As noted by Lachlan-Cope , the microphysical properties of cloud particles can have a major impact on the Earth's radiation budget. It is therefore important that they are correctly represented in climate models. Unfortunately, model cloud parameterizations are often empirically based upon measurements made in the Tropics and middle latitudes, so that their applicability for Antarctic clouds is questionable. For example, cloud physics schemes must provide for the radiative properties of thin clouds over Antarctica [Gallée and Gorodetskaya, 2010]. Furthermore, climate model simulations demonstrate the nonlocal as well as global impacts of changes in Antarctic cloud characteristics [Shibata and Chiba, 1990; Lubin et al., 1998; Gordon et al., 2000; Lachlan-Cope, 2010].
 This section discusses the modeling of Antarctic clouds with global and regional models, taking the opportunity to introduce important issues and technical details. Efforts with these models to represent Antarctic clouds within the hydrologic cycle have shown progress over the last two decades, but considerable work remains.
 Antarctic cloud modeling can be contrasted to that of the Arctic, where the clouds, especially the frequent summer mixed-phase clouds, are still not adequately represented in numerical models, but the challenges are well-recognized and concerted efforts have been organized to address the issues [e.g., Randall et al., 1998; Curry et al., 2000; Verlinde et al., 2007; Vavrus et al., 2009]. For the Antarctic region, there has been much less extensive research seeking to evaluate or improve the performance of cloud representations in numerical models. Nevertheless, the present cloud representations for Antarctica in current state-of-the-art numerical models appear to be far superior to those from the early systematic studies with global and regional models during the 1990s [e.g., Bromwich et al., 1995; Hines et al., 1997]. Much of that improvement is attributable to a general improvement in model cloud representations rather than a specific emphasis on Antarctic clouds.
5.2. Regional Models
 The 1990s saw the growth of regional modeling studies for Antarctica [e.g., Parish and Bromwich, 1991; Gallée, 1995; Hines et al., 1995, 1997; Bailey and Lynch, 2000a]. Cloud parameterizations within mesoscale models tend to be more detailed and computationally expensive than those of global models. The microphysics equations are complicated and will not be repeated here. An excellent example of a detailed set of prognostic equations for large-scale precipitation is given in the Appendix of Gallée . Condensate is typically divided into cloud (suspended) and precipitation (falling) particles and further divided into liquid and ice components. The various processes treated are condensation/evaporation/sublimation/deposition, melting/freezing, autoconversion (cloud to precipitation), accretion, and rain and snow fallout. Adequate representation of the clouds and hydrologic cycle in mesoscale models, however, has remained problematic, analogous to the global modeling studies. The overall advancement in model cloud parameterizations, regardless of climatic region, has offered hope for improved Antarctic representations. Early evaluations of performance of cloud representations for high southern latitudes demonstrate the inadequacy of parameterizations developed for other regions of the globe [e.g., Hines et al., 1997]. About the time of the Antarctic Weather Forecasting Workshop in May 2000 [Bromwich and Cassano, 2001], however, viable representations of Antarctic clouds began to more commonly appear in regional models.
 As an example, the fifth-generation Pennsylvania State University/NCAR Mesoscale Model (MM5) [Grell et al., 1994] contained the microphysics option of the single-moment, prognostic explicit scheme from Reisner et al. . The model has a traditional bulk microphysics scheme with three-dimensional prognostic equations for the mixing ratios of water vapor, cloud water, cloud ice, rain, and snow. One issue is the use of the Fletcher  IN formula that facilitates excessive number concentrations and unrealistically small particles at very low temperatures. An outcome can be a noticeable warm bias [e.g., Hines et al., 1997]. When the ice nuclei concentration of Meyers et al.  replaced that of Fletcher  in the polar-optimized version “Polar MM5,” however, fewer IN are present at low temperature and realistic simulations are possible for Antarctica [Guo et al., 2003]. An additional update was adapting the radiation scheme away from a diagnostic cloud fraction calculation to one that input the predicted liquid and ice mixing ratios. Accordingly, Polar MM5 with the modified Reisner scheme tackled multiple Antarctic applications including real-time synoptic forecasting [e.g., Powers et al., 2003; Bromwich et al., 2003, 2005]. Concurrent to the Antarctic application of Polar MM5, several regional high-resolution models had cloud microphysics sufficiently advanced to allow synoptic and climatic studies for Antarctica, including studies of Antarctic hydrology [e.g., Pavolonis et al., 2004; Gallée et al., 2005; van de Berg et al., 2006].
 Looking forward, advances in cloud modeling that are available to mesoscale models provide opportunities to treat an increasing set of physical phenomena. Mixed-phase clouds, ice fog, polar cloud sensitivity to aerosols, and the frequent diamond dust/clear-sky precipitation over Antarctica are possibly treatable with advanced parameterizations including “double-moment” schemes discussed below [e.g., Girard and Curry, 2001]. Additionally, the impact of aerosols on cloud physics is gaining attention among modelers. From the Antarctic perspective, observations suggest that polar clouds are highly sensitive to the number concentration of IN, which are highly influenced by atmospheric aerosol concentrations [e.g., Prenni et al., 2007]. Not surprisingly, early attention has focused more on the Arctic [e.g., Girard and Blanchet, 2001; Girard and Curry, 2001; Morrison et al., 2008]. Yet there are good reasons to consider the aerosol impact for the relatively pristine Antarctic environment. Furthermore, the seasonal pattern of aerosols at the South Pole is opposite to that in the Arctic with minima in the winter an order of magnitude smaller than the summer values [Hogan and Barnard, 1978; Park et al., 2004]. Thus, Antarctica provides an opportunity to study and model clouds in a unique clean environment. However, little work has yet been done to explore detailed microphysics over Antarctica with the most advanced cloud schemes, coinciding with the lack of detailed observations for comparisons.
 One recent mesoscale study has been performed with an advanced microphysics scheme and compared to recent observations. Gallée and Gorodetskaya  compared results of the Modèle Atmosphérique Régional to Dome C observations over the East Antarctic Plateau and achieved a representation of polar stratospheric clouds. Furthermore, they represented the impact of snow on the radiative fields by taking the effective radius of snow to be three times that of ice clouds.
 As to how cloud-aerosol issues might be addressed, it can be noted that the earlier generation of bulk microphysics schemes (known as “single-moment” schemes because they only carry cloud particle mass concentrations and not number concentrations) are poorly suited to the task of capturing aerosol sensitivities [Girard and Curry, 2001]. A more advanced set of schemes known as “double-moment” schemes seek to predict the number concentration for water and ice clouds in addition to predicting the mass of cloud condensate [Morrison and Grabowski, 2007; Philips et al., 2007; Morrison et al., 2008]. This allows for a more physical treatment of clouds and their aerosol interactions and also enables determination of key inputs to cloud radiative parameterizations such as effective particle radius [Girard and Curry, 2001; Morrison et al., 2008]. For example, the state-of-the-art Polar WRF mesoscale model [Hines and Bromwich, 2008; Bromwich et al., 2009; Hines et al., 2011], which has replaced Polar MM5, has the option for the Morrison two-moment microphysics scheme. It is plausible that these advanced schemes with appropriate revisions could treat ice fog and diamond dust/clear-sky precipitation [Girard and Blanchet, 2001; Girard and Curry, 2001]. Yet, to our knowledge, no studies have attempted to parameterize Antarctic clear-sky precipitation in this way, and only limited efforts have been attempted in the Arctic [Girard and Blanchet, 2001].
5.3. Operational Forecasting of Clouds in Antarctica
 Accurately forecasting clouds, especially low clouds, is critical to the support of Antarctic aviation operations. Both the Terminal Area Forecast and route forecast are required to provide estimates of low cloud cover (in octas) and base height (in feet) to ensure minimum safe altitudes and good visibility conditions at landing. Routine observations of cloud base and cover are only made at staffed Antarctic stations and many of the regular skiways and landing sites, with these observations traditionally provided by trained observers. Some landing sites now also rely on ceilometers to provide an estimate of the cloud base height (see section 2.1). The accuracy of the cloud base observations is dependent on the experience of the observer and the availability of reference markers to gauge the cloud height. These observations are also necessarily limited to staffed sites with large sections of the Antarctic continent devoid of any observations of cloud base or cover.
 This data void has led to a reliance on numerical weather prediction (NWP) output to provide information on cloud properties over the Antarctic region. Such NWP efforts include the Antarctic Mesoscale Prediction System (AMPS) [Powers et al., 2003; Bromwich et al., 2005], funded by the U.S. National Science Foundation, and an East Antarctic version of the Australian Bureau of Meteorology's Limited Area Prediction System, PolarLAPS [Adams, 2005; Adams and Powers, 2007].
 Since aviation forecasting is critical for Antarctic operations, accurate cloud representations are highly valued. However, verification of modeled prognostic clouds versus observations can be problematic. First, there are difficulties associated with obtaining objective cloud observations which are frequently reported in fractions or octas. Second, many modern cloud prediction schemes predict the mass of water substance but do not directly produce a cloud fraction that can be compared to observations. Diagnostic relationships, however, can be used to estimate cloud fraction from model results [e.g., Wyser and Jones, 2005; Fogt and Bromwich, 2008]. As an example, to generate its routine operational weather forecasts for Antarctica, the AMPS has successively used the mesoscale model Polar MM5 (until mid-2008) and the Polar WRF. A preexisting MM5 formula has been used to estimate the total CF from the forecast cloud liquid water (CLWP) and ice water (CIWP) paths within each model layer,
where the paths (in mass per unit area) are summed layer by layer from the surface to the top of the model. CLWP and CIWP are readily calculated from the cloud liquid water and cloud ice water mixing ratios, respectively (see details in the work by Fogt and Bromwich ). The constants, Cl and Ci, are longwave absorption coefficients that can be determined empirically or from cloud radiative properties. Fogt and Bromwich  found, empirically, optimal matches between MM5 AMPS forecasts and cloud observations in the vicinity of McMurdo Station by setting Cl and Ci to 75 m2 kg−1 and 170 m2 kg−1, respectively.
 In theory, the empirically estimated cloud fraction should be consistent with the radiative properties of fractional clouds. Hines et al.  found that the Fogt and Bromwich  coefficients are also applicable in the Arctic at Barrow, Alaska. However, a recent study suggests that the cloud impact on radiation may not be well simulated by the Polar WRF (A. B. Wilson et al., Evaluation of polar WRF forecasts on the Arctic System Reanalysis domain. Part II. Atmospheric hydrologic cycle, submitted to Journal of Geophysical Research, 2011). A further limitation of the formula is that it only works for total cloud fraction and is highly sensitive to low clouds, which tend to have higher water substance contents. To estimate only the high cloud fraction, for example, the formula would require modifications, as an overcast cirrus layer is typically denoted by small CIWP and no CLWP.