With ever increasing computing resources, climate and earth system models have seen considerable improvements over the years through the use of increasingly fine resolutions with more accurate physics. Increasingly advanced modeling and data assimilation techniques traditionally used for weather prediction are implemented to facilitate greater data-model integration and uncertainty quantification for climate and earth system monitoring and prediction.
Through a mix of invited and contributed presentations, this special two-day symposium solicits papers on recent progress and challenges on data-model integration and uncertainty quantification for various climate and earth system processes beyond weather phenomena, including, but not limited to, atmospheric composition and chemistry, biogeochemistry, hydrology, ocean, cryosphere, land surface, ecosystem, and the interaction and coupling among various processes. Particular emphasis will be given to the use of advanced modeling and data assimilation techniques to understand both the practical and intrinsic aspects of multiscale predictability of earth and climate systems. Practical predictability refers to the current capability of a modeling system under best practice given state-of-the-art models with state-of-the-art initial and boundary conditions. Intrinsic predictability refers to the limit of prediction at different temporal and spatial scales given nearly perfect initial conditions and nearly perfect models.
For additional information please contact co-chairs, Professor Fuqing Zhang, Penn State University (814-865-0470, firstname.lastname@example.org) and Professor Kerry Emanuel, MIT (617-253-2462, email@example.com).