North Atlantic Oscillation

NAO signal

Climate prediction problem

Data Analysis

Climate variability

• Air-sea interaction
  • Large scale
  • Upwelling
  • Sea ice
• Air-land interaction
  • Snow
• Local responses

Climate predictability

• Linear model

• Analog technique

Methodology

Predictive signals

• November signal 

• May signal

• April-October signal

The linear modelling approach is based on identification of joint modes of variability linking predictor and predictand variables.

• A subset of CCA pairs representing two fields is used to build a regression model (Von Storch, 1995) to estimate the anomalies of predictand (SLP, geopotential height at 500 hPa) from the anomalies of the predictor (May SST, November air surface temperature).

• The performance of the model is sensitively dependent on the number of EOFs and CCA pairs used in the regression model.

• Here, the CCA based models have been been trained for the interval 1961-1986 and were independently tested in the interval 1987-2001. Both models use 4 CCA modes derived from 6 EOFs from the paired fields.

Results

• November/DJFM model

• May/DJFM model

• April-October/DJFM model

References:

Bojariu, R., L.Gimeno, 2003: Predictability and numerical modelling of the North Atlantic Oscillation. Earth Science Reviews, Vol 63/1-2, 145-168.

Bojariu, R., Paliu D.,Gavrilescu T., Toma L., R. Povara 2000: Climate and economical aspects of drought prediction in Romania in the sustainable development perspective. In Proceedings book of the Central and Eastern European Workhop on Drought Mitigation ( L. Vermes and A. Szemessy eds.) ,12-15 Aprilie 2000, Budapest-Felsogod, Ungaria , 235-238.

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CCA

POP

MICOM

Roxana Bojariu Copyright © 2002-2008

NAO