1. Dynamics of the sea surface temperature on a global scale using satellite measurements G. Vysotskaya (1,2), A. Shevyrnogov (1,3) (1) Institute of Biophysics of SB RAS, Krasnoyarsk, Russia, (2) Institute of Computational Modelling of SB RAS, Krasnoyarsk, Russia (3) Siberian Federal University, Krasnoyarsk, Russia

2. 108 satellite MODIS images (2160 x 4320) – 7.2002-7.2011 and 300 AVHRR images (4096 x 8192) - 1985 - 2009 were used in this investigation. Sea surface temperature may be represented as a sum: C i,j (t) = F i,j (  (t)) +ξ i,j (  (t))+U i,j (t), where F i,j (m) is a mean for (i,j);  (t) is a number of a month for time t; ξ i,j (m) is a random variable, that describe natural variability of the ocean. It depends on (i,j) and a month. U i,j (t) is a some additional variable, that can be defined by climatic changes, human impact etc.

3. Norm data E i,j,m is a mean value for a point (i,j) and a month m D i,j,m is a variance for a point (i,j) and a month m. Define positive anomaly values as the upper 20% (p 0.8 )and negative anomaly as the lower 20% (p 0.2 )

4. Square of negative and positive anomalies by AVHRR data (60°N- 60°S)

8. Square of negative and positive anomalies by MODIS data (60°N- 60°S)

9. Square of positive anomalies by AVHRR data (30°N- 30°S)

10. Square of negative anomalies by AVHRR data (30°N- 30°S)

12. Square of negative and positive anomalies by AVHRR data (30°N- 30°S)

15. Conclusions Long-term changes of sea surface temperature are global. The largest positive anomalies appear almost synchronously in the different oceans. The largest negative anomalies usually are not synchronous in the different oceans Variability of SST in the Atlantic and Indian oceans is higher than in the Pacific ocean.