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               2019

June 6, 2019  The North American Multi-Model Ensemble (NMME) forecast products have been widely used for decision making since 2011. The quality of the prediction is an important focus for service improvement. Dr. Sarah Strazzo of Climate Prediction Center conducted a study on four types of NMME seasonal forecasts (uncalibrated1, Probability Anomaly Correlation (PAC) calibrated (van den Dool et al. 2017), “Calibration, Bridging, and Merging” (CBaM) post-processed (Strazzo et al. 2018), and “Bridged”2 NMME forecasts) over the period of 2012-2018 and presented her findings in the June teleconference. Her results showed overall more hits and correct negatives than false positives and misses from contingency table of verification. The observation of more above than below normal temperatures was captured by uncalibrated, PAC calibrated, and CBaM post-processed forecasts, among which uncalibrated and PAC calibrated forecasts over-predicted above normal temperatures while CBaM post-processed and “Bridged” over-predicted below normal temperatures. Moreover, PAC calibrated precipitation forecasts achieved impressive Heidke skill scores, and NMME forecasts, particularly the post-processed versions, tended to outperform ENSO-derived forecasts in general. Her results further showed “hits” also increased as predicted probabilities increased, a good indication of “forecast opportunity” potential. In her summary, Dr. Strazzo shared with audience her promising thoughts to leverage other predictability sources, specifically to improve the prediction of winter below normal temperatures.

1) NMME includes CFSv2, CanCM3, CanCM4, GFDL-CM2.1, GFDL-FLOR, NASA-GEOS, and NCAR-RSMAS-CCSM4 participant models. Probabilities are calculated as ensemble frequencies relative to model mean terciles.

2) Uses bridged forecasts (1-month lead) as a stand-in for empirical ENSO-derived forecasts.

References

Strazzo, S., D. Collins, A. Schepen, Q. J. Wang, E. Becker, and L. Jia, 2018: Seasonal prediction of North American temperature and precipitation using the Calibration, Bridging, and Merging (CBaM) method. NWS Sci. Technol. Infusion Clim. Bull., 42nd NOAA Annu. Clim. Diagn. Predict. Workshop, Norman, OK, National Oceanic and Atmospheric Administration, 177-180, doi:10.7289/V5/CDPW-NWS-42nd-2018.

van den Dool, H., E. Becker, L.-C. Chen, and Q. Zhang, 2017: The probability anomaly correlation and calibration of probabilistic forecasts. Wea. Forecasting, 199-206.

April 4, 2019  Drs. Huug van den Dool and Emily Becker of Climate Prediction Center (CPC) gave a joint presentation on the topics of 1) EOF patterns in 200 hPa height in NMME models and observation, and 2) NMME representation of stationary wave pattern and the “North American Dipole index” in the April NMME teleconference.

Principal Scientist Emeritus Huug van den Dool studied the time series of “all” model members (many realizations) versus that of observation (a single realization) to explain how using the ensemble mean as input can result in a simpler time series and lead to forecast overconfidence. His EOF analysis of 200 hPa height revealed that the 1st mode was AO-like for both observation and all models, while the explained variance was 30% in observation and a large range from 20% to 40% among “all” model members. It was also demonstrated that nearly 50% (ranging from 37% to 58%) of the variance, when using ensemble means, was explained by the 1st EOF mode (one degree of freedom). The forecast based on the ensemble mean could be too simple when a lot of the “noise” variance being eliminated. Dr. van den Dool also showed the trends appear mostly in EOF2, if not EOF1, with spatially uniform maps and mainly up or down time series for all models calculated from ensemble means.

CPC NMME Lead Dr. Becker explored the relationship between northern California (NorCal) precipitation and “North American Dipole” (NAD), a peak/trough of stationary wave pattern revealed in an early work by Wang et al. (2014). Her study demonstrated the relationship between DJF NAD index and NorCal precipitation was fairly strong (r=-0.55, Fig. 1 top panel) in observation and even stronger (r=-0.88) in NMME ensemble mean forecast but weaker (r=-0.16, Fig. 1 bottom panel) between forecasted NAD index and observed NorCal precipitation, though the former has some relationship with observed NAD index (r=0.36).

Reference

Wang, S.-Y., L. Hipps, R. R. Gillies, and J.-H. Yoon, 2014: Probable causes of the abnormal ridge accompanying the 2013-14 California drought: ENSO precursor and anthropogenic warming footprint. Geophys. Res. Lett., doi: 10.1002/2014GL059748

Archives   2018, 2017, 2016, 2015.