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Abstract: The response relationship between equivalent neutral wind speed anomaly (ENWSA) and sea-air temperature difference anomaly (SATDA) has been analyzed over four typical sea regions, i.e., the Kuroshio Extension, the Gulf Stream, the Brazil-Malvinas Confluence and the Agulhas Return Current. The results show that ENWSA is more sensitive to SATDA than sea surface temperature anomaly (SSTA), which implies that SATDA seems to be a more suitable parameter than SSTA to analyze the mesoscale air-sea interactions. Here, the slope of the linear relation between ENWSA and SATDA is defined as the air-sea coupling coefficient. It is found that the values of the coupling coefficient over the four typical sea areas have obvious seasonal variations and geographical differences. In order to reveal the reason of the seasonal variation and geographical difference of the coupling coefficient, the influences of some environmental background factors, such as the spatially averaged sea surface temperature (SST), the spatially averaged air temperature, the spatially averaged sea-air temperature difference and the spatially averaged equivalent neutral wind speed, on the coupling coefficient are discussed in detail. The results reveal that the background sea-air temperature difference is an important environmental factor which directly affects the magnitude of the coupling coefficients, meanwhile, the seasonal and geographical variations of the coupling coefficient.
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Figure 1. Four typical sea areas for studying in this paper. The map is the monthly-averaged equivalent neutral wind speed (ENWS) acquired by the advanced scatterometer in August 2013. GS. the Gulf Stream; KE. the Kuroshio Extension; BMC. the Brazil-Malvinas Confluence; ARC. the Agulhas Return Current.
Figure 2. The map of sea surface temperature anomaly (SSTA) overlaid as contours on equivalent neutral wind speed anomaly (ENWSA) at the Agulhas Return Current region in December 2010 (a), and the corresponding scatter plot (b); the map of sea-air temperature difference anomaly (SATDA) overlaid as contours on ENWSA (c), and the corresponding scatter plot (d). The parameter
$R$ in b and d denotes the correlation coefficient, and the solid thick line is the linear least square fitting line to the points. WSA. wind speed anomaly.
Figure 3. The values of the
${k_{{\text{SSTA}}}}$ (blue dashed curves) and${k_{{\text{SATDA}}}}$ (red solid curves) from January 2008 to December 2017 over the four typical regions, i.e. the Kuroshio Extension (a), the Gulf Stream (b), the Brazil-Malvinas Confluence (c) and the Agulhas Return Current (d) region.
Figure 4. The ENWS evaluated by Eq. (9) (a) and the coupling coefficient evaluated by Eq. (16) (b) as functions of the sea-air temperature difference
$\Delta T$ .
Figure 5. The coupling coefficient as functions of
$\Delta T$ for different${T_{\rm{w}}}$ and${T_{\rm{a}}}$ are presented in a and b, respectively.
Figure 6. The values of the coupling coefficient (red solid curve) and RMMSST, RMMWS, RMMAT and RMMSATD (blue dashed curve) from January 2008 to December 2017 over the Brazil-Malvinas Confluence region (a−d), and the corresponding scatter plots (e−h). The parameter R denotes the correlation coefficient.
Figure 7. The scatter plots of the background environmental parameters and the coupling coefficient over the four sea regions. The red solid circles, the green asterisks, the blue squares and the black pluses represent the data in the Kuroshio Extension (KE), the Gulf Stream (GS), the Brazil-Malvinas Confluence (BMC) and the Agulhas Return Current (ARC) regions respectively.
Figure 8. The linear and the nonlinear relationships between RMMSATD and the coupling coefficient. The dashed and the solid lines denote the linear (LFL) and the nonlinear fitting lines (NLFL), respectively. R and RMSE denote the correlation coefficient and the root-mean-square error.
Figure 9. The coupling coefficient (a) and the modified coupling coefficient (b) from January 2008 to December 2017 over the four regions.
Figure 10. The coupling coefficient (a) and the modified coupling coefficient (b) , the wind filed and SST data were acquired by QSCAT and AMSR-E from June 2002 to November 2009, respectively.
Table 1. The correlation coefficients between coupling coefficient and the environment parameters over the four areas
Sea area Environment parameters Correlation coefficient Sea area Environment parameters Correlation coefficient KE RMMSST 0.43 BMC RMMSST 0.37 RMMWS −0.88 RMMWS −0.71 RMMAT 0.65 RMMAT 0.66 RMMSATD −0.93 RMMSATD −0.94 GS RMMSST 0.76 ARC RMMSST 0.45 RMMWS −0.83 RMMWS −0.62 RMMAT 0.87 RMMAT 0.55 RMMSATD −0.89 RMMSATD −0.86 
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Table 2. The cross-correlation coefficients between the four environment parameters
RMMSST RMMWS RMMAT RMMSATD RMMSST 1 −0.66 0.97 −0.51 RMMWS −0.66 1 −0.79 0.85 RMMAT 0.97 −0.79 1 −0.70 RMMSATD −0.51 0.85 −0.70 1
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