Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment, Part II: Multi-layered cloud GCSS Polar Cloud Breakout Session II, June 4, 2008 Hugh Morrison National Center for Atmospheric Research Stephen Klein and Renata McCoy Lawrence Livermore National Laboratory +27 additional scientists

Mixed-Phase Arctic Cloud Experiment M-PACE took place at ARM’s Barrow site in October 2004 (Verlinde et al. 2007) M-PACE featured numerous aircraft flights that measured clouds and aerosols among other increased observations A variety of cloud types were observed A – multi-layer stratus B – boundary layer stratocumulus C – frontal clouds Cloud Barrow Day in October 2004 A BC ARM’s Barrow site

Period A – Multilayer mixed-phase stratus

Multilayering inferred from lidar

Observations Aircraft Observations Three aircraft flights during the period (Oct. 5, 6, 8) Liquid and ice water contents, effective radii, number concentrations were computed from the data (McFarquhar et al. 2007) CDFC-measured ice nuclei concentrations were very low (~0.1 L -1 ) (Prenni et al. 2007) Radar Observations Liquid and ice water contents were retrieved from the remote sensing Barrow (Shupe et al and Turner et al. 2007; Wang 2007)

Cloud Microphysics Microphysics# of SCMs# of CRMs Single moment with T- dependent liquid and ice 31 Single moment with independent liquid and ice 41 Double moment53 Bin Microphysics00 There is a broad distribution of microphysical complexity among the models

Model Configurations Models are initialized with data from ARM variational analysis over the MPACE domain Large-scale advection and winds from ARM variational analysis Model aerosols are fixed in time except for 2 models with prognostic ice nuclei Models simulate the period from 1400 UTC Oct 5 to 1400 UTC Oct 8

Participating Models Fourteen SCMs and four CRMs SCMs include –five operational climate models (CCCMA, ECHAM, GFDL, GISS, CAM3) –one weather model (NCEP) –four research models (ARCSCM, MCRAS, SCRIPPS, UWM) –four models which include single modifications to the base set (MCRASI, SCAM3- LIU, SCAM3-MG, and SCAM3-UW). (The modifications include cloud microphysics)

Participating Models CRMs include –two 3-dimensional models (METO, SAM). These models have horizontal resolutions of ~500 m and total domain of ~50 km x 50 km. –two 2-dimensional models (RAMS-CSU, UCLA- LARC). These models have horizontal resolutions of ~1 – 2 km with a total domain length of ~100 km

Results All models produce basic morphology of the cloud system. Nearly all models produce multiple layering of liquid, suggesting it is driven more by surface and large-scale advective forcing than details of model physics. However, the number of layers produced by the models is uncorrelated with key cloud parameters such as liquid and ice water path. Little difference in thermodynamic profiles

Cloud Fraction

On average both SCMs and CRMs overestimate the liquid water path (LWP) and strongly underestimate the ice water path (IWP), in contrast to the single-layer case in Part 1. However, during the brief period at the end of Oct. 7 when only low-level, single-layer cloud is present, models underestimate LWP and overestimate IWP consistent with Part 1. These results suggest key differences in the ability of models to simulate deep, multi-layer mixed-phase clouds versus shallow single layer mixed-phase clouds. This may reflect different physical processes in deep versus shallow clouds (e.g., “seeder-feeder” mechanism in deep clouds).

Timeseries of LWP and IWP

Downwelling LW flux at surface

Downwelling SW flux at surface

Impact of ice microphysics

liquid water path (g m -2 ) 1 mom. with ind. liq. & ice 2 mom. 1 mom. with T-dep. part. Observations M-PACE Period A Does the microphysics matter?

ice water path (g m -2 ) 1 mom. with ind. liq. & ice 2 mom. 1 mom. with T-dep. part. Observations M-PACE Period A Does the microphysics matter?

Conclusions In contrast to single layer Period B case, liquid water was overestimated and ice water underestimated, although scatter among models was large. In a brief period of shallow single layer cloud, LWP was underestimated and IWP overestimated as in Period B. Some evidence that increased complexity of microphysics led to improved LWP, but lots of scatter and reason for improvement are not clear.