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  • View in gallery

    Large-scale forcing for the (a) temperature and (b) moisture fields; (c) zonal and (d) meridional components of large-scale wind field; and surface (e) sensible and (f) latent heat fluxes over the SGP during the ARM 1997 IOP. Contour intervals are 6 K day−1 in (a), 3 g kg−1 day−1 in (b), and 5 m s−1 in (c) and (d).

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    The 26-day mean profiles of domain-averaged differences between the CRM and observations for (a) temperature and (b) water vapor mixing ratio. Horizontal bars are standard deviations of the 3-hourly means.

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    Three-hourly domain-averaged surface rainfall rates from the CRM (solid) and observations (dashed) over the SGP.

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    Three-hourly domain-averaged (a) cloud liquid water, (b) cloud ice water, and (c) cloud fraction from the CRM (solid) and observations (dashed). Note that observations for cloud ice water are not currently available.

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    Scatter diagrams between observed (obs) and simulated (CRM) daily mean longwave (FLW) and shortwave (FSW) net fluxes (W m−2) at the TOA and surface (SRF). Downward fluxes are positive.

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    Scatter diagram between the CRM and Surface Cloud Grid Value Added Product for the 3-hourly ratio of downward all-sky over clear-sky shortwave fluxes (dnsw) at the surface. Two thin solid lines represent observational uncertainty of 15%.

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    Examples of cloud fields used in the radiation calculations of (a) CRM, (b) GCM-like approach (GRC), and (c) diagnostic analysis (NCI). See text for explanation.

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    The 26-day mean profile of cloud fraction from the CRM. Horizontal bars are the standard deviations of 15-min cloud fraction and show the temporal variability of cloud fraction during the 26-day period.

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    The 26-day mean profiles of emissivity averaged over the 200 columns of the CRM domain (solid) and emissivity calculated using the domain-averaged cloud water path (dashed). Horizontal bars are the standard deviations of the domain-averaged emissivity, which show the spatial variability of emissivity during the 26-day period.

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    CRM-simulated cloud frequency (10−4) distribution as a function of the base and top heights for (left) ARM and (right) TOGA COARE.

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    Scatter diagrams of radiative fluxes from the CRM vs GRC for (a), (c) shortwave and (b), (d) longwave at (a), (b) TOA and (c), (d) SRF. The dotted points represent 15-min samples.

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    The 26-day mean profiles of (a) shortwave heating rates, (b) longwave heating rates, and (c) totalradiative heating rates from the CRM (solid), GRC (dashed), and diagnostic analysis (NCI; dotted).

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Cloud-Resolving Model Simulations over the ARM SGP

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  • 1 Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa
  • | 2 Illinois State Water Survey, University of Illinois at Urbana–Champaign, Urbana, and Illinois Department of Natural Resources, Champaign, Illinois
  • | 3 Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa
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Abstract

This study aims to combine the cloud-resolving model (CRM) simulations with the Department of Energy’s Atmospheric Radiation Measurement Program (ARM) observations to provide long-term comprehensive and physically consistent data that facilitate quantifying the effects of subgrid cloud–radiation interactions and ultimately to develop physically based parameterization of these interactions in general circulation models. The CRM is applied here to simulate the midlatitude cloud systems observed at the ARM southern Great Plains (SGP) site during the 1997 intensive observation period. As in the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), the CRM-simulated ensemble mean quantities such as cloud liquid water, cloud fraction, precipitation, and radiative fluxes are generally in line with the surface measurements, satellite, and radar retrievals. The CRM differences from the ARM estimates, when averaged over the entire period, are less than 5 W m−2 in both longwave and shortwave radiative fluxes at the top of the atmosphere and surface. Because of the different large-scale forcing and surface heat fluxes in ARM and TOGA COARE, the CRM produces different cloud distributions over the midlatitude continent and tropical ocean. However, diagnostic analyses show that the subgrid cloud variability has similar impact on the domain-averaged radiative fluxes and heating rates in ARM as in TOGA COARE.

Corresponding author address: Dr. Xiaoqing Wu, Iowa State University, 3010 Agronomy Hall, Ames, IA 50011. Email: wuxq@iastate.edu

Abstract

This study aims to combine the cloud-resolving model (CRM) simulations with the Department of Energy’s Atmospheric Radiation Measurement Program (ARM) observations to provide long-term comprehensive and physically consistent data that facilitate quantifying the effects of subgrid cloud–radiation interactions and ultimately to develop physically based parameterization of these interactions in general circulation models. The CRM is applied here to simulate the midlatitude cloud systems observed at the ARM southern Great Plains (SGP) site during the 1997 intensive observation period. As in the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), the CRM-simulated ensemble mean quantities such as cloud liquid water, cloud fraction, precipitation, and radiative fluxes are generally in line with the surface measurements, satellite, and radar retrievals. The CRM differences from the ARM estimates, when averaged over the entire period, are less than 5 W m−2 in both longwave and shortwave radiative fluxes at the top of the atmosphere and surface. Because of the different large-scale forcing and surface heat fluxes in ARM and TOGA COARE, the CRM produces different cloud distributions over the midlatitude continent and tropical ocean. However, diagnostic analyses show that the subgrid cloud variability has similar impact on the domain-averaged radiative fluxes and heating rates in ARM as in TOGA COARE.

Corresponding author address: Dr. Xiaoqing Wu, Iowa State University, 3010 Agronomy Hall, Ames, IA 50011. Email: wuxq@iastate.edu

1. Introduction

The atmospheric radiation budgets are strongly affected by the horizontal and vertical distributions of cloud systems. With the horizontal resolution of several hundred kilometers, current general circulation models (GCMs) need to represent convection and clouds in terms of grid-scale thermodynamic and dynamical variables. Simplistic assumptions have to be made in the treatment of cloud variability within a GCM grid box. It has long been recognized that the parameterization of subgrid cloud–radiation interactions, including cloud vertical overlap and optical property horizontal inhomogeneity, is one of major uncertainties in climate simulations (Manabe and Strickler 1964; Geleyn and Hollingsworth 1979; Stephens 1984). Numerous studies have been focused on developing methods to approximate the effects of subgrid interactions into the GCM radiation schemes (Morcrette and Fouquart 1986; Tian and Curry 1989; Cahalan et al. 1994; Tiedtke 1996; Liang and Wang 1997; Barker et al.1999; Fu et al. 2000; Collins 2001; Li 2002; Stephens et al. 2004; Li et al. 2005). However, the evaluation and implementation of these approaches is limited because of the lack of consistent, fine-resolution observations of cloud–radiation interactions.

Two major developments in the modeling and observational communities over the last decade provide a unique opportunity to facilitate progress in this area (GCSS 1994; Stokes and Schwartz 1994; Moncrieff et al. 1997). The cloud-resolving model (CRM) with the fine spatial resolution has emerged as a credible tool to simulate the long-term evolution of cloud systems (Grabowski et al. 1996; Xu and Randall 1996; Wu and Moncrieff 1996; Wu et al. 1998; Das et al. 1999; Donner et al. 1999; Li et al. 1999; Redelsperger et al. 2000a; Johnson et al. 2002; Tao et al. 2003, 2004; and many others). The Department of Energy Atmospheric Radiation Measurement Program (ARM) obtains, with an unprecedented scope, continuous field measurements of radiation and cloud fields using a new generation of in situ and remote sensing instruments (Ackerman and Stokes 2003). The CRM simulations validated by ARM observations will provide comprehensive and physically consistent two-dimensional (2D) or three-dimensional (3D) cloud-scale datasets, based on which more realistic GCM parameterization of subgrid cloud-radiation interactions can be developed.

Grabowski et al. (1996) outlined a formal framework for the use of traditional cloud-scale model (e.g., Soong and Ogura 1980; Soong and Tao 1980) to simulate cloud systems under the evolving large-scale temperature and moisture advections. The nonsquall cloud clusters, squall lines, scattered convection, and transitions among them were produced by the CRM over a 7-day period during phase III of the Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE). The qualitative agreement between modeled cloud systems and available radar observations was encouraging at the time. Clearly, more quantitative evaluations of CRM simulations against observations are needed. The Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) between November 1992 and February 1993 provided this opportunity. Wu et al. (1998, 1999) conducted a monthlong CRM simulation during TOGA COARE using the evolving large-scale forcing provided by Lin and Johnson (1996). The model results were extensively evaluated against various independent datasets such as longwave and shortwave radiative flux, cloud radiative forcing, surface sensible and latent heat fluxes, and airborne radar reflectivity. The general agreement between modeled and satellite-retrieved radiative fluxes gives confidence in the use of CRM-generated cloud and radiative properties to evaluate the cloud and radiation parameterization schemes of GCMs (Wu and Moncrieff 2001). The TOGA COARE observations and successful long-term CRM simulations help establish the first intercomparison project of CRMs under the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS; Moncrieff et al. 1997; Redelsperger et al. 2000b). While the development of CRMs benefited from the TOGA COARE, the uncertainties in the estimation of large-scale forcing and the shortwave radiation retrievals at the top of the atmosphere (TOA) during TOGA COARE have also been recognized (e.g., Wu et al. 2000; Wu and Moncrieff 2001).

ARM offers another dataset for the CRM simulation and its validation. Its intensive observation periods (IOPs) obtained several monthlong datasets including measurements of temperature, moisture and wind profiles, precipitation, surface sensible and latent heat fluxes, and surface and TOA radiative fluxes. The large-scale forcing data were constrained by column budgets of dry static energy and moisture using the variational analysis (Zhang and Lin 1997). These forcing data were used by several CRMs to simulate the midlatitude continental cloud systems and intercompare the CRMs and single-column models (SCMs) over several multiday periods during the IOPs (Ghan et al. 2000; Xie et al. 2002). The simulated cloud systems were in reasonable agreement with ARM observations and significantly better than those of SCMs. Luo et al. (2003) and Khairoutdinov and Randall (2003) conducted 2D and 3D CRM simulations for the entire 1997 IOP, respectively. Luo et al. focused on the comparison of CRM-produced cirrus clouds with ARM cloud radar observations. Khairoutdinov and Randall performed an ensemble of runs to test the sensitivity of simulations to the domain dimensionality and size, grid resolution, and microphysical representation. Recently, Tao et al. (2004) compared temperature, moisture, and moist static energy budgets between the CRM simulations of ARM and TOGA COARE cloud systems. The budgets suggested the relative importance of physical processes involved. Microphysical processes play a more important role in tropical oceanic cases, while radiative, surface sensible, and latent heat fluxes have more contributions in midlatitude continental cases.

Given the different large-scale forcing, surface heat fluxes, and temperature and moisture budgets between the ARM and TOGA COARE cloud systems and the differences in spatial and temporal structure of clouds between two regions presented in previous studies and in this study, it is natural to ask whether the effects of cloud horizontal inhomogeneity and vertical overlap on the domain-averaged radiative fluxes and heating rates are qualitatively similar for these two cases. This investigation will provide useful information for improving the representation of subgrid cloud distributions in the radiation parameterization. The objective of our study is to produce cloud systems observed during the 1997 IOP of ARM using the Iowa State University (ISU) CRM, validate the ensemble means against ARM observations, and examine the effects of subgrid cloud distributions on the radiative fluxes and heating rates in comparison with TOGA COARE (Wu and Moncrieff 2001; Wu and Liang 2005a). A brief description of the CRM, experimental design, and large-scale forcing data are presented in section 2. In section 3, the CRM-simulated ensemble means are validated against ARM observations. Using the CRM-produced cloud-scale properties, effects of cloud inhomogeneity and random overlap assumption on the estimation of radiative fluxes and heat rates are examined by the diagnostic analyses and compared with TOGA COARE. Summary and concluding remark are given in section 4.

2. Cloud-resolving model and large-scale forcing data

The CRM used in this study is based on the Clark–Hall model (Clark et al. 1996), which is a finite-difference formulation of the anelastic and nonhydrostatic equations. We improved various aspects of the model physics to make the CRM applicable to cloud system studies. In particular, an interactive cloud-radiation scheme based on the National Center for Atmospheric Research (NCAR) Community Climate Model version 3 (CCM3; Kiehl et al. 1996) is incorporated to calculate radiative heating rates and surface fluxes every 300 s (e.g., Grabowski et al. 1996; Wu et al. 1999). The cloud microphysics is explicitly predicted using the Kessler (1969) warm rain parameterization and the Koenig and Murray (1976) ice parameterization with an improved ice fall speed formulation (Wu et al. 1999). The cloud effective radius is assumed to be constant in the radiation calculation, with 10 μm for liquid clouds and 30 μm for ice clouds as in CCM3. The subgrid-scale mixing is parameterized using the first-order eddy diffusion scheme of Smagorinsky (1963). The 2D version of the CRM is aligned east–west and is 600 km long and 40 km deep. The model has a 3-km horizontal resolution and a stretched grid in the vertical (100 m at the surface, increasing to 1500 m at the model top) with a time step of 15 s. Periodic lateral boundary conditions are used to facilitate a mathematically consistent CRM framework (Grabowski et al. 1996). Free-slip bottom and top boundary conditions are applied together with a gravity wave absorber located between 16 km and the model top. Surface friction is calculated to model the momentum exchange between the air and ground.

Large-scale forcing datasets (temperature and moisture advections; Figs. 1a and 1b) are produced by the variational analysis of observations over the ARM Southern Great Plains site (SGP; approximately 400 km × 350 km bounded by 35.12°–38.31°N and 95.55°–99.33°W) during the 1997 IOP, which constrains the column-integrated mass, moisture, energy, and momentum (Zhang et al. 2001). These time-evolving data are integrated to force the CRM to produce cloud system variations that correspond to observations. Because it is difficult to obtain accurate momentum forcing, the model domain-averaged zonal and meridional wind components are relaxed to observations (Figs. 1c and 1d) using a 2-h time scale (e.g., Grabowski et al. 1996). Since the CRM is currently not coupled with any dynamic land surface model, the observed evolving surface latent and sensible heat fluxes (Figs. 1e and 1f) used in the variational analysis are prescribed for the simulation. The surface heat fluxes are characterized by strong diurnal variation throughout the IOP. Based on observational studies by Li et al. (2002) and Michalsky et al. (2003), surface albedos for direct/diffuse incident solar radiation are set to 0.05 and 0.25 for two spectral intervals (0.2–0.7 and 0.7–5.0 μm), respectively. Convection initiation is excited by adding random perturbations to the temperature and moisture fields within the boundary layer every 15 min during the simulation, with respective amplitude of 0.1 K and 0.1 g kg−1, as well as to the surface latent and sensible heat fluxes with 10% amplitude. All perturbations are imposed across the 2D domain but vanished when averaged over the domain. The perturbations during the simulation allowed gradual release of convective available potential energy ahead of the cold front to prevent unrealistic bursts of convection.

Several convective episodes can be easily identified by strong advective cooling (Fig. 1a) and moistening (Fig. 1b) during the IOP. For most cases, the cooling is well correlated with the moistening, where the peak of the former higher than the latter indicates impacts of convective transports of heat and moisture (Yanai et al. 1973). An exception is the period of 3–4 July when a cold front passed through the SGP site. Ahead of the front, strong moistening appeared near the surface on 3 July (Fig. 1b), but there was little temperature cooling (Fig. 1a). As the front passed through the site, cooling developed in early 4 July and moistening became deeper and higher. After the passage of the front, cooling is associated with drying below 3 km and moistening above 3 km during 4 July. The description of convective events for three subperiods (i.e., 26–30 June, 7–12 July, and 12–17 July) can be found in Xie et al. (2002).

3. CRM simulations of ARM cloud systems and comparison with TOGA COARE

a. Ensemble means and validation against ARM observations

The CRM simulation is performed for a period of 26 days (22 June–17 July) during the 1997 IOP since there is no convective event in the first three days (19–21 June) of the IOP. The simulation can be considered as a downscaling procedure, by which the cloud-scale properties can be produced by integrating the observed large-scale forcing with the CRM that explicitly resolves cloud dynamics and mesoscale circulation. The 26-day mean profiles and standard deviations of temperature and moisture differences between the CRM and observations are shown in Fig. 2. For the temperature field, the largest cold bias (1°C) is present in the layer of 13–14 km and the largest warm bias (2°C) is found at the surface. The temperature biases in the rest of the layers are smaller than 0.5°C. For the moisture field, moist biases appear in most layers except near the surface. The largest moisture bias (1 g kg−1) is around 2 km. These biases are comparable in magnitude to the TOGA COARE simulations (Wu et al. 1999). Various factors that might be responsible for these biases have been suggested, including the uncertainties in the model microphysics and the lack of large-scale advection of condensates (e.g., Grabowski et al. 1996; Wu et al. 2000).

Figure 3 compares the CRM-simulated and the observed 3-hourly precipitation. The model result is averaged over the 600-km horizontal domain. The CRM well reproduced most precipitation events during the IOP. The 26-day mean rainfall rates are 0.19 mm h−1 for the CRM and 0.20 mm h−1 for the observation. The perturbations added on temperature and moisture fields in the lower boundary improve the simulation of precipitation associated with the cold front during 3–4 July. The peak of large-scale moisture forcing appears in the lower troposphere several hours before the peak of large-scale temperature cooling occurs in the middle and upper troposphere in the cold front case (Figs. 1a and 1b). Without the perturbations, the moisture and convective available potential energy in the lower troposphere were accumulating as a result of the large-scale moisture advection as the cold front approached. When the cold front passed through the domain, convection developed and produced unrealistically greater precipitation compared to the observation. Additional considerations to the current CRM framework will be needed to simulate the development of cloud systems associated with the cold front.

The cloud properties also compare favorably with the ARM measurements. The evolution and magnitude of vertically integrated cloud liquid water path are consistent with the retrievals of ground-based millimeter-wave cloud radar (MMCR; Fig. 4a). The 26-day mean cloud liquid water paths (standard deviations of 3-hourly means) are 39.4 (56.8) for the CRM and 32.5 (49.1) g m−2 for the observation. Note that the observation is from measurements at the central facility of the SGP, but the model result represents the mean over the SGP domain, which partly explains the difference between the CRM and observations. The CRM cloud ice water path is shown in Fig. 4b, which can be evaluated when the observational estimate becomes available. The 26-day mean ice water path is 86.0 g m−2, which is twice the liquid water path with the largest amount associated with convection on 30 June and 4 July. In TOGA COARE simulations, it was also found that the ice water path is generally twice as large as the liquid ice path associated with strong convective systems (e.g., Wu et al. 1999). The standard deviation of 3-hourly means over 26 days is 16.2 g m−2. The reliable cloud ice measurement is crucial for further improving the treatment of ice nucleation process and ice crystal fall speed in the CRM microphysical scheme (Wu et al. 1999). The evolution of cloud fraction from the CRM is in general agreement with Geostationary Operational Environmental Satellite-7 (GOES-7) retrievals (Minnis et al. 1995; Fig. 4c). The CRM cloud fraction is calculated using a threshold of 0.2 g m−2 of column-integrated cloud liquid and ice water paths (Wu and Moncrieff 2001). The 26-day mean cloud fraction from the CRM is 0.54, which is larger than the satellite estimate 0.45, but the standard deviations of 3-hourly means are similar (0.31 for CRM and 0.32 for observation).

ARM measurements provide important data to evaluate the radiation budgets of the CRM simulation. Table 1 lists means and standard deviations of daily net shortwave and longwave fluxes at the TOA and surface averaged over the SGP domain for the 26-day period. In observations, TOA longwave and shortwave fluxes are derived from the GOES-7 retrieval (Minnis et al. 1995), surface shortwave radiative fluxes are estimated by the algorithm of Li et al. (2002), and surface longwave radiative fluxes are obtained using data measured at 22 solar infrared radiation stations (SIRS; http://www.arm.gov/instruments/instrument.php?id=sirs). The surface shortwave radiative flux averaged over 22 SIRS are about 30 W m−2 smaller than that obtained from Li’s estimates. This deficit was attributed to the inability of the limited number of SIRS in representing the surface inhomogeneity that has large impacts on the domain-averaged shortwave radiative fluxes (Li et al. 2002). The CRM values are averaged over the 600-km domain. The result shows that the CRM realistically simulates both longwave and shortwave radiative fluxes at TOA and surface, where the differences from the observational estimates are less than 5 W m−2. Standard deviations of daily mean fluxes also generally agree, with a slight underestimation in the model. The consistency between the CRM and ARM observations indirectly validates the simulated cloud systems, which may also provide some support to the CRM simulations of TOGA COARE cloud systems (Wu et al. 1999). Recall that there were large uncertainties in the estimation of TOA shortwave fluxes from satellite observations in TOGA COARE.

The quality of the CRM result can be further verified by the scatter diagrams of CRM versus observed daily mean fluxes. Figure 5 shows the corresponding CRM and observations for both longwave and shortwave fluxes at the TOA and surface. The daily means are around the diagonal lines, indicating the general agreement between the two, but scatter considerably with largest deviations of 50 W m−2 for several days during the period. Further improvement in the treatment of cloud ice microphysics may help reduce these differences. As demonstrated by Wu et al. (1999), the improved representation of ice fall speed based on observations significantly affected the radiation budgets during TOGA COARE. The use of constant cloud effective radius and surface albedo may also contribute to the discrepancy between the CRM and observations. The potential impacts of varied radius on the simulation were suggested by an offline radiation calculation using TOGA COARE CRM simulations with the doubling of the cloud ice effective radius (Wu et al. 1999). The effect of physically determined effective radius over land and ocean on the CRM simulation is an aspect requiring further investigation.

Figure 6 compares 3-hourly ratios of downward all-sky over clear-sky shortwave fluxes at the surface simulated by the CRM with the observational analysis from the Surface Cloud Grid Value Added Product (SfcCldGrid VAP; a detailed description can be found on the Web site http://science.arm.gov/vaps/sfccgl.stm). The use of ratio of observed shortwave fluxes eliminates the biases associated with the instruments, such as cosine response errors and calibration drifts (Long and Ackerman 2000). Most of the points (85%) during the 26-day period fall within the observational uncertainty of 15%. The 26-day means (standard deviations) are 0.85 (0.14) and 0.81 (0.16) for the CRM and observation, respectively. The impact from inconsistent clear versus cloudy conditions between the model and observations is partially depicted by the comparison of cloud liquid water in Fig. 4.

In general, the CRM performance is qualitatively similar in simulating the ARM SGP and TOGA COARE cloud systems when validated against observations. The CRM-simulated ensemble mean cloud and radiative properties are in general agreement with ARM-estimated properties. With the CRM-produced cloud-scale properties, the cloud distributions and their effects on the ensemble mean fluxes and heating rates can be investigated and compared with TOGA COARE simulations (Wu and Moncrieff 2001; Wu and Liang 2005a) to understand their dependence on the prevailing climate regimes.

b. Subgrid cloud effects on domain-averaged radiative flux and heating rate

Faithful downscaling of cloud and radiative properties allows the quantification of effects of horizontal and vertical cloud distributions on domain-averaged radiative fluxes and heating rates. Diagnostic calculations with the use of various CRM simulations have been adopted by increasing numbers of studies (e.g., Barker et al. 1999; Wu and Moncrieff 2001; Wu et al. 2002; Li and Barker 2002; Wu and Liang 2003, 2005a; Stephens et al. 2004; Xu 2005). Wu and Liang (2005a) presented a diagnostic approach to quantify the effects of TOGA COARE cloud systems on the radiation fluxes and heating rate. The same approach is used here to further investigate the impacts of cloud distributions on the domain-averaged radiative flux and heating rate for the ARM cloud systems that are characteristically different from the TOGA COARE.

In a snapshot of the CRM simulation (Fig. 7a), a grid box at a given level is considered to be completely overcast if the sum of liquid and ice water paths exceeds a threshold of 0.2 g m−2 or otherwise totally clear sky. The radiation calculations with different thresholds show that clouds with the water paths smaller than 0.2 g m−2 have negligible effect on the radiative fluxes (Wu and Moncrieff 2001). Radiative fluxes and heating rates are calculated for each column using the CCM3 radiative transfer scheme. The use of binary clouds makes the cloud vertical overlap assumptions irrelevant. As such, the vertical cloud cover structure is explicitly resolved. Since the cloud condensate profiles differ between individual columns, the radiative fluxes and heating rates averaged over all 200 columns of the domain thereby also include the effect of horizontal inhomogeneity of cloud optical properties.

Figure 7b shows the mean profile of cloud liquid and ice water mixing ratio averaged over the cloudy columns of Fig. 7a and the profile of cloud fraction over the domain. The radiative transfer is calculated using these profiles together with the mean temperature and moisture profiles. The cloud vertical overlap has to be treated by a certain cloud-overlap assumption because there is only one column for each time step. This calculation is what a typical GCM does, which will be referred to as the GCM-like radiation calculation (GRC). In the CCM3 radiation scheme used here, the radiative effect of vertically varying partial cloudiness is represented in approximation to the random overlap assumption for the shortwave radiation (Kiehl et al. 1996). It is approximated by modifying the cloud optical depth as τc = τcA3/2c, where τc is a cloud layer optical depth, Ac the cloud fraction of the corresponding model layer, and τc the scaled optical depth. The scaling was found to produce a result that is very close to the random overlap assumption. The difference of radiative fluxes and heating rates between the CRM and GRC approaches indicates the combined impacts of horizontal inhomogeneity and random overlap assumption of cloud condensates. The readers are referred to Kiehl et al. (1996) for the description of random overlap assumption for the longwave radiation.

Figure 7c shows the cloud distribution that excludes the horizontal inhomogeneity of cloud condensates in Fig. 7a by replacing cloudy columns with the mean profile of cloud condensates in Fig. 7b. Similar diagnostic analyses were performed by Barker et al. (1999) for three snapshots from our previous CRM simulation of GATE cloud systems (Wu and Moncrieff 1996). Here the analysis is based on monthlong statistics and considers both shortwave and longwave radiative fluxes. As such the conclusion is statistically more robust. The radiative transfer is calculated for each column and the mean radiative flux and heating rate are obtained by averaging all 200 columns. This diagnostic analysis preserves the cloud geometry but ignores in-cloud horizontal condensate inhomogeneity, and is referred to as NCI. The comparison between the CRM and NCI will quantify the impact of cloud condensate horizontal inhomogeneity. The difference between the GRC and NCI analyses will depict the cloud vertical overlap effect to a large extent, but not entirely because the radiative transfer into a specific layer depends on both cloud cover and optical property of adjacent layers.

Figures 8 and 9 show the vertical distributions of cloud fraction and emissivity, respectively. The mean and standard deviations are calculated based on daily averages over the 26-day integration period. As compared with the TOGA COARE (Wu and Liang 2005a), the ARM cloud systems have quite different vertical distributions with cloud cover peaks at lower altitudes and optically thinner clouds than TOGA COARE. Figure 10 further compares the cloud frequency distributions between the two cloud systems in terms of cloud-base and cloud-top heights. Clearly, the TOGA COARE has much more deep convections (with the base around 4 km) that penetrate to higher altitudes than the ARM. TOGA COARE clouds are also concentrated more in upper layers above 12 km than those of the ARM, which occur more often below 12 km. An important question is then whether the subgrid cloud radiative effects also differ between the ARM and TOGA COARE systems.

Table 2 compares the net radiative fluxes (downward flux minus upward flux) at the TOA and surface between the CRM and GRC analyses. The shortwave differences are more than 30 W m−2 at both the TOA and surface, while the corresponding longwave differences are approximately 28 and 9 W m−2. This shows that the GRC yields larger effective cloudiness and results in smaller domain-averaged net shortwave fluxes due to the larger reflection at the TOA and less insolation to the surface. For the longwave, the larger effective cloudiness will allow less longwave radiation emitted to space at the TOA and more longwave into the surface, which lead to smaller domain-averaged net longwave fluxes. In both circumstances, the GRC generates much larger biases than the CRM. The use of homogenous mean profiles of cloud condensate and random overlap assumption is the cause of differences between the CRM and GRC analyses.

These results are similar with those from the TOGA COARE calculations (Wu and Moncrieff 2001; Wu and Liang 2005a) and other studies that reported the biases in the radiative fluxes due to various cloud overlap assumptions (e.g., Barker et al. 1999; Fu et al. 2000; Li 2002; Stephens et al. 2004). Note that the TOGA COARE CRM simulation has a larger domain of 900 km than the 600 km for the ARM. In addition, a 20-day three-dimensional CRM simulation of TOGA COARE cloud systems (Wu and Guimond 2006) produced similar radiative budgets as in the two-dimensional CRM simulation. We expect that this similarity between the 2D and 3D simulations applies for the results of the ARM cloud systems.

The radiative effects of cloud inhomogeneity and random overlap assumption can be more clearly visualized from the scatter diagrams of CRM versus GRC. Figure 11 illustrates the instantaneous CRM and GRC correspondences of the domain-averaged fluxes for all 15-min samples. There exists a large spread of points, with the majority below and above the diagonal lines for the shortwave and longwave fluxes, respectively. Consequently, the GRC produces twice as large standard deviation of daily means as the CRM for the shortwave fluxes and larger deviation of daily means for longwave fluxes (Table 2). It further indicates that the cloud reflection in GRC was too high, which results in less shortwave energy reaching the surface and larger shortwave energy being reflected back to space in GRC than CRM, and the effective cloud amount in GRC is too large, which leads to smaller longwave energy to the space and larger longwave energy reflected back to the surface in GRC than CRM. The result suggests the need to improve the treatment of cloud subgrid variability in the NCAR CCM3 radiation scheme.

Table 2 also lists the statistics from the NCI analysis. The mean flux differences between the CRM and NCI are approximately half of those between the CRM and GRC. The result suggests that the condensate horizontal inhomogeneity is as important as cloud vertical overlap in the radiation calculation. With the vertical overlap of clouds as in the CRM, the NCI produces smaller standard deviations and root-mean-square (rms) errors of daily mean fluxes as compared to the GRC (Table 2).

Figure 12 compares 26-day mean profiles of shortwave, longwave, and total radiative heating rates from the CRM, GRC, and NCI. The differences among the profiles reflect those shown in the fluxes. The GRC produces larger (smaller) shortwave heating above (below) 5 km relative to the CRM (Fig. 12a). The NCI reduces the difference of shortwave heating between the CRM and GRC by one-third. The longwave cooling rates from the GRC are larger (smaller) above (below) 8 km than those from the CRM (Fig. 12b). The difference of longwave cooling between the CRM and GRC is reduced by one-third above 8 km and by half below. The total radiative heating rate is dominated by the longwave cooling rate (Fig. 12c). The GRC produces too much cooling above 8 km but too little below as compared to the CRM. The NCI that includes the CRM cloud cover structure eliminates the difference between the CRM and GRC by about half, while only slightly modifying the peak of the difference at 8 km. This indicates the importance of including the inhomogeneity effect.

4. Summary and concluding remarks

A 26-day CRM integration driven by the large-scale forcing data at the SGP site during the ARM 1997 IOP is conducted with the goal of producing a long-term (several weeks to a month), dynamically and thermodynamically consistent high-resolution dataset that reasonably represents cloud horizontal (inhomogeneity) and vertical (geometric association) distributions. The simulation of cloud systems is evaluated against various measurements including satellite and radar. It is demonstrated that the CRM can simulate many features of ARM cloud systems that generally agree with observations. These include precipitation, cloud liquid water path, and radiative fluxes at the TOA and surface. Considering the uncertainties in the observational estimates and the comparison of point measurements versus domain-averaged model properties, the similarities between the CRM simulation and ARM data are very promising. It offers the confidence for the use of CRM in analyzing effects of cloud distributions on the radiation budgets, and testing existing or developing new parameterizations that incorporate subgrid cloud-radiation interactions into the GCMs. We compare the CRM performance in representing the cloud systems between the ARM SGP and TOGA COARE. It is found that special treatment (such as adding perturbations to temperature and humidity in the boundary layer) is necessary for realistic simulation of precipitation during cold front passages, while it is not needed in the TOGA COARE simulation. The spatial and temporal structure of the cloud systems largely differs, where the TOGA COARE is identified with more deep convection than the ARM.

Despite the differences between ARM and TOGA COARE cloud systems, the cloud inhomogeneity and random overlap assumption have similar impacts on the domain-averaged radiative fluxes, each accounting for approximately half of the biases in TOA and surface longwave and shortwave radiative fluxes. Using the CRM simulation as a benchmark, it is demonstrated that the conventional GCM radiation calculation (GRC) greatly underestimates the shortwave and longwave fluxes at the TOA and surface due to the use of homogenous cloud condensates and unrealistic random overlap assumptions. The NCI analysis that preserves the CRM cloud cover structure and cloud condensate profile is conducted to further quantify the relative effects of the cloud inhomogeneity and random overlap assumption on the radiation calculation. The result indicates that both cloud horizontal inhomogeneity and vertical overlap are important and must be incorporated through a physically based treatment in order to accurately calculate radiative fluxes and heating rates over the grid box of GCMs. It should be pointed out that the random overlap assumption used in CCM3 has now been replaced by the maximum-random overlap assumption. The CRM simulation with the new radiation scheme can be also performed to examine the effects of maximum-random overlap assumption on the radiative fluxes and heating rates following the same analysis presented in this study.

The CRM simulation presented in this paper has been also used to evaluate the mosaic treatment (Liang and Wang 1997) of vertical overlap of clouds for GCMs. The result is presented in Liang and Wu (2005), which demonstrates that the consideration of cloud vertical overlap based on three cloud genera (i.e., deep convection, anvil, and stratiform clouds) can capture the dominant radiative effects of cloud vertical overlap simulated by the CRM. It also suggests that further improvement can be made for the mosaic approach by introducing the inhomogeneity factor for three cloud genera. Recently, the mosaic representation of cloud distribution is implemented in the radiation scheme of the CCM3 to investigate its impacts on global climate simulations. It is found that the CCM3 with the modified radiation scheme enables the use of more realistic cloud amounts as well as cloud water contents while producing net radiative fluxes closer to observations (Wu and Liang 2005b). Clearly, the CRM has shown a great potential for understanding cloud-related problems in GCMs. But the remaining differences between the model and observations also point to the need for the improvement of the CRM physics, especially the cloud microphysics. In this regard, the measurement of microphysical parameters such as the ice fall speed in the cloud systems over the ARM SGP will be critical.

Acknowledgments

We thank numerous ARM science team members for making ARM data available for the comparison with the CRM. This research was partly supported by the Biological and Environmental Research Program (BER), U.S. Department of Energy Grants DE-FG02-02ER63483 and DE-FG02-04ER63868, and National Oceanic and Atmospheric Administration Center for Atmospheric Sciences Grant 634554172523. The CRM simulations are performed on the Linux Clusters located in ISU. Computing support by Daryl Herzmann is greatly appreciated. The views expressed are those of the authors and do not necessarily reflect those of the sponsoring agencies or the Illinois State Water Survey.

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Fig. 1.
Fig. 1.

Large-scale forcing for the (a) temperature and (b) moisture fields; (c) zonal and (d) meridional components of large-scale wind field; and surface (e) sensible and (f) latent heat fluxes over the SGP during the ARM 1997 IOP. Contour intervals are 6 K day−1 in (a), 3 g kg−1 day−1 in (b), and 5 m s−1 in (c) and (d).

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 2.
Fig. 2.

The 26-day mean profiles of domain-averaged differences between the CRM and observations for (a) temperature and (b) water vapor mixing ratio. Horizontal bars are standard deviations of the 3-hourly means.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 3.
Fig. 3.

Three-hourly domain-averaged surface rainfall rates from the CRM (solid) and observations (dashed) over the SGP.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 4.
Fig. 4.

Three-hourly domain-averaged (a) cloud liquid water, (b) cloud ice water, and (c) cloud fraction from the CRM (solid) and observations (dashed). Note that observations for cloud ice water are not currently available.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 5.
Fig. 5.

Scatter diagrams between observed (obs) and simulated (CRM) daily mean longwave (FLW) and shortwave (FSW) net fluxes (W m−2) at the TOA and surface (SRF). Downward fluxes are positive.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 6.
Fig. 6.

Scatter diagram between the CRM and Surface Cloud Grid Value Added Product for the 3-hourly ratio of downward all-sky over clear-sky shortwave fluxes (dnsw) at the surface. Two thin solid lines represent observational uncertainty of 15%.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 7.
Fig. 7.

Examples of cloud fields used in the radiation calculations of (a) CRM, (b) GCM-like approach (GRC), and (c) diagnostic analysis (NCI). See text for explanation.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 8.
Fig. 8.

The 26-day mean profile of cloud fraction from the CRM. Horizontal bars are the standard deviations of 15-min cloud fraction and show the temporal variability of cloud fraction during the 26-day period.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 9.
Fig. 9.

The 26-day mean profiles of emissivity averaged over the 200 columns of the CRM domain (solid) and emissivity calculated using the domain-averaged cloud water path (dashed). Horizontal bars are the standard deviations of the domain-averaged emissivity, which show the spatial variability of emissivity during the 26-day period.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 10.
Fig. 10.

CRM-simulated cloud frequency (10−4) distribution as a function of the base and top heights for (left) ARM and (right) TOGA COARE.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 11.
Fig. 11.

Scatter diagrams of radiative fluxes from the CRM vs GRC for (a), (c) shortwave and (b), (d) longwave at (a), (b) TOA and (c), (d) SRF. The dotted points represent 15-min samples.

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Fig. 12.
Fig. 12.

The 26-day mean profiles of (a) shortwave heating rates, (b) longwave heating rates, and (c) totalradiative heating rates from the CRM (solid), GRC (dashed), and diagnostic analysis (NCI; dotted).

Citation: Monthly Weather Review 135, 8; 10.1175/MWR3438.1

Table 1.

The 26-day (22 Jun–17 Jul 1997) means and standard deviations of net longwave (LW) and shortwave (SW) radiative fluxes at the TOA and SRF from the CRM and obs. The net flux is defined as the downward flux minus the upward flux.

Table 1.
Table 2.

The 26-day means and standard deviation of net LW and SW radiative fluxes at the TOA and SRF from the CRM, GRC, and NCI, and rmse for the GRC and NCI.

Table 2.
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