CMIP6 climate mode comprehensive comparison and selection method and system

Abstract

The invention discloses a CMIP6 climate mode comprehensive comparison and selection method and a CMIP6 climate mode comprehensive comparison and selection system, and relates to the technical field of data processing. According to the first index data, respectively calculating a first index score of each climate mode for the simulation capability of one meteorological element and a second index score of each climate mode for the simulation capability of another meteorological element, and averaging the sum of the first index score and the second index score corresponding to each climate mode; obtaining a comprehensive index score corresponding to each climate mode; according to the comprehensive index scores from large to small, a preset number of climate modes are selected from all the climate modes in sequence, and the climate mode selection process comprehensively considers the simulation effect of all the climate elements when the different climate elements are simulated; therefore, a better simulation effect can be achieved when the selected climate mode simulates different meteorological elements.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a CMIP6 climate model comprehensive comparison method and system. Background Art

[0002] In recent years, the global climate has continued to warm, and climate change has become a focus of widespread concern in today's society. Climate warming will intensify global and regional water cycles, leading to frequent extreme hydrological events such as floods, droughts, extreme high temperatures and heavy precipitation, which have had a very serious impact on human safety, social economy and the ecological environment, thus increasing people's demand for accurate prediction of future climate change. In addition, accurate simulation and prediction of climate change can provide a scientific basis for policy making, risk assessment and resource management.

[0003] In order to simulate and predict climate change, CMIP6 (International Coupled Model Intercomparison Project Phase 6) brings together a variety of climate models developed by many scientific research institutions around the world. It contains a variety of climate forcing scenarios and has become one of the most effective tools for climate simulation and prediction internationally. However, due to differences in data structure, parameter settings, calculation methods, etc. among different climate models in CMIP6, there are significant differences in simulation performance and applicable scenarios among the models. Therefore, when studying the climate of a certain region, it becomes a crucial issue to select the climate model from CMIP6 that is most applicable to that region.

[0004] At present, the comparison and selection of climate models is usually achieved by statistically calculating indicators such as the correlation coefficient (R), standard deviation (STD) and root mean square error (RMSE) between the simulated values ​​and observed values ​​of each climate model in CMIP6. The simulation ability of each climate model is judged based on the above indicators to select the appropriate climate model. However, when selecting the appropriate climate model based on the above method, only a single meteorological element of each climate model is often evaluated. While the same climate model ensures the optimal simulation of a certain meteorological element, it often cannot achieve the optimal simulation of other meteorological elements, which leads to the problem of low climate prediction accuracy. Summary of the invention

[0005] The present disclosure provides a CMIP6 climate model comprehensive comparison method and system, the main purpose of which is to solve the problem that related technologies only evaluate a single meteorological element, resulting in the same climate model being unable to achieve optimal simulation of other meteorological elements while ensuring the optimal simulation of a certain meteorological element.

[0006] According to a first aspect of the present disclosure, a CMIP6 climate model comprehensive comparison method is provided, which includes:

[0007] Acquire CMIP6 climate model data and meteorological measured data of a target research area, wherein the spatial range of the CMIP6 climate model data includes the target research area;

[0008] Calculating first indicator data of each climate model in the CMIP6 climate model data based on the measured meteorological data, and obtaining indicator factors when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first indicator data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation;

[0009] Based on a preset basis function and according to the index factors, respectively calculate a first index score of the simulation capability of each climate model for one meteorological element and a second index score of the simulation capability of each climate model for another meteorological element, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element;

[0010] Taking an average of the sum of the first index score and the second index score corresponding to each climate mode to obtain a comprehensive index score corresponding to each climate mode;

[0011] Selecting a preset number of climate modes from the climate modes in descending order of the comprehensive index scores;

[0012] Based on the Bayesian model weighted averaging method, a weighted ensemble average is performed on the preset number of climate models to obtain a coupled climate model, and the coupled climate model is used to simulate the changing laws of future meteorological elements.

[0013] Optionally, the first indicator data of each climate model in the CMIP6 climate model data is calculated based on the meteorological measured data to obtain the indicator factors when each climate model in the climate models simulates at least two meteorological elements respectively, including:

[0014] Calculating in sequence the target normalized standard deviation, target spatial correlation coefficient, target mean deviation and target interannual standard deviation of each climate model in each climate model relative to the meteorological measured data;

[0015] Performing factor conversion on the target normalized standard deviation based on a normalized standard deviation conversion function to obtain a first conversion factor;

[0016] Performing factor conversion on the target spatial correlation coefficient based on the spatial correlation coefficient conversion function to obtain a second conversion factor;

[0017] Performing factor conversion on the target average deviation based on the average deviation conversion function to obtain a third conversion factor;

[0018] The target inter-annual standard deviation is factor-converted based on the inter-annual standard deviation conversion function to obtain a fourth conversion factor, and the index factor is obtained by weighting the first conversion factor, the second conversion factor, the third conversion factor and the fourth conversion factor, so as to obtain the index factor when each climate model simulates at least two meteorological elements respectively;

[0019] The formula of the normalized standard deviation conversion function is expressed as:

[0020] x1=-|x1-1|

[0021] Wherein, X1 is the first conversion factor, and x1 is the target normalized standard deviation;

[0022] The formula of the spatial correlation coefficient conversion function is expressed as:

[0023] X2=x2-1

[0024] Wherein, X2 is the second conversion factor, and x2 is the target space correlation coefficient;

[0025] The formula of the average deviation conversion function is expressed as:

[0026] X3=-|x3|

[0027] Wherein, X3 is the third conversion factor, and x3 is the target average deviation;

[0028] The formula of the interannual standard deviation conversion function is expressed as:

[0029] X4=-x4

[0030] Among them, X4 is the fourth conversion factor, and x4 is the target inter-annual standard deviation.

[0031] Optionally, the formula of the preset basis function is expressed as:

[0032] S(X)=β σ(X+δ)

[0033] Among them, δ represents tolerance, δ=|maxE(X)|, E(X) is the set of indicator factors corresponding to the one meteorological element when the climate models simulate the one meteorological element or the set of indicator factors corresponding to the another meteorological element when the climate models simulate the another meteorological element, X is the indicator factor corresponding to the one meteorological element when the climate models simulate the one meteorological element or the indicator factor corresponding to the another meteorological element when the climate models simulate the another meteorological element; σ is the correction factor; β is the base, and S(X) is the first index score or the second index score.

[0034] Optionally, the method comprises:

[0035] According to the CMIP6 climate model data and the meteorological measured data, spatial distribution schematic diagrams corresponding to the at least two meteorological elements in each climate model are drawn in sequence to obtain a first spatial distribution schematic diagram corresponding to one meteorological element in each climate model and a second spatial distribution schematic diagram corresponding to another meteorological element in each climate model;

[0036] Comparing the differences in spatial distribution characteristics between the first spatial distribution schematic diagrams under the various climate modes to obtain first spatial distribution characteristic differences, and comparing the differences in spatial distribution characteristics between the second spatial distribution schematic diagrams under the various climate modes to obtain second spatial distribution characteristic differences;

[0037] respectively calculating a first spatial correlation coefficient between first simulated data and first target measured data for simulating the one meteorological element by each climate model, wherein the first target measured data is data in the meteorological measured data corresponding to the first simulated data;

[0038] Respectively calculating a second spatial correlation coefficient between second simulated data and second target measured data for simulating the other meteorological element by each climate model, wherein the second target measured data is data corresponding to the second simulated data in the meteorological measured data, and the CMIP6 climate model data includes the first simulated data and the second simulated data;

[0039] Selecting a first target climate model with the best simulation effect on the spatial characteristics of the one meteorological element from the climate models according to the first spatial distribution characteristic difference and the first spatial correlation coefficient;

[0040] A second target climate model with the best simulation effect on the spatial characteristics of another meteorological element is selected from the climate models according to the second spatial distribution characteristic difference and the second spatial correlation coefficient.

[0041] Optionally, the method comprises:

[0042] Selecting a third target climate model with the best simulation effect on the temporal characteristics of the one meteorological element from the climate models according to the first interannual standard deviation, wherein the first interannual standard deviation is used to describe the difference in the interannual fluctuation range between the first simulation data and the first target measured data, so as to judge the simulation effect of the climate models on the temporal characteristics of the one meteorological element;

[0043] Selecting a fourth target climate model with the best simulation effect on the temporal characteristics of the another meteorological element from the climate models according to the second interannual standard deviation, wherein the second interannual standard deviation is used to describe the difference in the interannual fluctuation range between the second simulated data and the second target measured data so as to judge the simulation effect of the climate models on the temporal characteristics of the another meteorological element, and the first interannual standard deviation and the second interannual standard deviation are calculated by an interannual standard deviation function;

[0044] The formula of the interannual standard deviation function is expressed as:

[0045]

[0046] Among them, STD m Represents the interannual standard deviation of each climate model, STD o represents the interannual standard deviation of the meteorological measured data, and IVS represents the interannual standard deviation.

[0047] Optionally, the method comprises:

[0048] Calculating the spatial correlation coefficient, standard deviation and root mean square error of each climate model and the meteorological measured data;

[0049] Calculating a first performance score of each climate model when simulating the one meteorological element according to the spatial correlation coefficient, the standard deviation, and the root mean square error, and selecting a fifth target climate model with the best simulation performance for the one meteorological element from each climate model based on the first performance score;

[0050] A second performance score of each climate model when simulating the other meteorological element is calculated according to the spatial correlation coefficient, the standard deviation and the root mean square error, and a sixth target climate model with the best simulation performance for the other meteorological element is selected from the climate models based on the second performance score.

[0051] According to a second aspect of the present disclosure, a CMIP6 climate model comprehensive comparison and selection system is provided, comprising:

[0052] An acquisition unit, used for acquiring CMIP6 climate model data and meteorological measured data of a target research area, wherein the spatial range of the CMIP6 climate model data includes the target research area;

[0053] A first calculation unit is used to calculate first indicator data of each climate model in the CMIP6 climate model data based on the meteorological measured data, and obtain an indicator factor when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first indicator data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation;

[0054] A second calculation unit is used to calculate a first index score of the simulation ability of each climate model for one meteorological element and a second index score of the simulation ability of each climate model for another meteorological element according to the index factor based on a preset basis function, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element;

[0055] A third calculation unit, configured to average the sum of the first index score and the second index score corresponding to each climate mode to obtain a comprehensive index score corresponding to each climate mode;

[0056] A selection unit, configured to select a preset number of climate modes from the climate modes in descending order of the comprehensive index scores;

[0057] The processing unit is used to perform weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, and the coupled climate model is used to simulate the changing laws of future meteorological elements.

[0058] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0059] at least one processor; and

[0060] a memory communicatively connected to the at least one processor; wherein,

[0061] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0062] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0063] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0064] The CMIP6 climate model comprehensive comparison method and system provided by the present invention obtain CMIP6 climate model data and meteorological measured data of a target study area, wherein the spatial range of the CMIP6 climate model data includes the target study area; first index data of each climate model in the CMIP6 climate model data is calculated based on the meteorological measured data to obtain index factors when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first index data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation; and the index factors when each climate model simulates one meteorological element are calculated based on a preset basis function according to the index factors. a first index score of the ability of each climate model to simulate another meteorological element and a second index score of the ability of each climate model to simulate another meteorological element, wherein the at least two meteorological elements include the one meteorological element and the other meteorological element; averaging the sum of the first index score and the second index score corresponding to each climate model to obtain a comprehensive index score corresponding to each climate model; selecting a preset number of climate models from the climate models in descending order of the comprehensive index scores; performing weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, which is used to simulate the changing laws of future meteorological elements. Compared with the related art, the first indicator data of each climate model is calculated by the meteorological measured data and the CMIP6 climate model data, and the first index score of each climate model's simulation ability for one meteorological element and the second index score of each climate model's simulation ability for another meteorological element are respectively calculated according to the first indicator data, and the sum of the first index score and the second index score corresponding to each climate model is averaged to obtain the comprehensive index score corresponding to each climate model; a preset number of climate models are selected from the climate models in descending order according to the comprehensive index scores, and the selection process of the aforementioned climate model comprehensively considers the simulation effect of each climate element when simulating different meteorological elements, so that the selected climate model can achieve good simulation effect when simulating different meteorological elements, and further, a coupled climate model is obtained by weighted ensemble averaging of the selected climate models, thereby achieving an improvement in the simulation effect when using the coupled climate model to simulate different meteorological elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0066] Figure 1A schematic diagram of a CMIP6 climate model comprehensive comparison method provided in an embodiment of the present disclosure;

[0067] Figure 2 A schematic diagram of the spatial distribution of multi-year average precipitation and temperature under a climate model and a measured model provided in an embodiment of the present disclosure;

[0068] Figure 3 A comparison chart of simulation performance of various climate models for interannual variability of meteorological elements provided by an embodiment of the present disclosure;

[0069] Figure 4 A multi-mode evaluation Taylor diagram provided by an embodiment of the present disclosure;

[0070] Figure 5 A heat map showing the ranking of the three indicators of mean, root mean square error and correlation coefficient of each climate model provided in an embodiment of the present disclosure;

[0071] Figure 6 A comprehensive evaluation diagram of multi-factor simulation effects provided by an embodiment of the present disclosure;

[0072] Figure 7 A schematic diagram of the changes in future temperature and rainfall relative to historical periods under two scenarios provided in an embodiment of the present disclosure;

[0073] Figure 8 A schematic diagram of the structure of a CMIP6 climate model comprehensive comparison and selection system provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0074] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0075] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0076] In addition, the terms "first", "second", etc. in the specification and claims of the present disclosure 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 terms used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in sequences other than those illustrated or described herein.

[0077] The following describes the CMIP6 climate model comprehensive comparison method and system of the embodiments of the present disclosure with reference to the accompanying drawings.

[0078] In order to at least solve the problem that the related technology only evaluates a single meteorological element, resulting in the same climate model failing to achieve the best simulation of other meteorological elements while ensuring the best simulation of a certain meteorological element, this embodiment provides a CMIP6 climate model comprehensive comparison method.

[0079] Figure 1 A schematic diagram of a CMIP6 climate model comprehensive comparison method provided by an embodiment of the present disclosure. Figure 1 As shown, the method comprises the following steps:

[0080] Step 101, obtaining CMIP6 climate model data and meteorological measured data of a target research area, wherein the spatial range of the CMIP6 climate model data includes the target research area;

[0081] Step 102, calculating first indicator data of each climate model in the CMIP6 climate model data based on the measured meteorological data, and obtaining indicator factors when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first indicator data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation;

[0082] Step 103, respectively calculating a first index score of the simulation capability of each climate model for one meteorological element and a second index score of the simulation capability of each climate model for another meteorological element based on a preset basis function and the index factor, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element;

[0083] Step 104, averaging the sum of the first index score and the second index score corresponding to each climate mode to obtain a comprehensive index score corresponding to each climate mode;

[0084] Step 105, selecting a preset number of climate modes from the climate modes in descending order according to the comprehensive index scores;

[0085] Step 106, performing weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, wherein the coupled climate model is used to simulate the changing patterns of future meteorological elements.

[0086] The CMIP6 climate model comprehensive comparison method provided by the present invention obtains CMIP6 climate model data and meteorological measured data of a target study area, wherein the spatial range of the CMIP6 climate model data includes the target study area; first index data of each climate model in the CMIP6 climate model data is calculated based on the meteorological measured data to obtain index factors when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first index data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation; and the simulation energy of each climate model for one meteorological element is calculated based on the preset basis function according to the index factors. The method comprises the following steps: a first index score of the ability of each climate model to simulate another meteorological element and a second index score of the ability of each climate model to simulate another meteorological element, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element; averaging the sum of the first index score and the second index score corresponding to each climate model to obtain a comprehensive index score corresponding to each climate model; selecting a preset number of climate models from the climate models in descending order of the comprehensive index scores; performing weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, which is used to simulate the changing laws of future meteorological elements. Compared with the related art, the first indicator data of each climate model is calculated by the meteorological measured data and the CMIP6 climate model data, and the first index score of each climate model's simulation ability for one meteorological element and the second index score of each climate model's simulation ability for another meteorological element are respectively calculated according to the first indicator data, and the sum of the first index score and the second index score corresponding to each climate model is averaged to obtain the comprehensive index score corresponding to each climate model; a preset number of climate models are selected from the climate models in descending order according to the comprehensive index scores, and the selection process of the aforementioned climate model comprehensively considers the simulation effect of each climate element when simulating different meteorological elements, so that the selected climate model can achieve good simulation effect when simulating different meteorological elements, and further, a coupled climate model is obtained by weighted ensemble averaging of the selected climate models, thereby achieving an improvement in the simulation effect when using the coupled climate model to simulate different meteorological elements.

[0087] As a refinement of the disclosed embodiment, when executing step 101 to obtain CMIP6 climate model data and meteorological measured data of the target study area, and the spatial range of the CMIP6 climate model data includes the target study area, the following implementation methods may also be adopted but are not limited to, for example: downloading and collecting NEX-GDDP-CMIP6 climate model data set, the NEX-GDDP-CMIP6 climate model data set is the CMIP6 climate model data, the purpose of this step is to directly obtain 23 high-resolution downscaled CMIP6 climate models, all models of the NEX-GDDP-CMIP6 climate model data set have been uniformly downscaled to 0.25° based on bias correction and spatial decomposition methods. Download the daily value data set of basic meteorological elements of national ground meteorological stations (V3.0), collect the historical long-term series daily meteorological data of each station in the target study area, and obtain the meteorological measured data, which is used to evaluate and verify the simulation effect of each climate model. If the meteorological stations in the target study area are relatively sparse and dispersed, the fifth-generation global reanalysis dataset (ERA5) released by the European Centre for Medium-Range Weather Forecasts (ECWMF) can be collected as ground-based measured data, i.e., an effective supplement to the meteorological measured data. The meteorological models include, but are not limited to, 23 high-resolution downscaled CMIP6 climate models. Strict quality control is performed on the collected meteorological measured data, and a small number of missing values ​​and outliers in the data are processed in a unified and centralized manner to ensure the continuity, completeness and accuracy of the data. The length of the time series of the collected CMIP6 climate model historical data must be consistent with the historical data of the meteorological station, and the spatial range of the data must be ensured to cover the entire target study area.

[0088] As a refinement of the above embodiment, when executing step 102 to calculate the first indicator data of each climate model in the CMIP6 climate model data based on the meteorological measured data, and obtaining the indicator factors when each climate model in the climate models simulates at least two meteorological elements respectively, the following implementation methods can also be adopted but are not limited to, for example: sequentially calculating the target normalized standard deviation, target spatial correlation coefficient, target average deviation and target interannual standard deviation of each climate model in the climate models relative to the meteorological measured data; factoring the target normalized standard deviation based on the normalized standard deviation conversion function to obtain the first conversion factor. factor; based on the spatial correlation coefficient conversion function, the target spatial correlation coefficient is factored to obtain a second conversion factor; based on the average deviation conversion function, the target average deviation is factored to obtain a third conversion factor; based on the interannual standard deviation conversion function, the target interannual standard deviation is factored to obtain a fourth conversion factor, and the index factor is obtained by weighting the first conversion factor, the second conversion factor, the third conversion factor and the fourth conversion factor, so as to obtain the index factor when each climate model simulates at least two meteorological elements respectively; the formula of the normalized standard deviation conversion function is expressed as:

[0089] x1=-|x1-1|

[0090] Wherein, X1 is the first conversion factor, and x1 is the target normalized standard deviation;

[0091] The formula of the spatial correlation coefficient conversion function is expressed as:

[0092] X2=x2-1

[0093] Wherein, X2 is the second conversion factor, and x2 is the target space correlation coefficient;

[0094] The formula of the average deviation conversion function is expressed as:

[0095] X3=-|x3|

[0096] Wherein, X3 is the third conversion factor, and x3 is the target average deviation;

[0097] The formula of the interannual standard deviation conversion function is expressed as:

[0098] X4=-x4

[0099] Among them, X4 is the fourth conversion factor, and x4 is the target inter-annual standard deviation.

[0100] As a refinement of the above embodiment, the formula of the preset basis function is expressed as:

[0101] S(X)=βσ(X+δ)

[0102] Among them, δ represents the tolerance, δ is the absolute value of the maximum value in the E(X) set, that is, δ = |maxE(X)|, ensuring that the best performing model S(X) = 1; E(X) is the set of the indicator factors corresponding to the simulation of the one meteorological element by the climate models or the set of the indicator factors corresponding to the simulation of the other meteorological element by the climate models, X is the indicator factor corresponding to the simulation of the one meteorological element by the climate models or the indicator factor corresponding to the simulation of the other meteorological element by the climate models; σ is the correction factor, the purpose of which is to correct the order of magnitude difference between the correction coefficients, to ensure that S(X) ≤ 1, and select σ = aveE(x + δ); β is the base, the value of parameter β needs to be iteratively calibrated, and the β value that satisfies the maximum standard deviation of S(X) should be selected, S(X) is the first index score or the second index score.

[0103] To facilitate understanding of the above embodiment, this embodiment is further explained in conjunction with the detailed description of step 101, including: calculating the normalized standard deviation (NSTD), spatial correlation coefficient (R), mean deviation (MB) relative to the measured value and interannual standard deviation (IVS) of different climate models, and factoring the above four indicators according to the normalized standard deviation conversion function, the spatial correlation coefficient conversion function, the mean deviation conversion function and the interannual standard deviation conversion function to obtain the indicator factor corresponding to each climate model, and arranging the indicator factors X of 23 climate models into a set E (X∈E). In order to amplify the final score of the model with better simulation effect, an exponential function is further used as the basis function to score each climate model, and the climate model with the highest final score meets the most evaluation criteria.

[0104] In order to facilitate the understanding of the processes involved in step 104, step 105 and step 106, this embodiment provides an exemplary explanation by defining the at least two meteorological elements as rainfall and temperature, that is, the rainfall is one meteorological element, and the temperature is the other meteorological element. The specific explanation includes: averaging the sum of the simulation capability index scores of rainfall and temperature of each climate model, obtaining a comprehensive score of coupled temperature and precipitation different climate elements, performing a comprehensive ranking based on the comprehensive score, and finally selecting a climate model with excellent simulation performance for rainfall and temperature. The Bayesian model weighted average method is used to compare the output results of the top four climate models selected in the comprehensive ranking, and a weighted ensemble average is performed to obtain a coupled climate model. Set a future emission scenario, output the meteorological simulation value under the future scenario based on the coupled climate model, and then obtain the change trend of different future scenarios of each meteorological element. The simulation capability index score of rainfall and temperature of each climate model includes the first index score and the second index score, and the comprehensive score is the comprehensive index score.

[0105] As a refinement of the above embodiment, the method also includes: drawing spatial distribution schematic diagrams corresponding to the at least two meteorological elements in each climate mode in turn according to the CMIP6 climate model data and the meteorological measured data, to obtain a first spatial distribution schematic diagram corresponding to the one meteorological element in each climate mode and a second spatial distribution schematic diagram corresponding to the other meteorological element in each climate mode; comparing the spatial distribution characteristic differences between the first spatial distribution schematic diagrams in the various climate modes to obtain a first spatial distribution characteristic difference, and comparing the spatial distribution characteristic differences between the second spatial distribution schematic diagrams in the various climate modes to obtain a second spatial distribution characteristic difference; respectively calculating a first spatial correlation coefficient between the first simulated data and the first target measured data for simulating the one meteorological element in each climate mode, and obtaining a first spatial distribution characteristic difference. The first target measured data is the data in the meteorological measured data corresponding to the first simulation data; the second spatial correlation coefficient between the second simulation data and the second target measured data for simulating the other meteorological element by each climate model is calculated respectively, the second target measured data is the data in the meteorological measured data corresponding to the second simulation data, and the CMIP6 climate model data includes the first simulation data and the second simulation data; according to the first spatial distribution characteristic difference and the first spatial correlation coefficient, the first target climate model with the best simulation effect of the spatial characteristics of the one meteorological element is selected from the climate models; according to the second spatial distribution characteristic difference and the second spatial correlation coefficient, the second target climate model with the best simulation effect of the spatial characteristics of the other meteorological element is selected from the climate models.

[0106] As a refinement of the above embodiment, the method further includes: selecting a third target climate model with the best simulation effect on the time characteristics of the one meteorological element from the climate models according to the first interannual standard deviation, the first interannual standard deviation is used to describe the difference in the interannual fluctuation range between the first simulation data and the first target measured data, so as to judge the simulation effect of the climate models on the time characteristics of the one meteorological element; selecting a fourth target climate model with the best simulation effect on the time characteristics of the other meteorological element from the climate models according to the second interannual standard deviation, the second interannual standard deviation is used to describe the difference in the interannual fluctuation range between the second simulation data and the second target measured data, so as to judge the simulation effect of the climate models on the time characteristics of the other meteorological element, the first interannual standard deviation and the second interannual standard deviation are calculated by the interannual standard deviation function; the formula of the interannual standard deviation function is expressed as:

[0107]

[0108] Among them, STD m Represents the interannual standard deviation of each climate model, STD o represents the interannual standard deviation of the meteorological measured data, and IVS represents the interannual standard deviation.

[0109] In order to facilitate the understanding of the scheme involved in the above-mentioned embodiment, this embodiment provides an exemplary description, including: drawing a schematic diagram of the spatial distribution of the multi-year average rainfall and temperature under 23 climate models and measured models, that is, under the meteorological measured data, comparing and observing the differences in the spatial distribution characteristics of rainfall and temperature under each climate model, and statistically calculating the spatial correlation coefficient between each climate model and the measured value, that is, the spatial correlation coefficient between the simulated data of each climate model and its corresponding measured data, and preliminarily judging the climate model with better overall spatial simulation effect from the perspective of similar distribution patterns and high spatial correlation coefficients. The simulation performance of the meteorological interannual variability of each climate model is evaluated from the perspective of time change. Specifically, the difference in the interannual fluctuation amplitude of the simulated meteorological elements and the measured values ​​of each climate model is described by calculating the interannual standard deviation IVS index. The smaller the IVS, the better the climate model can simulate the interannual variability characteristics of the meteorological elements.

[0110] As a refinement of the above embodiment, the method also includes: calculating the spatial correlation coefficient, standard deviation and root mean square error between the climate models and the meteorological measured data; calculating a first performance score of the climate models when simulating the one meteorological element based on the spatial correlation coefficient, the standard deviation and the root mean square error, and selecting a fifth target climate model with the best simulation performance of the one meteorological element from the climate models based on the first performance score; calculating a second performance score of the climate models when simulating the other meteorological element based on the spatial correlation coefficient, the standard deviation and the root mean square error, and selecting a sixth target climate model with the best simulation performance of the other meteorological element from the climate models based on the second performance score.

[0111] In order to facilitate the understanding of the scheme involved in the above embodiment, this embodiment provides an exemplary description, including: based on the measured meteorological data and climate model data, three statistical indicators are calculated: spatial correlation coefficient (R), standard deviation (STD) and root mean square error (RMSE), and Taylor diagrams are drawn through the three indicators to intuitively show the performance of 23 climate models in three simulation performance indicators. According to the calculation results of STD, RMSE and R of each climate model, the performance of each indicator is scored and ranked respectively, and the simulation performance of 23 climate models for rainfall and temperature is comprehensively evaluated in combination with the three indicators.

[0112] In order to more intuitively demonstrate the implementation process of the present disclosure, the following is an example of a water diversion source area. The CMIP6 climate model comprehensive comparison method is used to determine the climate model with higher applicability and higher accuracy for rainfall and temperature in the area, and then the process of using the climate model to simulate and predict the future temperature and precipitation changes in the area is as follows:

[0113] The measured data of daily precipitation and average temperature from 1960 to 2020 at 8 meteorological stations (Qingshuihe, Shiqu, Ganzi, Xinlong, Daofu, Banma, Seda and Maerkang) in the water source area were collected. The data were all from the daily value data set of basic meteorological elements of national ground meteorological stations (V3.0). The data were strictly quality controlled, and a small number of missing values ​​and outliers were uniformly processed to ensure the continuity, integrity and accuracy of the data. At the same time, considering that the 8 meteorological stations are relatively scattered and insufficient to cover the entire study area, the fifth generation global reanalysis data set (ERA5) released by the European Center for Medium-term Weather Forecasts (ECWMF) was selected as an effective supplement to the ground measured data. Download and collect 23 high-resolution downscaled climate model data of NEX-GDDP-CMIP6 in the regional scope, and use each climate model to simulate and output the two key meteorological element data of precipitation and temperature in the study area from 1960 to 2020 (historical stage).

[0114] The simulation effects of 23 climate models on the spatial distribution characteristics of rainfall and temperature were evaluated. Based on the measured and model meteorological data collected in the study area, the spatial distribution maps of the multi-year average precipitation and multi-year average temperature from 1960 to 2020 under each climate model were drawn, such as Figure 2 shown. Figure 2 (a) is the spatial distribution map of the multi-year average precipitation of each climate model. It can be seen from the figure that the simulation results of the precipitation distribution characteristics of each climate model are generally similar, and can basically reflect the overall spatial variation trend of multi-year precipitation. At the same time, the simulation effect is better for the northwest region with less precipitation, while the precipitation simulation value in the southeast of the study area is slightly higher. The spatial correlation coefficient between the simulated precipitation values ​​of each climate model and the historical measured values ​​is further calculated, and the spatial correlation coefficient between each climate model and the measured value is above 0.86. Among them, CanESM5 has the best spatial simulation effect on rainfall, with a correlation coefficient of 0.88. Climate models such as CNRM-CM6-1 and INM-CM4-8 also have good simulation capabilities for the spatial distribution characteristics of precipitation. Figure 2(b) is the spatial distribution map of the multi-year average temperature of each climate model. Overall, the temperature in the study area simulated by most climate models is lower than the measured value, but they can roughly simulate the spatial distribution characteristics of the temperature, that is, the spatial characteristics of increasing from north to south. Among them, EC-Earth3-Veg-LR has the best simulation effect on the temperature in the study area, and its spatial correlation coefficient reaches 0.87. Secondly, climate models such as Noresm2-MM also show good ability to simulate spatial distribution characteristics. The IVS index is used to further measure the simulation performance of each climate model on the interannual variability of meteorological elements. It is found that in terms of the simulation effect of the temporal characteristics of precipitation, the best performing climate model is MPI-ESM1-2-HR, the worst simulation effect is CanESM5, and other climate models with good simulation effects include EC-Earth3, MRI-ESM2-0, CMCC-ESM2, etc. For temperature, the best performing climate model is Noresm2-MM, the worst performing climate model is CanESM5, and the other climate models that simulate the temporal variation of temperature over the historical period well include EC-Earth3-Veg-L, Taiesm1, IPSL-CM6A-LR and CNRM-ESM2-1. Figure 3 , Figure 3 (a) is a comparison chart of the IVS performance scores for precipitation, and (b) is a comparison chart of the IVS performance scores for temperature.

[0115] The spatial correlation coefficient (R), standard deviation (STD) and root mean square error (RMSE) between each climate model and the measured value were calculated and standardized. At the same time, the simulation performance of the 23 climate models selected in NEX-GDDP-CMIP6 for the historical period of temperature and precipitation in the study area was evaluated by drawing Taylor diagrams. According to the calculation results of STD, RMSE and R of each model, the comprehensive index ranking of each model was obtained. The best comprehensive simulation effect of precipitation is CanESM5, and the NroESM2-LM, INM-CM4-8, CNRM-CM6-1 and FGOALS-g3 climate models also have good simulation effects on precipitation. The climate models with better temperature simulation performance are NorESM2-LM, GISS-E2-1-G, CNRM-ESM2-1, CanESM5 and INM-CM5-0. The Taylor diagram can be found in Figure 4 , Figure 4 Figure (a) shows the Taylor diagram of precipitation in each climate model, and Figure (b) shows the Taylor diagram of temperature in each climate model. The ranking of the performance of each climate model on the three indicators of mean, root mean square error and correlation coefficient can be found in Figure 5 , Figure 5 Figure (a) shows the ranking of precipitation simulation performance, and figure (b) shows the ranking of temperature simulation performance.

[0116] Since the same climate model cannot achieve the optimal simulation of precipitation and temperature in time and space at the same time, the exponential function is further used as the basis function to score each climate model. The sum of the simulation ability index scores of each climate model for precipitation and temperature is averaged to obtain the comprehensive score of the coupled temperature and precipitation climate elements. The comprehensive ranking is carried out based on the comprehensive score, and finally the climate model with the best simulation performance of both precipitation and temperature meteorological elements is selected. The index scoring method is used to comprehensively evaluate the simulation performance of each climate model for temperature and precipitation. Figure 6 , Figure 6 Figure (a) shows the ranking of precipitation simulation, Figure (b) shows the ranking of temperature simulation, and Figure (c) shows the ranking of comprehensive simulation of precipitation and temperature. The comparison results show that EC-Earth3, MRI-ESM2-0, Noresm2-MM and MPI-ESM1-2-HR have the best simulation effects in the study area, among which the comprehensive scores of EC-Earth3 and MRI-ESM2-0 are both over 0.5, indicating excellent performance in both temperature and precipitation.

[0117] Based on the comprehensive evaluation results, the four climate models of EC-Earth3, MRI-ESM2-0, Noresm2-MM and MPI-ESM1-2-HR selected above were Bayesian integrated and coupled using the Bayesian model weighted average method to form a climate coupling model.

[0118] According to the climate coupling model, two emission scenarios SSP1-2.6 and SSP5-8.5 are set in the future period to predict the future temperature and precipitation trends in the study area. Considering the large difference between precipitation, its change is measured by the anomaly percentage (%), and the temperature difference is expressed as the anomaly value (℃). The trend prediction results are shown in Figure 7 , Figure 7 Figure (a) is a schematic diagram of the future precipitation changes relative to the historical period, and Figure (b) is a schematic diagram of the future temperature changes relative to the historical period. It can be seen that overall, the future temperature and precipitation in the study area show a fluctuating upward trend under the two scenarios. The average future precipitation increase under the two scenarios is 7.9% (SSP1-2.6) and 10.21% (SSP5-8.5). As for the temperature, by the end of the 21st century, the future temperature will rise by 1.64℃ under the two scenarios.

[0119] (SSP1-2.6) and 3.72℃(SSP5-8.5).

[0120] In summary, the present invention uses a multi-dimensional, multi-model and multi-factor climate model evaluation and comprehensive method to select the climate model with the best suitability for regional climate forecasting, and uses the weighted average coupling of the Bayesian model to obtain the optimal model, thereby optimizing the selection of regional climate models and more accurately predicting the changing patterns of future meteorological elements.

[0121] In summary, the embodiments of the present disclosure can achieve the following effects:

[0122] 1. Calculate the first index data of each climate model through the measured meteorological data and the CMIP6 climate model data, and calculate the first index score of each climate model's simulation ability for one meteorological element and the second index score of each climate model's simulation ability for another meteorological element according to the first index data, average the sum of the first index score and the second index score corresponding to each climate model to obtain the comprehensive index score corresponding to each climate model; select a preset number of climate models from each climate model in descending order according to the comprehensive index score, the selection process of the aforementioned climate model comprehensively considers the simulation effect of each climate element when simulating different meteorological elements, so that the selected climate model can achieve good simulation effect when simulating different meteorological elements, and further, obtain a coupled climate model by weighted ensemble averaging the selected climate models, thereby achieving the improvement of simulation effect when using the coupled climate model to simulate different meteorological elements.

[0123] 2. This paper introduces multiple evaluation indicators such as Taylor diagram and interannual standard deviation (IVS), which not only evaluates the consistency of the distribution characteristics of temperature and precipitation from the spatial dimension, but also evaluates the interannual changes from the temporal dimension, comprehensively reflecting the simulation performance of meteorological elements of different models in time and space, and improving the comprehensiveness and reliability of climate model evaluation.

[0124] 3. In view of the current situation that it is difficult for the same climate model to achieve the optimal temperature and precipitation simulation in time and space at the same time, the present invention applies a comprehensive ranking method based on multiple statistical indicators. By weighted aggregation of indicators such as the normalized standard deviation (NSTD), spatial correlation coefficient (R), and mean deviation (MB) of the climate model, a multi-factor comprehensive ranking method is formed, which effectively realizes the unified evaluation of multiple meteorological elements such as temperature and precipitation, and ensures the scientificity and objectivity of the ranking.

[0125] 4. This disclosure uses the Bayesian model averaging method (BMA) to perform weighted ensemble output on climate models with excellent evaluation. Compared with the traditional model averaging method, BMA further reduces the prediction error and uncertainty, significantly improves the accuracy of future meteorological change trend prediction, and is particularly suitable for climate simulation and future scenario analysis in water source areas, providing more guiding meteorological data support for water resources management and regulation.

[0126] Corresponding to the above-mentioned CMIP6 climate model comprehensive comparison method, the present invention also proposes a CMIP6 climate model comprehensive comparison system. Since the system embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the system embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0127] Figure 8 A schematic diagram of the structure of a CMIP6 climate model comprehensive comparison and selection system provided by an embodiment of the present disclosure is shown in FIG. Figure 8 As shown, including:

[0128] An acquisition unit 21 is used to acquire CMIP6 climate model data and meteorological measured data of a target research area, wherein the spatial range of the CMIP6 climate model data includes the target research area;

[0129] A first calculation unit 22 is used to calculate first indicator data of each climate model in the CMIP6 climate model data based on the meteorological measured data, and obtain an indicator factor when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first indicator data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation;

[0130] A second calculation unit 23 is used to calculate a first index score of the simulation capability of each climate model for one meteorological element and a second index score of the simulation capability of each climate model for another meteorological element based on a preset basis function and according to the index factor, respectively, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element;

[0131] A third calculation unit 24 is used to average the sum of the first index score and the second index score corresponding to each climate mode to obtain a comprehensive index score corresponding to each climate mode;

[0132] A selection unit 25, configured to select a preset number of climate modes from the climate modes in descending order of the comprehensive index scores;

[0133] The processing unit 26 is used to perform weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, and the coupled climate model is used to simulate the changing laws of future meteorological elements.

[0134] The CMIP6 climate model comprehensive comparison system provided by the present invention obtains CMIP6 climate model data and meteorological measured data of a target study area, wherein the spatial range of the CMIP6 climate model data includes the target study area; first index data of each climate model in the CMIP6 climate model data is calculated based on the meteorological measured data to obtain index factors when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first index data are normalized standard deviation, spatial correlation coefficient, average deviation and interannual standard deviation; and the simulation energy of each climate model for one meteorological element is calculated based on the preset basis function according to the index factors. The method comprises the following steps: a first index score of the ability of each climate model to simulate another meteorological element and a second index score of the ability of each climate model to simulate another meteorological element, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element; averaging the sum of the first index score and the second index score corresponding to each climate model to obtain a comprehensive index score corresponding to each climate model; selecting a preset number of climate models from the climate models in descending order of the comprehensive index scores; performing weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, which is used to simulate the changing laws of future meteorological elements. Compared with the related art, the first indicator data of each climate model is calculated by the meteorological measured data and the CMIP6 climate model data, and the first index score of each climate model's simulation ability for one meteorological element and the second index score of each climate model's simulation ability for another meteorological element are respectively calculated according to the first indicator data, and the sum of the first index score and the second index score corresponding to each climate model is averaged to obtain the comprehensive index score corresponding to each climate model; a preset number of climate models are selected from the climate models in descending order according to the comprehensive index scores, and the selection process of the aforementioned climate model comprehensively considers the simulation effect of each climate element when simulating different meteorological elements, so that the selected climate model can achieve good simulation effect when simulating different meteorological elements, and further, a coupled climate model is obtained by weighted ensemble averaging of the selected climate models, thereby achieving an improvement in the simulation effect when using the coupled climate model to simulate different meteorological elements.

[0135] It should be noted that the above explanation of the method embodiment is also applicable to the system of this embodiment, the principle is the same, and is no longer limited in this embodiment.

[0136] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0137] The present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the CMIP6 climate model comprehensive comparison method described in the above embodiment.

[0138] The present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the CMIP6 climate model comprehensive comparison method described in the above embodiment.

[0139] The present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the CMIP6 climate model comprehensive comparison method described in the above embodiment.

[0140] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A comprehensive comparison method of CMIP6 climate models, characterized in that: include: Acquire CMIP6 climate model data and meteorological measured data of a target research area, wherein the spatial range of the CMIP6 climate model data includes the target research area; Calculating first indicator data of each climate model in the CMIP6 climate model data based on the measured meteorological data, and obtaining indicator factors when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first indicator data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation; Based on a preset basis function and according to the index factors, respectively calculate a first index score of the simulation capability of each climate model for one meteorological element and a second index score of the simulation capability of each climate model for another meteorological element, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element; Taking an average of the sum of the first index score and the second index score corresponding to each climate mode to obtain a comprehensive index score corresponding to each climate mode; Selecting a preset number of climate modes from the climate modes in descending order of the comprehensive index scores; Based on the Bayesian model weighted averaging method, a weighted ensemble average is performed on the preset number of climate models to obtain a coupled climate model, and the coupled climate model is used to simulate the changing laws of future meteorological elements.

2. The method according to claim 1, characterized in that: The first index data of each climate model in the CMIP6 climate model data is calculated based on the meteorological measured data to obtain the index factors when each climate model in the climate models simulates at least two meteorological elements respectively, including: Calculating in sequence the target normalized standard deviation, target spatial correlation coefficient, target mean deviation and target interannual standard deviation of each climate model in each climate model relative to the meteorological measured data; Performing factor conversion on the target normalized standard deviation based on a normalized standard deviation conversion function to obtain a first conversion factor; Performing factor conversion on the target spatial correlation coefficient based on the spatial correlation coefficient conversion function to obtain a second conversion factor; Performing factor conversion on the target average deviation based on the average deviation conversion function to obtain a third conversion factor; The target inter-annual standard deviation is factor-converted based on the inter-annual standard deviation conversion function to obtain a fourth conversion factor, and the index factor is obtained by weighting the first conversion factor, the second conversion factor, the third conversion factor and the fourth conversion factor, so as to obtain the index factor when each climate model simulates at least two meteorological elements respectively; The formula of the normalized standard deviation conversion function is expressed as: x1=-|x1-1| Wherein, X1 is the first conversion factor, and x1 is the target normalized standard deviation; The formula of the spatial correlation coefficient conversion function is expressed as: X2=x2-1 Wherein, X2 is the second conversion factor, and x2 is the target space correlation coefficient; The formula of the average deviation conversion function is expressed as: X3=-|x3| Wherein, X3 is the third conversion factor, and x3 is the target average deviation; The formula of the interannual standard deviation conversion function is expressed as: X4=-x4 Among them, X4 is the fourth conversion factor, and x4 is the target inter-annual standard deviation.

3. The method according to claim 2, characterized in that The formula of the preset basis function is expressed as: S(X)=β σ(X+δ) Among them, δ represents tolerance, δ=|maxE(X)|, E(X) is the set of indicator factors corresponding to the one meteorological element when the climate models simulate the one meteorological element or the set of indicator factors corresponding to the another meteorological element when the climate models simulate the another meteorological element, X is the indicator factor corresponding to the one meteorological element when the climate models simulate the one meteorological element or the indicator factor corresponding to the another meteorological element when the climate models simulate the another meteorological element; σ is the correction factor; β is the base, and S(X) is the first index score or the second index score.

4. The method according to claim 1, characterized in that: The method comprises: According to the CMIP6 climate model data and the meteorological measured data, spatial distribution schematic diagrams corresponding to the at least two meteorological elements in each climate model are drawn in sequence to obtain a first spatial distribution schematic diagram corresponding to one meteorological element in each climate model and a second spatial distribution schematic diagram corresponding to another meteorological element in each climate model; Comparing the differences in spatial distribution characteristics between the first spatial distribution schematic diagrams under the various climate modes to obtain first spatial distribution characteristic differences, and comparing the differences in spatial distribution characteristics between the second spatial distribution schematic diagrams under the various climate modes to obtain second spatial distribution characteristic differences; respectively calculating a first spatial correlation coefficient between first simulated data and first target measured data for simulating the one meteorological element by each climate model, wherein the first target measured data is data in the meteorological measured data corresponding to the first simulated data; Respectively calculating a second spatial correlation coefficient between second simulated data and second target measured data for simulating the other meteorological element by each climate model, wherein the second target measured data is data corresponding to the second simulated data in the meteorological measured data, and the CMIP6 climate model data includes the first simulated data and the second simulated data; Selecting a first target climate model with the best simulation effect on the spatial characteristics of the one meteorological element from the climate models according to the first spatial distribution characteristic difference and the first spatial correlation coefficient; A second target climate model with the best simulation effect on the spatial characteristics of another meteorological element is selected from the climate models according to the second spatial distribution characteristic difference and the second spatial correlation coefficient.

5. The method according to claim 4, characterized in that The method comprises: Selecting a third target climate model with the best simulation effect on the temporal characteristics of the one meteorological element from the climate models according to the first interannual standard deviation, wherein the first interannual standard deviation is used to describe the difference in the interannual fluctuation range between the first simulation data and the first target measured data, so as to judge the simulation effect of the climate models on the temporal characteristics of the one meteorological element; Selecting a fourth target climate model with the best simulation effect on the temporal characteristics of the another meteorological element from the climate models according to the second interannual standard deviation, wherein the second interannual standard deviation is used to describe the difference in the interannual fluctuation range between the second simulated data and the second target measured data so as to judge the simulation effect of the climate models on the temporal characteristics of the another meteorological element, and the first interannual standard deviation and the second interannual standard deviation are calculated by an interannual standard deviation function; The formula of the interannual standard deviation function is expressed as: Among them, STD m Represents the interannual standard deviation of each climate model, STD o represents the interannual standard deviation of the meteorological measured data, and IVS represents the interannual standard deviation.

6. The method according to any one of claims 1 to 5, characterized in that: The method comprises: Calculating the spatial correlation coefficient, standard deviation and root mean square error of each climate model and the meteorological measured data; Calculating a first performance score of each climate model when simulating the one meteorological element according to the spatial correlation coefficient, the standard deviation, and the root mean square error, and selecting a fifth target climate model with the best simulation performance for the one meteorological element from each climate model based on the first performance score; A second performance score of each climate model when simulating the other meteorological element is calculated according to the spatial correlation coefficient, the standard deviation and the root mean square error, and a sixth target climate model with the best simulation performance for the other meteorological element is selected from the climate models based on the second performance score.

7. A CMIP6 climate model comprehensive comparison system, characterized in that: include: An acquisition unit, used for acquiring CMIP6 climate model data and meteorological measured data of a target research area, wherein the spatial range of the CMIP6 climate model data includes the target research area; A first calculation unit is used to calculate first indicator data of each climate model in the CMIP6 climate model data based on the meteorological measured data, and obtain an indicator factor when each climate model in the climate models simulates at least two meteorological elements respectively, wherein the types of the first indicator data are normalized standard deviation, spatial correlation coefficient, mean deviation and interannual standard deviation; A second calculation unit is used to calculate a first index score of the simulation ability of each climate model for one meteorological element and a second index score of the simulation ability of each climate model for another meteorological element according to the index factor based on a preset basis function, wherein the at least two meteorological elements include the one meteorological element and the another meteorological element; A third calculation unit is used to average the sum of the first index score and the second index score corresponding to each climate mode to obtain a comprehensive index score corresponding to each climate mode; A selection unit, configured to select a preset number of climate modes from the climate modes in descending order of the comprehensive index scores; The processing unit is used to perform weighted ensemble averaging on the preset number of climate models based on the Bayesian model weighted averaging method to obtain a coupled climate model, and the coupled climate model is used to simulate the changing laws of future meteorological elements.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

Images