5C.3 Exploring Seasonal-to-Decadal Predictability of Climate Extremes by Combining High-Resolution Climate Modeling with Big Data Analytics (Invited Presentation)

Tuesday, 14 January 2020: 11:00 AM
151A (Boston Convention and Exhibition Center)
Dan Fu, Texas A&M Univ., College Station, TX; International Laboratory for High-Resolution Earth System Prediction, College Station, TX; and P. Chang, S. Yeager, W. C. Hsu, G. Danabasoglu, L. Wu, and S. Zhang

Developing capability of predicting weather and climate extremes, such as tropical cyclones (TCs), on seasonal-to-decadal time scales is of great importance because extreme events can cause severe property damage and loss of human life. Tackling this scientific challenge is one of the key objectives of the newly established International Laboratory for High-Resolution Earth System Prediction (iHESP) – a trilateral collaboration among the Qingdao National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU) and National Center for Atmospheric Research (NCAR). We highlight some recent collaborative research within iHESP to advance the science of seasonal-to-decadal prediction of climate extremes. In particular, we will showcase results of a seasonal-to-decadal TC prediction system derived by combining a large ensemble of high-resolution climate model simulations with a convolutional neural network model.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner