Correlation between sea surface temperature and wind speed in Greenland Sea and their relationships with NAO variability

Bo QU*, Albert J. GABRIC, Jing-nan ZHU, Dao-rong LIN, Feng QIAN, Min ZHAO

1. School of Science, Nantong University, Nantong 226007, P. R. China

2. School of Environment, Griffith University, Brisbane 4111, Australia

Correlation between sea surface temperature and wind speed in Greenland Sea and their relationships with NAO variability

Bo QU*1, Albert J. GABRIC2, Jing-nan ZHU1, Dao-rong LIN1, Feng QIAN1, Min ZHAO1

1. School of Science, Nantong University, Nantong 226007, P. R. China

2. School of Environment, Griffith University, Brisbane 4111, Australia

The North Atlantic Oscillation (NAO) is one of the major causes of many recent changes in the Arctic Ocean. Generally, it is related to wind speed, sea surface temperature (SST), and sea ice cover. In this study, we analyzed the distributions of and correlations between SST, wind speed, NAO, and sea ice cover from 2003 to 2009 in the Greenland Sea at 10°W to 10°E, 65°N to 80°N. SST reached its peak in July, while wind speed reached its minimum in July. Seasonal variability of SST and wind speed was different for different regions. SST and wind speed mainly had negative correlations. Detailed correlation research was focused on the 75°N to 80°N band. Regression analysis shows that in this band, the variation of SST lagged three months behind that of wind speed. Ice cover and NAO had a positive correlation, and the correlation coefficient between ice cover and NAO in the year 2007 was 0.61. SST and NAO also had a positive correlation, and SST influenced NAO one month in advance. The correlation coefficients between SST and NAO reached 0.944 for the year 2005, 0.7 for the year 2008, and 0.74 for the year 2009 after shifting SST one month later. NAO also had a positive correlation with wind speed, and it also influenced wind speed one month in advance. The correlation coefficients between NAO and wind speed reached 0.783, 0.813, and 0.818 for the years 2004, 2005, and 2008, respectively, after shifting wind speed one month earlier.

correlation analysis; NAO; SST; wind speed; ice cover; Greenland Sea

1 Introduction

The Greenland Sea lies in the southeast of the Arctic Ocean. The dramatic change in the Arctic Ocean, especially the large reduction in sea ice cover, has a significant impact on the global climate. Conditions in the Arctic are very different from those at lower latitudes of the globe, and the Arctic remains one of the least explored and understood places on the earth.

The North Atlantic Oscillation (NAO) appears to be the cause of many recent changes in the Arctic. NAO refers to a redistribution of atmospheric mass between the Arctic and the subtropical Atlantic, and swings from positive to negative phases. Changes in NAO have been associated with a wide range of physical and biological responses in the North Atlantic and Arctic, including variations in wind speed, sea ice cover, latent and sensible heat flux,evaporation-precipitation, and sea surface temperature (SST) (Dickson et al. 2000).

Looking at the history of Arctic temperature, a sharp increase in Arctic temperature in the 1920s was one of the most fascinating climatic events in the 20th century. This Arctic warming was associated with an eastward shift of the icelandic low. From the mid-1950s on, NAO has had a trend of decline and reached extremely negative values in the 1960s. After 1970, NAO exhibited an upward trend, and this trend was persistent for several years (Fig. 1). Strong and sustained zonal circulation was established over the northern hemisphere during this period. As a result, the icelandic low has shifted eastward since the 1990s (Hurrell and Dickson 2004), and the Arctic warming has accelerated.

Fig. 1 History of winter NAO

Prediction with climate models shows that the temperature increase in the Arctic will continue to be about twice the global average temperature increase over the next century, with more warming in winter than in summer (Solomon et al. 2007). Decreases in sea ice extent and thickness are considered to continue over the next century, with some models predicting that the Arctic Ocean will be free of sea ice in late summer by the mid to late part of this century (Solomon et al. 2007). It is projected that the increase of SST will also persist throughout this century. SST is significantly correlated with NAO variability in the Arctic (Hurrell and Dickson 2004). NAO variability can explain about one-third of the northern hemisphere inter-annual surface temperature variance during winter. Czaja and Frankignoul (1999, 2002) found that North Atlantic SST anomalies have a so-called horseshoe shape in summer and fall, associated with NAO in early winter. They also suggested that the early winter NAO is influenced by but less correlated with SST. The strength of flow is related to the pressure gradient, so surface winds are strongest during winter with their values of between 5 and 10 m/s from southern to northern Europe (Hurrell and Dickson 2004).

In the eastern part of the Arctic Ocean, the sea ice extent increases during the negative phase of NAO and retreats during its positive phase (Dickson et al. 2000). One of the most visible ramifications of the increase of SST is the reduced area of sea ice cover in the Arctic polar region. Hu et al. (2002) studied the NAO influence on sea ice extent in the Eurasian coastal region; the influence was mainly due to stronger wind-driven ice export.

It was noted that the Arctic Ocean underwent a particularly warm period of 2005 to 2009. However, NAO has been declining since the 1990s. The discrepancy between the NAO index and Arctic temperature variability in recent years is termed the Arctic paradox (Overland and Wang 2005). It was expected that the Arctic would remain anomalously warm until about 2012. After that, there is likely to be a long-term cooling trend until about 2023. The cooling may start with a sharp decline of winter temperature (Overland and Wang 2005).

Apart from ice cover, SST, and wind speed, there are many other factors related to NAO variability. NAO also has a large effect on the ecological dynamics in both marine and terrestrial systems, such as temperature, winter severity, wind regulation, and oceanic circulation, hence influencing the amounts of phytoplankton, fish, and zooplankton, the spatial distribution of species (Ottersen et al. 2001), and the surface and deep waters (Dickson 1997). Tsimplis et al. (2006) suggested that a high positive NAO is related to storm surges and a strongly negative NAO is not the driving factor of the sea-level and SST variability. NAO can influence the hurricane frequency by changing SST. Elsner et al. (2006) used NAO and SST as predictors to forecast U.S. hurricanes six months in advance.

This paper focuses on the area of the Greenland Sea in the Southern Arctic, an important region of water mass exchange between the North Atlantic Ocean and Arctic Ocean, and attempts to analyze the regional distributions of SST, wind speed, sea ice cover, and their relationships with NAO.

2 Materials and methods

The study region is in the Greenland Sea (10°W to 10°E, 65°N to 80°N) (Fig. 2), and divided into four five-degree zonal bands. Satellite data of SST and wind speed were obtained from POET (http://poet.jpl.nasa.gov/) for the period of 2003 to 2009, at weekly (SST) and daily (wind speed) time intervals, respectively. Wind speed was converted into weekly data at a one-degree spatial resolution. Due to constraints in Arctic conditions, satellite data could only be obtained from 85°N southwards. Updated data of time series of the NAO index from NCEP/NCAR were used.

Fig. 2 Map of study region in Greenland Sea

Due to different correlations for different time periods and regions, it was difficult to conduct the correlation analysis in the entire study region overthe seven-year period. Regression analysis of the correlation and time-lag relationships between SST, wind speed, and NAO was carried out using statistical software EViews to obtain the general trends in the 75°N to 80°N band. Because the polar front is located in the south, more significant correlations occurred there compared with other regions. F-test and t-test regression analyses were conducted in the 75°N to 80°N band for the seven years.

3 Results and discussion

3.1 Correlation between SST and wind speed

3.1.1 Distribution of SST

SST plays an important role in controlling the ocean’s heat content and regulating climate. Generally, the peak of SST occurs in July. Fig. 3 shows the eight-day mean SST in the 70°N to 75°N band from 2003 to 2009. The highest spring SST occurred in the year 2004, and the highest summer SST occurred in the year 2003. An unusually high SST occurred in the 70°N to 75°N band during the spring of 2004. Generally, SST decreased towards the north, and the peaks of SST shifted from July to June.

Fig. 3 Eight-day mean SST in 70°N to 75°N band (10°W to 10°E) from 2003 to 2009

3.1.2 Distribution of wind speed

Strong winds usually occur more frequently along the coast than in the continental interiors. Arctic winds can sweep over huge ocean areas without a barrier. Generally, wind speed is lower in summer and higher in spring and autumn, reaching its highest value in winter and lowest value in July. The wind speed is higher at more northerly latitudes. The year 2008 was an unusual year with extremely high wind speed in spring. Fig. 4 shows the extreme wind speed (greater than 20 m/s) that occurred in the north of the study region during the spring of 2008.

3.1.3 Correlation between SST and wind speed

Fig. 4 Distribution of weekly mean wind speed in region of 65°N to 80°N (10°W to 10°E) in 2008

The relationship between SST and wind speed has been studied for decades. Generally, research has shown that SST and wind speed have a negative relationship (Hurrell 1995; Bjerknes 1964; Shukla and Misra 1997; Huang and Qiao 2009). It is due to the fact that the increasing wind speed would decrease the surface temperature by breaking down the surface water stratification, hence bringing colder subsurface water to the surface. Ng et al. (2010) found that sea surface emissivity (SSE) changes with wind speed, while SSE affects SST retrieval. Hence, wind speed affects SST retrieval. Moreover, they found that only a high wind speed of greater than 15 m/s had a significant effect on SST retrieval. Wang et al. (1999) pointed out that increasing surface wind speed would result in more evaporation and more SST cooling, which result in further increase of the surface wind speed, and vise versa. This wind speed-evaporation-SST feedback is interesting and can be explained in many situations. However, a study by Xie (2004) showed that SST and wind speed are positively correlated in a small-scale region, and that surface winds are locally higher over warm waters and lower over cool waters.

Fig. 5 shows the mean SST and wind speed in the study region. SST increased from spring to summer and reached its peak in July, while wind speed initially decreased from spring to summer, and then increased in late summer and autumn. The negative correlation between SST and wind speed implies that an increase of wind speed would lead to a decrease of SST. Would a decrease of SST after July cause an increase of wind speed in winter? The result is not convincible and needs further research on that.

Fig. 5 Eight-day mean SST and wind speed in study region

According to our study in smaller regions, at 65°N to 70°N, the year 2003 had the lowestsummer wind speed while SST was at its highest value in summer. The year 2007 had the highest wind speed in spring, while SST was at its lowest value around the similar time. This indicates a negative relationship between wind speed and SST. At 70°N to 75°N, a higher SST in late 2003 and early 2004 (extremely high in spring) possibly led the wind speed to reach its lowest value in March of 2004. This indicates that SST possibly had an effect on the wind speed in the region south of 75°N a few weeks to several months in advance. Fig. 6 shows the correlation between NAO and wind speed at 75°N to 80°N for the years 2003, 2007, 2008, and 2009. At 75°N to 80°N, the year 2008 had the strongest spring wind speed, while SST was at its relative low values in spring (Fig. 6). This again shows that the relationship between wind speed and SST is negative, and that there is a possible time lag of several months between them.

Fig. 6 Mean time series of SST and wind speed in 75°N to 80°N band (10°W to 10°E) for 2003, 2007, 2008, and 2009

Table 1 shows the peak time lag between NAO and wind speed from 2003 to 2009. When the peak of SST is ahead of that of wind speed, the sign is positive; otherwise, it is negative. Unlike in the southern region, it is more likely that wind speed influenced SST (showing more negative values of the peak time lag) in the northern region due to a stronger wind. The peak of wind speed usually arrived ahead of that of SST by about three months in the northern region, and the longest time lag was 21 weeks at 70°N to 75°N in 2006. From 2003 to 2004, SST influenced wind speed (showing positive values of the peak time lag) in the southern region, while wind speed influenced SST in the region north of 75°N. From 2005 to 2006, wind speed influenced SST (showing negative values of the peak time lag) in the region north of 70°N, while the peak time lag increased by about two months at 70°N to 75°N. It ispossibly due to the peak of SST that shifted later in the southern region. The opposite situation appeared after the year 2006, in which time lags were larger in the northern region than in the southern region.

Table 1 Peak time lag between SST and wind speed from 2003 to 2009

In the region north of 75°N, wind speed, with a leading time of several months (apart from the year 2009), could have an effect on SST. The strong warm summer wind could cause more ice melting and warmer temperatures in winter. Table 1 also shows the complexity of the leading effect between SST and wind speed for different years. Hence, which is the leading factor between wind speed and SST still remains a debate.

The year 2008 had stronger wind in spring than other years (Fig. 6). The unusual high wind in the autumn of 2009 changed usual high wind patterns in spring, hence changed the time lag between SST and wind speed. More ice melting could be one of the reasons.

We note that SST and wind speed are mostly negatively correlated in our study region. The relationship between SST and wind speed varies in different years for different latitude bands. We are more interested in the northern region at 75°N to 80°N where the F-test indicated a stronger correlation between SST and wind speed.

EViews regression analysis (Pang 2006) shows a correlation between wind speed and SST at 75°N to 80°N:

whereTis the value of SST, andVis the wind speed. The correlation coefficientR2= 0.180.

The lagged regression analysis at 75°N to 80°N using EViews and the lag distribution coefficient according to the Almon scheme of polynomical lag (Griffiths et al. 2008) prov ides the following relationship between wind speed and SST:

whereVi(i= 1, 2, 3) is the wind speedimonths ahead of the corresponding time of SST. The correlation coefficientR2= 0.913. Eq. (2) shows that wind speed has an influence on SST three months in advance.

3.2 Correlation between ice cover and NAO

NAO and winter ice cover has exhibited important interactions since the 1980s in the Arctic Ocean, as observed by Strong and Magnusdottir (2010). NAO might have an influence on the spatial distribution of winter ice via wind-driven effects and possibly heat flux effects inboth vertical and horizontal directions. Observation showed that a year with positive NAO indexes tended to have a high sea ice concentration in the Labrador Sea and a low ice concentration in the Barents Sea (Deser et al. 2000).

Ice cover at 70°N to 75°N was less than 6% due to the polar front effect. Hence, we emphasize the 75°N to 80°N band (north of the polar front). Fig. 7 shows the time series of monthly NAO and mean ice cover at 75°N to 80°N for the seven years. Ice cover and NAO had a strong correlation at 75°N to 80°N. The correlation coefficient between the two factors was 0.61 in the year 2007. Generally, in our study region, the increase of NAO led to the increase of ice cover, especially in recent years.

Fig. 7 Time series of monthly NAO and mean ice cover in 75°N to 80°N band (10°W to 10°E) from 2003 to 2009

3.3 Correlation between SST and NAO

The warming rate has accelerated since the 1970s, and increased globally since the mid-1970s at a rate approximately three times faster than the century-scale trend. Bjerknes (1964) found that NAO is strongly correlated with SST at a large time scale. However, the role of the air-sea interactions in the dynamics of NAO is not fully understood. Czaja and Frankignoul (1999, 2002) noted a significant correlation between the winter NAO and SST of the previous summer. Wang et al. (2004) used Granger causality to examine the relationship between NAO and SST over the North Atlantic Basin. They found that the influence of NAO in preceding seasons on the winter SST is very limited. However, they agreed with the simple time-lagged correlation between the winter NAO and SST of preceding seasons. Tsimplis et al. (2006) found that the sensitivity of SST to NAO is strongly time-dependent. More recent results suggested that when NAO is in its positive phase, the southerly anomaly appears in the Greenland Sea with the surface air temperature and SST increasing and the sea ice concentration decreasing accordingly (Liu et al. 2008). It was also found that when NAO is in its negative phase, the northerly anomaly appears in the Greenland Sea with SST decreasingand the sea ice concentration increasing accordingly.

Fig. 8 shows the monthly NAO and mean SST at 75°N to 80°N for the years 2005, 2008, and 2009. Visually there were relatively strong correlations between SST and NAO in the three years although there was a time lag between the two factors.

Fig. 8 Monthly NAO and SST in 75°N to 80°N band (10°W to 10°E) for 2005, 2008, and 2009

The variation of SST was usually one month ahead of NAO. After shifting NAO one month earlier, the correlation coefficients between SST and NAO were high, which were 0.944 for the year 2005, 0.7 for the year 2008, and 0.74 for the year 2009. The correlation in other years was not high, probably because of the longer time lag. According to Battisti et al. (1995), the winter NAO is related to SST of the previous summer, and sometimes, it is related to SST of next spring.

3.4 Correlation between wind speed and NAO

Wind speed changes associated with NAO are responsible for alterations of oceanic surface currents in the direction and strength. Currents are particularly influenced by wind conditions, especially in shallow areas (Ottersen et al. 2001).Observations show that at high latitudes, the magnitude of wind stress forcing will likely exert a significant influence on the large-scale wind-driven ocean circulation (Chhak and Moore 2007).

Fig. 9 is the time series of wind speed and NAO in the study region (10°W to 10°E, 65°N to 80°N) in the year 2008. The correlation between NAO and wind speed is obviously shown in the figure. In contrast to the correlation between SST and NAO, where SST usually influences NAO, the correlation between wind speed and NAO is opposite. We found that NAO influences wind speed almost one month in advance. The correlation between NAO and wind speed was more significant in the years 2004, 2005, and 2008 than in other years. After shifting NAO one month later, the correlation coefficients between the two factors were 0.783,0.813, and 0.818, respectively, for the years 2004, 2005, and 2008.

Fig. 9 Monthly mean NAO and wind speed in year 2008

3.5 Regression analysis of SST, wind speed, and NAO

We still used the 75°N to 80°N band to study the relationships between SST, wind speed, and NAO. The regression software EViews was used to do F-tests andt-tests. The regression equation is

whereIis the NAO index. The correlation coefficientR2= 0.367.

The EViews lagged regression analysis shows that the lag model is

whereVi(i= 1, 2, 3) is the wind speedimonths ahead of the corresponding time of SST, andI1is the NAO index one month ahead of the corresponding time of SST. The correlation coefficientR2= 0.919.

Eq. (4) shows that the variation of SST lags three months behind that of wind speed, which is the same result as before. However, when both the wind speed and NAO effects on SST are considered, the variation of NAO is one month ahead of that of SST. This result is different from the previous result where only the interaction between SST and NAO was considered. Thet-test shows that wind speed has a stronger effect on SST.

4 Conclusions

We studied the distributions of and correlation between SST and wind speed in the Greenland Sea (10°W to 10°E, 65°N to 80°N) over seven years (2003 to 2009). SST generally elevated from March to July and then decreased from July to September. Wind speed was higher in spring and autumn and lower in summer. SST and wind speed exhibited a strong negative correlation at various temporal lags. The correlation analysis was focused on the 75°N to 80°N band, and the results showed that the variation of SST lagged three months behind that of wind speed. Thet-test showed that wind speed had a strong effect on SST.

SST influenced NAO one month in advance, and NAO influenced wind speed also one month in advance. After shifting SST one month later, SST and NAO showed strong positive correlations in the years 2005, 2008, and 2009. The correlation coefficients between SST andNAO reached 0.944 for the year 2005, 0.7 for the year 2008, and 0.74 for the year 2009. In the years 2004, 2005, and 2008, correlations between wind speed and NAO were also strong, with correlation coefficients of 0.783, 0.813, and 0.818, respectively, after shifting NAO one month later. The ice cover and NAO showed an obvious positive correlation, and the correlation coefficient was 0.61 for the year 2007.

The relationship between SST and wind speed is not as simple as that one is dependent on the other. Our focus was placed on the 75°N to 80°N band, and the time lag and correlations between those factors are different in other regions. We suspect that the three-month time lag between SST and wind speed is not robust and may change with climate warming. The dominant force could alternate between wind speed and SST, depending on which one is stronger. The relationships between NAO, ice cover, SST, and wind speed are also complicated. The phases of NAO have a strong connection to ocean currents, ice cover, and glacier mass balance. Will strong wind cause a greater decline in sea ice and warming/cooling of SST? What are the different correlations of multi-parameters between the Arctic and Antarctic? Further research on NAO, ice cover, wind speed, and SST is expected to be carried out in the future to answer many uncertain questions.

Battisti, D. S., Bhatt, U. S., and Alexander, M. A. 1995. A modeling study of the interannual variability of sea-surface temperature in the wintertime North Atlantic Ocean.Journal of Climate, 8(12), 3067-3083. [doi:10.1175/1520-0442(1995)008＜3067:AMSOTI＞2.0.CO;2]

Bjerknes, J. 1964. Atlantic air-sea interaction.Advances in Geophysics, 10, 1-82. [doi:10.1016/S0065-2687(08) 60005-9]

Chhak, K. C., and Moore, A. M. 2007. The North Atlantic Oscillation as a source of stochastic forcing of the wind-driven ocean circulation.Dynamics of Atmospheres and Oceans, 43(3-4), 151-170. [doi:10.1016/ j.dynatmoce.2006.12.002]

Czaja, A., and Frankignoul, C. 1999. Influence of the North Atlantic SST on the atmospheric circulation.Geophysical Research Letters, 26(19), 2969-2972. [doi:10.1029/1999GL900613]

Czaja, A., and Frankignoul, C. 2002. Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation.Journal of Climate, 15(6), 606-623. [doi:10.1175/1520-0442(2002)015＜0606:OIOASA＞2.0.CO;2]

Deser, C., Wash, J. E., and Timlin, M. S. 2000. Arctic sea ice variability in the context of recent wintertime atmosphere circulation trends.Journal of Climate, 13(3), 617-633. [doi:10.1175/1520-0442(2000)013＜0617:ASIVIT＞2.0.CO;2]

Dickson, B. 1997. From the Labrador Sea to global change.Nature, 386(6626), 649-650. [doi:10.1038/ 386649a0]

Dickson, R. R., Osborn, T. J., Hurrell, J. W., Meincke, J., Blindheim, J., Adlandsvik, B., Vinje, T., Alekseev, G., and Maslowski, W. 2000. The Arctic Ocean response to the North Atlantic Oscillation.Journal of Climate, 13(15), 2671-2696. [doi:10.1175/1520-0442(2000)013＜2671:TAORTT＞2.0.CO;2]

Elsner, J. B., Murnane, R. J., and Jagger, T. H. 2006. Forecasting U.S. hurricanes 6 months in advance.Geophysical Research Letters, 33, L10704. [doi:10.1029/2006GL025693].

Griffiths, W. E., Hill, R. C., and Lim, G. C. 2008.Using EViews: For Principles of Econometrics. 3rd ed. Danvers: John Wiley & Sons.

Hu, A., Rooth, C., Bleck, R., and Deser, C. 2002. NAO influence on sea ice extent in the Eurasian coastal region.Geophysical Research Letters, 29(22), 2053. [doi:10.1029/2001GL014293]

Huang, C., and Qiao, F. 2009. The relationship between sea surface temperature anomaly and wind energy input in the Pacific Ocean.Progress in Natural Science, 19(10), 1409-1412. [doi:10.1016/j.pnsc.2009. 03.004]

Hurrell, J. W. 1995. Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation.Science, 269(5224), 676-679. [doi:10.1126/science.269.5224.676]

Hurrell, J. W., and Dickson, R. R. 2004. Climate variability over the North Atlantic. Stenseth, N. C., Ottersen, G., Hurrell, J. W., and Belgrano, A., eds.,Marine Ecosystems and Climate Variation: The North Atlantic: A Comparative Perspective. Oxford: Oxford University Press.

Liu, X., Liu, H., Li, W., Zhang, X., Yu, R., Yu, Y. 2008. Numerical simulation of atmosphere-ocean-sea ice interaction during tnterannual cycle in high northern latitudes.ACTA Meteorologica Sinica, 22(1), 119-128.

Ng, H. G., MatJafri, M. Z., Abdulah, K., and Wong, C. J. 2009. The effect of wind speed on SST retrival.Proceedings of Aerospace Conference, 2009 IEEE.

Ottersen, G., Planque, B., Belgrano, A., Post, E., Reid, P. C., and Stenseth, N. C. 2001. Ecological effects of the North Atlantic Oscillation.Oecologia, 128(1), 1-14. [doi:10.1007/s004420100655]

Overland, J. E., and Wang, M. 2005. The arctic climate paradox: The recent decrease of the Arctic Oscillation.Geophysical Research Letters, 32, L06701. [doi:10.1029/2004GL021752].

Pang, H. 2006.Econometrics. Beijing: Science Press. (in Chinese)

Shukla, J., and Misra, B. M. 1977. Relationships between sea surface temperature and wind speed over the central Arabian Sea, and monsoon rainfall over India.Monthly Weather Review, 105, 998-1002.

Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L. 2007.Climate Change 2007: The Physical Science Basis (Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change). Cambridge: Cambridge University Press.

Strong, C., and Magnusdottir, G. 2010. Modeled boreal winter sea ice variability and the North Atlantic Oscillation: A multi-century perspective.Climate Dynamics, 34(4), 515-525. [doi:10.1007/s00382-009-0550-7]

Tsimplis, M. N., Shaw, A. G. P., Flather, R. A., and Woolf, D. K. 2006. The influence of the North Atlantic Oscillation on the sea-level around the northern European coasts reconsidered: The thermosteric effects.Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 364(1841), 845-856. [doi:10.1098/rsta.2006.1740]

Wang, C., Weisberg, R. H., and Yang, H. 1999. Effects of the wind speed-evaporation-SST feedback on the El Ni?o-Southern Oscillation.Journal of Atmospheric Sciences, 56(10), 1391-1403. [doi:10.1175/1520-0469(1999)056＜1391:EOTWSE＞2.0.CO;2]

Wang, W., Anderson, B. T., Kaufmann, R. K., and Myneni, R. B. 2004. The relation between the North Atlantic Oscillation and SSTs in the North Atlantic Basin.Journal of Climate, 17(24), 4752-4759. [doi:10.1175/ JCLI-3186.1]

Xie, S. P. 2004. Satellite observations of cool ocean-atmosphere interaction.Bulletin of the American Meteorological Society, 85(2), 195-208. [doi:10.1175/BAMS-85-2-195]

(Edited by Ye SHI)

This work was supported by the National Natural Science Foundation of China (Grant No. 41276097).

*Corresponding author (e-mail:qubo62@gmail.com)

Received Apr. 2, 2011; accepted Apr. 30, 2012