Method for diagnosing and analyzing air-sea CO2 flux by using physical-biological coupling ocean model

Abstract

The invention provides a method for diagnosing and analyzing air-sea CO2 flux by using a physical-biological coupling ocean model, so that high-resolution ocean ecosystem simulation in offshore areas (118-132-degree E and 24-42-degree N) in China is realized, basic structural characteristics of the ecosystem in the areas are accurately simulated, particularly, an ocean carbon cycle process can be accurately depicted, and the method has a wide application prospect. On the basis, a set of mode post-processing method for air-sea CO2 flux is developed. And automatically processing the regional high-resolution physical-ecological coupled ocean simulation data, including preparation of model input data and evaluation and visualization of a mode operation result. An air-sea CO2 flux diagnostic analysis method is improved, marine physical process analysis is introduced, and the physical process causing air-sea CO2 flux change is analyzed from the perspective of the whole water body.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical simulation of ocean carbon cycle, and in particular to a method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model. Background Art

[0002] Since the Industrial Revolution, global anthropogenic CO2 emissions have continued to increase, which has not only led to rising global temperatures and frequent extreme events, but also caused a series of ecological and environmental problems such as ocean acidification and deoxidation. It is estimated that about 25% of the world's annual anthropogenic CO2 emissions are absorbed by the ocean. The ocean is a huge carbon reservoir. In the process of carbon absorption by the ocean, CO2 in the atmosphere must first enter the upper ocean through the sea-air interface, and then it can be used and fixed by phytoplankton and microorganisms to achieve the purpose of carbon absorption and carbon storage. Therefore, the sea-air CO2 flux is an important indicator to measure the carbon absorption capacity of the ocean. The sea-air CO2 flux is affected by many factors, such as wind speed, sea surface temperature, sea surface salinity, and the difference in CO2 partial pressure between the sea surface and the atmosphere. There is a large uncertainty in the estimation process. In addition, the diagnostic analysis of the factors affecting the sea-air CO2 flux is subject to the lack of observational data, and is generally carried out by model simulation methods. At present, the diagnostic analysis of the sea-air CO2 flux has the following main defects: 1. Ocean carbon flux observation data is sparse, and ocean carbon flux is affected by physical, chemical and biological factors, making it difficult to conduct real-time synchronous observations. Therefore, current observation-based carbon flux diagnosis generally uses delayed data or data approximation methods, which makes its estimation unreliable; 2. Whether global or regional ocean carbon cycle simulations, in the process of carbon flux diagnosis, most of them only analyze the gas transfer formula, while there are relatively few diagnostic analyses of the sea surface CO2 partial pressure; 3. The sea surface CO2 partial pressure is jointly affected by sea surface temperature, sea surface salinity, sea surface inorganic carbon concentration and sea surface alkalinity. The above four variables can be mapped to the sea surface CO2 partial pressure through a mathematical function relationship, but this diagnostic method ignores the mutual influence between the various variables. For example, sea surface temperature can also affect the ocean inorganic carbon concentration and sea surface alkalinity. Therefore, relying solely on the current mainstream method will result in certain diagnostic errors and will not be able to fully distinguish the impact of various variables on the sea surface CO2 partial pressure. Summary of the invention

[0003] In order to make up for the shortcomings of the prior art, the present invention provides a method for diagnosing and analyzing sea-air CO2 flux using a physical-biological coupled ocean model. The present invention realizes high-resolution marine ecosystem simulation in the coastal area of ​​China (118°E-132°E, 24°N-42°N), accurately simulates the basic structural characteristics of the ecosystem in this region, and especially accurately characterizes the marine carbon cycle process. On this basis, a set of model post-processing methods for sea-air CO2 flux is developed. Automated processing of regional high-resolution physical-ecological coupled ocean simulation data, including preparation of model input data, evaluation and visualization of model operation results. Improve the diagnostic analysis method of sea-air CO2 flux, introduce ocean physical process analysis, and analyze the physical processes that cause changes in sea-air CO2 flux from the perspective of the water body as a whole.

[0004] The present invention is achieved through the following technical solution: a method for diagnosing and analyzing sea-air CO2 flux using a physical-biological coupled ocean model, specifically comprising the following steps: Step S1, select a regional ocean model containing an ecological module, prepare input data sets, create atmospheric driving data sets, river flow and nutrient data, generate grid and terrain files for high-resolution models, and prepare model initial condition and boundary condition files; Step S2, perform model simulation and evaluation verification, download satellite observation sea surface CO2 partial pressure dataset and sea surface temperature reanalysis dataset (OISST dataset), compare them with model data, and display them in the form of spatial distribution map and time series map; Step S3, calculating the air-sea CO2 flux according to the output result of the above model; Step S4, applying Reynolds decomposition to the sea-air CO2 flux to obtain the contribution of kk0 and pCO2 to the sea-air CO2 flux respectively; Step S5, based on step S4, firstly apply an approximate estimation method to the sea surface pCO2 to decompose it into contributions of temperature factors and non-temperature factors; further apply Reynolds decomposition to the complete functional relationship of the sea surface pCO2 to obtain the contributions of sea surface temperature, sea surface salinity, sea surface inorganic carbon concentration and sea surface alkalinity to the sea surface pCO2 respectively; Step S6, on the basis of steps S4 and S5, the functional relationship of the sea surface pCO2 is substituted into the budget analysis equation to obtain the complete budget of the sea surface pCO2, including the time term, horizontal advection term, vertical advection term, horizontal diffusion and mixing term, vertical diffusion and mixing term, biological term, temperature term, freshwater flux term and residual term.

[0005] As a preferred solution, the regional ocean model in step S1 is selected as Regional Ocean Modeling System (ROMS) v3.9, the ecological module is selected as Carbon, Silicate and Nitrogen Ecosystem (CoSiNE), and the atmospheric driving data include shortwave radiation, longwave radiation, wind speed, air pressure, temperature, air humidity, precipitation and atmospheric CO2 partial pressure, among which the atmospheric CO2 partial pressure is from the NOAA website, using spatially uniform and temporally varying monthly average data, and the station is selected from the TAP station on the west coast of South Korea. The remaining atmospheric driving data are downloaded from the ERA5 dataset with a time resolution of 6 hours; the river flow data are from the statistical yearbook of the China River Sediment Bulletin, and the river nutrient data are from the climatological dataset based on field observations; the terrain is obtained by interpolating ETOPO2 to the model grid points; the initial and boundary ecological conditions are taken from the reanalysis dataset of the European Copernicus Marine Data Center, with a time resolution of 1 day and a spatial resolution of 0.25°×0.25°.

[0006] As a preferred solution, the model simulation time in step S2 is from January 1, 2010 to December 30, 2022, with a time step of 120s; the temperature verification data set is the sea surface temperature in the OISST data set and the HYCOM reanalysis data set, the chlorophyll verification data set is the chlorophyll data set observed by the GlobColour satellite and the sea surface chlorophyll data in the reanalysis data set of the European Copernicus Data Center, and the sea surface pCO2 verification data is extracted from the "Global Ocean Key Carbon Parameters Dataset" produced by the Second Institute of Oceanography, Ministry of Natural Resources; the spatial distribution map plots the seasonal average results of winter (December-February), spring (March-May), summer (June-August), and autumn (September-November), and the time series map plots the average climatic seasonal cycle in the East China Sea and the Yellow Sea.

[0007] As a preferred solution, the calculation of the sea-air CO2 flux in step S3 mainly adopts the gas transmission formula: Where FCO2 represents the air-sea CO2 flux, represents the gas transmission rate, represents the solubility of CO2 gas, and represent the surface seawater CO2 partial pressure and the atmospheric CO2 partial pressure respectively; in, and They represent the east-west and north-south wind speeds at 10 m above the sea surface, SST represents the sea surface temperature, and A, B, C, and D are all constants, which are 2073.1, -125.62, 3.6276, and -0.043219, respectively; Among them, SST and SSS represent sea surface temperature and sea surface salinity, respectively. , , , , and are constants, namely -60.2409, 93.4517, 23.3505, 0.023517, -0.023656 and 0.0047036; .

[0008] As a preferred solution, the Reynolds decomposition of the sea-air CO2 flux in step S4 is as follows: in, represents the abnormal value of air-sea CO2 flux, and represent the climatological averages of gas transfer rate and air-sea pCO2 difference, respectively. , and represent the anomalies of gas transfer rate, sea surface pCO2 and atmospheric pCO2, respectively.

[0009] As a preferred solution, in the process of analyzing the sea surface pCO2 budget in step S6, it is necessary to combine the complete functional relationship of the sea surface pCO2 with the budget equations of the sea surface inorganic carbon, sea surface alkalinity, sea surface temperature and sea surface salinity for derivation, wherein the complete functional relationship of pCO2 is shown in step S3, and the budget equations of the sea surface inorganic carbon, sea surface alkalinity, sea surface temperature and sea surface salinity adopt the following approximate method: Among them, H, V, B, F, FW, and Q represent the horizontal transport term, vertical transport term, biological term, sea-air CO2 flux term, freshwater flux term, and sea-air net heat flux term, respectively; Combining the above equations, we can get the sea surface pCO2 budget equation, the sea surface pCO2 vertical transport term, the sea surface pCO2 biological change term, the sea surface pCO2 sea-air pCO2 flux term, and the sea surface pCO2 budget equation are as follows: Among them, the left-hand side term of the above equation represents the time variation term of sea surface pCO2, and the right-hand side terms from left to right represent the sea surface pCO2 horizontal transport term, sea surface pCO2 vertical transport term, sea surface pCO2 biological change term, sea surface pCO2 sea-air pCO2 flux term, sea surface pCO2 temperature change term, sea surface pCO2 freshwater flux term and the remainder; the complete expression of each of the above terms is as follows: .

[0010] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art: 1. By adjusting local parameters and improving the model resolution, the basic structural characteristics of the marine ecosystem in China's coastal areas were successfully simulated, and the basic carbon cycle, nitrogen cycle and silicon cycle processes in the region were depicted, laying a model foundation for studying various ecological processes including sea-to-air CO2 flux, and effectively responding to various marine ecological and environmental problems under eutrophication scenarios. It can also provide a basis for decision-makers to protect the marine ecological environment; 2. Improve the diagnostic method of air-sea CO2 flux. On the basis of decomposing the sea surface pCO2 into temperature factors and non-temperature factors, further single variable decomposition is performed, so that the causes of changes in sea surface pCO2 can be obtained in more detail. In addition, in order to overcome the nonlinear interaction between different variables, the ocean budget analysis method is innovatively introduced and applied to the diagnostic analysis of sea surface pCO2, so as to directly obtain the physical process that causes changes in sea surface CO2 flux. 3. Write a complete set of model post-processing procedures that can quickly perform diagnostic analysis of sea-air CO2 flux after the model run is completed, so as to quickly identify the main factors and ocean processes that cause changes in sea-air CO2 flux.

[0011] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 Flowchart for regional high-resolution physical-ecological coupled ocean model construction, parameter optimization, and model validation; Figure 2 This is the spatial distribution map of the climate state of sea-air CO2 flux in China's coastal waters (2012-2022). Figure a shows the spatial distribution of sea-air CO2 flux in winter, Figure b shows the spatial distribution of sea-air CO2 flux in spring, Figure c shows the spatial distribution of sea-air CO2 flux in summer, and Figure d shows the spatial distribution of sea-air CO2 flux in autumn. Figure 3 Figure 2 is the spatial distribution of the anomaly of sea-air CO2 flux and its contributing factors in the summer of 2017 in the coastal waters of China. Figures a and e are the spatial distribution of the anomaly of sea-air CO2 flux in the summer of 2017, Figures b and f represent the spatial distribution of the contribution of the anomaly of kk0 to the anomaly of sea-air CO2 flux in the summer of 2017, Figures c and g are the spatial distribution of the contribution of the anomaly of the difference between the sea surface pCO2 and the atmospheric pCO2 to the anomaly of sea-air CO2 flux in the summer of 2017, and Figures d and h are the spatial distribution of the contribution of the anomaly of the nonlinear interaction term of kk0 and pCO2 to the anomaly of sea-air CO2 flux; Figure 4 is the Reynolds decomposition of sea surface pCO2 averaged over the East China Sea and the Yellow Sea in summer 2017; Figure 5 The contribution of different ocean physical and biological processes to the change of sea surface pCO2. DETAILED DESCRIPTION

[0013] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0014] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0015] Combine the following Figures 1 to 5 The method for performing diagnostic analysis of air-sea CO2 flux using the physical-biological coupled ocean model of an embodiment of the present invention is specifically described.

[0016] like Figures 1 to 5As shown, the present invention proposes a method for diagnosing and analyzing air-sea CO2 flux using a physical-biological coupled ocean model, specifically, it particularly relates to the construction and optimization of a high-resolution physical-ecological coupled ocean model in China's offshore area, model post-processing, and a method for diagnosing factors affecting air-sea CO2 flux. In view of the urgent need for the protection of China's offshore marine ecological environment, a regional high-resolution ocean model reflecting the characteristics of China's offshore ecological environment is constructed through model transplantation and parameter improvement, and subsequent air-sea CO2 flux diagnostic analysis is carried out, specifically including the following steps: Step S1, select the regional ocean model ROMS containing the ecological module, the ecological module is CoSiNE, such as Figure 1 As shown, a high-resolution (3km~8km) grid (118°E-132°E, 23°N-41°N) file of the offshore area of ​​China was then produced, input data sets were prepared, atmospheric driving (wind speed, temperature, air pressure, precipitation, air humidity, shortwave radiation and longwave radiation) files, ocean initial state files, ocean boundary condition files, tidal input (eight major tides) files, river input (flow, temperature, salinity and nutrients) files were created, and then the model was run; Step S2, conduct model evaluation verification and parameter adjustment, download satellite observation sea surface CO2 partial pressure data set and sea surface temperature reanalysis data set (OISST data set), compare them with model data, and display them in the form of spatial distribution map and time series map, such as Figure 1 If the basic spatial distribution is consistent with the time series change, the model verification is considered to be successful. Otherwise, the model verification is considered to be unsuccessful, and the ecological parameters are adjusted and re-run, and the model verification is performed again until the model verification is successful. Step S3, calculate the air-sea CO2 flux according to the output results of the above model, and the calculation formula is as follows: Where FCO2 represents the air-sea CO2 flux, represents the gas transmission rate, represents the solubility of CO2 gas, and represent the surface seawater CO2 partial pressure and the atmospheric CO2 partial pressure, respectively.

[0017] like Figure 2 As shown, the daily average sea-air CO2 flux is first calculated, and then the daily average sea-air CO2 flux of the 2012-2022 climate state is calculated. On this basis, the seasonal average sea-air pCO2 flux of winter (December 1 of the previous year to February 28 of the next year), spring (March 1 to May 31), summer (June 1 to August 31) and autumn (September 1 to November 30) are calculated respectively; Step S4, Reynolds decomposition is applied to the sea-air CO2 flux, and the specific decomposition formula is as follows: in, represents the abnormal value of air-sea CO2 flux, and represent the climatological averages of gas transfer rate and air-sea pCO2 difference, respectively. , and represent the anomalies of gas transfer rate, sea surface pCO2 and atmospheric pCO2, respectively.

[0018] like Figure 3 As shown in the figure, the Reynolds decomposition of the air-sea CO2 flux in the Yellow Sea and the East China Sea in the summer of 2017 is plotted according to the above method. The figures show the air-sea CO2 flux anomaly, the contribution of kk0 to the air-sea CO2 flux anomaly, the contribution of pCO2 to the air-sea CO2 flux anomaly and the contribution of the nonlinear effect of the above two to the air-sea CO2 flux anomaly. It can be clearly found from the figure that both kk0 and pCO2 have significant contributions to the air-sea CO2 flux anomaly. The synergistic effect of the two makes the air-sea CO2 flux in the summer of 2017 show a negative anomaly. Step S5, based on step S4, Reynolds decomposition is applied to the sea surface pCO2, and the decomposition formula is as follows: in, , , , and They represent sea surface pCO2 anomaly, sea surface inorganic carbon anomaly, sea surface alkalinity anomaly, sea surface temperature anomaly and sea surface salinity anomaly respectively.

[0019] According to the above decomposition method, the contribution of sea surface temperature, sea surface salinity, sea surface inorganic carbon concentration and sea surface alkalinity to sea surface pCO2 can be obtained respectively. The results are as follows: Figure 4 shown. Figure 4 The Reynolds decomposition of the average surface pCO2 in the East China Sea and the Yellow Sea in summer 2017 is shown. Figure 4 It can be seen that the contribution of temperature factors to sea surface pCO2 in the Yellow Sea and the East China Sea is slightly different, 61% and 33% respectively. Non-temperature factors also play a non-negligible role in regulating sea surface pCO2. In addition, alkalinity contributes the most among non-temperature factors, with contribution rates reaching 61% and 90% respectively. Step S6, on the basis of steps S4 and S5, the functional relationship of the sea surface pCO2 is brought into the budget analysis equation to obtain the budget of the sea surface pCO2, including the time term, horizontal advection term, vertical advection term, horizontal diffusion and mixing term, vertical diffusion and mixing term, biological term, temperature term, freshwater flux term and remainder term. The specific expression and derivation process are as follows: Among them, H, V, B, F, FW, and Q represent the horizontal transport term, vertical transport term, biological term, sea-air CO2 flux term, freshwater flux term, and sea-air net heat flux term, respectively.

[0020] Therefore, combining the above equations, we can get the sea surface pCO2 budget equation. Since the above equations use an approximate method and ignore some small terms, there will be a small error term when deriving the sea surface pCO2 budget equation. The sea surface pCO2 budget equation is as follows: Among them, the left term of the above equation represents the time variation term of sea surface pCO2, and the right term represents the sea surface pCO2 horizontal transport term, sea surface pCO2 vertical transport term, sea surface pCO2 biological change term, sea surface pCO2 sea-air CO2 flux term, sea surface pCO2 temperature change term, sea surface pCO2 freshwater flux term and the remainder term from left to right. The complete expression of each of the above terms is as follows: The above method was applied to the analysis of the pCO2 budget of the Yellow Sea surface in the summer of 2017. The results are as follows: Figure 5 As shown, Figure 5 The figure shows the contribution of different ocean physical and biological processes to the change of sea surface pCO2. It can be found that the temporal variation of sea surface pCO2 is mainly contributed by the horizontal transport term; the ocean continues to release CO2 to the atmosphere in summer, so the sea-air CO2 flux term is a negative contribution; the biological term and the temperature term have a mutually offsetting effect, and the contribution of the biological term is about one-third of the contribution of the temperature term.

[0021] In the description of the present invention, the term "plurality" refers to two or more than two. Unless otherwise clearly defined, the orientation or positional relationship indicated by the terms "upper" and "lower" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation of the present invention; the terms "connection", "installation", "fixation", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0023] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for diagnosing and analyzing air-sea CO2 flux using a physical-biological coupled ocean model, characterized in that: The specific steps include: Step S1, select a regional ocean model containing an ecological module, prepare input data sets, create atmospheric driving data sets, river flow and nutrient data, and generate grid and terrain files for high-resolution models. Prepare model initial condition and boundary condition files; Step S2, perform model simulation and evaluation verification, download satellite observation sea surface CO2 partial pressure data set and sea surface temperature reanalysis data set, compare them with model data, and display them in the form of spatial distribution map and time series map; Step S3, calculating the air-sea CO2 flux according to the output result of the above model; Step S4, applying Reynolds decomposition to the sea-air CO2 flux to obtain the contribution of kk0 and pCO2 to the sea-air CO2 flux respectively; Step S5, based on step S4, firstly apply an approximate estimation method to the sea surface pCO2 to decompose it into contributions of temperature factors and non-temperature factors; further apply Reynolds decomposition to the complete functional relationship of the sea surface pCO2 to obtain the contributions of sea surface temperature, sea surface salinity, sea surface inorganic carbon concentration and sea surface alkalinity to the sea surface pCO2 respectively; Step S6, on the basis of steps S4 and S5, the functional relationship of the sea surface pCO2 is substituted into the budget analysis equation to obtain the complete budget of the sea surface pCO2, including the time term, horizontal advection term, vertical advection term, horizontal diffusion and mixing term, vertical diffusion and mixing term, biological term, temperature term, freshwater flux term and residual term.

2. The method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model according to claim 1 is characterized in that The regional ocean model in step S1 is selected from Regional Ocean Modeling System (ROMS) v3.9, and the ecological module is selected from Carbon, Silicate and Nitrogen Ecosystem (CoSiNE). The atmospheric driving data include shortwave radiation, longwave radiation, wind speed, air pressure, temperature, air humidity, precipitation and atmospheric CO2 partial pressure, where the atmospheric CO2 partial pressure comes from the NOAA website, using spatially uniform and temporally varying monthly average data, and the station is selected from the TAP station on the west coast of South Korea. The remaining atmospheric driving data are downloaded from the ERA5 dataset with a time resolution of 6 hours; river flow data are from the statistical yearbook of the China River Sediment Bulletin, and river nutrient data are from a climatological dataset based on field observations; the terrain is obtained by interpolating ETOPO2 to the model grid points; the initial and boundary ecological conditions are taken from the European Copernicus Marine Data Center reanalysis dataset, with a time resolution of 1 day and a spatial resolution of 0.25°×0.25°.

3. The method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model according to claim 1 is characterized in that , the model simulation time in step S2 is from January 1, 2010 to December 30, 2022, with a time step of 120s; the temperature verification data set is the sea surface temperature in the OISST data set and the HYCOM reanalysis data set, the chlorophyll verification data set is the chlorophyll data set observed by the GlobColour satellite and the sea surface chlorophyll data in the European Copernicus Data Center reanalysis data set, and the sea surface pCO2 verification data is extracted from the "Global Ocean Key Carbon Parameters Dataset" produced by the Second Institute of Oceanography of the Ministry of Natural Resources; the spatial distribution map plots the seasonal average results of winter (December-February), spring (March-May), summer (June-August), and autumn (September-November), and the time series map plots the average climatic seasonal cycle in the East China Sea and the Yellow Sea.

4. The method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model according to claim 1 is characterized in that The calculation of the sea-air CO2 flux in step S3 mainly adopts the gas transmission formula: FCO2=k×k0×(pCO 2w -pCO 2a ) Where FCO2 represents the air-sea CO2 flux, k represents the gas transfer rate, k0 represents the CO2 gas solubility, and pCO 2w and pCO 2a represent the surface seawater CO2 partial pressure and atmospheric CO2 partial pressure respectively; Among them, U 10 and V 10 They represent the east-west and north-south wind speeds at 10 m above the sea surface, SST represents the sea surface temperature, and A, B, C, and D are all constants, which are 2073.1, -125.62, 3.6276, and -0.043219, respectively; Among them, SST and SSS represent sea surface temperature and sea surface salinity, respectively. A1, A2, A3, B1, B2 and B3 are constants, which are -60.2409, 93.4517, 23.3505, 0.023517, -0.023656 and 0.0047036, respectively.

5. The method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model according to claim 1 is characterized in that , the Reynolds decomposition of the sea-air CO2 flux in step S4 is as follows: Where FCO′2 represents the abnormal value of air-sea CO2 flux, and represent the climatological average of the gas transfer rate and the air-sea pCO2 difference, (kk0)′, pCO′ 2w and pCO′ 2a represent the anomalies of gas transfer rate, sea surface pCO2 and atmospheric pCO2, respectively.

6. The method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model according to claim 1, characterized in that ,The sea surface pCO2 anomaly in step S5 can be decomposed into temperature factors and non-temperature factors, and the expression is as follows: pCO′ 2w =pCO′ 2T +pCO′ 2NT Among them, pCO′ 2T and pCO′ 2NT They represent the sea surface pCO2 anomalies caused by temperature factors and non-temperature factors, respectively; The Reynolds decomposition of the complete functional relationship of the sea surface pCO2 flux is as follows: Among them, pCO′2, DIC′, Talk′, T′ and S′ represent the sea surface pCO2 anomaly, sea surface inorganic carbon anomaly, sea surface alkalinity anomaly, sea surface temperature anomaly and sea surface salinity anomaly, respectively.

7. The method for performing diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model according to claim 1, characterized in that In the process of analyzing the sea surface pCO2 budget in step S6, it is necessary to combine the complete functional relationship of sea surface pCO2 with the budget equations of sea surface inorganic carbon, sea surface alkalinity, sea surface temperature and sea surface salinity for derivation, wherein the complete functional relationship of pCO2 is shown in step S3, and the budget equations of sea surface inorganic carbon, sea surface alkalinity, sea surface temperature and sea surface salinity adopt the following approximate method: Among them, H, V, B, F, FW, and Q represent the horizontal transport term, vertical transport term, biological term, sea-air CO2 flux term, freshwater flux term, and sea-air net heat flux term, respectively; Combining the above equations, we can get the sea surface pCO2 budget equation, which is as follows: Among them, the left-hand side term of the above equation represents the time variation term of sea surface pCO2, and the right-hand side terms from left to right represent the sea surface pCO2 horizontal transport term, sea surface pCO2 vertical transport term, sea surface pCO2 biological change term, sea surface pCO2 sea-air pCO2 flux term, sea surface pCO2 temperature change term, sea surface pCO2 freshwater flux term and the remainder; the complete expression of each of the above terms is as follows:

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