Method for studying interannual variability of marine heatwaves in southern indian ocean

Abstract

Disclosed in the present invention is a method for studying the interannual variability of marine heatwaves in the Southern Indian Ocean. The method includes: on the basis of acquired data, exploring the spatial distribution characteristics and linear variation trend characteristics of marine heatwaves in the Southern Indian Ocean; analyzing the relationship between the intensity of marine heatwaves in the Southern Indian Ocean and an El Niño-Southern Oscillation (ENSO) process; analyzing the spatial evolution of marine heatwaves and sea surface temperature anomalies in the Southern Indian Ocean during El Niño events; analyzing the energy contributions of the ocean and atmosphere to marine heatwave events during El Niño events, and exploring the physical mechanism of marine heatwaves in the Southern Indian Ocean; and determining whether marine heatwaves in the Southern Indian Ocean are affected by shortwave radiation and latent heat modulated by the ENSO process. The present invention discovers that during El Niño events, within a study area, low cloud cover decreases while high cloud cover increases, the mixed-layer depth reduces, and shortwave radiation penetrates the sea surface; moreover, northwesterly winds anomalously intensify while climatological southeasterly winds are suppressed, thereby reducing evaporation, decreasing latent heat energy loss, and thus promoting warming.

Description

A method for studying the interannual variation of marine heatwaves in the southern Indian Ocean TECHNICAL FIELD

[0001] The present application relates to the interannual variation of marine heatwaves, and in particular to a method for studying the interannual variation of marine heatwaves in the southern Indian Ocean. BACKGROUND

[0002] Marine heatwaves refer to extreme high-temperature events in which the sea surface temperature in a certain sea area exceeds a certain threshold for more than five consecutive days. The duration of a marine heatwave can last for several months, and the coverage area can reach thousands of square kilometers. Since the industrial revolution, with the continuous increase in greenhouse gas emissions, the greenhouse effect has led to the absorption of a large amount of additional heat into the ocean. The absorbed energy is mainly concentrated in the upper layer of the ocean above 700 meters, which has led to a significant rise in the temperature of the ocean surface layer. In this case, the average intensity, duration, and frequency of marine heatwaves in most global ocean areas have shown a clear linear growth trend. According to statistics, from 1925 to 2016, the number of marine heatwave days worldwide increased by 54% per year.

[0003] In recent years, extreme marine heatwaves have caused serious and devastating impacts on ecosystems and human economic and social systems. Heatwaves can trigger the massive proliferation of harmful algae, which in turn leads to a decrease in nutrients in the ocean, causing an increase in the mortality rate of birds, fish, and marine mammals. Extremely high-temperature seawater also causes a large number of corals to bleach and sea grasses to die, having a devastating impact on the marine ecological structure. Marine heatwaves not only destroy marine ecosystems but also cause huge losses to human economic and social systems. Therefore, in recent years, more and more scientists have begun to pay attention to and study marine heatwaves. Marine heatwaves have occurred frequently in various global ocean areas, and the driving factors of marine heatwaves vary in different regions and at different times. Some studies have researched some marine heatwave events and their physical mechanisms. In 2003, a strong marine heatwave event occurred in the northern Mediterranean Sea, which was closely related to the abnormally high air temperature above the sea surface and the reduction in wind speed. From 2010 to 2011, a marine heatwave event occurred in western Australia, which was closely related to the La Nina event that year. The La Nina event caused the Levin flow to bring more warm water from low latitudes, while also intensifying the sea-air heat flux on the western coast of Australia, which together led to an increase in sea surface temperature. From 2013 to 2015, a record-breaking marine heatwave event occurred in the northeast Pacific Ocean, which was known as “The Blob”. This event was mainly affected by the abnormally high pressure ridge above the northeast Pacific Ocean and the weakening of the surface wind related to the Aleutian low. In addition, the marine heatwave event in the northeast Pacific Ocean in the summer of 2019 was called “Blob 2.0”, which was related to the sustained weakening of the high-pressure system over the North Pacific Ocean. In 2017, a relatively strong marine heatwave event also occurred in the southwest Atlantic Ocean, which was regulated by the tropical intraseasonal oscillation.

[0004] It is known that marine heat wave refers to an event of abnormal warming of sea temperature, the sea surface temperature in the Indian Ocean region presents obvious interannual variation, and the ocean and atmosphere changes in the Indian Ocean region are closely related to important climate phenomena such as El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). During El Nino, the surface sea water temperature in the equatorial Pacific rises to affect the sea surface temperature in the eastern Indian Ocean through the action of the 'atmospheric bridge', and the sea temperature in the Indian Ocean is 3 months later than the highest warm phase of the sea temperature in the equatorial mid-Pacific. In addition, it is found that the sea temperature in the Indian Ocean is also closely related to the Asian summer monsoon. The research on the marine heat wave in the Indian Ocean region shows that the summer marine heat wave in the Bay of Bengal is regulated by El Nino and is related to atmospheric heating and deepening of the thermocline. In addition, the equatorial western Indian Ocean region is affected by the sinking Rossby wave, which leads to the enhancement of the upper sea water convergence and inhibits the upwelling of cold water, thereby causing the marine heat wave event. In April to June 2010, the longest total number of days of marine heat wave was observed in the Arabian Sea, and it is pointed out that it is related to less latent heat loss and reduced mixed layer depth. Through the above analysis, the problems and defects of the prior art are that the research on the marine heat wave in the Indian Ocean in the prior art mainly concentrates on the Arabian Sea, the Bay of Bengal, the equatorial western Indian Ocean, the southeastern Indian Ocean and other regions, however, the mechanism of the marine heat wave in the southern Indian Ocean is not well understood and needs further in-depth research. The analysis of the influencing factors is not comprehensive: the mechanism research on the marine heat wave in the southern Indian Ocean in the prior art is not comprehensive, especially its connection with large-scale climate processes. SUMMARY

[0005] The purpose of the present application is to provide a method for researching the interannual variation law of the marine heat wave in the southern Indian Ocean, which solves the problem that the interannual variation law and the dynamic mechanism of the marine heat wave in the southern Indian Ocean (60°E-90°W, 15°S-25°S) are unclear. The present application researches and finds that each feature of the marine heat wave in the southern Indian Ocean presents a growth trend, the heat wave intensity lags behind El Nino by 3 months, and the heat wave is related to latent heat and shortwave radiation through the calculation of the mixed layer heat budget. Through the synthesis analysis, it is found that during El Nino, the low cloud in the research area decreases, the high cloud increases, and the mixed layer depth decreases, which is beneficial to the shortwave radiation entering the ocean surface and promoting the warming. At the same time, the northwest wind during El Nino period inhibits the climatological southeast wind, reduces the ocean evaporation, and reduces the lost latent heat energy, which is beneficial to the warming.

[0006] In order to achieve the above purpose, the present application provides a method for researching the interannual variation law of the marine heat wave in the southern Indian Ocean, which comprises:

[0007] Step one, obtaining SST data, OISST data set, ECMWF Reanalysis V5, three-dimensional temperature and salinity data, 3.4 index data;

[0008] Step two, according to the OISST data obtained to explore the spatial distribution characteristics and linear trend characteristics of the marine heat wave in the southern Indian Ocean;

[0009] Select 15°S to 25°S, 60°E to 90°E as the research area of the southern Indian Ocean heat wave, there is a marine heat wave in the area, the average intensity of the marine heat wave is 1.20℃, the maximum intensity is 1.50℃, the cumulative intensity is 18.06℃ / day, the duration is 14.12 days, the frequency is 2.74 times, and the total number of days is 45.22 days;

[0010] Step three, according to the OISST data obtained 3.4 index, through 3.4 index and the intensity of the southern Indian Ocean heat wave are analyzed in advance and lag correlation analysis of the relationship between the intensity of the southern Indian Ocean heat wave and the ENSO process;

[0011] The ENSO is El Nino-Southern Oscillation;

[0012] The time series of the intensity of the southern Indian Ocean heat wave calculated based on OISST and the Nino3.4 index have consistent time variation rules, and the lead-lag correlation analysis shows that the time series of the intensity of the southern Indian Ocean heat wave and the lag Nino3.4 index 3 months have a positive correlation;

[0013] Step four, analyze the spatial evolution of the southern Indian Ocean heat wave and the sea surface temperature anomaly during the El Nino event process;

[0014] The lead-lag correlation analysis in step three shows that the time series of the intensity of the southern Indian Ocean heat wave and the lag Nino3.4 index 3 months have a positive correlation, and further detailed analysis of the spatial evolution of the southern Indian Ocean heat wave during the development of El Nino, i.e. the spatial evolution characteristics of the southern Indian Ocean heat wave 6 months before the El Nino peak to 5 months after the El Nino peak; The spatial evolution of the sea surface temperature anomaly in the southern Indian Ocean during the development of El Nino is consistent with the spatial evolution of the southern Indian Ocean heat wave, so the El Nino event can regulate the intensity of the southern Indian Ocean heat wave by affecting the sea surface temperature in the southern Indian Ocean;

[0015] Step five, analyze the energy contribution of the ocean and the atmosphere to the marine heat wave event during the El Nino event process through the mixed layer heat budget;

[0016] The El Niño event obtained in the fourth step can regulate the intensity of the South Indian Ocean Oceanic Heat Wave by affecting the sea surface temperature of the South Indian Ocean, and further combining the SST data, the ECMWF Reanalysis V5 data, the three-dimensional temperature and salinity data, the method of calculating the mixed layer heat budget equation, the energy contribution of the ocean and the atmosphere to the ocean heat wave event in the development process of the El Niño is quantitatively analyzed, and the physical mechanism of the South Indian Ocean Oceanic Heat Wave is explored;

[0017] The time variation of the net surface heat flux is consistent with the trend item of the SST from 6 months ahead of the El Niño peak to 5 months behind the El Niño peak, that is, the net surface heat flux plays a role in the change of the South Indian Ocean Oceanic Heat Wave;

[0018] In the sixth step, whether the South Indian Ocean Oceanic Heat Wave is modulated by the shortwave radiation and latent heat in the ENSO process is determined through regression analysis and synthesis analysis;

[0019] In the fifth step, the net surface heat flux plays a role in the change of the South Indian Ocean Oceanic Heat Wave, and further combining the ECMWF Reanalysis V5 data, the contributions of the four components of the net surface heat flux, latent heat, sensible heat, longwave radiation and shortwave radiation, are analyzed, and it is obtained that the change of the net surface heat flux is affected by the shortwave radiation and the latent heat;

[0020] Since the shortwave radiation is related to the cloud cover, in order to explore the influence of the El Niño event on the cloud cover of the South Indian Ocean, the low cloud cover, the high cloud cover and the overall cloud cover of the ECMWF Reanalysis v5 are regressed to the Nino3.4 index, so as to analyze the influence of the El Niño event on the low cloud, the high cloud and the overall cloud;

[0021] Since the decrease of the mixed layer depth is conducive to the increase of the net surface heat flux, in order to explore the influence of the El Niño event on the mixed layer depth of the South Indian Ocean, the mixed layer depth is calculated by using the three-dimensional temperature and salinity data;

[0022] Since the change of the latent heat is related to the wind field, in order to explore the influence of the El Niño event on the wind field, the sea level pressure and the wind field of the ECMWF Reanalysis v5 are regressed to the Nino3.4 index to analyze the influence of the wind field on the change of the latent heat.

[0023] Preferably, in step two, the frequency, duration, total days and cumulative intensity all show an increasing trend in the whole Southern Indian Ocean in the study area, with 1.01 ± 0.25 times / 10 years, 4.18 ± 1.10 days / 10 years, 27.95 ± 7.04 days / 10 years and 5.70 ± 1.59 °C·days / 10 years, respectively, with higher increasing trends in the western part of the Southern Indian Ocean than in the eastern part: in the western part of the Southern Indian Ocean, the frequency of marine heat waves tends to 2 times per 10 years; the trends of duration, total days and cumulative intensity are similar in spatial distribution, with the highest increasing trend of duration of 7.5 days per 10 years in the western part, the highest increasing trend of total days of 40 days per 10 years, and the highest increasing trend of cumulative intensity of 10 °C per 10 years.

[0024] The frequency, duration, total days, maximum intensity and cumulative intensity of the marine heat wave in the Southern Indian Ocean all reach the 95% confidence test.

[0025] Preferably, in step three, the frequency, duration, total days and cumulative intensity of the marine heat wave in the Southern Indian Ocean all show an increasing trend in the study area, with 1.01 ± 0.25 times / 10 years, 4.18 ± 1.10 days / 10 years, 27.95 ± 7.04 days / 10 years and 5.70 ± 1.59 °C·days / 10 years, respectively, with higher increasing trends in the western part of the Southern Indian Ocean than in the eastern part: in the western part of the Southern Indian Ocean, the frequency of marine heat waves tends to 2 times per 10 years; the trends of duration, total days and cumulative intensity are similar in spatial distribution, with the highest increasing trend of duration of 7.5 days per 10 years in the western part, the highest increasing trend of total days of 40 days per 10 years, and the highest increasing trend of cumulative intensity of 10 °C per 10 years. 3.4 The region with an index greater than 0.5 times the standard deviation is considered an El Niño event; the positive correlation refers to the correlation coefficient between the intensity time series of the marine heat wave in the Southern Indian Ocean and the lagged Nino3.4 index of 3 months, which is up to 0.60, and passes the 95% confidence test.

[0026] Preferably, in step four, in the 4 months before the peak of the El Niño event, a high value of the marine heat wave appears at the location of 20°S, 74°E, with an intensity of more than 0.4 °C; in the 1 month before the peak of the El Niño event, a tilted high-intensity belt of the marine heat wave is formed; after 2 months of the peak of the El Niño event, the high-value region begins to expand, and finally after 4 months of the peak, the intensity and range reach the maximum. Therefore, the El Niño event can affect the intensity of the marine heat wave by affecting the sea surface temperature.

[0027] Preferably, in step five, the mixed layer heat budget equation is:

[0028] In formula (1), represents the trend term of temperature, represents the sea surface heat forcing term, which represents the contribution of the sea surface heat flux to the change of the sea surface temperature, wherein Q net represents the net surface heat flux, which is composed of the sum of longwave radiation, shortwave radiation, sensible heat and latent heat; is the horizontal advection heat flux term, is the vertical entrainment term; the residual R es contains the turbulent mixing at the bottom of the mixed layer, the horizontal mixing and diffusion, and the error of the numerical model; wherein ρ = 1025 kg / m 3 and C p= 3990 J / (kg·℃) are the density and specific heat capacity of seawater, respectively; h is the mixed layer depth, T is the average temperature of the mixed layer, T d is the temperature at the bottom of the mixed layer; V(u, v) represents the velocity vector, u and v are the zonal and meridional flow velocities, respectively; ω e is the vertical entrainment velocity,

[0029] More preferably, in formula (1), the value of T-T d is 0.5℃.

[0030] Preferably, in step six, the analysis of the influence of El Niño events on low clouds, high clouds and total clouds shows that the low clouds in the southern Indian Ocean exhibit a negative anomaly; the composite analysis shows that the spatial evolution of the low cloud anomaly and the high cloud anomaly 6 months ahead of the El Niño event to 5 months after the El Niño event indicates that the decrease in low clouds is conducive to the entry of shortwave radiation into the ocean, promoting ocean warming and the generation of ocean heat waves.

[0031] More preferably, the amount of high clouds in the equatorial Pacific increases, and the high clouds in the southern Indian Ocean also exhibit an increase, with a growth rate of 0.05, and a tilted "northwest-southeast" high cloud increase band appears in the southern Indian Ocean; during the El Niño period, the total clouds in the southern Indian Ocean exhibit an increasing trend consistent with the change in total clouds in the equatorial Pacific; the low clouds decrease, and the shortwave radiation penetrates through the low clouds to enter the ocean surface, which leads to an increase in the ocean surface temperature, and the increase in the sea surface temperature promotes air rising, which is conducive to the formation of high clouds, forming a positive feedback cycle;

[0032] There is a lag relationship between low clouds and ocean heat waves; the decrease in low clouds leads to an increase in the sea surface temperature, and the ocean surface temperature reaches a maximum in the 3rd to 4th month of the El Niño peak period;

[0033] Before and after the peak of the El Niño event, the high clouds in the southern Indian Ocean exhibit a positive anomaly before and after the peak of the El Niño event, and the positive anomaly of the high clouds reaches a maximum in the 3rd to 4th month after the peak of the El Niño event.

[0034] Preferably, in step six, the composite analysis of the mixed layer depth shows that the mixed layer depth in the southern Indian Ocean as a whole exhibits a decreasing trend 6 months ahead of the El Niño event to 5 months after the El Niño event, which is conducive to the warming of the ocean surface, thereby promoting the formation of ocean heat waves; the mixed layer depth in the study area as a whole decreases, and in the 6 months before the peak of the El Niño event, the mixed layer depth on the west side of the study area generally exhibits a negative anomaly; at 80°E in the study area, there is a positive anomaly of the mixed layer depth with a value of 6 meters; as the El Niño develops, this positive anomaly gradually weakens and becomes a negative anomaly 3 months after the peak; the negative anomaly signal of the mixed layer depth at 60°E gradually strengthens and reaches a maximum 1 month after the peak.

[0035] Preferably, in step six, the analysis of the influence of the wind field on the latent heat change shows that during the El Nino event, the South Indian Ocean high pressure is conducive to the rise of the sea surface temperature, while the abnormal northwest wind weakens the southeast wind of the climate state, thereby reducing the water evaporation and the latent heat energy loss of the ocean surface, so as to make the sea surface temperature increase and the heat wave form; the composite analysis of the 850 hPa geopotential height anomaly and the wind speed anomaly in the South Indian Ocean 6 months before the El Nino event to 5 months after the El Nino event shows that the negative anomaly of the geopotential height in the southwest position of the research area and the abnormal northwest wind reach the strongest in the 4 months of the El Nino peak period, which inhibits the sea water evaporation, weakens the ocean latent heat energy loss, and is conducive to the rise of the sea surface temperature and the generation of the ocean heat wave.

[0036] When the sea surface temperature in the eastern equatorial Pacific Ocean appears a positive anomaly, a “northwest-southeast” direction inclined sea surface temperature positive anomaly region appears in the South Indian Ocean, which corresponds to the distribution of high clouds.

[0037] The method for researching the interannual variation law of the South Indian Ocean ocean heat wave of the present application solves the problem that the interannual variation of the South Indian Ocean (60°E-90°W, 15°S-25°S) ocean heat wave and its dynamic mechanism are unclear, and has the following advantages:

[0038] 1. The occurrence of the South Indian Ocean ocean heat wave is significantly affected by the El Nino-Southern Oscillation (ENSO) event, and there is obvious interannual variation. The intensity of the ocean heat wave in the sea area is significantly positively correlated with the El Nino peak period 3-4 months later.

[0039] 2. The mixed layer heat budget analysis shows that the energy of the South Indian Ocean ocean heat wave mainly comes from the net surface heat flux, in which the shortwave radiation and the latent heat play a major role.

[0040] 3. In the 3-4 months after the El Nino peak period, the South Indian Ocean is controlled by a positive geopotential height anomaly, and the sinking air flow leads to a decrease in low clouds, which is conducive to the shortwave radiation entering the ocean surface. At the same time, the shallow mixed layer makes the ocean surface more easily warm, thereby promoting the formation of the ocean heat wave.

[0041] 4. The abnormal northwest wind suppresses the background southeast wind, reduces the evaporation and the ocean latent heat energy loss, so as to make the sea surface temperature rise and the ocean heat wave increase. DETAILED DESCRIPTION

[0042] Fig. 1 is the spatial distribution of six ocean heat wave indicators in the South Indian Ocean during 1982-2021.

[0043] Fig. 2 is the spatial distribution of the trend of six ocean heat wave indicators in the South Indian Ocean during 1982-2021.

[0044] Fig. 3 is a time series distribution of the weighted average of the ocean heat wave characteristic index in the study area of the southern Indian Ocean from 1982 to 2021.

[0045] Fig. 4 is 3.4 Anomalies and monthly ocean heat wave intensity and lead-lag correlation.

[0046] Fig. 5 is a synthetic evolution of the ocean heat wave intensity in the southern Indian Ocean before and after the peak of the El Niño event.

[0047] Fig. 6 is a synthetic evolution of the ocean surface temperature anomaly in the southern Indian Ocean before and after the peak of the El Niño event.

[0048] Fig. 7 is a mixed layer heat budget El Niño event evolution and heat flux evolution before and after the El Niño event.

[0049] Fig. 8 is a regression spatial distribution field of low clouds, high clouds and total clouds from 1982 to 2021.

[0050] Fig. 9 is a synthetic evolution of low cloud anomalies in the southern Indian Ocean before and after the peak of the El Niño event.

[0051] Fig. 10 is a synthetic evolution of high cloud anomalies in the southern Indian Ocean before and after the peak of the El Niño event.

[0052] Fig. 11 is a synthetic evolution of mixed layer depth anomalies in the southern Indian Ocean before and after the peak of the El Niño event.

[0053] Fig. 12 is a regression spatial distribution field and sea level pressure and wind speed.

[0054] Fig. 13 is a synthetic evolution of 850 hPa geopotential height anomalies and wind speed anomalies in the southern Indian Ocean before and after the peak of the El Niño event.

[0055] Fig. 14 is a method flowchart for studying the interannual variation of the ocean heat wave in the southern Indian Ocean. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0057] Embodiment 1

[0058] A method for studying the interannual variation of the ocean heat wave in the southern Indian Ocean, the method comprising:

[0059] Step 1, obtaining SST data, OISST data set, ECMWF Reanalysis V5, three-dimensional temperature and salinity data, 3.4 Exponential data

[0060] The SST (Sea Surface Temperature) observation data is derived from the Optimal Interpolation Sea Surface Temperature (OISST) V2.1 dataset of the National Oceanic and Atmospheric Administration (NOAA), and the selected data time span is from January 1, 1982 to December 31, 2021, with a spatial resolution of 0.25°x0.25°. The OISST dataset is based on the NOAA Advanced Very High Resolution Radiometer multi-channel SST product, and is corrected by measured data such as buoys and ships, and then processed by optimal interpolation method to generate a comprehensive product. Because this dataset has high spatial resolution and good continuity, it is widely used in the study of marine heat waves.

[0061] In order to study the influence of atmospheric processes on marine heat waves, the present invention uses the global climate fifth generation atmospheric reanalysis data (ECMWF Reanalysis V5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). This data includes monthly sea surface heat flux (positive downward), sea level pressure, 10-meter wind speed, low cloud cover, high cloud cover, total cloud cover, and sea surface temperature data.

[0062] The data of sea surface current field is derived from the global ocean-sea ice reanalysis data product ORAS5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). This data product has a horizontal resolution of 0.25° and a vertical distribution of 75 layers.

[0063] The three-dimensional temperature and salinity data come from the 4th edition of the EN series dataset of the UK Meteorological Bureau Hadley Center. This dataset uses the optimal interpolation algorithm to provide three-dimensional temperature and salinity data based on available profiles and objective analysis, and the present invention uses the temperature and salinity data in this dataset to calculate the mixed layer depth. The horizontal resolution of EN4.2.2 version is 1°x1°, there are 42 layers in the vertical direction, the depth range is 5-5500 meters, and the time span covered is from 1901 to 2022.

[0064] The present application adopts the Nino 3.4 region (3.4) index in the Physical Sciences Laboratory Global Climate Observing System provided by the National Oceanic and Atmospheric Administration (NOAA). 3.4) index in the Physical Sciences Laboratory Global Climate Observing System provided by the National Oceanic and Atmospheric Administration (NOAA).

[0065] The present application determines marine heatwaves based on daily sea surface temperature data. Marine heatwaves are defined as "an unseasonal prolonged event of abnormally warm water occurring at a particular location". Specifically, "unseasonal" means that a marine heatwave is an identifiable event with a clear start and end date, "prolonged" means at least 5 days, and "abnormally warm" means that the sea surface temperature is higher than a certain climate threshold. Quantitatively, a marine heatwave is at least 5 consecutive days of daily sea surface temperature higher than the 90th percentile of the 30-year climate mean; at least 2 days apart between two marine heatwave events, otherwise it is considered to be the same marine heatwave, and the specific characteristic indicators are shown in Table 1. In addition, the determination of the marine heatwave threshold is to calculate the 90th percentile threshold for each day by using all the daily temperature values of all years within 11 days centered on the day, and then to perform 31-day smoothing. This method can ensure sufficient sample size and the threshold will change with the season.

[0066] Table 1 Marine heatwave indicator definition

[0067] Note: D i is the duration of the i-th occurrence, N is the number of occurrences, T ij and SSTj and the corresponding threshold value on the j-th day during the marine heatwave.

[0068] Step two, according to the obtained OISST data to explore the spatial distribution characteristics and linear trend characteristics of marine heatwaves in the southern Indian Ocean

[0069] As shown in FIG. 1, the spatial distribution of six marine heatwave indicators in the southern Indian Ocean from 1982 to 2021, wherein (a)-(f) represent heatwave frequency, duration, total days, average intensity, maximum intensity and cumulative intensity, respectively. The values shown are annual averages, units are times, days, °C and (°C days). As can be seen from FIG. 1, significant marine heatwave phenomena are observed in the southern Indian Ocean, and the marine heatwave frequency in the southern Indian Ocean is relatively uniform in space, while the high value regions of the duration and total days are located on the west side of the southern Indian Ocean. The spatial distribution of average intensity and maximum intensity is relatively low on the west side of the southern Indian Ocean, while the cumulative intensity is high on the east side of Madagascar.

[0070] The present application selects a region with a large duration and total number of days, i.e. 15°S to 25°S, 60°E to 90°E (the region marked by a black box in FIG. 1) as the research region of the South Indian Ocean heat wave. In this region, the average intensity of the marine heat wave is 1.20°C, the maximum intensity is 1.50°C, the cumulative intensity is 18.06°C / day, the duration (Dur) is 14.12 days, the frequency is 2.74 times, and the total number of days is 45.22 days. In summary of the above six indicators, the marine heat wave in this region is characterized by a large number of days, long duration, high intensity, and high frequency.

[0071] As shown in FIG. 2, the spatial distribution of the trends of the six marine heat wave indicators in the South Indian Ocean during 1982-2021, wherein (a)-(f) represent the heat wave frequency, duration, total number of days, average intensity, maximum intensity, and cumulative intensity, respectively, and the black dots represent the test by 95% significance level (unit: / decade). As shown in FIG. 2, the spatial distribution trends of the six marine heat wave characteristic indicators in FIG. 1 are shown. As can be seen from FIG. 2(a) to FIG. 2(f), the frequency, duration, total number of days, and cumulative intensity of the marine heat wave all show a clear increasing trend in the entire South Indian Ocean. In particular, the growth trend in the western region is significantly higher than that in the eastern region. In the west of the South Indian Ocean, the frequency trend of the marine heat wave reaches 2 times per 10 years. The trends of the duration, total number of days, and cumulative intensity are similar in spatial distribution, with the highest growth trend of the duration reaching 7.5 days per 10 years in the western region, the growth trend of the total number of days reaching 40 days per 10 years, and the growth trend of the cumulative intensity reaching 10°C per 10 years. As can be seen from FIG. 2(d) and FIG. 2(e), the average intensity and maximum intensity of the marine heat wave show a negative trend in 15°S-10°S, especially in the northwest of Madagascar Island. However, the average intensity and maximum intensity also show an increasing trend in the entire study area (15°S-25°S, 60°E-90°E). In summary, in the study area of the present application, all six characteristic indicators of the marine heat wave in the South Indian Ocean show an increasing positive trend, and this trend may further strengthen with global warming.

[0072] The Mann-Kendall method is a statistical method commonly used to analyze trends in time series data. It can detect trends in data without making assumptions about the distribution of data, so it is widely used in various application scenarios. The basic principle of this method is to test the trend in the data based on the rank of the observed data points. By calculating the rank and using the rank to calculate the Mann-Kendall test statistic, the trend in the data and its significance level can be determined. This method is widely used in the fields of climate change, environmental science, hydrology, etc.

[0073] As shown in FIG. 3, the time series distribution of the weighted average of the characteristic indicators of the marine heat wave in the research area of the southern Indian Ocean from 1982 to 2021, wherein (a) is the frequency (unit: times / ten years), (b) is the duration (unit: days / ten years), (c) is the total days (unit: days / ten years), (d) is the average intensity (unit: ℃ / times / ten years), (e) is the maximum intensity (unit: ℃ / times / ten years), and (f) is the cumulative intensity (unit: ℃ / ten years). The red dotted line represents the linear trend, and the significance level of the p value is calculated by the Mann-Kendall trend test. As can be seen from FIG. 3, the characteristic indicators of the weighted average of the marine heat wave in the southern Indian Ocean from 1982 to 2021 show an obvious increasing trend. The significant high values of the frequency, duration and total days are distributed in 2019, while the high values of the average intensity and maximum intensity are located in 1997. As can be seen from (a) of FIG. 3, the growth rate of the frequency of marine heat waves is 1.01±0.25 times / 10 years, the correlation coefficient (R) is 0.74, and there are peaks in 1982, 1988, 1991, 1994, 1998, 2002, 2005, 2010, 2015 and 2019, and the frequency in 2019 is more than 6 times. As can be seen from (b) of FIG. 3, the growth rate of the duration is 4.18±1.10 days / 10 years, and there are three obvious peaks in 1997, 2016 and 2019. As can be seen from (c) of FIG. 3, the growth rate of the total days of the regional annual average is more significant, which is 27.95±7.04 days / 10 years, and there are obvious peaks in 2016 and 2019. As can be seen from (d)-(e) of FIG. 3, the average intensity and the maximum intensity of the regional annual average distribution are similar, reaching the maximum value in 1997, and there are higher values in 2016 and 2019, and the growth rates are 0.01±0.01℃ / 10 years and 0.04±0.02℃ / 10 years, respectively. As can be seen from (f) of FIG. 3, the cumulative intensity has three obvious peaks in 1997, 2016 and 2019.

[0074] Step three, calculating 3.4 Index, by 3.4 Index and the intensity of the marine heat wave in the southern Indian Ocean were analyzed by lead-lag correlation analysis

[0075] Previous studies have shown that ENSO can have an impact on the sea surface temperature in the Indian Ocean region. In view of this fact, the present application further considers whether ENSO will also affect the marine heat wave in the Indian Ocean region. Therefore, the present application will further explore the correlation between ENSO and the marine heat wave in the Indian Ocean in order to gain a deeper understanding. The present application plots 3.4 Index vs. time series of ocean heat wave intensity in the study region. The present invention shows that 3.4 Regions where the index is greater than 0.5 standard deviations are considered El Niño events.

[0076] As shown in FIG. 4, 3.4 Anomalies vs. lagged correlation of monthly ocean heat wave intensity, where (a) is 3.4 Anomalies (black line) and monthly ocean heat wave intensity in the South Indian Ocean (trend removed, magenta line). Red represents 3.4 Regions where the index is greater than 0.5 standard deviations, blue represents 3.4 Regions where the index is less than negative 0.5 standard deviations. (b) 3.4 Anomalies vs. lead-lagged correlation of monthly ocean heat wave intensity. Positive values represent that the heat wave intensity lags the 3.4 Index.

[0077] From FIG. 4(a), it can be observed that the time series of ocean heat wave intensity in the South Indian Ocean calculated based on OISST and the Nino3.4 index have consistent temporal variation, 3.4 Index anomalies and ocean heat wave intensity have a good corresponding relationship. When 3.4 appears a positive anomaly peak (i.e., the red region), the intensity of the heat wave will also peak. From FIG. 4(b), it can be observed that there is a certain lag response between the ocean heat wave intensity and 3.4 anomalies. In the study region, the heat wave intensity lags the 3.4 index by 3 months, the correlation coefficient can reach 0.60 at the maximum, and it passes the 95% confidence test, and there is a significant positive correlation between the two, i.e., in the lag El Niño year peak period, the ocean heat wave intensity in the South Indian Ocean region will be enhanced.

[0078] Step four, analysis of the spatial evolution of the South Indian Ocean heat wave and ocean surface temperature anomaly during the El Niño event

[0079] Based on FIG. 4, the lead-lag correlation analysis shows that the time series of ocean heat wave intensity in the South Indian Ocean and the lag Nino3.4 index 3 months have a positive correlation, and further detailed analysis of the spatial evolution of the South Indian Ocean ocean heat wave during the El Niño development process, i.e., the spatial evolution characteristics of the South Indian Ocean heat wave from 6 months ahead of the El Niño peak period to 5 months lag behind the El Niño peak period. The present invention synthesizes the ocean heat wave intensity in the South Indian Ocean during the El Niño period, and the evolution process of its spatial distribution is shown in FIG. 5.

[0080] As shown in Figure 5, the composite evolution of the ocean heat wave intensity in the southern Indian Ocean (color, unit: ℃) before and after the peak of the El Nino event, and the black box line represents the research area. As shown in Figure 5(c), in the first four months before the peak of the El Nino event, an ocean heat wave intensity value appeared at the position of 20°S, 74°E, and the intensity reached above 0.4℃. As shown in Figure 5(f), as the El Nino develops, the ocean heat wave intensity value slowly moves westward, and the surrounding heat wave intensity continuously increases. In the last month before the peak of the El Nino, a high-intensity belt is formed. As shown in Figures 5(i), 5(k) and 5(m), two months after the peak of the El Nino, the high-value area begins to expand, and finally four months after the peak, the intensity and range reach the maximum.

[0081] As shown in Figure 6, the composite evolution of the sea surface temperature anomaly in the southern Indian Ocean before and after the peak of the El Nino event (unit: ℃). The black box line represents the research area. Since the ocean heat wave is an extreme warming event of seawater in the ocean, the present application explores the composite evolution of the sea surface temperature anomaly to understand its relationship with the ocean heat wave. As shown in Figure 6, in the first six months before the peak and the last five months after the peak, there is a clear positive sea surface temperature anomaly in the west of the southern Indian Ocean, and the intensity of the anomaly continues to increase, and the range also increases. In addition, in the Mozambique Channel, there is a negative sea surface temperature anomaly in the first six months before the peak and the last two months after the peak, and the negative anomaly gradually decreases over time and finally disappears. In the third to fourth months after the peak of the El Nino, the positive sea surface temperature anomaly in the research area reaches the maximum value, and has a good correspondence with the intensity of the ocean heat wave. Therefore, ENSO can affect the sea surface temperature and in turn affect the intensity of the ocean heat wave, and by exploring the physical mechanism of the sea surface temperature anomaly, the present application can clarify the physical mechanism of the ocean heat wave in the research area.

[0082] Step five, analyzing the energy contribution of the ocean and the atmosphere to the ocean heat wave event during the El Nino event through mixed layer heat budget

[0083] In step four, it is found that the El Nino event can regulate the intensity of the ocean heat wave in the southern Indian Ocean by affecting the sea surface temperature in the southern Indian Ocean. Further combined with the SST data, the ECMWF Reanalysis V5 data, the three-dimensional temperature and salinity data, and the method of calculating the mixed layer heat budget equation, the energy contribution of the ocean and the atmosphere to the ocean heat wave event during the development of the El Nino is quantitatively analyzed, and the physical mechanism of the ocean heat wave in the southern Indian Ocean is explored.

[0084] The present application performs a mixed layer heat budget analysis. The mixed layer temperature heat budget equation can be expressed as:

[0085] In formula (1), represents the trend item of temperature, Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. net Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. es Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. 2 Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. p Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. d Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. d Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. e Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat.

[0086] Figure 7 shows the evolution of the mixed layer heat budget during El Niño events and the evolution of heat fluxes before and after El Niño events, where (a) is the evolution of the mixed layer heat budget during El Niño events (unit: ℃ / month). It includes the temperature trend term (black line), the net surface heat flux term (red line), the ocean advection term (green line), the vertical entrainment and diffusion term (blue line) and the residual term (magenta line). (b) is the evolution of heat fluxes before and after El Niño events (unit: W / m 2 ). It includes the heat flux term (black line), latent heat (cyan line), sensible heat (green line), longwave radiation (blue line) and shortwave radiation (red line). All terms are removed from the long-term trend. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat.

[0087] As shown by the results of (a) of Figure 7, before and after the El Niño event, because the residual term may include the turbulent mixing at the bottom, the mixing in the horizontal direction and the error between the data, and the residual term is opposite to the trend of the temperature change term, the increase in the mixed layer temperature is mainly affected by the net surface heat flux, and the advection term and the vertical entrainment term have little contribution to the change in the temperature trend line. In addition, because the temperature trend term changes from positive to negative at 3-4 months during the El Niño peak, the temperature of the mixed layer reaches the maximum, which also accords with the case that the intensity of the ocean heat wave lags behind the SST by 3-4 months in Figure 4. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat. Qnet represents the net surface heat flux, which is the sum of longwave radiation, shortwave radiation, sensible heat and latent heat.

[0088] Step six, determining whether the shortwave radiation and latent heat are modulated by ENSO processes to affect the South Indian Ocean heatwaves through regression analysis and composite analysis

[0089] As the previous results show that the net surface heat flux term plays a major role in the peak of the South Indian Ocean marine heatwaves. The present invention further analyzes the contribution of the four components of the net surface heat flux, latent heat, sensible heat, longwave radiation, and shortwave radiation, by combining the ECMWF Reanalysis V5 data.

[0090] As can be observed from Figure 7 (b), the accumulated heat flux reaches a maximum value within 3-4 months after the peak of El Niño. It is worth noting that the change in heat flux is mainly controlled by shortwave radiation, while latent heat also has a certain influence on heat flux. In addition, the contribution of sensible heat and longwave radiation to heat flux is relatively small.

[0091] Therefore, in summary, the energy of the South Indian Ocean marine heatwaves mainly comes from the net surface heat flux, which is mainly controlled by shortwave radiation and latent heat.

[0092] From the linear trend, with global warming, all six heatwave characteristic indicators show a gradually increasing trend. In addition, the present invention observes that the high-value years of certain characteristic indicators coincide with ENSO (El Niño-Southern Oscillation) years (for example, 1997-1998). Therefore, the present invention speculates that ENSO may have some relevance to the South Indian Ocean marine heatwaves. In order to further verify this hypothesis, the present invention plans to carry out related research work.

[0093] As the change in local cloud coverage can affect the heat flux between the atmosphere and the ocean, thereby affecting the sea surface temperature. In order to explore the impact of El Niño events on cloud coverage in the South Indian Ocean, thereby revealing the physical mechanism of the South Indian Ocean marine heatwaves, the present invention carries out regression analysis on three types of cloud coverage (low cloud, high cloud, and total cloud) from 1982 to 2021, and the regression spatial distribution fields of the three types of cloud coverage to 3.4 are obtained in Figure 8.

[0094] As shown in Figure 8, the regression spatial distribution fields of low cloud, high cloud, and total cloud from 1982 to 2021, where (a) is the regression spatial distribution field of low cloud (low cloud cover) to 3.4 from 1982 to 2021. The box represents the study area. (b) is the same as (a), but for high cloud (high cloud cover). (c) is the same as (a), but for total cloud (total cloud cover). As can be known from Figure 8 (a), the equatorial Pacific (i.e. 3.4 When the sea surface temperature increases, the low cloud amount decreases, and the low cloud amount in the southern Indian Ocean also shows a significant negative anomaly. The decrease in low clouds helps more shortwave radiation generated by the sun to enter the ocean surface, heating the sea surface, thereby facilitating the generation of ocean heat waves. Figure 8 (b) shows the spatial distribution of the high cloud regression to 3.4 When the sea surface temperature increases, the low cloud amount decreases, and the low cloud amount in the southern Indian Ocean also shows a significant negative anomaly. The decrease in low clouds helps more shortwave radiation generated by the sun to enter the ocean surface, heating the sea surface, thereby facilitating the generation of ocean heat waves. Figure 8 (b) shows the spatial distribution of the high cloud regression to

[0095] To further analyze how the low cloud amount in the southern Indian Ocean affects the ocean heat wave during the El Niño period, the inventors synthesized the low cloud cover anomaly during the El Niño period from 1982 to 2021 and showed its evolution process in Figure 9.

[0096] As shown in Figure 9, the evolution of the low cloud anomaly in the southern Indian Ocean before and after the peak of the El Niño event, where the black box line represents the study area. As can be seen from Figures 9 (a) to (g), during the first 6 months before the peak of the El Niño period to the peak, the low cloud in the entire study area showed a significant negative anomaly. As can be seen from Figures 9 (h) to (i), during the decline of the El Niño period, the low cloud in the western part of the study area gradually increased, but overall, the total amount decreased. The decrease in low clouds helps more shortwave radiation to reach the ocean surface, thereby heating the ocean. During the development of the El Niño, due to the large-scale decrease in low clouds, a large amount of shortwave radiation enters the ocean, promoting the warming of the ocean. In the 3-4 months after the peak of the El Niño, the sea surface temperature reaches the highest.

[0097] As shown in Figure 10, the evolution of the high cloud anomaly in the southern Indian Ocean before and after the peak of the El Niño event, where the black box line represents the study area. As can be seen from Figures 10 (j) and (k), the high cloud shows a significant positive anomaly before and after the peak of the El Niño, and the positive anomaly of the high cloud reaches the maximum value in the 3-4 months after the peak of the El Niño.

[0098] From the comprehensive analysis of Fig. 9 and Fig. 10, it can be found that there is a lag relationship between low clouds and ocean heat waves; the reduction of low clouds will lead to the gradual increase of sea surface temperature, and reach the maximum value in the 3-4 months of the peak of El Nino. However, the response of high clouds to sea surface temperature is relatively fast: when the sea surface temperature is high, high clouds will heat the atmosphere, making the upward airflow strengthen, thereby facilitating the formation of high clouds.

[0099] As shown in Fig. 11, the composite evolution of the mixed layer depth anomaly in the southern Indian Ocean before and after the peak of the El Nino event, wherein the unit is m, and the black box line represents the research area. As shown in Fig. 11, in the first 6 months of the peak of the El Nino, the mixed layer depth on the west side of the research area generally presents a negative anomaly. On the east side of the research area, especially at 80°E, there is a significant positive anomaly of the mixed layer depth, with a value of 6 meters. With the development of El Nino, this positive anomaly gradually weakens and even becomes negative after 3 months of the peak. In addition, as shown in Fig. 11 (h), the negative anomaly signal of the mixed layer depth at 60°E gradually strengthens and reaches the maximum value after 1 month of the peak. As shown in Fig. 11 (j), a positive anomaly signal appears at 20°S, 75°E after 3 months of the peak and continuously strengthens.

[0100] In summary, it can be found that in the 2-3 months of the lag of the peak of the El Nino, the mixed layer depth in the research area as a whole presents a decreasing trend, which is conducive to the warming of the ocean surface. Due to the increase of the net surface heat flux term and the decrease of the mixed layer depth h, the ocean is further heated, thereby promoting the formation of the ocean heat wave. p h, Q net of the ocean.

[0101] To explore how the sea surface pressure and wind affect the local ocean heat wave by affecting the sea surface temperature, the present application draws the regression graph of sea temperature, sea level pressure and wind direction.

[0102] As shown in Fig. 12, the regression spatial distribution field and sea level pressure and wind speed, wherein (a) the regression spatial distribution field of sea surface temperature and 3.4 index (unit: ℃) from 1982 to 2021, and the black box line represents the research area; (b) the regression spatial distribution field of sea level pressure (unit: hPa) and wind speed (unit: m / s) and 3.4 index from 1982 to 2021. As shown in Fig. 12 (a), the sea surface temperature (SST) from 1982 to 2021 is regressed to 3.4 index. 3.4 index. As shown in Fig. 12 (b), the sea level pressure (SLP) and wind speed (WS) from 1982 to 2021 are regressed to 3.4 index. 3.4 index. As shown in Fig. 12 (b), the sea level pressure (SLP) and wind speed (WS) from 1982 to 2021 are regressed to 3.4 index. 3.4 Exponentiation of the spatial field. It can be observed from the figure that when the eastern equatorial Pacific sea surface temperature (SST) has a significant positive anomaly, a "northwest-southeast" direction tilted positive SST anomaly region appears in the southern Indian Ocean, which corresponds to the distribution of high clouds. Combining the previous research, it can be concluded that the positive SST anomaly in the southern Indian Ocean is regulated by El Nino. As can be seen from Fig. 12(b), the sea surface temperature anomaly in the study area is mainly controlled by the positive sea level pressure. The high pressure is conducive to the rise of sea surface temperature, because the sinking air flow in the high pressure area can inhibit the wind speed on the ocean surface, reduce the heat loss, and thus make the sea surface temperature rise. In addition, the area is controlled by the abnormal northwest wind, while the study area is affected by the climatological southeast wind. The appearance of the abnormal northwest wind reduces the climatological southeast wind, thereby reducing water evaporation and ocean surface latent heat energy loss, thus promoting the increase of sea surface temperature.

[0103] As shown in Fig. 13, the composite evolution of the 850 hPa geopotential height anomaly and wind speed anomaly in the southern Indian Ocean before and after the peak of the El Nino event, wherein the black frame line represents the study area. As can be seen from Fig. 13, there is a negative anomaly signal of geopotential height in the southwest of the study area, and a positive anomaly signal of geopotential height in the northeast, which is located at the junction of the two signals, and thus is controlled by the abnormal northwest wind. During the El Nino period, the positive signal is strengthened, the negative signal is weakened, the gradient between the two signals is increased, and it can be seen from Fig. 13(k) that it reaches the strongest after 4 months of the El Nino peak. Therefore, the abnormal northwest wind is strengthened, which inhibits the sea water evaporation and the ocean latent heat energy loss, and is conducive to the rise of sea surface temperature, and the heat wave is generated.

[0104] Although the content of the present application has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as a limitation of the present application. After reading the above content, various modifications and alternatives of the present application will be apparent to those skilled in the art. Therefore, the protection scope of the present application should be defined by the appended claims.

Claims

1. A method for studying the interannual variability of the marine heat wave in the southern Indian Ocean, characterized in that, The method comprises: Step one, obtain SST data, OISST data set, ECMWF Reanalysis V5, three-dimensional temperature and salinity data, Exponential data; Step two, according to the obtained OISST data, the spatial distribution characteristics and linear variation trend characteristics of the marine heat wave in the southern Indian Ocean are explored; Select 15°S to 25°S, 60°E to 90°E as the research area of the southern Indian Ocean heat wave, there is a marine heat wave in the area, the average intensity of the marine heat wave is 1.20℃, the maximum intensity is 1.50℃, the cumulative intensity is 18.06℃ / day, the duration is 14.12 days, the frequency is 2.74 times, and the total number of days is 45.22 days; Step three, calculating from the acquired OISST data Exponent, by The relationship between the intensity of the southern Indian Ocean marine heat wave and the ENSO process is analyzed by leading and lagging correlation analysis between the index and the intensity of the southern Indian Ocean heat wave; The ENSO is El Nino-Southern Oscillation; The time series of the intensity of the southern Indian Ocean marine heat wave calculated based on OISST and the Nino3.4 index have consistent time variation rules, and the leading and lagging correlation analysis shows that the time series of the intensity of the southern Indian Ocean marine heat wave and the lagging Nino3.4 index 3 months are positively correlated; Step four, analyze the spatial evolution of the southern Indian Ocean heat wave and the marine surface temperature anomaly during the El Nino event process; The leading and lagging correlation analysis in step three shows that the time series of the intensity of the southern Indian Ocean marine heat wave and the lagging Nino3.4 index 3 months are positively correlated, and the spatial evolution of the southern Indian Ocean marine heat wave during the development of El Nino is further analyzed, that is, the spatial evolution characteristics of the southern Indian Ocean heat wave from 6 months before the El Nino peak to 5 months after the El Nino peak; The spatial evolution of the southern Indian Ocean marine heat wave is consistent with the spatial evolution of the southern Indian Ocean marine heat wave during the development of El Nino, so the El Nino event can regulate the intensity of the southern Indian Ocean marine heat wave by affecting the sea surface temperature of the southern Indian Ocean; Step five, analyze the energy contribution of the ocean and the atmosphere to the marine heat wave event during the El Nino event process through the mixed layer heat budget; In step four, the El Nino event can regulate the intensity of the southern Indian Ocean marine heat wave by affecting the sea surface temperature of the southern Indian Ocean, further combined with SST data, ECMWF Reanalysis V5 data, three-dimensional temperature and salinity data, the method of mixed layer heat budget equation calculation is used to quantitatively analyze the energy contribution of the ocean and the atmosphere to the marine heat wave event during the development of El Nino, and the physical mechanism of the southern Indian Ocean marine heat wave is explored; From 6 months before the El Nino peak to 5 months after the El Nino peak, the time variation of the net surface heat flux is consistent with the trend of SST, that is, the net surface heat flux plays a role in the change of the southern Indian Ocean marine heat wave; Step six, determine whether the southern Indian Ocean heat wave is affected by shortwave radiation and latent heat by regression analysis and synthesis analysis; In step five, the net surface heat flux plays a role in the change of the southern Indian Ocean marine heat wave, further combined with ECMWF Reanalysis V5 data to analyze the contribution of the four components of the net surface heat flux, latent heat, sensible heat, longwave radiation and shortwave radiation, it is found that the change of the net surface heat flux is affected by shortwave radiation and latent heat. Since the shortwave radiation is related to the cloud cover, in order to explore the influence of El Niño event on the cloud cover in the southern Indian Ocean, the low cloud cover, high cloud cover and total cloud cover of ECMWF Reanalysis v5 are regressed to the Nino3.4 index to analyze the influence of El Niño event on the low cloud, high cloud and total cloud; Since the decrease of the mixed layer depth is conducive to the increase of the net surface heat flux, in order to explore the influence of El Niño event on the mixed layer depth in the southern Indian Ocean, the mixed layer depth is calculated by using the three-dimensional temperature and salinity data; Since the latent heat change is related to the wind field, in order to explore the influence of El Niño event on the wind field, the sea level pressure and wind field of ECMWF Reanalysis v5 are regressed to the Nino3.4 index to analyze the influence of the wind field on the latent heat change.

2. The method of claim 1, wherein, In step two, in the study area, the frequency, duration, total days and cumulative intensity all show an increase in the whole southern Indian Ocean, which are 1.01±0.25 times / 10 years, 4.18±1.10 days / 10 years, 27.95±7.04 days / 10 years and 5.70±1.59℃·days / 10 years respectively, and the increase in the western region of the southern Indian Ocean is higher than that in the eastern region; In the western region of the southern Indian Ocean, the trend of the frequency of marine heat waves reaches 2 times per 10 years; The trends of the duration, total days and cumulative intensity are similar in spatial distribution, and the increase trend of the duration in the western region is up to 7.5 days per 10 years, the increase trend of the total days is up to 40 days per 10 years, and the increase trend of the cumulative intensity is up to 10℃ per 10 years.

3. The method of claim 1, wherein, In step three, the Regions with an index greater than 0.5 times the standard deviation are considered El Niño events; the positive correlation refers to the maximum correlation coefficient of 0.60 between the Indian Ocean Marine Heat Wave Intensity time series and the lagged Nino3.4 index 3 months, which passed the 95% confidence test.

4. The method of claim 1, wherein, In step four, in the first 4 months of the peak of El Niño, a high value of marine heat wave appears at the position of 20°S, 74°E, and the intensity reaches more than 0.4℃; in the last month of the El Niño period, a tilted high-intensity band of marine heat wave is formed; after 2 months of the El Niño period, the high-value area begins to expand, and finally after 4 months of the El Niño period, the intensity and range reach the maximum.

5. The method of claim 1, wherein, In step five, the mixed layer heat budget equation is: In formula (1), a trend term indicative of temperature, represents the sea surface heat flux contribution to the sea surface temperature change, where Q net represents the net surface heat flux, which is the sum of the longwave radiation, shortwave radiation, sensible heat, and latent heat; For the horizontal advection heat flux term, R is the residual term; and es The turbulent mixing at the bottom of the mixed layer, the horizontal mixing and diffusion, and the error of the numerical model are included in R; where p = 1025 kg / m 3 and C p = 3990 J / (kg °C) are the seawater density and the seawater specific heat capacity, respectively; h is the depth of the mixed layer, T is the average temperature of the mixed layer, T d is the temperature at the bottom of the mixed layer; V(u, v) represents the velocity vector, u and v are the zonal and meridional current velocities, respectively; ω e is the vertical entrainment velocity, 6. The method of claim 5, wherein, In equation (1), T-T d has a value of 0.5°C.

7. The method of claim 1, wherein, In step six, the analysis of the influence of El Niño event on the low cloud, high cloud and total cloud shows that the low cloud in the southern Indian Ocean presents a negative anomaly; the composite analysis shows that the spatial evolution of the low cloud anomaly and high cloud anomaly from 6 months ahead of the El Niño event to 5 months behind the El Niño event shows that the decrease of the low cloud is conducive to the shortwave radiation entering the ocean, promoting the ocean warming and the generation of marine heat waves.

8. The method of claim 7, wherein, In step six, the high cloud in the equatorial Pacific Ocean increases, and the high cloud in the southern Indian Ocean also increases, with an increase of 0.05, and a tilted "northwest-southeast” high cloud increase band appears in the southern Indian Ocean; during the El Niño period, the total cloud in the southern Indian Ocean presents an increasing trend, which is consistent with the change of the total cloud in the equatorial Pacific Ocean; the decrease of the low cloud causes the shortwave radiation to enter the ocean surface, which leads to the increase of the sea surface temperature, and the increase of the sea surface temperature promotes the air to rise, which is conducive to the formation of high cloud, forming a positive feedback cycle; There is a lag relationship between the low cloud and the marine heat wave; the decrease of the low cloud leads to the increase of the sea surface temperature, and the sea surface temperature reaches the highest in the 3rd-4th month of the El Niño period; The high cloud in the southern Indian Ocean showed positive anomalies before and after the El Niño peak, and the positive anomaly of high cloud reached the maximum in the third to fourth months after the El Niño peak.

9. The method of claim 1, wherein, In step six, the composite analysis of the mixed layer depth showed that the mixed layer depth in the southern Indian Ocean decreased from 6 months before the El Niño event to 5 months after the El Niño event, which was conducive to the warming of the ocean surface and thus promoted the formation of the ocean heat wave; the mixed layer depth in the study area decreased as a whole, and the mixed layer depth on the west side of the study area generally showed a negative anomaly 6 months before the El Niño peak; at 80°E in the study area, there was a positive anomaly of the mixed layer depth, with a value of 6 meters; as the El Niño developed, this positive anomaly gradually weakened and became negative 3 months after the El Niño peak; the negative anomaly of the mixed layer depth at 60°E gradually increased and reached the maximum 1 month after the El Niño peak.

10. The method of claim 1, wherein, In step six, the analysis of the influence of the wind field on the latent heat change showed that the high pressure in the southern Indian Ocean during the El Niño event was conducive to the warming of the sea surface temperature, and the anomalous weakening of the southeast wind in the climate state reduced water evaporation and the loss of latent heat energy on the ocean surface, thus increasing the sea surface temperature and forming a heat wave; the composite analysis of the 850 hPa geopotential height anomaly and wind speed anomaly in the southern Indian Ocean from 6 months before the El Niño event to 5 months after the El Niño event showed that the negative anomaly of geopotential height and the anomalous northwest wind in the southwest of the study area reached the strongest 4 months after the El Niño peak, which suppressed sea water evaporation, reduced the loss of ocean latent heat energy, and was conducive to the warming of the sea surface temperature and the formation of the ocean heat wave.

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