Adaptive Vertical Grid Enhancement for E3SM

Low and high clouds of shallow extent, like stratocumulus and high-level cirrus clouds, are poorly represented in state-of-the-art Earth System Models. This adversely affects estimates of cloud feedbacks, a major source of uncertainty in projections of future climate. We have demonstrated that coarse vertical model resolution is a reason for the failure of these models to adequately represent shallow/thin clouds. While horizontal resolution has become progressively finer, vertical resolution has not kept pace, despite documented improvement of model fidelity at high vertical resolution. We have developed a new computational framework, called the Framework for Improvement by Vertical Enhancement (FIVE), that will reduce low and high cloud bias in Earth System Models by producing results equivalent to a high vertical resolution simulation but at a reduced cost. This project will advance the simulation capability of E3SM, formerly called ACME, for boundary layer and cirrus clouds by implementing FIVE into E3SM. FIVE computes selected processes on a vertical grid with local high resolution in the boundary layer and at cirrus altitudes. In addition to the host model, variables on the locally high-resolution grid are predicted in parallel so that high-resolution information is retained. By exchanging tendencies with one another, the host model and high-resolution field are always synchronized. In our pilot study, a prototype FIVE produced superior results in a regional simulation of drizzling stratocumulus. This project further refines FIVE, specifically for use in E3SM. These developments will be carried out with both global and regional simulations, so that a wide range of E3SM’s planned horizontal resolution configurations (down to 15 km) is covered. We will also develop an “adaptive” version of FIVE, which smoothly and automatically adjusts vertical grid spacing of the locally high-resolution grid (i.e., Adaptive Vertical Grid) to reduce computational costs while retaining accuracy. 

Project Term: 
2018 to 2020
Project Type: 
University Funded Research

Publications:

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Research Highlights:

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