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Home » Development of an Adaptive Vertical Grid Scheme for Large Scale Models

Development of an Adaptive Vertical Grid Scheme for Large Scale Models

In recent years the combination of increasing computational capability and uncertainties in climate simulations due to clouds (or more broadly un- or under-resolved processes that must be parameterized) has led to enhanced interest in higher resolution global and regional models. In simple terms, the expectation is that significantly better simulations can be obtained by resolving cloud scale motions and relying less on cloud parameterizations. However, simulations capable of resolving cloud scale motions on global or large regional domains are computationally challenging. Simulations run on small domains (where fine grids can be applied) have demonstrated that horizontal grid spacings of less than 1 km are required to resolve many clouds. Likewise, vertical grids with grid point spacings less than 100 m are often needed in the boundary layer. From a climate modeling perspective, accurately capturing stratocumulus and the transition between stratocumulus and cumulus is critical because of the large role that these clouds play in the Earth radiation balance. One potential approach to increasing resolution with only modest increases in computational cost is to use an adaptive grid. In this approach, additional grid points are added to the (relatively coarse) model base grid only where needed, as determined by the model simulation itself. We have developed a Cloud Resolving Model (CRM) with an Adaptive Vertical Grid (AVG) and tested this model using case studies on small domains. These simulations show that the adaptive vertical grid is able to simulate clouds well when compared with simulations using much higher vertical resolution (and many more grid layers). We propose to test the adaptive vertical grid approach in global scale simulations by incorporating our AVG model into the Multiscale Modeling Framework (MMF) climate model. Output from MMF-AVG simulations will be rigorously compared with observations.

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