NMME

North American Multi-Model Ensemble

NMME (North-American Multi-Model Ensemble) is to improve intra-seasonal to interannual (ISI) operational predictions based on the leading US and Canada climate models.

Monthly Teleconferences               2017

April 6, 2017  The teleconference presentation by Sarah Strazzo of CPC/INNOVIM explored the idea that, while models may not consistently represent observed teleconnections, e.g. between ENSO and North American temperature and precipitation, they may coherently represent large scale climate variability such as ENSO, AO/NAO, NPO etc. Statistical bridging by Bayesian joint probability model was tested to possibly further improve forecast skill beyond what skill was achieved through calibration. Preliminary results for temperature forecasts showed that the bridging was more skillful than calibration in DJF, particularly over the northern United States, though calibration yielded higher skill relative to bridging overall. At longer leads, skill differences between calibration and bridging varied by model, and the difference generally decreased with lead time. Further testing was planned, such as the performance relative to ensemble regression, merging of bridged and calibrated forecasts, exploring the use of additional bridging predictors etc.

February 2, 2017  Dr. Tao Zhang of CIRES-University of Colorado presented a study on the difference of observed Southern California (SCAL) precipitation anomalies between two strong El Niņo years, namely 2015-16 and 1997-98, asking the question: Why are strong El Niņo events not “too big to fail”? The study showed that global SST forcing in 2016 was less effective than 1998 in yielding wet conditions in SCAL, and the 2016 below-average SCAL precipitation was mostly a symptom of internal atmospheric variability. Dr. Zhang concluded that the flavor of “El Niņo” was not the main cause for the weakened SCAL wetness; instead, differences in extratropical SST anomalies were the main driver for a weakened SCAL wet signal. The second presentation, by Dr. Amir Aghakouchak of University of California, Irvine, focused on improving seasonal drought prediction in California using a combined statistical-dynamical model approach. Overall, it demonstrated the hybrid framework performed better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME).

Archives   2015, 2016