Method for vortex-upflow coordinated regulation of chlorophyll-a concentration variability

Abstract

The application provides a method for vortex-uplift flow coordinated regulation chlorophyll-a concentration variability, relates to the field of ocean remote sensing and environmental monitoring technology, obtains multi-source satellite and reanalysis data set, constructs smooth time series; identifies dominant variability period in chlorophyll-a concentration, sea surface temperature and sea surface height anomaly, extracts monthly anomaly value sequence; applies complex empirical orthogonal function analysis to sea surface height anomaly, extracts its dominant spatial variability mode, identifies vortex distribution position and shape; according to vortex type and season, classifies and filters target vortex, carries out normalization processing to chlorophyll-a concentration and aligns to vortex center, analyzes spatial response relationship between vortex and chlorophyll-a concentration; in combination with surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, density time-depth profile graph and CHL-SLA composite graph are drawn, and the influence mechanism of vortex-uplift flow on chlorophyll-a horizontal distribution is analyzed.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of marine remote sensing and environmental monitoring, and particularly relates to a method for vortex-upwelling coordinated regulation of chlorophyll-a concentration variability. BACKGROUND

[0002] In the past two decades, the driving mechanism of chlorophyll-a concentration (CHL) variability in the Somali upwelling region has been a research hotspot in the field of ocean science, but there has been controversy. Previous studies have shown that factors such as wind stress enhancement, sea surface temperature changes and mesoscale vortex structure have affected the spatial and temporal distribution of CHL in the region to varying degrees. However, the interaction mechanism between these factors has not been clarified, especially the coordinated regulation of vortex and upwelling under the background of strong monsoon still lacks systematic quantitative research. Therefore, in-depth exploration of the dynamic process and spatiotemporal evolution characteristics of the dominant CHL variability has become the key to understanding the changes in regional ocean primary productivity.

[0003] Traditional remote sensing analysis methods have limitations in analyzing the coupling characteristics between mesoscale eddies and upwelling, and it is difficult to fully capture their nonlinear propagation characteristics and spatial response patterns. Therefore, it is urgent to build a new research framework based on multi-source data fusion and multi-temporal and spatial scale analysis method to realize the identification and process mechanism understanding of the vortex-upwelling coordinated action. SUMMARY

[0004] In order to solve the above technical problems, the application provides a method for vortex-upwelling coordinated regulation of chlorophyll-a concentration variability, which integrates wavelet and complex empirical orthogonal function (CEOF) coordinated mode extraction, chlorophyll-a concentration (CHL) anomaly analysis, vortex center CHL fusion, and horizontal-section data joint analysis methods, and aims to reveal the regulation mechanism of mesoscale eddies (especially anticyclones such as Great Whirl, GW) and monsoon-driven coastal upwelling on the spatiotemporal distribution of CHL.

[0005] A method for vortex-upwelling coordinated regulation of chlorophyll-a concentration variability, comprising the following steps:

[0006] (1) Obtain multi-source satellite and reanalysis data sets, including chlorophyll-a concentration (CHL), sea surface temperature (SST), sea level anomaly (SLA), wind speed and Argo float data, time fuse the daily data of chlorophyll-a concentration, sea surface temperature and sea level anomaly, generate monthly average data, and construct a smooth time series;

[0007] (2) Continuous wavelet transform is performed on the stationary time series to identify dominant variation periods in chlorophyll-a concentration, sea surface temperature and sea level anomaly, and a multiple seasonal-trend decomposition method is used to separate long-term trend, seasonal cycle and residual components to extract monthly anomaly value series;

[0008] (3) Complex empirical orthogonal function analysis is applied to sea level anomaly to extract its dominant spatial variation mode to identify vortex distribution location and shape;

[0009] (4) Target vortices are screened according to vortex type and season, chlorophyll-a concentration is normalized and aligned to the vortex center, and the spatial response relationship between the vortex and chlorophyll-a concentration is analyzed;

[0010] (5) Surface chlorophyll-a concentration, sea level anomaly and vertical Argo float data are combined to draw density time-depth profile and CHL-SLA composite map to analyze the influence mechanism of vortex-upwelling on the horizontal distribution of chlorophyll-a.

[0011] Preferably, in step (3), the complex empirical orthogonal function analysis simultaneously analyzes the amplitude and phase information of sea level anomaly variation, and identifies the structure of mesoscale cyclone and anticyclone vortex.

[0012] Preferably, when identifying the structure of mesoscale cyclone and anticyclone vortex, the vortex center and polarity are determined by calculating the gradient and Hessian matrix of sea level anomaly, the vortex boundary is determined by finding the maximum closed contour, and the equivalent radius of the vortex is calculated.

[0013] Preferably, in step (4), the chlorophyll-a concentration is normalized and aligned to the vortex center, and is standardized according to the equivalent radius range of the vortex to analyze the spatial distribution characteristics of chlorophyll-a concentration inside and around the vortex.

[0014] Preferably, in step (5), the horizontal-section data joint analysis includes: for Argo float trajectory, a density time-depth profile is drawn, daily chlorophyll-a concentration and sea level anomaly corresponding to the time and location are obtained, a CHL-SLA spatial composite map is generated, Argo float trajectory is superimposed on the CHL-SLA spatial composite map, and the influence of vortex-upwelling system on the horizontal distribution of chlorophyll-a concentration is comprehensively analyzed.

[0015] Preferably, when performing horizontal-section data joint analysis, Argo float profile data is used, which includes temperature, salinity, pressure and density, and mixed layer depth MLD is calculated according to the density method:

[0016] ;

[0017] wherein z represents depth, This represents the density of seawater at depth z. Density representing the reference depth, This represents the threshold.

[0018] Preferably, in step (2), continuous wavelet transform is used to extract periodic and cyclical components from the stationary time series, decomposing the data into a time-frequency space. For data with a uniform time step... time series x n Wavelet coefficients, n=1,...,N for:

[0019] ;

[0020] Among them, wavelet coefficients This represents the time series at time point n and scale s. With wavelet basis functions The degree of similarity, For wavelet power spectrum, For the index of the summation variable.

[0021] Preferably, identifying cyclones and anticyclones through sea surface height anomaly contour lines and extracting vortex polarity, center position, and radius includes the following steps:

[0022] ① Polarity determination:

[0023] ;

[0024] Indicates the center point of the vortex SLA scalar field, denoted by , H represents the Hessian matrix, and detH represents the determinant;

[0025] Polarity determination: When the trace of H tr H < 0, it is a local maximum, indicating an anticyclone; when the trace of H tr H > 0, it is a local minimum, indicating a cyclone.

[0026] ② Construct closed contour lines:

[0027] Let S be the set of saddle points, and let s be the extreme values ​​of the boundary contour lines. ; For the center point of the vortex The SLA scalar field of the connected saddle points s;

[0028] Boundary equivalence of the anticyclone center for: ;

[0029] cyclone center boundary isopleth for: ;

[0030] vortex boundary is a closed contour :

[0031] ;

[0032] is an internal connected domain, represents the boundary contour, is a point on the closed contour SLA scalar field;

[0033] ③Calculate the vortex center point :

[0034] ;

[0035] ④Calculate the closed domain area A and the perimeter L:

[0036] , ;

[0037] Wherein, a represents the area element of the internal connected domain, represents the unit length on the closed contour.

[0038] Use the equal-area radius as the vortex radius :

[0039] .

[0040] Compared with the prior art, the present application has the beneficial effects as follows:

[0041] The present application proposes a comprehensive method combining wavelet analysis, modal extraction, anomaly identification, vortex center complex and horizontal-section data joint analysis, which is used for revealing the action mechanism of mesoscale vortex and upwelling on the distribution of marine CHL. The present application introduces Argo buoy section data, and systematically analyzes the comprehensive influence of vortex and upwelling on MLD, SLA and CHL concentration, which can effectively reveal the coupling relationship between physical process and biological response. The present application has significant innovation and advantage in multi-source data fusion, complex dynamic process separation and ecological effect identification, and can more comprehensively and accurately depict the regulation mechanism of vortex and upwelling on the marine ecological environment. The method of the present application has good universality, and can be applied to other typical upwelling and dynamic active sea areas in the world, thereby providing new technical means and scientific basis for marine ecological environment monitoring, resource assessment and climate change impact research. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is the monthly time series data from 1997 to 2020, wherein, Figure 1Figure 1 (a) is a time series plot of CHL between 1997-2004, Figure 1 Figure 1 (b) is a time series plot of SLA and SST between 1997-2024, Figure 1 Figure 1 (c) is a time series plot of wind speed horizontal and vertical components between 1997-2020, Figure 1 Figure 1 (d) is a time series plot of wind stress between 1997-2020;

[0043] Figure 2 Figure 2 is a correlation plot between CHL and SLA, SST, wind speed U component and wind speed V component for each month between 1997-2024;

[0044] Figure 3 Figure 3 is a time series data of monthly average CHL, SLA and SST between 1997-2024, wherein Figure 3 Figure 3 (a) is a time series plot of monthly average CHL, Figure 3 Figure 3 (b) is a time series plot of monthly average SLA, Figure 3 Figure 3 (c) is a time series plot of monthly average SST;

[0045] Figure 4 Figure 4 is a time series and energy spectrum of CHL, SST and SLA between 1998-2024, wherein Figure 4 Figure 4 (a) (d) (g) are time series plots of interannual monthly CHL, SST and SLA, Figure 4 Figure 4 (b) (e) (h) are energy spectrum plots of CHL, SST and SLA, Figure 4 Figure 4 (c) (f) (i) are average spectrum plots of CHL, SST and SLA;

[0046] Figure 5 Figure 5 is a scatter plot between CHL monthly anomalies and SST and SLA anomalies between 1997-2024, wherein Figure 5 Figure 5 (a) is a scatter plot of CHL-SST, Figure 5 Figure 5 (b) is a scatter plot of CHL-SLA, Figure 5 Figure 5 (c) is a scatter plot of SLA-SST;

[0047] Figure 6 Figure 6 is the first mode of SLA in CEOF analysis;

[0048] Figure 7 Figure 7 is a spatial distribution of cyclonic and anticyclonic eddy frequency, wherein, Figure 7 Figure 7 (a) is a spatial frequency distribution plot of cyclonic eddies; Figure 7 Figure 7 (b) is a spatial frequency distribution plot of anticyclonic eddies;

[0049] Figure 8 Figure 8 is a plot of CHL fusion at eddy center.

[0050] Figure 9 Fig. 16 is a monthly mean MLD spatial distribution map in the anticyclone and cyclone type eddies;

[0051] Figure 10 Fig. 17 is a monthly mean MLD temporal distribution in the anticyclone and cyclone type eddies;

[0052] Figure 11 Fig. 18 is a monthly mean MLD temporal distribution in the anticyclone type eddies; Figure 11 Fig. 18(a) is a monthly mean MLD temporal distribution in the anticyclone type eddies; Figure 11 Fig. 18(b) is a monthly mean MLD temporal distribution in the cyclone type eddies;

[0053] Figure 12 Fig. 19 is a density time-depth profile and surface CHL and SLA spatial and temporal distribution map based on Argo buoy trajectory drawing. DETAILED DESCRIPTION

[0054] The present application will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present application. These are all within the scope of protection of the present application.

[0055] Example 1

[0056] 1. Multi-source remote sensing and reanalysis data acquisition

[0057] The present application adopts multi-source satellite and reanalysis datasets, wherein the chlorophyll-a concentration (CHL) is from the Ocean Color Climate Change Initiative (OC-CCI) of the European Space Agency, which is a three-level gridded product with a daily time resolution, a 4-kilometer spatial resolution, and a coverage period from 1997 to 2024; the sea surface temperature (SST) is from the AVHRR remote sensing product of the National Environmental Information Center (NOAA NCEI), which has the same time and spatial resolution as CHL, and a time span from 1981 to 2023; the wind speed data is based on the ERA-Interim reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF), which selects the wind speed information at sea level 10 meters, with a daily time resolution, a 0.12° spatial resolution, and a coverage period from 1997 to 2020; the sea level anomaly (SLA) is from the Copernicus Marine Service, which is a four-level fusion product with a daily time resolution and a 0.25° spatial resolution, and a time range from 1993 to 2024; the Argo buoy data is from the International Argo Data website, with a time resolution of about 10 days, a spatial resolution of 3°, and a time range from 1998 to 2025.

[0058] 2. Wavelet analysis and trend decomposition

[0059] Firstly, the daily data of CHL, SST and SLA are time-fused to generate corresponding monthly average data. Then, the monthly data is de-trended to construct a stationary time series for subsequent spectral analysis. Based on the time series, the continuous wavelet transform (CWT) method is used to identify the dominant variation periods in CHL, SST, SLA and other variables, such as annual cycle, semi-annual cycle and short-term sudden fluctuations.

[0060] On this basis, further use the multiple seasonal-trend decomposition method (MSTL) to separate the long-term trend, seasonal cycle and residual components in the time series. This method can effectively strip the seasonal signal and extract monthly anomalies with independent significance. The final anomaly sequence can be used to analyze the correlation between CHL, SST and SLA, and provide theoretical support for subsequent collaborative regulation mechanism research.

[0061] 3. CEOF analysis and mesoscale eddy identification

[0062] The CEOF analysis method is applied to SLA to extract its dominant spatiotemporal variation mode. Compared with the traditional EOF method, CEOF can analyze both amplitude and phase information, and is suitable for revealing the propagation characteristics of mesoscale dynamic processes, especially having significant advantages in identifying rotating structures (such as eddies).

[0063] The first principal mode extracted by CEOF decomposition can effectively capture typical mesoscale anticyclonic structures in the Somali upwelling region, especially the persistent and large-scale GW. Simultaneously, this mode also reveals a hook-shaped transport path extending from the coast to the open ocean, reflecting the horizontal diffusion process of coastal upwelling under the guidance of the anticyclone.

[0064] 4. CHL Composite Analysis Method with Vortex Center

[0065] This composite analysis method can systematically characterize the regulatory effects of cyclones and anticyclones on CHL concentration, providing important support for understanding the impact mechanism of mesoscale dynamic processes on surface primary productivity.

[0066] Target vortices were categorized and screened according to vortex type (cyclone / anticyclone) and season (June-September) to obtain their center coordinates and equivalent radii. For each vortex, normalization was performed, aligning the CHL image to the vortex center and standardizing it according to the vortex radius range. The spatial distribution characteristics of CHL for each type of vortex in different months were statistically analyzed and overlaid with SLA contour lines to analyze the spatial response relationship between vortices and CHL. The specific steps of this method are as follows:

[0067] (1) Data acquisition and preprocessing

[0068] Level 4 fused SLA data was acquired from the Copernicus Ocean Service, with a daily temporal resolution, a spatial resolution of 0.25°, and a time range of 1993–2024; this data was used for eddy identification. Simultaneously, Level 3 CHL data was acquired from the European Space Agency's Ocean Color Climate Change Programme, with a daily temporal resolution, a spatial resolution of 4 km, and a time range of 1997–2024. From the daily CHL data, monthly fused CHL data was first calculated. Then, the average of the monthly fused data for all years was calculated, and this average was subtracted from the monthly fused CHL data to obtain the monthly CHL anomaly data. Finally, the average of the monthly CHL anomaly data for all years was calculated to obtain the monthly CHL anomaly data.

[0069] (2) Vortex recognition and feature extraction

[0070] Cyclones and anticyclones are identified using SLA contour lines, and vortex polarity (cyclone / anticyclone), center position, and radius are extracted. The specific calculation process is as follows:

[0071] ① Polarity determination:

[0072] ;

[0073] Indicates the center point of the vortex SLA scalar field, Indicates the center coordinates, denotes a local extremum, H denotes the Hessian matrix, and detH denotes the determinant.

[0074] Polarity: When trH (trace of H) < 0, it is a local maximum, an anticyclone; when trH > 0, it is a local minimum, a cyclone.

[0075] 2. Boundary contour (maximal closed contour):

[0076] Let S be the set of saddle points, and s be the extremum of the boundary contour, which makes detH < 0. s is a saddle point in the set S .

[0077] SLA scalar field of the relevant saddle point s connected with the vortex center point .

[0078] Anticyclone (central maximum):

[0079] ;

[0080] Boundary contour of the anticyclone center.

[0081] Cyclone (central minimum):

[0082] ;

[0083] Boundary contour of the cyclone center.

[0084] Vortex boundary is the corresponding closed contour:

[0085] = interior connected domain;

[0086] denotes the closed contour, denotes the boundary contour, SLA scalar field of the point on the closed contour.

[0087] 3. Calculate the vortex center point :

[0088] ;

[0089] 4. Radius (given by the closed domain geometry):

[0090] Area and perimeter of the closed domain:

[0091] , ;

[0092] A denotes area, a denotes area element of the interior connected domain, L denotes perimeter, denotes the unit length along the closed contour.

[0093] Using the equal-area radius as the vortex radius:

[0094]

[0095] denotes the vortex radius.

[0096] 5. Horizontal-section data joint analysis method

[0097] To clarify the influence mechanism of the vortex-upwelling on the horizontal distribution of CHL, the surface CHL and SLA data, and the vertical Argo float data were used. For a specific Argo float trajectory, the density time-depth profile was plotted using the Argo float pressure and density data. At the same time, the daily scale CHL and SLA data corresponding to each Argo float data were obtained. Thus, these data were compared. In addition, the starting time of this specific Argo float trajectory was found, and the composite graph of CHL and SLA on the same day was plotted, and the trajectory was superimposed on the graph. From the graph, the horizontal distribution of CHL in the vortex and upwelling area, and the trajectory distribution of the Argo float can be seen.

[0098] Example 2

[0099] (I) Research area and data processing

[0100] This example takes the Somali upwelling area as the research object, and the time period is the southwest monsoon period (June-September) from 1997 to 2024. Based on the OC-CCI, AVHRR, ERA-Interim, and Copernicus Marine Service satellite and reanalysis data, the CHL, SST, wind speed, and SLA variables were obtained, and the CHL composite graph of the study area during the monsoon period was obtained.

[0101] Through the superposition of SLA contour lines, it was found that the coastal water body showed negative SLA and CHL concentration higher than 1 mg / m³, indicating the existence of upwelling-driven nutrient enhancement phenomenon. In the open sea area, positive SLA corresponded to lower CHL concentration (<0.5 mg / m 3 ), reflecting the inhibition of biological productivity. This distribution pattern preliminarily characterizes the interaction between the monsoon-induced upwelling and the large-scale vortex dynamics, especially the important role of the "Great Whirl" in regulating the regional productivity.

[0102] (II) Time series variability analysis

[0103] Using monthly time series, such as Figure 1 ​The annual and seasonal variation trends of CHL, SLA, SST, wind speed components (U, V) and wind stress (curl τ ) are calculated respectively. Figure 1 Figure 1 (a) shows that CHL has an increasing trend from 1997 to 2004, a decreasing trend from 2004 to 2012, and a gentle trend from 2012 to 2024. Figure 1 Figure 1 (b) shows that SLA and SST have an increasing trend from 1997 to 2024, and have similar variation trends. Figure 1 Figure 1 (c) shows that the vertical component of wind speed (V) has a weak decreasing trend throughout the interval, while the horizontal component of wind speed (U) has a gentle trend throughout the interval. Figure 1 Figure 1 (d) shows that wind stress has no significant variation trend throughout the period.

[0104] (Three) Correlation coefficient of CHL and other parameters

[0105] Based on the time series data in Figure 1 , the correlation between CHL and SLA, SST, wind speed U component and wind speed V component is calculated, as shown in Figure 2 , the results show that CHL has a significant negative correlation with SLA and SST (r is -0.83 and -0.59 respectively), indicating that SLA and SST are the two main influencing factors of CHL.

[0106] (Four) Monthly variability of CHL, SST and SLA

[0107] The above time series data is averaged by year to obtain monthly average data, as shown in Figure 3 , the results show that CHL concentration increases significantly during the southwest monsoon period, reaching a peak in September, exceeding 0.5 mg / m³. SLA shows a strong positive anomaly, reflecting the dominant role of large eddies in the upwelling area. At the same time, SST and CHL show an inverse correlation, reaching a minimum value (<20°C) in July, and then gradually increasing with the advance of the monsoon, which may be related to the strengthening of large eddies.

[0108] (Five) Wavelet analysis and dominant mode identification

[0109] Continuous Wavelet Transform (CWT) is used to extract periodic and cyclic components from time series. This method decomposes data into a time-frequency space, revealing the dominant modes of variation and their evolution over time. It strikes a balance between time localization (suitable for low-frequency, slow-changing patterns) and frequency localization (suitable for high-frequency, rapidly changing oscillations). In other words, wavelet analysis can accurately identify the frequency of low-frequency events, while high-frequency oscillations have higher temporal resolution but weaker frequency localization. Morlet wavelets are widely used in ecological research due to their good balance between time and frequency localization. For a time series with uniform time steps (n=1,...,N), the wavelet coefficients

[0110] are calculated as:

[0111] The wavelet coefficient represents the similarity between the time series and the wavelet basis function at time point n and scale s. The square of the modulus of the wavelet coefficient is the wavelet power spectrum, which measures the energy intensity at time point n and scale s. The summation variable index traverses all N time points in the convolution calculation.

[0112] Due to the limited length of the time series, significant edge effects occur at higher scales. The global power spectrum is the average of the wavelet power over time, reflecting the strength of the dominant variation mode and undergoing scale normalization. The statistical significance of the wavelet power is tested by comparing it with the first-order autoregressive (AR1) red noise background. In this invention, the starting scale s0 is set to twice the time resolution of the time series; the discrete scale interval d j is set to 1 / 12 (dividing each octave into 12 sub-octaves), the number of octaves is 4.7, and finally 39 scales are obtained, covering a time range from 2 months to 4.5 years.

[0113] First, the monthly data in Figure 1 are detrended to construct a stationary time series, such as Figure 4 (a)(d)(g), then the Morlet wavelet function is used to perform CWT transformation on the CHL, SST, and SLA sequences, extract their power spectra, such as Figure 4 (b)(e)(h), and the normalized global power spectrum, such as Figure 4 (c)(f)(i).

[0114] Figure 4 ​The results in Fig. 2(a) show that CHL concentration increased before 2004, then gradually decreased, and stabilized around 2012. Since then, CHL concentration has generally remained below 0.3 mg / m3, with occasional peaks slightly above this threshold. SST did not show a clear long-term trend, but exhibited seasonal fluctuations, varying between 17-28 °C, and was generally lower during periods of higher CHL concentration. This suggests that higher temperatures can suppress phytoplankton growth, while lower temperatures can promote CHL increase by increasing nutrient supply. SLA was generally positive, was significantly affected by GW, but fluctuated between -0.02 and 0.02 m, reflecting complex oceanic dynamics.

[0115] Figure 4 Fig. 2(b)(e)(h) show the power spectra of CHL, SST, and SLA, respectively, while Figure 4 Fig. 2(c)(f)(i) show their corresponding global power spectra, i.e., the results of averaging the power over time. The Y-axis represents the period (in years), corresponding to the frequency of the cyclic variations in each set of data. Wavelet transforms allow for multi-resolution analysis, enabling the detection of both short-term fluctuations and long-term trends. Color shading in the figures represents the power intensity, with darker red colors representing stronger spectral signals and lighter colors representing weaker signals. Black contour lines represent the 95% confidence level, indicating statistically significant variations. The shaded region is the "cone of influence" (COI), where edge effects can affect the interpretation of results at the boundaries of the data. Global power spectra are used to identify dominant modes of variation, with red dashed lines representing the 95% confidence level, which is the threshold for determining statistical significance.

[0116] The results show that seasonal cycles, particularly annual and semi-annual periods, are the dominant modes driving CHL and SST variations. Before 2004, SLA gradually shifted from being dominated by annual periods to being dominated by high-frequency oscillations, which may be related to the increasing trend in CHL during this period. Overall, SLA exhibited alternating dominance of high-frequency oscillations and annual periods in different time periods, reflecting its dynamic spatiotemporal interaction characteristics.

[0117] (VI) Outlier extraction and scatter analysis

[0118] The present application adopts a multiple seasonal trend decomposition method to remove the main periodic and cyclic components identified by CWT. MSTL uses the Loess (locally weighted scatterplot smoothing) method to decompose the time series, extracting the trend, seasonal, and residual terms. Loess is a non-parametric method that can effectively estimate nonlinear trends and changing seasonality by fitting smooth curves to the data locally. MSTL is particularly suitable for time series data with multiple seasonal patterns, and can separate multiple periodic events simultaneously. In addition, MSTL can capture the changes in seasonality over time, making it very suitable for processing non-stationary data such as CHL, which is influenced by both long-term environmental changes and seasonal cycles. The residual term represents the part that cannot be explained by the trend and seasonality, reflecting the abnormal signals in the time series. By estimating and separating multiple cyclic events, MSTL, combined with the results of wavelet analysis, helps to identify these abnormalities. Wavelet analysis provides time-frequency features that highlight cyclic changes, while MSTL isolates long-term trends and seasonal components, allowing for a more comprehensive understanding of periodic behavior and abnormal changes.

[0119] The abnormal values extracted using the MSTL method after wavelet analysis were analyzed using scatter plots, as shown in FIG. 6, which is a scatter plot of CHL monthly anomalies and SST and SLA anomalies from 1997 to 2024: Figure 5 Figure 5 (a) CHL-SST scatter plot; Figure 5 (b) CHL-SLA scatter plot; Figure 5 (c) SLA-SST scatter plot. The SST-CHL and SLA-CHL scatter plots both show a negative correlation between CHL and SST and SLA, with a stronger correlation between CHL and SLA (-0.33) and a correlation of -0.27 with SST. This further emphasizes the important role of SST and SLA as the main driving factors of CHL variability. In addition, there is a positive correlation between SST and SLA, indicating that there is a correlation between internal ocean dynamic processes. In the scatter plot, the red line represents the regression line, and the gray shaded area represents the 95% confidence interval.

[0120] (Seven) CEOF spatial mode analysis

[0121] CEOF was used to analyze the dynamic evolution characteristics of the SLA field and extract its main modes, as shown in FIG. 7, which is a diagram of the first four spatial modes of the SLA field: Figure 6 ​The results show the first mode in the CEOF analysis, which captures 29% of the variance in the SLA dynamics. CEOF has a prominent advantage in detecting the moving features, revealing a cyclonic structure in the open-ocean region, which is a typical feature of GW. In addition, a hook-like structure extends from the coastal region to the open-ocean waters north of the GW, indicating that the coastal waters are transported to the open sea. This mode is mainly shaped by the interaction between the GW and the coastal upwelling, which is clearly reflected in the spatial distribution of CHL and SLA. The phase relationship between these dominant signals indicates that the GW plays a key role in enhancing the upwelling, strengthening the nutrient transport, and regional biological productivity.

[0122] The CEOF results include multiple spatial modes, each with a variance contribution rate, and the larger the value, the smaller the variance contribution rate in order. The first spatial mode selected in this embodiment has a variance contribution rate of 29.02%. As shown in FIG. 6, there is a cyclonic structure in the range of 50.5°E-55°E and 6.5°N-10°N, and the position of the structure coincides with the position of the Great Whirl, indicating the dominant role of the Great Whirl in this region. Above the Great Whirl, there is a hook-like structure that extends from the beginning of the upwelling region to the open sea. This embodiment does not perform quantitative analysis on the results, but only assumes the influence of the cyclone on the CHL distribution according to the results reflecting the influence of the cyclone on the upwelling region. Figure 6

[0123] The above contents all indicate that SLA has an important influence on the variation of CHL. To further reveal the dynamic mechanism, the next step of the research will focus on the interaction between the cyclone (including cyclones and anticyclones, especially the GW) and the coastal upwelling, and explore how these processes jointly affect the short-term fluctuations and long-term changes of CHL.

[0124] (Eight) Cyclone type and frequency distribution

[0125] The CEOF analysis highlights significant cyclonic activity in the open-ocean upwelling region, which is dominated by the GW. By plotting the spatial frequency of cyclones by type, it can be confirmed that the region highlighted by CEOF is mainly affected by anticyclonic cyclones, which are likely to be the GW itself. In contrast, cyclonic cyclones are more frequent in the northeast and northwest of the GW center, which is consistent with the hook-like structure observed in the CEOF analysis. The spatial distribution of cyclonic and anticyclonic cyclones in the Somali upwelling region shows that the darker the color, the higher the frequency of cyclone occurrence. Near 8°N, 53°E, the frequency of anticyclonic cyclone occurrence reaches a significant maximum, which corresponds to the core region of the GW. As shown in FIG. 7, the spatial distribution of the frequency of occurrence of cyclonic cyclones and anticyclonic cyclones is as follows: Figure 7 Figure 7 (a) cyclonic cyclones (CEs);​​Figure 7 The middle (b) is an anticyclonic eddy (AE).

[0126] (Nine) Composite analysis centered on eddies

[0127] To evaluate the influence of eddies on CHL distribution, the present application adopts a composite analysis method centered on eddies. Figure 8 The CHL fusion map of the eddy center in the Somali upwelling region from June to September is shown, where the left figure is an anticyclonic eddy, and the right figure is a cyclonic eddy. CHL data is normalized according to the eddy radius, and fusion analysis is performed within a range of twice the eddy radius. The CHL in the core area of the anticyclonic eddy is low (<0.5 mg / m³), while the CHL in the western and northwestern peripheral areas is significantly increased (>0.6 mg / m³), which is highly consistent with the spatial distribution characteristics of the hook structure in CEOF. The cyclonic eddy shows a relatively high CHL concentration (>0.65 mg / m³), often exceeding 0.8 mg / m³ near the upwelling side, but its CHL spatial distribution is relatively scattered and incoherent in time.

[0128] This peripheral CHL enhancement is consistent with the CEOF modal characteristics derived from SLA, reflecting the interaction between GW and upwelling. The hook structure in CEOF outlines the CHL path along the north side of GW in a clockwise direction, indicating that high CHL water is transported to the open sea. In contrast, the cyclonic eddy is characterized by a relatively high CHL concentration (>0.65 mg / m³), often exceeding 0.8 mg / m³ near the upwelling side, but its spatial distribution pattern is not as coherent as that of the anticyclonic eddy, and it fluctuates greatly in time.

[0129] The two snapshots in September 2018 and 2019 highlight the transport of high-concentration CHL (about 1 mg / m³ or higher) along the periphery of GW, while significant increases in CHL concentration are also observed in the interior of nearby cyclonic eddies, as shown. GW obviously plays an important role in the redistribution of high-CHL water to the open sea. Compared with 2018, the GW structure is less obvious in 2019, when the high-CHL band along the coast is significantly narrowed. This indicates that GW not only plays a key role in the upwelling process, but also is an important dynamic mechanism for the outward transport of high-CHL water; cyclonic eddies may enhance CHL concentration by lifting the isopycnic surface to flip the nutrient-rich water.

[0130] (Ten) Joint analysis of horizontal and profile data

[0131] To further understand the control mechanism of eddy-upwelling on the spatial distribution of CHL in the horizontal direction, Argo float profile data, including temperature, salinity, pressure, and density, are used.

[0132] First, the MLD (Medium Depth) of the hybrid layer is calculated using the density method, as shown in the following formula:

[0133] ;

[0134] Where z represents depth (m, positive downwards). This represents the density of seawater at depth z (kg / m³). 3 ), Density at a reference depth (10m) The threshold value is 0.03 kg / m³. 3 ).

[0135] Then, the monthly average MLD for each Argo buoy in the study area was calculated, such as... Figure 9 As shown, the results indicate that the monthly average MLD is deeper during the monsoon season, especially the southwest monsoon season (June-September), suggesting that increased wind speed can deepen the mixing depth of water bodies. Furthermore, using the vortex center composite analysis method, the spatial distribution of MLD within one radius of the subcyclone vortex and the cyclone vortex was calculated separately, as shown... Figure 10 As shown in the figure. The results show that the MLD is deeper in the center of the anticyclone and shallower at the edge; the MLD is shallower in the center of the cyclone and deeper at the edge. To quantify the temporal distribution of MLD, the monthly average MLD in anticyclones and cyclones was calculated separately, as shown in the figure. Figure 11 As shown. The results show that, Figure 11 In the middle (a) region, the MLD is generally deeper in anticyclones, while... Figure 11 The shallower cyclone is in the middle (b) cyclone.

[0136] Furthermore, the relationship between CHL, MLD, and SLA is analyzed using specific Argo buoy trajectory data. For example... Figure 12 As shown, Argo buoy trajectory data (number 1900182) was used. This trajectory began on September 9, 2004, and its starting point was located inside the anticyclone in the right-hand image. Around this time (July-October 2004), the density-time-depth profile shows that from the surface to 300 meters (corresponding to a water pressure of 300 dbar), the water density was relatively low and homogeneous, indicating a deeper MLD (Medium Depth). The corresponding SLA (Solar Acidity) was higher and positive, while the CHL (Cryptographic Hypoxia) was lower. This suggests that the anticyclonic eddy deepens the MLD, preventing bottom nutrients from reaching the surface and thus inhibiting phytoplankton growth. In contrast, from July-October 2005, the water pressure was higher and more uniform, consistent with upwelling characteristics, resulting in a lower SLA and a higher CHL. This indicates that upwelling brings high-density, nutrient-rich seawater from the bottom to the surface, promoting phytoplankton growth.

[0137] The specific embodiments of the present application are described above. It needs to be understood that the present application is not limited to the specific embodiments described above, and various changes or modifications can be made by those skilled in the art within the scope of the claims, which do not affect the essential content of the present application. The embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily without conflict.

Claims

1. A method for vortex-upflow co-regulation of chlorophyll-a concentration variability, characterized by, The method comprises the following steps: (1) Obtain multi-source satellite and reanalysis datasets, including chlorophyll-a concentration, sea surface temperature, sea surface height anomaly, wind speed and Argo float data, time fuse daily data of chlorophyll-a concentration, sea surface temperature and sea surface height anomaly to generate monthly average data, and construct a smooth time series; (2) Perform continuous wavelet transform on the smooth time series to identify dominant variation periods in chlorophyll-a concentration, sea surface temperature and sea surface height anomaly, separate long-term trend, seasonal cycle and residual components by using multiple seasonal-trend decomposition method, and extract monthly anomaly value sequence; (3) Apply complex empirical orthogonal function analysis to the sea surface height anomaly to extract its dominant spatial variation mode and identify the distribution position and shape of the vortex; The complex empirical orthogonal function analysis simultaneously analyzes the amplitude and phase information of the sea surface height anomaly variation and identifies the mesoscale cyclone and anticyclone vortex structure; When identifying the mesoscale cyclone and anticyclone vortex structure, the vortex center and polarity are determined by calculating the gradient and Hessian matrix of the sea surface height anomaly, the vortex boundary is determined by finding the maximum closed contour, and the equivalent radius of the vortex is calculated; The cyclone and anticyclone are identified by the sea surface height anomaly contour, and the vortex polarity, center position and radius are extracted, comprising the following steps: ① Polarity judgment: ； denotes the vortex center point of the SLA scalar field, denotes a local extremum, H denotes the Hessian matrix, and detH denotes the determinant; Polarity judgment: When the trace of H is tr H < 0, it is a local maximum, which is an anticyclone; when the trace of H is tr H > 0, it is a local minimum, which is a cyclone; ② Construct closed contour: Let S be the set of saddle points, and s be a saddle point of the boundary contour, ; SLA scalar field for the relevant saddle point s that is connected to the vortex center point ; Boundary contour of anticyclone center Is: ; Boundary contour of the cyclone center is: ; The vortex boundary is a closed contour : ； is an interior connected domain, represents a boundary contour, is a SLA scalar field for a point on a closed contour.

3. Calculate vortex center point : ； ④ Calculate the closed domain area A and the perimeter L: ， ； wherein a denotes an area element of the interior connected domain, denotes the unit length on the closed contour line; Using equal-area radius as swirl radius : ； (4) According to the vortex type and seasonal classification, the target vortex is screened, the chlorophyll-a concentration is normalized and aligned to the vortex center, and the spatial response relationship between the vortex and the chlorophyll-a concentration is analyzed; (5) Combine the surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo float data to draw the density time-depth profile and CHL-SLA composite diagram, and analyze the influence mechanism of the vortex-upwelling system on the horizontal distribution of chlorophyll-a.

2. The method of vortex-upwelling co-regulation of chlorophyll-a concentration variability according to claim 1, wherein, In step (4), the chlorophyll-a concentration is normalized and aligned to the vortex center, and is standardized according to the equivalent radius range of the vortex, so as to analyze the spatial distribution characteristics of the chlorophyll-a concentration inside and around the vortex.

3. The method of vortex-upwelling co-regulation of chlorophyll-a concentration variability according to claim 1, wherein, In step (5), the horizontal-profile data joint analysis includes: for the Argo float trajectory, draw the density time-depth profile, obtain the daily chlorophyll-a concentration and sea surface height anomaly corresponding to the time and position, generate the CHL-SLA spatial composite diagram, superimpose the Argo float trajectory on the CHL-SLA spatial composite diagram, and comprehensively analyze the influence of the vortex-upwelling system on the horizontal distribution of chlorophyll-a concentration.

4. The method of vortex-upwelling co-regulation of chlorophyll-a concentration variability according to claim 3, wherein, In the horizontal-profile data joint analysis, Argo float profile data is used, which includes temperature, salinity, pressure and density, and the mixed layer depth MLD is calculated according to the density method: ； where z denotes the depth, denotes the density of the sea water at depth z, denotes the density of the reference depth, denotes the threshold value.

5. The method of vortex-upwelling co-regulation of chlorophyll-a concentration variability according to claim 1, wherein, In step (2), the periodic and cyclical components are extracted from the stationary time series using continuous wavelet transform, which decomposes the data into time-frequency space. For a time series x n , n = 1,...,N, with uniform time step , the wavelet coefficients are: ； where the wavelet coefficients denote the time series at time point n and scale s, the degree of similarity to the wavelet basis function is the wavelet power spectrum, is the summation variable index.

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