# Presentation: 718889

## Intercomparison of Model Simulations of Mixed-Phase Clouds Observed during M-PACE 

Where does GFS Stand ?

**Where does GFS Stand ?**

**Fanglin Yang**
**NCEP/EMC Physics Meeting,  12/05/2008**

## M-PACE DOE ARM Mixed-Phase Arctic Cloud Experiment 

*27 Sept ~ 22 October, 2004*
-  Northern Alaska and adjacent Arctic Ocean
- Measurements were made from both in-situ and remote (aircraft) sensors.

- Citation

- Proteus

- Aerosonde Payloads

- Credit:  Hans Verlinde

## Model Intercomparison 

- This study is jointly supported by the GCSS (global energy and water experiment cloud system) Polar Cloud Working Group and the ARM Cloud Modeling Working Group.
- Project leaders:  Stephen Klein from PCMDI, and Hugh Morrison from NCAR
- 17 single-column model (SCM) and 9 cloud-resolving models (CRM) participated.
- All models were initialized from observational analyses, including cloud water, representing the atmosphere mean state over the domain on the right.
- Advective forcings (tendencies) and surface fluxes were specified using ECMWF analyses.**  **
- Lower boundary condition was specified as an ocean surface with a temperature of 274.01K.** **

## NCEP GFS SCM 

- The NCEP SCM was written by **Shrinivas Moorthi ** based on the 2005 version of the GFS,  and modified by Fanglin Yang for this M-PACE project.
- Two versions of the SCM, 64 and 640 vertical layers, were run with and without ice microphysics.
- Hua-Lu Pan provided vertical profiles of Oct-2004 operational GFS forecasts at the M-PACE sites.

##  17 Participating SCMs

** ****17 Participating SCMs**

- Single Momentum with T-Dependence

##  9 Participating CRM

## Case A: Multi-Layer Mixed-Phase Clouds, Oct 5-8, 2004

**Case A: Multi-Layer Mixed-Phase Clouds, Oct 5-8, 2004**

- Operational
- GFS 24hr fcst

- ARM Obs

- GFS-SCM 24hr fcst forced by ops GFS tendencies

- GFS-SCM 72hr fcst forced by ops GFS tendencies

- GFS-SCM 72hr fcst forced by MPACE tendencies

- GFS-SCM 72hr fcst forced by MPACE tendencies, no ice physics

## Case A: Observations

**Retrieved liquid water path (LWP) and ice water path (IWP) from ground-based remote sensing, averaged during the period 000 UTC Oct. 6 to 1400 UTC Oct. 8.**

## Case A:  Model Simulations

**Modeled liquid water path (LWP) and ice water path (IWP) for the baseline and sensitivity tests with no ice microphysics and increased vertical resolution. ‘1-M T-dep’, ‘1-M Ind’, and ‘2-M’ refer to the models using one-moment microphysics schemes with T-dependent partitioning, one-moment schemes with independent liquid and ice, and two-moment schemes, respectively. Asterisk (*) indicates models that did not include precipitation ice. Median IWP values are derived only from models that include both cloud and precipitation ice.**

## Case A:  Summary

**For All Models:** generally **overestimated** liquid water path (**LWP**),  and strongly **underestimated** ice water path (**IWP**) with large spread among the models.
- Models employing **simple microphysics** schemes with **temperature-based partitioning** of the cloud liquid and ice masses **are not able to** produce results consistent with observations.
- Models with a more sophisticated, two-moment treatment of the cloud microphysics produce a somewhat smaller liquid water path that is closer to observations.
**NCEP GFS-SCM**:  **largely** **underestimated** LWP; slightly underestimated IWP but close to other models.

## Case B:   Single Layer Mixed-Phase Stratocumulus, Oct 9-10, 2004 

**MODIS** **composite, October 9, 2004**.
- The boundary layer clouds occurred when cold air above the sea ice to the northeast of Alaska flowed over the ice-free Beaufort Sea inducing the significant surface heat fluxes responsible for cloud formation. The sea ice is visible in the upper right corner of the image. The clouds were observed in the northeasterly flow between the ARM stations of Barrow and Oliktok Point on the coast of snow-covered Alaska. As is common in **“cold-air outbreak” stratocumulus**, boundary layer “rolls” or “cloud streets” developed with a horizontal scale that increases in the downstream direction.

## Case B:  Observations

- Median condensate water paths and inter-quartile ranges in parentheses from observations for the study period.

## Case B:  Simulations, Mean Stats

## Case B:  Simulations, Individual Models

## Case B:  Summary

**Observation****:** a well-mixed boundary layer with a cloud top temperature of –15 oC, dominant by water cloud.
**All Models**: generally **underestimated** LWP;  mean IWP is close to the observed.
**NCEP GFS-SCM**:  compared to other SCMs tends to have even lower LWP;  IWP is more realistic

## Percent SW reached the surface to TOA SW flux

- Scatterplot of **solar transmission** and total condensate water path. Solar transmission is computed as the average downward component to the broadband solar radiation at the surface divided by the average insolation at the top-of-the-atmosphere. Model results are indicated with symbols with the same convention as in Figure 7. The observations from the radiation measurements and remote sensing retrievals at Barrow are indicated by OBS. The results of STREAMER radiation calculations performed with an ice-free ocean or snow-covered land surface are indicated by S-O and S-L, respectively.

***condensate water path (g m******-2******)***

***solar***
***transmission***

## surface downward longwave radiation(W m-2)

***condensate water path (g m******-2******)***

***surface downward longwave radiation(W m-2)***

## Conclusion

- Model simulations have large spread.  No single factor is found to lead a good or bad simulation. ** **
- However, models with more sophisticated microphysics are somewhat better, but really not much.
**NCEP GFS-SCM**:  ***tends to produce less LWP than other models.***
- 	GFS LWP is underestimated for both case A and B.  Most models overestimated for case A.  GFS IWP is not much different from others.
*Should  the T-dependent partitioning of LWP and IWP in the GFS be tuned?*   Earlier studies (Curry 200; McFarquhar &Cober 2004) showed that , unlike the mid-latitude cloud, Arctic mixed-phase cloud has little temperature dependence for the amount of LWP versus IWP.
**Take home message**:  no consensus among models. It is still a challenge for models to capture Arctic mixed-phase clouds.