Method and system for quantifying the relative contribution of climate mode coupling period to ITF transport

Abstract

The present invention discloses a method and system for quantifying the relative contribution of climate mode coupling period to ITF transport, the present invention comprises calculating the average flow of the upper and lower layers of the ITF based on multi-source reanalysis ocean current data; obtaining index data representing ENSO and IOD climate factors respectively, calculating the standard deviation of each index data, and determining significant ENSO and IOD single action events according to the standard deviation of each index data within a limited time range; determining the sub-mode when ENSO and IOD act alone in combination with the average flow of the upper and lower layers of the ITF; using the integrated similar circulation method CCA to determine the relative contribution of the index data representing ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period, and determining the dominant climate factor. The present invention aims to solve the problem that it is difficult to quantify the relative contribution to the ITF flow during the climate mode coupling period and determine the dominant climate factor.

Description

Technical Field

[0001] The present invention belongs to the technical field of physical oceanography, and in particular relates to a method and system for quantifying the relative contribution of climate mode coupling periods to ITF transport. Background Art

[0002] The ITF (Indonesian Throughflow) is a stable and strong current connecting the Pacific and Indian Oceans, and its interannual variation in flow is modulated by the ENSO (El Nino-Southern Oscillation) and IOD (Indian Ocean Dipole) climate factors. However, different types of climate events always occur at the same time, which hinders further clarification of the relative contributions of ENSO and IOD to the ITF flow. For example, El Niño / La Niña and positive IOD / negative IOD events often occur at the same time. Although convenient regression methods are often used to study this problem, simply regressing the ITF flow using an index is difficult to reflect the nonlinear and complex modulation effects of different climate factors on the ITF. At present, the use of machine learning methods can solve this problem, but it relies on large data sets and a lot of calculations. Therefore, on this issue, how to achieve a relatively convenient quantitative method that can retain the complex effects of climate factors on the ITF has become a key technical problem that needs to be solved urgently. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a method and system for quantifying the relative contribution of climate mode coupling to ITF transport is provided. The present invention aims to solve the problem that it is difficult to quantify the relative contribution to ITF flow during climate mode coupling and determine the dominant climate factors.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method to quantify the relative contribution of climate mode coupling periods to ITF transport includes:

[0006] The average flow of the upper and lower layers of the ITF is calculated based on reanalysis current data from multiple sources;

[0007] Obtain index data representing ENSO and IOD climate factors respectively, calculate the standard deviation of each index data, and determine significant ENSO and IOD independent action events based on the standard deviation of each index data within a limited time range;

[0008] The submodes of ENSO and IOD acting alone are determined based on the significant ENSO and IOD acting alone events and the average flow in the upper and lower layers of the ITF.

[0009] According to the sub-modes when ENSO and IOD act alone, combined with the average flow of the upper and lower layers of the ITF, the integrated similar circulation method CCA is used to determine the relative contribution of the index data representing the ENSO and IOD climate factors during the climate mode coupling period to the ITF transport flow, and the dominant climate factor is determined based on the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period.

[0010] Optionally, the average flow of the upper and lower layers of the ITF is the average of the flow of the upper and lower layers of the ITF of all reanalyzed current data, and the calculation function expression of the flow of the upper and lower layers of the ITF of each reanalyzed current data is:

[0011] ,

[0012] In the above formula, is the flow rate of the upper or lower layer of ITF, is the number of vertical grid layers, is the number of horizontal grid points, Perpendicular to the horizontal grid and The flow velocity on the cross section of a vertical grid, is the distance between two adjacent grid points in the horizontal grid, is the distance between two adjacent vertical layers in the vertical grid, and the vertical grid number and the horizontal grid number Satisfied , .

[0013] Optionally, the calculation of the average flow of the upper and lower layers of the ITF based on the multi-source reanalysis current data also includes using mooring observation data to screen the multi-source reanalysis current data: for each candidate reanalysis current data, the correlation coefficient R and the root mean square error RMSE between the reanalysis current data and the given mooring observation data are calculated respectively, and the reanalysis current data whose correlation coefficient R and the root mean square error RMSE meet the requirements are screened out from the candidate reanalysis current data as the multi-source reanalysis current data.

[0014] Optionally, when the index data representing the ENSO and IOD climate factors are respectively obtained, the index data representing the ENSO climate factor is the Niño 3.4 climate index data, and the index data representing the IOD climate factor is the DMI climate index data.

[0015] Optionally, the calculation function expression of the standard deviation of each index data is:

[0016] ,

[0017] ,

[0018] In the above formula, is the standard deviation, is the sequence length of the exponential data, is the jth index value of the index data, is the sequence average of the index data. Determining significant ENSO and IOD independent action events based on the standard deviation of each index data within a limited time range means that index data greater than a specified multiple of the standard deviation is regarded as a significant ENSO or IOD independent action event.

[0019] Optionally, the use of the integrated similar circulation method CCA to determine the relative contribution of the index data representing the ENSO and IOD climate factors during the climate mode coupling period to the ITF transport flow includes: first, a multiple regression is performed on the average flow of the upper and lower layers of the ITF under the sub-mode when ENSO and IOD act alone, and the regression term of the multiple regression is the average flow change sequence of the corresponding upper or lower sub-mode, and the average flow change sequence of the sub-mode is longer than or equal to the target time series. When the regression term is selected, the sub-mode is sequentially intercepted according to the length of the target time series. The time series of the sub-mode is a, and the time series of the target is b, then the number of regression terms selected is Each regression term contains a regression coefficient, so as to obtain the sub-mode when ENSO acts alone. Regression coefficients , and the sub-modal when IOD acts alone Regression coefficients ; Then the relative contribution of the index data representing the ENSO or IOD climate factors to the ITF transport flow during the climate mode coupling period is calculated according to the following formula:

[0020] ,

[0021] In the above formula, is the relative contribution of the index data representing the ENSO or IOD climate factor to the ITF transport flow during the climate mode coupling period, is the component of ITF flow change caused by ENSO climate factor, is the component of ITF flow change caused by IOD climate factors, and are the partial regression coefficients, is the residual term, and:

[0022] ,

[0023] ,

[0024] ,

[0025] ,

[0026] In the above formula, is the number of regression coefficients of the submode when ENSO acts alone, is the first submode when ENSO acts alone. regression coefficients, 1≤ ≤N, is the average flow series of the upper or lower layer of the ITF under the submode when ENSO acts alone. " indicates that the positive anomaly is El Niño. " indicates that negative anomalies are La Niña, is the number of regression coefficients of the sub-mode when IOD acts alone, is the mth regression coefficient of the sub-mode when IOD acts alone, 1≤ ≤M, is the average flow sequence of the upper or lower layer of the ITF under the sub-mode when the IOD acts alone. " indicates that the positive anomaly is a positive IOD. " indicates that the negative anomaly is a negative IOD. and are partial regression coefficients respectively.

[0027] Optionally, the method of determining the dominant climate factor based on the relative contribution of index data representing ENSO and IOD climate factors to ITF transport flow during the climate mode coupling period includes: performing a significance test on the regression coefficients of the sub-mode when ENSO acts alone, and performing a significance test on the regression coefficients of the sub-mode when IOD acts alone; if some regression coefficients fail the significance test, then retaining the sub-mode corresponding to the regression coefficient of the significance test and performing multiple regression again until the regression coefficients of all sub-modes pass the significance test; adding and forwarding all regression coefficients of the sub-mode when ENSO acts alone that pass the significance test to obtain the contribution of ENSO acts alone, and adding and forwarding all regression coefficients of the sub-mode when IOD acts alone that pass the significance test to obtain the contribution of IOD acts alone; and determining the climate factor corresponding to the larger contribution between the contribution of ENSO acts alone and the contribution of IOD acts alone as the dominant climate factor.

[0028] In addition, the present invention also provides a system for quantifying the relative contribution of the climate mode coupling period to the ITF transmission, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the method for quantifying the relative contribution of the climate mode coupling period to the ITF transmission.

[0029] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored, and the computer program or instruction is programmed or configured to execute the method of quantifying the relative contribution of the climate mode coupling period to ITF transmission through a processor.

[0030] In addition, the present invention also provides a computer program product, comprising a computer program or instructions, which are programmed or configured to execute the method of quantifying the relative contribution of the climate mode coupling period to the ITF transport through a processor.

[0031] Compared with the prior art, the present invention mainly has the following advantages:

[0032] 1. The present invention calculates the average flow of the upper and lower layers of the ITF based on multi-source reanalysis current data. By combining multi-source high temporal and spatial resolution reanalysis data and real-time observation data for mutual verification, the problem of difficulty in reflecting the monthly changes in ITF flow is solved.

[0033] 2. The present invention includes the use of an integrated similar circulation method CCA to determine the relative contribution of index data representing ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period. By utilizing the idea of ​​the integrated similar circulation method CCA to decouple and quantify the relative contribution of different climate factors to the ITF flow change during the ENSO and IOD coupling period, the problem of separating the relative contributions of different climate factors to ITF during the coupling period is solved.

[0034] 3. The present invention includes determining the dominant climate factors according to the relative contribution of index data representing ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period, which can determine the dominant climate factors affecting the ITF flow in different periods, and solves the problem of the difficulty in separating the effects of ENSO and IOD climate factors on ITF. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0036] Figure 2 1 is a diagram of the ITF data verification results drawn in an embodiment of the present invention, wherein (a) is the data verification result of the ITF upper layer (0–300m), and (b) is the data verification result of the ITF lower layer (300–760m).

[0037] Figure 3 1 is an average flow diagram of the upper and lower layers of the ITF drawn in an embodiment of the present invention, wherein (a) is an average flow diagram of the upper and lower layers, and (b) is an average flow diagram of the lower layer.

[0038] Figure 4 ENSO and IOD climate index change diagrams drawn in an embodiment of the present invention, wherein (a) is Niño 3.4 climate index data representing index data of ENSO climate factors, and (b) is DMI climate index representing index data of IOD climate factors.

[0039] Figure 5 The figure is a monthly variation diagram of the El Niño submodal climate index and the average flow of the upper and lower layers of the ITF drawn in an embodiment of the present invention, wherein (a) is the El Niño submodal climate index, (b) is the monthly variation diagram of the upper layer average flow, and (c) is the monthly variation diagram of the lower layer average flow.

[0040] Figure 6 The monthly variation diagram of the positive IOD submodal climate index and the upper and lower layer average flow of ITF drawn in an embodiment of the present invention, wherein (a) is the DMI climate index data, (b) is the monthly variation diagram of the upper layer average flow, and (c) is the monthly variation diagram of the lower layer average flow.

[0041] Figure 7 The monthly changes of climate index and ITF upper and lower layer average flow during the coupling of El Niño and positive IOD events drawn in an embodiment of the present invention, wherein (a) is the change of upper layer flow of ITF inflow and the change of Niño 3.4 index and DMI index in the corresponding period, and (b) is the change of lower layer flow of ITF inflow and the change of Niño 3.4 index and DMI index in the corresponding period.

[0042] Figure 8 1 is a spatial distribution diagram of sea surface height anomaly during the independent and coupled periods of El Niño and positive IOD events drawn in an embodiment of the present invention, wherein (a) is the spatial distribution of sea surface height anomaly (SLA) during the El Niño event unrelated to IOD, (b) is the spatial distribution of sea surface height anomaly during the positive IOD event unrelated to ENSO, and (c) is the spatial distribution of sea surface height anomaly during the coupled period of El Niño and positive IOD events. DETAILED DESCRIPTION

[0043] like Figure 1 As shown, the method of quantifying the relative contribution of the climate mode coupling period to the ITF transmission in this embodiment includes:

[0044] S1, the average flow of the upper and lower layers of the ITF is calculated based on reanalysis current data from multiple sources;

[0045] S2, respectively obtain the index data representing the ENSO and IOD climate factors, calculate the standard deviation of each index data, and determine the significant ENSO and IOD independent action events based on the standard deviation of each index data within a limited time range;

[0046] S3, based on the significant ENSO and IOD independent events combined with the average flow in the upper and lower layers of the ITF, the submodes of ENSO and IOD independent events are determined;

[0047] S4, based on the sub-modes when ENSO and IOD act alone, combined with the average flow of the upper and lower layers of the ITF, the integrated similar circulation method CCA is used to determine the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period, and the dominant climate factor is determined based on the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period.

[0048] It should be noted that the multi-source reanalysis ocean current data in step S1 can be selected according to actual needs. For example, as an optional implementation, the multi-source reanalysis ocean current data in this embodiment include: data monitored by the Copernicus Marine Environment Monitoring Service (CMEMS) (CMEMS data), Hybrid Coordinate Ocean Model (HYCOM) data, Simple Ocean Data Assimilation (SODA 3.4.2) data and OGCM For Earth Simulator (OFES) data. Four types of reanalysis ocean current data. Specifically, for CMEMS data in this embodiment, the global reanalysis data version LOBAL_MULTIYEAR_PHY_001_030 is used, the horizontal resolution of the data is 1 / 12°×1 / 12° (about 8 km×8 km at the equator), the vertical resolution is 50 layers, and the temporal resolution is monthly. The data of CMEMS data absorbs many existing observation data. The data time range used is 1993-2022, covering the most recent period of altimeter data (starting from the launch of TOPEX / Poseidon and ERS-1 satellites in the early 1990s). In this embodiment, for HYCOM data, the data in the time range of 1993–2012, 2013–2017, and 2018–2019 use HYCOM's GLBu0.08 / expt_19.0, GLBu0.08 / expt_90.9, and GLBv0.08 / expt_93.0 versions, respectively. The horizontal resolution of the data is 0.08°×0.08°, the vertical resolution is 40 layers, the time resolution is daily, and the data time range used is 1993–2019. In this embodiment, for SODA 3.4.2 data, the horizontal resolution of the data is 0.5°×0.5°, and the vertical resolution is 50 layers. The data time range used is 1993–2019. In this embodiment, for OFES data, it is forced by NCEP winds. The horizontal resolution of the data is 0.1°×0.1°, the vertical resolution is 54 layers, and the time resolution is monthly. The data time range used is 1993-2017. Using the above four reanalysis data, the flow of ITF in a specific section can be further calculated. In addition, the upper layer and the lower layer in step S1 can be divided according to actual needs. For example, as an optional implementation, the upper layer and the lower layer in this embodiment are due to the 300m layer (thermocline) as the boundary, and 0-300m and 300-760m often have different change characteristics.

[0049] In this embodiment, the average flow rate of the upper and lower layers of the ITF is the average value of the flow rate of the upper and lower layers of the ITF of all reanalyzed current data. The flow rate of the upper and lower layers of the ITF of each type of reanalyzed current data can be calculated using the volume flux method. The calculation function expression of the flow rate of the corresponding upper and lower layers of the ITF is:

[0050] ,

[0051] In the above formula, is the flow rate of the upper or lower layer of ITF, is the number of vertical grid layers, is the number of horizontal grid points, Perpendicular to the horizontal grid and The flow velocity on the cross section of a vertical grid, is the distance between two adjacent grid points in the horizontal grid, is the distance between two adjacent vertical layers in the vertical grid, and the vertical grid number and the horizontal grid number Satisfied , .

[0052] In step S1 of this embodiment, when calculating the average flow of the upper and lower layers of the ITF based on the multi-source reanalysis current data, the multi-source reanalysis current data is also screened using the mooring observation data: for each candidate reanalysis current data, the correlation coefficient R and the root mean square error RMSE between the reanalysis current data and the given mooring observation data are calculated respectively, and the reanalysis current data whose correlation coefficient R and the root mean square error RMSE meet the requirements are screened from the candidate reanalysis current data as the multi-source reanalysis current data. It should be noted that the correlation coefficient R and the root mean square error RMSE are both well-known statistical indicators, so their specific calculation function expressions are not described in detail here.

[0053] It should be noted that mooring observation data is a well-known observation data used for comparison and verification. A specific data set is selected according to actual needs. For example, as an optional implementation, the mooring observation data in this embodiment is the Makassar Strait observation data, and the Makassar Strait observation data is specifically the mooring observation data of the International Nusantara Stratification and Transport Program (INSTANT) and the Monitoring ITF Program (MITF) project. The International Nusantara Stratification and Transport Program (INSTANT) project has two mooring stations in the Labani Strait, located at 2°51.9′S, 118°27.3′E and 2°51.5′S, 118°37.7′E respectively. The time range of the International Nusantara Stratification and Transport Program (INSTANT) project is from January 2004 to November 2006. The Monitoring ITF Program (MITF) project retained a moored station at 2°51.9′S, 118°27.3′E, and the time range of use was from November 2006 to August 2017. There were no measured data from August 2011 to August 2013 due to loss of equipment signal. Figure 2 This is an ITF data verification result diagram drawn in an embodiment of the present invention, Figure 2 (a) is the data verification result of the upper layer of ITF (0–300m). Figure 2 (b) in the figure is the data verification result of the lower layer of ITF (300–760m). Figure 2 The verification results show that the four reanalysis current data, namely, CMEMS data, HYCOM data, SODA 3.4.2 data and OFES data, all show phase characteristics consistent with the moored observation data, that is, the ITF flow is strongest in the northern summer and weakest in the northern winter. Among them, the negative value of the flow (Sv) indicates a southward flow into the Indonesian Sea. In the upper layer, OFES data show good simulation performance, while CMEMS data, HYCOM data and SODA 3.4.2 data show larger ITF flows. In the lower layer, CMEMS data and SODA 3.4.2 data well characterize the changes in ITF flows, while HYCOM data and OFES data show larger and smaller ITF flows.

[0054] In order to verify the results specifically and quantitatively, in this embodiment, the correlation coefficient R and root mean square error RMSE between the reanalyzed ocean current data and the given mooring observation data are calculated for four types of reanalyzed ocean current data, namely CMEMS data, HYCOM data, SODA 3.4.2 data and OFES data. The results are shown in Table 1.

[0055] Table 1 Correlation coefficients R and root mean square errors RMSE between the reanalysis current data and mooring observation data

[0056]

[0057] The results in Table 1 are further quantitatively compared and show that the correlation coefficient R and root mean square error RMSE of the four independent reanalysis current data, CMEMS data, HYCOM data, SODA 3.4.2 data and OFES data, with the Makassar Strait mooring data are greater than 0.75 and 1.75 Sv in the upper layer (0-300m). Among them, the correlation coefficient R and root mean square error RMSE of CMEMS data and OFES data with the Makassar Strait mooring data are the largest and smallest, 0.87 and 1.79 Sv, respectively. In the lower layer (300–760m), the correlation between HYCOM data and SODA data and the Makassar Strait mooring data is weak, with correlation coefficients R of 0.39 and 0.55, respectively. However, the correlation coefficient R of the four independent reanalysis current data, CMEMS data, HYCOM data, SODA 3.4.2 data and OFES data, with the Makassar Strait mooring data after averaging is 0.72. Therefore, the simulation biases of the four independent reanalysis current data, namely, CMEMS data, HYCOM data, SODA 3.4.2 data and OFES data, can be greatly offset by the ensemble average, and this average result is further used to decouple the relative contributions of ENSO and IOD to the ITF flow.

[0058] Figure 3 The average flow diagrams of the upper and lower layers of the ITF drawn in this embodiment, where (a) is the average flow diagram of the upper and lower layers, and (b) is the average flow diagram of the lower layer. Figure 3 The average discharges in the upper and lower layers of the middle ITF were calculated from three sections, namely the Sulawesi Sea section (125°E, 1°N–6°N), the Maluku Sea section (125°E–127.5°E, 0.5°N), and the Halmahera Sea section (128°E–131°E, 0.5°S).

[0059] In this embodiment, when the index data representing the ENSO and IOD climate factors are obtained respectively, the index data representing the ENSO climate factor is the Niño 3.4 climate index data, and the index data representing the IOD climate factor is the DMI climate index data. Figure 4 ENSO and IOD climate index change diagrams drawn in this embodiment, where (a) is the Niño 3.4 climate index data representing the index data of the ENSO climate factor, and (b) is the DMI climate index representing the index data of the IOD climate factor. Figure 4The figure shows the monthly changes of the Niño 3.4 index and the DMI index from 1993 to 2022, where the orange and blue bars indicate that the absolute values ​​are greater than one standard deviation of the Niño 3.4 index and half a standard deviation of the DMI index, respectively. The horizontal axis represents the monthly changes for two consecutive years, where (0) represents the first year and (+1) represents the second year. J, A, J, and O represent January, April, July, and October, respectively. Figure 3 (b) and from Figure 4 As can be seen in (b), ENSO and IOD can occur independently of each other, that is, there are significant ENSO (IOD) events when there are no significant IOD (ENSO) events.

[0060] In step S3 of this embodiment, when determining the sub-mode when ENSO and IOD act alone according to the significant ENSO and IOD individual events combined with the average flow of the upper and lower layers of the ITF, when judging the climate events classified by type within a limited time range according to the changes in the climate index corresponding to ENSO and IOD, the significant event judgment can be judged according to the standard deviation of the index sequence within the limited time range. The calculation function expression of the standard deviation of each index data in this embodiment is:

[0061] ,

[0062] ,

[0063] In the above formula, is the standard deviation, is the sequence length of the exponential data, is the jth index value of the index data, is the sequence average of the index data, and the determination of significant ENSO and IOD independent action events according to the standard deviation of each index data within a limited time range refers to taking the index data greater than a specified multiple of the standard deviation as a significant ENSO or IOD independent action event. The above-mentioned specified multiple can be specified according to actual needs. For example, as an optional implementation, in this embodiment, significant ENSO and IOD events are determined according to 1 times and 0.5 times the standard deviation of the Niño 3.4 climate index and the DMI climate index within a limited time range, respectively.

[0064] In this embodiment, when determining the sub-modes of ENSO and IOD acting alone in combination with ITF flow in step S3, obtaining significant ENSO and IOD events can be selected according to actual needs. For example, as an optional implementation, in step S3 of this embodiment, ENSO and IOD positive abnormal events are obtained independently of each other as corresponding sub-modes, namely, El Niño sub-mode and positive IOD event sub-mode. The acquisition of sub-modes needs to be combined with the actual changes in ITF.

[0065] Figure 5 : The monthly variation diagram of the El Niño submodal climate index and the upper and lower layer average flow of ITF drawn in this embodiment, wherein (a) is the El Niño submodal climate index, (b) is the monthly variation diagram of the upper layer average flow, (c) is the monthly variation diagram of the lower layer average flow, J(0) represents January of the first year of the selected two consecutive years, A(0) represents April of the first year of the selected two consecutive years, O(0) represents October of the first year of the selected two consecutive years, J(+1) represents January of the second year of the selected two consecutive years, A(+1) represents April of the second year of the selected two consecutive years, O(+1) represents October of the second year of the selected two consecutive years, Figure 6 and Figure 7 The above symbols have the same meaning. Figure 5 It can be seen that during the El Niño submode, from September to January of the following year, there is a significant weakening change in the ITF inflow to the upper layer, e.g. Figure 5 In addition, it can be found that El Niño lags behind the ITF upper layer transport by 0–2 months. For the ITF inflow to the lower layer, the enhanced change trend exists from October to February of the following year, as shown in (b) of Figure 2. Figure 5 Combined with the flow changes, the ITF flow from September to March of the following year is defined as the flow submode during the El Niño period.

[0066] Figure 6 The monthly changes of the positive IOD sub-mode climate index and the ITF inflow into the upper and lower layers drawn in this embodiment, where (a) is the DMI climate index data, (b) is the monthly changes of the upper layer average flow, and (c) is the monthly changes of the lower layer average flow. Figure 6 It can be seen that during the positive IOD event submode, the ITF inflow upper layer traffic has almost no lead or lag in response to the positive IOD event, e.g. Figure 6 As shown in (b) in Figure 1, the upper layer traffic shows an increasing trend when the positive IOD event occurs. Figure 6 As shown in (c), the ITF flow rate lags behind the positive IOD event by 3-4 months and then weakens. Combined with the flow changes, the ITF flow rate corresponding to February to September is defined as the flow submode during the positive IOD period.

[0067] In step S4, based on the obtained ENSO and IOD sub-modes, and combined with the idea of ​​the integrated similar circulation method (Constructed Circulation Analogue, CCA), that is, multi-source linear regression decoupling is used to quantify the relative contribution of ITF flow changes during ENSO and IOD coupling events and determine the dominant climate factors. Specifically, in this embodiment, the integrated similar circulation method CCA is used to determine the relative contribution of index data representing ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period, including: first, a multivariate regression is performed on the average flow of the upper and lower layers of the ITF under the sub-mode when ENSO and IOD act alone, and the regression term of the multivariate regression is the average flow change sequence of the corresponding upper or lower sub-mode, and the average flow change sequence of the sub-mode is longer than or equal to the target time series. When selecting the regression term, the sub-mode is sequentially intercepted according to the length of the target time series. The time series of the sub-mode is a, and the time series of the target is b, then the number of regression terms selected is Each regression term contains a regression coefficient, so as to obtain the sub-mode when ENSO acts alone. Regression coefficients , and the sub-modal when IOD acts alone Regression coefficients ; Then the relative contribution of the index data representing the ENSO or IOD climate factors to the ITF transport flow during the climate mode coupling period is calculated according to the following formula:

[0068] ,

[0069] In the above formula, is the relative contribution of the index data representing the ENSO or IOD climate factor to the ITF transport flow during the climate mode coupling period, is the component of ITF flow change caused by ENSO climate factor, is the component of ITF flow change caused by IOD climate factors, and are the partial regression coefficients, is the residual term, and:

[0070] ,

[0071] ,

[0072] ,

[0073] ,

[0074] In the above formula, is the number of regression coefficients of the submode when ENSO acts alone, is the first submode when ENSO acts alone. regression coefficients, 1≤ ≤N, is the average flow series of the upper or lower layer of the ITF under the submode when ENSO acts alone. " indicates that the positive anomaly is El Niño. " indicates that negative anomalies are La Niña, is the number of regression coefficients of the sub-mode when IOD acts alone, is the mth regression coefficient of the sub-mode when IOD acts alone, 1≤ ≤M, is the average flow sequence of the upper or lower layer of the ITF under the sub-mode when the IOD acts alone. " indicates that the positive anomaly is a positive IOD. " indicates that the negative anomaly is a negative IOD. and are partial regression coefficients respectively.

[0075] In this embodiment, the dominant climate factor is determined based on the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period, including: performing a significance test on the regression coefficients of the sub-mode when ENSO acts alone, and performing a significance test on the regression coefficients of the sub-mode when IOD acts alone. If some regression coefficients fail the significance test, the sub-mode corresponding to the regression coefficient of the significance test is retained and multiple regression is performed again until the regression coefficients of all sub-modes pass the significance test; all regression coefficients of the sub-mode when ENSO acts alone that pass the significance test are added and forward processed to obtain the contribution of ENSO acts alone, and all regression coefficients of the sub-mode when IOD acts alone that pass the significance test are added and forward processed to obtain the contribution of IOD acts alone; and the climate factor corresponding to the larger contribution of ENSO acts alone and IOD acts alone is determined as the dominant climate factor. Among them, the significance test is a method in well-known statistics used to determine whether the experimental results are statistically significant, and the significance level (usually represented by α) is the probability that the researcher is willing to bear of incorrectly rejecting the null hypothesis (i.e., making a type I error) in the hypothesis test. As an optional implementation, the present embodiment adopts a 95% significance test, in which the significance level is usually set to 5% (i.e., α=0.05) in the 95% significance test.

[0076] In order to prove the scientific nature of the dominant climate factors obtained, the following methods are considered in this example for testing and further explanation and verification. The research premise is that ENSO and IOD events play an important role in the change of ITF flow on an interannual scale, but the two often occur at the same time, so it is difficult to determine which climate factor dominates the change of ITF flow during the coupling of the two climate factors and it is difficult to quantify their relative contributions. Different types of ENSO and IOD events have different effects on the changes of ITF flow at different levels, as shown in Table 2 below.

[0077] Table 2 Effects of different types of ENSO and IOD events on the changes in ITF flow at different levels

[0078]

[0079] When studying further interpretation, the quantitative results obtained are revealed in combination with the ITF flow changes in the corresponding period, the sea level changes in the typical area and the transmission of inter-oceanic signals. In the upper layer, the sea level changes have a stronger impact on the ITF flow changes, while in the lower layer, the transmission of inter-oceanic signals (Rossby waves propagating from the Pacific Ocean to the Indian Ocean caused by ENSO events and Kelvin waves propagating from the Indian Ocean to the Pacific Ocean stimulated by IOD events) cause the thermocline to rise and fall, which has a stronger impact on the ITF flow changes. In summary, by combining the flow changes and the sea level changes in the typical area with the relevant information such as the sea level changes in the typical area, the scientific nature of the obtained dominant climate factors can be revealed. In this embodiment, the relative contribution of the El Niño and positive IOD events to the ITF flow during the coupling of 1997-1998 was decoupled and quantified. Figure 7 Monthly changes of climate indices and ITF inflows to the upper and lower layers during the coupled El Niño and positive IOD events plotted in this example. Figure 7 (a) and Figure 7 (b) in the figure shows the changes in the upper and lower layer flow of the ITF inflow and the changes in the Niño 3.4 index and DMI index during the corresponding period. Figure 7 (a) and Figure 7 As can be seen from (b) in the figure, when El Niño and positive IOD events occur simultaneously, the positive IOD event usually peaks in the boreal autumn, while the corresponding El Niño event is in a strong development period and peaks in the boreal winter. At the same time, the ITF flow tends to show weakening and strengthening changes in the upper and lower layers, respectively.

[0080] In step S4, the CCA method is used to decouple and quantify the dominant climate factors of the changes in the ITF inflow into the upper and lower layers (the Res term is small enough to be ignored), and then forward processing is performed to obtain the relative contribution ratio of each climate factor. The dominant climate factor is defined as the climate factor with a large value in the regression coefficient ratio. The relevant results can be seen in Table 3 below.

[0081] Table 3 Relative contribution ratio of each climate factor

[0082]

[0083] As shown in Table 3, the contribution ratios of ENSO and IOD to the ITF inflow to the upper and lower layers reached 12:1 and 1.7:1, respectively, indicating that the strong El Niño event dominated the changes in ITF during this period. The reason why the dominant factor of ITF inflow to the upper layer is the ENSO climate factor can be explained by the pressure gradient between the Pacific and Indian Oceans.

[0084] Figure 8 1 is a spatial distribution map of sea surface height anomalies during independent and coupled El Niño and positive IOD events drawn in an embodiment of the present invention. Figure 8 (a), (b), and (c) in the figure show the spatial distribution of sea surface height anomalies (SLA) during El Niño events unrelated to IOD, during positive IOD events unrelated to ENSO, and during the coupling of El Niño and positive IOD events. The framed areas NWP and SEI in the figure represent the northwest tropical Pacific (NWP, 6°N–16°N, 125°E–155°E) and the southeast Indian Ocean (SEI, 6°S–16°S, 85°E–115°E), respectively. During El Niño events unrelated to IOD, as shown in Figure 8 In (a), the SLA difference between NWP and SEI is -0.11 m, which further verifies the reduction of ITF inflow into the upper layer. At the same time, the westward propagating Rossby wave enhances the pressure gradient in the lower layer between the Pacific and Indian Oceans by raising the depth of the thermocline on the inflow side, further increasing the ITF inflow into the lower layer. For positive IOD events not related to ENSO, Figure 8 In (b), both NWP and SEI show positive SLA, and the difference in SLA is 0.08 m, which further promotes the enhancement of ITF inflow into the upper layer. In the lower layer, the weakening of ITF flow is closely related to the upwelling caused by the eastward propagation of Kelvin waves, which reduces the pressure gradient between the Pacific and Indian Oceans by raising the depth of the thermocline on the Indian Ocean side, further weakening the ITF inflow into the lower layer. During the coupling of the 1997–1998 El Niño and positive IOD events, Figure 8In (c), the SLA difference between NWP and SEI is -0.16 m, and its variation shows similar variation characteristics during the El Niño event that is unrelated to IOD. At the same time, the SLA of NWP reached -0.20 m in 1997–1998, which may be the main reason for the 12:1 contribution ratio of ENSO and IOD to the upper layer of ITF inflow. It can be seen that the relative contribution of different climate factors to the upper and lower layers of ITF inflow is quantified by the CCA method.

[0085] In summary, the method of this embodiment includes: obtaining reanalysis data from multiple sources, calculating flow based on the reanalysis ocean current data from multiple sources, and verifying the data with the existing mooring observation data, thereby obtaining reliable average flow data of the upper and lower layers of the ITF based on the multi-source reanalysis data; obtaining climate indices representing ENSO and IOD climate factors, wherein the climate indices representing ENSO and IOD climate factors include the Niño 3.4 climate index and the DMI climate index; judging climate events classified by type within a limited time range according to changes in the climate indices representing ENSO and IOD, and determining the sub-modes when ENSO and IOD act alone in combination with the ITF flow; based on the obtained ENSO and IOD sub-modes, and in combination with the idea of ​​the CCA (integrated similar circulation) method, that is, multi-source linear regression decoupling quantifies the relative contribution of ITF flow changes during ENSO and IOD coupling events and ultimately determines the dominant climate factors. The method of quantifying the relative contribution of climate mode coupling to ITF transport in this embodiment aims to solve the problems of difficulty in reflecting the monthly changes in ITF flow and difficulty in separating the relative contributions of different climate factors to ITF during the coupling period, and to achieve the purpose of quantifying the relative contribution of climate mode coupling to ITF flow by using the CCA (integrated similar circulation method) method combined with multi-source high-resolution reanalysis data.

[0086] In addition, this embodiment also provides a system for quantifying the relative contribution of the climate mode coupling period to the ITF transmission, including a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the method for quantifying the relative contribution of the climate mode coupling period to the ITF transmission.

[0087] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the method of quantifying the relative contribution of the climate mode coupling period to the ITF transmission through a processor.

[0088] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the method of quantifying the relative contribution of the climate mode coupling period to the ITF transmission through a processor.

[0089] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for quantifying the relative contribution of climate mode coupling periods to ITF transport, characterized in that: include: The average flow of the upper and lower layers of the ITF is calculated based on reanalysis current data from multiple sources; Obtain index data representing ENSO and IOD climate factors respectively, calculate the standard deviation of each index data, and determine significant ENSO and IOD independent action events based on the standard deviation of each index data within a limited time range; The submodes of ENSO and IOD acting alone are determined based on the significant ENSO and IOD acting alone events and the average flow in the upper and lower layers of the ITF. According to the sub-modes when ENSO and IOD act alone, combined with the average flow of the upper and lower layers of the ITF, the integrated similar circulation method CCA is used to determine the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period, and the dominant climate factor is determined according to the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period. The average flow of the upper and lower layers of the ITF is the average of the flow of the upper and lower layers of the ITF of all reanalysis current data, and the calculation function expression of the flow of the upper and lower layers of the ITF of each reanalysis current data is: ， In the above formula, is the flow rate of the upper or lower layer of ITF, is the number of vertical grid layers, is the number of horizontal grid points, Perpendicular to the horizontal grid and The flow velocity on the cross section of a vertical grid, is the distance between two adjacent grid points in the horizontal grid, is the distance between two adjacent vertical layers in the vertical grid, and the vertical grid number and the horizontal grid number Satisfied , .

2. The method for quantifying the relative contribution of the climate mode coupling period to the ITF transport according to claim 1, characterized in that: The calculation of the average flow of the upper and lower layers of the ITF based on the multi-source reanalysis current data also includes using mooring observation data to screen the multi-source reanalysis current data: for each candidate reanalysis current data, the correlation coefficient R and the root mean square error RMSE between the reanalysis current data and the given mooring observation data are calculated respectively, and the reanalysis current data whose correlation coefficient R and the root mean square error RMSE meet the requirements are screened out from the candidate reanalysis current data as the multi-source reanalysis current data.

3. The method for quantifying the relative contribution of climate mode coupling periods to ITF transport according to claim 1, characterized in that: When the index data representing the ENSO and IOD climate factors are obtained respectively, the index data representing the ENSO climate factor is the Niño 3.4 climate index data, and the index data representing the IOD climate factor is the DMI climate index data.

4. The method for quantifying the relative contribution of climate mode coupling periods to ITF transport according to claim 1, characterized in that: The calculation function expression of the standard deviation of each index data is: ， ， In the above formula, is the standard deviation, is the sequence length of the exponential data, is the jth index value of the index data, is the sequence average of the index data. Determining significant ENSO and IOD independent action events based on the standard deviation of each index data within a limited time range means that index data greater than a specified multiple of the standard deviation is regarded as a significant ENSO or IOD independent action event.

5. The method for quantifying the relative contribution of climate mode coupling period to ITF transport according to claim 1, characterized in that: The method of using the integrated similar circulation method CCA to determine the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period includes: firstly, a multiple regression is performed on the average flow of the upper and lower layers of the ITF under the sub-mode when ENSO and IOD act alone, and the regression term of the multiple regression is the average flow change sequence of the corresponding upper or lower sub-mode, and the average flow change sequence of the sub-mode is longer than or equal to the target time series. When the regression term is selected, the sub-mode is sequentially intercepted according to the length of the target time series. The time series of the sub-mode is a, and the time series of the target is b, then the number of regression terms selected is Each regression term contains a regression coefficient, so as to obtain the sub-mode when ENSO acts alone. Regression coefficients , and the sub-modal when IOD acts alone Regression coefficients ; Then the relative contribution of the index data representing the ENSO or IOD climate factors to the ITF transport flow during the climate mode coupling period is calculated according to the following formula: ， In the above formula, is the relative contribution of the index data representing the ENSO or IOD climate factor to the ITF transport flow during the climate mode coupling period, is the component of ITF flow change caused by ENSO climate factor, is the component of ITF flow change caused by IOD climate factors, and are the partial regression coefficients, is the residual term, and there is: ， ， ， ， In the above formula, is the number of regression coefficients of the submode when ENSO acts alone, is the first submode when ENSO acts alone. regression coefficients, 1≤ ≤N, is the average flow series of the upper or lower layer of the ITF under the submode when ENSO acts alone. " indicates that the positive anomaly is El Niño. The " " indicates that negative anomalies are La Niña, is the number of regression coefficients of the sub-mode when IOD acts alone, is the mth regression coefficient of the sub-mode when IOD acts alone, 1≤ ≤M, is the average flow sequence of the upper or lower layer of the ITF under the sub-mode when the IOD acts alone. " indicates that the positive anomaly is a positive IOD. The " " indicates that the negative anomaly is a negative IOD, and are partial regression coefficients respectively.

6. The method for quantifying the relative contribution of climate mode coupling periods to ITF transport according to claim 1, characterized in that: The method of determining the dominant climate factor according to the relative contribution of the index data representing the ENSO and IOD climate factors to the ITF transport flow during the climate mode coupling period includes: performing a significance test on the regression coefficients of the sub-mode when ENSO acts alone, and performing a significance test on the regression coefficients of the sub-mode when IOD acts alone; if some regression coefficients fail the significance test, the sub-mode corresponding to the regression coefficient of the significance test is retained and multiple regression is performed again until the regression coefficients of all sub-modes pass the significance test; all regression coefficients of the sub-mode when ENSO acts alone that pass the significance test are added and forward processed to obtain the contribution when ENSO acts alone, and all regression coefficients of the sub-mode when IOD acts alone that pass the significance test are added and forward processed to obtain the contribution when IOD acts alone; and the climate factor corresponding to the larger contribution between the contribution when ENSO acts alone and the contribution when IOD acts alone is determined as the dominant climate factor.

7. A system for quantifying the relative contribution of climate mode coupling periods to ITF transport, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to perform the method of quantifying the relative contribution of the climate mode coupling period to the ITF transport as claimed in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instructions are programmed or configured to execute, through a processor, the method for quantifying the relative contribution of the climate mode coupling period to the ITF transport as claimed in any one of claims 1 to 6.

9. A computer program product comprising a computer program or instructions, characterized in that The computer program or instructions are programmed or configured to execute, through a processor, the method for quantifying the relative contribution of the climate mode coupling period to the ITF transport as claimed in any one of claims 1 to 6.

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