A method for diagnostic analysis of air-sea CO2 flux using a physical-biological coupled ocean model

Abstract

This paper provides a method for diagnosing and analyzing air-sea CO2 fluxes using a physical-biological coupled ocean model. This method achieves high-resolution simulation of the marine ecosystem in the China coastal area (118°E-132°E, 24°N-42°N), accurately simulating the basic structural characteristics of the regional ecosystem, particularly the marine carbon cycle. Based on this, a model post-processing method for air-sea CO2 fluxes has been developed. This automated processing of regional high-resolution physical-ecological coupled ocean simulation data includes the preparation of model input data, evaluation of model results, and visualization. The air-sea CO2 flux diagnostic analysis method is improved by incorporating ocean physical process analysis, analyzing the physical processes that cause changes in the air-sea CO2 flux from a holistic perspective.

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 deoxygenation. 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 before it can be used and fixed by phytoplankton and microorganisms to achieve the purpose of carbon absorption and storage. Therefore, the air-sea CO2 flux is an important indicator to measure the carbon absorption capacity of the ocean. The air-sea 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 air-sea CO2 flux is subject to the lack of observational data, and is generally carried out by model simulation methods. The current diagnostic analysis of the air-sea CO2 flux has the following main defects:

[0003] 1. Observational data on ocean carbon flux are sparse, and ocean carbon flux is affected by physical, chemical, and biological factors, making real-time synchronous observation difficult. Therefore, current observation-based carbon flux diagnostics generally use delayed data or data approximation methods, resulting in unreliable estimates.

[0004] 2. Whether global or regional ocean carbon cycle simulations, most studies only analyze gas transport equations during carbon flux diagnosis, while relatively few studies have conducted further diagnostic analysis of sea surface CO2 partial pressure.

[0005] 3. Sea surface CO2 partial pressure is jointly influenced by sea surface temperature, sea surface salinity, sea surface inorganic carbon concentration, and sea surface alkalinity. These four variables can be mapped to sea surface CO2 partial pressure through a mathematical function relationship. However, this diagnostic method ignores the mutual influence of each variable. For example, sea surface temperature can also affect ocean inorganic carbon concentration and sea surface alkalinity. Therefore, relying solely on current mainstream methods will result in certain diagnostic errors and cannot fully distinguish the impact of each variable on sea surface CO2 partial pressure. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention provides a method for diagnosing and analyzing air-sea CO2 fluxes using a physical-biological coupled ocean model. This method achieves high-resolution marine ecosystem simulation in the coastal waters of China (118°E-132°E, 24°N-42°N), accurately simulating the basic structural characteristics of the ecosystem in this region, particularly accurately depicting the marine carbon cycle. On this basis, a model post-processing method for air-sea CO2 fluxes is developed. Automated processing of regional high-resolution physical-ecological coupled ocean simulation data includes preparation of model input data, evaluation of model operation results, and visualization. Improved methods for diagnosing and analyzing air-sea CO2 fluxes are introduced, introducing ocean physical process analysis to analyze the physical processes that cause changes in air-sea CO2 fluxes from the perspective of the water body as a whole.

[0007] 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:

[0008] Step S1: Select a regional ocean model that includes an ecological module, prepare input datasets, create atmospheric driving datasets, river flow and nutrient data, generate grid and terrain files for the high-resolution model, and prepare model initial condition and boundary condition files;

[0009] Step S2: Perform model simulation and evaluation verification. Download the satellite-observed sea surface CO2 partial pressure dataset and the sea surface temperature reanalysis dataset (OISST dataset), compare them with the model data, and display them in the form of spatial distribution maps and time series graphs.

[0010] Step S3, calculating the air-sea CO2 flux based on the output of the above model;

[0011] Step S4, applying Reynolds decomposition to the air-sea CO2 flux to obtain the contribution of kk0 and pCO2 to the air-sea CO2 flux respectively;

[0012] Step S5: Based on step S4, firstly, an approximate estimation method is applied to the sea surface pCO2 to decompose it into contributions of temperature factors and non-temperature factors; then, Reynolds decomposition is applied 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;

[0013] Step S6: Based on 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.

[0014] As a preferred solution, the regional ocean model in step S1 is selected as Regional Ocean Modeling System (ROMS) v3.9, and the ecological module is selected as 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. The atmospheric CO2 partial pressure is obtained from the NOAA website, using spatially uniform and temporally varying monthly average data. The station is selected as TAP station on the west coast of South Korea. The remaining atmospheric driving data are downloaded from the ERA5 dataset with a temporal resolution of 6 hours; river discharge data are obtained from the statistical yearbook "China River Sediment Bulletin", and river nutrient data are obtained from a climatological dataset based on field observations; topography is obtained by interpolating ETOPO2 to the model grid points; initial and boundary ecological conditions are both obtained from the European Copernicus Marine Data Center reanalysis dataset with a temporal resolution of 1 day and a spatial resolution of 0.25°×0.25°.

[0015] 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 dataset is the sea surface temperature in the OISST dataset and the HYCOM reanalysis dataset, the chlorophyll verification dataset is the chlorophyll dataset observed by the GlobColour satellite and the sea surface chlorophyll data in the European Copernicus Data Center reanalysis dataset, 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), respectively, and the time series map plots the average seasonal cycle of the climate state in the East China Sea and Yellow Sea regions.

[0016] As a preferred solution, the calculation of the sea-air CO2 flux in step S3 mainly adopts the gas transmission formula:

[0017]

[0018] 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 atmospheric CO2 partial pressure, respectively;

[0019]

[0020] in, and represent the east-west and north-south wind speeds at 10 m above the sea surface, respectively. SST represents the sea surface temperature. A, B, C, and D are all constants, which are 2073.1, -125.62, 3.6276, and -0.043219, respectively.

[0021]

[0022]

[0023] 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;

[0024] .

[0025] As a preferred solution, the Reynolds decomposition of the air-sea CO2 flux in step S4 is as follows:

[0026]

[0027] in, represents the abnormal value of air-sea CO2 flux, and represent the climatological averages of gas transfer rate and air-sea pCO2 difference, 、 and represent the anomalies of gas transfer rate, sea surface pCO2, and atmospheric pCO2, respectively.

[0028] As a preferred solution, in the sea surface pCO2 budget analysis process 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. The complete functional relationship of pCO2 is shown in step S3. The budget equations of sea surface inorganic carbon, sea surface alkalinity, sea surface temperature, and sea surface salinity adopt the following approximate method:

[0029]

[0030]

[0031]

[0032]

[0033] 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;

[0034] Combining the above equations, we can obtain 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:

[0035]

[0036] The left-hand side of the equation represents the temporal variation of surface pCO2, and the right-hand side, from left to right, represents the horizontal pCO2 transport term, the vertical pCO2 transport term, the biological pCO2 change term, the air-sea pCO2 flux term, the temperature variation term, the freshwater pCO2 flux term, and the remainder. The complete expressions of each term are as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] .

[0044] Due to the adoption of the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0045] 1. By adjusting local parameters and improving model resolution, the model successfully simulates the basic structural characteristics of the marine ecosystem in the coastal waters of China. It can depict the basic carbon, nitrogen, and silicon cycles in the region, laying a model foundation for studying various ecological processes, including sea-to-air CO2 flux. It can effectively address various marine ecological and environmental issues under eutrophication scenarios and provide a basis for decision-makers to protect the marine ecological environment.

[0046] 2. Improve the diagnostic method for air-sea CO2 flux. Beyond the existing decomposition of surface CO2 into temperature and non-temperature factors, further univariate decomposition is performed, enabling a more detailed understanding of the causes of surface CO2 variations. Furthermore, to overcome the nonlinear interactions between different variables, an innovative ocean budget analysis method is introduced and applied to the diagnostic analysis of surface CO2, directly deriving the physical processes that cause changes in the air-sea CO2 flux.

[0047] 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 facilitate the rapid identification of the main factors and ocean processes that cause changes in sea-air CO2 flux.

[0048] Additional aspects and advantages of the invention will become apparent from the description which follows, or may be learned by practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0050] Figure 1 Flowchart for regional high-resolution physical-ecological coupled ocean model construction, parameter optimization, and model validation;

[0051] Figure 2 Figure 1 shows the spatial distribution of the air-sea CO2 flux over China's coastal waters during the 2012-2022 period. Figure a shows the spatial distribution of the air-sea CO2 flux in winter, Figure b shows the spatial distribution of the air-sea CO2 flux in spring, Figure c shows the spatial distribution of the air-sea CO2 flux in summer, and Figure d shows the spatial distribution of the air-sea CO2 flux in autumn.

[0052] Figure 3 Figures 2 and 3 show the spatial distribution of the air-sea CO2 flux anomaly and its contributing factors in the summer of 2017 in the coastal waters of China. Figures a and e show the spatial distribution of the air-sea CO2 flux anomaly in the Yellow Sea, and Figures eh and 5 show the spatial distribution of the air-sea CO2 flux anomaly in the summer of 2017. Figures b and f show the spatial distribution of the contribution of the kk0 anomaly to the air-sea CO2 flux anomaly in the summer of 2017. Figures c and g show the spatial distribution of the contribution of the difference between the sea surface pCO2 and the atmospheric pCO2 to the air-sea CO2 flux anomaly in the summer of 2017. Figures d and h show the spatial distribution of the contribution of the nonlinear interaction term between kk0 and pCO2 to the air-sea CO2 flux anomaly.

[0053] Figure 4 is the Reynolds decomposition of surface pCO2 averaged over the East China Sea and the Yellow Sea in the summer of 2017;

[0054] Figure 5 The contributions of different ocean physical and biological processes to changes in sea surface pCO2. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned objects, 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, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0056] 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 scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0057] The following combination Figures 1 to 5 The method for performing air-sea CO2 flux diagnostic analysis using the physical-biological coupled ocean model of an embodiment of the present invention is described in detail.

[0058] like Figures 1 to 5 As shown, the present invention proposes a method for diagnosing and analyzing air-sea CO2 flux using a physical-biological coupled ocean model. Specifically, it relates to the construction and optimization of a high-resolution physical-ecological coupled ocean model for the Chinese coastal region, model post-processing, and a diagnostic method for factors affecting the air-sea CO2 flux. In response to the urgent need for protecting the marine ecological environment in China's coastal waters, a regional high-resolution ocean model reflecting the ecological environment characteristics of China's coastal waters is constructed through model transplantation and parameter improvement, and subsequent air-sea CO2 flux diagnostic analysis is performed. The method specifically includes the following steps:

[0059] Step S1: Select the regional ocean model ROMS that includes the ecological module, where the ecological module is CoSiNE. Figure 1 As shown, a high-resolution (3km~8km) grid file (118°E-132°E, 23°N-41°N) of the China coastal area was then produced, and input datasets were prepared. The 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 tidal components) files, and river input (discharge, temperature, salinity, and nutrients) files were created, and then the model was run;

[0060] Step S2: Model evaluation and parameter adjustment. Download the satellite observation sea surface CO2 partial pressure dataset and the sea surface temperature reanalysis dataset (OISST dataset), compare them with the model data, and display them in the form of spatial distribution maps and time series graphs, such as Figure 1If the basic spatial distribution is consistent with the time series change, the model is considered to have passed the verification. Otherwise, the model is considered to have failed the verification. The ecological parameters are adjusted and the model is re-run, and the model is verified again until the model passes the verification.

[0061] Step S3: Calculate the air-sea CO2 flux based on the output of the above model. The calculation formula is as follows:

[0062]

[0063] 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.

[0064] 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 following year), spring (March 1 to May 31), summer (June 1 to August 31), and autumn (September 1 to November 30) are calculated respectively;

[0065] Step S4: Apply Reynolds decomposition to the air-sea CO2 flux. The specific decomposition formula is as follows:

[0066]

[0067] in, represents the abnormal value of air-sea CO2 flux, and represent the climatological averages of gas transfer rate and air-sea pCO2 difference, 、 and represent the anomalies of gas transfer rate, sea surface pCO2, and atmospheric pCO2, respectively.

[0068] 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 respectively 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.

[0069] Step S5: Based on step S4, Reynolds decomposition is applied to the sea surface pCO2. The decomposition formula is as follows:

[0070]

[0071] 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.

[0072] 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 figure shows the Reynolds decomposition of the average surface pCO2 in the East China Sea and the Yellow Sea in the summer of 2017. Figure 4 As can be seen from the data, temperature in the Yellow Sea and the East China Sea contributes slightly different amounts to sea surface pCO2, accounting for 61% and 33% respectively. Non-temperature factors also play a significant role in regulating sea surface pCO2. Furthermore, alkalinity contributes the most among non-temperature factors, reaching 61% and 90% respectively.

[0073] In step S6, based on steps S4 and S5, the functional relationship of the sea surface pCO2 is substituted into the budget analysis equation to obtain the sea surface pCO2 budget, 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:

[0074]

[0075]

[0076]

[0077]

[0078] 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.

[0079] Therefore, combining the above equations, we can get the sea surface pCO2 budget equation. Since the above equation uses an approximate method and ignores 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:

[0080]

[0081] The left-hand side of the equation represents the temporal variation of surface CO2, and the right-hand side, from left to right, represents the horizontal transport of surface CO2, the vertical transport of surface CO2, the biological variation of surface CO2, the air-sea CO2 flux, the temperature variation of surface CO2, the freshwater flux, and the remainder. The complete expressions for each of these terms are as follows:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] 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 contribution of different ocean physical and biological processes to the change of sea surface pCO2 is shown. It can be seen from the figure 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 has 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.

[0090] In the description of the present invention, the term "plurality" refers to two or more than two. Unless otherwise expressly defined, the orientations or positional relationships indicated by the terms "upper" and "lower" are based on the orientations or positional relationships shown in the accompanying drawings. They are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limitations on the present invention. The terms "connect," "install," and "fix" should be understood in a broad sense. For example, "connection" can mean a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0091] Throughout this specification, terms such as "one embodiment," "some embodiments," and "specific embodiments" mean that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0092] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for diagnostic analysis of 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 that includes an ecological module, prepare input datasets, create atmospheric driving datasets, river flow and nutrient data, generate grid and terrain files for the high-resolution model, and prepare model initial condition and boundary condition files; Step S2: Perform model simulation and evaluation verification. Download the satellite-observed sea surface CO2 partial pressure dataset and the sea surface temperature reanalysis dataset, compare them with the model data, and display them in the form of spatial distribution maps and time series graphs. Step S3, calculating the air-sea CO2 flux based on the output of the above model; the calculation of the air-sea CO2 flux 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 transmission 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 represent the east-west and north-south wind speeds at 10 m above the sea surface, respectively. SST represents the sea surface temperature. A, B, C, and D are all constants, which are 2073.1, -125.62, 3.6276, and -0.043219, respectively. Where 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; Step S4: Apply Reynolds decomposition to the air-sea CO2 flux to obtain the contribution of kk0 and pCO2 to the air-sea CO2 flux. The Reynolds decomposition of the air-sea CO2 flux is as follows: Where FCO′2 represents the abnormal value of the air-sea CO2 flux, and represents the climatological average of the gas transfer rate multiplied by the CO2 gas solubility and the air-sea pCO2 difference, (kk0)′, pCO′ 2w and pCO′ 2a represent the outliers of gas transfer rate multiplied by CO2 gas solubility, sea surface pCO2, and atmospheric pCO2, respectively; Step S5: Based on step S4, firstly, an approximate estimation method is applied to the sea surface pCO2 to decompose it into contributions of temperature factors and non-temperature factors; then, Reynolds decomposition is applied 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; The sea surface pCO2 anomaly can be decomposed into temperature factors and non-temperature factors, as shown in the following expression: 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. Step S6: Based on 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. During the sea surface pCO2 budget analysis, 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. The complete functional relationship of pCO2 is shown in step S3. The budget equations of sea surface inorganic carbon, sea surface alkalinity, sea surface temperature, and sea surface salinity use 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: The left-hand side of the equation represents the temporal variation of surface pCO2, and the right-hand side, from left to right, represents the horizontal pCO2 transport term, the vertical pCO2 transport term, the biological pCO2 change term, the air-sea pCO2 flux term, the temperature variation term, the freshwater pCO2 flux term, and the remainder. The complete expressions of each term are as follows:

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 In step S1, the regional ocean model selected is Regional Ocean Modeling System v3.9, and the ecological module selected is Carbon, Silicate and Nitrogen Ecosystem. The atmospheric driving data include shortwave radiation, longwave radiation, wind speed, air pressure, temperature, air humidity, precipitation and atmospheric CO2 partial pressure. The atmospheric CO2 partial pressure is obtained from the NOAA website, using spatially uniform and temporally varying monthly average data. The station is 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 "China River Sediment Bulletin", and river nutrient data are from a climatological dataset based on field observations; topography is obtained by interpolating ETOPO2 to the model grid points; initial and boundary ecological conditions are both 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 dataset is the sea surface temperature in the OISST dataset and the HYCOM reanalysis dataset, the chlorophyll verification dataset is the chlorophyll dataset observed by the GlobColour satellite and the sea surface chlorophyll data in the European Copernicus Data Center reanalysis dataset, 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 December-February in winter, March-May in spring, June-August in summer, and September-November in autumn, and the time series map plots the average seasonal cycle of the climate state in the East China Sea and the Yellow Sea.

Images