, and then the average of the five correlations is plotted. The observational datasets include NLDAS2 for (a)–(d), (f), and (h)–(l), Daymet for (e), and SNODAS for (g). Plots are generally ordered by variable with the strongest to weakest correlations.

  • View in gallery
    Fig. 12.

    Time series of daily lake surface temperature (°C) for Lakes (a) Superior, (b) Huron, (c) Ontario, (d) Michigan, and (e) Erie during November 2014–March 2015 from the Great Lakes Surface Environmental Analysis (GLSEA, black), MorrL (red), Nud1Ddep (blue), and Nud1D (purple).

  • View in gallery
    Fig. 13.

    Time series of daily percent ice cover for Lakes (a) Superior, (b) Huron, (c) Ontario, (d) Michigan, and (e) Erie during November 2014–March 2015 from the GLERL Great Lakes Ice Cover Database (black), MorrL (red), Nud1Ddep (blue), and Nud1D (purple).

  • View in gallery
    Fig. 14.

    Time series of daily (left) 2-m air temperature (°C), (center) downward surface shortwave radiation (W m−2), and (right) 10-m wind speed for (a)–(c) November 2014, (d)–(f) December 2014, (g)–(i) January 2015, (j)–(l) February 2015, and (m)–(o) March 2015 at Stannard Rock based on Great Lakes Evaporation Network (GLEN) observations and eight NU-WRF model simulations.

  • View in gallery
    Fig. 15.

    Time series of daily (left) sensible heat flux (W m−2) and (right) latent heat flux (W m−2) for (a),(b) November 2014, (c),(d) December 2014, (e),(f) January 2015, (g),(h) February 2015, and (i),(j) March 2015 at Stannard Rock based on Great Lakes Evaporation Network (GLEN) observations and eight NU-WRF model simulations.

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Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

Michael NotaroaNelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin

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Yafang ZhongbSpace Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

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Pengfei XuecDepartment of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, Michigan

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Christa Peters-LidarddHydrosphere, Biosphere, and Geophysics Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Carlos CruzeNASA Goddard Space Flight Center, Greenbelt, Maryland

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Eric KempeNASA Goddard Space Flight Center, Greenbelt, Maryland

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David KristovichfIllinois State Water Survey, University of Illinois at Urbana–Champaign, Champaign, Illinois

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Mark KuliegNOAA/National Environmental Satellite, Data, and Information Service, Madison, Wisconsin

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Junming WangfIllinois State Water Survey, University of Illinois at Urbana–Champaign, Champaign, Illinois

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Chenfu HuangcDepartment of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, Michigan

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Stephen J. VavrusaNelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin

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Abstract

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socioeconomic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake–atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014/15 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Significance Statement

Climate change poses a serious threat to the vital natural resources of the Laurentian Great Lakes region. Complex lake–atmosphere interactions and limnological processes are a challenge for regional climate models. To address the threat of climate change, there is a clear need to further evaluate and develop modeling tools for the Great Lakes Basin. Here, we evaluate the regional performance of the National Aeronautics and Space Administration’s regional climate model at high spatial resolution in support of ongoing efforts to develop the next generation modeling tool for the Great Lakes region.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yafang Zhong, yafangzhong@wisc.edu

Abstract

As Earth’s largest collection of freshwater, the Laurentian Great Lakes have enormous ecological and socioeconomic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake–atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014/15 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Significance Statement

Climate change poses a serious threat to the vital natural resources of the Laurentian Great Lakes region. Complex lake–atmosphere interactions and limnological processes are a challenge for regional climate models. To address the threat of climate change, there is a clear need to further evaluate and develop modeling tools for the Great Lakes Basin. Here, we evaluate the regional performance of the National Aeronautics and Space Administration’s regional climate model at high spatial resolution in support of ongoing efforts to develop the next generation modeling tool for the Great Lakes region.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yafang Zhong, yafangzhong@wisc.edu

1. Introduction

The Laurentian Great Lakes are the Earth’s largest collection of freshwater and an invaluable resource to society and wildlife (Botts and Krushelnicki 1988). The Great Lakes megaregion is home to over 55 million people (Todorovich 2009). The lakes critically support the United States’ and Canadian economies through impacts on shipping, drinking water, power production, manufacturing, fishing, and recreation (Vaccaro and Read 2011). The basin contains a rich diversity of fish, animals, and plants (Crossman and Cudmore 1998) and ecologically valuable wetlands.

The Great Lakes exert a prominent effect on regional climate due to their large thermal inertia, variability as a moisture source to the atmosphere, and contrasts in moisture, heat, friction, and radiation compared to adjacent land (Changnon and Jones 1972; Scott and Huff 1997; Chuang and Sousounis 2003; Notaro et al. 2013a). Heat and moisture fluxes destabilize and moisten the boundary layer during autumn–winter (Bates et al. 1993; Blanken et al. 2011). The lakes’ relative warmth and resulting enhanced low-level convergence make the basin a preferred region of wintertime cyclogenesis (Petterssen and Calabrese 1959; Colucci 1976; Eichenlaub 1979). Lake-induced precipitation peaks during September–March when cloud cover and precipitation are enhanced downwind of the lakes (Niziol et al. 1995; Scott and Huff 1996; Kristovich and Laird 1998). Overlake turbulent fluxes and lake-effect precipitation are dampened by mid- to late winter (February–March) as ice cover becomes extensive (Niziol et al. 1995; Brown and Duguay 2010).

The Great Lakes region has experienced dramatic climatic and limnologic changes (Kling et al. 2003; Wuebbles and Hayhoe 2004; Wuebbles et al. 2010; Sharma et al. 2018), including a regime shift in lake-surface temperature (LST) and ice cover (Van Cleave et al. 2014). During 1900–2010, annual air temperatures rose by 0.88°C in the Midwest United States (Kunkel et al. 2013; Schoof 2013; Pryor et al. 2014; Zobel et al. 2017, 2018). Due to mutual surface–atmosphere warming (Manabe and Wetherald 1967) and resulting earlier lake stratification, Lake Superior’s surface water temperatures increased by 2.5°C during July–September of 1979–2006, exceeding the regional atmospheric warming rate (Austin and Colman 2007; Zhong et al. 2016; Ye et al. 2019). The lakes’ ice cover declined by 71% during 1973–2010 due to the aforementioned mutual surface–atmosphere warming (Wang et al. 2012; Mason et al. 2016). Rising lake temperatures, ice cover reductions, and increased frequency of intense cyclones supported a long-term positive trend in lake-effect snowfall (Burnett et al. 2003; Ellis and Johnson 2004; Kunkel et al. 2009), which locally reversed over portions of the Great Lakes Basin in recent decades (Bard and Kristovich 2012; Hartnett et al. 2014; Suriano and Leathers 2017; Clark et al. 2020). Heavy precipitation events have become more frequent (Kunkel et al. 2003, 2012; Easterling et al. 2000; Winkler et al. 2012), with an invigorated hydrologic cycle generating extreme lake level variations (Gronewold et al. 2013).

Given the importance of lake–atmosphere interactions and pronounced climate change in the Great Lakes Basin, there is a need to generate, evaluate, and improve climate modeling for the region. Large lakes and their regional climate influence are poorly resolved in coarse global climate models (Mallard et al. 2014, 2015; Briley et al. 2017). The Great Lakes’ representation across the Coupled Model Intercomparison Project global climate models varies broadly among land, wet soil, ocean, or inland lake grid cells, with the most advanced representation in the Coupled Model Intercomparison Project global climate models based on 1D lake models (none are coupled to 3D lake models) with inappropriate assumptions for deep lakes (Roeckner et al. 2003; Briley et al. 2017). One rudimentary regional climate modeling approach consists of extracting sea surface temperatures from the initial and lateral boundary conditions datasets over the Atlantic Ocean, Pacific Ocean, or Hudson Bay and applying those oceanic sea surface temperature values as LST boundary conditions for the Great Lakes (Mallard et al. 2015; Spero et al. 2016; Sharma et al. 2018). Such erroneous LSTs, retrieved from oceans rather than lakes, can negatively impact simulated pressure and air temperature regionwide (Spero et al. 2016). Alternatively, regional climate models that apply historical, remotely sensed or reanalysis-based LSTs, rather than a coupled lake model, neglect hydrodynamic feedbacks and are impractical tools for developing climate projections (Sharma et al. 2018).

Regional climate models have been employed in an array of Great Lakes studies. Zhong et al. (2012) demonstrated the ability of select regional climate models to capture the lakes’ impacts on regional climate and outperform global climate models. The Regional Climate Model version 4, coupled to a 1D lake model, was applied to examine the lakes’ influence on atmospheric circulation, stability, moisture, and temperature; highlight model skill in capturing variability and trends in air temperature, ice cover, and snowfall; elucidate the mechanisms behind recent lake warming; and formulate winter severity projections (Notaro et al. 2013a,b, 2014, 2015, 2016; Zhong et al. 2016). Applying the “Providing Regional Climates for Impacts Studies” regional climate model, Zhang et al. (2020) projected that wintertime precipitation in the Great Lakes Basin would increase during this century. The Weather Research and Forecasting (WRF; Skamarock et al. 2008) Model is a commonly used regional climate model for the Great Lakes Basin. According to Shi et al. (2010), the nested WRF Model with 1-km grid spacing accurately simulated snowfall and cloud patterns from Canadian snowstorms. Wright et al. (2013) revealed a close association between Great Lakes’ ice cover distribution and resulting snowfall pattern in WRF and concluded that coarse models cannot capture local water–ice–atmosphere interactions that regulate snowband intensity and distribution. Insua-Costa and Miguez-Macho (2018) estimated that, during lake-effect snowstorms in November 2014, 30%–50% of WRF-simulated precipitation downwind of the lakes originated from lake evaporation, similar to those estimated from observed water and ice fluxes (e.g., Kristovich and Braham 1998) Applying nested WRF with 3-km grid spacing, Shi and Xue (2019) determined that resolving LST spatial variations enhances surface wind convergence, vertical motion, and lake-effect snowfall on the lee sides of the Great Lakes. The WRF-based findings of Sharma et al. (2019) included enhanced skill due to spectral nudging (Rockel et al. 2008; Wang and Kotamarthi 2013), better performance during winter than summer, and successfully simulated lake-effect precipitation at both 12- and 4-km grid spacing. Complex lake–atmosphere interactions and lake-effect snowfall morphology require high-resolution modeling (Notaro et al. 2013a,b, 2015; Wright et al. 2013; Briley et al. 2017; Xiao et al. 2018; Shi and Xue 2019). Future climate projections for the Great Lakes Basin were developed by Gula and Peltier (2012) and Peltier et al. (2018) using WRF either uncoupled or coupled to the Freshwater Lake Model (Mironov 2008). Peltier et al. (2018) identified a wintertime cold bias in WRF coupled to the Freshwater Lake Model across the Great Lakes Basin.

More advanced regional climate models typically represent the Great Lakes using 1D lake models, which incorporate coupled lake–atmosphere interactions and can generally capture the broad spatiotemporal patterns of LSTs and ice cover (Gula and Peltier 2012; Notaro et al. 2013b), but are characterized by serious limitations. These shortcomings for large lakes include the lack of dynamic lake circulation, explicit horizontal mixing, or ice motion; an oversimplified stratification process; assumed instantaneous mixing of instabilities; and deficient treatment of eddy diffusivity (Martynov et al. 2010; Stepanenko et al. 2010; Bennington et al. 2014; Mallard et al. 2014, 2015; Gu et al. 2015; Sharma et al. 2018). Such regional climate models, coupled to a 1D lake model, generate excessive ice cover due to the absence of horizontal mixing and ice movement (Bennington et al. 2010; Notaro et al. 2013b; Xiao et al. 2016). One-dimensional lake models commonly produce an anomalously early stratification and positive bias in summertime LST (Bennington et al. 2014). Charusombat et al. (2018) revealed that WRF coupled to a 1D lake model, adapted from the Community Land Model version 4.5 (Subin et al. 2012; Oleson et al. 2013), produces excessive sensible and latent heat fluxes, compared to Great Lakes Evaporation Network measurements, that can be largely resolved by modifying the roughness length scales. One common approach to reduce vertical temperature profile errors in 1D lake models is to artificially enhance the vertical eddy diffusivity of deep lakes to imitate the neglected dynamic circulation and vertical mixing processes (Subin et al. 2012; Bennington et al. 2014; Lofgren 2014; Gu et al. 2015; Mallard et al. 2015). Nonetheless, 1D lake models remain incapable of representing key dynamic and thermodynamic processes of deep lakes (Xiao et al. 2016; Xue et al. 2017). Continued progress is needed to interactively couple high-resolution regional climate models to 3D lake models in order to resolve shear instabilities, mixing episodes, Ekman suction, upwelling, downwelling, coastal currents and jets, seiches, and ice motion (Martynov et al. 2010; Bennington et al. 2010, 2014; Beletsky et al. 2012; Fujisaki et al. 2013), and minimize LST and ice cover biases (Notaro et al. 2013b; Xue et al. 2015, 2017; Sharma et al. 2018; Ye et al. 2019).

The authors developed an advanced Great Lakes Basin modeling tool, consisting of the NASA-Unified Weather Research and Forecasting (NU-WRF; Peters-Lidard et al. 2015) model, nested to 3-km grid spacing, interactively coupled to the Finite Volume Community Ocean Model (Chen et al. 2003) to represent 3D lake hydrodynamics. This tool will benefit subsequent assessments of historical and future climatic and limnological changes, representing variability and change in lake temperature, ice cover, and lake circulation, along with providing a high-resolution, convection-permitting depiction of precipitation extremes. In support of this development process, the current paper explores the cold season performance of the current NU-WRF version across the Great Lakes Basin, including the identification of regionally optimal schemes and the impacts of 1D lake model coupling, spectral nudging, and the choice of cumulus parameterization, microphysics, longwave and shortwave radiation, and planetary boundary layer and surface layer schemes. The authors present data and methods in section 2, results in section 3, and discussion and conclusions in section 4.

2. Data and methodology

a. Model description and experimental design

NU-WRF is a state-of-the-art observation-driven integrated modeling system that represents aerosol, cloud, precipitation, and land processes at satellite-resolved, convection-permitting scales. It was developed based on the National Center for Atmospheric Research Advanced Research WRF Model coupled with chemistry (WRF-Chem; Grell et al. 2005; Skamarock et al. 2008), with enhanced physics coupling and optimal use of NASA’s satellite products. The WRF dynamical core is coupled to the Goddard Space Flight Center Land Information System (Kumar et al. 2006; Peters-Lidard et al. 2007, 2015) and Goddard Chemistry Aerosol Radiation and Transport model (Chin et al. 2000), while incorporating multiple NASA-based microphysics and radiation packages (Wu et al. 2016). NU-WRF simulations here apply the Noah Land Surface Model, which prognostically computes soil moisture and temperature, permits fractional snow cover, and incorporates freeze–thaw soil physics (Mitchell 2001).

The current NU-WRF version permits two crude treatments of large lakes. Either LSTs can be provided by skin surface temperatures from the boundary condition dataset, without including a lake model or two-way lake–atmosphere interactions, or the atmosphere can be two-way coupled to the 1D Lake, Ice, Snow, and Sediment Simulator (Subin et al. 2012) from the Community Land Model version 4.5 (Oleson et al. 2013) with modifications by Gu et al. (2015). This 1D mass and energy balance scheme applies 0–5 snow layers on top of lake ice, 10 water layers (5-cm depth for top layer), and 10 soil layers at the lake’s bottom. This lake model initially generated reasonable LSTs for shallow Lake Erie but vast biases for deep Lake Superior due to an underestimated vertical heat transfer. However, by amplifying the eddy diffusion parameter, Gu et al. (2015) reduced these LST biases in an artificial manner that does not directly address the key 3D processes in deep lakes.

The performance of NU-WRF and optimal model configuration are explored for the Great Lakes region during a select cold season with active lake-effect snowfall. Twenty simulations (Table 1) are generated, including eight primary runs (“Nud”: with spectral nudging and temporally invariant November LSTs; “NoNud”: without nudging and with temporally invariant LSTs that are fixed at the initial warm November state; “NudVary”: with nudging and temporally varying LSTs; “NoNudVary”: without nudging and with temporally varying LSTs; “Nud1D”: with 1D lake model and uniform lake depths; “Nud1Ddep”: with 1D lake model and spatially varying lake depths; “MorrNoL”: without 1D lake model and with Morrison combination; “MorrL”: with 1D lake model and Morrison combination) for November 2014–March 2015 and 12 supplemental runs for only February 2015 (when temperature biases are most pronounced) to limit computational costs. The vertical resolution is assigned to 61 levels. The one-way nested configuration consists of an outer domain with 15-km grid spacing and inner domain with 3-km grid spacing (Fig. 1). Initial and lateral boundary conditions are provided by either the Global Data Assimilation System 0-h analysis or European Centre for Medium-Range Weather Forecasts interim reanalysis. Lake treatment includes LSTs provided as boundary conditions based on Global Data Assimilation System skin surface temperatures; or generated by application of a 1D lake model either with uniform (50 m for all lakes) or spatially varying lake depths, the latter based on the Kourzeneva (2010) dataset. Some simulations include spectral nudging to the large-scale atmospheric fields (wind components, air temperature, and geopotential height above the planetary boundary layer and specific humidity at all levels) to an approximate 600-km wavelength, which is the wavelength specified in numerous prior studies (Ferraro et al. 2017; Iguchi et al. 2017; Lee et al. 2017; Loikith et al. 2018).

Table 1.

Summary of the configuration applied in 20 NU-WRF simulations, with the first eight runs (in italics) covering November 2014–March 2015 and the remaining runs covering only February 2015. Columns include the name of the simulation and options for lateral boundary conditions [Global Data Assimilation System (GDAS) or European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-I)], lake physics (1D lake model turned on or off), spatially varying lake depths, activation of spectral nudging, cumulus parameterization in the outer domain [Kain–Fritsch (KF) or Modified Tiedtke (ModT)], thermal roughness length [default or vegetation-dependent scheme (vegdep)], microphysics [Thompson et al. graupel scheme version 3.1, Morrison 2-moment scheme, or Goddard 3-class ice scheme], longwave radiation scheme [Goddard, stand-along Rapid Radiative Transfer Model (RRTM), or Rapid Radiative Transfer Model for General Circulation Models (RRTMG)], shortwave radiation scheme [Goddard, RRTMG, or Community Atmosphere Model (CAM)], planetary boundary layer scheme [Yonsei University (YSU), Mellor–Yamada–Nakanishi–Niino Level 2.5 (MYNN2.5), or Mellor–Yamada–Janjić (MYJ)], surface layer scheme [old MM5, MYNN surface layer, Monin–Obukhov–Janjić (MOJ), or revised MM5 Monin–Obukhov], and activation of Noah Land Surface Model snow physics changes. Nud and NoNud apply persistent November 2014 lake surface temperatures throughout their entire simulations.

Table 1.
Fig. 1.
Fig. 1.

Elevation maps (m) for the (a) outer domain with 15-km grid spacing and (b) inner domain with 3-km grid spacing.

Citation: Journal of Hydrometeorology 22, 9; 10.1175/JHM-D-21-0025.1

Applied cumulus parameterization optio