Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Emil M Constantinescu Project Name: UQCLM Division: MCS Project title: Uncertainty Quantification for Land Model in Climate Science Associated funding: Climate Science for a Sustainable Energy Future; PI: Emil Constantinescu; site PI: Ian Foster Other Systems: Science: In this project we study uncertainty quantification (UQ) methods applied to Community Land Model (CLM). CLM is the land model for the Community Earth System Model (CESM) and the Community Atmosphere Model (CAM). It consists of several sets of large and nonlinear complex systems. We aim to develop intrusive uncertainty quantification methods and to demonstrate their computational advantage over a differentiated version of CLM with automatic differentiation (OpenAD) which will open the way for gradient-based optimization for calibration in land testbed. Project description: In order to improve carbon cycling within Earth System Models, crop representation for corn, spring wheat, and soybean species has been incorporated into the latest version of the Community Land Model (CLM), the land surface model in the Community Earth System Model. As a means to evaluate and improve the CLM-Crop model, we have implemented a calibration strategy that will be used to fine-tune model specific parameters. The calibration process uses a series of observational data of the process, and is used to set of parameters in the computer model as well as estimate their uncertainty. The computer model that we are working on is the crop model component of the CESM/ACME model improved by Argonne process model development team. To calibrate parameters of the crop model, we use the MCMC method. However, running MCMC samplers for complex models such as crop model typically requires thousands of model evaluations. This model evaluation is usually computationally intensive. Hence thousands of model evaluations exhaust available computer resources. And the situation is even worse if we only have a faint idea about the parameters because we don't know how to choose a well-proposed distribution. By using an efficient MCMC method, we will save a lot of computing cost. This method will automatically provide the variance for the next-step proposal distribution by communications between parallel chains. Industry partnership: Project URL: http://www.mcs.anl.gov/~emconsta/Projects_CSSEF.php Current FY Hours Used: undetermined amount New FY Requested allocation: 499000 Q1: 124000 Q2: 125000 Q3: 125000 Q4: 125000 Justification: Storage requirements: Thank You, The LCRC Accounts System