Although Global Climate Models (GCMs) are the most crucial analysis tool used to understand climate systems, they are highly sensitive to the representation of clouds and their feedbacks, which introduce the largest uncertainties in the prediction of climate sensitivity (S). The Polar Regions are predicted to be most affected by global warming, and observations appear to be confirming this.  It is thus paramount to realistically represent clouds in Polar Regions, especially the Arctic where cloud cover is most extensive. A study by Sanderson et al. (2008) found that 70% of the ensemble variance in the global feedback parameter (λ), where λ = 1/S, was due to two leading factors, (i) the entrainment coefficient and (ii) the ice fall velocity. Another GCM study by Mitchell et al. (2008) relates the findings in Sanderson et al. (2008) more intimately to cirrus microphysics. It showed that the global radiation balance was sensitive to the relative concentrations of small ice crystals through their affect on the mass-weighted ice fall velocity, V_m. Thus, the characterization of V_m in clouds is critical for accurately estimating climate sensitivity and the earth's radiation budget in general. Moreover, V_m is a critical parameter affecting cloud lifetime, ice water path (IWP) and coverage.

The representation of V_m and effective diameter D_e in GCMs can be achieved by incorporating new and improved PSD schemes to parameterize V_m and D_e accurately.  Moreover, a PSD scheme can supply the a priori information needed in cloud remote sensing retrievals, and also be used to validate the ice microphysics schemes in cloud and climate models. In this study data from two Arctic field projects (MPACE and ISDAC) has been used to parameterize V_m. Both these experiments were conducted over the ARM site in Barrow, Alaska. MPACE cloud data was representative of Arctic Fall whereas data from the ISDAC study was obtained during Arctic Spring. As such, data from these two field campaigns can be combined together for a broad representation of Arctic clouds.

A temperature dependent particle size distribution (PSD) scheme has been developed for MPACE (figure 1), based on the 1D-C, 2D-C and HVPS probes during all-ice conditions.  A bimodal PSD framework was adopted for MPACE PSD development, where each mode of the PSD is described as a gamma function of the form:

N(D) = No D^ν exp(-λD) ,

where λ is the slope parameter, ν is the dispersion or width parameter and No relates λ and ν to the PSD (or mode) number concentration N or IWC. Further details on this scheme, can be found in Mitchell et al. (2010, JAS).

A different approach will be applied to estimate V_m from ISDAC data using a relatively new probe called the 2 dimensional -Stereo (or 2D-S) probe. This probe measures ice particle number, projected area and mass and uses an ice particle projected area-mass relationship to estimate the size-resolved mass concentrations.  The PSD number concentration N(D) can then be used to obtain the ice mass sedimentation rate which is the product of the IWC and the mass-weighted fall velocity, V_m.  The mathematical representation of V_m is given as:

V_m = ∑ v(D)m(D)N(D)ΔD / ∑ m(D)N(D)ΔD ,

where v(D) = ice particle fall velocity, m(D) = ice particle mass, N(D) = size distribution and D = ice particle maximum dimension.  Using this equation, the measured PSD of size-resolved number, area and mass concentration can be used to solve for V_m exactly.  Ice particle projected area- and mass-dimension power law expressions, used in the calculation of v(D), are derived from the 2D-S measurements.  The PSD integrated 2D-S ice particle mass measurements were consistent with CVI measurements during the TC4 field campaign.  Both V_m and D_e will be related to cloud temperature and IWC, where D_e is also calculated directly from the 2D-S measurements.  This method has also been successfully applied to data from the TC4 experiment and is being presented by Mitchell et al. in this conference.

A comparison of V_m betweenMPACE and ISDAC will help us understand potential differences in the cirrus life-cyle during Fall and Spring, whereas a similar comparison regarding D_e will help us understand potential differences in cirrus cloud optical properties.  Potential differences in IWC will also be examined to address potential differences in cloud extinction/optical depth. 

Figure 1. The above figure shows example PSD curves from MPACE at two different temperature regimes (a) 0 to -5 ^◦C and (b) -50 to -55 ^◦C