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Evidence for strengthening of the tropical Pacific Ocean surface wind speed during 1979–2001

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Abstract

Using multiple surface wind speed (SWS) data sets and trend empirical orthogonal function analysis, we have explored the trend in SWS associated with the large-scale tropical Pacific atmospheric circulation for the period 1979–2001. The present research provides a robust evidence of strengthening of the tropical Pacific Ocean SWS during this period and the magnitude is generally in line with the finding of Wentz et al. The strengthening in SWS is closely associated with the so-called La Niña-like sea surface temperature (SST) trend pattern rather than the changes in the ENSO, ENSO Modoki, or PDO. The present results, together with those from some recent climate model simulations, suggest that global warming forcing may have caused an intensification of SWS in the tropical Pacific Ocean by inducing the La Niña-like SST trend pattern due to ocean dynamics. Meanwhile, the strengthening in the tropical Pacific Ocean surface trade winds may also feedback to enhance the La Niña-like SST trend pattern under the positive wind-upwelling dynamic feedback mechanism.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 40975029), the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant No. XDA05090402), and the Ph. D. Programs Foundation of Ministry of Education of China (Grant No.20093402110004). We would like to thank two anonymous reviewers for their valuable comments. We also acknowledge Dr. Abdel. Hannachi for providing the MATLAB calculation procedure of the TEOF analysis and Dr. Christopher W. Fairall for providing the MATLAB calculation procedure of the COARE3.0 algorithm. The GISTEMP T g index has been downloaded from http://data.giss.nasa.gov/gistemp/.

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Correspondence to Baohua Ren.

Appendix

Appendix

The regression equation between the two normalized variables X, Y is given as below:

$$ {\widetilde{Y}_i} = A + B{X_i} $$
(5)

where \( \widetilde{{{Y_i}}} \) is the fitted value of Y i using the method of least square; i = 1, 2, …, n. The two constants A and B are obtained as follows:

$$ B = {S_{{xy}}}/{S_{{xx}}} $$
(6)
$$ A = \overline {{Y_i}} - B\overline {{X_i}} $$
(7)

where (¯) indicates the mean; S xy and S xx are the covariance between X and Y and the variance of X, respectively. Because X and Y are the normalized variables, \( \overline {{X_i}} = \overline {{Y_i}} = 0 \) and \( {S_{{xx}}} = {S_{{yy}}} = 1 \), where S yy is the variance of Y. Therefore, Eq. 5 is shown as below:

$$ \widetilde{{{Y_i}}} = ({S_{{xy}}}/\sqrt {{{S_{{xx}}}{S_{{yy}}}}} ){X_i} = r{X_i} $$
(8)

where r is the correlation value between X and Y. Thus, \( \widetilde{{{Y_i}}} \) can be regarded as the component of Y that can be explained by X. The ratio of the variances of \( \widetilde{{{Y_i}}} \) and Y i indicates the ratio of the component of Y that can be explained by X in the total Y variance, which is expressed as follows:

$$ {\text{ratio}} = {S_{{\widetilde{y}\widetilde{y}}}}/{S_{{yy}}} = {r^2}{S_{{xx}}}/{S_{{yy}}} = {r^2} $$
(9)

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Li, G., Ren, B. Evidence for strengthening of the tropical Pacific Ocean surface wind speed during 1979–2001. Theor Appl Climatol 107, 59–72 (2012). https://doi.org/10.1007/s00704-011-0463-3

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