A method for determining the variation characteristics and mechanism of the Pacific Ocean basin-wide SECC by using the flow velocity field based on the temperature-salinity inversion

Abstract

The application provides a method for determining the variation characteristics and mechanism of the Pacific whole basin SECC based on a temperature-salinity inversion flow field, obtains monthly average Argo temperature-salinity data; deduces AGCs by using the monthly average Argo temperature-salinity data, draws an AGCs flow field map according to the AGCs, and delimits the spatial range of the SECC each month, quantifies the seasonal and interannual variations of the longitude and latitude boundary, vertical depth, flow axis, flow velocity and flow of the SECC, determines the variation law of the flow velocity, flow axis position and flow of the SECC in different seasons or years, and combines a sensitivity experiment method to clarify the regulation mechanism of the wind stress curl anomaly and ENSO event on the SECC. The application solves the blank of the three-dimensional range of the SECC in the whole basin and the variation of the three-dimensional range, flow axis, flow velocity and flow of the SECC on the seasonal and interannual scales and the dynamic mechanism in the traditional research, and reveals the law and physical mechanism of the seasonal and interannual variations of the SECC.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of Pacific basin SECC change characteristics, specifically to a kind of flow velocity field based on temperature and salinity inversion determines the method of Pacific basin SECC change characteristics and mechanism. BACKGROUND

[0002] The South Equatorial Countercurrent (SECC) is an important component of the equatorial current system in the Pacific Ocean, despite its profound impact on ocean dynamics and climate systems, but there is still a problem of insufficient description of its characteristics. SECC is an eastward current flowing from the western Pacific to the eastern Pacific, embedded in the South Equatorial Current (SEC), with an average flow rate of a few centimeters per second, but it carries a large amount of water and heat transport. SECC is mainly supplied by the backflow of SEC and the North Queensland Current (NQC) near the Solomon Islands. The strength, direction, and depth of SECC vary significantly on seasonal and interannual scales, which are closely related to wind stress curl, large-scale air-sea interaction in the tropical Pacific, and other factors, and play a crucial role in the modulation of surrounding mesoscale eddy energy through baroclinic instability.

[0003] The South Equatorial Countercurrent (SECC) is a backflow part of the equatorial current, located in the upper layer of the equatorial current, and is usually closely related to large-scale climate events such as El Niño and La Niña. As a countercurrent, SECC has an important influence on the thermocline structure, water temperature variation, and marine biological productivity of the eastern Pacific Ocean. It plays a regulating role in the exchange of heat, momentum, and matter between the atmosphere and the ocean, and thus affects the climate system and marine ecology. Early studies based on hydrological and current measurements from one or a few ship tracks have detected the strong seasonal and interannual variations of the Pacific SECC. And the dynamic link between SECC variation and surface wind has been found. Researchers have used satellite altimeter data and a 1.5-layer long Rossby wave model to diagnose the causes of the seasonal variation of the sea surface SECC, which is averaged at 13°S–8°S and 170°E–180°E. It is found that the SECC reaches its maximum in March and minimum in August, and the SECC north and south of 10°S is significantly affected by the interannual variation of the trade wind and the western Pacific monsoon, respectively, resulting in a phase jump in the annual sea surface height or thermocline across the SECC. However, the specific characteristics and dynamic mechanisms related to the seasonal variation of SECC in different regions are still poorly understood, let alone its interannual variation, which has hardly been recorded.

[0004] The Argo program, an international initiative launched in 1999, has revolutionized our understanding of the global ocean by systematically collecting temperature and salinity profiles in the upper 2000 m using autonomous Argo float systems. Using Argo data, previous studies have explored the characteristics of changes in the Pacific North Equatorial Current and South Equatorial Current, and evaluated the impact of local and remote wind forcing on changes, but no one has focused on the seasonal and interannual variations of the SECC based on Argo data.

[0005] Previous studies have some understanding of the seasonal variation of the SECC, but there are still deficiencies in the study of interannual variation and long-term dynamics. Through the above analysis, the problems and defects of the prior art are:

[0006] (1) Current research mostly focuses on certain specific regions and lacks systematic global-scale analysis; unable to achieve simultaneous quantification of SECC three-dimensional parameters (latitude, longitude, vertical range, flow velocity, flow axis, flow volume) based on temperature and salinity inversion in the entire ocean basin;

[0007] (2) Existing research has revealed the relationship between wind stress curl anomalies and sea surface height changes, but the study of how wind field anomalies regulate the seasonal and interannual variation of the SECC is still lacking, especially in quantitatively diagnosing the relative contribution of local and remote wind field anomalies to the SECC variation;

[0008] (3) Although the effects of wind stress curl and large-scale climate events (such as El Nino and La Nina) on the SECC have been studied to some extent, the specific response mechanisms of the SECC in these climate events, especially the response differences in different regions, are still not clear. SUMMARY

[0009] The purpose of the present application is to provide a method for determining the variation characteristics and mechanisms of the Pacific SECC in the entire ocean basin based on temperature and salinity inversion, to solve the technical problems of the variation and dynamic mechanism of the SECC in the three-dimensional range (longitude, latitude, depth) and the seasonal and interannual scales in the entire ocean basin, to achieve the determination and quantification of the variation of the three-dimensional range, flow axis, flow velocity, flow volume, etc. of the Pacific SECC in the entire ocean basin, and to reveal the rules and physical mechanisms of the seasonal and interannual variations of the SECC.

[0010] To achieve the above purpose, the present application provides a method for determining the variation characteristics and mechanisms of the Pacific SECC in the entire ocean basin based on temperature and salinity inversion, comprising the following steps:

[0011] Step one: Obtain monthly mean Argo temperature and salinity data; obtain AVISO ocean absolute dynamic height and sea surface geostrophic current velocity dataset; obtain OSCAR monthly mean sea surface current velocity data, ORAS5 surface wind stress data, Niño3.4 index, and use the Niño3.4 index to represent ENSO events;

[0012] Step two: Use the P-vector method to inverse the monthly mean Argo temperature and salinity data flow field, obtain AGCs, and use the OSCAR sea surface current velocity data to verify the AGCs;

[0013] Step three: Draw the AGCs flow field map according to the AGCs, and demarcate the spatial range of the SECC each month, quantify the seasonal and interannual variations of the SECC in longitude, latitude, vertical depth, flow axis, flow velocity, and flow volume, and determine the variation law of the flow velocity, flow axis position, and flow volume of the SECC in different seasons;

[0014] Step four: Remove the climatological monthly mean, detrend, and moving average of the AVISO ocean absolute dynamic height and AGCs to obtain interannual anomalies, and analyze the interannual variation law of the SECC in the six seasons of the ENSO development year and the recession year through synthetic analysis method;

[0015] Step five: Based on the seasonal variation law obtained in step three and the interannual variation law obtained in step four, establish a wind stress-sea current dynamic response mechanism, and through sensitivity experiment, determine the dynamic mechanism of the SECC seasonal and interannual variation caused by wind stress curl anomaly in different regions.

[0016] Further, the AGCs obtaining method in step two is:

[0017] (1) Calculate the potential density of each site according to the monthly mean Argo temperature and salinity data ;

[0018] (2) Calculate the three-dimensional flow velocity field using the P-vector method and the potential density to obtain the AGCs.

[0019] Further, the AGCs are obtained by calculating the three-dimensional flow velocity field using the P-vector method and the potential density , which is specifically:

[0020] Calculate the potential vorticity gradient: calculate the potential vorticity , , as the Coriolis parameter, as the vertical gradient of the potential density, and the potential vorticity is obtained by calculating the gradient of the potential vorticity along the x, y, and z directions ;

[0021] Construct the P-vector: where is the density gradient and the vorticity gradient , the cross product of is the unit vector representing the direction of the geostrophic flow;

[0022] Solving the velocity field, we obtain the monthly mean absolute geostrophic flow: ;

[0023] where is a scalar function determined by the thermal wind relation, which relates the density gradient and the flow velocity, represents the AGCs; are the spatial coordinates, representing longitude, latitude, and depth, respectively; , which is the constructed P vector.

[0024] Further, the criteria for the spatial range of each monthly SECC in step three are: the flow direction is eastward, the region is continuously distributed in the longitude and latitude directions, and it has a clear principal axis structure of flow velocity.

[0025] Further, the synthetic analysis method in step four is as follows:

[0026] (1) Define an El Niño event if the Niño3.4 index exceeds +0.5°C for five consecutive months, and define a La Niña event if it is below -0.5°C for five consecutive months. Define the starting time of each event as the development year, and the following year as the decay year.

[0027] (2) Remove the climatological monthly mean and perform detrending and 13-month moving average processing on the AVISO ocean absolute dynamic height and AGCs to obtain the interannual anomaly.

[0028] (3) Around each determined ENSO event, extract the SECC flow velocity anomaly, flow axis position anomaly, and flow volume anomaly data at the key seasonal time nodes in the development year and the decay year. Perform arithmetic averaging on the anomaly fields of similar events in the same season to obtain the synthetic average anomaly field. Compare and analyze the differences between the synthetic average anomaly fields of El Niño and La Niña, as well as the evolution of different phases of the same event, to reveal the interannual variation of SECC.

[0029] Further, the formula for calculating the synthetic average anomaly field is: ;

[0030] where is the synthetic average anomaly field, N is the number of events, is the anomaly value of the th event in the specified season, x and y represent the longitude and latitude coordinates, respectively, and season represents the specific season.

[0031] Further, the interannual variation rule of SECC is that: in El Niño period, SECC is horizontally expanded, vertically contracted, and the flow axis is northward, and the summer flow is multiplied to 10 Sv in recession year; in La Niña period, it is vertically deepened, the flow axis is southward, and the range is contracted to the west of 160°W.

[0032] Further, step five is specifically as follows:

[0033] The wind stress-sea current dynamic response mechanism is established: the wind stress curl is obtained according to the ORAS5 surface wind stress, and the sea surface height anomaly is obtained by using the AVISO global ocean absolute dynamic height; the wind stress-sea current dynamic response mechanism is established based on the linear vorticity equation theory, the reduced gravity coefficient and the wind dissipation coefficient are optimized by using the error minimization principle, and the causal relationship between the wind stress curl and the sea surface height anomaly is analyzed; and the dynamic mechanism of SECC seasonal variation and the dynamic mechanism of SECC interannual variation are determined according to the sensitivity experiment.

[0034] Further, the wind stress-sea current dynamic response mechanism is established based on the following linear vorticity equation: ;

[0035] In the formula, is the sea surface height anomaly, represents the phase velocity of the first-order Rossby wave, is the reduced gravity, is the gravitational acceleration, is the reference density, represents the surface wind stress, is the Coriolis parameter, is the dissipation coefficient.

[0036] Further, the dynamic mechanism of SECC seasonal variation is that the seasonal anomaly of wind stress curl in the region of 180°W-140°W is the core driving force, which adjusts the sea surface height gradient through exciting Rossby wave, and then drives the SECC seasonal variation;

[0037] The dynamic mechanism of SECC interannual variation is that the interannual anomaly of wind stress curl in the region of 180°W-140°W is the dominant mechanism, which regulates the south equatorial counter-current flow and the flow axis change of the whole basin through Rossby wave; the interannual anomaly in the region of 160°E-180° adjusts the range of the west of SECC through local Ekman pumping; and the interannual anomaly in the region east of 140°W affects the change of the east boundary of SECC.

[0038] The beneficial effects of the present application are:

[0039] 1. The Pacific full basin SECC three-dimensional structure and multi-parameter synchronous accurate quantification are realized. In view of the problems of existing research regional limitation and lack of systematic full basin analysis, the present application utilizes Argo temperature and salinity profile data, inverses monthly average absolute geostrophic current (AGCs) through the P vector method, and formulates clear spatial range demarcation standards (eastward flow direction, continuous distribution, and main flow axis), so as to systematically and monthly determine the longitude and latitude boundary, vertical depth, flow axis, flow velocity and flow rate of the SECC, fill the gap of the existing technology in the recognition of the three-dimensional fine structure of the full basin SECC, and accurately reveal the seasonal variation law (such as the widest space and the largest flow velocity / flow rate in spring, the longest zonal extension in autumn, etc.).

[0040] 2. The quantitative regulation and control effect of ENSO events on the three-dimensional structure and flow rate of the SECC are revealed. In view of the unclear response mechanism of the SECC in climate events, the present application quantitatively analyzes the regulation and control law of ENSO events on the SECC by using synthesis analysis method: in the El Niño period, the SECC shows horizontal expansion, vertical contraction, northward shift of the flow axis, and the flow rate is doubled to 10 Sv in summer of the recession year; in the La Niña period, the SECC shows vertical deepening, southward deviation of the flow axis, and the range is contracted to the west of 160°W. This clearly shows the key characteristics of the interannual variation of the SECC and its specific association with ENSO.

[0041] 3. The dominant mechanism of wind stress curl anomaly in different regions on the change of the SECC is determined through zoned dynamic mechanism diagnosis and sensitivity experiment. In view of the defect of insufficient research on the regulation and control mechanism of wind field anomaly, the present application establishes a wind stress-sea current dynamic response mechanism, and proves through delicate sensitivity experiment (zoned removal of wind stress curl signal) that the wind stress curl anomaly in the 180°W-140°W region is the core driving force of the seasonal and interannual variation of the full basin SECC (especially the flow rate and flow axis variation), and it is clear that the anomaly in the 160°E-180° region and the east of 140°W region respectively modulates the west and east range of the SECC through local Ekman pumping and affecting the eastern boundary, so as to completely reveal the dynamic cause of the change of the SECC. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a spatial distribution and correlation distribution diagram of the climatological average value of the Pacific surface zonal flow under the AGCs and OSCAR data provided by the present application;

[0043] Figure 2 is the climatological monthly average value of the Pacific surface zonal flow under the AGC data provided by the present application;

[0044] Figure 3 is the climatological monthly average value of the zonal flow along the 10°S zonal section (background fill color) provided by the present application.

[0045] Figure 4 is the climatological monthly mean of the Pacific AVISO surface zonal flow provided by the present application;

[0046] Figure 5 is the seasonal flow axis and transport of SECC provided by the present application;

[0047] Figure 6 is the composite interannual anomaly chart of different seasonal wind stress curl and sea surface height during El Nino and La Nina events provided by the present application;

[0048] Figure 7 is the composite interannual anomaly chart of surface zonal flow, wind stress and sea surface height during El Nino and La Nina events in different seasons provided by the present application;

[0049] Figure 8 is the interannual anomaly chart of zonal flow along 10°S during El Nino and La Nina events in different seasons provided by the present application;

[0050] Figure 9 is the interannual anomaly chart of zonal flow along 8°S during El Nino and La Nina events in different seasons provided by the present application;

[0051] Figure 10 is the composite flow axis and transport of different seasons during El Nino and La Nina event development year (Y0) and decay year (Y1) provided by the present application;

[0052] Figure 11 is the interannual anomaly chart of zonal flow speed and sea surface height meridional gradient of regions A and B provided by the present application;

[0053] Figure 12 is the comparison chart of AVISO measured sea surface height anomaly and simulated sea surface height anomaly on the seasonal variation scale along 10°S, and the sensitivity experimental result chart of wind stress curl anomaly of three regions provided by the present application;

[0054] Figure 13 is the comparison chart of AVISO measured sea surface height anomaly and simulated sea surface height anomaly on the interannual variation scale along 10°S, and the sensitivity experimental result chart of wind stress curl anomaly of three regions provided by the present application. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to examples. It should be understood that the specific examples described herein are only used to explain the present application, and are not used to limit the present application.

[0056] The application principles of the present application are further described below in combination with the drawings and specific embodiments.

[0057] A method for determining the Pacific Ocean basin SECC change characteristics and mechanism based on the temperature-salinity inversion, comprising:

[0058] Step one: obtain monthly average Argo temperature-salinity data; obtain the global ocean absolute dynamic height (ADT) and sea surface geostrophic current data set published by AVISO; obtain OSCAR monthly average sea surface current data, ORAS5 surface wind stress data, and Niño3.4 index, and use the Niño3.4 index to represent the ENSO event.

[0059] Among them, the monthly average Argo temperature-salinity data is a global ocean observation plan, which obtains temperature and salinity profile data at a depth of 0-2000 meters through the automatic profile float distributed globally; the temperature-salinity data is the optimal interpolation grid data made by the Scripps Institute of Oceanography of the University of California, San Diego, with a spatial resolution of 1°×1°, containing 58 vertical layers, a depth range of 2.5 to 1975 dbar, and a data time range of January 2004 to December 2022.

[0060] AVISO is a satellite oceanography data center established by the French National Space Research Center CNES, and the ADT data is based on the observation of the actual height of the sea surface by satellite altimeter relative to the reference surface; the data is distributed by the Copernicus Marine Environment Monitoring Service (CMEMS), with a spatial resolution of 0.25°×0.25°, a time distribution rate of 1 day, and used to estimate the sea surface geostrophic current.

[0061] OSCAR is a real-time analysis product of ocean surface currents, which is based on satellite sea surface height and wind field inversion, combined with Ekman flow and geostrophic flow theory, by Earth and Space Research; the data has a spatial resolution of 0.25°×0.25°, a time span of 1993 to present, and a time interval of 5 days.

[0062] ORAS5 is the fifth generation global ocean reanalysis data set published by the European Medium-Range Weather Forecast Center ECMWF, and the ORAS5 data has a spatial resolution of 0.25°×0.25°, a time range of 1958 to present, and surface wind stress for estimating Ekman pumping and sea level response under the interaction of sea and air; ORAS5 data is distributed by the Copernicus Climate Data Store platform.

[0063] The Niño3.4 index is an important indicator of the intensity of ENSO events, defined as the sea surface temperature (SST) anomaly in the region of 170°W-120°W, 5°S-5°N. When the index is greater than 0.5°C for five consecutive months, it is defined as an El Niño event, and less than -0.5°C as a La Niña event. The index is provided by the Physical Science Laboratory (PSL) of the National Oceanic and Atmospheric Administration (NOAA).

[0064] Step two: Use the P-vector method to retrieve the flow field from the monthly average Argo temperature and salinity data to obtain the monthly average absolute geostrophic currents (AGCs). The specific method is as follows:

[0065] (1) Calculate the potential density of each point according to the monthly average Argo temperature and salinity data .

[0066] (2) Calculate the three-dimensional flow velocity field using the P-vector method and potential density to obtain the monthly average absolute geostrophic currents (AGCs).

[0067] Calculate the potential vorticity gradient: calculate the potential vorticity as the Coriolis parameter, the vertical gradient of potential density, and the potential vorticity is calculated along the x, y, and z directions to obtain the potential vorticity gradient . .

[0068] Construct the P-vector: where is the cross product of the density gradient and the potential vorticity gradient , and is the unit vector representing the direction of the geostrophic flow.

[0069] Solve the flow velocity field to obtain the monthly average absolute geostrophic currents: ;

[0070] where is a scalar function determined by the thermal wind relationship, which relates the density gradient and the flow velocity, and represents the AGCs. is the spatial coordinate, representing longitude, latitude, and depth, respectively, and

[0071] is the unit P-vector constructed in the second step.Based on the monthly average Argo temperature and salinity data obtained in step one, the AGCs from January 2004 to December 2022 were calculated monthly using the P-vector method, and then the three-dimensional flow field of the SECC was obtained. In order to verify the rationality of the P-vector method and the reliability of the results, the monthly average sea surface flow velocity of OSCAR (which combines satellite altimeter and wind field calculation, suitable for geostrophic scale flow field observation) was used to compare and analyze the surface flow velocity results of AGCs (the surface flow velocity results of AGCs are directly obtained from the calculation of AGCs from January 2004 to December 2022, and the monthly average AGCs of all time ranges and latitude and longitude ranges with the shallowest depth coordinate are obtained). The comparison indicators include flow velocity direction consistency, flow velocity amplitude correlation coefficient, etc., to ensure that AGCs can truly reflect the climate state and variation characteristics of SECC.

[0072] As shown in Figure 1 , the climate state (2004-2022 average) of the SECC surface flow velocity distribution, where (a) is the climate state flow velocity map of the derived data AGCs, (b) is the climate state flow velocity map of the OSCAR sea surface flow velocity data, (c) is the climate state correlation distribution of AGCs and OSCAR sea surface flow velocity data, (d) is the climate state RMS difference of AGCs and OSCAR sea surface flow velocity data; the dashed line represents the zero zonal flow velocity line. Red dots represent grid points of correlation coefficients that do not pass the 90% confidence level significance test. It can be seen from Figure 1 that the climate state flow velocity map of AGCs is consistent with the climate state flow velocity map of OSCAR sea surface flow velocity, and from the correlation distribution graph (c) and the RMS difference graph (d), it can be seen that in the study time (from January 2004 to December 2022), AGCs and OSCAR data have good correlation (the average correlation is above 0.5) at each spatial grid point in the main study area (150°E-140°W, 4°S-15°S), and pass the 90% confidence level significance test, and the RMS difference is kept within ±0.03ms -1 in the main body of SECC. The results show that the AGCs surface flow field and OSCAR flow velocity are in good agreement in terms of time variation or amplitude, and can effectively reproduce the structure, path, and seasonal and interannual variations of SECC. In the average state, SECC is mainly located between 5°-11°S and 150°E-160°W, and its flow axis shows a northwest-southeast orientation.

[0073] Step three: Draw AGCs flow field map according to AGCs, and delimit the spatial range of SECC each month, quantify the seasonal and interannual variations of the latitude and longitude boundaries, vertical depth, flow axis, flow velocity, flow rate, etc. of SECC, and determine the variation law of flow velocity, flow axis position and flow rate of SECC in different seasons. The specific method is as follows:

[0074] (1) The spatial extent of the SECC was determined by drawing the AGCs stream function chart with the stream direction to the east (positive zonal velocity component), the region continuously distributed in the zonal and meridional directions, and the obvious structure of the main axis of the velocity.

[0075] (2) Based on the spatial extent of the SECC, the meridional and zonal boundaries (5°S-13°S), the vertical depth (0-300 m), the peak velocity (0.29 m / s), the displacement of the axis (1.8 degrees northward), and the flux (12.3 Sv) were extracted, and the seasonal variation of the SECC was determined: the spatial extent was the widest and the velocity / flux was the largest in spring, the zonal extension was the longest (to 140°W) and the axis was the most northern (6°S-9°S) in autumn, and the extent was contracted and the velocity was the weakest in summer.

[0076] As shown in Figure 2 , the monthly mean of the zonal flow on the surface of the Pacific Ocean in the AGCS. The contour interval is 0.1ms -1 . The dashed line represents the zero zonal flow line. As shown in Figure 3 , the monthly mean of the zonal flow along the 10°S zonal section (background fill). The velocity contour interval is 0.1ms -1 . The dashed line indicates the potential density minus 1000 kgm -3 . As shown in Figure 2 and Figure 3 , in the northern hemisphere spring (March to May, Figure 2 (a)-(c)), the SECC is the widest in the meridional direction (5°S-13°S) but the narrowest in the zonal direction (150°E-170°W), reaching the maximum velocity (0.29ms -1 ) of the year. In the western basin west of the date line, the SECC extends to a depth of more than 300 meters, and the zonal flow velocity in the upper 150 meters is mainly between 0.1-0.3 ms -1 (a)-(c) of Figure 2 , (a)-(c) of Figure 3 . In summer (June to August, Figure 2 (d)-(f)), the SECC in the western basin begins to weaken, and both the meridional and zonal ranges are contracted. At the same time, the SECC in the eastern basin begins to develop, but it is not connected with the western branch. In this season, the SECC is the narrowest in the meridional direction and presents an intermittent zonal feature, with a zonal flow velocity usually lower than 0.1ms -1 . In the vertical direction, the SECC is mainly concentrated in the upper 150 meters, and by August, the depth is shallower than 100 meters in most of the SECC region (d)-(f) of Figure 3 . From September, the SECC in the western basin begins to recover and connects with the eastern branch, and in autumn (September to November, Figure 2(g)–(i)) reaches its longest latitudinal range (150°E–140°W). As the SECC strengthens, its vertical range also deepens to approximately 200 meters. Figure 3 (g) – (i)). In winter (December to February, Figure 2 (j)–(l)), the SECC in the western basin continued to intensify, while the SECC in the eastern basin began to contract. Ultimately, the SECC west of 160°E was comparable in intensity to that in autumn, while the intensity east of 175°E reached its maximum, and the SECC between the two longitudes was slightly weaker than in spring, with a vertical depth approaching 300 meters, slightly shallower than in spring. Figure 3 (j) – (l)). Therefore, the SECC flow velocity and range exhibit significant seasonal variations.

[0077] like Figure 4 The figure shows the climatological monthly average of the zonal surface currents of the Pacific AVISO. The contour line spacing is 0.1 ms. -1 The dashed line represents a latitudinal velocity of zero. (From...) Figure 4 It can be seen that the spatial distribution of SECC is related to Figure 2 The results (based on AGCs) are highly consistent, demonstrating the reliability of AGCs based on temperature-salinity inversion in characterizing the surface path and intensity of SECC, thus corroborating the effectiveness of the P-vector method inversion and the credibility of subsequent analysis results.

[0078] like Figure 5 As shown, the seasonal flow axis and transport volume of the SECC (within the upper 260 meters) (unit: Sv). The solid blue line, the circled orange line, the beige dashed line, and the circled purple line represent spring, summer, autumn, and winter, respectively. Figure 5 As shown in (a), when the SECC flows eastward west of the Date Line, it gradually shifts southward and then takes a near-latitudinal orientation. Generally, the current axis reaches its northernmost position in autumn, ranging from 6°S in the northwest to approximately 9°S in the east (beige dashed line). Conversely, it reaches its southernmost position in spring, extending from 7.5°S in the northwest to approximately 11°S in the east (blue line). In summer (orange line with circles) and winter (purple dashed line with circles), the current axis exhibits an anti-symmetrical structure on both sides of the Date Line. West of the Date Line, the summer current axis (6°S–11°S) is further south than the winter current axis (7°S–10°S), while east of the Date Line, the summer current axis is further north than the winter current axis (summer ~8.5°S vs. winter ~9.5°S). Figure 5As shown in (b), the maximum transport occurs approximately at 160°E in spring, summer, and autumn, with values ​​of 12.3 Sv, 8.7 Sv, and 6.5 Sv, respectively. In winter, the peak transport shifts to around 167°E, reaching 9.7 Sv. In contrast, summer transport is the weakest, generally remaining below 2 Sv in most areas. The maximum transport west and east of 165°E occurs in spring and winter, respectively, and is generally greater than 4 Sv except east of 175°W. In autumn, the transport west of 160°E is similar to that in winter, then decreases as the flow moves eastward. Between 172°W and 160°W, the autumn transport is greater than that in spring but less than that in winter.

[0079] Step 4: Remove the climatological monthly average from the AVISO absolute ocean dynamic height and AGCs (i.e., subtract the corresponding climatological monthly average data from the monthly absolute ocean dynamic height and AGCs; the climatological monthly average data here is obtained by calculating the climatological average for each January, February, etc., between January 2004 and December 2022, resulting in a total of twelve climatological monthly average data, corresponding to the climatological monthly average for January, February, etc., respectively), and then perform trend removal and moving average (13 months) processing to obtain the interannual anomaly. Then, through composite analysis, analyze the interannual variation of SECC in the six seasons of ENSO development and decline years. That is, during the El Niño event: the SECC expands horizontally but contracts vertically, the velocity anomaly advances from north to south, and the flow axis shifts northward; during the La Niña event: the SECC deepens vertically, and the flow axis shifts southward; in the summer of the decline year, the SECC flow is the largest (10 Sv) during the El Niño event, which is twice that of the climatological and La Niña event periods.

[0080] (1) The detrending process in step four is as follows:

[0081] Assuming that the ocean absolute dynamic height of AVISO, after removing the climatic monthly mean, can be decomposed into... ,in It is a linear trend term. The remaining components after detrending are then subjected to parameter fitting to solve for the parameters. a (Slope) and b (Intercept), making the trend line Compared with the original data Sum of squared residuals between To minimize this, the optimal fitted trend line is subtracted from the original data to obtain the clean, detrended time series as the result. .

[0082] (2) The synthetic analysis method in step four is as follows:

[0083] First, using the Niño3.4 index obtained in step one, an El Niño event is defined as the Niño3.4 index being above +0.5℃ for 5 consecutive months; a La Niña event is defined as the Niño3.4 index being below -0.5℃ for 5 consecutive months. Furthermore, the starting time of each event is defined as the development year (Year0, Y0), and the following year as the decay year (Year1, Y1). Then, the climatological monthly mean of the AVISO data obtained in step one and the AGCs data obtained in step two are removed and processed by detrending and 13-month moving average to obtain interannual anomalies. Then, for each specific ENSO event, the anomaly field data of key seasons (such as summer of the development year, autumn of the development year, winter of the maturity period, spring of the decay year, and summer of the decay year) are extracted in its development year (Y0) and decay year (Y1). For example, for a certain El Niño event, the SECC velocity anomaly, current axis position anomaly, and flow anomaly data of time nodes such as June-August 2009 (Y0 summer), September-November 2009 (Y0 autumn), and March-May 2010 (Y1 spring) are extracted. Then, the anomaly fields of similar events (all El Niño or all La Niña) in the same season are arithmetically averaged to obtain the composite average anomaly field. For example, by averaging the SECC velocity anomaly fields of all five El Niño events in the summer of their decaying year (Y1 summer), a spatial distribution map of "synthetic average SECC velocity anomaly in the summer of the El Niño decaying year" is obtained.

[0084] The formula for calculating the average anomaly field after synthesis is as follows: ;

[0085] in, It is the average anomalous field after synthesis, and N is the number of events (e.g., 5 for El Niño). It is the first The outliers of the sub-event in a specified season, where x and y represent longitude and latitude coordinates respectively, and season represents a specific season, such as summer in the El Niño development year. Finally, by comparing and analyzing the differences between the El Niño and La Niña composite fields, as well as the evolution of different phases of the same event (such as the comparison between development and decay years), the interannual variation of SECC is revealed.

[0086] like Figure 6 As shown, where Figure 6 Seasonal wind stress curl of (a)-(f) El Niño and (g)-(l) La Niña events (color in; unit: Nm) -3The composite interannual anomaly of ENSO events and sea level (contour lines; unit: cm). Blue (red) contour lines represent the zero line for absolute zonal velocity (sea level anomaly). The 0s and 1s in parentheses represent the development and decline years of the ENSO event, respectively. Figure 6 It can be seen that during El Niño cycles, the SECC region is dominated by strong southwesterly wind anomalies, while in the eastern basin, mainly north of 8°S, southeasterly wind anomalies dominate from the developing autumn to the weakening spring. According to Ekman's pumping dynamics, the negative wind stress anomalies in the SECC region during El Niño (see...) Figure 6 (a)-(e)) will lead to a decrease in sea surface height, in contrast to the observed sea surface height pattern (decreasing in the west and rising in the east of the SECC), during La Niña years, the SECC region is affected by anomalies in northeasterly and normal wind stress curl (see Figure 6 (f)-(l)), while the central basin is dominated by southeasterly winds.

[0087] like Figure 7 As shown, surface zonal currents during (a)-(e) El Niño and (f)-(g) La Niña events in different seasons (filled in; unit: ms) -1 Wind stress (vector; unit: Nm) -2 The composite interannual anomaly of ENSO event and sea surface height (black contour lines; unit: cm). The blue (red) contour lines represent the zero line for absolute zonal current velocity (sea surface height anomaly). The 0 and 1 in parentheses represent the development year and decay year of the ENSO event, respectively. Figure 8 It is well known that ENSO's moderating effect on the tropical Pacific circulation system is widely recognized (e.g., Kessler and Cravatte, 2013). The zero-isotropy line of absolute velocity (blue line) and the interannual anomaly of surface wind stress (vector) are also superimposed on... Figure 7 Above. Overall, during El Niño and La Niña, the spatial extent and flow velocity of the SECC change in opposite ways. During El Niño ( Figure 7 (a)-(f)) The SECC region, enclosed by isolines of zero velocity west of 140°W, expands both zonally and meridionally, reaching its maximum extent during the mature stage (12°–5°S, 150°E–140°W). For most of the El Niño year, the southwestern part of the SECC experiences negative velocity anomalies, while other regions are predominantly positive. This contrast is most pronounced during El Niño winters, with maximum positive and negative velocity anomalies reaching 0.11 ms. -1 and -0.08ms -1 ( Figure 7 (d)). However, the southward propagation of positive velocity anomalies is significant, and correspondingly, by the summer of El Niño decay, almost the entire SECC region is dominated by positive velocity anomalies, while in La Niña years ( Figure 7(g)-(l)), the SECC range shrinks significantly, limited to west of 160°W, with negative velocity anomalies spreading from north to south.

[0088] like Figure 8 and Figure 9 As shown, interannual anomalies of zonal currents along 10°S or 8°S during (a)-(e) El Niño and (f)-(g) La Niña events in different seasons (coloring; unit: ms). -1 The composite absolute zonal velocities during the ENSO event are superimposed for comparison; solid and dashed contour lines represent eastward and westward flows, respectively. Zero-velocity contour lines are marked in red (contour interval: 0.02 ms). -1 ). Figure 8 This shows the synthetic zonal currents (isolines) and interannual anomalies (background fill) within 350 meters above 10°S during ENSO events. During El Niño, the SECC (surrounded by red isolines above 300 meters) expands eastward, but its vertical extent contracts during the development phase and becomes shallowest in the mature phase, mainly confined to the upper 150 meters. The zonal current velocity west of 155°W gradually weakens, reaching a minimum in the mature phase, with the strongest negative anomaly (-0.04 ms) appearing near 165°E. -1 East of 155°W, the SECC, though intermittent, intensifies from autumn and peaks in winter. During La Niña events, the SECC deepens vertically, reaching 2 cm / s at maturity. -1 The contour lines extend to over 250 meters. Flow velocity increases west of 170°W, reaching a maximum anomaly of 0.06 ms. -1 The flow velocity weakened east of 170°W, with the maximum anomaly being 0.01 ms. -1 Along 8°S, the latitudinal range of the SECC narrows, but its variability is similar to that along 10°S, except that the velocity anomaly reversal occurs earlier in the western sector. Figure 9 ).

[0089] like Figure 10 As shown, the composite (a)-(f) flow axes and (g)-(l) transport volumes for different seasons in the development (Y0) and decline (Y1) years of El Niño (orange dotted line) and La Niña (green dashed line) events. Seasonal flow axes and transport volumes (cyan line) are superimposed for comparison. Figure 10 It can be seen that with the changes in the range and velocity of the SECC during El Niño and La Niña years, its flow axis ( Figure 10 The (a)-(f) and transport (10 of (g)-(l)) experienced significant interannual variability. During El Niño ( Figure 10 (The orange lines with dots in (c)-(e)) show the transition from developing autumn to weakening spring, compared to La Niña years ( Figure 10(c)-(e) (green dashed line), the SECC shifts northward by up to 1.8 degrees of latitude. Although the meridional current widens during El Niño, its transport volume is comparable to that during La Niña, both weaker than the climatological seasonal average, due to the shallowing and reverse effects of changes in current velocity in the north and south (intensification in the north and weakening in the south). Figure 10 (g)-(j)). After maturity, the SECC of the entire basin intensifies, leading to an increase in transport relative to La Niña conditions ( Figure 10 (k)). During the declining summer, SECC transport is almost twice the climatological seasonal average (cyan line) and the La Niña value, reaching a maximum transport of ~10 Sv near 157°E. Figure 10 (l)).

[0090] like Figure 11 As shown, the interannual anomalies of zonal current velocity (red line) and meridional sea surface height gradient (blue line) are observed in regions A (6°–8.8°S, 168°E–175°W) and B (9.2°–11°S, 165°E–175°W). Figure 11 It can be seen that the interannual anomalies of the SECC show a strong correlation with the meridional gradients of sea surface height in regions A and B. The correlation coefficients reach 0.89 and 0.72, respectively, both passing the 99% significance test. The effective degrees of freedom for the interannual anomalies of the SECC in regions A and B are 36 and 44, respectively. This indicates that the interannual variability of the SECC is closely related to the changes in the meridional gradient of sea surface height.

[0091] Quantitative analysis reveals the regulatory patterns of ENSO events on the SECC: During the El Niño period, the SECC expands horizontally, contracts vertically, and shifts its axis northward, with summer flow doubling to 10 Sv (at 157°E) during the recession year; during the La Niña period, the SECC deepens vertically (>250 meters), shifts its axis southward, and shrinks to west of 160°W; interannual SECC anomalies are significantly correlated with the meridional gradient of sea surface height (r=0.89).

[0092] The specific method for estimating the effective degrees of freedom is as follows:

[0093] This study uses a simplified formula mentioned by Bretherton et al. in their 1999 paper "The effective number of spatial degrees of freedom of a time-varying field" published in the Journal of Climate for estimation: ;

[0094] in, T The original length of the sequence. and The lag-one autocorrelation coefficients of the two sequences, respectively, The effective degrees of freedom required.

[0095] Step five: Based on the SECC seasonal variation law in step three and the SECC interannual variation law in step four, the wind stress-sea current dynamic response mechanism is established to further explore the dynamic mechanism of the SECC seasonal variation law and the SECC interannual variation law, analyze the causal relationship between the wind stress curl and the sea surface height anomaly, and adjust the reduced gravity coefficient, the wind dissipation coefficient and other parameters to optimize the simulation effect, conduct a sensitivity experiment, and analyze the dynamic mechanism of the interannual anomaly of the wind stress curl in different regions on the SECC interannual variation and the dynamic mechanism of the SECC seasonal variation. The specific method is as follows:

[0096] (1) Establish the wind stress-sea current dynamic response mechanism

[0097] The wind stress curl is obtained according to the ORAS5 surface wind stress, and the sea surface height anomaly (SLA / SSHa) is obtained by using the AVISO global ocean absolute dynamic height.

[0098] The wind stress-sea current dynamic response mechanism is established based on the linear vorticity equation theory. First, the physical framework with the wind stress curl as the core forcing term is established, and the wind stress curl driving the sea surface height anomaly is described by equation (3) h' and westward propagation C R * h' / x of the dynamic process. In order to accurately simulate the actual ocean response, the key parameters are optimized and adjusted by using the error minimization principle: the reduced gravity coefficient is determined to be 0.038 ms -2 , reflecting the strength of the South Pacific stratification; according to the difference in research scale, the dissipation coefficient ε is set to seasonal variation 1 / (2 years) and interannual variation 1 / (5 years); the Rossby wave phase speed C RThe latitude dependence value was determined using Chelton's observations (reference: Chelton, DB, deSzoeke, RA, Schlax, MG, El Naggar, K., & Siwertz, N. (1998). Geographical variability of the firstbaroclinic Rossby radius of deformation. Journal of Physical Oceanography, 28(3), 433–460.).

[0099] The causal relationship between wind stress curl and sea surface height anomaly is analyzed. The causal relationship between wind stress curl and sea surface height anomaly is quantitatively analyzed through the integral solution of equation (4). Specifically, at any location ( x , y At that moment t sea ​​surface height h It is all the upstream points on its east side ( x' , y The wind stress curl forced through time delay (x'-x) / C R (Characterizing the time required for the Rossby wave to propagate from the forcing point to the target point) and attenuation (Characterizing energy dissipation during propagation) Modulated cumulative response. This integral relationship clearly quantifies the spatiotemporal correspondence between wind stress curl anomaly as cause and sea surface height anomaly as effect. The anomalous signal from the eastern wind field propagates westward through Rossby waves, gradually influencing the sea surface height distribution in the downstream sea area, thus establishing a dynamic causal relationship between the two.

[0100] The wind stress-ocean current dynamic response mechanism is established based on the following linear eddy covariance equation: (3);

[0101] (4);

[0102] In formulas (3) and (4), It is an anomaly in sea surface height. The phase velocity representing a first-order Rossby wave, This is the reduced gravity (set to 0.038 ms). -2 ), It is the acceleration due to gravity (set to 9.8 ms). -2 ), This is the reference density (set to 1025 kg / m³). -3 ), Represents surface wind stress, It is the Coriolis parameter. It is the dissipation coefficient (set to 1 / (2 years) when studying seasonal variation, and 1 / (5 years) when studying interannual variation). The simulated sea surface height anomaly field is shown in the model. It is the longitude of the eastern boundary of the integral. The target point whose longitude needs to be calculated for sea surface height anomalies is... It is a time lag term, representing the upstream point. x' The effects of wind stress forcing at a certain location need to be investigated. It takes time for the message to reach the target point. x , It is the integral variable, representing the direction from the eastern boundary. x e To the target point x Longitude of the upstream point between them It is an integral variable, representing a tiny line segment along the propagation path. It is the latitude where the integral is located.

[0103] (2) Sensitivity test

[0104] Sensitivity experiments included the removal of three groups of regional wind stress curl seasonal anomalies: 140°W–70°W; 180°–140°W; and 160°E–180°.

[0105] (3) The driving mechanism of seasonal variation in SECC

[0106] like Figure 12 As shown, the seasonal variation of sea surface height anomalies along 10°S, where (a) is AVISO satellite observations and (b)-(e) are model simulations driven by wind stress curl anomalies (unit: m). In (c)-(e), the seasonal signals of wind stress curl were removed in regions 140°W–70°W, 180°–140°W, and 160°E–180°, respectively. Figure 12 As shown, the seasonal variation along the SECC at 10°S was re-examined, and the simulation results generally agree well with AVISO observations. Figure 12 (a) and (b) identified the dynamic mechanism of SECC seasonal variation. To identify key forcing regions, three sensitivity experiments were conducted, sequentially removing areas east of 140°W ( Figure 12 (c)), between 180°W and 140°W ( Figure 12 (d) and between 160°E and 180° ( Figure 12 (e) seasonal signal of wind stress curl anomaly. Figure 12(d) reveals that the wind stress curl anomaly in the 180°W–140°W region is the main driver of the seasonal variation of the SECC, while the anomaly east of 140°W plays a fundamental role in the SECC between 150°W–140°W. It was found that the wind stress curl anomaly in the 180°W–140°W region is the core driving force regulating the seasonal variation of the SECC. It adjusts the sea surface height gradient by stimulating Rossby waves, which in turn adjust the sea surface height gradient through their westward wave characteristics. The wind stress curl anomaly first stimulates the sea surface height anomaly through Ekman pumping, and this anomaly signal is then locked in Rossby waves and propagates westward. The propagation of the wave is essentially a westward dispersion of energy and potential vortex disturbances, which reconstructs the spatial distribution of sea surface height along its path, thereby directly changing its meridional and zonal gradients. Specifically, the wave structure of alternating peaks (positive anomalies) and troughs (negative anomalies) itself constitutes a new pressure gradient field, which in turn drives ocean current changes through geostrophic equilibrium, and in turn drives SECC changes: strong wind stress anomalies in spring lead to SECC expansion and strengthening, weak wind stress in the west in summer causes SECC contraction, and eastern anomalies in autumn regulate the northward shift of the flow axis.

[0107] (4) The driving mechanism of interannual variation of SECC

[0108] like Figure 13 As shown, the interannual variation of sea surface height anomalies along 10°S originates from... Figure 13 (a) AVISO satellite observations and Figure 13 (b)-(e) Model simulations driven by wind stress curl anomalies (unit: m). Figure 13 In (c)-(e), the interannual signal of wind stress curl was removed in regions 140°W–70°W, 180°W–140°W and 160°E–180°E, respectively.

[0109] Sensitivity experiments using the regional removal of wind stress curl anomalies confirmed that the interannual wind stress curl anomalies in the 180°–140°W region are the dominant mechanism driving changes in the entire sea basin's SECC (especially in terms of discharge and current axis). The anomalies in this region, by stimulating westward-propagating Rossby waves, adjust the meridional gradient of the sea surface height field, thereby controlling the core changes of the SECC through geostrophic balance. Anomalies in the 160°E–180° region mainly modulate changes in the western extent of the SECC through local Ekman pumping processes. Anomalies east of 140°W primarily affect changes in the eastern boundary of the SECC.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for determining the characteristics and mechanisms of SECC variation across the entire Pacific Ocean basin based on current field determined by temperature-salinity inversion, characterized in that, Includes the following steps: Step 1: Obtain monthly mean Argo temperature and salinity data; obtain AVISO ocean absolute dynamic height and sea surface geostrophic velocity datasets; obtain OSCAR monthly mean sea surface velocity data, ORAS5 surface wind stress data, and Niño 3.4 index, and use the Niño 3.4 index to characterize ENSO events; Step 2: Use the P-vector method to invert the flow field of the monthly average Argo temperature-salinity data to obtain AGCs; and use OSCAR sea surface velocity data to verify the AGCs. Step 3: Draw the flow field diagram of AGCs based on AGCs, and delineate the spatial range of SECCs each month. Quantify the seasonal and interannual variations of the latitude and longitude boundaries, vertical depth, flow axis, flow velocity, and flow rate of SECCs, and determine the variation patterns of flow velocity, flow axis position, and flow rate of SECCs in different seasons. Step 4: Remove the climatological monthly mean, detrended and moving average processing from the AVISO ocean absolute dynamic height and AGCs to obtain interannual anomalies, and analyze the interannual variation of SECC in the six seasons of ENSO development and decline years using composite analysis. The synthetic analysis method specifically includes: (1) An El Niño event is defined as the Niño 3.4 index being above +0.5℃ for 5 consecutive months; a La Niña event is defined as the Niño index being below -0.5℃ for 5 consecutive months; the start time of each event is defined as the development year, and the following year is the decay year; (2) Remove the climatological monthly mean from the AVISO and AGCs and perform detrending and 13-month moving average processing to obtain the interannual anomalies; (3) For each specific ENSO event, extract the SECC velocity anomaly, flow axis position anomaly, and flow anomaly data at key seasonal time nodes in its development and decay years. Take the arithmetic mean of the anomaly fields of the same type of event in the same season to obtain the composite average anomaly field. Compare and analyze the differences between the composite average anomaly fields of El Niño and La Niña and the evolution of the same event in different phases to reveal the interannual variation law of SECC. Step 5: Based on the seasonal variation patterns obtained in Step 3 and the interannual variation patterns obtained in Step 4, wind stress curl is obtained from the surface wind stress of ORAS5, and sea surface height anomalies are obtained using AVISO global ocean absolute dynamic height. A wind stress-ocean current dynamic response mechanism is established based on the linear eddy equation theory. The reduced gravity coefficient and wind dissipation coefficient are optimized using the error minimization principle, and the causal relationship between wind stress curl and sea surface height anomalies is analyzed. The dynamic mechanisms of seasonal and interannual SECC variation are determined based on sensitivity experiments.

2. The method according to claim 1, characterized in that, The method for obtaining AGCs in step two is as follows: (1) Calculate the potential density of each point based on the monthly average Argo temperature and salinity data. ; (2) Using the P-vector method and potential density Calculate the three-dimensional flow velocity field to obtain AGCs.

3. The method according to claim 2, characterized in that, Using the P-vector method and potential density The three-dimensional velocity field is calculated to obtain AGCs as follows: Calculate the potential vortex gradient: based on the potential density Calculate potential vorticity , Coriolis parameters, The vertical gradient of the potential density is for the potential vortex. The potential vortex gradient is obtained by calculating the gradients along the x, y, and z directions respectively. ; Construct the P vector: ,in Density gradient With potential vortex gradient cross product, A unit vector characterizing the direction of geostrophic flow; Solving for the velocity field yields the monthly average absolute flow: In the formula, It is a scalar function determined by the thermo-wind relationship, relating density gradient and velocity. Represents AGCs; These are spatial coordinates, representing longitude, latitude, and depth, respectively. This is the constructed P vector.

4. The method according to claim 1, characterized in that, The standard for the spatial extent of the SECC in step three is: the flow direction is eastward, the area is continuously distributed in the latitude and longitude directions, and there is a clear main velocity axis structure.

5. The method according to claim 1, characterized in that, The formula for calculating the synthesized average anomaly field is as follows: in, This is the synthesized average anomalous field, where N is the number of events. It is the first The outlier of this event in a specified season, where x and y represent longitude and latitude coordinates respectively, and season represents the specific season.

6. The method according to claim 1, characterized in that, The interannual variation pattern of the SECC is as follows: during the El Niño period, the SECC expands horizontally, contracts vertically, and shifts its flow axis northward, with the summer flow doubling to 10 Sv during the recession year; during the La Niña period, the vertical flow deepens, the flow axis shifts southward, and the range shrinks to west of 160°W.

7. The method according to claim 1, characterized in that, The wind stress-ocean current dynamic response mechanism is established based on the following linear eddy covariance equation: In the formula, It is an anomaly in sea surface height. The phase velocity representing a first-order Rossby wave, It is reduced gravity. It is gravitational acceleration. It is a reference density. Represents surface wind stress, It is the Coriolis parameter. It is the dissipation coefficient. t represents the longitude of the target point for which the sea surface height anomaly needs to be calculated, and t represents the time.

8. The method according to claim 1, characterized in that, The driving mechanism of SECC seasonal variation is as follows: the seasonal anomaly of wind stress curl in the 180°W–140°W region is the core driving force, which drives SECC seasonal variation by stimulating Rossby waves to adjust the sea surface height gradient. The dynamic mechanisms of the interannual variation of the SECC are as follows: the interannual anomaly of wind stress curl in the 180°W–140°W region is the dominant mechanism, which modulates the South Equatorial Countercurrent flow and axis changes throughout the basin through Rossby waves; the interannual anomaly in the 160°E–180° region modulates the western extent of the SECC through local Ekman pumping; and the interannual anomaly in the region east of 140°W affects the changes in the eastern boundary of the SECC.

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