Abstract ID: 002
Sensitivities of Subseasonal Coupled Earth System Model Simulations to Changes in Parameterizations of Convection, Cloud Microphysics, and Planetary Boundary Layer
Lead Author: Benjamin Green
CU/CIRES and NOAA/Global Systems Laboratory, United States of America
Keywords: S2S modeling, Model bias, Parameterization schemes, Physics testing
Abstract: 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, the National Oceanic and Atmospheric Administration (NOAA) has been developing a fully-coupled Earth system model under the Unified Forecast System framework which will be responsible for global (ensemble) predictions at lead times of 0-35 days. The development has involved several prototype runs consisting of bimonthly initializations over a 7-year period for a total of 168 cases.
This study 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 is uniformly better or worse, 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. Additional convective parameterization and cloud microphysics tests using different baselines shows that attempting to generalize results between or within modeling systems may be misguided.
Shan Sun (NOAA/Global Systems Laboratory)
Eric Sinsky (NOAA/Climate Prediction Center)