Hello, A yearly allocation for the LCRC cluster has been requested with the following updated information: Submitter/PI: Robert Jacob Project Name: foam Division: MCS Project title: Global Climate Modeling with the Fast Ocean Atmosphere Model Associated funding: Department of Energy, Office of Science National Science Foundation Other Systems: external: NERSC, NCAR Science: Climate models treat the atmosphere and ocean as interacting fluids. They contain numerical methods to solve the basic partial differential equations which are in turn derived from the Navier-Stokes equations for a fluid on a rotating sphere. They also contain detailed parameterizations to compute the internal and external forcing terms that come from such diverse phenomena as the passage of radiation through the atmosphere, the release of latent heat by phase changes of water and the effects of friction and unresolved turbulent scales. In general, climate model development is focused on increasing the fidelity of the simulation compared to the observations. On long time scales, the climate is controlled mostly by the ocean because it is a slower, more dense fluid then the atmosphere and has a higher heat capacity. For some problems in climate science, such as understanding the different climates of the geologic past and the internal variability of the climate, it may not be necessary to have the most accurate representation of the atmosphere in the climate model being used. Project description: Our major objective is to test the application of the Ensemble Kalman Filter to FOAM for tuning model parameters in a coupled mode. In spite of decades of effort, coupled ocean-atmosphere general circulation models (OAGCM) like FOAM still suffer from significant model biases, notably in the tropics. A significant part of these biases is caused by uncertainties in the model parameters. So far, however, parameter tuning in OAGCMs remains rudimentary. We will use our time to develop a strategy of a systematic parameter optimization in an OAGCM. In Fy12, we have successfully developed adaptive coupled ensemble Kalman Filter system (AcEnKF) for a simultaneous estimation of model parameter and state. We have optimized a parameter – solar penetration depth by assimilating monthly mean observation of sea surface temperature (SST) and sea surface salinity (SSS) in the perfect model framework. Next, we will further investigate AcEnKF in FOAM. Based on our previous work, the parameter estimation is sensitive to ensemble size and some updating technics. And it is also sensitive to the response of model to the parameters. We will further investigate the AcEnKF in FOAM this year: -We will investigate multi-parameter estimations in FOAM. Some atmosphere and coupler parameters will be estimated in our perfect model experiments. -We will further investigate the effect of observation quality and ensemble size to the parameter estimation. For this request, we'll assume fifty members will be used for each experiment. We expect about 150 different sets of experiments for parameter estimation. Each integration should take 30 years such that the total model years are: Parameter estimation experiments: 50x30x150 =225,000 years. Good parameter estimation requires good data assimilation. We also need to refine our pure data assimilation skills that will provide support for us to estimate parameter using real observation in the future. - We plan to use our newly built FOAM data assimilation system to do some sensitivity tests by using the real ARGO observational network to further check if we can reconstruct the ocean state, especially, the sub-surface ocean. - Given the initial condition we get from the first stage, we will do some forecast experiments to check the predictability of longer-term variability in FOAM model. - The second stage we will imply the real observation to the system to produce a data assimilation and decadal forecast product. We expect about 60 different sets of experiments for data assimilation. By average, each experiment takes 30yr integration with an ensemble size of 50. Data assimilation experiments: 50x30x60 =90,000 years We have measured a cost of 2.133 core-hours/simulated year leading to a request of 640000 core-hours. This gives us the total hours for model experiments as Total cpu for experiments: (225000+90,000)yearsx2.133 = 692K core-hours With some minor time for testing, we request a total of 700k core-hours for FY13. Project URL: http://www.mcs.anl.gov/foam Current FY Hours Used: undetermined amount New FY Requested allocation: 700000 Q1: 150000 Q2: 150000 Q3: 200000 Q4: 200000 Justification: We have measured a cost of 2.133 core-hours/simulated year. The total experiments are independent and can be run simultaneously. FOAM is very scalable between 4 and 64 processors. We choose to run an individual ensemble member on 16 or 32 because the additional parallelism comes from the ensemble members. We could effectively use the entire machine if it was available. Thank You, The LCRC Accounts System