Correlation Analysis Of SST Anomaly With Sea Surface Wind And China’s Climate Based On Satellite Observations

The influence of sea surface temperature anomaly (SSTA) on wind speed field is one of important studies to research the air-sea interaction. This study requires combining the marine and atmospheric system as a whole in order to determine its effects on climate (e.g. precipitation and air temperature). It is vital to understand the effects of air-sea interaction on ocean environmental changes as well as the human survival. Air-sea interaction studies include two main aspects:one is influence of SSTA on wind speed field on typical areas where mesoscale phenomena is obvious, such as Agulhas Current, Brazil-Malvinas, Gulf of Mexico, Kuroshio, the other is influence of subtropical dipole pattern (SDP), EL Nino3 index (NIN03) and southern oscillation index (SOI) three-factors principal component Flon climate of China.Based on satellite data of AMSR-E and QuikSCAT, the influence of SSTA, richardson number R1 and roughness length of sea surface z/L on surface wind field is studied over the above typical areas. The experiment results reveal that, the influence of SSTA, R, and z/L are relate to SSTA>0 and SSTA<0. SSTA> 0 the influence is weak, while SSTA< 0 is strong. There is linear relation between SSTA gradient and wind divergence, wind curl. Downwind components of SSTA gradient are linear to wind divergence. Crosswind components of SSTA gradient is linear to wind curl. The influence of SSTA on WSA (wind speed anomaly) varies in magnitude depending on the spatial scales ranging from hundreds of meters to thousands of kilometers, thus the variations on different spatial scales are investigated. The SSTA data of AMSR-E and WSA data from QuikSCAT in the same period are used to study coupling characteristics between SSTA and WSA on different spatial scale. The analysis show that cross correlation coefficient first increase and then decrease with the increasement of spatial scale.SDP, NIN03 and SOI are the combined action of SSTA and air-sea interaction, inducing significant influence on climate change. Monthly sea surface temperature data from Hadley, sea level pressure data from NCEP/NCAR and site data of precipitation/air-temperature are used to analyze the correlations between SDP, NIN03, SOI principal component Fl and precipitation or temperature of China, by means of principal components analysis (PCA) from 1961-2013. The main Results show that correlation between the first principal components Fl and precipitation/air-temperature reflect main characteristic of three variables. And the first principal component has different regional and seasonal effect on precipitation/air-temperature of China.