Method for detecting climatic change signal of drought event under high temperature-dry condition

Abstract

The invention relates to the field of carbon neutralization, and discloses a drought event climate change signal detection method under a high temperature-dry condition, and the method comprises the following steps: data acquisition; evaluating the dependency relationship of the high temperature-atmospheric dryness composite event and the severity of the composite event; evaluating the response of the regional extreme drought to the change of the high temperature-atmospheric drying condition; estimating occurrence probabilities of different grades of high temperature-atmospheric drying conditions in the future; quantifying the probability of occurrence of extreme high temperature-atmospheric dryness-drought events exceeding the observation history in historical and future regions; clarifying dominant factors influencing historical period changes; identifying a human activity signal of a regional high temperature-atmospheric dryness composite event; analyzing whether the control emission can limit the increase of the severity of the high temperature-atmospheric dryness composite event or not; the method has the beneficial effects that the additional influence of high temperature and atmospheric dryness on drought is quantified from an urgent angle, the average state of high temperature and atmospheric dryness during drought is clarified, and the leading effect of high temperature and atmospheric dryness on drought is clarified.

Description

Technical Field

[0001] The present invention relates to the field of carbon neutrality, and in particular to a method for detecting climate change signals of drought events under high temperature and dry conditions. Background Art

[0002] In the context of global warming, high temperature-atmospheric dryness-drought compound events are hot topics in current research and have received widespread attention. The simultaneous occurrence of high temperature and atmospheric dryness compound conditions will increase the duration and severity of droughts, causing huge social and economic losses. However, there is currently a lack of research on the response of regional extreme droughts to high temperature and atmospheric dryness conditions. In addition, high temperature and atmospheric dryness are important factors causing droughts. Existing studies have detected human activity signals in changes in precipitation and temperature, but there is a lack of research on the detection of human activity signals in compound events. With the continued emission of greenhouse gases in the future, the probability and severity of high temperature-atmospheric dryness compound events are expected to increase further. It is necessary to explore the role of controlling temperature rise and greenhouse gas emissions in reducing the severity of high temperature-atmospheric dryness compound events, and emphasize the importance of energy conservation and emission reduction; studying how drought responds to the continued increase in high temperature-atmospheric dryness compound events can provide a basis for decision makers, thereby promoting the optimization of disaster prevention and mitigation measures and reducing the pressure and economic losses caused by extreme climate on the ecological environment. Summary of the invention

[0003] The purpose of the present invention is to propose a method for detecting climate change signals of drought events under high temperature and dry conditions, so as to solve the technical problem of the current lack of research on the response of regional extreme drought to high temperature and atmospheric dry conditions.

[0004] The present invention provides a method for detecting climate change signals of drought events under high temperature and dry conditions, comprising the following steps:

[0005] Step S1: Data collection: collecting drought event climate change data;

[0006] Step S2: Combine the data obtained in step S1 to calculate the normalized time series of air temperature, precipitation, surface soil moisture and scPDSI, and evaluate the dependency of high temperature-atmospheric dryness composite events and the severity of the composite events;

[0007] Step S3: Response of regional extreme drought to high temperature and atmospheric dryness conditions in historical periods; Combine the temperature, precipitation, surface soil moisture and scPDSI normalized time series calculated in step S2 to evaluate the response of regional extreme drought to changes in high temperature-atmospheric dryness conditions;

[0008] Step S4: Temporal patterns and dependencies of high temperature-atmospheric dryness composite conditions under the background of anthropogenic climate change; combining the standardized time series of temperature and precipitation obtained in step S2, using the CMIP6 future scenario, to estimate the probability of occurrence of different levels of high temperature-atmospheric dryness conditions in the future;

[0009] Step S5: Estimation of the recurrence probability of regional extreme drought compound events driven by high temperature and atmospheric dryness; Calculate the additional impact of high temperature and atmospheric dryness on drought respectively, and clarify the driving factors affecting regional extreme drought; Based on the driving factors, quantify the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding the observed history every 10 years;

[0010] Step S6: The spatiotemporal evolution characteristics and driving factors of the severity of the high temperature-atmospheric dryness composite event; Combined with the precipitation and temperature data obtained in step S1, the standardized precipitation index and standardized temperature index based on the original data and the linear trend removed are calculated respectively, and the SCEI is calculated based on these two indices to clarify the dominant factors affecting the changes in the SCEI in the historical period;

[0011] Step S7: Detection of climate change signals of high temperature-atmospheric dryness composite events; using a detection and attribution method based on rotated empirical orthogonal decomposition and an optimal fingerprint method to identify human activity signals of regional high temperature-atmospheric dryness composite events.

[0012] A storage medium stores instructions and data for implementing a method for detecting climate change signals of drought events under high temperature and dry conditions.

[0013] A device for detecting climate change signals of drought events under high temperature and dry conditions, comprising: a processor and the storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for detecting climate change signals of drought events under high temperature and dry conditions.

[0014] The beneficial effects provided by the present invention are:

[0015] (1) The present invention makes up for the lack of response of the probability of occurrence of regional extreme drought events to high temperature-atmospheric dryness in previous studies. And the present invention analyzes the changes in the probability of occurrence and dependency of high temperature-atmospheric dryness from a century-long perspective. The present invention uses a future high emission scenario to quantify the additional impact of high temperature and atmospheric dryness on drought from an urgency perspective, clarifies the average state of high temperature and atmospheric dryness when drought occurs, and clarifies the dominant role of high temperature and atmospheric dryness. The present invention uses two sets of observational data sets and single model large sample data sets to increase the credibility of the results. The present invention is suitable for global and regional research.

[0016] (2) This paper quantifies the severity of the composite high temperature-atmospheric dryness event, and uses the detection and attribution method based on the rotational empirical orthogonal decomposition and the optimal fingerprint method to identify the anthropogenic climate change signal. It also analyzes the severity of the high temperature-atmospheric dryness event under different temperature rise levels and emission scenarios, emphasizing the important role of controlling temperature rise and emissions in controlling the severity of composite events. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of the method of the present invention;

[0018] Figure 2 Schematic diagram of the standardized time series of temperature, precipitation, self-calibrated Palmer Drought Index and surface soil moisture in the middle and lower reaches of the Yangtze River;

[0019] Figure 3 Schematic diagram of the relationship between temperature and precipitation in summer and autumn in the middle and lower reaches of the Yangtze River and the changes in this relationship over the past 20 years and the past 100 years;

[0020] Figure 4 Schematic diagram of the three-dimensional relationship between temperature, precipitation and drought in the middle and lower reaches of the Yangtze River in summer and autumn and the comparison between the past 20 years and the past 100 years;

[0021] Figure 5 Schematic diagram of the changes in precipitation, temperature and high temperature-atmospheric dryness probability caused by anthropogenic climate change in the middle and lower reaches of the Yangtze River based on model data during the historical period;

[0022] Figure 6 Schematic diagram of future projections of different levels of high temperature-atmospheric dryness combined conditions under high emission scenarios;

[0023] Figure 7 Schematic diagram of the changes in high temperatures, atmospheric dryness, and droughts of the same magnitude as in the summer and autumn of 2019 in the middle and lower reaches of the Yangtze River over the past two centuries;

[0024] Figure 8 Schematic diagram of the temporal evolution of the standardized temperature index, standardized precipitation index, and standardized composite event index in summer and autumn in the middle and lower reaches of the Yangtze River during the historical period;

[0025] Fig. 9 Schematic diagram of the three-dimensional relationship and changes in different periods of the standardized temperature index, standardized precipitation index and standard composite event index in summer and autumn in the middle and lower reaches of the Yangtze River;

[0026] Fig.10 Schematic diagram of the changing trend of high temperature-atmospheric dryness complex events under different forcing scenarios based on model data;

[0027] Fig.11 Schematic diagram of the identification of anthropogenic climate change signals for high temperature-atmospheric dryness composite events based on the rotational empirical orthogonal decomposition detection attribution method;

[0028] Fig.12 Schematic diagram of quantitative detection and attribution of composite dry heat event index based on the optimal fingerprint method;

[0029] Fig.13 Schematic diagram of the estimated change trend of the composite dry heat event index under different temperature rise levels;

[0030] Fig.14 Schematic diagram of future projections of the standardized precipitation index, standardized temperature index and composite dry-heat event index under different emission scenarios;

[0031] Fig.15 It is a working schematic diagram of the hardware device of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0033] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0034] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the process of the present invention.

[0035] The present invention provides a method for detecting climate change signals of drought events under high temperature and dry conditions, comprising the following steps:

[0036] Step S1: Data collection: collecting drought event climate change data;

[0037] As an embodiment, the present invention collects regional precipitation, temperature data and self-calibrated Palmer Drought Index (scPDSI) based on observations and surface soil moisture data; collects temperature, precipitation and surface soil moisture, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature data of the sixth phase of the International Coupled Comparison Program CMIP6; as well as elevation data and effective soil moisture data; single model large sample set data comes from the Seamless System for Prediction and Earth System Research (SPEAR) data set of the Geophysical Fluid Dynamics Laboratory, including precipitation and temperature data.

[0038] In step S1, in order to verify the reliability of the results, two sets of observation-based data of different lengths were collected. The observation-based precipitation, temperature and scPDSI data collected are on a centennial scale, aiming to increase the research cases of regional extreme droughts and compare the changes in the past 20 years with the historical period. The collected single model large sample data set uses a high-resolution model to improve the accuracy of regional scale simulation, which can effectively verify the results of CMIP6. The single model large sample set data only provides a combination of internal variability, avoiding the model construction differences in CMIP6 and reducing the uncertainty of the model.

[0039] Step S2: Combine the data obtained in step S1 to calculate the normalized time series of air temperature, precipitation, surface soil moisture and scPDSI, and evaluate the dependency of high temperature-atmospheric dryness composite events and the severity of the composite events;

[0040] As an embodiment, the present invention combines the data obtained in step S1 and uses the Penman-Monteis formula recommended by the Food and Agriculture Organization of the United Nations to calculate potential evapotranspiration; uses potential evapotranspiration, precipitation and effective soil moisture to calculate scPDSI; for horizontal comparison of different variables and different data sets, precipitation, air temperature, surface soil moisture and scPDSI are standardized. Using the standardized data, the probability of the joint occurrence of low precipitation and high temperature is calculated; the Copula function is used to calculate the probability multiplication factor for evaluating the high temperature-atmospheric dryness composite event dependency, and the standardized composite event index (SCEI) for evaluating the severity of the composite event.

[0041] In step S2, precipitation, temperature, surface soil moisture and scPDSI are standardized and calculated to obtain the standard deviation of the anomaly (y), which is calculated as follows:

[0042]

[0043] Where x is the time series of precipitation, temperature, surface soil moisture and scPDSI, x ref is the reference period time series, It is expressed as the mean value of the reference period, detrend is the linear detrend, and δ is the standard deviation. The calculated result is called the "standard deviation of the deviation", and "SD" is set as its unit.

[0044] The variables of CMIP6 are used: sensible heat flux, latent heat flux, relative humidity, wind speed, maximum temperature and minimum temperature. The Penman-Montetus formula is used to calculate PET in combination with elevation data, and then scPDSI is calculated by combining precipitation and effective soil moisture content. The Penman-Montetus formula is as follows:

[0045]

[0046] Where Δ is the slope of the saturated water vapor pressure curve, R n is the net radiation of the surface, G is the soil heat flux density, T is the daily average temperature at 2 meters, γ is the psychrometric constant, u2 is the wind speed at 2 meters, e s -e a is the difference between the saturated vapor pressure and the actual vapor pressure.

[0047] The Copula function is used to calculate the probability multiplication factor to measure the dependence between precipitation and temperature. The calculation formula is as follows:

[0048] p=f(T>prob 90 ∩P <prob 10 )

[0049] Where f represents the joint cumulative probability, T represents the temperature, P represents the precipitation, and prob 90 and prob 10 Respectively represent the 90th or 10th percentile exceeding the reference period, and p represents the joint probability of temperature and precipitation under this condition. The probability multiplication factor is the ratio of the joint probability to the probability of assuming independent distribution. This step assumes that the temperature and precipitation are respectively greater than their 90th percentile and lower than the 10th percentile. Therefore, the probability of independent distribution is 0.1×0.1. The probability multiplication factor of the present invention can be finally simplified to p / 0.01. The probability multiplication factor is greater than 1 and the larger it is, the stronger the dependence of high temperature and atmospheric dryness.

[0050] Similarly, the present invention uses the Copula function to construct the SCEI, and the variables used are the standardized precipitation index and the standardized temperature index. The calculation formula is as follows:

[0051] P(X>x∩Y≤y)=P(Y≤y)-P(X≤x∩Y≤y)

[0052] Where P represents the joint probability distribution of the variables, X and Y are the marginal probability distributions of the standardized temperature index and standardized precipitation index of the random variables, respectively, and x and y represent the values ​​corresponding to each random variable. After calculating the joint probability, the SCEI is obtained after transformation using the standard normal distribution, which is used to measure the severity of high temperature-atmospheric dryness.

[0053] Step S3: Response of regional extreme drought to high temperature and atmospheric dryness conditions in historical periods; Combine the temperature, precipitation, surface soil moisture and scPDSI normalized time series calculated in step S2 to evaluate the response of regional extreme drought to changes in high temperature-atmospheric dryness conditions;

[0054] It should be noted that step S3 is specifically as follows: combining the temperature, precipitation, surface soil moisture and scPDSI standardized time series calculated in step S2, comparing the changes in the probability of regional extreme drought occurring simultaneously with high temperature, atmospheric dryness and high temperature-atmospheric dryness in the past 100 years and the past 20 years, and analyzing the temporal evolution characteristics of drought and high temperature and atmospheric dryness in historical periods; constructing the coupling relationship between regional temperature and precipitation, and comparing the changes in this coupling relationship in the past 20 years and the past 100 years; combining scPDSI and surface soil moisture, constructing the three-dimensional relationship and changes of temperature, precipitation and drought, and reflecting the response of regional extreme drought to changes in high temperature-atmospheric dryness conditions through the distribution of regional extreme drought in the two-dimensional coordinates of temperature-precipitation in different periods.

[0055] Step S4: Temporal patterns and dependencies of high temperature-atmospheric dryness composite conditions under the background of anthropogenic climate change; combining the standardized time series of temperature and precipitation obtained in step S2, using the CMIP6 future scenario, to estimate the probability of occurrence of different levels of high temperature-atmospheric dryness conditions in the future;

[0056] It should be noted that step S4 is specifically as follows: combining the standardized time series obtained in step S2, comparing the changing trends of different levels of high temperature-atmospheric dryness composite conditions of CMIP6 under historical full forcing experiments and natural forcing experiments only, and analyzing the role of anthropogenic climate change in high temperature-atmospheric dryness composite conditions; calculating the probability multiplication factor, analyzing the changes in the interdependence of high temperature and atmospheric dryness conditions in the future relative to historical periods; using future scenarios of CMIP6, including low, medium and high emission scenarios, to estimate the probability of occurrence of different levels of high temperature-atmospheric dryness conditions in the future.

[0057] In step S4, in order to facilitate the comparison of the probability multiplication factors of the historical period and the future period, the historical period is used as the reference period for both periods. When predicting the probability of different levels of high temperature-dry atmosphere conditions in the future, the historical full forcing and future high emission scenarios are used, with a total of 200 years as the reference period, to reduce the deviation caused by the extreme temperature increase under the future high emission scenario.

[0058] Step S5: Estimation of the recurrence probability of regional extreme drought compound events driven by high temperature and atmospheric dryness; Calculate the additional impact of high temperature and atmospheric dryness on drought respectively, and clarify the driving factors affecting regional extreme drought; Based on the driving factors, quantify the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding the observed history every 10 years;

[0059] It should be noted that in step S5, the recurrence probability of regional extreme drought complex events driven by high temperature and atmospheric dryness is estimated; the additional impact of high temperature and atmospheric dryness on drought is calculated respectively to clarify the most important driving factors affecting regional extreme drought; taking the most extreme high temperature-atmospheric dryness-drought event observed in regional history as the standard, and the regional average values ​​of high temperature, atmospheric dryness and drought of this event as the evaluation threshold, the model simulation data is used to calculate the historical and future occurrence probabilities of high temperature, atmospheric dryness and high temperature-atmospheric dryness composite conditions under the evaluation threshold; the changes in the probability of drought events of this level occurring in historical and future periods of multi-model model simulation data are calculated; the changes in the average state of future precipitation and temperature relative to historical periods when extreme drought events occur are evaluated, and the changes in the degree of dependence of regional extreme drought on high temperature and atmospheric dryness under high emission scenarios are clarified; finally, the probability of the occurrence of extreme high temperature-atmospheric dryness-drought events exceeding the observed historical period is quantified for every 10 years.

[0060] To calculate the additional impact of high temperature and atmospheric dryness, step S5 is as follows:

[0061] S51: Calculate the average values ​​of scPDSI and surface soil moisture when the temperature exceeds the 90th percentile of the reference period and / or the precipitation is less than the 10th percentile of the reference period, respectively. The formula is as follows:

[0062] D dry-hot =mean(D|T>prob 90 ∩P<prob 10 )

[0063] D hot =mean(D|T>prob 90 )

[0064] D dry =mean(D|P<prob 10 )

[0065] Where D dry-hot represents the average value of scPDSI or surface soil moisture under the conditions where the temperature exceeds the 90th percentile of the reference period and the precipitation is less than the 10th percentile of the reference period, D hot and D dry denote the average values ​​of scPDSI or surface soil moisture when only temperature exceeds the 90th percentile and only precipitation is less than the 10th percentile, respectively, and D denotes drought measured by scPDSI or surface soil moisture.

[0066] S52: Additional effects of high temperatures and atmospheric dryness (AE hot and AE dry ) can be expressed as the following formula: AE dry=D dry-hot -D hot , A.E. hot =D dry-hot -D dry .

[0067] Step S6: The spatiotemporal evolution characteristics and driving factors of the severity of the high temperature-atmospheric dryness composite event; Combined with the precipitation and temperature data obtained in step S1, the standardized precipitation index and standardized temperature index based on the original data and the linear trend removed are calculated respectively, and the SCEI is calculated based on these two indices to clarify the dominant factors affecting the changes in the SCEI in the historical period;

[0068] Combined with the precipitation and temperature data obtained in step S1, the standardized precipitation index and standardized temperature index based on the original data and the linear trend removed are calculated respectively, and the SCEI is calculated based on these two indices to quantify the temporal evolution trend of the severity of the original data and the high temperature-atmospheric dryness composite event with the linear trend removed. The Pearson correlation coefficient of the SCEI calculated based on the original data and the data with the linear trend removed is calculated to determine whether the fluctuation of the SCEI is caused by the long-term trend or the coupling of precipitation and temperature. The three-dimensional relationship between the standardized precipitation index, the standardized temperature index and the SCEI is constructed, and the historical period is divided into three different stages to determine the response of the SCEI to temperature and precipitation in different periods. The multivariate linear regression method is used to quantify the contribution of changes in the regional standardized precipitation index and the standardized temperature index to the change of the SCEI, and to clarify the dominant factors affecting the changes in the SCEI in the historical period.

[0069] To quantify the contribution of the standardized precipitation index and the standardized temperature index to the SCEI, the specific formula of the multivariate linear regression method in this step is as follows:

[0070] ΔSCEI=a×ΔSTI+b×ΔSPI+c

[0071] Where a and b are regression coefficients, c is a constant, Δ is the difference between the variables in two periods, STI and SPI represent the standardized temperature index and standardized precipitation index, respectively.

[0072] Step S7: Detection of climate change signals of high temperature-atmospheric dryness composite events; using a detection and attribution method based on rotated empirical orthogonal decomposition and an optimal fingerprint method to identify human activity signals of regional high temperature-atmospheric dryness composite events.

[0073] Specifically, step S7 combines the regional spatial SCEI calculated in step 2 to perform a rotational empirical orthogonal decomposition on the SCEI three-dimensional spatiotemporal data of the grid points in the region to obtain a fingerprint pattern, and projects the obtained fingerprint pattern to the observed SCEI, quantifies the signal-to-noise ratio to calculate the significance level of the warming signal in the SCEI, and obtains the start time of the warming signal to verify the reliability of the signal.

[0074] The fingerprint method is used to compare the change amplitude of the observed and simulated SCEI. Combined with the SCEI calculated by experiments before the industrial revolution, the single-variable and double-variable optimal fingerprint methods are used to evaluate the scale factors of different external forcings and detect the signals of external forcing. Based on the scale factor and the trend of SCEI change, the contribution of external forcing to the observed SCEI change is quantified.

[0075] The two detection attribution methods are as follows:

[0076] S71: Detection and attribution method based on rotating empirical orthogonal decomposition. First, the three-dimensional SCEI data of the CMIP6 historical full forcing and future high emission scenarios are selected, and the rotating empirical orthogonal decomposition is used to extract the spatial fingerprint pattern of the main mode, that is, the dominant empirical orthogonal function of the spatial pattern with the maximum variance obtained by the spatiotemporal covariance matrix. The spatial fingerprint pattern is projected onto the observed SCEI and the SCEI of the pre-industrial revolution experiment. The projection formula is as follows:

[0077]

[0078] Where Proj(x) represents the projection value of each year, F(i, j) is the leading empirical orthogonal function, A(i, j) is the actual area of ​​each grid point, and SCEI(i, j, x) represents the observed or simulated SCEI value of each grid point at longitude i and latitude j.

[0079] Then the trend value of the projected time series is calculated, and the trend values ​​of the observed data and the CMIP6 historical full forcing projection for L years are respectively used as the signal S(L), and the standard deviation of the trend distribution of the SCEI of the CMIP6 pre-industrial revolution experiment for L years is used as the noise N(L), and the dimensionless signal-to-noise ratio SNR=S(L) / N(L) is calculated. Where L is a preset value. If the SNR exceeds 1.64 or 2.57, it means that the signal is detected at a confidence level of 90% and 99%, respectively. Based on the model data, the continuously increasing L value is set, and the calculated SNR is used to calculate the time when the signal appears, that is, the time when the forced signal begins to appear relative to the natural climate variability.

[0080] S72: Detect and quantify the impact of external forcing on observed SCEI changes using the optimal fingerprint method. This method uses multiple linear regression and assumes that the observed changes are a response to external forcing and natural climate variability. The formula is as follows:

[0081]

[0082] Where y is the observed SCEI, X is the simulated SCEI under external forcing, is the sampling uncertainty of X, β is the scaling factor to adjust the fingerprint to match the observation, and ε is the residual term associated with natural climate variability. The scaling factor is estimated using ordinary least squares and total least squares.

[0083] Step S8: Estimation of the severity of future high temperature-atmospheric dryness composite events under different temperature rise levels and emission scenarios; Combined with the CMI P6 temperature data obtained in step S1, calculate the temperature rise level relative to the pre-industrial revolution (1861-1900), correspond the SCEI with the temperature rise level, and analyze the relationship between the severity of high temperature-atmospheric dryness and temperature rise. Combined with the SCEI calculated by CMI P6 different future emission scenarios obtained in S1 and S2, analyze whether controlling emissions can limit the increase in the severity of high temperature-atmospheric dryness composite events.

[0084] In the method of the present invention, the mutation point of the severity change trend of the high temperature-atmospheric dryness composite event is calculated in step S8, and the mutation point is obtained by the parameter-free Pettitt detection method. In order to explore the reasons for the trend mutation point of the high temperature-atmospheric dryness composite event, the present invention estimates the future change trend of the standardized precipitation index and the standardized temperature index, and clarifies their impact on the SCEI.

[0085] As an example, the present invention takes the summer and autumn seasons (August-November) in the middle and lower reaches of the Yangtze River in China (110°-119°E, 25°-34°N) from 1901 to 2020 as an example to further describe the present invention. The implementation case is used to illustrate the present invention, but this case is not used to limit the scope of application of the present invention. It is also applicable to different regions and other time periods.

[0086] The specific steps of the method of the present invention are as follows:

[0087] (1) Collection of basic data;

[0088] In this example, the temperature and precipitation from the National Science and Technology Basic Conditions Platform-National Earth System Science Data Center were collected, covering the period from 1901 to 2020 with a resolution of 1 km; the CN05 gridded observation dataset from the National Climate Change Research Center of China, covering the period from 1961 to 2022 with a resolution of 0.25°; the scPDSI data version 4.07 from the Climate Research Unit (CRU), covering the period from 1901 to 2022 with a resolution of 0.5°, and the surface soil moisture from the fifth generation European Reanalysis data (ERA5) of the European Centre for Medium-Range Weather Forecasts, covering the period from 1961 to 2020 with a resolution of 0.1°.

[0089] Based on the data from CMIP6, the historical and future experimental patterns of changes in the frequency of high temperature-atmospheric dryness composite events and droughts are shown in the following table:

[0090] Table 1 CMIP6 multi-model ensemble simulations for assessing hot and dry conditions and drought

[0091]

[0092]

[0093] The patterns of historical and future experiments on the change in severity of high temperature and atmospheric dryness events are shown in the following table:

[0094] Table 2 Multi-model ensemble for assessing the severity of high temperature-atmospheric dryness combined events

[0095]

[0096]

[0097] Note: * indicates that the data of this mode is available, - indicates that it is not available;

[0098] This example also collects precipitation and temperature data from the Geophysical Fluid Dynamics Laboratory's Seamless System for Prediction and Earth System Research (SPEAR) dataset, a total of 30 sets, including historical full forcing and future high emission scenario SSP585, covering 1921–2100 with a horizontal resolution of 0.5°.

[0099] (2) Response of regional extreme drought to high temperature and atmospheric dryness in historical periods

[0100] This example standardizes temperature, precipitation, scPDSI, and surface soil moisture. The result is called "standard deviation of anomalies" with "SD" as its unit. Using 2001-2020 as the last 20 years and 1901-2000 as the last century, it is found that the probability of extreme drought (scPDSI <–1) from the last century to the last 20 years increased from 18% to 35%, and the probability of drought and atmospheric dryness occurring simultaneously in these two periods increased from 57% to 75%. However, when analyzing the probability of atmospheric dryness alone, there is no significant change in the two periods, which are 21% and 20%, respectively (e.g. Figure 2 (shown in Figure 2). Although atmospheric dryness is a prerequisite for drought, high temperatures can promote the occurrence and development of drought. Based on this, it is assumed that high temperatures affect the probability of drought. The probability of high temperatures accompanying drought has increased from 33% to 100% in the past 20 years ( Figure 2 The probability of high temperature itself increased from 36% to 90%, which also led to an increase in the combined probability of high temperature and atmospheric dryness (from 15% to 45%; e.g. Figure 2 As shown in Figure 2 ). This proves that high temperature increases the probability of atmospheric dryness turning into drought. Further analysis of the temperature-precipitation coupling relationship and its changes shows that temperature and precipitation are negatively correlated, and the difference between the past 20 years and the past century reveals the increase in the probability of high temperature-atmospheric dryness coupling (e.g. Figure 3 As shown in Figure 2). On this basis, drought is added to construct a three-dimensional relationship between precipitation, temperature and drought. In the past century, droughts were mainly distributed in areas with negative standard deviations of precipitation anomalies, while in the past 20 years, the probability of droughts being distributed in areas with high temperature and dry atmosphere increased from 33.3% to 85.7%. CMIP6 provides more samples, and the probability of droughts in the two periods increased from 44.3% to 73.4% (as shown in Figure 2). Figure 4 The changes in surface soil moisture verified the results of scPDSI. The changes in the relationship between drought and high temperature-atmospheric dryness during the historical period revealed that high temperature-atmospheric dryness has become the most important factor driving regional extreme drought.

[0101] (3) Temporal patterns and dependencies of high temperature and atmospheric dryness complex conditions driven by human factors

[0102] In this implementation case, the observed probability of high temperature and atmospheric dryness was observed, and the model data was used to analyze the impact of anthropogenic climate change on the probability change of this condition, and to estimate the future high temperature and atmospheric dryness compound conditions. Under the historical full forcing scenario, precipitation showed a significant downward trend of –0.49SD / century, and temperature rose significantly, by 0.87SD / century (e.g. Figure 5However, when considering only natural forcing, neither precipitation nor temperature changes significantly. This reflects that human-driven precipitation reduction and temperature increase. The change in high temperature-atmospheric dryness increases by 17% per century, and also does not change significantly under natural forcing alone ( Figure 5 ), therefore, anthropogenic climate change has driven the increase of high temperature-atmospheric dryness composite conditions. In order to evaluate the changes in the probability and dependence of the simultaneous occurrence of high temperature and atmospheric dryness, the probability multiplication factor was further calculated. During the period 1961-2020, the average probability multiplication factor of the middle and lower reaches of the Yangtze River in the study area of ​​the implementation case was 2.09, while in the future period 2041-2100, the probability multiplication factor was 11, an increase of 5.5 times, indicating that the probability and dependence of the simultaneous occurrence of high temperature-atmospheric dryness have greatly increased. The future development of high temperature-atmospheric dryness conditions was further predicted, and four levels of high temperature-atmospheric dryness conditions were set, such as Figure 6 As shown, it can be found that the probability of atmospheric dry conditions and mild and moderate high temperature-atmospheric dry conditions in the future are decreasing, while the probability of extreme and abnormal high temperature-atmospheric dry conditions is increasing, and finally they all coincide with atmospheric dry conditions.

[0103] (4) Estimation of the recurrence probability of regional extreme drought complex events driven by high temperature and atmospheric dryness

[0104] The study area in the implementation case experienced the most severe summer-autumn drought observed in 2019, which lasted from August to November, accompanied by the most severe atmospheric dryness and the second most severe high temperature conditions in 120 years. The thresholds of high temperature, atmospheric dryness and drought are set as the regional average values ​​of temperature, precipitation and scPDSI or surface soil moisture during the summer-autumn drought in the middle and lower reaches of the Yangtze River in 2019. The probability of an event exceeding the severity of the summer-autumn drought in 2019 is analyzed by model data using this threshold, and the role of high temperature and atmospheric dryness on drought is clarified. First, the impact of the combined effects of high temperature and atmospheric dryness on drought in historical and future periods is calculated respectively. It can be found that the high temperature-atmospheric dryness conditions exceeding the same threshold will cause more severe drought in the future period (2041–2100), with a regional average of –1.56SD, while the regional average in the historical period (1961–2020) is –1SD. Considering only the additional effect of high temperature, the regional average value of drought caused in the historical period is –0.1SD, while in the future it is –0.001SD. Considering only the additional impact of atmospheric dryness, the drought caused in the historical period was –0.54SD, while the drought caused in the future period was more severe, with a regional average of –0.91SD. This shows that atmospheric dry conditions play a dominant role in the occurrence of drought in the future period. Due to the increase in the probability of high temperature, drought is more sensitive to atmospheric dry conditions, that is, the same degree of atmospheric dryness will cause more severe drought in the future. Further evaluating the changes in the probability of the simultaneous occurrence of high temperature and atmospheric dry conditions exceeding the severity of 2019, the probability of this level of conditions increased from 0.05% in the historical period to 1.22% in the future period, corresponding to the increase in the probability of high temperature from 0.73% to 91.6%, and the probability of atmospheric dry conditions from 0.68% to 1.25%, indicating that the increase in high temperature conditions in the future leads to an increase in the probability of high temperature-atmospheric dryness composite conditions.

[0105] The present invention further evaluates the probability of drought events exceeding the 2019 summer and autumn periods in the historical and future periods based on model data, such as Figure 7 As shown, it can be found that the drought characterized by scPDSI increased from 1.59% in the historical period to 17.82% in the future period. Correspondingly, when drought occurs, the state of high temperature increases from 0.54SD to 7.48SD, and the state of atmospheric dryness increases from –0.76SD to –0.19SD. This shows that in the future, due to the great increase in the probability of high temperature, even if the atmospheric dryness is far lower than that in the historical period, it can cause the occurrence of summer and autumn droughts of the same severity as in 2019. From the perspective of two centuries, the probability of high temperature-atmospheric dryness increases from none to 1.45% / century, and the probability of droughts exceeding the severity of 2019 increases from 2.3% in the 20th century to 14.9% in the 21st century.

[0106] (5) Spatial and temporal variation of the severity of high temperature-atmospheric dryness combined events and their driving factors

[0107] The present invention calculates the original time series and the time series without linear trend of the Standardized Precipitation Index (SPI), Standardized Temperature Index (STI) and Standardized Composite Event Index (SCEI), and calculates the Pearson correlation coefficient between the indexes. Figure 8 As shown in the figure, from 1901 to 2022, the observed STI increased significantly, increasing by 0.78 per century, while the SPI did not change significantly. The SCEI under the joint influence decreased by –0.81 per century, indicating an increase in the severity of high temperature-atmospheric dryness composite events. From 1901 to 1960, the correlation coefficient between SPI and STI was –0.08, while the correlation coefficient from 1961 to 2022 was –0.19, indicating that the negative correlation between precipitation and temperature has increased in the past 62 years. The correlation coefficient between the SCEI calculated by removing the linear trend of SPI and STI and the SCEI calculated by the original data is 0.91, indicating that the fluctuation of SCEI is mainly affected by the coupling relationship between high temperature and atmospheric dryness. The three-dimensional spatial distribution of SPI, STI and SCEI was constructed. The probability of the occurrence of extreme high temperature-atmospheric dryness composite events increased significantly after 1981, reaching 1.32%, compared with 0.59% in the previous two periods (1901–1950) and 0.57% in 1951–1980. It has increased significantly in the past 40 years (e.g. Fig. 9 The distribution of extreme high temperature-atmospheric dryness composite events is in the direction of high temperature, while the natural forcing and detrended data alone do not show obvious changes, which reveals that anthropogenic climate change increases the probability of high temperature-atmospheric dryness composite events and increases their dependence on high temperature.

[0108] The present invention uses multiple linear regression to quantitatively analyze the contribution of changes in SPI and STI to changes in SCEI. During 1901-2020, the observed STI dominated the change in SCEI, contributing 82.3%. The model-simulated STI is still the dominant factor, with 60.3% contributed by historical full forcing and 57.1% contributed by natural forcing alone. Regardless of whether anthropogenic climate change is considered, STI is the dominant factor in SCEI. With further greenhouse gas emissions in the future, the contribution of STI will further increase, reaching 79.4% in the future low emission scenario and 88.4% in the high emission scenario.

[0109] (6) Spatiotemporal detection and attribution of anthropogenic climate change for high temperature-atmospheric dryness complex events

[0110] The trend of the severity of the high temperature-atmospheric dryness composite event under the full historical forcing of CMIP6 is close to the observed value, with the SCEI decreasing by –0.7 per century. However, under natural forcing only, the SCEI has no significant change, which indicates that anthropogenic climate change has led to a decrease in the SCEI and aggravated the severity of the high temperature-atmospheric dryness composite event (e.g. Fig.10 shown).

[0111] Two detection and attribution methods are further used to identify anthropogenic climate change signals. Using the detection and attribution method based on the rotational empirical orthogonal decomposition (such as Fig.11 (shown in Figure 2), the spatial fingerprint of the main mode of the three-dimensional spatiotemporal data of the regional grid under the historical full forcing and future high emission scenarios is extracted. This fingerprint pattern explains 97.8% of the total variance in the SCEI and captures the signal of the original SCEI very well. Due to the full forcing and high emission scenarios adopted, the fingerprint signal in this method comes from anthropogenic climate warming. Consistent with the overall regional decline of the observed SCEI, the fingerprint shows an overall negative value in the middle and lower reaches of the Yangtze River, that is, the severity of the high temperature-atmospheric dryness composite event has increased. The spatial fingerprint pattern is projected onto the observed SCEI, and the resulting projection sequence shows a significant upward trend, indicating that the signal of anthropogenic climate warming can be captured. The signal-to-noise ratio calculated on this basis is 3.77, indicating that the signal of anthropogenic climate change can be detected at a confidence interval of 99%. The time of signal occurrence estimated by the full forcing and high emission scenarios, the signal of human activities was detected at the confidence intervals of 90% and 99% in 2017 and 2036, respectively, verifying the reliability of the anthropogenic climate warming signal in the historical period.

[0112] Another quantitative detection and attribution method is the optimal fingerprint method (such as Fig.12 The signals of total forcing and greenhouse gas forcing are reliably detected, with the range of scaling factors for total forcing including 1, indicating that total forcing effectively reproduces the observed SCEI changes. In contrast, the signal scaling factor for greenhouse gas forcing is less than 1, indicating that considering greenhouse gases alone overestimates the observed SCEI changes. The bivariate detection separates the signal of natural forcing alone and detects the signals of total forcing and greenhouse gases equally reliably. Total forcing contributes 83.6% of the decrease in SCEI between 1901 and 2020, while greenhouse gas forcing contributes 86.7%.

[0113] (7) Prediction of the severity of future high temperature and atmospheric dryness combined events

[0114] Considering that anthropogenic climate warming is the main driver of the severity of high temperature and atmospheric dryness events, it is necessary to analyze how the SCEI responds to the continued increase in greenhouse gas emissions under future scenarios. Fig.13As shown in the figure, SCEI will continue to decline before 3.5℃, and above 3.5℃, SCEI will fluctuate around an extremely low value. If the temperature rise is controlled at 1.5℃ or 2℃, SCEI will be between –1.7 and –2.1, effectively controlling the temperature rise and helping to alleviate the deterioration of the severity of the high temperature-atmospheric dryness complex event.

[0115] The present invention uses the non-parametric Pettitt test method to detect the mutation points of SCEI time changes in four future scenarios. Fig.14 As shown in the figure, under the four scenarios, the mutation point appears around 2050. Before the mutation point, SCEI shows a significant decline, from 1.92 to 4.3 per century from low emission to high emission scenario. After the mutation point, SCEI fluctuates around low values, with low and high emissions at -2.38 and -2.95 respectively. Controlling emissions can effectively alleviate the severity of high temperature-atmospheric dryness composite events.

[0116] In order to explore the factors behind the changes in SCEI, the present invention analyzes the future time series of SPI and STI. STI shows a significant upward trend, from low to high, rising by 1.28 to 5.74 per century, and SPI also shows a significant upward trend, especially under the low emission scenario, where the SPI increases the most. Although the increase in SPI partially alleviates the aggravation of the severity of the high temperature-atmospheric dryness composite event by STI, in future emission scenarios, when the warming level exceeds 3.5°C, SCEI will fluctuate at a new normal severity level that exceeds the most extreme value in the historical period.

[0117] See also Fig.15 , Fig.15 4 is a schematic diagram of the working of the hardware device of an embodiment of the present invention, and the hardware device specifically includes: a drought event climate change signal detection device 401 under high temperature and dry conditions, a processor 402 and a storage medium 403.

[0118] A device 401 for detecting climate change signals of drought events under high temperature and dry conditions: The device 401 for detecting climate change signals of drought events under high temperature and dry conditions implements a method for detecting climate change signals of drought events under high temperature and dry conditions.

[0119] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the method for detecting climate change signals of drought events under high temperature and dry conditions.

[0120] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the method for detecting climate change signals of drought events under high temperature and dry conditions.

[0121] The beneficial effects of the present invention are:

[0122] (1) The present invention makes up for the lack of response of the probability of occurrence of regional extreme drought events to high temperature-atmospheric dryness in previous studies. And the present invention analyzes the changes in the probability of occurrence and dependency of high temperature-atmospheric dryness from a century-long perspective. The present invention uses a future high emission scenario to quantify the additional impact of high temperature and atmospheric dryness on drought from an urgency perspective, clarifies the average state of high temperature and atmospheric dryness when drought occurs, and clarifies the dominant role of high temperature and atmospheric dryness. The present invention uses two sets of observational data sets and single model large sample data sets to increase the credibility of the results. The present invention is suitable for global and regional research.

[0123] (2) This paper quantifies the severity of the composite high temperature-atmospheric dryness event, and uses the detection and attribution method based on the rotational empirical orthogonal decomposition and the optimal fingerprint method to identify the anthropogenic climate change signal. It also analyzes the severity of the high temperature-atmospheric dryness event under different temperature rise levels and emission scenarios, emphasizing the important role of controlling temperature rise and emissions in controlling the severity of composite events.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting climate change signals of drought events under high temperature and dry conditions, characterized in that: The method comprises the following steps: Step S1: Data collection: collecting drought event climate change data; Step S2: Combine the data obtained in step S1 to calculate the normalized time series of air temperature, precipitation, surface soil moisture and scPDSI, and evaluate the dependency of high temperature-atmospheric dryness composite events and the severity of the composite events; Step S3: Response of regional extreme drought to high temperature and atmospheric dryness conditions in historical periods; Combine the temperature, precipitation, surface soil moisture and scPDSI normalized time series calculated in step S2 to evaluate the response of regional extreme drought to changes in high temperature-atmospheric dryness conditions; Step S4: Temporal patterns and dependencies of high temperature-atmospheric dryness composite conditions under the background of anthropogenic climate change; combining the standardized time series of temperature and precipitation obtained in step S2, using the CMIP6 future scenario, to estimate the probability of occurrence of different levels of high temperature-atmospheric dryness conditions in the future; Step S5: Estimation of the recurrence probability of regional extreme drought compound events driven by high temperature and atmospheric dryness; Calculate the additional impact of high temperature and atmospheric dryness on drought respectively, and clarify the driving factors affecting regional extreme drought; Based on the driving factors, quantify the probability of historical and future regional extreme high temperature-atmospheric dryness-drought events exceeding the observed history every 10 years; Step S6: The spatiotemporal evolution characteristics and driving factors of the severity of the high temperature-atmospheric dryness composite event; Combined with the precipitation and temperature data obtained in step S1, the standardized precipitation index and standardized temperature index based on the original data and the linear trend removed are calculated respectively, and the SCEI is calculated based on these two indices to clarify the dominant factors affecting the changes in the SCEI in the historical period; Step S7: Detection of climate change signals of high temperature-atmospheric dryness composite events; using a detection and attribution method based on rotational empirical orthogonal decomposition and an optimal fingerprint method to identify human activity signals of regional high temperature-atmospheric dryness composite events; Step S8: Estimation of the severity of future high temperature-atmospheric dryness complex events under different temperature rise levels and emission scenarios; Combined with the data obtained in step S1, calculate the temperature rise level relative to the pre-industrial revolution, correspond the SCEI with the temperature rise level, and analyze the relationship between the severity of high temperature-atmospheric dryness and temperature rise; Combined with the SCEI calculated for different future emission scenarios of CMIP6 obtained in S1 and S2, analyze whether controlling emissions can limit the increase in the severity of high temperature-atmospheric dryness complex events.

2. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 1, characterized in that: In step S1, the climate change data of drought events include: regional precipitation, temperature data and self-calibrated Palmer Drought Index scPDSI based on observations and surface soil moisture data; temperature, precipitation, surface soil moisture, latent heat flux, sensible heat flux, relative humidity, wind speed, maximum temperature, minimum temperature data, as well as elevation and effective soil moisture data of the sixth phase of the International Coupled Comparison Program CMIP6; a single model large sample set from the SPEAR data set of the Geophysical Fluid Dynamics Laboratory's Seamless System for Prediction and Earth System Research, including precipitation and temperature data.

3. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 2, characterized in that: Step S2 is specifically as follows: the Penman-Monteis formula is used to calculate potential evapotranspiration; the self-calibrated Palmer Drought Index (scPDSI) is calculated using potential evapotranspiration, precipitation and effective soil moisture content; precipitation, temperature, surface soil moisture and the self-calibrated Palmer Drought Index (scPDSI) are standardized for horizontal comparison of different variables and different data sets; the probability of the joint occurrence of low precipitation and high temperature is calculated using the standardized data; the Copula function is used to calculate the probability multiplication factor for evaluating the dependency of high temperature-atmospheric dryness composite events and the standardized composite event index SCEI for evaluating the severity of composite events.

4. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 3, characterized in that: Step S3 is specifically as follows: Combined with the temperature, precipitation, surface soil moisture and scPDSI standardized time series calculated in step S2, the changes in the probability of regional extreme drought occurring simultaneously with high temperature, atmospheric dryness, and high temperature-atmospheric dryness in the past 100 years and the past 20 years are compared, and the temporal evolution characteristics of drought, high temperature, and atmospheric dryness in historical periods are analyzed; The coupling relationship between regional temperature and precipitation was constructed, and the changes in this coupling relationship in the past 20 years and the past 100 years were compared; the three-dimensional relationship and changes of temperature, precipitation and drought were constructed by combining scPDSI and surface soil moisture, and the distribution of regional extreme drought in the two-dimensional coordinates of temperature and precipitation in different periods was used to reflect the response of regional extreme drought to changes in high temperature and atmospheric dryness conditions.

5. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 4, characterized in that: Step S4 specifically comprises: combining the scPDSI standardized time series obtained in step S2, comparing the changing trends of different levels of high temperature-atmospheric dryness composite conditions of CMIP6 under historical full forcing experiments and natural forcing experiments only, and analyzing the role of anthropogenic climate change in high temperature-atmospheric dryness composite conditions; The probability multiplication factor is calculated to analyze the changes in the interdependence between high temperature and atmospheric dryness in the future relative to historical periods; the probability of occurrence of different levels of high temperature-atmospheric dryness in the future is estimated using the CMIP6 future scenarios.

6. A method for detecting climate change signals of drought events under high temperature and dry conditions as claimed in claim 5, characterized in that: Step S5 is specifically as follows: respectively calculating the additional impact of high temperature and atmospheric dryness on drought, clarifying the driving factors affecting regional extreme drought; taking the most extreme high temperature-atmospheric dryness-drought event observed in regional history as the standard, taking the regional average values ​​of high temperature, atmospheric dryness and drought of the event as the evaluation threshold, and using model simulation data to calculate the historical and future occurrence probabilities of high temperature, atmospheric dryness and high temperature-atmospheric dryness composite conditions under the evaluation threshold; calculating the changes in the probability of drought events of this level occurring in historical and future periods of multi-model model simulation data; Evaluate the changes in the average state of precipitation and temperature in the future relative to the historical period when extreme drought events occur, clarify the changes in the dependence of regional extreme drought on high temperature and atmospheric dryness under high emission scenarios, and finally, quantify the probability of historical and future regions experiencing extreme high temperature-atmospheric dryness-drought events exceeding the observed historical level every 10 years.

7. A method for detecting climate change signals of drought events under high temperature and dry conditions as claimed in claim 6, characterized in that: Step S6 is specifically as follows: combining the precipitation and temperature data obtained in step S1, respectively calculating the standardized precipitation index and the standardized temperature index based on the original data and the linear trend removed, and calculating the SCEI based on these two indices to quantify the time evolution trend of the severity of the original data and the high temperature-atmospheric dryness composite event with the linear trend removed; The Pearson correlation coefficient of SCEI calculated based on the original data and the data with the linear trend removed was calculated to determine whether the fluctuation of SCEI was caused by the long-term trend or precipitation-temperature coupling; a three-dimensional relationship between the standardized precipitation index, the standardized temperature index and SCEI was constructed, and the historical period was divided into three different stages to determine the response of SCEI to temperature and precipitation in different periods; the multivariate linear regression method was used to quantify the contribution of changes in the regional standardized precipitation index and standardized temperature index to the change of SCEI, and to clarify the dominant factors affecting the changes in SCEI in the historical period.

8. The method for detecting climate change signals of drought events under high temperature and dry conditions according to claim 7, characterized in that: Step S7 is specifically as follows: S71: Detection and attribution method based on rotating empirical orthogonal decomposition. First, the three-dimensional SCEI data of the CMIP6 historical full forcing and future high emission scenarios are selected. Rotating empirical orthogonal decomposition is used to extract the spatial fingerprint pattern of the main mode, that is, the dominant empirical orthogonal function of the spatial mode with the maximum variance obtained by the spatiotemporal covariance matrix. The spatial fingerprint pattern is projected onto the observed SCEI and the SCEI of the pre-industrial revolution experiment. The projection formula is as follows: Where Proj(x) represents the projection value of each year, F(i,j) is the dominant empirical orthogonal function, A(i,j) is the actual area of ​​each grid point, and SCEI(i,j,x) represents the observed or simulated SCEI value of each grid point at longitude i and latitude j; Then, the trend value of the projected time series is calculated, and the trend values ​​of the observed data and the CMIP6 historical full forcing projection for L years are respectively used as the signal S(L), and the standard deviation of the trend distribution of the SCEI of the CMIP6 pre-industrial revolution experiment for L years is used as the noise N(L). The dimensionless signal-to-noise ratio SNR=S(L) / N(L) is calculated, where L is a preset value. If the SNR exceeds 1.64 or 2.57, it indicates that the signal is detected at the confidence level of 90% and 99%, respectively. Based on the model data, a continuously increasing L value is set, and the calculated SNR is used to calculate the time when the signal appears, that is, the time when the forced signal begins to appear relative to the natural climate variability. S72: An optimal fingerprint method is used to detect and quantify the impact of external forcing on observed SCEI changes; this method uses multiple linear regression and assumes that the observed changes are a response to external forcing and natural climate variability. The formula is as follows: Where y is the observed SCEI, X is the simulated SCEI under external forcing, is the sampling uncertainty of X, β is the scaling factor that adjusts the fingerprint to match the observation, and ε is the residual term associated with natural climate variability. The scaling factor is estimated using ordinary least squares and total least squares.

9. A storage medium, characterized in that: The storage medium stores instructions and data for implementing a method for detecting climate change signals of drought events under high temperature and dry conditions as described in any one of claims 1 to 8.

10. A device for detecting climate change signals of drought events under high temperature and dry conditions, characterized in that: include: Processor and storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for detecting climate change signals of drought events under high temperature and dry conditions as described in any one of claims 1 to 8.

Images