Improving Climate Predictability
With this goal in mind, USGCRP began convening an annual U.S. Climate Modeling Summit in 2015, bringing together institutional leaders from six leading climate model development centers and operational-climate-prediction centers in the United States. As recommended by the National Research Council, the 2016 Summit was dedicated to topical exchanges among centers with the goal of facilitating coordination on specific items of shared interest, including the upcoming 6th Coupled Model Intercomparison Project and other joint modeling activities, and opportunities and challenges for modeling with high resolution and advanced physical representation, which includes bridging timescales across weather and climate prediction and projection. Topics were discussed in the context of USGCRP research priorities, the evolving condition of Federal supercomputing and software, and interfaces with impacts and assessment models. Participants included representatives from NOAA’s Earth System Models; NASA’s Goddard Institute for Space Studies Model and Global Modeling Assimilation Office; the Community Earth System Model, which is hosted by the National Center for Atmospheric Research (NCAR) and funded by NSF and DOE; and the Accelerated Climate Model for Energy, funded by DOE with participation from eight national laboratories, NCAR, academic institutions, and the private sector.
Demand for reliable climate information at regional-to-local scales is also growing. To help address the common challenges both communities face in improving model resolution, DOE and NOAA jointly hosted a workshop on High-Resolution Coupling and Initialization to Improve Predictability and Predictions in Climate Models, September 30–October 2, 2015, with over 40 participants from both the prediction and projection communities. Participants summarized the current state of research surrounding high-resolution climate modeling, identified common challenges across communities, and proposed a collaborative research framework for quantifying the benefits of high-resolution coupled modeling for reducing model biases and for improving prediction skill on sub-seasonal to seasonal scales.