Methods, devices, and media for el nino event identification and warning of a transition to la nina

Abstract

The application relates to the field of atmospheric science and discloses an El Nino event identification and La Nina transition early warning method, equipment and medium. The method uses the sea surface temperature and the near-surface 2-meter air temperature data in the fifth-generation atmospheric reanalysis product ERA5 of the European Centre for Medium-Range Weather Forecasts, calculates the lag cross-correlation coefficient between the sea temperature grid points and the air temperature grid points, and constructs a directed network between the global air temperature and the equatorial mid-east Pacific and equatorial mid-Pacific sea temperature. The average link strength is used to quantify the importance of the air temperature node, the spatial and temporal changes of the link number and the link strength are combined, the key area with a leading response to the El Nino is identified, and the transition of the El Nino event to the La Nina event is early warned. The application helps us to deeply understand the influence of the El Nino event on the global climate system, improves the prediction accuracy of the La Nina event, and thus provides a scientific basis for the formulation of disaster prevention and response strategies.

Description

Technical Field

[0001] This invention relates to the field of atmospheric science, and in particular to a method, device and medium for identifying El Niño events and providing early warning of their transition to La Niña. Background Technology

[0002] El Niño-Southern Oscillation (ENSO) is a core manifestation of the tropical Pacific interannual oscillation and one of the most significant ocean-atmosphere coupled climate phenomena globally, exerting a profound impact on the global climate system. El Niño events can be categorized into eastern and central types, exhibiting significant differences in the distribution and evolution of sea surface temperature anomalies, and their impacts on global climate also differ. However, regardless of the type, El Niño events have a significant impact on global temperature, precipitation patterns, fisheries resources, food security, and economic development. Therefore, a thorough understanding of the atmosphere's response mechanisms to different types of El Niño events is crucial for improving climate forecasting capabilities and developing disaster prevention strategies. Summary of the Invention

[0003] The purpose of this invention is to propose a method, device, and medium for identifying El Niño events and providing early warning of their transition to La Niña, so as to reveal in depth the atmospheric response mechanism to different types of El Niño events, improve climate prediction capabilities, and formulate corresponding disaster prevention strategies.

[0004] Specifically, the present invention provides a method for identifying El Niño events and providing early warning of their transition to La Niña, comprising the following steps:

[0005] S1. Data Acquisition: Acquire sea surface temperature and 2-meter air temperature data from the European Centre for Medium-Range Weather Forecasts' fifth-generation atmospheric reanalysis product, ERA5.

[0006] S2. Data preprocessing: Using the data collected in step S1, calculate the daily average sea surface temperature and air temperature. Define 1979-2019 as the baseline state. Subtract the obtained daily average sea surface temperature and air temperature from the baseline state temperature to obtain the anomaly time series.

[0007] S3. Construction of a climate network based on cross-correlation coefficients: Combining the anomaly time series obtained in step S2, the air temperature grid and sea surface temperature grid are divided into two sets. For each pair of nodes i and j in different sets, the time-lag cross-correlation coefficient between them is calculated. The strength and direction of the link are represented by the peak value of the absolute value of the cross-correlation coefficient and the corresponding time lag, and a directed network of air temperature and sea surface temperature is constructed.

[0008] S4. Network Link Feature Quantification: Set an appropriate cross-correlation threshold to remove accidental links that are insensitive to El Niño events; focus on links from air temperature to sea surface temperature, and calculate the number of links and average link strength at air temperature grid points;

[0009] S5. Atmospheric response quantification of eastern and central El Niño: Combining the methods described in steps S3 and S4, calculate the frequency distribution of the link strength of the global temperature-equatorial central and eastern Pacific sea surface temperature network, the spatiotemporal distribution of the number of links and the link strength, identify key regions with advanced responses to El Niño events, and identify early warning signals of the transition from El Niño events to La Niña events based on the link strength of key regions.

[0010] S6. Atmospheric response quantification of Central El Niño Type I and II: Combining the methods described in steps S3 and S4, calculate the frequency distribution of the link strength, the spatiotemporal distribution of the number of links and the link strength of the global temperature-equatorial mid-Pacific sea surface temperature network, identify key regions with advanced responses to the two types of CP El Niño events, and identify early warning signals for two consecutive La Niña events following an El Niño event based on the link strength of the key regions.

[0011] A storage medium storing instructions and data for implementing a method for El Niño event identification and early warning of transition to La Niña.

[0012] An El Niño event identification and La Niña transition early warning device includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement an El Niño event identification and La Niña transition early warning method.

[0013] The beneficial effects provided by this invention are:

[0014] (1) This invention reveals the interaction between the atmosphere and El Niño events and their key regions through complex network analysis. This not only deepens the understanding of the impact of El Niño events on the global climate system, but also provides scientific support for governments and international organizations to formulate more efficient climate change adaptation programs.

[0015] (2) This invention proposes a method for early warning of possible La Niña events in the current or following year during El Niño events, which helps to improve the accuracy of La Niña event prediction, provides a scientific basis for formulating disaster prevention and response strategies, and thus effectively reduces the losses caused by climate anomalies.

[0016] (3) This invention explores the atmospheric advance response of different types of El Niño events by constructing an air temperature and sea temperature network based on different sea temperature regions, providing a new perspective for climate science research and promoting further research and understanding of El Niño events and climate change. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0018] Figure 2 The graph shows the frequency distribution of link strength in normal years and the difference between the frequency distribution of link strength in five El Niño years and normal years.

[0019] Figure 3 Spatial distribution map of gridded temperature link intensity in normal years and five El Niño years;

[0020] Figure 4 Spatial distribution of grid link numbers for temperature in normal years and five El Niño years;

[0021] Figure 5 A three-point smoothing plot showing the total link strength across different ranges in the El Niño key region over time, and a schematic diagram illustrating the duration of EP and CP type El Niño events;

[0022] Figure 6 The diagram shows the three-point smoothing plot of the average link strength over time and the duration of El Niño and La Niña events of type EP, CP-I, and CP-II.

[0023] Figure 7 Spatial distribution map of gridded temperature linkage intensity in normal years and CP-I and CP-II El Niño years;

[0024] Figure 8 Spatial distribution map of grid link number of temperature in normal years and CP-I and CP-II El Niño years;

[0025] Figure 9 Three-point smoothing plot of the total link strength over time in different ranges of the key region of CP-II El Niño, and schematic diagram of the duration of EP, CP-I, CP-II El Niño and La Niña events;

[0026] Figure 10 This is a schematic diagram of the hardware device of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0028] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0029] Please refer to Figure 1 This invention provides a method for identifying El Niño events and providing early warning of their transition to La Niña, comprising the following steps:

[0030] S1. Data Acquisition: Acquire sea surface temperature and 2-meter air temperature data from the European Centre for Medium-Range Weather Forecasts' fifth-generation atmospheric reanalysis product, ERA5.

[0031] In step S1, the time span of the collected ERA5 dataset is from January 1979 to December 2020, and the spatial resolution of the data is selected as 5°×5°.

[0032] S2. Data preprocessing: Using the data collected in step S1, calculate the daily average sea surface temperature and air temperature. Define 1979-2019 as the baseline state. Subtract the obtained daily average sea surface temperature and air temperature from the baseline state temperature to obtain the anomaly time series.

[0033] In step S2, the formulas for calculating the time series of sea surface temperature and air temperature anomalies are as follows:

[0034]

[0035] in, Let y = temperature on day i in year y (excluding February 29th of leap years to ensure that each year of the grid point has 365 days), where y = 1 represents 1979, and so on. This represents summing the temperatures of day i in all years of the baseline state and averaging them.

[0036] S3. Construction of a climate network based on cross-correlation coefficients; Combining the anomaly time series obtained in step S2, the air temperature grid and sea surface temperature grid are divided into two sets. For each pair of nodes i and j in different sets, the time-lag cross-correlation coefficient between them is calculated. The strength and direction of the link are represented by the peak value of the absolute value of the cross-correlation coefficient and the corresponding time lag, and a directed network of air temperature and sea surface temperature is constructed.

[0037] Step S3 is as follows:

[0038] The anomaly time series obtained in step S2 are divided into two sets: one containing all global temperature grid points (2664 in total), and the other containing all sea surface temperature grid points in a selected ocean region (55 in total). For each pair of nodes i and j in different sets, the cross-correlation coefficient between them is calculated using the following formula:

[0039]

[0040] in It is a time series T i (d) standard deviation, where τ is the time interval, τ∈[0,τ] max ], τ max =200 days, y represents the start time of the time series when the time interval of each window is 0, and c i,j (τ)=C j,i (-τ). The time lag corresponding to the maximum absolute value of the cross-correlation coefficient is expressed as... The symbol represents the direction of the link between nodes i and j, when In this case, the link points from i to j. The absolute value of the cross-correlation coefficient represents the link strength. Thus, a directed climate network of air temperature and sea surface temperature is established based on the cross-correlation coefficient.

[0041] S4. Network link feature quantification; set an appropriate cross-correlation threshold to remove accidental links that are insensitive to El Niño events; focus on links from air temperature to sea surface temperature, and calculate the number of links and average link strength at air temperature grid points;

[0042] Step S4 is as follows:

[0043] An appropriate threshold Δ is introduced to exclude links that are weakly associated with El Niño events. The adjacency matrix defined by the threshold is as follows:

[0044]

[0045] Here, H(X) is the Heaviside step function, defined as H(X≥0)=1 and H(x<0)=0.

[0046] Consider the link from air temperature to sea surface temperature, i.e. The formula for the degree of the grid points in the link is as follows:

[0047]

[0048] This is the sum of the cross-correlation coefficients of all nodes at a given grid point that exceed a threshold. The average link strength at a grid point is defined as follows:

[0049]

[0050] That is, the ratio of the degree of a grid point to the number of links to that grid point. This is the number of links from the air temperature grid point to the sea surface temperature grid point, and its formula is as follows:

[0051]

[0052] S5. Atmospheric response quantification of Eastern (EP) and Central (CP) El Niño types; combining the methods described in steps S3 and S4, calculate the frequency distribution of the link strength of the global temperature-equatorial central and eastern Pacific sea surface temperature network, the spatiotemporal distribution of the number of links and the link strength, identify key regions with advanced responses to El Niño events, and identify early warning signals for the transition of El Niño events to La Niña events based on the link strength of key regions.

[0053] Step S5 is as follows:

[0054] Step S51: Establish a grid network of sea surface temperatures in the central and eastern equatorial Pacific region (170°W-120°W, 10°S-10°N); define the occurrence and dissipation of El Niño events according to the Nino3.4 index: a Nino3.4 index > 0.5℃ and lasting for at least 5 months is an El Niño event; a Nino3.4 index < -0.5℃ and lasting for at least 5 months is a La Niña event. Define the time and value of the absolute value of the Nino3.4 index reaching its maximum as the peak time and peak intensity of the event, respectively. An event with a peak intensity > 0.5℃ and < 1.3℃ is defined as a weak event; ≥ 1.3℃ and < 2℃ as a moderate event; ≥ 2℃ and < 2.5℃ as a strong event; and ≥ 2.5℃ as an extremely strong event.

[0055] Step S52: Considering the different sea surface temperature anomaly regions and their varying impacts among different types of El Niño events, the NEP and NCP indices are used to distinguish between EP-type and CP-type El Niño events. The formulas for calculating these two indices are as follows:

[0056] NEP = N3 - αN4

[0057] NCP = N4 - αN3

[0058] Wherein, N3 is the regional average of sea surface temperature anomalies in the Nino3 region (150°W-90°W, 5°S-5°N), and N4 is the regional average of sea surface temperature anomalies in the Nino4 region (160°E-150°W, 5°S-5°N). When N3*N4 is greater than 0, α equals 0.4; otherwise, α equals 0. The distinction rules are as follows: (1) If the absolute value of the NEP index reaches or exceeds 0.5°C and lasts for at least three months, it is an EP-type event; (2) If the absolute value of the NCP index reaches or exceeds 0.5°C and lasts for at least three months, it is a CP-type event; (3) If an event satisfies both of the above conditions, the type of the event peak shall prevail.

[0059] Step S53: Based on the Nino3.4 index, five El Niño events of moderate to high intensity are selected for analysis. The changes in the number and intensity of links during El Niño events are analyzed using frequency distribution histograms of link strength, graphs showing the number of links over time, and probability density distribution graphs of link time intervals. Through spatiotemporal distribution maps of link strength and number, key regions with advanced responses to El Niño events are identified. Furthermore, the temporal evolution of link strength within these key regions is analyzed to determine early warning signals indicating the transition from El Niño to La Niña events.

[0060] S6. Quantification of atmospheric response to Central El Niño Type I and II; Combining the methods described in steps S3 and S4, calculate the frequency distribution of the link strength, the spatiotemporal distribution of the number of links and the link strength of the global temperature-equatorial mid-Pacific sea surface temperature network, identify key regions with advanced responses to the two types of CP El Niño events, and identify early warning signals for two consecutive La Niña events following an El Niño event based on the link strength of the key regions.

[0061] Step S6 is as follows:

[0062] Step S61: Establish a grid of sea surface temperatures in the central equatorial Pacific region (160°E-150°W, 10°S-10°N); use the EMI index to distinguish between EP and CP El Niño events, the calculation formula of which is as follows:

[0063] EMI = [SSTA] C -0.5×[SSTA] W -0.5×[SSTA] E

[0064] [SSTA] C [SSTA] W [SSTA] E These represent the sea surface temperature anomaly averages for regions C (165°E-140°W, 10°S-10°N), W (125°E-145°E, 10°S-20°N), and E (110°W-70°W, 15°S-5°N), respectively. The MII index is used to further distinguish between Central El Niño Type I (CP-I) and Central El Niño Type II (CP-II) events. A CP-II El Niño event is defined as an MII event if the MII index is greater than one standard deviation; otherwise, it is a CP-I event.

[0065] Step S62: Eight CP-type El Niño events were selected for analysis based on EMI; the characteristics of CP-type El Niño links and their differences from EP-type El Niño were analyzed through link strength frequency distribution maps and link time interval probability density distribution maps; key regions of CP-I and CP-II type El Niño were identified through spatiotemporal distribution maps of link strength and link number; finally, the signals of two consecutive La Niña events following the El Niño event were further explored through temporal evolution analysis of link strength within the key regions.

[0066] As an example of implementation, the present invention will be further described. The example is used to illustrate the present invention, but it does not limit the scope of application of the present invention. It is also applicable to different regions and other time periods.

[0067] The flowchart of the method for studying the influence of the atmosphere on El Niño events based on complex network methods of this invention is as follows: Figure 1 As shown in the attached diagram, the following explanation is provided:

[0068] (1) Data Acquisition

[0069] This implementation case uses hourly sea surface temperature and hourly 2-meter atmospheric temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5 Atmospheric Reanalysis (ERA5) product. The dataset covers the period from January 1979 to December 2020 and uses a spatial resolution of 5°×5°.

[0070] (2) Data preprocessing

[0071] Based on the data described in step (1), the daily average sea surface temperature and atmospheric temperature are calculated. The period from 1979 to 2019 is defined as the baseline state. The multi-year average daily temperature of the baseline state is calculated. The anomaly time series is obtained by subtracting the daily average temperature of the baseline state from the daily average temperature. This case study selects two ocean regions for research: one is the central and eastern equatorial Pacific region (170°W-120°W, 10°S-10°N), and the other is the central equatorial Pacific region (160°E-150°W, 10°S-10°N). The former mainly studies the characteristics of general El Niño events, while the latter mainly studies the characteristics of the two types of CP-type El Niño events.

[0072] (3) Construction of climate network based on cross-correlation coefficient

[0073] The anomaly time series were divided into two sets: one containing all global temperature grid points (2664 in total), and the other containing all sea surface temperature grid points in a selected ocean region (55 in total). The time window was set to 365 days, and the time lag was set to 0-200 days. For each pair of nodes i and j in different sets, their cross-correlation coefficient was calculated in each time window. The peak value of the absolute value of the cross-correlation coefficient represents the link strength, and the corresponding time lag represents the direction of the link between nodes i and j.

[0074] (4) Quantification of network link features

[0075] Next, a threshold is introduced to exclude links that are insensitive to El Niño events: Analysis of the frequency distribution of link strength and the temporal evolution of link numbers under different thresholds reveals that the number of links with absolute strength values ​​less than 0.2 is very small, and their changes are not significant during El Niño years (e.g., Figure 2 Furthermore, we focus not only on strong links but also on changes in weak links. A threshold of 0.2 results in significant fluctuations in the number of links; therefore, this case uses a threshold of 0.2. This case primarily considers links from air temperature to sea surface temperature, i.e. The link is defined as the degree of a temperature grid point as the sum of all cross-correlation coefficients of that grid point exceeding a threshold within a time window. The number of links for each grid point within the window is calculated, and the ratio of the degree of that grid point to its number of links within the window is calculated, i.e., the average link strength.

[0076] (5) Quantification of atmospheric response to eastern and central El Niño

[0077] A network was established by selecting sea surface temperature grid points in the central and eastern equatorial Pacific region (170°W-120°W, 10°S-10°N) and global temperature grid points to analyze the atmospheric response to eastern (EP) and central (CP) El Niño events.

[0078] In this implementation case, five El Niño events of moderate or greater intensity from 1979 to 2020 were selected for specific analysis based on the Nino3.4 index (1982, 1987, 1997, 2009, and 2015). EP and CP type events were determined based on the NEP and NCP indices, as shown in Table 1.

[0079] Table 1. Five El Niño events of moderate or greater intensity.

[0080]

[0081] like Figure 2 Overall, in normal years, the vast majority of El Niño links are weak (within the range of -0.6 to 0.6). However, in the five El Niño years, the number of weak links decreased significantly compared to normal years, while the number of strong links (absolute strength greater than 0.5) increased, and the decrease in weak links far exceeded the increase in strong links. This may be because, as the El Niño event develops, the direction of most links changes from sea surface temperature to air temperature. Analyzing the frequency changes in link strength across different El Niño years reveals that EP-type El Niño events (… Figure 2 When events b, c, and d occur, positive links decrease more than negative links; while CP-type El Niño events ( Figure 2 e) During this period, the number of positive and negative links decreased by roughly the same amount. In addition, there were three super El Niño events ( Figure 2 (b, d, f) showed a significantly greater increase in negative links, while the number of positive and negative links increased by roughly the same amount during the two moderate El Niño events, and was much smaller than the number of negative links increased during the super El Niño event.

[0082] like Figure 3 As shown, the linkage strength is significantly higher and more concentrated in certain regions during El Niño years. Specifically, areas with high negative linkage strength exist in the tropical Atlantic, northern and eastern Australia, while areas with high positive linkage strength exist in the central and eastern equatorial Pacific and the southeastern Pacific. In the three super El Niño events ( Figure 3 (b, d, f) Strong negative links exist between the tropical Atlantic and Indonesian regions, whereas during the two moderate El Niño years, only one region showed a strong link. For example... Figure 4 In normal years, atmospheric temperature generally correlates with sea surface temperature in the central and eastern equatorial Pacific, but most temperature grid points show relatively few links. However, in El Niño years, some atmospheric regions establish links with most or even all (55) of the ocean grid points, while other regions are completely disconnected from the ocean. Figure 3 The study found that regions with high link numbers largely overlap with regions with high link strength, indicating that atmospheric grid points in these regions establish more high-intensity links during El Niño years, and that the atmospheric advance response to the ocean exhibits strong locality. Further analysis of the average link strength and link number changes in these regions identified key areas with significant influence on sea surface temperature in the central and eastern equatorial Pacific: the eastern equatorial Pacific, the tropical Atlantic, and northern Australia.

[0083] from Figure 5 As can be seen, in the early and middle stages of an El Niño event, a large number of weak links break, while in the later stages, these weak links are re-established. In CP-type El Niño events, the total strength of weak links reaches its lowest value earlier than in EP-type El Niño events. Figure 5 It can be observed that during El Niño events, the total strength of strong links generally shows an upward trend. The magnitude of change in CP-type El Niño events is smaller than that in EP-type El Niño events, and the trends are inconsistent. This may indicate that the aforementioned regions are not key regions for CP-type El Niño events. Therefore, the next step will focus on analyzing CP-type El Niño and identifying its key regions. Further analysis reveals that when the average link strength in key regions during El Niño exceeds a certain threshold (0.47), a La Niña event is likely to occur in the same year or the following year after its end.

[0084] (6) Quantification of atmospheric response to central El Niño I and II

[0085] A network was established by selecting sea surface temperature grids in the central equatorial Pacific region (160°E-150°W, 10°S-10°N) and global temperature grids to analyze the atmospheric leading response to two types of CP-type El Niño events. In this step, the EMI index was used to identify CP-type El Niño events, and the MII index was used to distinguish between CP-I and CP-II type El Niño events.

[0086] like Figure 6As shown, during EP-type El Niño events, the average link strength shows an upward trend, peaking at the end of the event. This peak is slightly smaller than the average link strength calculated in the central and eastern equatorial Pacific, which may indicate that the atmosphere has a greater influence on the central and eastern equatorial Pacific during EP-type El Niño events. During CP-I and CP-II type El Niño events, the average link strength also shows an upward trend, with the increase positively correlated with the intensity of the event itself. For La Niña events, if they occur immediately after a strong EP-type El Niño event or after the previous La Niña event, the average link strength decreases monotonically; this indicates that the strong link between the atmosphere and sea surface temperature in the central equatorial Pacific basin has largely broken down. If a La Niña event occurs after a CP-type El Niño event or a weak EP-type El Niño event, its average link strength first increases and then decreases.

[0087] like Figure 7 As shown, both CP-I and CP-II El Niño events exhibit a strong positive link in the central and eastern equatorial Pacific region. However, during CP-II El Niño years, a significant negative link also exists in the tropical Atlantic. Overall, compared to CP-I events, CP-II El Niño years show a stronger link between atmospheric temperature and sea surface temperature grid points. Figure 8 As shown, compared to normal years, the number of links in the extra-equatorial region is significantly increased during CP-I El Niño events. Combined with... Figure 7 It can be seen that for CP-I type El Niño events, although the number of links in the equatorial region grid is not high, strong links also exist in the temperature grid within the central and eastern equatorial Pacific region. This may indicate that the strong links between atmospheric temperature and sea surface temperature grid in the central equatorial Pacific basin are localized during El Niño years. For CP-II type El Niño events, overall, most of the high-linkage areas are concentrated in the tropics, and the link strength is relatively strong. There are also a certain number of strong negative links in the Atlantic region. Further analysis of the spatial distribution of average link strength and link number identifies the key regions with a significant impact on sea surface temperature in the central equatorial Pacific, namely the central equatorial Pacific, the northern tropical Atlantic, and the northern temperate Atlantic.

[0088] like Figure 9As shown in (a), during CP-I El Niño events, the total strength of weak links first decreases and then increases, reaching a peak at the end of the event. During CP-II El Niño events, the total strength of weak links shows a decreasing trend, with a slight rebound later. For La Niña events, the total strength of weak links mostly shows a decreasing trend. However, in the two La Niña events following the 1997 and 2009 El Niño events, the total strength of weak links increased instead of decreasing. During an El Niño event, if the total strength of weak links in the critical region falls below a certain threshold, and in the subsequent La Niña event, the total strength of weak links in the critical region shows an increasing trend, then it is very likely that another La Niña event will occur after the event ends.

[0089] like Figure 9 As shown in (b), during CP-I El Niño events, the sum of strong link strengths first peaks and then declines rapidly in the later stages of the event. During CP-II El Niño events, the sum of strong link strengths steadily increases and peaks after the event ends. For La Niña events that immediately follow El Niño (within the same year), the sum of link strengths first increases and then decreases. Furthermore, after most La Niña events, the sum of strong link strengths increases again before the start of the next El Niño event, and then declines rapidly.

[0090] Please see Figure 10 , Figure 10 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: an El Niño event identification and early warning device for transition to La Niña 401, a processor 402, and a storage medium 403.

[0091] An El Niño event identification and La Niña transition early warning device 401: The El Niño event identification and La Niña transition early warning device 401 implements the El Niño event identification and La Niña transition early warning method.

[0092] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the method for identifying El Niño events and providing early warning of their transition to La Niña.

[0093] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the method for identifying El Niño events and providing early warning of their transition to La Niña.

[0094] The beneficial effects of this invention are:

[0095] (1) This invention reveals the interaction between the atmosphere and El Niño events and their key regions through complex network analysis. This not only deepens the understanding of the impact of El Niño events on the global climate system, but also provides scientific support for governments and international organizations to formulate more efficient climate change adaptation programs.

[0096] (2) This invention proposes a method for early warning of possible La Niña events in the current or following year during El Niño events, which helps to improve the accuracy of La Niña event prediction, provides a scientific basis for formulating disaster prevention and response strategies, and thus effectively reduces the losses caused by climate anomalies.

[0097] (3) This invention explores the atmospheric advance response of different types of El Niño events by constructing an air temperature and sea temperature network based on different sea temperature regions, providing a new perspective for climate science research and promoting further research and understanding of El Niño events and climate change.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for El Nino event identification and La Nina transition early warning, characterized in that: The method comprises the following steps: S1, data collection: collecting sea surface temperature and 2-meter air temperature data in the ERA5 atmospheric reanalysis product of the European Medium-term Weather Forecasting Center; S2, data preprocessing: using the data collected in step S1, calculating daily average sea surface temperature and air temperature, defining 1979-2019 as the reference state, and subtracting the reference state temperature from the obtained daily average sea surface temperature and air temperature to obtain the anomaly time series; S3, climate network construction based on cross-correlation coefficient: combining the anomaly time series obtained in step S2, dividing the air temperature grid points and the sea temperature grid points into two sets, and calculating the lag cross-correlation coefficient between each pair of nodes i and j in different sets; The peak value of the absolute value of the cross-correlation coefficient and the corresponding time lag represent the strength and direction of the link, and a directed network of air temperature and sea temperature is constructed; S4, network link feature quantification: setting a proper cross-correlation coefficient threshold to remove accidental links that are not sensitive to El Nino events; calculating the link number and average link strength of the air temperature grid points from the air temperature to the sea temperature link; S5, atmospheric response quantification of eastern and central El Nino: combining the methods described in steps S3 and S4, calculating the frequency distribution of the link strength of the global air temperature-equatorial mid-east Pacific sea temperature network, the spatial and temporal distribution of the link number and link strength, identifying the key areas that respond to El Nino events in advance, and identifying the early warning signals of the transition of El Nino events to La Nina events based on the link strength of the key areas; S6, atmospheric response quantification of central El Nino type I and type II: combining the methods described in steps S3 and S4, calculating the frequency distribution of the link strength of the global air temperature-equatorial mid-Pacific sea temperature network, the spatial and temporal distribution of the link number and link strength, identifying the key areas that respond to CP type two El Nino events in advance, and identifying the early warning signals of the occurrence of two consecutive La Nina events after an El Nino event based on the link strength of the key areas.

2. A method of El Niño event identification and warning of a transition to La Niña as claimed in claim 1, wherein: In step S2, the daily average temperature is calculated based on the hourly sea temperature and air temperature data in step S1; then, 1979-2019 is defined as the reference state, and the daily temperature of the reference state multi-year average is calculated; the daily average temperature is subtracted from the reference state temperature to obtain the anomaly time series, and the formula is as follows: where, Tij is the temperature at grid point i on day j in year y, where y = 1 represents 1979, and so on; represents the average of the temperatures on day i in all years of the reference state.

3. A method of El Niño event identification and warning of a transition to La Niña as claimed in claim 2, wherein: Step S3 is as follows: The anomaly time series obtained in step S2 is divided into two sets, one containing all global air temperature grid points and the other containing all sea temperature grid points in the selected ocean area; for each pair of nodes i and j in different sets, the lag cross-correlation coefficient between them is calculated, and the formula is as follows: wherein is the standard deviation of the time series ), is the time interval, , days, y represents the starting time of the time series for each window time interval of 0, and ; the time lag corresponding to the maximum value of the absolute value of the cross-correlation coefficient is represented as , The sign of the cross-correlation coefficient represents the direction of the link between the i and j nodes, and when , the link is directed from i to j; the absolute value of the cross-correlation coefficient represents the link strength, thereby establishing a directed climate network of air temperature and sea surface temperature based on the cross-correlation coefficient.

4. A method of El Niño event identification and warning of a transition to La Niña as claimed in claim 3, wherein: Step S4 is as follows: A threshold value Δ is introduced to exclude links that are not sensitive to El Nino events, and the adjacency matrix defined by the threshold value is as follows: Where H(X) is the Heaviside step function, defined as H(X≥0) = 1 and H(X<0) = 0; Consider the linkage from air temperature to sea temperature, i.e. The degree formula of the grid is as follows: That is, the sum of all cross-correlation coefficients of a grid point that exceed the threshold value; the average link strength of the grid point is defined as follows: i.e. the ratio of the degree of the grid point to the number of links of the grid point; wherein is the number of links of the grid point to the grid point of the sea surface temperature, which is calculated as follows: .

5. A method of El Niño event identification and warning of a transition to La Niña as claimed in claim 4, wherein: Step S5 is as follows: Step S51: Selecting sea surface temperature grid points in the equatorial eastern Pacific region to establish a network; defining the occurrence and disappearance of El Niño events according to the Nino3.4 index, when the Nino3.4 index is greater than a first preset value ℃ and the event lasts more than a second preset value, it is an El Niño event; when the Nino3.4 index is less than a third preset value and lasts more than a second preset value, it is a La Niña event; The time and value of the absolute value of the Nino3.4 index reaching the maximum value are defined as the peak time and peak intensity of the event respectively; according to the peak event and the peak intensity, the event is divided into weak intensity event, medium intensity event, strong intensity event and super strong intensity event; Step S52: Considering that the sea surface temperature anomaly regions of different types of El Niño events are different and their influences are different, the eastern type El Niño index and the central type El Niño index are used to distinguish eastern type and central type El Niño events, and the two index calculation formulas are as follows: Wherein, N3 is the regional average of the sea surface temperature anomaly in Nino3 region, N4 is the regional average of the sea surface temperature anomaly in Nino4 region; when N3*N4>0, α is equal to 0.4, otherwise α is equal to 0; the differentiation rule is as follows: (1) if the absolute value of NEP index reaches or exceeds 0.5 ℃ and lasts at least three months, it is an EP type event; (2) if the absolute value of NCP index reaches or exceeds 0.5 ℃ and lasts at least three months, it is a CP type event; (3) if an event meets the above two points at the same time, the type of peak event is used as the criterion; Step S53: Selecting five El Niño events with medium and above intensity according to Nino3.4 index for analysis; analyzing the change of the number and intensity of links during El Niño events by linking the frequency distribution histogram of intensity, the number of links changing with time, and the probability density distribution diagram of link time interval; identifying the key area with advanced response to El Niño events by the spatiotemporal distribution diagram of link intensity and link number, and determining the early warning signal of El Niño event to La Niña event by the time evolution analysis of link intensity in the key area.

6. A method of El Niño event identification and warning of a transition to La Niña as claimed in claim 5, wherein: The peak intensity of the event is greater than 0.5 ℃ and less than 1.3 ℃, which is defined as a weak intensity event; greater than or equal to 1.3 ℃ and less than 2 ℃, which is defined as a medium intensity event; greater than or equal to 2 ℃ and less than 2.5 ℃, which is defined as a strong intensity event; greater than or equal to 2.5 ℃, which is defined as a super strong intensity event.

7. A method of El Niño event identification and warning of a transition to La Niña as claimed in claim 6, wherein: Step S6 is as follows: Step S61: Selecting sea surface temperature grid points in the equatorial eastern Pacific region to establish a network; using the El Niño-Modoki index to identify CP type El Niño events, and the calculation formula is as follows: The sea surface temperature anomaly averages of C, W, and E regions are denoted by C, W, and E, respectively; the Modoki II index is used to further distinguish the central Pacific El Niño I and central Pacific El Niño II events, and when the MII index is greater than 1 standard deviation, it is defined as a CP-II type El Niño event, otherwise it is a CP-I type event; Step S62: Selecting eight CP type El Niño events according to EMI for analysis; analyzing the characteristics of central type El Niño links and their differences with eastern type El Niño by the frequency distribution histogram of link intensity and the probability density distribution diagram of link time interval; identifying the key area of CP-I and CP-II type El Niño by the spatiotemporal distribution diagram of link intensity and link number; finally, exploring the signal of two consecutive La Niña events after El Niño event by the time evolution analysis of link intensity in the key area.

8. A storage medium characterized by: The storage medium stores instructions and data for implementing the method for identifying an El Nino event and early warning of a transition to a La Nina according to any one of claims 1-7.

9. An El Niño event identification and La Niña transition early warning device, characterized by: Comprise: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium for implementing the method for identifying an El Nino event and early warning of a transition to a La Nina according to any one of claims 1-7.

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