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

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Page 1: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

Information-based potentialclimate predictability

Youmin Tang

University of Northern British Columbia, Canada

Page 2: 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, 637-648.

Page 3: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

Mutual Info. MI

.

])()(

),(ln[),(

pvp

vpvpMI

Page 4: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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,

Page 5: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

Multiple Model Ensemble

ENSEMBLES project stream-2 Hindcasts (1-tier forecast) http://ensembles.ecmwf.int/thredds/ensembles/stream2/seasonal/atmospheric/monthly.html

UKMO, ECMWF, MF, CMCC_INGV, IFM_GEOMAR

Page 6: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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

Page 7: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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

Page 8: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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

Page 9: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

The time series of PrCA mode 1 and mode 2

Page 10: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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

Page 11: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

Correlation RMSE

Page 12: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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.

Page 13: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

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.

Page 14: Information-based potential climate predictability Youmin Tang University of Northern British Columbia, Canada

Thank You

http://web.unbc.ca/~ytang

[email protected]