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.

NMME Data    

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Publications    

2015    2014   2012-13

The North American Multi-Model Ensemble (NMME) is an experimental multi-model seasonal forecasting system, consisting of coupled models from US and Canadian modeling centers, including NOAA/NCEP, NOAA/GFDL, IRI, NCAR, NASA, and Canada’s CMC..

The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) than any single model ensemble to meet the specific tailored regional prediction and decision support needs of a large community of climate information users.

The need for the development of NMME operational predictive capability was recommended in US National Academies report “Assessment of Intraseasonal to Interannual Climate Prediction and Predictability”. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and   8    April    2011),     a    collaborative    and

coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule.

Important decisions in sectors ranging from food and water security and public health, to emergency management and national security, rely on forecast information at subseasonal to seasonal (S2S) timescales (i.e., lead times from 3-4 weeks to as much as 9-13 months), which is beyond traditional weather forecasts, and often at shorter leads or at higher spatial and temporal resolutions than the current seasonal forecasts.  Further research has been done to test an NMME protocol as applicable to sub-seasonal probabilistic quantitative prediction. The NMME ongoing development is to evolve the current seasonal system into a system able to meet operational requirements and user needs for shorter lead times of several weeks, and to design the protocol and experiments for an NMME operational sub-seasonal forecast system.

Toward Improving Operational
Sub-seasonal to Seasonal Forecasts