Three-Dimensional Week-Long Simulations of TOGA COARE Convective Systems Using the MM5 Mesoscale Model

Transcription

1 2326 JOURNAL OF THE ATMOSPHERIC SCIENCES Three-Dimensional Week-Long Simulations of TOGA COARE Convective Systems Using the MM5 Mesoscale Model HUI SU, SHUYI S. CHEN, AND CHRISTOPHER S. BRETHERTON Department of Atmospheric Sciences, University of Washington, Seattle, Washington (Manuscript received 12 August 1997, in final form 21 September 1998) ABSTRACT A three-dimensional nonhydrostatic mesoscale model, the Pennsylvania State University/National Center for Atmospheric Research mesoscale model (MM5), is used to simulate the evolution of convective systems over the intensive flux array (IFA) during the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment, during December The model is driven by a time-varying IFA mean forcing based on the average advective tendencies of temperature and moisture over the IFA. The domain-averaged horizontal wind is kept close to the observed IFA mean using Newtonian relaxation. Periodic lateral boundary conditions are imposed. Simulations with three horizontal grid spacings, 2, 15, and 60 km, are conducted. With 15- and 60-km resolution, subgrid-scale cumulus convection is parameterized while mesoscale convective organization is explicitly resolved over a (600 km) 2 domain. With 2-km resolution, convection is fully resolved over a (210 km) 2 domain. Despite their different horizontal resolution and different treatment of moist convection, the simulations all produce very similar temporal variability in domain-averaged temperature and relative humidity profiles. They also closely resemble each other in various statistical properties of convective systems. A comprehensive comparison of the 15- and 2-km model results against observations is performed. The domain-averaged cloud amount and precipitation agree well with observations. Some shortcomings are noted. During suppressed convective periods, the model tends to have greater areal coverage of rainfall and more cirrus anvil clouds than observed. Over the 8-day period, both models produce mean temperature drifts about 2 K colder than observed. A histogram of modeled cloud-top temperature captures the observed breaks between convective episodes but shows excessive and persistent cold cirrus clouds. A radar reflectivity histogram shows that the 15-km model slightly overpredicts radar reflectivity and that the 2-km model has too high and temporally homogeneous reflectivities. The modelsimulated cloud cluster size is somewhat smaller than the observed. Surface sensible and latent heat fluxes are overestimated by 50% 100%, due both to shortcomings in the surface flux calculations in the model and modelproduced mean temperature and humidity biases. Downwelling solar flux at the surface is underestimated mainly because of the simple shortwave radiation scheme. This study suggests that large-domain simulations using the MM5 with 15-km resolution can be a useful tool for further study of tropical convective organization and its interaction with large-scale circulation. 1. Introduction The parameterization of cumulus convection and cloud-related processes in general circulation models (GCMs) and numerical weather prediction models has been a challenging problem in atmospheric research for years. Convection vigorously interacts with its largescale environment through precipitation; latent heating; eddy fluxes of heat, moisture, and momentum; cloudradiation feedback; microphysical processes; and air sea interaction. These processes dominate tropical climate variability and climate change. Cloud-resolving models (CRMs) have been widely used in studying the Corresponding author address: Christopher S. Bretherton, Department of Atmospheric Sciences, Box , University of Washington, Seattle, WA breth@atmos.washington.edu interaction between large-scale flow and convection (e.g., Lau et al. 1993, 1994; Sui et el. 1994; Xu and Randall 1996; Grabowski et al. 1996). Cloud-scale results from CRM simulations have also been used to evaluate GCM parameterization schemes (Gregory and Miller 1989). Due to computational limitations, most of these studies have used two-dimensional models. Although there exist a few three-dimensional simulations of deep convective ensembles, they were performed with limited horizontal domains (less than 200 km in each direction) (e.g., Robe and Emanuel 1996) or integrated over short time periods of a few hours (e.g., Tao and Soong 1986; Trier et al. 1996). On the other hand, observational studies revealed that a large portion of cloud clusters over tropical oceanic warm pool have horizontal dimensions exceeding 300 km (Mapes and Houze 1993; Chen et al. 1996). Therefore, the small model domains of previous 1999 American Meteorological Society

2 15 JULY 1999 SU ET AL studies cannot reproduce the structure or even the ensemble statistical characteristics of big convective systems. Recently, Wu et al. (1997) have performed a threedimensional simulation with a spatial domain of (400 km) 2 and integrated over a period of 1 week. While such simulations will be commonplace in a few years, they stretch the limits of current computer power. Mesoscale models provide us another avenue to study the convective organization and its interaction with the large-scale environment. They have finer resolution and better parameterization of cloud-related processes than GCMs but coarser resolution than CRMs. They allow us to employ a much bigger domain than used in CRMs at a reasonable computational cost. For instance, it is attractive to use a 15-km resolution, for which individual convective cells are parameterized, while broader circulations within mesoscale convective systems can be explicitly resolved. In the future, this approach may be computationally feasible even for global models. In this study, we use the nonhydrostatic version of the fifth-generation Pennsylvania State University/National Center for Atmospheric Research mesoscale model (MM5) with modifications of lateral boundary conditions and observed intensive flux array (IFA) mean forcing (see section 2) to simulate the evolution of tropical deep convection during a week-long period of the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE). The goal of our modeling study is to compare MM5 simulations at three spatial resolutions (2, 15, and 60 km) with a variety of TOGA COARE observations. The MM5 and its predecessors have been widely and successfully used for simulations of both extratropical and tropical mesoscale convective systems (MCSs) and oceanic cyclones (e.g., Zhang and Fritsch 1986; Chen and Frank 1993; Dudhia 1989, 1993; Kuo et al. 1996). Our study is the first to apply it in the same manner as in many CRM studies, for example, using periodic boundary conditions and specified forcing, and integrating over many days. We focus on two identically forced simulations. One uses a domain of (600 km) 2 with 15-km resolution, parameterized convection, and a simple explicit moisture (prognostic microphysical parameterization) scheme. The other uses a (210 km) 2 domain with 2-km grid spacing and explicitly resolved convection. We also conduct a sensitivity test using 60- km grid spacing with the same forcing and model physics as the 15-km run. Section 2 describes the main features of the model with emphasis on the application of IFA mean forcing. Section 3 presents results of the model observation intercomparison. Discussion and conclusions are given in section Model description and experimental design The nonhydrostatic version of the MM5 (version 2) is used in this study. A general description of the MM5 can be found in Dudhia (1993) and Grell et al. (1994). There are 24 vertical levels, spaced at intervals of 0.01 (about 10 mb) near the surface to 0.05 (about 50 mb) above 900 mb. We use the Kain Fritsch cumulus parameterization (Kain and Fritsch 1990) when horizontal resolution of 15 or 60 km is used. We increase the threshold value of boundary layer convergence WKLCL/ZLCL in the trigger function in the Kain Fritsch parameterization scheme from s 1 to s 1 to force convection to be more closely associated with resolvedscale upward motion. The grid-scale microphysical parameterization used in all model simulations is described in Hsie (1984) and Dudhia (1989) and includes explicit treatment of cloud water, rainwater, ice, and snow. Icephase processes are assumed when the temperature is below 0 C, where cloud water is treated as cloud ice and rain is treated as snow (Dudhia 1989). Prognostic equations of these variables include the effect of gridscale advection of water species, phase changes such as condensation and evaporation, freezing and melting, as well as sublimation and deposition. Although there are various options on ice-phase processes in the MM5, we use the simple-ice scheme (Dudhia 1989), mostly for computational efficiency. This scheme treats the microphysical processes of ice and snow in the same way as most other CRMs, except it does not include supercooled water and unmelted snow. Observational studies (e.g., Houze and Churchill 1984; Gamache 1990) have shown that there is very little supercooled water and hail/graupel in tropical oceanic convective systems. This is consistent with the fact that convective updrafts are generally weaker in tropical convective systems than their counterparts over land in the midlatitude (e.g., LeMone and Zipser 1980; Lucas et al. 1994). Furthermore, we conducted a sensitivity test using a mixedphase microphysics option in the MM5, which permits the existence of supercooled water and unmelted snow. It does not significantly affect the ensemble properties of the convection and convective organization in our simulation. We employ an upper radiative boundary condition (Klemp and Durran 1983). The Blackadar high-resolution PBL (Zhang and Anthes 1982) and the longwave and shortwave radiation scheme of Dudhia (1989) are used. We slightly modify the cloud optical depth calculation to allow a different effective radius of cloud ice particles ( 50 m) compared with that of cloud water droplets ( 10 m) for scattering of shortwave radiation by clouds. A similar distinction between snow ( 250 m) and rainwater droplets ( 100 m) is also made. The modification has no significant impact on the model dynamics but yields better downwelling solar radiative fluxes at the surface. Our simulations are over the open ocean of the equatorial western Pacific warm pool with a uniform fixed sea surface temperature (SST) of 29 C. The MM5 was modified to run with periodic boundary conditions at both north south and east west bound-

3 2328 JOURNAL OF THE ATMOSPHERIC SCIENCES aries, as is usually done in other CRM simulations. Periodic boundary conditions are a natural choice for simulations of a small part of an active convective region and are convenient for budget analysis. To satisfy a periodic north south boundary condition, the Coriolis parameter must be constant over the domain. In our experiments, the Coriolis parameter is set to zero because the center of the domain, which is 2 S, 156 E, the center of the IFA region during TOGA COARE, is near the equator. Periodic boundary conditions do not explicitly permit net horizontal convergence of mass or advected quantities into the model domain at any level. However, convection observed within a control volume such as the IFA is associated with strong horizontal convergence/ divergence of mass and other quantities. To best match the model simulations with observations, we drive the model with an external forcing. We call it IFA mean forcing, which is often referred to as large-scale forcing in CRM studies (e.g., Grabowski et al. 1996). For our case, the term large-scale forcing would be misleading because the size of the IFA is only km across. The forcing calculated from observed sounding data represents the collective effects of both largescale circulation and convective activity within the IFA. It is applied as tendencies of temperature, moisture, or momentum uniformly distributed over the domain, which match the observed advective tendency profiles deduced from IFA budgets. The model equations for prognostic variables are modified to include the forcing tendency: A A A, (2.1) t t t MM5 forcing where A stands for temperature T, water vapor mixing ratio q, and zonal and meridional wind components u and. The first term on the right-hand side of the equation represents the original tendency terms in the MM5. The forcing terms for temperature and water vapor are expressed as T (V T) T (2.2) t p C p q t forcing forcing (V T) q, (2.3) p where is the horizontal wind vector, is vertical pressure velocity, is the specific volume, and C p is the specific heat of air at constant pressure. Overbars refer to horizontally averaged quantities, which are from the IFA mean soundings during TOGA COARE. A formal derivation can be found in Grabowski et al. (1996). This approach treats IFA mean forcing as an externally specified cause of convection. Within an IFA-sized region, comparable in size to a large MCS, it can be more appropriate to think of convection as inducing the TABLE 1. Setup of the three simulations. Domain size Grid size (km) Convective scheme MM5EP (210 km) 2 2 microphysical scheme MM5KF (600 km) 2 15 Kain Fritsch cumulus microphysical scheme MM5KF60 (600 km) 2 60 Kain Fritsch cumulus microphysical scheme forcing. One MCS advecting into the IFA region can completely change the IFA mean forcing profile. The model will respond to such a change in forcing by rapid, uniform cooling and moistening, which amplifies convection, but may select the wrong convective organization and possibly even the wrong mean thermodynamic profiles. Calculations of horizontal momentum forcing are not available for the IFA region. Instead of using horizontal momentum forcing, we relax horizontal winds to observed mean wind profiles over the IFA region (U and V) using the nudging terms (Grabowski et al. 1996) u t t forcing forcing û U (2.4) ˆ V, (2.5) where û and ˆ are domain-averaged winds and 2 h in all of our simulations. Grabowski et al. (1996) found that their CRM results were not sensitive to the choice of of 1, 2, or 3 h. The three experiments conducted are summarized in Table 1. In the first experiment, MM5EP, we use 2-km grid resolution within a (210 km) 2 domain. In the second experiment, MM5KF, we use 15-km grid spacing within a (600 km) 2 domain, equivalent to the size of the IFA where the mean forcing is applicable. Subgrid-scale convection is parameterized using the Kain Fritsch parameterization scheme. The third experiment, MM5KF60, is conducted with 60-km resolution and the same domain and model physics as MM5KF. All experiments are initiated with an IFA mean sounding at 0000 UTC on 19 December with random temperature perturbations ( 0.5 K) applied to the lowest 1 km of model levels at the first time step. Simulations are conducted through the 8-day period ending at 0000 UTC 27 December We focus on the comparison of the first two experiments with observations and present results from MM5KF60 in a separate section. To evaluate model results against observations, we use TOGA COARE sounding data processed by Ciesielski et al. (1997). Rainfall amount and surface heat fluxes are obtained from the Colorado State University TOGA COARE IFA data site (Lin and Johnson 1996a, b). Other observational datasets used in this study in-

4 15 JULY 1999 SU ET AL FIG. 1. Time height plots of advective tendencies of (a) temperature and (b) water vapor mixing ratio; and time evolution of (c) zonal wind and (d) meridional wind averaged over IFA from 0000 UTC 19 Dec to 0000 UTC 27 Dec Contour intervals are 5 K day 1 in(a),2gkg 1 day 1 in (b), and 5ms 1 in (c) and (d). clude hourly infrared (IR) satellite data from the Japanese Geosynchronous Meteorological Satellite (GMS), International Satellite Cloud Climatology Project (ISCCP) cloud cover and radiative flux data derived from the ISCCP cloud observations (Rossow and Zhang 1995; Zhang et al. 1995), and Massachusetts Institute of Technology (MIT) radar data on the R/V Vickers (Leary 1997, personal communication). 3. Model results a. General features: Domain-averaged properties The observed evolution of the IFA mean forcing over the 8-day period is shown in Fig. 1. The average advective tendencies of temperature and moisture are characterized by tropospheric cooling and moistening with modulations on periods of 1 and 2 days. The strongest cooling and moistening occurred on 20, 22, and 24 December, when three large convective systems were observed over the IFA (Chen et al. 1996). In contrast, adiabatic warming and drying in the middle troposphere prevailed on 23 December, which tended to stabilize the atmosphere and suppress convection. A layer of westerly winds in the lower atmosphere deepened as time progressed with winds increasing up to 10 m s 1. Upper levels were dominated by strong easterlies, with the largest easterly shear between 10 and 15 km. The meridional wind was relatively weak, with midtropospheric northerlies and upper-tropospheric southerlies up to 10 ms 1 through most of the period. Figure 2 displays the time evolution of domain-averaged temperature changes from initial temperature profiles for observed IFA mean sounding and two model experiments. The IFA mean sounding shows a diurnal variation of temperature, with a cooling trend in the upper troposphere of 2 K over the 8 days. Periods of

5 2330 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 2. Time evolution of domain-averaged temperature deviations from initial temperature profiles for observed data and two model experiments. Contour intervals are 1 K. Solid and dashed contours correspond to positive and negative values, respectively. Heavy bars indicate periods of strong forcing (maximum cooling exceeds 15 K day 1 ). strong convective forcing with a maximum IFA mean advective temperature tendency less than 15 K day 1 are indicated by heavy bars near the bottom of each panel in Fig. 2. During the suppressed periods on 23 and December, the temperature became warmer just above the boundary layer. The model-produced domain-averaged temperature changes capture the variation of temperature of a 1-day period and the lowertropospheric warming during the suppressed periods but drift toward tropospheric temperatures that are colder than observed value by 2 4 K. Both simulations generate similar temperature biases, while MM5KF has smaller bias in the upper troposphere. We conclude that the cold drift is not caused by the use of the cumulus parameterization. The cold bias is not a unique problem of the MM5. A variety of models have been used to simulate this period of convection over the IFA in TOGA COARE using a similar approach, as specified by the GCSS Work Group 4 Model Intercomparison Project (Krueger et al. 1998, manuscript submitted to J. Atmos. Sci.; hereafter K98). Although the magnitude of the cold drift varies, the trend exists in every model, including several CRMs and single column models. While no consensus has been reached regarding the cause of this cold bias, it may partly be due to errors in the calculation of observed forcing profiles (e.g., Burks 1998). Emanuel and Zivkovic (1998) showed that the calculated forcings from the observed soundings were not consistent with the observed trends in IFAaveraged, troposphere-integrated moist static energy, a quantity whose budget can be calculated independent of any knowledge about the convection. The inconsistency is of the correct sign and magnitude to explain the cold bias. Meanwhile, Zipser and Johnson (1998) pointed out systematic biases in radiosonde humidities

6 15 JULY 1999 SU ET AL FIG. 3. Time evolution of domain-averaged relative humidity for observed data and two model experiments. Contour intervals are 10%. Data above 14 km are not shown. Heavy bars are as in Fig. 2. during TOGA COARE. Correction of the observed datasets is ongoing. On the other hand, the modeled cold bias could also reflect systematic problems with cloud microphysics, or possibly even a difference between the observed convection and convection generated by imposed uniform forcing. The observed and model-produced domain-averaged relative humidity variations are shown in Fig. 3. Above the freezing level (5.5 km), relative humidity is computed with respect to ice saturation. Above 14 km, measurements of relative humidity become unreliable so this region is not shown in the figure. The model-produced relative humidity variations are well correlated with observations between the top of boundary layer and 14 km. However, the modeled relative humidity tends to be 10% 20% higher than observed. Again, we see close similarity between the two model experiments. MM5KF produces better drying trends on 23 and December compared with the observations than MM5EP (Fig. 3). Both model simulations yield very high relative humidities, near 100% with respect to ice, between 14 and 16 km (not shown). The differences of temperature, water vapor mixing ratio, and relative humidity between model simulations and observations averaged over days 2 8 (0000 UTC December), are shown in Fig. 4. Day 1 is excluded in the temporal average because of model spinup. Both models have cold biases of 2 K throughout the troposphere and are systematically slightly moist between 2 and 8 km, but dry just above the boundary layer. The models both have a high relative humidity bias of 15%, associated primarily with the cold bias rather than the mixing ratio biases. Again, MM5KF produces smaller cold bias in the upper troposphere and slightly smaller moistening in the middle troposphere and drying above the boundary layer than MM5EP. Figure 5 shows the time height section of domainaveraged total condensate, which is the sum of cloud

7 2332 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 4. Domain and 2 8-day average of difference between modelproduced and observed profiles for (a) temperature, (b) water vapor mixing ratio, and (c) relative humidity. Data above 14 km are not shown. water (ice) and rainwater (snow). The evolution of simulated total condensate displays a diurnal and 2-day periodicity in convective activity similar to the IFA mean forcing. The maximum hydrometeor mixing ratio is located at 6 km, just above the freezing level of 5.5 km. Both experiments simulate deep convective cloud tops near the tropopause or even penetrating it, with slightly higher cloud tops in MM5KF. The amount of condensate in MM5KF is smaller than that in MM5EP, probably because condensate in parameterized convection is handled internally by the parameterization scheme and not stored as a model output field. Therefore, it is not included in the 15-km grid resolved-scale averages in Fig. 5a. The vertical profiles of spatial and temporal averages for various species of water substance from both model simulations are shown in Fig. 6. Total condensate is dominated by cloud ice in the upper troposphere above 10 km, while snow prevails from 5 to 9 km and peaks around 6 km. MM5KF has a similar amount of cloud ice to MM5EP, but only one-third as much snow and rain water. Again, this is likely due to the exclusion of the contribution from parameterized convection in Fig. 6a. The vertical distribution of each species of hydrometeor is comparable for both runs. Figure 7 compares the time series of the modeled surface precipitation rates with precipitation rates deduced from the IFA moisture budget (Lin and Johnson 1996a). The excellent agreement is to be expected, since the IFA mean moisture forcing is based on the same moisture budget. Significant rainfall differences would require extreme differences in surface moisture fluxes or storage between the model and observations, which is not the case here. The rainfall rates in MM5EP are systematically slightly higher than MM5KF. This may be related to a higher surface moisture flux in MM5EP, which we discuss shortly. Figure 8 compares the model-simulated fractional cloud coverage with the satellite-derived value from ISCCP data (Fig. 8). The ISCCP data are averaged over 3-h intervals and 2.5 grid spacing (Rossow and Zhang 1995; Zhang et al. 1995). From these data, an IFA mean is constructed to compare with model domain-averaged properties. A cloudy column in the model is defined as a column whose integrated cloud water plus ice path is greater than 0.02 kg m 2. After the first 24 h of model spinup, both models agree well with ISCCP cloud coverage, with nearly 100% cloud cover except during the two suppressed periods, 23 and 25 December. During the second suppressed period, MM5EP produces excessive clearing. We also compare the model-simulated fractional area of rainfall to the shipborne MIT radar measurements over an area of (80 km) 2 within the IFA (Fig. 9). The radar estimate was kindly provided by Dr. Colleen A. Leary of Texas Technology University. On strong convective days, the radar-derived coverage is about 50% 60% and reaches higher than 90% on 24 December. On suppressed days such as 23 and 25 December, sparse or no precipitation falls within the range of the radar. Overall, the modeled evolution of rainfall coverage is well correlated with observed radar measurements. A rainy point in the model is defined as a grid box where the hourly rainfall rate exceeds 0.2 mm h 1. The rain cov-

8 15 JULY 1999 SU ET AL FIG. 5. Time evolution of domain-averaged total condensate on the resolved scale produced by two experiments: (a) MM5KF and (b) MM5EP. Contour interval is 0.1 g kg 1. The dashed lines are 0.01 g kg 1 contours, which mark the outline of clouds. erage in MM5KF and MM5EP is strikingly similar. The model simulates well the average and trend in rain coverage but does not produce the two extremes the almost 100% and 0% area fraction of rain shown by the radar measurements. This may relate to the much smaller areal coverage of the radar than the model domains. Convective development is closely related to surface processes. Figure 10 shows contemporaneous observations of surface air temperature, water vapor mixing ratio, and equivalent potential temperature from IMET buoy measurements at heights of 2 3 m at 1.75 S, 156 E, near the center of the IFA; MM5KF-modeled values at the center of the domain (2 S, 156 E); and the domain averages. The corresponding results from MM5EP are similar to MM5KF and are not shown here. The IMET data are described in detail in Weller and Anderson (1996). The model values are at the lowest level (about 40 m above the surface). The systematic 3 gkg 1 dry bias of the model output compared to the buoy data may partially result from the difference in observation heights, as it is apparent even at the initial time before there is any systematic bias in the model thermodynamic profiles. Hence, we focus on the temporal variations of these variables, rather than the absolute values. During periods of extensive convection, the surface temperature and moisture decrease due to ventilation of the boundary layer by convective downdrafts. This cold pool dynamics is reproduced in the simulation with the Kain Fritsch parameterization, despite the cold bias superimposed on the temperature time series. Although the 2-km fully resolved run MM5EP has similar time variations in surface temperature and moisture, the simulated strength of the cold pool is much weaker than the observed value and MM5KF (not shown). Model-simulated surface latent and sensible fluxes capture the variations of observed surface fluxes but are greater than observed values by about 50% (latent) and 100% (sensible) (Fig. 11). The observed sensible and latent heat fluxes are averages of several buoys in the IFA (Lin and Johnson 1996b) based on bulk aerodynamic formulas optimized for TOGA COARE (Fairall et al. 1996). The latent heat flux variations are primarily due to variations in surface wind speed, while sensible heat flux is more correlated with air sea temperature difference, as found observationally by Lin and Johnson (1996a). The significantly higher surface flux values in both simulations appear to be mainly due to the flux algorithm inside the Blackadar high-resolution PBL parameterization currently used in the MM5. We calculated the surface sensible and latent heat fluxes in the MM5 at the first time step using the COARE flux and Blackadar algorithm. The observed initial latent heat flux is 65 W m 2. In MM5KF, the initial latent heat flux shown in Fig. 11, calculated by the Blackadar flux algorithm, is 102 W m 2. However, the latent heat flux calculated by the COARE flux algorithm is 66 W m 2, very close to the observed. In a cloud-resolving model

9 2334 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 6. Domain and 2 8-day average of total condensate produced in two experiments: (a) MM5KF and (b) MM5EP. Here Qc and Qi are cloud water and ice mixing ratio, while Qr and Qs stand for rainwater and snow mixing ratio. study of a TOGA COARE squall line, Wang et al. (1996) found that the TOGA COARE flux algorithm produced 20% smaller latent and sensible heat fluxes than those obtained from the surface flux calculation in the Blackadar-type PBL parameterization over a 12-h period. The sensitivity of the MM5 simulations to different surface flux algorithms over a long time period (e.g., several days) is currently under investigation and the results will be reported in an upcoming paper. The model sensible heat fluxes are further enhanced during the simulations by an overestimation of the air sea temperature difference due to the models cold bias and the use of time-invariant SST. The observed SST dropped about C during the 8-day period (Chen et al. 1996). For climate studies, the radiative properties of the cloud ensemble are particularly important. To accurately simulate the radiative fluxes requires an accurate characterization of the hydrometeor distribution and size spectrum, along with an accurate radiative transfer parameterization. The Dudhia (1989) longwave radiative scheme employs a broadband emissivity method (Stephens 1984). It accounts for longwave absorption by water vapor, CO 2, cloud water and ice, rain, and snow. The shortwave radiative scheme is also a simple band scheme. It accounts for clear-air scattering and water vapor absorption. The treatment of clouds is highly simplified. Clouds above a given level are assumed to absorb and scatter downwelling solar radiation based only on the solar zenith angle and a vertically integrated condensate path above that level. A lookup table based on Stephens (1978) is used to calculate the cumulative absorption and cloud albedo at each level, from which the upwelling and downwelling shortwave fluxes are deduced. This table assumes that all the condensate is liquid water droplets with a 10- m effective radius. Since precipitation has a larger typical radius, it is weighted with a proportionality constant of 0.1. As mentioned earlier, we modified the scheme to crudely dis-

10 15 JULY 1999 SU ET AL FIG. 7. Time series of surface precipitation rate over IFA and domain-averaged model results. Numbers inside legend box are averaged rainfall rates in mm day 1 from Dec for each dataset. tinguish between frozen and liquid condensate by weighting cloud ice by 0.2 and snow by 0.04 in the condensate path, in inverse proportion to the ratio of their assumed effective radii (50 and 250 m) to that of cloud droplets. We compared surface downwelling shortwave radiation in the models with ISCCP algorithms based on top-of-the-atmosphere observations (Fig. 12). Compared to observations processed by Burks (1998), the ISCCP downwelling solar flux at the surface is about 27 W m 2 higher. The 7-day averages of IMET downwelling solar flux is 130 W m 2. Both model simulations produce less surface shortwave downward flux than ISCCP, but are more comparable to the in situ observations. The 2 8-day averages of ISCCP, MM5KF, and MM5EP downwelling solar flux at surface are 155, 109, and 93 W m 2, respectively. MM5KF produces slightly better surface solar flux compared to observations than MM5EP, especially on 24 December, because the cloud is not as optically thick as that in MM5EP (Fig. 12). Using larger values, 1 and 2.5 mm, for the effective radii of raindrops and snow in the cloud optical depth calculation increases the simulated downward solar fluxes at the surface by 6%. We calculated the modeled domain-averaged outgoing longwave radiation (OLR) and albedo (the percentage of downwelling solar flux reflected back to space at the top of the atmosphere) values. For MM5KF, the 2 8-day averaged OLR is 110 Wm 2 and the albedo is For MM5EP, the corresponding values are 126 W m 2 and 0.64, while the observed values are 166 W m 2 and (varies with datasets; K98). The modeled low OLR, high albedo, and low surface downward solar fluxes may be due to the simplified radiative scheme and deficiencies in the MM5 microphysics parameterization, which causes the MM5 to produce excessive highly reflective cold clouds. FIG. 8. Time series of fractional coverage of cloud over IFA for ISCCP data and two model simulations. b. Organization of convective systems Analysis of the IFA average rain rate and fractional rain area based on MIT radar measurements reveals that when area-averaged rainfall rate is higher, the maximum rain intensity inside the domain is larger, and the rain fractional coverage is greater. We calculate a synthetic radar reflectivity from the model-generated precipitation (rainwater and snow) using an algorithm kindly provided by Brad S. Ferrier and described in Ferrier (1994). The horizontal maps of model-calculated radar reflectivity at the radar base scan height (z 1.5 km) from MM5KF and MM5EP at two different times (Fig. 13), along with corresponding MIT radar images (Fig. 14), are shown. Note the different domain sizes of the images FIG. 9. Time series of fractional area of rainfall for MIT radar data over an (80 km) 2 region within the IFA, and two model experiments. Data interval is 1 h. Radar data were not available before 21 Dec.

11 2336 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 10. Time series of surface (a) air temperature, (b) water vapor mixing ratio, and (c) equivalent potential temperature for IMET buoy measurements at 1.75 S, 156 E, and experiment MM5KF produced corresponding time series at the center of domain (2 S, 156 E) and averages over the domain from Dec. in Fig. 13. In Fig. 14, the most reliable radar reflectivity measurements are within 100 km of the radar location due to possible attenuation. Because our simulations are initiated with a mean IFA sounding and forced by uniform adiabatic cooling and moistening, we do not expect to find a point-to-point match in the convective patterns. However, the spatial distribution of the MM5 radar reflectivity qualitatively agrees with observations. On 23 December, organized convection is highly suppressed. Both MM5 simulations and the MIT radar image display scattered and weak precipitation (Figs. 13a,c, and 14a). At 1800 UTC 24 December, the radar shows a broad area of strong reflectivity to the south of the IFA center (2 S, 156 E) and a moderate reflectivity on the northeast corner of the region (Fig. 14b). Widespread and intense convection is captured by the model (Figs. 13b,d). In MM5EP, more intense and isolated convective cells are embedded in moderately precipitating regions. The MM5KF rainfall pattern is smoother than MM5EP, partly because of the difference in grid resolution. To provide a statistical perspective on the convective organization over the entire model simulation period, we compare the time series of modeled and observed histograms of radar reflectivity. The contours shown in Fig. 15 are percentage area coverage of radar reflectivity within a 2-dBZ interval. The model-derived radar reflectivity values are systematically higher than observed. MIT radar reflectivity rarely exceeds 40 dbz but model-derived reflectivities reach more than 48 dbz in MM5KF and 52 dbz in MM5EP. This is perhaps due to the simplified precipitation size distribution assumed by the MM5 for microphysical calculations and used in the algorithm of reflectivity calculation. The lower reflectivities found in MM5KF are likely due to the exclusion of condensate within the parameterized convection. Overall, the evolution of modeled reflectivity in

12 15 JULY 1999 SU ET AL FIG. 11. Time series of observed and modeled area-averaged surface sensible and latent heat fluxes. MM5KF is consistent with the radar observations. Convective breaks on 23 December and the second half of 25 December are clearly shown. High reflectivity observed on 26 December were not reproduced by MM5KF, because the precipitation on this day is mainly parameterized. The 2-km resolution MM5EP produces less variation in reflectivity than the observations (Figs. 15a, c). Another commonly used indicator of cloud structure is cloud-top temperature. The satellite-deduced cloudtop temperature is based on IR radiance with an optical depth-weighted function to correct water vapor effect. The model-produced cloud-top temperature is assumed to be the temperature of the atmosphere at the level where the optical depth of cloud below the model top (50 mb) is unity. To compute the optical depth, we use constant absorption coefficients and m 2 g 1 for cloud ice and water particles, which are slightly FIG. 12. Time series of downwelling solar radiative flux at surface for ISCCP data and two model simulations.

13 2338 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 13. Horizontal maps of radar reflectivity at base scan height (z 1.5 km) calculated from two model simulations for suppressed convective period (0100 UTC Dec 23) and active convective period (1800 UTC Dec 24). less than those used in the model radiation scheme (Dudhia 1989), because the satellite-derived cloud-top temperature is from the m spectrum, while the model longwave radiation scheme is broadband. Our absorption coefficients are similar to those given by Stephens (1978) for the water vapor window region ( m), which we assume will approximate the coefficients for the m band. Histograms of cloud-top temperature for the first two simulations are compared with the GMS satellite data from Chen et al. (1996) in Fig. 16. The contours are area percentage covered by cloud-top temperature within a 5-K interval over the IFA region or model domains during December. The satellite data clearly display a 2-day local periodicity in IR cloud-top temperature variation. Three convective events (22, 24, and 26 December) started with a rapid development of very cold cloud-top temperatures less than 200 K, weakening

14 15 JULY 1999 SU ET AL FIG. 14. Horizontal maps of MIT radar reflectivity at base scan height (z 1.5 km) over the same area as the domain of MM5KF for (a) suppressed convective period (0100 UTC 23 Dec) and (b) active convective period (1800 UTC 24 Dec). Dashed circle in each panel indicates the radar detection range. and collapsing to warmer cloud shields over a 2-day period. During the two relatively suppressed convective periods (23 and 25 December), the satellite-observed cloud-top temperature is dominated by values between 260 and 290 K. In both simulations, there is also a 2-day periodicity at the very cold cloud-top temperature range (e.g., 200 K), but it is not nearly as pronounced in the warmer temperature range as in the observations. On 23 and 25 December, although most of the model domain is precipitation free, there is still a thin layer of cirrus clouds remaining in the upper levels in both models. Possible reasons for the discrepancy may be a combination of inaccurate treatment of microphysical processes of cirrus anvil, the neglect of net hydrometeor advection into and out of the domain (which may be significant in the upper troposphere), and the difference in the spectrum bands used for the cloud-top temperature calculation in model simulations and satellite observations. Another important measure of the cloud population is the size distribution of cloud systems. The observational study by Chen et al. (1996) showed the size distribution of cloud clusters within the IFA region during TOGA COARE. A cloud cluster is defined by a closed contour of IR cloud-top temperature less than 208 K. Figure 17 shows the observed and modeled population of IFA cloud clusters during December. The largest possible size of cloud clusters is limited by the model domain. Only MM5KF is used, due to the small domain size of MM5EP. Each dot represents a cloud cluster with a specified size indicated by its equivalent radius, defined as R e (A/ ) 1/2, where A is the cluster area. Four classes of clusters by size are defined in Chen et al. (1996). Each size quartile contributes 25% to the total area of cloud top colder than 208 K over the entire GMS satellite domain (90 S 90 N, 80 E 160 W) during TOGA COARE. Comparing the satellite cloud cluster occurrence (Fig. 17a) with that generated in MM5KF over a (600 km) 2 domain (Fig. 17b), we see that the model produces a large number of small clusters of class 1 but fails to reproduce big clusters of classes 3 and 4. Over the 7-day period from 20 to 26 December, satelliteobserved class-4 clusters account for 49.8% of the total cluster area, while the modeled class 4 contributes only 7.3%. For class-3 clusters, the percentage for each dataset is 27.0% and 22.8%, respectively. But the modeled class-1 clusters compose 40.0% of the total area of cold cloud-top temperature, compared to 9.6% in the observations. The inability of MM5KF to produce large clusters may partly result from the limited model domain. This problem is even worse in MM5EP, which has a domain size only about one-tenth of that of MM5KF. We are currently investigating cloud-clustering in larger domain simulations. MM5KF correctly captures the development of larger clusters on 21, 22, 24, and 26 December. However, the last period of larger clusters in the model begins early on 25 December, which is much earlier than observed. As most of the model-produced cloud clusters are small in size, they are also short lived. A typical lifetime of the cloud clusters within the model domain is 5 10 h, whereas the observation indicates a much longer lifetime for large cloud systems (Chen et al. 1996). c. Coarse domain simulation Could a simulation with an even coarser resolution than 15 km give us a reasonably good representation of tropical convection? To answer this question, we conduct a simulation using 60-km grid spacing over the same (600 km) 2 domain used in MM5KF with the same IFA mean forcing and model physics. The coarse domain simulation produces very similar domain-averaged properties of convective systems in response to evolving mean forcing. This is because the domain-averaged energy and moisture budgets are controlled by the uniform external forcing. However, significant difference exists in the hor-

15 2340 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 15. Percent area of radar reflectivity at 1.5-km level within 2-dBZ interval over the IFA or two model domains from the period Dec Contours are drawn at 0.1%, 1%, 5%, 10%, and 20% (per 2 dbz h 1 ); (a) MIT radar data, (b) MM5KF, and (c) MM5EP. Time marker starts from 0000 UTC 20 Dec MIT radar data on 20 Dec are not available. izontal structure of convective systems. Figure 18 shows the horizontal map of radar reflectivity from the 60-km simulation at base scan height (z 1.5 km) at 1800 UTC on 24 December 1992 (Fig. 18a), compared with the corresponding map from MM5KF averaged onto 60-km grids (Fig. 18b). The 60-km simulation displays less horizontal variability than MM5KF (Fig. 18b) and the observed radar image (Fig. 14b). It is unclear from this comparison whether 60-km resolution would reproduce observed meso- scale ( km scale) convective organization skillfully given a larger simulation domain. However, since individual convective systems are not

16 15 JULY 1999 SU ET AL FIG. 16. Percent area of cloud-top temperature within 5-K interval over the IFA or two model domains from the period Dec Contours are drawn at 1%, 5%, 10%, 20%, and 30% (per 5 K h 1 ); (a) satellite data, (b) MM5KF, and (c) MM5EP. Time marker starts from 0000 UTC 20 Dec apparent at 60-km resolution, such a simulation cannot convincingly address the role of the stochastic nature of convection, for example, whether individual convective systems will spontaneously cluster. It remains open whether the more detailed and realistic-looking organization of a 15-km simulation actually leads to a better representation of the convective ensemble. 4. Discussion and conclusions The MM5 is able to simulate many aspects of the evolution of deep convection in response to evolving mean conditions over the IFA in TOGA COARE, using either a combined implicit explicit treatment of convection over a large domain or fully explicit treatment

17 2342 JOURNAL OF THE ATMOSPHERIC SCIENCES FIG. 17. Time series of occurrence of cloud clusters (closed contours of IR temperature 208 K) within IFA as a function of their sizes; (a) satellite data, (b) MM5KF. of convection over a small domain. The model simulations reproduce much of the observed temporal variability in the temperature and relative humidity profiles but develop similar biases. All simulations drift about 2 K colder than observations over the 8 simulated days throughout the troposphere. This bias may reflect errors in the specified IFA mean forcing, the model physics, or even the technique of using specified large-scale forcing to induce convection rather than vice versa. Many other models have also given similar cold biases with the same forcings and the forcings are not thermodynamically consistent with the observed mean evolution of moist static energy, suggesting that the forcings are at least partly at fault. The relative humidity is 10% 15% too high through the troposphere in both models; this seems more likely to be mainly a problem with the model physics. In all experiments, cloud and rainfall amount and their fractional coverage resemble the observations in magnitude and time variation. During the relatively suppressed convective periods, the models tend to have greater areal coverage of rainfall and more cirrus anvil clouds than observed. MM5KF-simulated size distribution of cloud systems is generally smaller than the observed. The use of specified uniform IFA mean forcing and a limited domain size may tend to suppress convective organization associated with synoptic-scale circulations. Histograms of cloud-top temperature and radar reflectivity from the 15- and 2-km runs show similar evolutions. While they are broadly similar to observations, the models generate cirrus that persist excessively long into periods of suppressed convection for reasons we do not understand. The modeled radar reflectivity are systematically somewhat higher than observed, suggesting biases in the Marshall Palmer distributions of hydrometeors assumed by the model. The temporal variation, however, is consistent with that observed. The temporal variability of the modeled surface heat and moisture fluxes agrees with observations. However, surface latent and sensible heat fluxes in the model are 50% 100% greater than observed values. This seems to primarily be due to the surface flux algorithm used in the Blackadar high-resolution PBL parameterization scheme in the MM5. Further sensitivity tests of the different flux algorithms in the MM5 are being conducted to help us understand the interaction of convection with boundary layer processes. The downwelling solar radiative flux at surface is underestimated by about 20%.

18 15 JULY 1999 SU ET AL the simplified microphysical parameterization in the MM5. The model simulation with 60-km resolution produces similar domain-averaged properties to other simulations, but there are not enough grid points to represent mesoscale structure in the 60-km run. Further testing is needed to determine if substantial differences in convective organization emerge between 15- and 60-km runs if a larger domain or a weaker forcing is used. The striking similarity between the 15-km run with parameterized convection and the 2-km run with explicitly resolved convection, and the agreement of both runs with a diverse variety of TOGA COARE observations, suggest the usefulness of the MM5 with 15-km grid resolution in studying the interaction of tropical deep convective organization and large-scale circulations. In some respects, the 15-km run is in fact superior to the 2-km run. Because there exists a significant difference between observed and modeled relative humidity, surface fluxes, histograms of cloud-top temperature and radar reflectivity, size distribution of cloud clusters, etc., there is a need for further improvement of the MM5 physical parameterizations. One useful complement to studies such as described here would be to use the MM5 to simulate a well-observed tropical mesoscale convective system (e.g., the TOGA COARE 22 February 1993 squall line system, chosen as case 1 of the 1997 GCSS Working Group 4 Model Intercomparison Project). FIG. 18. Horizontal maps of radar reflectivity at base scan height (z 1.5 km) for active convective period (1800 UTC 24 Dec) calculated from (a) MM5KF60 and (b) MM5KF averaged onto 60-km grids. This suggests the need to improve the shortwave radiation scheme in the MM5. The model run with 15-km resolution and parameterized convection and the one with 2-km resolution and explicitly resolved convection produce similar time evolution of domain-averaged temperature and moisture profiles. Various statistical properties of the convection such as fractional coverage of rainfall and cloud are also very close in both simulations. Differences in total condensate and radar reflectivity distribution are mainly due to the condensate within deep convective towers. When convection is parameterized, this condensate, which may amount to more than half of the overall condensate, is not resolved by the model and cannot be included in these statistics. Compared to observations, the cooling and drying of boundary layer during active convective period in MM5EP is not as pronounced as MM5KF. This indicates that even 2-km grid spacing may not be adequate to fully resolve the effects of small-scale convective updrafts and downdrafts. It may also be due to Acknowledgments. The authors would like to thank Dr. Brad Ferrier for providing the code of radar reflectivity calculation algorithm, Dr. Sandra Yuter for the MIT radar reflectivity histogram, Kay Dewar for graphical assistance, and Dr. Dennis L. Hartmann and Dr. Robert A. Houze for helpful comments on this manuscript. The authors also benefited from the discussions with Dr. John Kain and Dr. Jimy Dudhia about the Kain Fritsch parameterization and the radiation scheme used in the simulations. The authors are grateful to two reviewers valuable comments, which helped us to improve the manuscript. The authors would like to thank the ISCCP, Goddard Institute for Space Studies, and the Distributed Active Archive Center (Code 902.2) at the Goddard Space Flight Center in Greenbelt, Maryland, for the production and distribution of ISCCP flux data, respectively. These activities are sponsored by NASA s Mission to Planet Earth Program. This work is supported by NASA EOS Grant NAGW and a NOAA TOGA COARE grant under Cooperative Agreement NA37RJ0198. REFERENCES Burks, J. E., 1998: Radiative fluxes and heating rates during TOGA COARE over the Intensive Flux Array. M.S. thesis, Department of Meteorology, University of Utah, 85 pp. [Available from Department of Meteorology, 819 Browning Bldg., University of Utah, Salt Lake City, UT ]