The NOAA CPO Modeling, Analysis, Prediction, and Projections (MAPP) program will host a webinar on the topic of climate predictability and predictions on Tuesday, March 18. During this webinar, speakers will discuss the National Multi Model Ensemble, other subseasonal to seasonal prediction efforts, and predictability of Asian Summer monsoon precipitation. This work is relevant to ongoing activities of the NOAA Climate Prediction Task Force, which will also be discussed.
Ben Kirtman -- Some Recent Prediction Activities in the NMME Experiment and the Climate Prediction Task Force -- The purpose of this talk is to provide an update on some recent prediction activities in the NMME experiment and the Climate Prediction Task Force. With respect the NMME project, the status and description of some of the phase-II experiments and model upgrades will be described. In particular, results from CCSM4 prediction in comparison with the phase-I CCSM3 prediction and the whole NMME suite will be described. The NMME project also includes efforts at developing a multi-model sub-seasonal multi-model forecast activity, which will be described. The status of the NMME phase-II data server will also be discussed. Finally, ongoing effort in the Climate Prediction Task Force will be presented.
Arun Kumar -- Understanding Climate Predictability and Advancing Predictions: The Sub-Seasonal to Seasonal Prediction Project -- There remain numerous outstanding issues in understanding sources and limits of predictability on sub-seasonal and seasonal time scales. These issues also include providing guidance on practical questions addressing the design of forecast systems, e.g., model resolution, initialization and generation of perturbations etc. Use of model forecast data from multiple prediction systems will help advance some of basic understanding and other practical issues on sub-seasonal and seasonal predictions. With that goal in mind a ‘Sub-Seasonal to Seasonal (S2S)’ prediction project was jointly initiated by the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). This presentation will provide an overview of the S2S project.
Bin Wang -- Predictable Mode Analysis of Asian Summer Monsoon Rainfall Predictability -- To what extent the Asian summer monsoon (ASM) rainfall is predictable has been an important but long-standing issue in climate science. Here we introduce a predictable mode analysis (PMA) method to estimate the predictability of the ASM rainfall. The PMA is an integral approach combining empirical analysis, physical understanding and hindcast experiments. The empirical analysis detects most important patterns; the understanding of physical processes governing these patterns establishes the physical basis for empirical prediction; and the empirical and dynamical models’ predictions determine predictable modes. The potential predictability can then be estimated by the fractional variance accounted for by the “predictable” modes. This approach also provides a bias correction of spatial patterns to improve prediction skills.
For the ASM rainfall during June-July-August, we identify four major modes of variability by analysis of the 1979-2010 observation: (1) El Niño and Southern Oscillation (ENSO) developing mode, (2) Indo-Pacific coupled mode which is sustained by a positive thermodynamic feedback with the aid of background mean flows and mean precipitation, (3) the Indian Ocean dipole (IOD) mode, and (4) a trend mode. If these four modes are perfectly predicted, about 47% of the total variance can be captured over the entire Asian-Australia monsoon domain. We show that these modes can be predicted reasonably well by a physical-empirical prediction model as well as the atmosphere-ocean coupled models’ multi-model ensemble (MME). The empirical and dynamical coupled models have comparable prediction skills and complementary strengths in predicting the ASM precipitation. The results suggest that the four major modes may be regarded as “predictable” modes, and the PMA provides a useful approach for assessing the seasonal predictability and improve prediction skill.