# Presentation: 270969

## What does a CRM need to model cloud-aerosol interaction 

_**Acknowledgments**_
- This research was supported by the DOE Atmospheric Radiation Measurements Program (ARM) and by the PNNL Directed Research and Development (LDRD) program as part of the Aerosol Climate Initiative. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC02-05CH11231. PNNL is operated by Battelle for the U.S. Department of Energy.

_**Motivation**_
- Mixed-phase clouds are wide-spread and long-lived in the Arctic.
- Their response to changing aerosol forcing is not well understood.
- Second indirect effect is expected to be stronger than in warm cloud.
- Cloud-resolving models (CRMs) need to simulate mixed-phase cloud properties correctly to provide a reliable information on cloud-aerosol interaction.

_**Summary**_
- The mixed-phase clouds are not uniform; spatial structure is important.
- Spectral bin microphysics scheme provides a tool to test uncertain nucleation mechanisms but ...
- ... global models can only handle a two-moment scheme, at best.
- Models with prognostic IN are rare but needed (*Fridlind et al. 2007*)

_**What is needed ?**_
-  IN concentrations measured by CFDC (condensation freezing and deposition modes) are  often comparable to observed ice particle (IP) concentrations.
-  IN are depleted quickly by precipitation.
- Therefore,
-  a ***prognostic IN concentration(s)*** is needed, and
-  another ***nucleation mechanism*** and/or
-  ***IN source*** is needed to maintain the IP concentration and the mixed-phase structure of clouds for prolonged period of time.

**What does a CRM need to model cloud-aerosol interaction **
**in mixed-phase clouds?**
**Mikhail Ovtchinnikov****1****, Jiwen Fan****1****, Jennifer Comstock****1****, Sally McFarlane****1****, **
**Alexander Khain****2****, Vaughn Phillips****3**
**1***** Pacific Northwest National Laboratory, Richland , WA, USA; *****2 *****The Hebrew University of Jerusalem, Jerusalem, Israel; *****3 *****University of Hawaii, Honolulu, USA***

_**REFERENCES**_
- Fridlind et al (2007), J. Geophys. Res., doi:10.1029/2007JD008646.
- Khain, A. P., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips (2004), J. Atmos. Sci., 61, 2963–2982.
- Khairoutdinov and Randall (2003), J. Atmos. Sci., 60, 607–625.
- McFarquhar et al (2007), J. Geophys. Res., doi:10.1029/2007JD008633.
- Phillips V. T. J., L. Donner, and S. T. Garner (2007), J. Atmos. Sci., 64, 738–761.

_**Approach**_
- Use the System for Atmospheric Modeling (SAM) with three microphysics options:
-  **standard bulk (1MOM)**: predicted non-precipitating and precipitating condensed water mixing ratios; saturation adjustment-based condensation; temperature dependent liquid-ice partitioning (*Khairoutdinov and Randall, 2003*)
-  **limited 2-moment bulk (2MOM)**: predicted number and mass mixing ratios for cloud liquid and ice and mass mixing ratios for rain, snow, and graupel; linearized supersaturation prediction scheme (*Phillips et al, 2007*)
-  **spectral bin microphysics (SBM)**: predicted size distributions for CCN, cloud droplets, 3 types of ice crystals, snow, graupel, and hail (*Khain et al. 2004*); added CCN regeneration scheme, prognostic ice nucleus number mixing ratio is available

_**Cloud properties**_
- 12-hour 2D simulations, dx=100m, dz=20m, dt=2s

-  Negligible cloud ice amount;
-  Most ice is in precipitation;
-  Liquid and ice amounts in cloud are anti-correlated.

**Total liquid water                     **

**Total ice water                     **

_**LWP and IWP**_
- Mixed-Phase Arctic Cloud Experiment (**M-PACE**) **October 9-10, 2004** case (McFarquhar et al. 2007).
- Both liquid and ice water paths are highly sensitive to the treatment of microphysics and in case of 2MOM and SBM to  the  concentrations of CCN  and IN.

**Total ice water                     **

**Total liquid water                     **

-  Nearly equal split between cloud liquid and cloud ice;
-  Liquid and ice amounts in cloud are diagnosed and therefore highly correlated in time and in space.

*LWP and IWP are important but *
*	**	**	**	**do not tell the whole story.*

-  Comparable amount of ice in the form of primary ice crystals and snow;
-  Complex spatial structure with no unique liquid-ice relation.

- MODEL

- AIRCRAFT OBS

**Cloud**
**Condensation**
**Nuclei**

**Aerosol**

**Ice Nuclei**

**Cloud **
**droplets**

**Ice Particles**

- uncertain

- IN  recycling and pre-
-       activation

- evaporative
- freezing ?

- evaporation
-           IN ?

**1MOM**

**2MOM**

**SBM**