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Uncertainty Quantification in Climate Modeling

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Wednesday, 11 September 2019, 12:00

Wednesday, September 11, 2019. 12:00PM. Uncertainty Quantification in Climate Modeling. Yun Qian, Pacific Northwest National Laboratory. Sponsored by NOAA GFDL. More information here


DOE - Energy Exascale Earth System Model (E3SM) is a new model and has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one-at-a-time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or the improvement in one target variable may lead to degradation in another target variable. To better understand the model behaviors and physics, we conducted a large number of short simulations in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection and cloud macrophysics and microphysics were perturbed simultaneously using the Latin Hypercube sampling method. From the Perturbed Parameters Ensemble (PPE) simulations and use of different skill score functions, we identified the most sensitive parameters and their global spatial distribution, quantified how the model responds to changes of the parameters, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Results from this analysis provide a more comprehensive picture of the E3SM model behavior at global scale as well as at process level in different cloud regimes. The difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model structural uncertainty (embedded errors) to inform future development efforts.


 In this talk, besides presenting above parametric sensitivity results in E3SM atmosphere model, I will also highlight a few applications using Uncertainty Quantification technics in model calibration and optimization, wind energy and aerosol study conducted at PNNL. Finally, I will discuss a few challenges and paths forward in this area. 

Location  NOAA GFDL, Smagorinsky Seminar Room, Princeton University, Princeton, NJ.