1. A Cloud Resolving Model with an Adaptive Vertical Grid Roger Marchand and Thomas Ackerman - University of Washington, Joint Institute for the Study of Atmosphere and Ocean (JISAO) Baseline – Fixed Grid Motivation Over the last few years a new type of global climate model (GCM) has emerged in which a two-dimensional or small three-dimensional cloud resolving model (CRM) is embedded into each grid cell of a GCM. This new approach is frequently called a Multiscale Modeling Framework (MMF). Because of the large computational burden associated with this approach, the CRM in the MMF has been run using an undesirably coarse grid, typically with horizontal grid spacings of 1 or 4 km and 24 vertical levels. Comparisons of MMF output against observational data show that the model has a number of shortcomings that are likely due to insufficient resolution in the cloud resolving model. In particular, boundary layer clouds are often found to have too little coverage and be too low in altitude. Decreasing the horizontal and vertical grid spacing in the MMF such that boundary layer cloud processes are well resolved will be difficult to achieve computationally. One potential approach is to use a cloud resolving model with an adaptive vertical grid (that is, a model that is able to add vertical layers where and when needed) rather than trying to use a fixed grid with high vertical resolution throughout the boundary layer. Here we present preliminary results of simulations of the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) field study using a version the System for Atmospheric Modeling (SAM) modified to include an adaptive vertical grid. Simulations with the adaptive vertical grid are able to capture the stratocumulus cloud by adding only a modest number of layers (just where they are needed, principally near the inversion and to a lesser degree near cloud base). Background While atmospheric sciences models more commonly use fixed grids than adaptive grids, the potential benefits of adaptive grids have attracted some attention. Much of the research in this area has focused on the application of adaptive horizontal grids for global or large regional scale modeling (e.g., Bacon et al. 2007, Barros and Garcia 2004, Iselin et al. 2005, Jablonowski et al. 2006, and Lauter et al. 2007). Examples of atmospheric models using vertical adaptive grids include Dietachmayer and Droegemeier (1992) and Fielder and Trapp (1993), who developed the Continuous Dynamic Grid Adaption (CDGA) scheme and used this scheme to model rising thermals, as well as Skamrock and Klemp (1993) and Stevens and Bretherton (1999) who used an Automated Mesh Refinement (AMR) approach in which two-way nested grids (that is, overlying grids with differing grid spacings) are used to decrease the grid spacing in a portion of the domain. In all of these studies, the authors found that model simulations with adaptive grids compared well against simulations where the same model was run with high resolution throughout the domain. Given the significant reductions in computational burden associated with adaptive grids, why aren’t adaptive grids more widely used? There appears to be a variety of answers including the increased complexity of coding an adaptive scheme and the reliance of many atmospheric models on parameterizations of sub-grid processes (such as cloud convection) that are to some degree resolution dependent. But perhaps the most significant factor in reducing the use of adaptive grids is simply that there is no fundamental theory that describes how (or when) to reduce grid resolution, and thus all criteria are to some degree subjective and must be chosen by the researcher. Thus for many research projects (where the computational burden is far below that posed by the MMF), the use of adaptive grids introduces additional unwanted complexity to the analysis that outweighs the benefits associated with the computation savings. The “baseline” figures (above) shows the results of several cloud resolving model simulations (using SAM) for a case from DYCOMS-II. These simulations use a fixed vertical grid spacing ranging from 100 m to 5 m and initial conditions and forcing data identical to that specified by Stevens et al. (2005). The figures show that simulations with less than about 10 m vertical grid spacing significantly underestimate cloud liquid water and cloud cover for this case. Accurate simulation of the cloud water content for these clouds requires accurately capturing the entrainment of air from above the inversion into the cloud layer, which is difficult when the inversion is strong enough to restrict mixing over a small vertical distances. Adaptive Vertical Grid The figures to the right show simulations of this same case using SAM modified to adaptively add vertical layers when they are needed. We refer to this model as SAM-AVG, which stand for System for Atmospheric Modeling (SAM) - adaptive vertical grid (AVG). As the figure shows, SAM- AVG is able to capture the evolution of the cloud reasonably well when compared with results of a simulation using a very high (5 m) vertical grid. The AVG simulation starts with 16 levels (100 m vertical resolution) between the surface and 1.6 km. The location of the cloud top appears to be somewhat low, and we have found that the location is somewhat sensitive to the placement of the initial grid points and how forcing data for added layers is applied. The lower panels show the temporal evolution of layer placement for this case. The model determines where additional vertical layers are needed by examining the ratio of the total water flux contributed by the sub-grid scale scheme to the total water flux. If this ratio is more than 10%, layers are added, and if the ratio (for new layers) drops below 1% layers are removed. New layers are created by dividing an existing level into two levels, and there are some additional constraints to ensure that the vertical spacing between neighboring levels in never more than a factor of two. Future Research While promising, the results shown here are for just one case, and more cases need to be examined. We hope to apply the SAM-AVG to several other case studies including the Atlantic Tradewind Experiment (ATEX) and Atlantic Stratocumulus Transition Experiment (ASTEX). Adaptive grids are not commonly used in cloud resolving models and a variety of criteria for adding or removing layer should also be evaluated. Ultimately we hope to incorporate SAM-AVG into the MMF.