The NOAA CPO Modeling, Analysis, Prediction, and Projections (MAPP) program hosted a webinar on the topic of Seasonal Prediction: Achievements and New Frontiers on Wednesday, October 29, 2014. The announcement is provided below; you are invited to remotely join the session.
Ben Kirtman -- The North American Multi Model Ensemble: Status and Science Update -- The North American Multi-Model Ensemble (NMME; Kirtman et al. 2014) experiment is an unprecedented effort to improve intraseasonal to interannual (ISI) operational predictions based on the leading North American climate models. As a result of two Climate Test Bed (CTB) NMME workshops (February 18 and April 8, 2011) a collaborative and coordinated implementation strategy for the phase-I NMME prediction system was developed and is now fully functioning. The core activity of phase-I was to make multi-model (from the major US and Canadian modeling centers) seasonal predictions in real-time on the NOAA operational schedule. Participating models are necessarily global coupled ocean-atmosphere-land-sea ice models. The NMME phase-I project was supported by the NOAA Climate Program Office (CPO) MAPP (Modeling, Analysis, Predictions and Projections) Program, and critically leverages existing seasonal prediction activities at the major US and Canadian modeling centers. The NMME Phase-II project for the period of August 2012 – July 2015 is supported by NOAA/CPO with contributions from DOE, NASA, and NSF. The phase-II objectives include: (i) Continued real-time forecasts and incorporating updated models; (ii) Coordinated predictability research that identifies the benefit of the multi-model approach, examines how best to combine models, and guides model development and applications; (iii) Developing and evaluating an intraseasonal protocol; (iv) Continued and enhanced data distribution to facilitate use of NMME data. This presentation will document that NMME is making substantial progress in all of its goals, is a key asset to operational forecasters, enables predictability and prediction research, informs model development, is critical input for a host of application models and is being extensively used by the private sector.
We also include some specific examples of how the NMME data is being currently used in applications that are not directly funded by this project, and a non-exhaustive compilation of peer reviewed NMME results. We will also discuss the projected changes in the length of tropical cyclone season, which were obtained in Dwyer et al. 2014. We considered 2 sets of simulations: (i) high-resolution climate model (HiRAM) forced with SST anomalies from the CMIP3 and CMIP5 models and (ii) dynamical downscaling of the CMIP3 and CMIP5 model outputs that generates synthetic TC tracks. We measured season length using 3 different metrics. While the HiRAM model projects shorter seasons in most basins, the CMIP5 downscaling projects longer seasons. The changes in the length of TC season by basin can be largely explained by the annual mean TC frequency changes in each basin. Furthermore, while in most cases there is a projection for a timing shift of the TC annual cycle to later in the year in the North Atlantic, in the western North Pacific, the projections are opposite, i.e. a shift towards earlier in the year.
Eric Guilyardi -- Understanding ENSO in climate models: from statistics to process-based metrics -- The ability of coupled ocean-atmosphere general circulation models (CGCMs) to simulate the ENSO has largely improved over the last decades. Nevertheless, the diversity of model simulations of present-day El Nino characteristics, and the lack of significant progress in the latest generation of models (i.e. from CMIP3 to CMIP5), indicate current limitations in our ability to model this climate phenomenon and therefore anticipate changes in its properties on short and long time scales. A recent body of studies shows that the atmosphere GCM may lie at the heart of these limitations. ENSO in CGCMs is for example very sensitive to the representation of atmospheric convection and of tropical clouds. Nevertheless the diversity and complexity of the processes involved has so far been a severe limiting factor for the understanding of this sensitivity.
Lisa Goddard -- Opportunities for Predictions and Prediction Research -- Abstract TBD