Subseasonal prediction remains a uniquely challenging problem because the timescale involved cannot take much advantage of the memory imparted by atmospheric initial conditions (leveraged for predictions shorter than ~2 weeks), or of the slowly-evolving boundary forcings (leveraged for predictions longer than ~3 months). Regardless, interest in subseasonal prediction has grown substantially over the past decade owing to the identification of so-called “forecasts of opportunity” and the potential benefits of these forecasts to numerous sectors of society. Recognizing the demand for subseasonal forecasts, NOAA has been developing a fully-coupled Earth system model under the Unified Forecast System (UFS) framework which will be responsible for global (ensemble) predictions at lead times of 0-35 days. The development has involved several prototype coupled UFS runs consisting of bimonthly initializations over a 7-year period for a total of 168 cases.
This webinar presents results from a study that leverages these existing baseline prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and boundary layer on 35-d forecasts. It is found that no particular configuration of atmospheric physics within the coupled UFS is uniformly better or worse for subseasonal prediction, based on several metrics including mean-state biases and skill scores for the Madden-Julian Oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3-4, indicating that some subseasonal biases may be mitigated through tuning at shorter timescales. An additional convective parameterization test using a different baseline shows that attempting to generalize specific results within UFS may be misguided.
Dr. Ben Green is a Research Scientist at the University of Colorado CIRES and works at the NOAA Global Systems Laboratory. He has been affiliated with these institutions since 2015, when he joined the team as a postdoc. He is a member of the GSL subseasonal-to-seasonal branch and works on the coupled UFS.
His research interests focus on numerical modeling of the Earth system at multiple timescales, with a particular emphasis on parameterizations for air-sea interaction. Ben has his Bachelor, Master, and PhD degrees in meteorology from Penn State University.
