(mm/day)
72
C
48
24
0 170
173
176
179
182 185 188 Time (Julian Day)
191
194
197
Figure 1. Time series of observed surface precipitation rates (hPi) during the summer 1997 Intensive Observation Period of the Atmospheric Radiation Measurement program. The horizontal lines inside the plot show the durations of the three sub-periods chosen for this intercomparison study. TABLE 7. Sub-case A B C
S UMMA RY OF CHA RACTERISTICS OF SUB - CASES FOR THIS INTERCO MPARISON STU DY Duration
2330 UTC 26–30 June 1997 (Julian day 178 to 182) 2330 UTC 7–12 July 1997 (Julian day 189 to 194) 2330 UTC 12–17 July 1997 (Julian day 194 to 199)
Characteristics of convection A major precipitation event with a maximum precipitation rate of 3.5 mm h¡1 on Julian day 181, and weak precipitation events on Julian day 179. Three moderate precipitation events with maximum precipitation rates of approximately 1.0 mm h¡1 (Julian days 190, 191.5 to 193), with a very short (3–5 h) break period between the second and third events. A moderate precipitation event (»1 mm h¡1 ) on Julian day 198 and a few weaker ones ( 0, cloudg; (3) ¾
where ½ is the density of air, w is the vertical velocity, and ¾ is the updraught area which satis es the criterion of cloud occurrence mentioned earlier. Downdraught mass uxes (Md ) are composed of saturated downdraughts (ds), which satisfy the cloud occurrence criterion, and unsaturated downdraughts (du) with precipitation: Z Mds D .½w d¾ / if fw < 0, cloudg; (4) ¾ Z Mdu D .½w d¾p / if fw < 0, precipitationg; (5) ¾p
where ¾p is the precipitation area, which is identi ed using a larger threshold (10¡4 kg kg¡1 ) than that used for de ning the hydrometeor fraction. Because many different scales of motion are present in CRM simulations, the diagnosed mass uxes include contributions not only from convective-scale (individual strong draughts) and mesoscale circulations (weak stratiform precipitation), but also from gravity waves. Other criteria on de ning updraughts and downdraughts have also been used in the literature, mainly using the draught intensity (e.g. Tao et al. 1987; Gray 2000). The consistency of Mc , which is the sum of Mu and Md , among the models is very good for the mean pro les, as indicated by the small differences from the consensus of all models (thick black dashed line in Fig. 12(a)). For comparison, the observed largescale mass ux, M (½w where w is the large-scale vertical velocity), is also shown. Most CRMs produce compensating subsidence in the environment of the middle and upper troposphere, i.e. Mc is greater than M, except for UKLEM and the middle troposphere of CNRM and UCLA/CSU (Fig. 12(a)). That is, downdraughts are relatively strong in these three models (Fig. 12(c)). Another consistent feature among the models is the lack of compensating subsidence in the lower troposphere and the negative Mc in the PBL of all models. The consensus shows the zero-subsidence level at approximately 5 km. This feature is due to the presence of strong precipitating (unsaturated) downdraughts and to the high cloud-base heights (very small Mu below 1 km). The presence of large-scale horizontal advective heating and drying in the lower troposphere (Fig. 2) may favour strong downdraught activity in model simulations so that the compensating subsidence is not produced.
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Figure 12.
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Same as Fig. 9 except for the net cloud mass ux (Mc , (a)), updraught (Mu , (b)) and downdraught (Md , (c)) mass uxes. The thick black dashed line shows the consensus of all models.
A detailed analysis of the downdraughts from CRM simulations is required in order to understand this feature and to improve cumulus parametrizations in GCMs. The mean pro les of Mu and Md are also quite consistent among the CRMs (Figs. 12(b) and (c)). Apparently, their inter-model differences are greater than those of Mc (Fig. 12(a)) because they respond more directly to the differences in cloud microphysics representations. The inter-model differences in Mu (Fig. 12(b)) are consistent with those in cloud-water mixing ratios (Fig. 9(a)) and cloud-ice mixing ratios (Fig. 9(b)), except for the large Mu in the upper troposphere of CNRM and UCLA/CSU (perhaps contributed by gravity waves). For example, CNRM and GFDL have the largest cloud-water mixing ratios (Fig. 9(a)) and the largest Mu in the lower troposphere. The smallest cloud-ice mixing ratios correspond to the smallest Mu in the upper tropospheres of EULAG and GFDL (Fig. 9(b)). Beyond these inter-model differences, there is a strong consensus among CRMs towards comparable magnitudes in Mu and Md at most heights. As a result, Mc appears as a relatively small residual of these two mass uxes (Fig. 12). This feature does not appear in the simulations of tropical oceanic convection (e.g. Xu and Randall 2000a) and may be characteristic of midlatitude convection over land. A change of thresholds used for diagnosis of updraught and downdraught areas is unlikely to impact on this result. Clearly, this result stresses the equally important roles of updraughts and downdraughts in midlatitude convection over land. It is probably essential that cloud-related parametrizations capture this feature for a proper representation of these convective systems (Xie et al. 2001). Further analyses from contributing groups are needed to isolate contributions from convective and mesoscale processes, as well as from gravity waves, especially in the
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upper troposphere. The partitioning of convective and mesoscale processes (Tao and Simpson 1989; Xu 1995) is an approach well suited to understanding the physical processes leading to these mass- ux pro les. (g) Discussion The agreements between simulations and observations are rather remarkable in many aspects of the Case 3 simulations, for example, intensity of convective events and the timing of some events, and temperature and speci c-humidity evolution. Some noticeable disagreements are, however, present among the CRMs. Chie y, the initial convective precipitation events in the CRM simulations of all sub-cases tend to be delayed relative to observations (Figs. 3(a), (b) and (c)). Probable causes for this are: 1) the coarse horizontal resolutions (1–3 km), 2) the lack of initial mesoscale circulations due to initialization from horizontally homogeneous soundings, and 3) the initial uniform surface uxes. Most of these causes are related to oversimpli cations in the initiation procedure, not to shortcomings in the models. The delayed occurrence of the initial precipitation events leads to signi cant departures of simulated thermodynamic pro les from observations (Figs. 6 and 7), which also impact on the simulations of cloud elds and cloud properties in the rst one to two days. In the present study, a variety of observations of cloud properties such as cloud liquid-water path, column cloud fraction and hydrometeor fraction are available for comparisons with model simulations (Figs. 8 and 11). In general, there are broad agreements with observations for all CRMs, especially in the sub-period-averaged intensities and magnitudes. Some inter-model differences in cloud microphysics parametrizations are readily revealed. It is, however, dif cult to pinpoint the causes of the differences between simulations and observations because of large uncertainties in observations, i.e. point measurements vs. areal averages, and in the best-suited de nitions of cloud boundaries (lateral, top and bottom) used in the CRM diagnoses. The de nitions of cloud boundaries in the CRM diagnoses are not consistent with those of cloud-property measurements. For example, the column cloud fractions are all severely underestimated, compared with either MMCR or satellite observations (Figs. 8(d), (e) and (f), Table 9). The hydrometeor fractions show moderate inter-model differences at all heights (Fig. 11), due perhaps to the small thresholds used in the diagnosis of precipitating fractions. Updraught and downdraught mass uxes also show some inter-model differences among the models though much smaller than those from SCMs (Fig. 12) (Xie et al. 2001). Methods of diagnosing Mu and Md need to be re ned because of the presence of multiple-scale processes in the models, as in the real atmosphere. The mass- ux pro les are not available from observations but are needed for evaluating cumulus parametrizations, in addition to the diagnoses of cumulus transports of heat, moisture and momentum. To further understand the differences between simulations and observations and the inter-model differences, further analyses of observations are needed, based upon Mesonet measurements, gridded satellite and radar precipitation data, to improve the variational analysis of the forcing data, e.g. obtaining the horizontal condensate advection. Furthermore, model sensitivity studies will be helpful to reduce the extent of disagreements between models and observations, for example, sensitivities to horizontal or vertical resolutions, representations of microphysical processes, and relaxations of oversimpli cations in the initiation and forcing methods. In addition, some differences between 2-D and 3-D results also need to be further analysed because some 3-D results do not show any superiority of the additional dimension. Sensitivity studies by some
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contributing groups would help nd out the causes of some inter-model differences and de ciencies found in this study, and address some issues raised in this study, especially those related to cloud microphysics representations. Additional sensitivity studies are also needed to allow cloud–radiation interactions and the interactions between clouds and land-surface processes in the simulations of midlatitude convection. These sensitivity studies are beyond the scope of this intercomparison but should provide very useful ndings in the future.
5.
C ONCLUSIONS
In summary, this intercomparison study has shown: ² CRMs can reasonably simulate midlatitude continental summer convection observed at the ARM CART site in terms of convective intensity, temperature and speci chumidity evolution. ² Delayed occurrences of the initial precipitation events are a common feature of all three sub-cases among the CRMs, especially Sub-case B. ² Observed cloud properties are extensively used to identify some model de ciencies in representations of cloud microphysical processes. ² The 2-D results are very close to those produced by the 3-D versions of the same models; some differences between 2-D and 3-D simulations are noticed and are due probably to the limited domain size and the differences between 2-D and 3-D dynamics. ² Cloud mass uxes, condensate mixing ratios and hydrometeor fractions produced by all CRMs are similar. Some inter-model differences in cloud properties are likely to be related to those in the parametrizations of microphysical processes. ² The magnitudes of the updraught and downdraught mass uxes are more comparable than those produced by simulations of tropical oceanic deep convection.
ACKNOWLEDGEMENTS
This research was partially supported by the Environmental Sciences Division of the US Department of Energy as part of ARM, under grants DE-FG03-95ER61968 (Khairoutdinov, Randall and Xu), DE-FG03-94ER61769 (Krueger), and DE-FG0298ER62570 (Zhang), and Contract W-7405-Eng-48 to LLNL (Cederwall, Xie and Yio). The work at the National Atmospheric and Space Administration (NASA) Langley Research Center (Xu and Wang) was partially supported by the NASA Earth Observation System/Interdisciplinary Science Program. Work at GFDL (Donner and Seman) was partially supported by NASA Contract RR1BNC97. Johnson’s and Tao’s work is supported by the NASA Headquarters Atmospheric Dynamics and Thermodynamics Program and the NASA Tropical Rainfall Measuring Mission. Zhang’s research was also partly supported by the National Science Foundation under grant ATM9701950 to the State University of New York at Stony Brook. Work at NCAR (Grabowski) was supported by NCAR’s Clouds and Climate Program. The Met Of ce (Petch) acknowledges support from the European Union contract EVK2 CT199900051 for the EUROpean Cloud Systems (EUROCS) program. The simulations by F. Guichard were run on a Cray C90 at NCAR and she was partly funded by the European Program EUROCS during the course of this work. F. Guichard also acknowledges J.-L. Redelsperger and J. Dudhia for their help with installing CNRM CRM on the Cray C-90.
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