A method, device, electronic device and storage medium for attributing climate change

Abstract

The present application discloses a method, device, electronic device and storage medium for attributing climate change. The method comprises: obtaining first data of climate elements in a target area within a target period, and second data of a circulation index within the target period; determining the circulation component of the climate elements at a target time within the target period according to the first data and the second data. This case analyzes three-dimensional climate elements through a one-dimensional circulation index having only a time dimension, and determines to what extent the climate elements are affected by the circulation index, thereby providing an exact analysis method for attributing the one-dimensional circulation index to the three-dimensional climate elements, which helps to have a deeper understanding of the driving factors of climate change, and provides a more accurate and stable scientific basis for climate prediction, and also has important reference significance for the formulation of climate response strategies.

Description

Technical Field

[0001] The present application relates to the field of climate analysis technology, and in particular to a climate change attribution method, device, electronic device and storage medium. Background Art

[0002] The Earth-Atmosphere System includes components such as the atmosphere, land surface, ocean, and biosphere, as well as the energy and material exchange processes between these systems. The detection and attribution research of climate change aims to qualitatively or quantitatively reveal the spatiotemporal evolution of the Earth-Atmosphere System and explore the driving principles and influencing factors of climate change.

[0003] Currently, mathematical statistics and numerical modeling methods are widely used in the field of earth sciences to attribute the evolution of climate factors, but these methods are difficult to fully and accurately express all the factors that can affect the climate, resulting in poor accuracy in the attribution of climate change. Summary of the invention

[0004] The present application provides a climate change attribution method, device, electronic device and storage medium to provide a cause analysis of one-dimensional circulation indicators for three-dimensional climate elements, thereby improving the accuracy of climate cause analysis.

[0005] According to one aspect of the present application, a method for attributing climate change is provided, the method comprising:

[0006] Acquire first data of climate elements of a target area within a target period, and second data of a circulation index within the target period;

[0007] The circulation component of the climate element at the target time within the target period is determined based on the first data and the second data.

[0008] According to another aspect of the present application, a climate change attribution device is provided, the device comprising:

[0009] A climate data acquisition module, used to acquire first data of climate elements of a target area within a target period, and second data of a circulation index within the target period;

[0010] The circulation component determination module is used to determine the circulation component of the climate element at the target time within the target period according to the first data and the second data.

[0011] According to another aspect of the present application, an electronic device is provided, the electronic device comprising:

[0012] at least one processor; and

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the climate change attribution method described in any embodiment of the present application.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the climate change attribution method described in any embodiment of the present application when executed.

[0016] According to another aspect of the present application, a computer program product is provided, the computer program product comprising a computer program, and the computer program, when executed by a processor, implements the climate change attribution method according to any embodiment of the present application.

[0017] In the technical solution of the embodiment of the present application, the first data of the climate elements of the target area in the target period and the second data of the circulation index in the target period are calculated to obtain the circulation component of the climate elements at the target time in the target period. It can be understood that this case analyzes the three-dimensional climate elements through a one-dimensional circulation index with only a time dimension to determine the extent to which the climate elements are affected by the circulation index, thereby providing a precise analysis method for the attribution of the one-dimensional circulation index to the three-dimensional climate elements, which helps to have a deeper understanding of the driving factors of climate change, and provides a more accurate and stable scientific basis for climate prediction, and also has important reference significance for the formulation of climate response strategies.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a flow chart of a climate change attribution method provided according to Embodiment 1 of the present application;

[0021] Figure 2 is a schematic diagram of the Euclidean distance between circulation indices at different times provided in Example 1 of the present application;

[0022] Figure 3is a schematic diagram of a climate change attribution analysis process provided according to Embodiment 1 of the present application;

[0023] Figure 4 is a structural schematic diagram of a climate change attribution device provided according to Embodiment 2 of the present application;

[0024] Figure 5 It is a schematic diagram of the structure of an electronic device for implementing the climate change attribution method of an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 A flow chart of a climate change attribution method is provided for the first embodiment of the present application. This embodiment can be applied to explain the degree of change of three-dimensional climate elements using a one-dimensional circulation index. The method can be executed by a climate change attribution device. The climate change attribution device can be implemented in the form of hardware and / or software. The climate change attribution device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S110, obtaining first data of climate elements of a target area within a target period, and second data of a circulation index within the target period.

[0030] Among them, the target area can be any geographical range, and can include but is not limited to multiple spatial scales from a certain region to the world. It should be noted that the target area can be a certain geographical range, such as the Beijing-Tianjin-Hebei region, eastern China or the Eurasian continent, etc., and the embodiments of this application are not exhaustive here. The target period may be a time range that needs to be studied, such as a few years or a few months. Climate elements can be any element that constitutes the atmospheric conditions in the target area, for example, it may include precipitation, temperature, and evaporation. It should be noted that the research objects of climate change attribution are mainly near-ground elements such as temperature and precipitation. The embodiments of this application and subsequent implementation methods will take precipitation as an example to explain the climate change attribution method proposed in this application.

[0031] The first data may be all the data of climate elements within the target period, and the first data represents any variable field of the geoatmospheric system within a certain time and space range, such as the annual cumulative precipitation anomaly data from 2000 to 2024.

[0032] The circulation index may be any parameter that characterizes the strength of atmospheric circulation in a certain area, such as but not limited to PDO (Pacific Decadal Oscillations) and AMO (Atlantic Multidecadal Oscillation). The second data of the circulation index in the target period may be a collection of the circulation indexes of all times in the target period.

[0033] Exemplarily, taking the annual cumulative precipitation anomaly of Tianjin as an example, the first data of the climate element of the target area in the target period can be the difference between the annual precipitation of Tianjin in 2000-2024 and the long-term climate state (for example, the unit can be mm), and the time range and spatial range here are only used as examples. The second data of the circulation index in the target period can be the PDO data of each year in the same 2000-2024 25 years. It can be seen that the first data has a spatiotemporal range, that is, the spatial range (limitation of longitude range and latitude range) and the time range constitutes three-dimensional information. The second data only needs to limit the time range, and the coverage time range and time resolution of the second data must be consistent with the climate element to have research value. It can be understood that the impact of ocean signals on near-ground climate change is wide-ranging. The climate change attribution method proposed in this application is also intended to analyze the impact of a one-dimensional circulation index on three-dimensional climate elements. It should be noted that both the first data and the second data can be obtained through professional means such as observation and data fusion, statistical analysis, etc. in the meteorological field. The embodiment of the present application is about how to analyze the relationship between the circulation index and climate elements, and does not limit the method of obtaining the first data and the second data.

[0034] S120, determining the circulation component of the climate element at the target time in the target period according to the first data and the second data. The target time may be any time range, time step or moment in the target period, for example, 2024 in 2000-2024 may be used as the target time. The circulation component of the climate element may be the part of the climate element that changes due to the influence of the circulation index.

[0035] For example, continuing the previous example, based on the annual precipitation anomaly data of Tianjin from 2000 to 2024, a total of 25 years, and the PDO data from 2000 to 2024, a total of 25 years, the annual precipitation in Tianjin in 2024 is taken as an example to calculate its circulation component. The long-term recorded PDO index and its corresponding precipitation field are calculated to analyze the component affected by PDO in the precipitation anomaly in 2024. For example, the influence weight of the circulation index of each year on the circulation index in 2024 can be calculated first, and then the circulation component can be calculated by the weighted average of the precipitation anomaly.

[0036] Continuing with the previous example, assuming that the spatial average annual cumulative precipitation anomaly of all grid points in Tianjin in 2024 is 20 mm, and the mean value of the circulation component in 2024 calculated in the previous steps is 5 mm, the ratio of the circulation component to the original data of the weather element is calculated, and 25% is obtained, which is the degree of influence of the circulation index on the change of climate elements. Alternatively, this example can be directly interpreted in meteorology as 5 mm of the 20 mm average annual cumulative precipitation anomaly in Tianjin in 2024 is caused by the influence of the PDO positive phase circulation type.

[0037] In the technical solution of the embodiment of the present application, the first data of the climate elements of the target area in the target period and the second data of the circulation index in the target period are calculated to obtain the circulation component of the climate elements at the target time in the target period. It can be understood that this case analyzes the three-dimensional climate elements through a one-dimensional circulation index with only a time dimension to determine the extent to which the climate elements are affected by the circulation index, thereby providing a precise analysis method for the attribution of the one-dimensional circulation index to the three-dimensional climate elements, which helps to have a deeper understanding of the driving factors of climate change, and provides a more accurate and stable scientific basis for climate prediction, and also has important reference significance for the formulation of climate response strategies.

[0038] In an optional implementation manner, determining the circulation component of the climate element at the target time within the target period according to the first data and the second data in S120 may include:

[0039] S121. All first data except the target time are used as climate element samples, and all second data except the target time are used as circulation index samples.

[0040] Among them, in order to study the impact of the circulation index of the target time on the climate, any time in the target period is taken as the target time for study (it can be understood that each time in the target period will be processed by the method proposed in this application), and the first data and second data of other times in the target period other than the target time are analyzed to find out the influencing factors from the impact of long-term circulation on the climate. Therefore, all the first data except the target time are first taken as climate element samples, and all the second data except the target time are taken as circulation index samples.

[0041] Continuing with the previous example, if we want to study the degree to which the annual cumulative precipitation anomaly in 2024 in the target area is affected by PDO, the PDO for 24 years except 2024, that is, 2000-2023, a total of 24 years, will be used as circulation index samples; similarly, the annual cumulative precipitation anomaly data for 24 years from 2000 to 2023 will be used as climate element samples.

[0042] S122. Calculate the influence weight of each circulation index sample according to the circulation index sample and the target second data at the target time.

[0043] Among them, the influence weight of the circulation index sample can be the degree of influence of the second data of each time point in the entire target period on the climate elements of the target time point, and the influence weight can be used to calculate the circulation component. For example, the circulation index sample of each time point and the target second data can be weighted by the inverse distance weight method to determine the influence weight of the circulation index sample of each other time point on the target second data. Alternatively, a machine learning regression model can be pre-trained, and the model inputs the target second data of the target time point and the circulation index samples of each time point except the target time point, and the model outputs the value of the influence weight corresponding to each circulation index sample. Exemplarily, continuing the previous example, the PDO value of each year from 2000 to 2023 and the PDO value in 2024 are used to calculate a total of 24 influence weights from 2000 to 2023.

[0044] S123, performing weighted average calculation on each impact weight and each climate element sample at a corresponding time, and obtaining an estimated value of the climate element affected by the circulation index at a target time.

[0045] Since the impact weights calculated in the previous steps correspond to the circulation index samples at each time, correspondingly, when performing weighted average calculations, the corresponding climate element samples at each time are also required. The result value of the weighted average calculation of each impact weight and each climate element sample during climate element valuation is used to characterize the estimated value of the climate element at the target time affected by the circulation index.

[0046] Exemplarily, continuing the previous example, the impact weights of each year from 2000 to 2023 are weighted averaged with the annual precipitation data of the corresponding years, and the result is an estimate of the impact of PDO on annual precipitation in 2024.

[0047] S124. Determine the circulation components based on the climate element valuation.

[0048] It is understandable that the climate element estimates obtained based on a small amount of data still have certain errors. Then, the climate element estimate can be directly used as the circulation component; or the sample size can be expanded by using, for example, the empirical Monte Carlo method. The empirical Monte Carlo method is a statistical simulation technique that estimates the probability of an event or the mathematical expectation of a random variable through random sampling. In climatological statistical analysis, this method can be used to expand the sample size in the time dimension and improve statistical credibility. Among them, the concept of resampling is to simulate different climate scenarios by randomly extracting samples from long-term recorded data, thereby expanding the sample size. The characteristics of this method can be learned directly from the data. On the basis of expanding the sample size, the climate element estimate is calculated multiple times, and the average value is taken as the circulation component, thereby reducing the error.

[0049] In the above implementation, the influence weights of each circulation index sample are calculated according to the circulation index sample and the target second data at the target time. Based on these influence weights, the climate element samples at the corresponding time are weighted averaged to obtain the climate element valuation, and the circulation component in the change of the environmental element is further determined. This provides a strategy for quantitative calculation of the impact of the circulation index on the climate elements. The obtained circulation component can directly reflect the quantitative basis for the impact of the circulation index on the climate requirements of the target area.

[0050] In a further embodiment, the step of calculating the influence weight of each circulation index sample according to the circulation index sample and the target second data of the target time in S122 includes:

[0051] A1. Randomly select a first preset number of samples to be processed from the circulation index samples.

[0052] Among them, the first preset number can be the number selected from all circulation index samples, so the first preset number needs to be less than or equal to the total number of circulation index samples, and these randomly selected circulation index samples are used as samples to be processed for subsequent calculations. Of course, the method of randomly selecting samples can adopt any random sampling algorithm in the relevant technology, and the embodiment of the present application is not limited to this. For example, continuing the previous example, 15 PDO data of 15 years in 24 years between 2000 and 2023 are randomly selected as samples to be processed.

[0053] A2. Calculate a first preset number of influence weights between each sample to be processed and the target second data.

[0054] These samples to be processed are processed in the manner proposed in the aforementioned real-time method, and the impact weights are calculated using these samples to be processed and the target second data. Since the number of samples to be processed is the first preset number, the impact weights of these time times also correspond to the first preset number.

[0055] Continuing with the previous example, the 15 randomly sampled PDO data are compared with the PDO data of 2024 using the inverse distance weighting method to obtain 15 impact weights corresponding to these 15 years.

[0056] A3. In response to the number of climate element estimates not satisfying the second preset number, return to executing the operation of randomly selecting a first preset number of samples to be processed from the circulation index samples until the first preset number of influence weights between each sample to be processed and the target second data are calculated.

[0057] In response to the number of climate element estimates satisfying a second preset number, determining the circulation component according to the climate element estimates in S124 may include: taking an average of the second preset number of climate element estimates as the circulation component.

[0058] As mentioned above, the climate factor estimates obtained based on data from a shorter target period still have certain errors, so the sample size can be expanded to calculate the climate factor estimates multiple times.

[0059] The second preset number is actually the number of times random sampling is repeated. The second preset number can be set by technicians in the relevant field based on experiments or manual experience, such as repeating random sampling 100 times, which is not limited in the present embodiment.

[0060] Continuing with the previous example, it should be noted that in the implementation of the present application, the samples to be processed for 15 years are not randomly sampled once and then the randomly sampled samples are copied 100 times; instead, the PDO data of all years from 2000 to 2023 are randomly sampled 100 times each time. The 15 PDO data obtained by random sampling each time are not all the same. The purpose is to expand the sample size and reduce the calculation error in the overall operation. The 15 PDO data obtained by random sampling each time have annual precipitation data for the corresponding year. These PDO data are calculated with the PDO of 2024 to obtain the impact weights of each year. Then, through the impact weights of each year and the annual precipitation data of the corresponding year, an estimated climate element affected by PDO can be calculated, and the average value of the 100 climate element estimates calculated is calculated, and the calculation result is used as the circulation component.

[0061] In the above implementation, by resampling the circulation index samples, the sample size is expanded, the error in statistical operation can be reduced, and random resampling from the circulation index samples can obtain more information about the overall data, which helps to make the calculated climate element estimates closer to the overall data; on this basis, the average value of these calculated climate element estimates is taken as the circulation component, which can obtain a circulation component result that is more in line with the overall data trend while fitting the overall data as closely as possible, thereby helping to improve the accuracy of climate cause analysis.

[0062] In an optional implementation, the calculation of the first preset number of influence weights between each sample to be processed and the target second data in A2 may include: calculating the Euclidean distance between each sample to be processed and the target second data; and using the reciprocal of each Euclidean distance as each influence weight.

[0063] In the embodiment of the present application, the inverse distance weighting method is adopted, firstly, the Euclidean distance between all randomly selected samples to be processed and the target second data is calculated, and then the inverse of the Euclidean distance is used as the influence weight corresponding to each sample to be processed. Of course, the method of calculating the Euclidean distance between two data can adopt any algorithm in the relevant technology, and the embodiment of the present application is not limited to this.

[0064] The above implementation method provides a practical solution for calculating the impact weight of the second data at each time. Through the inverse distance weighting method, the inverse of the Euclidean distance between each sample to be processed and the target second data is used as the impact weight. The distance between each sample to be processed and the target second data can be considered. The closer the distance to the target second data, the greater the weight, and the farther the distance to the target second data, the smaller the weight. Then, the inverse of the Euclidean distance as the impact weight can accurately reflect the impact of the second data at each time on the target second data, which helps to improve the accuracy of the circulation component, thereby improving the accuracy of the climate change attribution analysis.

[0065] While determining the circulation component as described above, in another optional implementation, the method may further include:

[0066] B1. Determine the target circulation estimate at the target time based on each sample to be processed and each impact weight.

[0067] Among them, the target circulation estimate can be an estimate of the second data of the target time obtained by calculating the to-be-processed samples of each time and the influence weights calculated in the aforementioned real-time manner. Although the second data of the target time already exists, the estimate of the circulation index of the target time (i.e., the target circulation estimate) is estimated by comprehensively estimating the second data of other time periods in the past. The target circulation estimate can be used for comparison with the existing second data of the target time, so as to understand the changes or influences of the circulation index data itself. Exemplarily, the target circulation estimate can be calculated by weighted average similar to the aforementioned embodiment, or a machine learning model can be pre-trained, which inputs each to-be-processed sample and the influence weights of the corresponding time calculated, and the model outputs the target circulation estimate of the target time.

[0068] B2. Determine the historical reconstruction features corresponding to the circulation components based on the target circulation estimate and the circulation components at the target time.

[0069] Among them, the historical reconstruction characteristics can be used to characterize the causes of the circulation components that can explain the climate elements at each time in the target period. The historical reconstruction characteristics can include the intensity and phase of the specific circulation index of the circulation components that cause changes in climate elements at each time in the target period.

[0070] Continuing with the previous example, the target circulation estimate is calculated based on the PDO data of each year from 2000 to 2023. This target circulation estimate can represent the PDO data of 2024 estimated based on the PDO situation of historical years, corresponding to the circulation component of the precipitation anomaly in 2024, thereby obtaining the corresponding changes in the annual cumulative precipitation anomaly in Tianjin under this PDO estimate, that is, the background of the North Pacific sea surface temperature anomaly. This will facilitate technical personnel in related fields to explore the corresponding "atmosphere-ocean" interaction process based on the change of circulation index, and will help further study the physical mechanism of how large-scale atmospheric circulation changes related to the circulation index affect the evolution of climate elements.

[0071] In a further optional implementation manner, determining the target circulation estimation at the target time according to each sample to be processed and each influence weight in B1 may include:

[0072] B11. Perform weighted average calculation on the first preset number of samples to be processed and the first preset number of influence weights to obtain a circulation index estimation.

[0073] Similar to the calculation of climate element valuation in the aforementioned embodiment, each sample to be processed is weighted averaged with the impact weight of the corresponding time to obtain the circulation index valuation of the target time.

[0074] B12. In response to the number of circulation index estimates not satisfying the second preset number, return to executing the operation of randomly selecting a first preset number of samples to be processed from the circulation index samples until the first preset number of influence weights between each sample to be processed and the target second data are calculated.

[0075] Similar to the calculation of climate factor valuation in the aforementioned embodiment, a resampling operation is performed to expand the sample size.

[0076] B13. In response to the number of circulation index estimates satisfying a second preset number, taking an average of the second preset number of circulation index estimates as a target circulation estimate.

[0077] After each resampling, a circulation index estimate corresponding to the current sampling is calculated, and the average of all circulation index estimates is taken as the target circulation estimate.

[0078] For example, continuing the previous example, we randomly sampled 100 times from the original PDO data of all years from 2000 to 2023 each time. The 15 PDOs obtained from each random sampling are not all the same. The purpose is to expand the sample size and reduce the calculation error in the overall operation. The 15 PDOs obtained from each random sampling and the impact weights of the corresponding years are used to calculate the PDO valuation. The average value of the 100 PDO valuations is calculated, and the calculation result is used as the target valuation for 2024, and then compared with the actual PDO in 2024.

[0079] In the above implementation, while calculating the circulation component, the circulation index itself at each time can also be calculated by using the influence weight of the intermediate calculation to obtain the circulation estimate of the target time, which is further compared with the real circulation index of the target time to determine the influence of the circulation index itself. This provides a practical solution for technicians in related fields to study the changes and influences of the circulation index itself, which helps to improve the exploration of the indicator data of the circulation index.

[0080] On the basis of the foregoing embodiments and implementation methods, the embodiments of the present application further provide a preferred implementation method, which summarizes or supplements the contents of the foregoing embodiments, as follows.

[0081] First, the spatial and temporal characteristics of the target climate element field and the circulation index that may affect its evolution are determined by prior correlation. The so-called prior correlation means that the selected circulation index has the same time dimension as the climate element to be studied, that is, the consistency of time range and resolution. At the same time, considering that the circulation index usually represents the large-scale circulation or sea-air coupling characteristics over a period of time, the above-mentioned time resolution can be selected at the monthly scale or above.

[0082] Then, the circulation component of the climate element at the target time is calculated. Among them, the objective weighting method is first used to calculate the weight coefficient of the circulation index at each time (equivalent to the influence weight in the above embodiment). The objective weighting method is a type of indicator weighting method. The main feature is that the indicator weight is determined based on the objective nature of the data, thereby avoiding the bias and subjectivity that may be caused by the subjective weighting method. The objective weighting method used in the real-time mode of this application is the inverse distance weighting method, that is, the inverse of the Euclidean distance is used as the weight coefficient, such as Figure 2 shown.

[0083] Correspondingly, the formula of the inverse distance weighting method is as follows:

[0084]

[0085] in, represents the weight coefficient, Refers to the circulation index at the target time. Represents the circulation index sequence of historical times other than the target time (the sample size is N-1, where N represents the total duration of the target period, for example, 2000-2024 is 25 years, N=25). The reciprocal of the Euclidean distance between the circulation index of each time and the circulation index of the target time is used as the weight coefficient of the circulation index of each time. On this basis, the circulation components of each time are calculated as follows:

[0086]

[0087] in, It represents the estimated climate element at the target time obtained by weighted interpolation of climate elements at historical times. Represents climate elements of historical times.

[0088] like Figure 3 As shown in the figure, Na samples of circulation index are randomly selected from the circulation index of all N-1 times except the target time, and the weight coefficients are calculated respectively. Then, the climate elements corresponding to the Na samples are selected, and based on these weight coefficients, the climate element estimation is obtained by weighted average calculation of these climate elements. Since the sampling is repeated M times, a climate element estimation can be calculated each time. Accordingly, the average value of these M climate element estimations is calculated, and the calculation result is used as the circulation component of the climate element of the target time.

[0089] The purpose of resampling the weighted interpolation process above is to expand the sample size and reduce the calculation error in statistical operations. Resampling is a common technique in statistics, which involves repeatedly extracting subsets from sample data and analyzing each subset to obtain more information about the overall data. By resampling and analyzing multiple times, the distribution of the statistic in the sample can be more accurately estimated, thereby reducing the error of random sampling.

[0090] While determining the circulation component, the embodiment of the present application may also include:

[0091]

[0092] in, A single element representing a historical circulation index sequence (e.g. PDO data for a certain year), It represents the estimated circulation index at the target time obtained by weighted interpolation of the circulation index at the historical time. Since the sampling is repeated M times, a circulation index estimate can be calculated each time. Accordingly, the average value of these M circulation index estimates is calculated, and the calculated result is used as the target circulation estimate of the climate element at the target time.

[0093] The implementation method of the present application realizes the quantitative attribution of three-dimensional climate elements using a one-dimensional circulation index. In particular, considering the key impact of the teleconnection index in the study of climate change attribution, this method studies the three-dimensional climate element field through the input of the one-dimensional circulation index, obtains the degree of change of the climate elements caused by the circulation index, and ensures the simplicity of calculation on the basis of following the physical mechanism. The climate elements at each time in the target period are dynamically adjusted to obtain an estimated field that is consistent with the size of the original three-dimensional element field. Its physical meaning is the part of the element change caused by the circulation factor, that is, the circulation component of the climate element.

[0094] Embodiment 2

[0095] Figure 4 This is a schematic diagram of the structure of a climate change attribution device provided in Example 2 of this application. Figure 4 As shown, the device 200 includes:

[0096] A climate data acquisition module 210 is used to acquire first data of climate elements of a target area within a target period, and second data of a circulation index within the target period;

[0097] The circulation component determination module 220 is used to determine the circulation component of the climate element at the target time within the target period according to the first data and the second data.

[0098] In the technical solution of the embodiment of the present application, the first data of the climate elements of the target area in the target period and the second data of the circulation index in the target period are calculated to obtain the circulation component of the climate elements at the target time in the target period. It can be understood that this case analyzes the three-dimensional climate elements through a one-dimensional circulation index with only a time dimension to determine the extent to which the climate elements are affected by the circulation index, thereby providing a precise analysis method for the attribution of the one-dimensional circulation index to the three-dimensional climate elements, which helps to have a deeper understanding of the driving factors of climate change, and provides a more accurate and stable scientific basis for climate prediction, and also has important reference significance for the formulation of climate response strategies.

[0099] In an optional implementation, the circulation component determination module 220 may include:

[0100] A circulation index sample determination unit, used to use all first data except the target time as climate element samples, and use all second data except the target time as circulation index samples;

[0101] An influence weight calculation unit, used to calculate the influence weight of each circulation index sample according to the circulation index sample and the target second data of the target time;

[0102] A climate element estimation determination unit, used for performing weighted average calculation on each impact weight and each climate element sample at a corresponding time, to obtain an estimation of the climate element affected by the circulation index at a target time;

[0103] The circulation component determination unit is used to determine the circulation component according to the climate element estimation.

[0104] In an optional implementation, the influence weight calculation unit may include:

[0105] A sample extraction subunit to be processed, used for randomly extracting a first preset number of samples to be processed from the circulation index samples;

[0106] An influence weight calculation subunit, used to calculate a first preset number of influence weights between each sample to be processed and the target second data;

[0107] a repeated sampling subunit, configured to return to executing the operation of randomly sampling a first preset number of samples to be processed from the circulation index samples in response to the number of climate element estimates not satisfying a second preset number, until the first preset number of influence weights between each sample to be processed and the target second data is calculated;

[0108] In response to the number of climate element estimates satisfying a second preset number, the circulation component determination unit may be specifically configured to:

[0109] The average of the second preset number of climate element estimates is used as the circulation component.

[0110] In an optional implementation manner, the influence weight calculation subunit may include:

[0111] The Euclidean distance calculation slave unit is used to calculate the Euclidean distance between each sample to be processed and the target second data;

[0112] The weight determination slave unit is used to take the inverse of each Euclidean distance as each influence weight.

[0113] In an optional implementation, the device 200 may further include:

[0114] A target circulation estimation determination module is used to determine the target circulation estimation at the target time according to each sample to be processed and each influence weight;

[0115] The historical reconstruction feature determination module is used to determine the historical reconstruction feature corresponding to the circulation component according to the target circulation estimation and the circulation component at the target time.

[0116] In an optional implementation, the target circulation estimation determination module may include:

[0117] A circulation index estimation determination unit, configured to perform weighted average calculation on a first preset number of samples to be processed and a first preset number of influence weights to obtain a circulation index estimation;

[0118] a resampling unit, configured to, in response to the number of circulation index estimates not satisfying a second preset number, return to executing the operation of randomly selecting a first preset number of samples to be processed from the circulation index samples until the first preset number of influence weights between each sample to be processed and the target second data are calculated;

[0119] The target circulation estimate determination unit is configured to, in response to the number of circulation index estimates satisfying a second preset number, use an average value of the second preset number of circulation index estimates as the target circulation estimate.

[0120] The climate change attribution device provided in the embodiments of the present application can execute the climate change attribution method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects for executing each method.

[0121] Embodiment 3

[0122] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0123] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0124] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the climate change attribution method.

[0126] In some embodiments, the climate change attribution method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the climate change attribution method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the climate change attribution method in any other appropriate manner (e.g., by means of firmware).

[0127] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0132] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0133] The present application also discloses a computer program product, which includes a computer program. When the computer program is executed by a processor, the computer program implements the climate change attribution method provided in any embodiment of the present application. The program product and the climate change attribution method disclosed in each embodiment of the present application belong to the same inventive concept, so they are not described here in detail.

[0134] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0135] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for attributing climate change, characterized in that: The method comprises: Acquire first data of climate elements of a target area within a target period, and second data of a circulation index within the target period; Determine the circulation component of the climate element at a target time within the target period according to the first data and the second data; Determining the circulation component of the climate element at the target time within the target period according to the first data and the second data includes: Taking all the first data except the target time as climate element samples, and taking all the second data except the target time as circulation index samples; Calculating the influence weight of each circulation index sample according to the circulation index sample and the target second data of the target time; Perform weighted average calculation on the impact weights and climate element samples at the corresponding time to obtain an estimated value of the climate element affected by the circulation index at the target time; The circulation component is determined based on the climate element estimate.

2. The method according to claim 1, characterized in that: The calculating the influence weight of each circulation index sample according to the circulation index sample and the target second data of the target time includes: Randomly selecting a first preset number of samples to be processed from the circulation index samples; Calculating a first preset number of influence weights between each of the samples to be processed and the target second data; In response to the number of the climate element estimates not satisfying the second preset number, returning to execute the operation of randomly selecting a first preset number of samples to be processed from the circulation index samples until the first preset number of influence weights between each of the samples to be processed and the target second data are calculated; In response to the number of the climate element estimates satisfying a second preset number, determining the circulation component according to the climate element estimates includes: An average value of a second preset number of climate element estimates is used as the circulation component.

3. The method according to claim 2, characterized in that The calculating the influence weight of a first preset number between each of the samples to be processed and the target second data includes: Calculating the Euclidean distance between each of the samples to be processed and the target second data; The reciprocal of each Euclidean distance is used as each influence weight.

4. The method according to claim 2, characterized in that: The method further comprises: Determining a target circulation estimate for the target time according to each of the samples to be processed and each of the influence weights; A historical reconstruction feature corresponding to the circulation component is determined based on the target circulation estimate and the circulation component at a target time.

5. The method according to claim 4, characterized in that The step of determining the target circulation estimation at the target time according to each of the samples to be processed and each of the influence weights includes: Performing weighted average calculation on the first preset number of samples to be processed and the first preset number of influence weights to obtain a circulation index estimate; In response to the number of the circulation index estimates not satisfying the second preset number, returning to execute the operation of randomly selecting a first preset number of samples to be processed from the circulation index samples until the first preset number of influence weights between each of the samples to be processed and the target second data are calculated; In response to the number of the circulation index estimates satisfying the second preset number, an average value of the second preset number of the circulation index estimates is used as a target circulation estimate.

6. A climate change attribution device, characterized in that: The device comprises: A first data acquisition module is used to acquire first data of climate elements of a target area within a target period, and second data of a circulation index within the target period; A circulation component determination module is used to determine the circulation component of the climate element at the target time within the target period based on the first data and the second data; including: taking all the first data except the target time as climate element samples, and taking all the second data except the target time as circulation index samples; calculating the influence weight of each circulation index sample based on the circulation index sample and the target second data of the target time; performing weighted average calculation on each of the influence weights and each of the climate element samples of the corresponding time to obtain an estimated value of the climate element affected by the circulation index at the target time; and determining the circulation component based on the estimated value of the climate element.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so as to enable the at least one processor to perform the climate change attribution method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the climate change attribution method described in any one of claims 1 to 5 when executed.

9. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the climate change attribution method according to any one of claims 1 to 5.

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