Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada.

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Presentation transcript:

Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

Potential Predictability Signal-to-Noise Ratio (SNR) Rowell, D. P. (1998), Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations, J. Clim., 11, 109 – 120. Peng, P., A. Kumar, W. Wang (2009), An analysis of seasonal predictability in coupled model forecasts, Clim. Dyn., 36,

Mutual Info. MI.

Relative Entropy （ Gaussian). Yang, D. Tang, Y and Zhang, Y and Yang X, 2011: JGR-Atmosphere.Yang, D. Tang, Y and Zhang, Y and Yang X, 2011: JGR-Atmosphere,

Multiple Model Ensemble ENSEMBLES project stream-2 Hindcasts (1-tier forecast) onal/atmospheric/monthly.html onal/atmospheric/monthly.html UKMO, ECMWF, MF, CMCC_INGV, IFM_GEOMAR

MI-based potential predictability and its difference from SNR- based measure

For 2-seasons prediction (the calendar season is the target time of prediction, such as MAM meaning the prediction starting from Nov.

The most predictable pattern of the NA TAS at the lead of one season for different seasons (prediction target) Predictable Component Analysis （ PrCA) PI  Maximum ; SNR  Maximum

The time series of PrCA mode 1 and mode 2

The SST patterns associated with the PrCA mode 1 and mode 2 of TAS.

Correlation RMSE

The predicted time series of the first PrCA mode against the observation counterpart. Same as above but for the prediction of the first principal component.

Conclusions Signal-to-noise ratio (SNR) is a special case of information-based predictability measure. When the ensemble spread changes with initial condition, SNR often underestimates the potential predictability. The most predicable component of the Northern America climate (temperature) is the interannual mode and a long-term trend (the global warming). The most predicable interannual variability is highly related to the ENSO.

Thank You