A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model

• Main Authors: Covey, C, Brandon, S, Bremer, P T, Domyancis, D, Garaizar, X, Johannesson, G, Klein, R, Klein, S A, Lucas, D D, Tannahill, J, Zhang, Y
• Other Authors: United States. Department of Energy.
• Format: Report
• Language: English
• Published: Lawrence Livermore National Laboratory 2011
• Subjects: Weather; Clouds; Carbon Dioxide; Seas; Climates; Sensitivity; Simulation; Boundary Conditions; Climate Models; 54 Environmental Sciences; Metrics; Boundary Layers; Accuracy; Algorithms; Sea ice
• Online Access: https://doi.org/10.2172/1035301; http://digital.library.unt.edu/ark:/67531/metadc835362/

Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide.