A Coupled Air Sea Mesoscale Model: Experiments in Atmospheric Sensitivity to Marine Roughness

208 MONTHLY WEATHER REVIEW A Coupled Air Sea Mesoscale Model: Experiments in Atmospheric Sensitivity to Marine Roughness JORDAN G. POWERS Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, Colorado MARK T. STOELINGA Department of Atmospheric Sciences, University of Washington, Seattle, Washington (Manuscript received 23 April 1998, in final form 6 January 1999) ABSTRACT A coupled air sea numerical model comprising a mesoscale atmospheric model, a marine circulation model, and a surface wave model is presented. The coupled model is tested through simulations of an event of frontal passage through the Lake Erie region. Experiments investigate the effects of different sea surface roughness parameterizations on the atmospheric simulations. The coupled system s components are the fifth-generation Pennsylvania State University National Center for Atmospheric Research Mesoscale Model (MM5), the Princeton Numerical Ocean Model (POM), and the GLERL Donelan Wave Model (GDM). The finest of the MM5 s three nested grids covers Lake Erie, on which the POM and GDM operate. The MM5 provides surface heat and momentum fluxes to the POM, and the POM returns lake surface temperatures to the MM5. The MM5 provides 10-m winds to the GDM, and the GDM returns sea state information to the MM5. The MM5 uses this sea state information in calculating overwater roughness lengths (z 0 s). Experiments varying the MM5 s roughness parameterization over Lake Erie are performed, resulting in a broad range of z 0 s. It is found that wave model coupling can significantly increase overwater roughnesses in the MM5, leading to increased surface heat and moisture fluxes and to changes in PBL characteristics. The impacts on the atmosphere from marine model coupling can appear far downstream of the coupled zones. The accuracy of the mesoscale atmospheric simulation appears sensitive to the assumptions behind the marine roughness parameterizations used. The results suggest that, for consistent forecast improvement, marine roughness parameterizations should account for wave age. In addition, it is found that accounting for wave movement in an air sea coupling scheme can be a significant factor in the calculation of surface stresses and, with them, surface heat fluxes over marine areas. Thus, the approach with which a coupling scheme implements sea-statedependent roughness parameterizations can be as influential as the parameterizations themselves. 1. Introduction Until recently, the bulk of work in coupled air sea numerical modeling has addressed large-scale conditions and interactions. Atmosphere ocean model coupling over the past decade has been most prevalent in general circulation models, which target climatic variability and seasonal cycles (see, e.g., Mechoso et al. 1995; Huang and Schneider 1995; Schneider et al. 1997; Frey et al. 1997), and specialized models directed toward large-scale phenomena such as ENSO (Zebiak and Cane 1987; Battisti 1988; Chen et al. 1997). In contrast, the coupling of atmospheric mesoscale models with ma- Corresponding author address: Dr. Jordan G. Powers, Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO 80307. E-mail: powers@ncar.ucar.edu rine models to create systems excelling at high-resolution simulations on short ( 1 week) timescales and limited spatial scales has received much less attention. These types of coupled systems, however, offer the promise of reproducing with greater detail the air sea interactions governing mesoscale weather and marine conditions. A goal in coupling atmospheric and marine mesoscale models is to capture best the mutual forcing and thus to simulate best the response of the component systems (i.e., air and sea). The aim is therefore to improve the accuracy of simulations on the mesoscale, whether historical or real time. The potential targets of mesoscale air sea models are manifold and include (i) hurricane intensification, tracking, and environment modification (see, e.g., Ginis et al. 1997; Marks et al. 1996); (ii) oceanic cyclogenesis (Doyle 1995); (iii) lake effect phenomena, (iv) coastal surges and fogs, (v) storm wave and surge prediction, and (vi) oceanic squirts (see, 2000 American Meteorological Society

JANUARY 2000 POWERS AND STOELINGA 209 e.g., Ramp et al. 1991; Barton et al. 1993). This paper introduces a mesoscale air sea modeling system developed for high-resolution, coupled atmospheric and marine simulations. One existing coupled mesoscale model is the U.S. Navy s Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS; Hodur 1997). It is composed of a nonhydrostatic atmospheric model and a hydrostatic ocean model and, in some applications, a surface wave model (Doyle 1995). In COAMPS s coupling scheme the atmospheric model provides heat, moisture, and momentum fluxes to the ocean model; the ocean model provides sea surface temperatures to the atmospheric model (Hodur 1997). Results from COAMPS in applications of real-time forecasting of marine wind conditions and of simulations of idealized hurricanes have been positive, demonstrating benefits to the atmospheric simulations from the marine models (see Hodur 1997). In coupling COAMPS to a marine wave model [the Wave Model (WAM); Hasselmann et al. 1988], Doyle (1995) has shown that accounting for sea state in an atmospheric mesoscale model can have significant impacts on surface roughnesses. Doyle s (1995) coupled simulations have indicated that patterns of surface roughness from simulated wave fields can modify frontal structures, cyclone central pressure, and cyclolysis rates. As the results below will show, marine roughness lengths can be a linchpin in coupled air sea mesoscale models, strongly influencing the strength of communication across the atmosphere ocean interface. The work herein addresses the construction and testing of a coupled air sea mesoscale modeling system. The system comprises a mesoscale atmospheric model, an ocean circulation model, and a surface wave model: (i) the fifth-generation Pennsylvania State University National Center for Atmospheric Research Mesoscale Model 5 (MM5), (ii) the Princeton Ocean Model (Blumberg and Mellor 1987; Mellor 1996), and (iii) the Great Lakes Environmental Research Laboratory (GLERL) Donelan Wave Model (Donelan 1977; Schwab et al. 1984) The meteorological motivations for building the system have been to (i) develop a mesoscale model that better accounts for air sea interaction and (ii) improve mesoscale simulations of atmospheric conditions sensitive to surface wave activity, sea surface temperatures, and marine circulations. The coupled model has also served as a vehicle for the exploration of distributed supercomputing via high data rate networks (Powers et al. 1997). The purposes of this paper are to present the coupled mesoscale model and to examine its performance and sensitivities. The latter is pursued through experiments investigating PBL conditions as sea surface roughness parameterizations are varied. The vehicle for model testing is a case of the passage of a strong cold front through the Lake Erie region, where Lake Erie is the basin in which the marine models operate. The case presented a situation with strong lake surface fluxes and a potentially important role of high-amplitude wave activity. Both the nature and extent of the effects of marine model coupling are examined, and the focus is on the impacts of coupling on surface fluxes and PBL temperature and moisture forecasts. Note that the MM5 results are stressed here, not the ocean and wave model output. Section 2 describes the models used, while section 3 explains the coupling design. The test case is summarized, and the experiments are described, in section 4. Section 5 presents the atmospheric modeling results. Section 6 concludes with a summary of the work and its findings. 2. Model components a. MM5 The coupled system centers around the MM5, a nonhydrostatic, primitive equation mesoscale forecast model. This section summarizes the MM5 s configuration for the experiments performed. For full model details the reader may consult Dudhia (1993), Grell et al. (1995), and Haagenson et al. (1994). The MM5 here has three domains (denoted D1, D2, D3) covering the midwestern and eastern United States and southeastern Canada. Figures 1a and 1b show D1 D3. The horizontal grid sizes of D1, D2, and D3 are 18 km, 6 km, and 2 km, respectively. It is over Lake Erie, and within D3, that the marine models operate and the model coupling occurs. In the vertical, the model has 27 levels (see Grell et al. 1995) from the ground to 100 mb. The simulations employ relaxation lateral boundary conditions (Grell et al. 1995). The first guess fields come from National Centers for Environmental Prediction gridded analyses, which are objectively reanalyzed with upper-air and surface observations (Manning and Haagenson 1992). Moist processes are simulated through an explicit scheme and a cumulus parameterization. Both of these packages are active on the 18- and 6-km grids, but only the explicit scheme is active on the 2-km grid. The explicit scheme is described in Grell et al. (1995) and includes the mixed-phase ice microphysics of Reisner et al. (1993). The explicit scheme has separate prognostic equations for water vapor, cloud water, cloud ice, rainwater, and snow. The cumulus parameterization is that of Grell (1993). The PBL scheme used is the Blackadar high-resolution model (Blackadar 1979; Zhang and Anthes 1982). This scheme is employed because of its history of use and understood performance in the MM5. It does, however, necessarily involve simplifications to boundary layer surface interactions. b. POM The Princeton Numerical Ocean Model (POM) is the second component of the modeling system and is ap-

210 MONTHLY WEATHER REVIEW FIG. 1. MM5 domains used in coupled model experiments. Grid sizes: domain 1 (D1), 18 km; domain 2 (D2), 6 km; domain 3 (D3), 2 km. The lake and wave model grids (not shown) cover Lake Erie and lie within D3. Stations and locations mentioned in text indicated in (b): (a) D1, D2. (b) D2, D3. Stations referenced in text shown. The S marks location of sounding shown in Fig. 9. plied here to Lake Erie. It will at times be referred to as the lake model. For full model details the reader may consult Blumberg and Mellor (1987), O Connor (1991), and Mellor (1996) (see also Kelley et al. 1998). The POM is a three-dimensional, hydrostatic, primitive equation model designed to simulate marine circulations. Its horizontal grid size is 2 km, and its grid points are coincident with a subset of the points of the MM5 s 2-km grid (D3; Fig. 1a). The model uses a bathymetry-following coordinate (see, e.g., Mellor 1996), and 11 levels from the lake bottom to the surface are used. The model accounts for complex coastline configurations as well as bathymetry. The model s predicted variables are velocity, temperature, salinity, turbulence kinetic energy, turbulence length scale, and free surface elevation. At the lateral boundaries in the closed basin of Lake Erie normal and tangential velocities are set to zero. The model represents both a baroclinic wave mode and a free upper surface with a barotropic wave mode. Predictions of velocity, temperature, and turbulence are associated with the baroclinic mode, while predictions of the surface elevation and the vertically averaged velocity are associated with the barotropic mode (see Kelley et al. 1993; Mellor 1996). The lake model is initialized by running it alone for a two-week period leading up to the starting time of the coupled run. During this period, the model is forced by surface momentum and heat fluxes calculated from observed conditions interpolated to the grid and input every hour (Kelley 1995). To begin the spinup period, the model s surface temperatures are initialized with gridinterpolated marine observations from land stations, buoys, and ships. An idealized thermal profile yields the subsurface temperatures. Velocities are initialized to zero. The lake model has been used for daily real-time marine forecasting for the Great Lakes as part of the Great Lakes Forecasting System (GLFS). GLFS is run by the Great Lakes Forecasting System Laboratory at The Ohio State University, located in Columbus, Ohio. c. GDM The GLERL Donelan Wave Model (GDM) is the third component in the modeling system and simulates the surface wave activity on Lake Erie. It will at times be referred to as the wave model. For full model details the reader may consult Donelan (1977) and Schwab et al. (1984). The GDM is a two-dimensional, parametric dynamical model that predicts water surface elevation standard deviation (i.e., rms surface displacement), wave period, wave direction, and wave phase speed. It is based on a local momentum balance equation described by M v M w, (1) t where M is the total momentum, ( / x, / y), v is the group velocity, and w is the portion of the form stress that changes the momentum in the gravity wave spectrum (Liu et al. 1984; Donelan 1977). The spectral form is taken from Hasselmann et al. (1973), accounts for wave age in spectral shape, and is given in Donelan (1977). The GDM assumes an equipartition of kinetic and potential wave energies and a linear deep water dispersion relation (Schwab et al. 1984). The lack of shallow water effects in the model can at times affect simulated wave height and, hence, the coupled model response (discussed in section 5). The wave model operates on the same 2-km surface grid as the lake model and, thus, also lies within MM5 D3. At the beginning of the coupled model run, the wave model has an active wave field, produced by its running for a prior 48-h period. During this spinup period, the model is forced by grid-interpolated observed winds,

JANUARY 2000 POWERS AND STOELINGA 211 FIG. 2. Coupled modeling system schematic. Transmissions of variables are depicted with arrows, and variables are described in text. with the winds being input hourly. The wave model has been used for daily real-time wave forecasting for the Great Lakes as part of the GLFS. 3. Coupled model structure The model coupling takes the form of the exchange of atmospheric surface layer and lake surface parameters at overlapping time steps. The coupling occurs between the 2-km MM5 domain (D3) and the lake and wave model grids, and the mode of linkage is most aptly described as synchronous or tightly coupled (Rosmond 1992). Figure 2 diagrams the coupled modeling system. The arrows show the variables exchanged, with the information from the marine models being sent into the MM5 s PBL scheme. The variables abbreviated in Fig. 2 are Q, surface heat flux; M u and M, surface momentum fluxes (u and components); SST, sea surface temperature (here, Lake Erie surface temperature); U 10, 10- m winds; c p, wave phase velocity; and, standard deviation of water surface elevation. a. MM5 POM The MM5 s input to the lake model consists of momentum fluxes and heat fluxes (sensible, latent, radiative). The 2-km MM5 domain (D3) sends its flux information every 50 s. The lake model requires from the MM5 that portion of the total momentum flux across the air sea interface that goes into driving the lake current. This is given by M, (2) w where w is the density of water at the surface and is the part of the total surface wind stress that goes into lake current. Because the standard MM5 does not partition the stress in this way, the division must be introduced. Over water, the surface stress can be separated into two components and written s w, (3) in a formalism described by Longuet-Higgins (1977) and Taylor and Gent (1978). Here s is the skin drag or skin friction component of, and w is the form drag component: s is attributed to viscous shear stress on the water surface and to drag on capillary and short gravity waves; w is attributed to the dominant gravity waves in a wide band around the peak of the spectrum (see Taylor and Gent 1978). While all of the skin drag ( s ) goes into the current, the form drag ( w ) has components going into both lake current ( wc ) and wave momentum ( wm ). In the wave model, the fraction of wave-induced stress that goes into wave momentum is m [the value used in the GDM version obtained from the GLFS lab 0.028; see, e.g., Donelan (1977)]. Thus is s wc s (1 m ) w. (4) The task remaining is to determine the separate surface stresses, s and w. In general, surface stress is given by u * u *, (5) where is the surface air density and u * is the friction velocity. In the MM5, the friction velocity is computed by kv u*, (6) (1 m) where z a z 0 ln. (7) Here k is von Kármán s constant (0.4), V is the model surface wind velocity (i.e., the wind at the model s lowest vertical level), z a is the height of the lowest model level ( 36 m above ground level), z 0 is the roughness length, and m is a stability-dependent factor that increases u * in lower-stability regimes. The approach in deriving the partitioned stresses is to evaluate Eqs. (5), (6), and (7) for the two components, using the known parameters associated with each type of stress: u* u*, (8) s,w s,w s,w kv s,w u* s,w, (9) (1 m) s,w z a z 0s,w ln. (10) s,w

212 MONTHLY WEATHER REVIEW Here the subscript pair s, w shows that these equations can return the stress associated with either the skin drag (subscript s ) or the wave drag (subscript w ). The known parameters mentioned above are z 0s, z 0w, V s, and V w, but some explanation of these is required. First are the roughness length components, z 0s and z 0w. Here, z 0s is the roughness length associated with skin friction. From Taylor and Gent (1978) it is taken as 0.005 cm. In addition, z 0w is the roughness length associated with form drag. Two parameterizations of z 0w are used in the coupled model experiments. In the first, z 0w is taken as z 0w 0.033, (11) where is the standard deviation of the water surface elevation, or rms surface displacement. This parameterization is based on observations that have indicated that an upper value of z 0 over water is approximately 1/30 (Donelan 1982, 1990). The parameterization is thus used to produce a simulation in which marine roughness is maximized. The second parameterization of z 0w follows an empirical formula presented by Donelan et al. (1993). From data taken over the North Sea (Smith et al. 1992) and Lake Ontario (Donelan 1990), Donelan et al. (1993) found roughness due to waves best approximated by 2.6 U 4 10 c p z (6.7 10 ). (12) 0w Here, U 10 is the 10-m wind speed, and c p is the phase speed of the waves at the peak of the spectrum. In Eqs. (11) and (12), the wave model provides and c p. Note that the standard MM5 approach to overwater roughness is a modified Charnock relation (Charnock 1955), with z 0 given by 2 0.032u z0 * 0.0001. (13) g Here, 0.0001 m is a base value resulting in a minimum roughness length of 0.01 cm over water. Consider now the model wind partitioning appearing in Eq. (9). Separate values for surface wind (V s, V w ) are considered for the stresses induced by skin and wave drag because where roughness elements (e.g., surface gravity waves) are moving with respect to a fixed reference frame, the correct interpretation of V [Eq. (6)] is that it is the wind relative to the motion of the roughness elements. Therefore, in the stress partitioning described by Eqs. (8) and (9), the velocities V s and V w should be relative to their respective roughness elements. The roughness elements associated with skin friction are slow enough so that attributing to them a velocity of zero is a good approximation. Therefore, V s is the model surface wind velocity, that is, the velocity relative to a surface with static roughness elements. In contrast, V w is the wave-relative wind. With z 0s, z 0w, V s, and V w in hand, it is straightforward to evaluate the stress partitions and, therefore, to determine. A few hurdles, however, arise if the equation set (8) (10) is to be used in the stress calculation in the MM5 s Blackadar PBL parameterization. 1) The determination of the PBL stability regime depends on a bulk Richardson number, which in turn depends on V. If two variants of V are used for the two stress partitions, an ambiguity would arise as to which regime the PBL is in. For this reason, a single, effective value of V is desirable, one that can define the PBL regime unambiguously. This new quantity shall be referred to as the effective wind and be denoted V e. 2) Similarly, surface heat and moisture fluxes depend on z 0 and u *, but partitioning heat and moisture fluxes according to skin friction and form drag further complicates the problem and is not intuitively apparent. Thus, single, effective values of z 0 and u * are desired for calculating surface heat and moisture fluxes. 3) The factor m is independent of z 0 and V in the three stable ( stable, mechanically driven turbulence, and forced convection ) regimes considered by the Blackadar PBL scheme, but is weakly dependent on z 0 in the fourth (unstable) regime ( free convection ). This further motivates the definition of a single, effective value of z 0 so that one value of m can be defined and used in Eq. (9) for both skin and form drag in the unstable regime. The approach in using the effective values of friction velocity, surface wind, and roughness length (u * e, V e, and z 0e, respectively) is to assume that they apply in an expanded form of Eq. (3). Thus, the total effective stress and friction velocity can be defined as in Eqs. (5) and (6): where u* u* and (14) e e e kv u* e e, (15) (1 m) e z a z 0e ln. (16) e Equations (8) (10) and (14) (16) can be substituted into Eq. (3) to yield V e V e V s V s V w Vw. (17) 2 2 2 e s w Since there is only one equation and two unknowns ( e and V e ), another constraint is required. To that end, the effective roughness length z 0e [which enters into Eq. (17) through e ] is defined as dependent on z 0w and z 0s ; it is defined from Eq. (17) in the special case where V s V w V e, which allows a solution for e :

JANUARY 2000 POWERS AND STOELINGA 213 s w e 2 2 1/2. (18) ( ) s The effective roughness length z 0e is then calculated in the MM5 as follows: z 0e z a e e. (19) Note that in the flux equations there are not separate effective roughness lengths for momentum and heat. In the latent flux calculation [Eqs. (26) and (27), discussed below], however, there is an effective reduction in z 0 through the accounting for a molecular sublayer (Oncley and Dudhia 1995). The single-length simplification used here follows the standard (uncoupled) MM5 approach. Using Eq. (18) in Eq. (17) yields the x and y components of V e : 1 2 2 e 2 w s s s w w w u ( uv uv) and (20) V e 1 2 2 e 2 w s s s w w ( V V ), (21) V e where ( s 2, w 2 ) 1/2 and V e V e ; V s, u s, and s are the total ground-relative wind and its components; and V w, u w, and w are the total wave-relative wind and its components; which are calculated by the MM5 from the wave phase velocity information received from the wave model. Here, V e [( ue 2 e 2 ) 1/2 ], u e, and e are used in determining the component surface momentum fluxes (M u, M ) to be passed to the lake model: ( u e, e) Mu,. (22) w V e Note that V e does not account for surface drift, which is small, 3% of the 10-m wind speed (Taylor and Gent 1978). Another lower-order complexity excluded is the potential effect of surface current on wave steepness. In addition to the surface momentum fluxes, the MM5 provides to the lake model the surface heat fluxes (Q): Hs Es Rn Q. (23) c Here, c pw is the specific heat of water, w is the density of water, and H s, E s, and R n are the sensible, latent, and net radiative fluxes, respectively. Grell et al. (1995) provide the detailed formulas. b. POM MM5 The lake model s input to the MM5 consists of lake surface temperatures, T. This field is provided to the MM5 s 2-km domain every lake time step (50 s) and becomes the MM5 ground (i.e., lower boundary) temperature over the Lake Erie points. Thus, in the coupled system, the MM5 s Lake Erie surface temperatures vary temporally, whereas normally they are fixed throughout a simulation. pw w c. MM5 GDM The MM5 s input to the wave model consists of the 10-m u and wind components. These are provided to the wave model every 50 s. In this system, the MM5 does not send its internally computed surface stresses to the wave model. Because the wave model assumes neutral stratification, however, the surface stresses it calculates from the 10-m MM5 winds may not always equal the MM5 stresses, which account for deviations from neutrality. Thus, for the case simulated, the GDM s neutrality assumption means that the calculated surface stresses in the GDM would be slightly lower than those in the MM5 for the majority of the simulation period. The MM5 s sending of 10-m winds rather than stresses was done because the GDM is built to receive 10-m winds. Future work may address sending stresses to, or the neutrality in, the wave model. d. GDM MM5 The wave model s input to the MM5 consists of (i), the standard deviation of the water surface elevation; (ii) c p, the phase speed of the waves at the spectral peak (c p c p ); and (iii) the propagation direction of the waves at the spectral peak. The information is sent to the MM5 every 50 s. The MM5 uses c p, the directional information, and in calculating u w, w, and z 0w. As described above, the first parameterization for z 0w is that given by Eq. (11) (Donelan 1982, 1990) and is aimed at maximizing z 0w. The second is that given by Eq. (12) (Donelan et al. 1993). In this formulation, U 10 /c p is inverse wave age (see, e.g., Donelan 1990). Wave age may also be expressed as c p /u * (see, e.g., Volkov 1970; Nordeng 1991). The use of the second parameterization is aimed at investigating the influence of wave age on lake roughness. The issue of partially accounting for wave motion through Eq. (12) while also using an effective wind is discussed in section 5, below. Note that in subsequent discussions, wave motion refers to the phase velocity of surface waves, not to the orbital motion of fluid elements. 4. Test case and experiments performed a. Event simulated The test case features the passage of a cold front through the Lake Erie region on 16 17 October 1992. Postfrontal west to northwesterly surface winds of 30 kt were sustained over the lake for over 15 h, and gusts to over 50 kt were recorded (Kelley et al. 1993). These conditions generated large waves (significant wave height 3.5 m) and a significant seiche and storm surge. Figure 1b shows the Lake Erie region and the stations discussed. For future reference, the eastern basin of Lake Erie extends roughly from ERI eastward, the west-

214 MONTHLY WEATHER REVIEW FIG. 3. Surface analyses for 1200 UTC 16 Oct; and 0000 and 1200 UTC 17 Oct 1992. Sea level pressure (mb) solid, contour interval 4 mb. Temperature ( C) dashed, contour interval 2 C. Winds: half barb 5 kt, full barb 10 kt. Isobars objectively analyzed; fronts subjectively analyzed. (a) 1200 UTC 16 Oct, (b) 0000 UTC 17 Oct, and (c) 1200 UTC 17 Oct. ern basin extends from buoy 45005 westward, and the central basin encompasses the remainder (see Fig. 1b). At 1200 UTC 16 October 1992, the cold front driving the event is poised on the western end of Lake Erie (Fig. 3a). An intensifying low of approximately 1001 mb is centered over eastern Lake Huron, and southerly flow of up to 25 kt in the warm sector prevails over the eastern reaches of the Lake Erie. Farther to the east, a warm stationary front runs through southeastern Ontario and then southward into New York and Pennsylvania. At 500 mb (not shown) an eastward-progressing shortwave trough is located over Minnesota and Wisconsin, and the observations indicate a 100-kt (51 m s 1 ) jet streak in its base. By 0000 UTC 17 October, the surface low has occluded, deepened to at least 990 mb, and moved northeastward to the Ontario Quebec border (Fig. 3b). The cold front has swept through the Great Lakes region and now runs through central New York and Pennsylvania. The postfrontal surface flow over Lake Erie is westerly, with individual reports of winds of up to 45 kt. Satellite imagery (Fig. 4) reveals a well-defined edge in the midto high-level cloud associated with the front, particularly through central Pennsylvania and New York. Farther west, into the cold sector, low-level cloud appears (marked A in Fig. 4). This is present along and downwind (i.e., east and southeast) of the southeastern shore of Lake Erie. By 1200 UTC 17 October (Fig. 3c) the cold front has moved off the U.S. east coast. The Lake Erie region is in a postfrontal regime, with cold, dry air and brisk winds prevailing. The winds over the lake, diminished since 0000 UTC, now average less than 25 kt. In Fig. 3c, the surface air temperature analysis hints at an effect that will be the focus of model analyses in section 5: the warm air pool over Lake Erie. This local maximum (4 C isotherm) reflects lake-induced warming of the cold, postfrontal flow, and this zone will be referred

JANUARY 2000 POWERS AND STOELINGA 215 FIG. 4.GOES-7 4-km IR imagery for 0001 UTC 17 Oct 1992 over the eastern United States and southeastern Canada. Synoptic features indicated. The A identifies area of low cloud downwind of Lake Erie. to as the lake-effect warm pool (LWP). The airmass modification results from the relatively high lake surface temperatures (LSTs), which average over 14 C. b. Experiments performed Table 1 lists the experiments performed. All are 24-h simulations initialized at 1200 UTC 16 October 1992 as described in section 2. Experiment identifiers are NC for a noncoupled run, FC for fully coupled runs, and PC for a partially coupled run. Experiment NC is a noncoupled simulation in which the MM5 runs alone. The value of z 0e over all bodies of water is given by the Charnock relation in Eq. (13). Two FC experiments have all three models interacting, but use different roughness parameterizations. These ex- TABLE 1. Summary of coupled model experiments. Roughness lengths (z 0 ) and wave (form drag) component (z 0w ) expressed in m; grid sizes expressed in km. Here, u * friction velocity (m s 1 ), c p phase speed of waves at spectral peak (m s 1 ), U 10 10-m wind speed (m s 1 ), and rms surface displacement (m). All experiments feature the 18-, 6-, 2-km three-domain configuration with 27 vertical levels. Coupling occurs on the 2-km MM5 grid. Expt Coupling configuration Lake Erie roughness parameterization NC FCZ30 FCD93 PC Noncoupled Fully coupled Fully coupled Partially coupled Charnock MM5 control run Max roughness MM5 POM GDM Wave age dependent MM5 POM GDM Min roughness MM5 POM GDM; no GDM input to MM5 z 0 0.032(u * /g) 0.0001 z 0w 1/30 z 0w 0.00064 (U 10 /c p ) 2.6 z 0 0.0001

216 MONTHLY WEATHER REVIEW FIG. 5. Surface mesoanalyses (subjective) for 0000 and 1200 UTC 17 Oct 1992. Isotherms solid, contour interval 1.0 C for T 12 C, 2.0 C for T 12 C; 6.5 C isotherm in (b) dashed. Fronts indicated. Winds: half barb 5 kt, full barb 10 kt. (a) 0000 UTC 17 Oct, and (b) 1200 UTC 17 Oct. periments employ the two approaches to estimating z 0w given in Eqs. (11) and (12). The experiment using the former is FCZ30; the experiment using the latter is FCD93. The final experiment is a partially coupled run. Here, all three models operate, but the MM5 receives no input from the wave model. Overlake roughness equals the base value in the MM5 s standard approach to marine z 0 s [Eq. (13)] so that z 0e over Lake Erie is a constant 0.01 cm. The suite of experiments thus offers a range of Lake Erie roughnesses, from a maximum (FCZ30) to a minimum (PC). In all of the experiments, the initial lake surface temperatures are the same. Over time, the Lake Erie temperatures in PC, FCZ30, and FCD93 change due to the lake model coupling, while those in NC remain at their initial values. 5. Experiment results The experiment analyses presented will focus on the area of the coupling, the 2-km MM5 grid (D3). It is found that the main synoptic aspectsthe low center in Ontario and its cold front trailing through the Lake Erie region (Fig. 3)are simulated virtually identically across the experiments. On the mesoscale, however, significant variations in PBL and surface layer characteristics appear. a. Meteorological conditions The simulated surface air temperatures reflect the air sea coupling and thus will be a starting point for the experiment analyses. To examine the observations first, Figs. 5a and 5b present mesoanalyses of the Lake Erie region for 0000 and 1200 UTC 17 October 1992. By 0000 UTC (Fig. 5a) the cold front has cleared the eastern end of the lake. The postfrontal surface flow is strong, with observations of sustained winds over the lake at up to 45 kt (23 m s 1 ). The prominent LWP has maxima of 10.5 C, in contrast to the cold ( 6.5 ) pocket in western Pennsylvania and southwestern New York to the east and the 5.5 6 C temperatures on the upwind (northwest) lakeshore. By 1200 UTC 17 October (Fig. 5b), the cold front has moved off the U.S. east coast. Over Lake Erie are sustained northwesterlies at up to 30 kt. The LWP s maximum temperatures of over 6.5 C contrast with those upwind and downwind at 2.2 3.5 C. Figures 6a h present the surface temperature results for the experiments at hours 12 and 24. In all of the runs the temperatures along the upper and left (west/ northwest) boundaries of the domains are the same because these areas are upwind of the coupled subarea of the domain (i.e., Lake Erie); they have no disparity in lower boundary conditions. The consistency in upwind surface air temperatures also means that any temperature differences along the downwind shore are due solely to coupling scheme differences. Note, too, that the model temperatures along the upwind shore of Lake Erie, of about 6.5 C, are verified (cf. Fig. 5). Experiment NC at hour 12 is shown in Fig. 6a. Experiment NC produces a distinct LWP with maximum intensity along the New York shore in the eastern basin. Compared to observation, both the magnitude and the extent of the warming are underforecast. The highest observed temperatures in the LWP were 10.5 C, while those in NC are approximately 9.5 C. In addition, the observed 9 C area (Fig. 5a) is significantly larger than the NC counterpart (Fig. 6a). In FCZ30 (Fig. 6b) maximum temperatures in the LWP exceed 9.9 C, and FCZ30 s 9 C area is over three times the size of NC s. Although this is a significant enhancement over NC, FCZ30 still represents an underforecast of the lake effect warming (cf. Fig. 5a).

JANUARY 2000 POWERS AND STOELINGA 217 FIG. 6. Model surface air temperatures for experiments NC, FCZ30, FCD93, and PC at simulation hours 12 and 24 (0000, 1200 UTC 17 Oct 1992). Contour interval 1 C. Surface wind vectors plotted; vector magnitude 35ms 1 /vector interval. The 2-km grid (D3) is shown. (a) NC, hour 12; (b) FCZ30, hour 12; (c) FCD93, hour 12; (d) PC, hour 12; (e) NC, hour 24; (f) FCZ30, hour 24; (g) FCD93, hour 24; and (h) PC, hour 24. FCD93 s LWP (Fig. 6c) has virtually the same magnitude and extent as NC s. Thus, like NC, FCD93 underforecasts the LWP strength and extent. Last, PC s LWP (Fig. 6d) is the weakest, with no 9 C area produced. At 1200 UTC 17 October the LWP remains prominent and has observed maxima of over 6.5 C (Fig. 5b). While NC s 6 C area (Fig. 6e) agrees with observation, NC s maximum LWP temperatures are warmer (over 7.5 C). Nevertheless, NC (and FCD93) represents the best forecast for this hour. The FCD93 results (Fig. 6g) are almost identical to those of NC. In FCZ30 (Fig. 6f), the LWP (maxima 8 C) continues to be stronger than in NC. Compared to observation, FCZ30 is now overforecasting the LWP. This contrasts with the hour 12 situation, in which FCZ30 was not overpredictive of LWP strength and, rather, was the most accurate simulation. As for PC (Fig. 6h), it has again significantly underforecast the strength of the warm pool. Table 2 summarizes the model results over Lake Erie for hours 12 and 24. The warmest simulation (in terms of LWP strength and extent) is FCZ30, and the coolest is PC. FCZ30 surface air temperatures average about 0.5 C warmer over the lake than those in NC, while PC surface air temperatures average about 0.5 C cooler. Although experiment differences are qualitatively consistent throughout the simulation period, model performance with respect to observation varies over time. For example, at hour 12 FCZ30 best reproduces the observed LWP; at hour 24 NC and FCD93 have the bestverified LWPs. To examine the LWPs more closely, Fig. 7 presents time series of the observed and modeled surface air temperatures for Erie, Pennsylvania (ERI; location shown in Fig. 1b). The sharp drop in the observed record

218 MONTHLY WEATHER REVIEW TABLE 2. Surface and PBL characteristics over Lake Erie in model experiments. Values are averages for all Lake Erie points in the 2-km MM5 grid at simulation hours 12 and 24 (0000, 1200 UTC 17 Oct 1992). The s denote the absolute or percentage differences between the given experiment and NC. All values at surface, except those for PBLH; T air temperature, U wind speed, q mixing ratio, RH relative humidity, PBLH height of PBL. Expt T ( C) T ( C) U (m s 1 ) U (m s 1 ) q (g kg 1 ) RH (%) RH (%) PBLH (m) PBLH (%) Hour 12 NC FCZ30 FCD93 PC Hour 24 NC FCZ30 FCD93 PC 7.62 8.10 7.62 7.09 6.08 6.65 6.12 5.58 0.48 0 0.53 0.57 0.04 0.50 19.9 20.4 20.6 20.5 11.2 11.6 11.4 11.0 0.5 0.7 0.6 0.4 0.2 0.2 4.10 3.91 4.01 4.11 3.38 3.12 3.29 3.51 62 57 61 65 57 51 56 62 5 1 3 6 1 5 1029 1168 1046 895 1031 1211 1044 868 14 1.7 13 18 1.3 14 (solid line) after hour 6 (1800 UTC 16 October 1992) marks the passage of the cold front (FROPA, indicated in Fig. 7). The post-fropa traces are consistent with the results seen in the surface analyses (Fig. 6) and in the overlake averages (Table 2). FCZ30 is the warmest simulation, and PC is the coolest, with NC and FCD93 intermediate and quite similar. At hour 12, although all of the model temperatures are underforecasts, FCZ30 is the closest to observation. At hour 24, NC and FCD93 are the best simulations, while FCZ30 presents an overprediction and PC a continuing underprediction. Regarding the vertical extent of the experiment differences, it is found that at 900 mb (not shown) the FIG. 7. Time series of surface air temperature ( C) for ERI for observed and model data. Period shown is 1200 UTC 16 Oct 1200 UTC 17 Oct 1992. Abscissa shows time in hours from 1200 UTC 16 Oct. Curves identified in plot. Location of ERI shown in Fig. 1b. Approximate time of observed frontal passage at this location marked FROPA. downwind reaches of the lake in FCZ30 are 0.5 1.0 C warmer than in NC, while in PC they are 0.5 1.0 C cooler than in NC. At 850 mb the temperature differences (T ) persist, but are reduced. By 700 mb, no significant FCZ30 NC or PC NC temperature differences appear. Little difference between FCD93 and NC appears at 900 mb or above. Figure 8 examines the horizontal extent of the experiment differences, showing surface T on the 6-km grid (D2). Figure 8a reveals the FCZ30 NC difference as a plume of warmer air temperatures streaming off Lake Erie. The plume extends 300 km downwind into west-central Pennsylvania and even into the coarsest (18 km) grid (D1). The PC NC difference plume (Fig. 8c) is negative, reflects maximum temperature deficits in PC of 1.0, and extends downstream (i.e., southeast) of the lake for over 300 km. 1 The FCD93 NC results (Fig. 8b) show no significant difference signal. Vertical structure differences in the simulations have been analyzed through observed and model soundings for Pittsburgh, Pennsylvania (PIT; location in Fig. 1b), and for model soundings along the immediate downwind shore of the lake. PIT is the observed sounding closest to Lake Erie in the downstream direction. At hour 24 each of the experiments reproduces a frontal inversion and concomitant backing in the winds above 800 mb observed at PIT (soundings not shown); temperature and wind profiles above 650 mb are also verified. All of the experiments display essentially adiabatic layers to 850 mb as observed, although NC produces a thin saturated layer at this point from 910 to 870 mb. The satellite imagery (Fig. 4) does show patches of low cloud in the vicinity of this point. For a comparison of the model vertical structures along the downwind shore of the lake, Figs. 9a d present soundings at hour 24 for a location between Cleveland, 1 The FCZ30 NC and PC NC differences downstream of Lake Huron reflect patches of precipitation, and thus low-level diabatic cooling, which are out of phase in the experiments.

JANUARY 2000 POWERS AND STOELINGA 219 FIG. 8. Model surface temperature differences and surface winds on 6-km grid (D2) at simulation hour 24 (1200 UTC 17 Oct 1992). FCZ30 NC, FCD93 NC, and PC NC differences shown. Contour interval 0.25 C; positive values solid, negative values dashed; zero contour suppressed. Wind vectors plotted; vector magnitude 30ms 1 /vector interval. (a) FCZ30 NC T; FCZ30 winds. (b) FCD93 NC T; FCD93 winds. (c) PC NC T; PC winds. Ohio (CLE), and ERI (labeled S in Fig. 1b). FCZ30 (Fig. 9b) yields the warmest ( 281.5 K), deepest (170 mb), and driest (q 2.70 g kg 1 ) PBL, while PC (Fig. 9d) yields the coolest ( 280 K), shallowest (110 mb), and moistest (q 3.14 g kg 1 ) PBL. The NC and FCD93 (Figs. 9a,c) soundings are virtually identical and display PBL parameters between the FCZ30 and PC extremes. For temperatures on the lower boundary, that is, the Lake Erie surface, Fig. 10 presents hourly time series of surface water temperatures during the simulation period at National Data Buoy Center (NDBC) buoy 45005, located in Fig. 1b. The observed (solid) curve has a stepwise appearance because the instrument has recorded in whole degrees Fahrenheit. Although FROPA occurs, and strong upward heat fluxes begin, at buoy 45005 at approximately 1530 UTC 16 October, the lack of immediate cooling reflects the lag associated with the reporting of the temperature measurement. The NC time series in Fig. 10 is constant and reflects the standard MM5 approach of fixing water temperatures. The error possible from fixed SSTs becomes apparent by comparing the observed and NC curves: by the end of the period NC has not accounted for at least 1.1 C of overall cooling. After frontal passage, FCZ30, FCD93, and PC all show sustained temperature decreases that are consistent with the now strongly upward sensible heat fluxes. The results show a range of responses: FCZ30 s overall temperature decrease is the most at 1.2 C, while PC s is 60% of this, and FCD93 s is 83% of this. FCZ30 s surface water temperature simulation is the closest to observation. The sensitivity of the surface wind speeds to varying overwater roughnesses is reflected in Table 2 (U). The FCZ30 overlake, surface wind speed averages for hours 12 and 24 are 0.45 m s 1 higher than those in NC (U ) despite overlake z 0e s, which are significantly greater than those in NC (Table 3). This result in part reflects FCZ30 s deeper PBL (discussed in section 5c; Fig. 9), which is associated with greater mixing of higher-mo-

220 MONTHLY WEATHER REVIEW FIG. 9. Model soundings for point near downwind shore of Lake Erie at hour 24 (1200 UTC 17 Oct 1992). Temperature solid; dewpoint dashed. Winds: full barb 10 kt, pennant 50 kt. Sounding location labeled S in Fig. 1b. Soundings plotted to 400 mb. (a) NC, (b) FCZ30, (c) FCD93, and (d) PC. mentum air from the free troposphere. Also, FCZ30 employs the effective wind. Accounting for the motion of the waves offsets the increased roughness in the calculation of the surface stress (u * e) (presented in Table 3 and discussed below). The overlake wind speed averages in FCD93 and PC, on the other hand, reflect roughnesses much lower than those of FCZ30. Their surface winds are thus generally higher than those of NC (cf. PC at hour 24, where a PBL depth reduction is decreasing momentum mixing). Table 2 also reveals the sensitivity of surface moisture parameters to coupling and experiment configuration. The results show that differences in average, overlake, surface mixing ratio (q ) and relative humidity (RH) are inversely related to surface roughness differences. At hour 24 for example, a comparison of rough FCZ30 and smooth PC shows RH FCZ30 51% and q FCZ30 3.12 g kg 1, while RH PC 62% and q PC 3.51 g kg 1. These results are contrary to an expectation of increased moisture flux with increased roughness [Eqs. (26) and (27); discussed below]. To examine this, Figs. 11a d present the hour 24 surface relative humidity for NC and the surface relative humidity differences for FCZ30 NC, FCD93 NC, and PC NC. First, the NC RH patterns (Fig. 11a) show that surface relative humidity actually decreases in the downstream direction over the lake. Second, the difference plots reveal humidity deficits in FCZ30 and FCD93 compared

JANUARY 2000 POWERS AND STOELINGA 221 In the Lake Erie region, model cloud does not extend much above 700 mb. The actual cloud tops in this area were relatively low (warm), and from the imagery are estimated at below 500 mb. All of the experiments produce cloud over and east of the downwind half of Lake Erie corresponding to cloud area A in Fig. 4. FCZ30 (Fig. 12b) shows the greatest extent of downstream cloud; PC (Fig. 12d) shows the least. The intermediate NC and FCD93 (Figs. 12a,c) results are very similar in terms of coverage and maximum cloud water and ice mixing ratios. At this hour, FCZ30 best reproduces the observed low-level cloud. FIG. 10. Time series of surface water temperature at NDBC buoy 45005. Observed, NC, FCZ30, FCD93, and PC records plotted. Curves identified in plot. Period shown corresponds to 1200 UTC 16 Oct 1200 UTC 17 Oct 1992. Model values have been adjusted through the subtraction of an initial temperature difference at this location of 0.4 C (i.e., LST model LST obs ), done to allow the temperature trends to be compared more easily and to show more clearly overall temperature changes. Location of buoy 45005 shown in Fig. 1b. Approximate time of observed frontal passage at this location marked FROPA. to NC (Figs. 11b,c) and humidity surpluses in PC compared to NC (Fig. 11d). These results are interpreted in section 5c. For a final look at the meteorological conditions, Fig. 4 presents the IR imagery for 0001 UTC 17 October (simulation hour 12). Low cloud extends from the downwind (eastern/southeastern) half of the lake into western New York and northwestern Pennsylvania (areas identified A in Fig. 4). The model low-level cloud, represented by the 800-mb combined cloud water cloud ice field, is shown in Figs. 12a d. b. Marine conditions Given that the experiments are largely defined by lake roughness parameterizations, the simulation differences will be analyzed in light of the overlake effective roughness lengths (z 0e ). Figures 13a c thus present z 0e results for hour 24. Because PC s z 0e s are invariant at 0.01 cm, they are not shown. The NC z 0e s (Fig. 13a) are maximized at 0.12 0.14 cm from the eastern part of the central basin to the eastern basin. The FCD93 z 0e s (Fig. 13b) are maximized at 0.5 0.7 cm in the northern part of the central basin. As seen through Fig. 14a, this siting is consistent with the region of youngest waves. FCZ30 (Fig. 13c) has produced the greatest z 0e s, which range from 2 to 5.5 cm. Maxima in FCZ30 occur in the southeastern central basin and along the southern reaches of the eastern basin where significant wave heights (H SIG ), shown in Fig. 14b, are maximized. This reflects the z 0w component of z 0e in FCZ30 being a function of, where is directly related to significant wave height through the formula H SIG 4. Table 3 presents the average Lake Erie z 0e s for hours 12 and 24. The range is over two orders of magnitude: PC 0.01 cm and FCZ30 1.0 cm. The NC and FCD93 values fall between 0.1 and 1.0 cm, although those of FCD93 are roughly two to five times those of NC. In contrast, FCZ30 has average z 0e s of over 6 cm and 4 cm at hours 12 and 24, respectively. TABLE 3. Surface characteristics over Lake Erie in model experiments. Values are averages for all Lake Erie points in the 2-km MM5 grid at simulation hours 12 and 24 (0000, 1200 UTC 17 Oct 1992). The s denote the absolute or percentage differences between the given experiment and NC; z 0e effective roughness length, swh significant wave height, c p /u * wave age, u *e effective friction velocity, T *e temperature in H s expression, V e effective wind, H s sensible heat flux, and E s latent heat flux. Expt z 0t (cm) swh u *e (m) c p /u * (m s 1 ) T *e ( C) V e (m s 1 ) H s (W m 2 ) H s (%) E s (W m 2 ) E s (%) Hour 12 NC FCZ30 FCD93 PC Hour 24 NC FCZ30 FCD93 PC 0.29 6.84 0.80 0.01 0.11 4.36 0.50 0.01 3.6 3.8 1.9 1.9 11 14 18 21 0.994 1.021 0.838 0.720 0.498 0.492 0.427 0.388 0.91 1.33 1.01 0.67 0.98 1.37 1.08 0.77 19.9 12.3 14.6 20.5 11.2 6.4 7.8 11.0 378 553 370 220 217 432 283 171 46 2.1 42 59 4.4 37 625 641 551 456 457 483 410 325 2.6 12 27 5.7 10 29

222 MONTHLY WEATHER REVIEW FIG. 11. Surface RH and RH differences at simulation hour 24 (1200 UTC 17 Oct 1992). The 2-km grid (D3) is shown. (a) NC RH. Contour interval 10%. Surface wind vectors shown. (b) FCZ30 NC RH. Contour interval 5%; positive value solid, negative values dashed. (c) FCD93 NC RH. Contouring as in (b). (d) PC NC RH. Contouring as in (b). FIG. 12. Model 800-mb cloud and winds for experiments NC, FCZ30, FCD93, and PC at simulation hour 12 (0000 UTC 17 Oct 1992). Cloud field is sum of cloud water and cloud ice mixing ratios. Initial contour (boldface) 0.001 g kg 1 ; other contours begin at 0.1 g kg 1 with a 0.1 g kg 1 interval. The 800-mb wind vectors are plotted; vector magnitude 55 m s 1 /vector interval. The 2-km grid (D3) is shown. (a) NC, (b) FCZ30, (c) FCD93, and (d) PC.