A method to study the interannual variation of marine heat waves in the South Indian Ocean

Abstract

The present invention discloses a method for studying the interannual variation law of the South Indian Ocean heat wave, which comprises: exploring the spatial distribution characteristics and linear variation trend characteristics of the South Indian Ocean heat wave according to the acquired data, analyzing the relationship between the intensity of the South Indian Ocean heat wave and the El Nino-Southern Oscillation (ENSO) process, analyzing the spatial evolution of the South Indian Ocean heat wave and the sea surface temperature anomaly during the El Nino event, analyzing the energy contribution of the ocean and atmosphere to the marine heat wave event during the El Nino event, exploring the physical mechanism of the South Indian Ocean heat wave, and determining whether the South Indian Ocean heat wave is affected by the shortwave radiation and latent heat modulated by the ENSO process. The present invention finds that during the El Nino period, the low clouds in the study area decrease, the high clouds increase, the depth of the mixed layer decreases, the shortwave radiation enters the ocean surface, and the northwest wind abnormally strengthens, the southeast wind of the climate state is suppressed, the evaporation decreases, the latent heat energy loss decreases, and the warming is promoted.

Description

Technical Field

[0001] The invention relates to an interannual variation of an ocean heat wave, and in particular to a method for studying the interannual variation law of an ocean heat wave in the South Indian Ocean. Background Art

[0002] Marine heatwaves refer to extreme high temperature events in a certain sea area where the sea surface temperature exceeds a certain threshold for more than five consecutive days. They may last for several months and cover thousands of square kilometers. In recent years, more and more scientists have begun to pay attention to and study marine heatwaves. Marine heatwaves frequently occur in various seas around the world, and the driving factors of marine heatwaves in different regions and at different times are different. Some studies have studied some marine heatwave events and their physical mechanisms. In 2003, the northern Mediterranean experienced a strong marine heatwave event, which was closely related to abnormally high temperatures and reduced wind speeds above the sea surface. From 2010 to 2011, the marine heatwave event in western Australia was closely related to the La Niña event of that year. The La Niña event caused the Livermore Flow to bring more warm water from low latitudes, while intensifying the sea-air heat flux on the west coast of Australia, which together led to an increase in sea surface temperatures. Between 2013 and 2015, a record-breaking marine heatwave event occurred in the northeastern Pacific, known as “The Blob,” which was primarily influenced by an abnormal high-pressure ridge over the northeastern Pacific and weakening surface winds associated with the Aleutian Low. In addition, the summer 2019 marine heatwave event in the northeastern Pacific was known as “Blob 2.0,” which was associated with the continued weakening of the high-pressure system over the North Pacific. In 2017, a strong marine heatwave event also occurred in the southwestern Atlantic, which was regulated by the tropical intraseasonal oscillation.

[0003] As we all know, marine heat waves refer to abnormal warming events in the ocean temperature. The sea surface temperature in the Indian Ocean region shows obvious interannual changes, and the ocean and atmospheric changes in the Indian Ocean region are closely related to important climate phenomena such as the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). During El Nino, the rise in surface sea temperature in the equatorial Pacific will affect the sea surface temperature in the eastern Indian Ocean through the role 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 central Pacific. In addition, studies have found that the sea temperature in the Indian Ocean is also closely related to the Asian summer monsoon. Studies on heat waves in the Indian Ocean region have shown that summer marine heat waves in the Bay of Bengal are regulated by El Nino and are related to atmospheric heating and the deepening of the thermocline. In addition, the equatorial western Indian Ocean region is affected by the sinking Rossby wave, which leads to enhanced convergence of upper seawater, inhibits the upwelling of cold water, and thus triggers marine heat wave events. From April to June 2010, the longest marine heat wave was observed in the Arabian Sea. Studies have shown that it is related to less latent heat loss and reduced mixed layer depth. Through the above analysis, the problems and defects of the existing technology are as follows: the existing technology mainly focuses on the research of marine heat waves in the Indian Ocean, such as the Arabian Sea, the Bay of Bengal, the equatorial western Indian Ocean, and the southeastern Indian Ocean. However, the understanding of the mechanism of marine heat waves in the South Indian Ocean is still incomplete and needs further in-depth research. Incomplete analysis of influencing factors: The existing technology does not comprehensively study the mechanism of marine heat waves in the South Indian Ocean, especially its connection with large-scale climate processes. Summary of the invention

[0004] The purpose of the present invention is to provide a method for studying the interannual variation law of marine heat waves in the South Indian Ocean, and solve the problem that the interannual variation law and dynamic mechanism of marine heat waves in the South Indian Ocean (60°E-90°W, 15°S-25°S) are unclear. The present invention has found that all characteristics of marine heat waves in the South Indian Ocean show an increasing trend, and the intensity of heat waves lags behind El Niño by 3 months. Through the calculation of the heat balance of the mixed layer, it is concluded that the heat wave is related to latent heat and shortwave radiation. Through synthetic analysis, it is found that during the El Niño period, low clouds in the study area decrease, high clouds increase, and the depth of the mixed layer decreases, which is conducive to the entry of shortwave radiation into the ocean surface and promotes warming. At the same time, the northwest wind during the El Niño period suppresses the southeast wind of the climate state, reduces ocean evaporation, and reduces the loss of latent heat energy, which is conducive to warming.

[0005] In order to achieve the above object, the present invention provides a method for studying the interannual variation law of marine heat waves in the South Indian Ocean, the method comprising:

[0006] Step 1: Obtain Niño 3.4 index data, OISST data set and SST data with a spatial resolution of 0.25°×0.25°. Also obtain ECMWF Reanalysis V5, sea surface current data with a horizontal resolution of 0.25° and a vertical distribution of 75 layers, and three-dimensional temperature and salinity data with a horizontal resolution of 1°×1° and 42 layers in the vertical direction. Use the temperature and salinity data of the three-dimensional temperature and salinity data to calculate the depth of the mixed layer and determine the marine heat wave based on the sea surface temperature data.

[0007] Step 2: The study area of ​​the South Indian Ocean marine heatwave was selected to explore the spatial distribution characteristics and time series distribution characteristics of the six indicators of the South Indian Ocean marine heatwave. The coordinates of the study area are 15°S to 25°S and 60°E to 90°E. The six indicators of marine heatwave are average intensity, maximum intensity, cumulative intensity, duration, frequency and total number of days.

[0008] Step 3: Explore whether ENSO affects marine heat waves in the Indian Ocean. Consider areas where the Niño 3.4 index is greater than 0.5 times the standard deviation as El Niño events. Draw a time series diagram of the Niño 3.4 index and the intensity of marine heat waves. Perform a lead-lag correlation analysis between the Niño 3.4 index and the intensity of marine heat waves to analyze the relationship between the intensity of marine heat waves in the South Indian Ocean and the ENSO process.

[0009] Step 4: Analyze the spatial evolution of the South Indian Ocean heatwave during the development of the El Niño event, synthesize the intensity of the South Indian Ocean heatwave during the El Niño period, analyze the synthetic evolution of the sea surface temperature anomaly, study the relationship between the sea surface temperature anomaly and the ocean heatwave, and determine that ENSO affects the intensity of the ocean heatwave by affecting the sea surface temperature. By exploring the physical mechanism of the sea surface temperature anomaly, the physical mechanism of the generation of the ocean heatwave in the study area is obtained;

[0010] Step 5: Combine SST data, ECMWF Reanalysis V5 data, 3D temperature and salinity data and use the mixed layer heat budget equation to quantitatively analyze the energy contribution of the ocean and atmosphere to marine heatwave events during the development of El Niño, and explore the physical mechanism of marine heatwaves in the South Indian Ocean;

[0011] Step 6: Combined with ECMWF Reanalysis V5 data, the contributions of latent heat, sensible heat, longwave radiation and shortwave radiation to the net surface heat flux are analyzed to obtain the energy source of the South Indian Ocean marine heat wave. Through regression analysis, the impact of El Niño events on the cloud cover in the South Indian Ocean is obtained, and how sea surface pressure and wind affect the marine heat wave by affecting the sea surface temperature is determined.

[0012] Preferably, in step one, the depth range of the mixed layer depth is 5 to 5500 meters; the duration of the ocean heat wave is ≥5 days, the interval between two adjacent ocean heat waves is ≥2 days, and the daily sea surface temperature data is higher than the 90th percentile of the 30-year climate average.

[0013] Preferably, in step 2, the average intensity is 1.20°C, the maximum intensity is 1.50°C, the cumulative intensity is 18.06°C / day, the duration is 14.12 days, the frequency is 2.74 times, and the total number of days is 45.22 days.

[0014] Preferably, in step five, the heat balance equation of the mixed layer is:

[0015] (1)

[0016] In formula (1), represents the trend term of temperature, represents the sea surface thermal forcing term, which indicates the contribution of sea surface heat flux to the sea surface temperature change, where represents the net surface heat flux, which is composed of the sum of long-wave radiation, short-wave radiation, sensible heat and latent heat; is the horizontal advective heat flux term, is the vertical entrainment item; the remaining item It includes turbulent mixing at the bottom of the mixed layer, horizontal mixing and diffusion, and errors in the numerical model; and are the density and specific heat of seawater respectively; is the depth of the mixed layer, is the average temperature of the mixed layer, is the temperature at the bottom of the mixed layer; represents the velocity vector, and are the latitudinal and meridional flow velocities, respectively; Indicates the vertical winding speed, .

[0017] More preferably, in formula (1), The value is 0.5℃.

[0018] Preferably, in step six, the regression analysis is to convert the three cloud amounts into the Niño 3.4 index to obtain the spatial distribution field of low clouds, high clouds and whole clouds in the South Indian Ocean cloud layer, and to derive the impact of the El Niño event on the cloud coverage in the South Indian Ocean.

[0019] More preferably, in step six, the spatial distribution field of low clouds, high clouds and whole clouds in the South Indian Ocean cloud layer is to analyze how the low cloud cover in the South Indian Ocean affects the marine heat wave during the El Niño period, and synthesize the abnormal evolution of the low cloud coverage during the El Niño period; before and after the peak of the El Niño event, the abnormal evolution of the high cloud in the South Indian Ocean is synthesized; before and after the peak of the El Niño event, the abnormal evolution of the mixed layer depth in the South Indian Ocean is synthesized, and the overall change law of the mixed layer depth within 2 to 3 months after the peak of the El Niño period is obtained, and the impact on the marine heat wave is obtained.

[0020] Preferably, in step six, how the sea surface pressure and wind affect the ocean heat wave by affecting the sea surface temperature is obtained by drawing a regression diagram of the sea surface temperature, sea level pressure and wind direction, and synthesizing the evolution of the 850hPa potential height anomaly and wind speed anomaly in the South Indian Ocean.

[0021] The method of the present invention for studying the interannual variation law of the ocean heat wave in the South Indian Ocean solves the problem that the interannual variation and dynamic mechanism of the ocean heat wave in the South Indian Ocean (60°E-90°W, 15°S-25°S) are unclear, and has the following advantages:

[0022] 1. The occurrence of marine heatwaves in the South Indian Ocean is significantly affected by the El Nino-Southern Oscillation (ENSO) event, and there is a significant interannual variation. The intensity of marine heatwaves in this sea area lags significantly behind the peak of El Nino by 3 to 4 months, showing a significant positive correlation.

[0023] 2. The mixed layer heat budget analysis shows that the energy of the South Indian Ocean marine heat wave mainly comes from the net surface heat flux, among which shortwave radiation and latent heat play a major role.

[0024] 3. Three to four months after the peak of El Niño, the sky over the South Indian Ocean is controlled by abnormal positive geopotential heights, and the downward airflow leads to a decrease in low clouds, which is conducive to the entry of short-wave radiation into the ocean surface. At the same time, the shallow mixed layer makes it easier for the ocean surface to warm up, which in turn promotes the formation of marine heat waves.

[0025] 4. The abnormal northwest wind suppresses the southeast wind in the background field, reducing evaporation and ocean latent heat energy loss, thereby increasing sea surface temperature and marine heat waves. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Figure 4 shows the spatial distribution of six marine heatwave indicators in the South Indian Ocean from 1982 to 2021.

[0027] Figure 2 Spatial distribution of trends in six marine heatwave indicators in the South Indian Ocean from 1982 to 2021.

[0028] Figure 3It is the time series distribution of the weighted average of marine heatwave characteristic indicators in the South Indian Ocean study area from 1982 to 2021.

[0029] Figure 4 The lead-lag correlation between the Niño 3.4 anomaly and the monthly marine heatwave intensity.

[0030] Figure 5 Composite evolution of the intensity of marine heat waves in the South Indian Ocean before and after the peak of the El Niño event.

[0031] Figure 6 Composite evolution of sea surface temperature anomalies in the South Indian Ocean before and after the peak of the El Niño event.

[0032] Figure 7 The diagram shows the evolution of the mixed layer heat budget during an El Niño event and the evolution of the heat flux before and after the El Niño event.

[0033] Figure 8 This is the regression spatial distribution field of low clouds, high clouds and whole clouds from 1982 to 2021.

[0034] Fig. 9 Composite evolution of low cloud anomalies over the South Indian Ocean before and after the peak of the El Niño event.

[0035] Fig.10 Composite evolution of high cloud anomalies over the South Indian Ocean before and after the peak of the El Niño event.

[0036] Fig.11 Composite evolution of mixed layer depth anomaly in the South Indian Ocean before and after the peak of the El Niño event.

[0037] Fig.12 is the regression spatial distribution field and sea level pressure and wind speed.

[0038] Fig.13 This is the composite evolution of the 850hPa geopotential height anomaly and wind speed anomaly in the South Indian Ocean before and after the peak of the El Niño event. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] A method for studying the interannual variation of marine heat waves in the South Indian Ocean, the method comprising:

[0041] Step 1: Obtain Niño 3.4 index data (Niño 3.4 index in the Global Climate Observing System provided by the Physical Sciences Laboratory of the National Oceanic and Atmospheric Administration (NOAA) of the United States), OISST dataset (OISST dataset is a comprehensive product generated based on NOAA's Advanced Very High Resolution Radiometer multi-channel SST product, which is corrected by measured data from buoys, ships, etc., and then processed by the optimal interpolation method. Due to its high spatial resolution and good continuity, this dataset is widely used in the study of marine heat waves) and SST data with a spatial resolution of 0.25°×0.25° (SST (Sea Surface Temperature) data comes from the Optimal Interpolation Sea Surface Temperature (OISST) V2.1 of the National Oceanic and Atmospheric Administration (NOAA) of the United States. The selected data spans from January 1, 1982 to December 31, 2021. ECMWF Reanalysis V5 (ECMWF Reanalysis V5 is the fifth generation of atmospheric reanalysis data for global climate provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), which includes monthly sea surface heat flux (positive downward), sea level pressure, 10-meter wind speed, low cloud cover, high cloud cover, overall cloud cover, and sea surface temperature) was also obtained. The horizontal resolution is 0.The data of sea surface flow field with a horizontal resolution of 25° and 75 layers in vertical distribution (the data of sea surface flow field comes from the global ocean-sea ice reanalysis data product ORAS5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF)) and three-dimensional temperature and salinity data with a horizontal resolution of 1°×1° and 42 layers in vertical direction (the three-dimensional temperature and salinity data comes from the 4th version of the "EN" series data set of the Hadley Center of the British Meteorological Office, which uses the best interpolation algorithm and provides three-dimensional temperature and salinity data based on available profiles and objective analysis, covering a time span of 1901 to 2022); the temperature and salinity data of the three-dimensional temperature and salinity data are used to calculate the mixed layer depth, and the depth range of the mixed layer depth is 5~5500 meters. The present invention determines marine heat waves based on sea surface temperature data. .

[0042] Marine heatwaves are defined as "discontinuous, long-term, abnormally warm water events that occur at a specific location". Specifically, "discontinuous" means that a marine heatwave is an identifiable event with a clear start and end date, "long-term" means that it lasts at least 5 days, and "abnormally warm" means that the sea surface temperature is above a specific climate threshold. Quantitatively, a marine heatwave lasts ≥ 5 days, the daily sea surface temperature data is higher than the 90th percentile of the 30-year climate average, and the interval between two adjacent marine heatwaves is ≥ 2 days, otherwise it will be regarded as the same marine heatwave. The specific characteristic indicators are detailed in Table 1. In addition, the determination of the marine heatwave threshold is to use the daily temperature values ​​of all years within 11 days centered on the current day, calculate the 90th percentile threshold corresponding to each day, and then perform a 31-day smoothing process. This method can ensure that there is enough sample size and the threshold will change with the seasons.

[0043] Table 1 Definition of marine heatwave indicators

[0044]

[0045] Note: D i is the duration of the i-th occurrence, N is the number of occurrences, T ij and is the SST on day j during a marine heatwave and the corresponding threshold.

[0046] Step 2: The study area of ​​the South Indian Ocean marine heatwave was selected, and the spatial distribution characteristics and time series distribution characteristics of the six indicators of the South Indian Ocean marine heatwave were explored based on the OISST data obtained. The coordinates of the study area are 15°S to 25°S and 60°E to 90°E ( Figure 1The six indicators of marine heatwaves are average intensity, maximum intensity, cumulative intensity, duration, frequency and total number of days.

[0047] like Figure 1 Figure 2 shows the spatial distribution of six marine heatwave indicators in the South Indian Ocean from 1982 to 2021, where (a) to (f) represent heatwave frequency, duration, total number of days per year, average intensity, maximum intensity, and cumulative intensity, respectively. The values ​​shown are annual averages in times, days, ℃, and (℃ days). Figure 1 It can be seen that significant marine heatwave phenomena are observed in the South Indian Ocean region. The frequency of marine heatwaves in the South Indian Ocean is relatively evenly distributed in space, while the high-value areas of duration and total number of days are located in the west of the South Indian Ocean. The spatial distribution of average intensity and maximum intensity is relatively low in the west of the South Indian Ocean, while the cumulative intensity is high in the east of Madagascar Island.

[0048] The average intensity of the marine heatwave was 1.20°C, the maximum intensity was 1.50°C, the cumulative intensity was 18.06°C / day, the duration (Dur) was 14.12 days, the frequency was 2.74 times, and the total number of days was 45.22 days. Based on the above six indicators, the characteristics of the marine heatwave in this area are many days, long duration, high intensity, and high frequency.

[0049] like Figure 2 Figure 2 shows the spatial distribution of trends in six marine heatwave indicators in the South Indian Ocean from 1982 to 2021, where (a) to (f) represent heatwave frequency, duration, total number of days per year, average intensity, maximum intensity, and cumulative intensity, respectively. Black dots indicate those that passed the 95% significance level test (unit: / decade). Figure 2 Shown Figure 1 The spatial distribution trends of six marine heat wave characteristic indicators are shown in Figure 2. Figure 2 (a) to Figure 2 (f) shows that the frequency, duration, total number of days and cumulative intensity of marine heat waves 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 western part of the South Indian Ocean, the frequency trend of marine heat waves has reached 2 times every 10 years. The trends in duration, total number of days and cumulative intensity are similar in spatial distribution. The duration growth trend in the western region can reach up to 7.5 days per decade, the total number of days can reach 40 days per decade, and the cumulative intensity can reach 10°C per decade. From Figure 2 (d) and Figure 2(e) shows that the average and maximum intensity of marine heat waves have a negative trend at 15°S-10°S, especially the northwest side of Madagascar Island has the largest negative trend. However, in the entire study area (15°S-25°S, 60°E-90°E), the average and maximum intensity also show an increasing trend. In summary, in the study area of ​​this invention, the six characteristic indicators of marine heat waves in the South Indian Ocean all show an increasing positive trend, and this trend may be further strengthened with global warming.

[0050] 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 the data, so it has been widely used in various application scenarios. The basic principle of this method is to test the trend in the data based on the ranks of the observed data points. By calculating the ranks and using the ranks to calculate the statistics of the Mann-Kendall test, the trend in the data and its significance level can be determined. This method is widely used in fields such as climate change, environmental science, and hydrology.

[0051] like Figure 3 As shown, the time series distribution of the weighted average characteristic indicators of marine heatwaves in the South Indian Ocean study area from 1982 to 2021, where (a) is the frequency of occurrence (unit: times / decade), (b) is the duration (unit: days / decade), (c) is the total number of days (unit: days / decade), (d) is the average intensity (unit: ℃ / times / decade), (e) is the maximum intensity (unit: ℃ / times / decade), and (f) is the cumulative intensity (unit: ℃ / decade). The red dotted line represents the linear trend, and the significance level of the p value was calculated by the Mann-Kendall trend test. From Figure 3 It can be seen that the weighted average characteristic indicators of the South Indian Ocean heatwave region from 1982 to 2021 showed a clear growth trend. The significant high values ​​of frequency, duration and total number of days were distributed in 2019, while the high values ​​of average intensity and maximum intensity were located in 1997. Figure 3 From (a), we can see that the increase rate of marine heatwave frequency is 1.01±0.25 times / 10 years, with a correlation coefficient (R) of 0.74. Peaks occurred in 1982, 1988, 1991, 1994, 1998, 2002, 2005, 2010, 2015 and 2019, with the frequency exceeding 6 times in 2019. Figure 3 From (b), we can see that the growth rate of duration is 4.18±1.10 days / 10 years, with three obvious peaks in 1997, 2016 and 2019. Figure 3From (c), we can see that the regional annual average growth rate of the total number of days is quite significant, which is 27.95±7.04 days / 10 years, and there are also obvious peaks in 2016 and 2019. Figure 3 From (d) to (e), we can see that the regional annual average distribution of the average intensity and the maximum intensity is similar, reaching the maximum in 1997, and also showing higher values ​​in 2016 and 2019, with growth rates of 0.01±0.01 degrees Celsius / 10 years and 0.04±0.02 degrees Celsius / 10 years, respectively. Figure 3 From (f), we can see that the cumulative intensity has three obvious peaks in 1997, 2016 and 2019.

[0052] Step 3: Explore whether ENSO will affect the marine heat waves in the Indian Ocean region. Calculate the Niño 3.4 index based on the acquired OISST data, regard the areas where the Niño 3.4 index is greater than 0.5 times the standard deviation as El Niño events, plot the time series diagram of the Niño3.4 index and the intensity of marine heat waves, and conduct lead-lag correlation analysis between the Niño 3.4 index and the intensity of marine heat waves to explore the relationship between the intensity of marine heat waves in the South Indian Ocean and the ENSO process.

[0053] like Figure 4 As shown, the lead-lag correlation between the Niño 3.4 anomaly and the monthly marine heatwave intensity, where (a) is the Niño 3.4 anomaly (black line) and the monthly marine heatwave intensity in the South Indian Ocean (detrended, magenta line). Red represents areas where Niño 3.4 is greater than 0.5 standard deviations, and blue represents areas where Niño 3.4 is less than negative 0.5 standard deviations. (b) is the lead-lag correlation between the Niño 3.4 anomaly and the monthly marine heatwave intensity. Positive values ​​indicate that the heatwave intensity lags behind the Niño 3.4 index.

[0054] from Figure 4 It can be observed from (a) that the time series of the intensity of the South Indian Ocean heat wave calculated based on OISST has a consistent temporal variation pattern with the Nino3.4 index, and there is a good correspondence between the anomaly of the Niño 3.4 index and the intensity of the ocean heat wave. When the positive anomaly peak of Niño 3.4 appears (i.e. the red area), the intensity of the heat wave will also peak. Figure 4 (b) shows that there is a certain lag response between the intensity of marine heat waves and the Niño 3.4 anomaly. In the study area, the correlation coefficient between the heat wave intensity and the Niño 3.4 index lags by 3 months, and the maximum value is 0.60, which passes the 95% confidence test. There is a significant positive correlation between the two, that is, the intensity of marine heat waves in the South Indian Ocean region will increase when lagging behind the peak of the El Niño year by 3 months.

[0055] Step 4: The lead-lag correlation analysis obtained in step 3 shows that the time series of the intensity of the South Indian Ocean marine heatwave is positively correlated with the lagged Nino3.4 index of 3 months. The spatial evolution of the South Indian Ocean marine heatwave during the development of the El Niño event is further analyzed in detail (i.e., the spatial evolution characteristics of the South Indian Ocean heatwave from 6 months ahead of the peak of El Niño to 5 months behind the peak of El Niño). The intensity of the South Indian Ocean marine heatwave during the El Niño period is synthesized, the synthetic evolution of the sea surface temperature anomaly is analyzed, the relationship between the sea surface temperature anomaly and the marine heatwave is studied, and it is determined that ENSO affects the intensity of the marine heatwave by affecting the sea surface temperature. By exploring the physical mechanism of the sea surface temperature anomaly, the physical mechanism of the generation of marine heatwaves in the study area is obtained.

[0056] like Figure 5 As shown in Figure 2, the composite evolution of the intensity of the South Indian Ocean heat wave before and after the peak of the El Niño event (coloring, unit: °C), and the black frame line indicates the study area. Figure 5 From (c), we can see that in the first four months of the El Niño peak, a strong marine heat wave occurred at 20°S, 74°E, with an intensity of more than 0.4°C. Figure 5 From (f), we can see that as El Niño develops, the intensity of ocean heat waves slowly moves westward, and the intensity of surrounding heat waves continues to increase. One month before the peak of El Niño, an inclined high-intensity belt is formed. Figure 5 From (i) and (k), we can see that two months after the peak of El Niño, the high value area begins to expand, and finally reaches its maximum intensity and range four months after the peak.

[0057] like Figure 6 As shown in the figure, the synthetic evolution of the sea surface temperature anomaly in the South Indian Ocean before and after the peak of the El Niño event (unit: °C). The black frame line indicates the study area. Since marine heat waves are extreme warming events in the ocean, this paper explores the synthetic evolution of sea surface temperature anomalies to understand their relationship with marine heat waves. Figure 6 It can be seen that there is an obvious positive anomaly of sea temperature in the western part of the South Indian Ocean from 6 months ahead of the peak to 5 months behind the peak, and the intensity of this anomaly continues to increase and the range is also increasing. In addition, in the Mozambique Channel, there is a negative anomaly of sea temperature in the strait from the first six months to the last two months of the El Niño peak, and the negative anomaly gradually decreases over time and eventually disappears. 3 to 4 months after the El Niño peak, the positive anomaly of sea temperature in the study area reaches the maximum value, and has a good correspondence with the intensity of the ocean heat wave. Therefore, ENSO can affect the intensity of ocean heat waves by affecting the sea surface temperature. By exploring the physical mechanism of the sea surface temperature anomaly, the present invention can explain the physical mechanism of the generation of ocean heat waves in the study area.

[0058] Step 5: In step 4, it was found that El Niño events can regulate the intensity of marine heat waves in the South Indian Ocean by affecting the sea surface temperature. Further combined with SST data, ECMWF Reanalysis V5 data, and three-dimensional temperature and salinity data, the mixed layer heat budget equation was used to quantitatively analyze the energy contribution of the ocean and atmosphere to marine heat wave events during the development of El Niño, and to explore the physical mechanism of marine heat waves in the South Indian Ocean.

[0059] The present invention performs a heat balance analysis of the mixed layer. The heat balance equation of the mixed layer temperature can be expressed as:

[0060] (1)

[0061] In formula (1), represents the trend term of temperature, represents the sea surface thermal forcing term, which indicates the contribution of sea surface heat flux to the change of sea surface temperature (SST), where It represents the net surface heat flux, which is composed of the sum of long-wave radiation, short-wave radiation, sensible heat and latent heat. is the horizontal advective heat flux term, It is the vertical entrainment item. It includes turbulent mixing at the bottom of the mixed layer, horizontal mixing and diffusion, and errors in the numerical model. and are the density and specific heat of seawater respectively. is the depth of the mixed layer, is the average temperature of the mixed layer, is the temperature at the bottom of the mixed layer. When calculating the depth of the mixed layer, the temperature difference between the upper and lower temperatures is set to 0.5°C. Considered as a fixed value of 0.5°C. represents the velocity vector, and are the latitudinal and meridional flow velocities, respectively. Indicates the vertical winding speed, To obtain the anomaly value of each term in the equation, the corresponding climatological mean value was subtracted from each term in the equation.

[0062] like Figure 7As shown, the evolution of the mixed layer heat budget El Niño event and the evolution of heat flux before and after the El Niño event, where (a) is the evolution of the mixed layer heat budget El Niño event (unit: ℃ / month). Including temperature trend term (black line), net surface heat flux term (red line), ocean advection term (green line), vertical entrainment and diffusion term (blue line) and residual term (magenta line). (b) is the evolution of heat flux before and after the El Niño event (unit: W / m²). Including heat flux term (black line), latent heat (cyan line), sensible heat (green line), longwave radiation (blue line), shortwave radiation (red line). All terms have removed the long-term trend.

[0063] Depend on Figure 7 The results of (a) show that before and after the El Niño event, the temperature increase of the mixed layer is mainly affected by the net surface heat flux, and the advection term and the vertical entrainment term have a small contribution to the change of the temperature trend line, because the residual term may include turbulent mixing at the bottom layer, horizontal mixing, and errors between data, and the residual term has an opposite trend to the temperature change term. In addition, since the temperature trend term turns from positive to negative at 3 to 4 months during the peak of the El Niño, the temperature of the mixed layer reaches its maximum at this time, which is also consistent with Figure 4 The intensity of the ocean heat wave lags behind the Niño 3.4 index by 3 to 4 months. At the same time, the net surface heat flux also turns from positive to negative in the 3 to 4 months of the peak of El Niño, which can be explained by the accumulation of heat. At this time, the mixed layer obtains the most heat, which is conducive to the generation and maintenance of ocean heat waves.

[0064] Step 6: Since the previous results show that the net surface heat flux term plays a major contribution to the peak of the South Indian Ocean marine heat wave, we further combine the ECMWF Reanalysis V5 data to analyze the contributions of latent heat, sensible heat, longwave radiation and shortwave radiation of the net surface heat flux to derive the energy source of the South Indian Ocean marine heat wave.

[0065] Depend on Figure 7 (b) It can be observed that the accumulated heat flux reaches its maximum value within 3 to 4 months after the peak of El Niño. It is worth noting that the change of 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.

[0066] Therefore, in summary, the energy of the South Indian Ocean marine heat wave mainly comes from the net surface heat flux, which is mainly controlled by shortwave radiation and latent heat.

[0067] From the linear trend, with global warming, the six heat wave characteristic indicators all show a gradual increase. In addition, the present invention observed that the high-value years of some characteristic indicators are consistent with the ENSO (El Nino Southern Oscillation) years (for example, 1997-1998). Therefore, the present invention speculates that ENSO may have a certain correlation with the South Indian Ocean marine heat wave. In order to further verify this hypothesis, the present invention plans to carry out relevant research work.

[0068] Since changes in local cloud cover may affect the heat flux between the sea and the air, and thus affect the sea surface temperature. In order to explore the impact of the El Niño event on the cloud cover in the South Indian Ocean, and thus reveal the physical mechanism of the marine heat wave in the South Indian Ocean, this paper regresses the three types of cloud cover (low cloud, high cloud, and whole cloud) from 1982 to 2021, converts the three types of cloud cover to the Niño 3.4 index, and obtains the spatial distribution field of low cloud, high cloud, and whole cloud in the South Indian Ocean, and obtains the impact of the El Niño event on the cloud cover in the South Indian Ocean.

[0069] like Figure 8 As shown in the figure, the regression spatial distribution field of low cloud, high cloud and total cloud from 1982 to 2021, where (a) the regression spatial distribution field of low cloud cover and Niño 3.4 from 1982 to 2021. The box represents the study area. (b) Same as (a), but high cloud cover. (c) Same as (a), but total cloud cover. Figure 8 From (a), we can see that when the sea surface temperature in the equatorial Pacific (i.e., Niño 3.4 region) rises, the amount of low clouds decreases, and at the same time, the amount of low clouds in the southern Indian Ocean also shows a significant negative anomaly. The reduction of low clouds helps the short-wave radiation generated by the sun to enter the ocean surface, heating the sea surface, which is conducive to the generation of marine heat waves. Figure 8 (b) shows the spatial distribution of high clouds returning to Niño 3.4. The cloud cover in the equatorial Pacific has increased significantly. At this time, high clouds in the study area have also increased significantly. An inclined "northwest-southeast" high cloud increase belt has appeared in the South Indian Ocean. Higher ocean surface temperatures can increase the water vapor content in the atmosphere, thereby promoting convective activity, making the air rise more intense, which is conducive to the formation of high clouds. High-level clouds have a certain reflection and scattering effect on solar radiation, but compared to low clouds, the reflection and scattering effects of high clouds are not strong. From Figure 8(c) shows that during the El Niño period, the total cloud in the South Indian Ocean shows an increasing trend, which is consistent with the change in the total cloud in the equatorial Pacific. This further shows that there is a certain feedback effect between clouds and sea surface temperature: the reduction of low clouds is conducive to the entry of shortwave radiation into the ocean, promoting ocean warming, while the increase in sea surface temperature also strengthens the rise of air, which is conducive to the formation of high clouds. This "positive feedback mechanism" leads to a further increase in sea surface temperature, thereby maintaining or intensifying the formation of low and high clouds, forming a positive feedback cycle.

[0070] To further analyze how low cloud cover in the South Indian Ocean affects marine heatwaves during El Niño, we synthesized the anomalies of low cloud cover during El Niño from 1982 to 2021 and displayed their evolution in Fig. 9 middle.

[0071] like Fig. 9 As shown in Figure 2, the composite evolution of low cloud anomalies in the South Indian Ocean before and after the peak of the El Niño event, where the black frame line indicates the study area. Fig. 9 It can be seen from (a) to (g) that from the first six months to the peak of El Niño, the low clouds in the entire study area show obvious negative anomalies. Fig. 9 As can be seen from (h) to (i) of the graph, during the decline of El Niño, low clouds in the western part of the study area gradually increased, but overall, the total amount still decreased. The reduction of low clouds is conducive to more shortwave radiation reaching the ocean surface, thereby heating the ocean. During the development of El Niño, due to the large-scale reduction of low clouds, a large amount of shortwave radiation enters the ocean, promoting ocean warming. The ocean surface temperature reaches its highest level 3 to 4 months after the peak of El Niño.

[0072] like Fig.10 As shown in Figure 2, the composite evolution of high cloud anomalies in the South Indian Ocean before and after the peak of the El Niño event, where the black frame indicates the study area. Fig.10 From (j) and (k), we can see that high clouds show obvious positive anomalies before and after the peak of El Niño, and the positive anomalies of high clouds reach their maximum value in the 3rd to 4th month after the peak of El Niño.

[0073] Comprehensive analysis Fig. 9 and Fig.10 It is known that there is a lag relationship between low clouds and marine heat waves; the reduction of low clouds will lead to a gradual increase in sea surface temperature, reaching a maximum value in the third to fourth months of the El Niño peak period. 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 updraft stronger, which is conducive to the formation of high clouds.

[0074] like Fig.11As shown in Figure 2, the composite evolution of the mixed layer depth anomaly in the South Indian Ocean before and after the peak of the El Niño event, where the unit is m and the black frame line represents the study area. Fig.11 It can be seen that in the first six months of the peak period of El Niño, the depth of the mixed layer in the west of the study area generally showed a negative anomaly. In the east of the study area, especially at 80°E, there was an obvious positive anomaly of the mixed layer depth, reaching 6 meters. As El Niño developed, this positive anomaly gradually weakened and even turned into a negative anomaly three months after the peak period. In addition, Fig.11 From (h), we can see that the negative anomaly signal of the mixed layer depth at 60°E gradually increases and reaches the maximum value one month after the peak period. Fig.11 From (j), we can see that three months after the peak, a positive anomaly signal appeared at 20°S, 75°E and continued to strengthen.

[0075] In summary, the present invention can find that within 2 to 3 months after the peak of El Niño, the depth of the mixed layer in the study area shows an overall decreasing trend, which is conducive to the warming of the ocean surface. , The increase in the depth of the mixed layer h The decrease in ocean temperature is more conducive to the heating of the ocean, thus promoting the formation of marine heat waves.

[0076] In order to explore how sea surface pressure and wind affect the local marine heat waves by affecting the sea surface temperature, the present invention draws a regression map of sea temperature, sea level pressure and wind direction, and synthesizes the evolution of the 850hPa potential height anomaly and wind speed anomaly in the South Indian Ocean.

[0077] like Fig.12 As shown, the regression spatial distribution field and sea level pressure and wind speed, including (a) the regression spatial distribution field of sea surface temperature and Niño 3.4 index from 1982 to 2021 (unit: ℃), the black frame line represents the study area; (b) the regression spatial distribution field of sea level pressure (unit: hPa) and wind speed (unit: m / s) and Niño 3.4 index from 1982 to 2021. Fig.12 (a) shows the spatial field obtained by regressing the sea surface temperature (SST) from 1982 to 2021 to the Niño 3.4 index. It can be observed from the figure that when there is an obvious positive anomaly in the sea temperature in the equatorial eastern Pacific, there will also be a "northwest-southeast" inclined sea temperature positive anomaly area in the southern Indian Ocean, corresponding to the distribution of high clouds. Combined with the previous research, the present invention can conclude that the positive sea temperature anomaly in the southern Indian Ocean will be regulated by El Niño. Fig.12It can be seen from (b) that in the study area, the sea temperature anomaly is mainly controlled by the positive sea level pressure. High pressure control is conducive to the increase of sea surface temperature, because the sinking airflow in the high pressure area can suppress 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 abnormal northwest winds, while the study area is affected by the climatic southeast winds. The emergence of abnormal northwest winds reduces the climatic southeast winds, thereby reducing water evaporation and latent heat energy loss on the ocean surface, thereby promoting the increase of sea surface temperature.

[0078] like Fig.13 As shown in Figure 2, the composite evolution of the 850hPa geopotential height anomaly and wind speed anomaly in the South Indian Ocean before and after the peak of the El Niño event, where the black frame line represents the study area. Fig.13 It can be seen that there is a negative anomaly signal of potential height in the southwest of the study area, and a positive anomaly signal of potential height in the northeast. It is located at the junction of the two signals and is therefore controlled by the abnormal northwest wind. During the El Niño period, the positive signal strengthens, the negative signal weakens, and the gradient between the two signals increases. Fig.13 As can be seen in (k), the El Niño peaks 4 months later. Therefore, the abnormal northwest wind strengthens, suppressing the evaporation of seawater and the loss of ocean latent heat energy, which is conducive to the rise of sea temperature and the generation of heat waves.

[0079] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A method for studying the interannual variation of marine heat waves in the South Indian Ocean, characterized in that: The method includes: Step 1: Obtain Niño 3.4 index data, OISST dataset, and SST data with a spatial resolution of 0.25°×0.25°. Also obtain ECMWF Reanalysis V5 data, as well as sea surface flow data with a horizontal resolution of 0.25° and a vertical distribution of 75 layers, and three-dimensional temperature and salinity data with a horizontal resolution of 1°×1° and 42 layers in the vertical direction. Use the three-dimensional temperature and salinity data to calculate the depth of the mixed layer and determine the marine heat wave based on the sea surface temperature data. The depth of the mixed layer ranges from 5 to 5,500 meters; the duration of the ocean heat wave is ≥ 5 days, the interval between two adjacent ocean heat waves is ≥ 2 days, and the daily sea surface temperature data is higher than the 90th percentile of the 30-year climate average; Step 2: Select the study area of ​​the South Indian Ocean marine heatwave and explore the spatial distribution characteristics and time series distribution characteristics of the six indicators of the South Indian Ocean marine heatwave. The coordinates of the study area are 15°S to 25°S and 60°E to 90°E. The six indicators of marine heatwave are average intensity, maximum intensity, cumulative intensity, duration, frequency and total number of days. The average intensity is 1.20°C, the maximum intensity is 1.50°C, the cumulative intensity is 18.06°C, the duration is 14.12 days, the frequency is 2.74 times, and the total number of days is 45.22 days; Step 3: Explore whether ENSO affects marine heat waves in the Indian Ocean. Consider areas where the Niño 3.4 index is greater than 0.5 times the standard deviation as El Niño events. Draw a time series diagram of the Niño 3.4 index and the intensity of marine heat waves. Perform a lead-lag correlation analysis between the Niño 3.4 index and the intensity of marine heat waves to obtain the relationship between the intensity of marine heat waves in the South Indian Ocean and the ENSO process. Step 4: Analyze the spatial evolution of the South Indian Ocean heatwave during the development of the El Niño event, synthesize the intensity of the South Indian Ocean heatwave during the El Niño period, analyze the synthetic evolution of the sea surface temperature anomaly, study the relationship between the sea surface temperature anomaly and the ocean heatwave, determine that ENSO affects the intensity of the ocean heatwave by affecting the sea surface temperature, and by exploring the physical mechanism of the sea surface temperature anomaly, derive the physical mechanism of the generation of the ocean heatwave in the study area; Step 5: Combine SST data, ECMWF Reanalysis V5 data, 3D temperature and salinity data and use the mixed layer heat budget equation to quantitatively analyze the energy contribution of the ocean and atmosphere to marine heatwave events during the development of El Niño, and explore the physical mechanism of marine heatwaves in the South Indian Ocean; The heat balance equation of the mixed layer is: (1) In formula (1), represents the trend term of temperature, represents the sea surface thermal forcing term, which indicates the contribution of sea surface heat flux to the sea surface temperature change, where represents the net surface heat flux, which is composed of the sum of long-wave radiation, short-wave radiation, sensible heat and latent heat; is the horizontal advective heat flux term, is the vertical entrainment item; the remaining item It includes turbulent mixing at the bottom of the mixed layer, horizontal mixing and diffusion, and errors in the numerical model; and are the density and specific heat of seawater respectively; h is the depth of the mixed layer, T is the average temperature of the mixed layer, Td is the temperature at the bottom of the mixed layer; V = represents the velocity vector, and are the latitudinal and meridional flow velocities, respectively; Indicates the vertical winding speed, ; Step 6: Combined with ECMWF Reanalysis V5 data, analyze the contribution of latent heat, sensible heat, longwave radiation and shortwave radiation to the net surface heat flux, and derive the energy source of the South Indian Ocean marine heat wave. Through regression analysis, derive the impact of El Niño events on the cloud cover in the South Indian Ocean, and determine how sea surface pressure and wind affect the marine heat wave by affecting the sea surface temperature. The regression analysis is to regress the three types of cloud cover to the Niño 3.4 index to obtain the spatial distribution field of low clouds, high clouds and whole clouds in the South Indian Ocean, and to obtain the impact of the El Niño event on the cloud cover in the South Indian Ocean; The spatial distribution fields of low clouds, high clouds and whole clouds in the South Indian Ocean cloud layer are obtained, including: analyzing how the low cloud cover in the South Indian Ocean affects the marine heat wave during the El Niño period, and synthesizing the abnormal evolution of low cloud coverage during the El Niño period; synthesizing the abnormal evolution of high clouds in the South Indian Ocean before and after the peak of the El Niño event, and at the same time synthesizing the abnormal evolution of the mixed layer depth in the South Indian Ocean, and deriving the overall change law of the mixed layer depth within 2 to 3 months after the peak of the El Niño period, and its impact on the marine heat wave.

2. The method according to claim 1, characterized in that In formula (1), The value is 0.5℃.

3. The method according to claim 1, characterized in that In step six, how the sea surface pressure and wind affect the ocean heat wave by affecting the sea surface temperature is obtained by synthesizing the evolution of the 850hPa potential height anomaly and wind speed anomaly in the South Indian Ocean when drawing the regression diagram of the sea surface temperature, sea level pressure and wind direction.

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