Qinghai-Tibet Plateau land-gas coupling strength prediction method and system based on CMIP6 model and emergence constraint

Abstract

The invention belongs to the technical field of ecological information, and particularly relates to a Qinghai-Tibet Plateau land-gas coupling strength prediction method and system based on a CMIP6 model and emergence constraints. The method comprises the following steps: collecting monthly total precipitation and soil humidity data of a historical period and a future period in nine earth system models in a CMIP6 model, and constructing a data set; performing data preprocessing on the data set through spatial resolution unification and multi-source data fusion; constructing an emergence constraint model based on the preprocessed data; and calculating a land-gas coupling strength index according to a coupling measurement method based on the emergence constraint model. According to the method, by means of emerging constraint, the uncertainty of the model is reduced, the standard deviation of the land-gas coupling strength after constraint is reduced by 65.12%, the prediction precision of the space-time trend is improved, and the problems that a CMIP6 model is underestimated to the northeast and southwest of the Qinghai-Tibet Plateau and is overestimated to the west and northwest of the Qinghai-Tibet Plateau are solved.

Description

Technical Field

[0001] The present invention belongs to the field of ecological information technology, and specifically relates to a method and system for predicting the land-atmosphere coupling intensity of the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints. Background Art

[0002] As the "Roof of the World," the Qinghai-Tibet Plateau's vegetation changes and land-atmosphere coupling have significant impacts on regional and even global climate and ecosystems. CMIP6 (Coupled Model Intercomparison Project 6), a leading global climate modeling system, simulates the interactions between the atmosphere, land, and ocean to predict future climate trends.

[0003] The land-atmosphere coupling intensity refers to the degree of interaction between land surface conditions (such as soil moisture and temperature) and atmospheric processes (such as precipitation and temperature).

[0004] The CMIP6 Earth System Model (EM6) exhibits significant uncertainty in its predictions of the strength of land-atmosphere coupling over the Tibetan Plateau: it overestimates trends in the water cycle and thermal coupling in arid regions and underestimates them in humid regions. This uncertainty stems from the model's underestimation of soil moisture limitations and its inadequate consideration of the impact of cold-season processes on soil moisture, such as permafrost thaw and snow phase transitions. Furthermore, the model's inadequate portrayal of topographic influences on precipitation over the plateau leads to biased predictions of land-atmosphere coupling, severely limiting our understanding of the future global water, carbon, and energy cycles. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention provides a method and system for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergent constraints. This method aims to integrate historical observation data with CMIP6 model outputs to construct a dynamic constraint model, reduce the CMIP6 model prediction variance, improve the accuracy of land-atmosphere coupling intensity prediction over the Qinghai-Tibet Plateau, and address the systematic bias of existing models in arid / humid areas.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints, the method comprising:

[0008] S1. Collect monthly total precipitation and soil moisture data for the historical and future periods in nine Earth system models in the CMIP6 model to construct a dataset.

[0009] S2, preprocessing the dataset by unifying spatial resolution and fusing multi-source data;

[0010] S3, constructing an emergent constraint model based on the preprocessed data;

[0011] S4. Based on the emergence constraint model, the land-atmosphere coupling intensity index is calculated according to the coupling measurement method.

[0012] Preferably, the data preprocessing of the data set by spatial resolution unification and multi-source data fusion in S2 includes:

[0013] S21, spatial resolution uniformity: resample all data in the dataset to ;

[0014] S22. Multi-source data fusion: Calculate the pixel-scale average value for the same type of data in the dataset.

[0015] Preferably, the emergent constraint model in S3 includes:

[0016] ;

[0017] in, is the Earth system variable in the future period after constraint, where and are the average values ​​of 9 different Earth system models, y and x, respectively. y is the monthly total precipitation and soil moisture data for the future period, and x is the monthly total precipitation and soil moisture data for the historical period. is the average of the total precipitation and soil moisture observed in the current Earth system, is the scale factor after correction;

[0018] ;

[0019] Where a is the scaling factor and SNR is the signal-to-noise ratio.

[0020] Variance of Earth system variables for preferred, constrained future periods for:

[0021] ;

[0022] in, is the correlation coefficient between y and x, is the standard deviation of y.

[0023] Preferably, the calculation of the land-atmosphere coupling intensity index in S4 based on the emergence constraint model and the coupling metric method includes:

[0024] ;

[0025] Where I(SM_P) is the land-atmosphere coupling intensity index, SM is the monthly mean soil moisture, and P is the monthly total precipitation. and are the standard deviations of soil moisture and precipitation, represents the linear regression slope between precipitation and soil moisture, and r(SM,P) is the correlation coefficient between precipitation and soil moisture.

[0026] The present invention also provides a Qinghai-Tibet Plateau land-atmosphere coupling intensity prediction system based on the CMIP6 model and emergence constraints, the system is used to implement the above-mentioned prediction method, the system includes: an acquisition module, a preprocessing module, a constraint module and a land-atmosphere coupling module;

[0027] The acquisition module is used to collect monthly total precipitation and soil moisture data for the historical and future periods in the nine Earth system models in the CMIP6 model and construct a dataset;

[0028] The preprocessing module is used to preprocess the data set by unifying spatial resolution and fusing multi-source data;

[0029] Constraint module, used to build emergent constraint models based on preprocessed data;

[0030] The land-atmosphere coupling module is used to calculate the land-atmosphere coupling intensity index based on the emergence constraint model and the coupling measurement method.

[0031] Preferably, the pre-processing module includes: a spatial resolution unification unit and a multi-source data fusion unit;

[0032] A spatial resolution unit that resamples all data in the dataset to a uniform value using bilinear interpolation. ;

[0033] The multi-source data fusion unit is used to calculate the pixel-scale average value of the same type of data in the dataset.

[0034] Preferably, the constraint module includes:

[0035] ;

[0036] in, is the Earth system variable in the future period after constraint, where and are the average values ​​of 9 different Earth system models, y and x, respectively. y is the monthly total precipitation and soil moisture data for the future period, and x is the monthly total precipitation and soil moisture data for the historical period. is the average of the total precipitation and soil moisture observed in the current Earth system, is the scale factor after correction;

[0037] ;

[0038] Where a is the scaling factor and SNR is the signal-to-noise ratio.

[0039] Variance of Earth system variables for preferred, constrained future periods for:

[0040] ;

[0041] in, is the correlation coefficient between y and x, is the standard deviation of y.

[0042] Preferably, the land-air coupling module includes:

[0043] ;

[0044] Where I(SM_P) is the land-atmosphere coupling intensity index, SM is the monthly mean soil moisture, and P is the monthly total precipitation. and are the standard deviations of soil moisture and precipitation, represents the linear regression slope between precipitation and soil moisture, and r(SM,P) is the correlation coefficient between precipitation and soil moisture.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention uses the means of emergence constraints to reduce model uncertainty. After the constraints, the standard deviation of the land-atmosphere coupling intensity is reduced by 65.12%, and the accuracy of spatiotemporal trend prediction is improved. This solves the problem that the CMIP6 model underestimates the northeastern and southwestern Qinghai-Tibet Plateau, while overestimating the western and northwestern Qinghai-Tibet Plateau. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 The figure is a flow chart of a method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Example 1

[0052] like Figure 1 As shown, the present invention provides a method for predicting the land-atmosphere coupling intensity in the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints, comprising:

[0053] S1. Collect monthly total precipitation and soil moisture data for the historical and future periods in nine Earth system models in the CMIP6 model to construct a dataset.

[0054] S2, preprocessing the dataset by unifying spatial resolution and fusing multi-source data;

[0055] S3, constructing an emergent constraint model based on the preprocessed data;

[0056] S4. Based on the emergence constraint model, the land-atmosphere coupling intensity index is calculated according to the coupling measurement method.

[0057] Furthermore, the specific implementation process of the present invention is as follows:

[0058] S1. Collect monthly total precipitation (P) and soil moisture data (SM) for the historical period (2000-2020) and the monthly total precipitation (P) and soil moisture data (SM) for the future period (2015-2100) (SSP126, SSP245, SSP370 and SSP585) from the nine Earth system models in the CMIP6 model, as shown in Table 1.

[0059] Table 1

[0060]

[0061] Among them, SSP describes the possible development of future society in the absence of climate change or climate policy, as shown in Table 2.

[0062] Table 2

[0063]

[0064] S2. Data preprocessing of the dataset by spatial resolution unification and multi-source data fusion, including:

[0065] S21, spatial resolution uniformity: resample all data in the dataset to , ensuring spatial consistency.

[0066] S22. Multi-source data fusion: Calculate the pixel-scale average value for the same type of data in the dataset (such as precipitation data from different sources) to reduce the bias of a single data source.

[0067] S3. Construct an emergence constraint model based on the preprocessed data.

[0068] Emergent constraints provide a quantitative probabilistic model for analysis based on observational data and current climate sensitivity. They also provide a robust approach for constraining future predictions of Earth system characteristics because they account for the correlation between current and future Earth system characteristics, the deviation between observational data and the model ensemble mean, and the uncertainty of observational data. The main formula for the emergent constraint is:

[0069] ;

[0070] Formula 1 uses the least squares linear regression method to establish the emerging constraint relationship between x and y. is the Earth system variable (monthly total precipitation and soil moisture data) in the future period after constraints, where and are the average values ​​of 9 different Earth system models, y and x, respectively. y is the collected monthly total precipitation and soil moisture data for the future period, and x is the collected monthly total precipitation and soil moisture data for the simulated historical period. is the average of the total precipitation and soil moisture observed in the current Earth system, is the corrected scaling factor:

[0071] ;

[0072] Where a is the scaling factor and SNR is the signal-to-noise ratio.

[0073] ;

[0074] The signal-to-noise ratio defines the relative strength of the variability of historical data to the variability of current observations, where is the variance of the monthly total precipitation and soil moisture data of the historical period simulated by the system under different models, is the variance of monthly total precipitation and soil moisture data from different observation datasets.

[0075] ;

[0076] in, is the correlation coefficient between y and x, and are the standard deviations of y and x, respectively, in an ensemble of nine different Earth system models.

[0077] Therefore, the main formula of the emergence constraint can also be written as:

[0078] ;

[0079] Variance of Earth system variables for constrained future periods for:

[0080] .

[0081] S4. Based on the emergence constraint model, the land-atmosphere coupling intensity index is calculated according to the coupling measurement method.

[0082] The potential connection between soil moisture and precipitation at the ground and in the atmosphere was assessed based on the coupling metric method. The land-atmosphere coupling strength index was calculated.

[0083] ;

[0084] Where I(SM_P) is the land-atmosphere coupling intensity index, SM is the monthly mean soil moisture, and P is the monthly total precipitation. and are the standard deviations of soil moisture and precipitation, represents the linear regression slope between precipitation and soil moisture, and r(SM,P) is the correlation coefficient between precipitation and soil moisture.

[0085] Application of land-atmosphere coupling strength:

[0086] The land-atmosphere coupling intensity is a key indicator for measuring the efficiency of material and energy exchange between land and atmosphere. Its value can be used to reveal the land-atmosphere interaction mechanism, predict climate and ecosystem feedback, and optimize model simulation accuracy.

[0087] Revealing the driving mechanisms of atmospheric circulation: The coupling strength between surface temperature and 500 hPa geopotential height derived from satellite remote sensing can be used to quantify the timing of plateau heating's impact on the monsoon. For example, when the surface temperature in the central Tibetan Plateau exceeds 20°C in May and the coupling coefficient with the South Asian High Ridge is greater than 0.6, it typically indicates the onset of the East Asian summer monsoon 10–15 days later.

[0088] Climate simulation and prediction: Incorporating data on land-atmosphere coupling strength into climate models and improving model parameterization schemes can enhance the simulation and prediction capabilities of the climate of the Qinghai-Tibet Plateau and surrounding areas, including temperature and precipitation. For example, the application of models such as CLDAS-V2.0 and FGOALS-f3-H has improved the simulation of precipitation and temperature in the Qinghai-Tibet Plateau.

[0089] Disaster early warning and response: Monitor changes in the intensity of land-atmosphere coupling to predict extreme weather events such as droughts, floods, rainstorms, and heat waves in advance, providing support for disaster early warning, prevention, and mitigation. If strengthened land-atmosphere coupling is found to increase the intensity and probability of extreme high-temperature events on the Qinghai-Tibet Plateau, proactive response measures can be implemented.

[0090] By adopting this technical solution, the standard deviation of the constrained land-atmosphere coupling intensity was reduced by 65.12%, and the accuracy of spatiotemporal trend prediction was improved.

[0091] In summary, this paper proposes a constraint model based on the statistical correlation of historical and future variables. Using linear regression, this model maps historical CMIP6 model outputs to future projections. By employing emergent constraints, model uncertainty is reduced. The standard deviation of the land-atmosphere coupling intensity decreases by 65.12% after the constraints are implemented, improving the accuracy of spatiotemporal trend predictions and addressing the CMIP6 model's underestimation of the land-atmosphere coupling intensity over the northeastern and southwestern Tibetan Plateau.

[0092] Example 2

[0093] The present invention also provides a Qinghai-Tibet Plateau land-atmosphere coupling intensity prediction system based on the CMIP6 model and emergence constraints, which is used to implement the prediction method of the above embodiment. The system includes: an acquisition module, a preprocessing module, a constraint module, and a land-atmosphere coupling module;

[0094] The acquisition module is used to collect monthly total precipitation and soil moisture data for the historical and future periods in the nine Earth system models in the CMIP6 model and construct a dataset;

[0095] The preprocessing module is used to preprocess the data set by unifying spatial resolution and fusing multi-source data;

[0096] Constraint module, used to build emergent constraint models based on preprocessed data;

[0097] The land-atmosphere coupling module is used to calculate the land-atmosphere coupling intensity index based on the emergence constraint model and the coupling measurement method.

[0098] Specifically, the preprocessing module includes: a spatial resolution unification unit and a multi-source data fusion unit;

[0099] A spatial resolution unit that resamples all data in the dataset to a uniform value using bilinear interpolation. ;

[0100] The multi-source data fusion unit is used to calculate the pixel-scale average value of the same type of data in the dataset.

[0101] Constraint modules, including:

[0102] ;

[0103] in, is the Earth system variable in the future period after constraint, where and are the average values ​​of 9 different Earth system models, y and x, respectively. y is the monthly total precipitation and soil moisture data for the future period, and x is the monthly total precipitation and soil moisture data for the historical period. is the average of the total precipitation and soil moisture observed in the current Earth system, is the scale factor after correction;

[0104] ;

[0105] Where a is the scaling factor and SNR is the signal-to-noise ratio.

[0106] Variance of Earth system variables for constrained future periods for:

[0107] ;

[0108] in, is the correlation coefficient between y and x, is the standard deviation of y.

[0109] Land-atmosphere coupling module, including:

[0110] ;

[0111] Where I(SM_P) is the land-atmosphere coupling intensity index, SM is the monthly mean soil moisture, and P is the monthly total precipitation. and are the standard deviations of soil moisture and precipitation, represents the linear regression slope between precipitation and soil moisture, and r(SM,P) is the correlation coefficient between precipitation and soil moisture.

[0112] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints, characterized by: The method comprises: S1. Collect monthly total precipitation and soil moisture data for the historical and future periods in nine Earth system models in the CMIP6 model to construct a dataset. S2, preprocessing the dataset by unifying spatial resolution and fusing multi-source data; S3, constructing an emergent constraint model based on the preprocessed data; S4. Based on the emergence constraint model, the land-atmosphere coupling intensity index is calculated according to the coupling measurement method.

2. The method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints according to claim 1 is characterized in that: In S2, data preprocessing is performed on the dataset by unifying spatial resolution and fusing multi-source data, including: S21, unified spatial resolution: all data in the dataset were resampled to 0.1° × 0.1° using bilinear interpolation; S22. Multi-source data fusion: Calculate the pixel-scale average value for the same type of data in the dataset.

3. The method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints according to claim 1, characterized in that: The emergent constraint model in S3 includes: ； in, is the Earth system variable in the future period after constraint, where and are the average values ​​of 9 different Earth system models, y and x, respectively. y is the monthly total precipitation and soil moisture data for the future period, and x is the monthly total precipitation and soil moisture data for the historical period. is the average of the total precipitation and soil moisture observed in the current Earth system, is the scale factor after correction; ； Where a is the scaling factor and SNR is the signal-to-noise ratio.

4. The method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints according to claim 3 is characterized in that: Variance of Earth system variables for constrained future periods for: ； in, is the correlation coefficient between y and x, is the standard deviation of y.

5. The method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints according to claim 4, characterized in that: In S4, based on the emergence constraint model, the land-atmosphere coupling intensity index is calculated according to the coupling measurement method, including: ； Where I(SM_P) is the land-atmosphere coupling intensity index, SM is the monthly mean soil moisture, and P is the monthly total precipitation. and are the standard deviations of soil moisture and precipitation, represents the linear regression slope between precipitation and soil moisture, and r(SM,P) is the correlation coefficient between precipitation and soil moisture.

6. A system for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints, the system being used to implement the method for predicting the intensity of land-atmosphere coupling over the Qinghai-Tibet Plateau based on the CMIP6 model and emergence constraints according to any one of claims 1 to 5, characterized in that: The system includes: an acquisition module, a pre-processing module, a constraint module and a land-atmosphere coupling module; The acquisition module is used to collect monthly total precipitation and soil moisture data for the historical and future periods in the nine Earth system models in the CMIP6 model and construct a dataset; The preprocessing module is used to preprocess the data set by unifying spatial resolution and fusing multi-source data; Constraint module, used to build emergent constraint models based on preprocessed data; The land-atmosphere coupling module is used to calculate the land-atmosphere coupling intensity index based on the emergence constraint model and the coupling measurement method.

7. The Qinghai-Tibet Plateau land-atmosphere coupling intensity prediction system based on the CMIP6 model and emergence constraints according to claim 6, characterized in that: The pre-processing module includes: a spatial resolution unification unit and a multi-source data fusion unit; The spatial resolution uniform unit is used to resample all data in the dataset to 0.1° × 0.1° using bilinear interpolation; The multi-source data fusion unit is used to calculate the pixel-scale average value of the same type of data in the dataset.

8. The Qinghai-Tibet Plateau land-atmosphere coupling intensity prediction system based on the CMIP6 model and emergence constraints according to claim 6, characterized in that: The constraint module includes: ； in, is the Earth system variable in the future period after constraint, where and are the average values ​​of 9 different Earth system models, y and x, respectively. y is the monthly total precipitation and soil moisture data for the future period, and x is the monthly total precipitation and soil moisture data for the historical period. is the average of the total precipitation and soil moisture observed in the current Earth system, is the scale factor after correction; ； Where a is the scaling factor and SNR is the signal-to-noise ratio.

9. The Qinghai-Tibet Plateau land-atmosphere coupling intensity prediction system based on the CMIP6 model and emergence constraints according to claim 8, characterized in that: Variance of Earth system variables for constrained future periods for: ； in, is the correlation coefficient between y and x, is the standard deviation of y.

10. The Qinghai-Tibet Plateau land-atmosphere coupling intensity prediction system based on the CMIP6 model and emergence constraints according to claim 9, characterized in that: The land-atmosphere coupling module includes: ； Where I(SM_P) is the land-atmosphere coupling intensity index, SM is the monthly mean soil moisture, and P is the monthly total precipitation. and are the standard deviations of soil moisture and precipitation, represents the linear regression slope between precipitation and soil moisture, and r(SM,P) is the correlation coefficient between precipitation and soil moisture.

Images