CONCLUSIONS & FUTURE WORK

Using TWP-ICE Observations and CRM Simulations to Retrieve Cloud Microphysics Processes

Xiping Zeng1,2, Wei-Kuo Tao2, Shaocheng Xie3, Minghua Zhang4, and Steve Lang2

1GEST/UMBC, Greenbelt 2NASA/GSFC, Greenbelt 3LLNL, Livermore 4Stony Brook University, New York

CONCLUSIONS & FUTURE WORK

• Simulations show that TWP-ICE cloud ensembles are sensitive to IN and ice crystal enhancement factor. However, they are not so sensitive as the ARM-SGP ones, which resembles that of SCSMEX/NESA and KWAJEX simulations (Zeng et al. 2008a).

• Present and other sensitivity experiments reveal that the modeled cloud ensembles are close to observations when Ni is high in the Tropics. In contrast, they are close to observation when Ni is low in middle latitudes.

• Suppose that the ensemble average of IN concentration in the Tropics is close to or less than that in middle latitudes. Thus, the ice crystal enhancement factor in the Tropics is about 103 times that in middle latitudes, which is consistent with the model of Blyth and Latham (1997) based on observations.

• Future study will address the proper representation of IN and ice crystal multiplication in CRM simulations.

Acknowledgements: This work was supported by the DOE ARM Program.

ARM-SGP SIMULATIONS

Three ARM-SGP campaigns, conducted in the summer of 1997, spring of 2000 and summer of 2002, provided large-scale forcing data to drive the model. Their numerical simulations lasted for 20, 29 and 20 days, respectively. Fig. 4 displays the time-pressure cross sections of ice water content from observations and the two numerical experiments. Fig. 5 displays the modeling biases of three variables against Ni at -10C.

Fig. 1 Schematic of a convective cloud with a cirrus anvil that modulates solar and infrared radiation. Stars and circles denote ice crystals and water drops, respectively. The Bergeron process exists in the mixed-phase region in the middle troposphere.

Fig. 5 Modeling biases of upward infrared flux at the top of the atmosphere (thin lines in the lower part), precipitable water (thick lines) and surface precipitation rate (thin lines in the upper part) versus the IN concentration at -10°C in midlatitudes (or the ARM-SGP site). Red and blue lines represent the results in spring and summer, respectively.

Fig. 4 Time-pressure cross section of ice water content for the ARM-SGP-2000 numerical experiments and field observations. The upper, middle and lower panels represent the retrievals (observations) and the experiments with low and high IN concentration, respectively.

INTRODUCTION

Ice nuclei (IN), a form of aerosol particles, can change clouds via the Bergeron process and thus modulate radiation (see Fig. 1). Previous Cloud-Resolving Model (CRM) simulations revealed that cloud ensembles and radiation are sensitive to IN concentration as well as the ice crystal enhancement factor (Zeng et al. 2008a, b). In this study, TWP-ICE and ARM-SGP observations are used to analyze the difference in the ice crystal enhancement factor between the Tropics and midlatitudes.

NUMERICAL SIMULATIONS The number concentration of active natural IN changes with air temperature T as (Fletcher 1962)

Ni=n0exp[(T0-T)]

where n0 is typically about 10-8 cm-3 with variations of several orders of magnitude; =0.6 and can range from 0.4 to 0.8; and T0=273.16 K. Thus, the conversion rate of cloud ice to snow due to vapor deposition in the Bergeron process is directionally proportional to

3qi-mI50-1Ni

where qi is the mixing ratio of cloud ice, mI50 the mass of a 50 m size crystal, the ice crystal enhancement factor (Zeng et al. 2008a). Obviously, n0 works as one factor in the conversion rate.

Three-dimensional CRM simulations, driven with TWP-ICE and ARM-SGP forcing, are carried out to show the sensitivity of cloud ensembles to n0 in the Tropics and midlatitudes, using the same model setup as the previous ones (Tao et al. 2003, Zeng et al. 2007, 2008a). Due to lack of sufficient observations of and Ni, the modeled cloud ensembles are compared with observations to diagnose n0. If the cloud ensembles with a particular n0 agree with observations, the value can be treated as an in-situ n0.

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TWP-ICE SIMULATIONS

Two TWP-ICE simulations were conducted over the mainland subdomain.They started at 1500 UTC 19 January 2006 and lasted for 5 days. They used (, n0)=(0.4, 1.2x10-9 cm-3) and (0.6, 1.2x10-6 cm-

3), which are denoted as low and high IN concentrations, respectively. Fig. 2 displays the modeled surface precipitation rate and precipitable water in contrast to observations. Fig. 3 displays the time-pressure cross sections of modeled ice water content.

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REFERENCES

1. Blyth, A. M. and J. Latham, 1997: A multi-thermal model of cumulus glaciation via the Hallett-Mossop process. Quart. J. Roy. Meteor. Soc., 123, 1185-1198.

2. Fletcher, N. H., 1962: The physics of Rain Clouds. The Cambridge University Press, 386 pp.

3. Tao, W.-K., J. Simpson, D. Baker, S. Braun, M.-D. Chou, B. Ferrier, D. Johnson, A. Khain, S. Lang, B. Lynn, C.-L. Shie, D. Starr, C.-H. Sui, Y. Wang and P. Wetzel, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97-137.

4. Zeng, X., W.-K. Tao, M. Zhang, C. Peters-Lidard, S. Lang, J. Simpson, S. Kumar, S. Xie, J. L. Eastman, C.-L. Shie and J. V. Geiger, 2007: Evaluating clouds in long-term cloud-resolving model simulations with observational data. J. Atmos. Sci., 64, 4153-4177.

5. Zeng, X., W.-K. Tao, S. Lang, A. Y. Hou, M. Zhang and J. Simpson, 2008a: On the sensitivity of atmospheric ensembles to cloud microphyics in long-term cloud-resolving model simulations. J. Meteor. Soc. Japan. (in press).

6. Zeng, X., W.-K. Tao, M. Zhang, A. Y. Hou, S. Xie, S. Lang, X. Li, D. Starr, X. Li, and J. Simpson, 2008b: The indirect effect of ice nuclei on atmospheric radiation. Submitted to J. Atmos. Sci.


Fig. 2 The modeled surface precipitation rate versus time (left) and precipitable water versus time (right). Black, green and red lines represent the results from observations, the numerical simulations with the low and high IN concentrations, respectively.



Fig. 3 Time-pressure cross section of ice water content for the TWP-ICE numerical experiments. The left and right panels represent the experiments with low and high IN concentration, respectively.


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CONCLUSIONS & FUTURE WORK