CN117592316B - Sea gas carbon flux reconstruction method, system and device based on remote sensing data assimilation - Google Patents
Sea gas carbon flux reconstruction method, system and device based on remote sensing data assimilation Download PDFInfo
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Abstract
The invention discloses a sea gas carbon flux reconstruction method, a system and a device based on remote sensing data assimilation, wherein the method comprises the following steps: constructing a three-dimensional carbon circulation model based on dissolved inorganic carbon, dissolved organic carbon and relevant marine carbon influencing factors in the granular organic carbon; acquiring ocean salinity data, and establishing an ocean total alkalinity parameterization model by combining the correlation of the ocean total alkalinity and the ocean salinity data; acquiring remote sensing carbon dioxide partial pressure data, coupling the remote sensing carbon dioxide partial pressure data with a three-dimensional carbon circulation model, and combining a total alkalinity parameterization model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data; based on the carbon dioxide partial pressure data, the carbon dioxide partial pressure difference between sea gas interfaces is obtained, and further sea gas carbon flux data is obtained. The method solves the problem of the deficiency of the sea gas carbon flux data in the existing remote sensing data, remarkably improves the simulation precision of the carbon circulation related parameters, and provides data support for sea research.
Description
Technical Field
The invention relates to the field of sea gas measurement, in particular to a sea gas carbon flux reconstruction method, system and device based on remote sensing data assimilation.
Background
The energy and substance exchange between the ocean and the atmosphere is an important research content of biological earth circulation, in an inter-restriction ocean system, the sea carbon flux is an important way for realizing the interaction between the ocean and the atmosphere, a large part of carbon in the atmosphere enters the ocean in the form of carbon dioxide, and meanwhile, the sea carbon flux represents the energy exchange range between the ocean and the atmosphere and influences global climate change to a certain extent, so that the method has important practical significance for the research of the sea carbon flux.
At present, conventional observation methods of ocean carbon flux in the prior art mainly comprise remote sensing, a box method, a direct measurement method and the like, the carbon flux is calculated by observing the change condition of the atmospheric carbon dioxide content by the observation method based on remote sensing, but the method cannot directly observe, the obtained ocean carbon flux related data is easily influenced by weather, and the condition of data loss exists; the observation precision based on the box method is greatly influenced by a measuring instrument, the measuring range is small, and the error is large; direct measurement cannot be used for long-term in-situ sea carbon flux observation.
Therefore, the limitation of the current calculation mode of the sea carbon flux leads to very inaccurate calculation results, and thus, the relevant research of sea data is seriously influenced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a sea gas carbon flux reconstruction method, a sea gas carbon flux reconstruction system and a sea gas carbon flux reconstruction device based on remote sensing data assimilation.
In order to solve the problems, the invention is solved by the following technical scheme:
a sea gas carbon flux reconstruction method based on remote sensing data assimilation comprises the following steps:
constructing a three-dimensional carbon circulation model based on marine carbon influencing factors, wherein the marine carbon influencing factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
acquiring ocean salinity data, and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model into a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data;
based on the carbon dioxide partial pressure data, obtaining a carbon dioxide partial pressure difference between sea interfaces, and constructing a simulated sea carbon flux model based on the carbon dioxide partial pressure difference between sea interfaces, so as to obtain sea carbon flux data.
As an embodiment, the constructing the ocean total alkalinity parameterized model includes the following steps:
based on ocean salinity data, performing polynomial fitting on ocean total alkalinity, and obtaining an ocean total alkalinity parameterized model through the relationship between balance parameter and fitting precision, wherein the model is expressed as follows:
wherein,indicating total sea alkalinity->Representing marine salinity data, < > 10 >>、/>、/>、/>Representing a constant.
As an implementation manner, the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model are coupled to a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model, and further obtain carbon dioxide partial pressure data, which comprises the following steps:
assimilating the dissolved inorganic carbon linear model in the three-dimensional carbon circulation model based on remote sensing carbon dioxide partial pressure data to obtain a dissolved inorganic carbon change model;
based on a carbonic acid-carbonate system balance equation and combining the total ocean alkalinity, obtaining the total ocean alkalinity parameter;
based on ocean total alkalinity parameters and a dissolved inorganic carbon change model, a simulated carbon dioxide partial pressure model is established, and carbon dioxide partial pressure data are obtained.
As an embodiment, the dissolved inorganic carbon change model is expressed as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Partial pressure data of carbon dioxide representing a three-dimensional carbon cycle model, +.>Represents dissolved inorganic carbon, and p1 represents a constant.
As an embodiment, the model for simulating partial pressure of carbon dioxide is expressed as follows:
wherein,represents the solubility of carbon dioxide in seawater, +.>、/>Represents the equilibrium constant of carbon dioxide in seawater, +.>Represent the fugacity constant, < >>Indicate temperature,/->Indicating atmospheric pressure, ++>Represents the data of the partial pressure of the simulated carbon dioxide,representing the total alkalinity parameter of the ocean.
As an implementation manner, the carbon dioxide partial pressure difference between the sea-gas interfaces is obtained by the following calculation method:
the simulated sea carbon flux model is expressed as follows:
wherein,representing the gas transmission coefficient, +.>，/>Represents the solubility of carbon dioxide in seawater, +.>Represents the partial pressure difference of carbon dioxide between sea-gas interfaces, +.>Represents the wind speed at the sea level of 10 meters, +.>Represents schmitt number,/->Data representing partial pressure of carbon dioxide of surface sea water, +.>Partial pressure data of carbon dioxide representing the sea surface atmosphere, +.>Represents sea gas carbon flux data.
A sea-air carbon flux reconstruction system based on remote sensing data assimilation comprises a sea-air carbon model building module, an alkalinity model building module, a partial pressure data calculation module and a sea-air carbon flux calculation module;
the ocean carbon model building module is used for building a three-dimensional carbon circulation model based on ocean carbon influence factors, wherein the ocean carbon influence factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
the alkalinity model construction module is used for acquiring ocean salinity data and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
the partial pressure data calculation module is used for acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterization model into the three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data;
the sea carbon flux calculation module obtains the carbon dioxide partial pressure difference between sea interfaces based on the carbon dioxide partial pressure data, and constructs a simulated sea carbon flux model based on the carbon dioxide partial pressure difference between the sea interfaces, so as to obtain sea carbon flux data.
As an embodiment, the partial pressure data calculation module is configured to:
assimilating the dissolved inorganic carbon linear model in the three-dimensional carbon circulation model based on remote sensing carbon dioxide partial pressure data to obtain a dissolved inorganic carbon change model;
based on a carbonic acid-carbonate system balance equation and combining the total ocean alkalinity, obtaining the total ocean alkalinity parameter;
based on ocean total alkalinity parameters and a dissolved inorganic carbon change model, establishing a simulated carbon dioxide partial pressure model to obtain carbon dioxide partial pressure data;
wherein the dissolved inorganic carbon change model is represented as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Representing carbon dioxide partial pressure of three-dimensional carbon circulation modelData,/->Represents dissolved inorganic carbon, and p1 represents a constant;
wherein, the simulated carbon dioxide partial pressure model is expressed as follows:
wherein,represents the solubility of carbon dioxide in seawater, +.>、/>Represents the equilibrium constant of carbon dioxide in seawater, +.>Represent the fugacity constant, < >>Indicate temperature,/->Indicating atmospheric pressure, ++>Represents the data of the partial pressure of the simulated carbon dioxide,representing the total alkalinity parameter of the ocean.
A computer readable storage medium storing a computer program which when executed by a processor performs the method of:
constructing a three-dimensional carbon circulation model based on marine carbon influencing factors, wherein the marine carbon influencing factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
acquiring ocean salinity data, and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model into a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data;
based on the carbon dioxide partial pressure data, obtaining a carbon dioxide partial pressure difference between sea interfaces, and constructing a simulated sea carbon flux model based on the carbon dioxide partial pressure difference between sea interfaces, so as to obtain sea carbon flux data.
A marine carbon flux reconstruction device based on remote sensing data assimilation, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor performs the following method when executing the computer program:
constructing a three-dimensional carbon circulation model based on marine carbon influencing factors, wherein the marine carbon influencing factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
acquiring ocean salinity data, and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model into a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data;
based on the carbon dioxide partial pressure data, obtaining a carbon dioxide partial pressure difference between sea interfaces, and constructing a simulated sea carbon flux model based on the carbon dioxide partial pressure difference between sea interfaces, so as to obtain sea carbon flux data.
The invention has the remarkable technical effects due to the adoption of the technical scheme:
the method solves the problem of data missing in the existing method for acquiring the sea gas carbon flux based on the remote sensing data, and simultaneously avoids the influence of weather conditions and environmental problems on the data in the process of acquiring the remote sensing data.
Meanwhile, based on the method, the accuracy of carbon dioxide partial pressure calculation is improved through data assimilation, so that the simulation accuracy of carbon circulation related parameters is improved, and powerful support is provided for ocean research.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is an overall schematic of the system of the present invention;
FIG. 3 is a schematic representation of a marine total alkalinity fitting of the present invention;
FIG. 4 is a diagram showing the comparison of the model coupling results with the measured data.
Detailed Description
The present invention will be described in further detail with reference to the following examples, which are illustrative of the present invention and are not intended to limit the present invention thereto.
Example 1:
a sea gas carbon flux reconstruction method based on remote sensing data assimilation, as shown in figure 1, comprises the following steps:
s100, constructing a three-dimensional carbon circulation model based on ocean carbon influence factors, wherein the ocean carbon influence factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
s200, acquiring ocean salinity data, and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
s300, acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model into a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model, so as to obtain carbon dioxide partial pressure data;
s400, based on the carbon dioxide partial pressure data, obtaining a carbon dioxide partial pressure difference between sea interfaces, and constructing a simulated sea carbon flux model based on the carbon dioxide partial pressure difference between the sea interfaces, so as to obtain sea carbon flux data.
The method solves the problem that the sea gas carbon flux data obtained based on the remote sensing data is missing, avoids the influence of ocean weather and environment on the remote sensing data, improves the accuracy of carbon flux data calculation based on data fitting, and provides data support for ocean environment research.
Three-dimensional carbon cycle models are common in the art and describe the exchange and conversion processes of carbon between the atmosphere, the ocean and the biosphere, including biological pumps, dissolution pumps and bio-geochemical processes. The ocean total alkalinity parameterization scheme is mainly used for describing the relationship between the ocean total alkalinity and the pCO2, and the accuracy of remote sensing pCO2 data can be optimized by adjusting the ocean total alkalinity. The sea total alkalinity parameterization scheme plays a key role in sea gas flux calculation. The calculation results of the three-dimensional carbon circulation model and the ocean total alkalinity parameterization scheme are combined with the remote sensing pCO2 data, so that the accuracy and the reliability of the data can be improved. This helps to better understand the global carbon cycling process and provides powerful support for climate change research. Considering the existence form of ocean carbon, the influence on the carbon circulation model mainly comprises dissolving inorganic carbon DIC, dissolving organic carbon DOC and granular organic carbon POC, and the approximate ratio of the three existence forms is 2000:38:1.
The influencing factors of the content of dissolved inorganic carbon DIC in the ocean include salinity, photosynthesis of marine organisms, remineralization of organic matters and dissolution and precipitation of calcium carbonate. As the salinity changes, in general, the higher the salinity in the ocean is, the higher the dissolved inorganic carbon DIC is, and the seawater salinity is closely related to precipitation, evaporation, fresh water input, formation and melting of sea ice and other processes, in the related research of oceanography, the dissolved inorganic carbon linear model is in a linear relationship, and the comparison of the dissolved inorganic carbon DIC is usually carried out by correcting to the same salinity level through normalization treatment, wherein the normalization process is expressed as follows:
wherein,represents normalized dissolved inorganic carbon, +.>Represents salinity;
the essence of the photosynthesis of the marine organism is that the dissolved inorganic carbon DIC in the seawater is converted into organic carbon through a biochemical process, so that the strength of the photosynthesis of the marine organism can influence the content of the DIC in the ocean, and the content of the DIC in the seawater is generally lower in a sea area or interval with stronger photosynthesis, and is higher in the opposite direction; the remineralization process of marine organics produces carbon dioxide, which is rapidly hydrolyzed to HCO 3-and CO 3 2- Ions, thereby increasing the content of DIC in the ocean, and the influence of the remineralization process of the ocean organic matters is particularly important to the content of DIC in the deep water body; utilization of CO in the ocean during the growth of marine calcareous organisms 3 2- Ion synthesis of CaCO thereof 3 The synthesis process of the shell or bone can lead to the reduction of the DIC content of seawater, when CaCO 3 The hulls or bones dissolve after delivery into the mid-deep ocean, resulting in an increase in DIC content in the ocean body of water.
The change factors influencing the dissolution of the organic carbon DOC are four, wherein the process of producing the dissolution of the organic carbon DOC comprises phytoplankton excretion, dissolution of the granular organic carbon POC and dissolution of a substrate, the process of consuming the dissolution of the organic carbon DOC comprises oxidation decomposition of the DOC, and the expression of the change of the concentration of the dissolution of the organic carbon DOC along with the time is as follows:
the bacterial to dissolved organic carbon DOC mineralization rate as a function is expressed as follows:
the dissolution formula of the substrate is expressed as follows:
wherein,indicates the oxidative decomposition rate of the dissolved organic carbon, < + >>Indicates the dissolution rate of dissolved organic carbon in the matrix,/->Representing the thickness of the bottom layer in the model,/->Represents the dissolved oxygen half-saturation constant of the dissolved organic carbon of bacterial decomposition,represents the decomposition rate of the dissolved organic carbon, +.>Represents the dissolution rate at 0 ℃,>representing the temperature coefficient>Represents the oxygen inhibition factor.
Factors influencing the production of particulate organic carbon POC include phytoplankton death, zooplankton death and zooplankton excretion, and factors for consumption include zooplankton ingestion, oxidative decomposition of particulate organic carbon POC, and dissolution sedimentation of particulate organic carbon POC, and the expression of the concentration of particulate organic carbon POC over time is as follows:
wherein,indicating the rate of bacterial decomposition of the particulate organic carbon, < >>Represents the rate at which the particulate organic carbon dissolves,represents the sedimentation velocity of the particulate organic carbon, +.>Represents the decomposition rate of the granular organic carbon at 0 ℃, ->Representing the temperature coefficient>Indicating the half-saturation constant associated with oxidative decomposition of the particulate organic carbon.
That is, the model formed by the above influencing factors is a three-dimensional carbon circulation model, and the three-dimensional carbon circulation model outputs dissolved inorganic carbon, dissolved organic carbon and granular organic carbon.
In step S200, ocean salinity data is acquired, and an ocean total alkalinity parameterization model is constructed based on the ocean total alkalinity and the ocean salinity data, including the following steps:
based on ocean salinity data, performing polynomial fitting on ocean total alkalinity, and obtaining an ocean total alkalinity parameterized model through the relationship between balance parameter and fitting precision, wherein the model is expressed as follows:
wherein,indicating total sea alkalinity->Representing marine salinity data, < > 10 >>、/>、/>、/>Representing a constant.
The ocean total alkalinity change in the ocean has very high correlation with the ocean salinity data, polynomial fitting parameters are obtained through polynomial fitting according to the ocean salinity data obtained through observation, and then the ocean total alkalinity parameterized model is obtained through balance parameters and calculated quantity.
For example, in one particular embodiment, a particular polynomial fitting process is as follows:
the quadratic polynomial fitting results are expressed as follows:
the result of the third order polynomial fitting is expressed as follows:
the fourth order polynomial fit results are expressed as follows:
that is to say that the first and second,、/>、/>、/>the polynomial fitting result graph is shown in fig. 3, and the fitting result precision and the fitting parameter quantity are balanced, and the polynomial fitting result of three times is selected as a sea total alkalinity parameterized model.
In step S300, the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model are coupled to a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model, and further obtain carbon dioxide partial pressure data, which comprises the following steps:
obtaining remote sensing carbon dioxide partial pressure data, assimilating the remote sensing carbon dioxide partial pressure data and a dissolved inorganic carbon linear model in a three-dimensional carbon circulation model to obtain a dissolved inorganic carbon change model, wherein the dissolved inorganic carbon change model is expressed as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Partial pressure data of carbon dioxide representing a three-dimensional carbon cycle model, +.>Represents dissolved inorganic carbon, and p1 represents a constant;
based on a carbonic acid-carbonate system balance equation and combining the total ocean alkalinity, obtaining the total ocean alkalinity parameter;
based on ocean total alkalinity parameters and a dissolved inorganic carbon change model, a simulated carbon dioxide partial pressure model is established, and carbon dioxide partial pressure data are obtained.
In this embodiment, a linear model of dissolved inorganic carbon in a three-dimensional carbon circulation model is mainly used, and since the three-dimensional carbon circulation model is a well-known model in the art and is very complex, the change model of dissolved inorganic carbon is obtained by assimilating remote sensing carbon dioxide partial pressure data with the linear model of dissolved inorganic carbon in the three-dimensional carbon circulation model. The main influencing factors of pCO2 are DIC dissolved inorganic carbon, temperature, ocean total alkalinity and fresh water, in a carbonate-based system, ocean total alkalinity, dissolved inorganic carbon, seawater pH and simulated carbon dioxide partial pressure data, a model for simulating carbon dioxide partial pressure data is built by arbitrarily selecting known amounts, and further carbon dioxide partial pressure data is obtained.
In this embodiment, the model for simulating partial pressure of carbon dioxide is expressed as follows:
the relevant parameters involved in the calculation process are expressed as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Partial pressure data of carbon dioxide representing a three-dimensional carbon cycle model, +.>Represents dissolved inorganic carbon,/->Represents the solubility of carbon dioxide in seawater, +.>、/>Represents the equilibrium constant of carbon dioxide in seawater, +.>Represent the fugacity constant, < >>Indicate temperature,/->Indicating atmospheric pressure, ++>Representing simulated carbon dioxide partial pressure data,/">Representing the total alkalinity parameter of the ocean.
Referring to fig. 4, fig. 4 shows a clear comparison of errors between remote sensing carbon dioxide partial pressure data, coupled carbon dioxide partial pressure data, uncoupled carbon dioxide partial pressure data, and measured carbon dioxide partial pressure data, respectively.
In step S400, based on the carbon dioxide partial pressure data, a carbon dioxide partial pressure difference between sea interfaces is obtained, and a simulated sea carbon flux model is constructed based on the carbon dioxide partial pressure difference between sea interfaces, so as to obtain sea carbon flux data, comprising the following steps:
in this embodiment, the specific calculation formula of the sea carbon flux data is as follows:
wherein,representing the gas transmission coefficient, +.>，/>Represents the solubility of carbon dioxide in seawater, +.>Representation ofCarbon dioxide partial pressure difference between sea-air interfaces +.>Represents the wind speed at the sea level of 10 meters, +.>Represents schmitt number,/->Data representing partial pressure of carbon dioxide of surface sea water, +.>Partial pressure data of carbon dioxide representing the sea surface atmosphere, +.>Representing sea gas carbon flux data;
if it is>0，/>Positive, when the ocean releases carbon dioxide into the atmosphere, the ocean area is the source of carbon dioxide in the atmosphere, if +.><0，/>Is negative, and the ocean absorbs carbon dioxide into the atmosphere, and the sea area is the sink of the carbon dioxide in the atmosphere.
Example 2:
a sea-air carbon flux reconstruction system based on remote sensing data assimilation is shown in fig. 2, and comprises a sea-air carbon model building module 100, an alkalinity model building module 200, a partial pressure data calculation module 300 and a sea-air carbon flux calculation module 400;
the marine carbon model building module 100 is configured to build a three-dimensional carbon circulation model based on marine carbon influencing factors, where the marine carbon influencing factors include dissolved inorganic carbon, dissolved organic carbon, and particulate organic carbon;
the alkalinity model construction module 200 acquires ocean salinity data and constructs an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
the partial pressure data calculation module 300 acquires remote sensing carbon dioxide partial pressure data, and couples the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterization model to the three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model, so as to obtain carbon dioxide partial pressure data;
the sea carbon flux calculation module 400 obtains the partial pressure difference of carbon dioxide between sea interfaces based on the partial pressure data of carbon dioxide, and constructs a simulated sea carbon flux model based on the partial pressure difference of carbon dioxide between sea interfaces, thereby obtaining sea carbon flux data.
In one embodiment, the partial pressure data calculation module 300 is configured to:
assimilating the dissolved inorganic carbon linear model in the three-dimensional carbon circulation model based on remote sensing carbon dioxide partial pressure data to obtain a dissolved inorganic carbon change model;
based on a carbonic acid-carbonate system balance equation and combining the total ocean alkalinity, obtaining the total ocean alkalinity parameter;
based on ocean total alkalinity parameters and a dissolved inorganic carbon change model, establishing a simulated carbon dioxide partial pressure model to obtain carbon dioxide partial pressure data;
wherein the dissolved inorganic carbon change model is represented as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Carbon dioxide partial pressure data representing a three-dimensional carbon circulation model,/>represents dissolved inorganic carbon, and p1 represents a constant;
wherein, the simulated carbon dioxide partial pressure model is expressed as follows:
wherein,represents the solubility of carbon dioxide in seawater, +.>、/>Represents the equilibrium constant of carbon dioxide in seawater, +.>Represent the fugacity constant, < >>Indicate temperature,/->Indicating atmospheric pressure, ++>Represents the data of the partial pressure of the simulated carbon dioxide,representing the total alkalinity parameter of the ocean.
All changes and modifications that come within the spirit and scope of the invention are desired to be protected and all equivalent thereto are deemed to be within the scope of the invention.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that identical and similar parts of each embodiment are mutually referred to.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that:
reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
In addition, the specific embodiments described in the present specification may differ in terms of parts, shapes of components, names, and the like. All equivalent or simple changes of the structure, characteristics and principle according to the inventive concept are included in the protection scope of the present invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions in a similar manner without departing from the scope of the invention as defined in the accompanying claims.
Claims (4)
1. A sea gas carbon flux reconstruction method based on remote sensing data assimilation is characterized by comprising the following steps:
constructing a three-dimensional carbon circulation model based on marine carbon influencing factors, wherein the marine carbon influencing factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
acquiring ocean salinity data, and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model into a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data;
based on the carbon dioxide partial pressure data, obtaining a carbon dioxide partial pressure difference between sea-air interfaces, and constructing a simulated sea-air carbon flux model based on the carbon dioxide partial pressure difference between the sea-air interfaces, so as to obtain sea-air carbon flux data;
the method for constructing the ocean total alkalinity parameterized model comprises the following steps of:
based on ocean salinity data, performing polynomial fitting on ocean total alkalinity, and obtaining an ocean total alkalinity parameterized model through the relationship between balance parameter and fitting precision, wherein the model is expressed as follows:
wherein,indicating total sea alkalinity->Representing marine salinity data, < > 10 >>、/>、/>、/>Representing a constant;
the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterized model are coupled into a three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model, and further obtain carbon dioxide partial pressure data, and the method comprises the following steps:
assimilating the dissolved inorganic carbon linear model in the three-dimensional carbon circulation model based on remote sensing carbon dioxide partial pressure data to obtain a dissolved inorganic carbon change model;
based on a carbonic acid-carbonate system balance equation and combining the total ocean alkalinity, obtaining the total ocean alkalinity parameter;
based on ocean total alkalinity parameters and a dissolved inorganic carbon change model, establishing a simulated carbon dioxide partial pressure model to obtain carbon dioxide partial pressure data;
the dissolved inorganic carbon change model is expressed as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Partial pressure data of carbon dioxide representing a three-dimensional carbon cycle model, +.>Represents dissolved inorganic carbon, and p1 represents a constant;
the simulated carbon dioxide partial pressure model is expressed as follows:
wherein,represents the solubility of carbon dioxide in seawater, +.>、/>Represents the equilibrium constant of carbon dioxide in seawater,represent the fugacity constant, < >>Indicate temperature,/->Indicating atmospheric pressure, ++>Represents the data of the partial pressure of the simulated carbon dioxide,representing the ocean total alkalinity parameter;
the carbon dioxide partial pressure difference between the sea-gas interfaces is obtained by the following calculation method:
the simulated sea carbon flux model is expressed as follows:
wherein,representing the gas transmission coefficient, +.>，/>Represents the solubility of carbon dioxide in seawater, +.>Represents the partial pressure difference of carbon dioxide between sea-gas interfaces, +.>Represents the wind speed at the sea level of 10 meters, +.>Representation->Coefficient of->Data representing partial pressure of carbon dioxide of surface sea water, +.>Partial pressure data of carbon dioxide representing the sea surface atmosphere, +.>Represents sea gas carbon flux data.
2. The sea-air carbon flux reconstruction system based on remote sensing data assimilation is characterized by comprising a sea-air carbon model building module, an alkalinity model building module, a partial pressure data calculation module and a sea-air carbon flux calculation module;
the ocean carbon model building module is used for building a three-dimensional carbon circulation model based on ocean carbon influence factors, wherein the ocean carbon influence factors comprise dissolved inorganic carbon, dissolved organic carbon and granular organic carbon;
the alkalinity model construction module is used for acquiring ocean salinity data and constructing an ocean total alkalinity parameterization model based on the ocean total alkalinity and the ocean salinity data;
the partial pressure data calculation module is used for acquiring remote sensing carbon dioxide partial pressure data, and coupling the remote sensing carbon dioxide partial pressure data and the ocean total alkalinity parameterization model into the three-dimensional carbon circulation model to obtain a simulated carbon dioxide partial pressure model so as to obtain carbon dioxide partial pressure data;
the sea carbon flux calculation module is used for obtaining the carbon dioxide partial pressure difference between sea interfaces based on the carbon dioxide partial pressure data, constructing a simulated sea carbon flux model based on the carbon dioxide partial pressure difference between the sea interfaces, and further obtaining sea carbon flux data;
the partial pressure data calculation module is configured to:
assimilating the dissolved inorganic carbon linear model in the three-dimensional carbon circulation model based on remote sensing carbon dioxide partial pressure data to obtain a dissolved inorganic carbon change model;
based on a carbonic acid-carbonate system balance equation and combining the total ocean alkalinity, obtaining the total ocean alkalinity parameter;
based on ocean total alkalinity parameters and a dissolved inorganic carbon change model, establishing a simulated carbon dioxide partial pressure model to obtain carbon dioxide partial pressure data;
wherein the dissolved inorganic carbon change model is represented as follows:
wherein,representing remote sensing carbon dioxide partial pressure data,/->Partial pressure data of carbon dioxide representing a three-dimensional carbon cycle model, +.>Represents dissolved inorganic carbon, and p1 represents a constant;
wherein, the simulated carbon dioxide partial pressure model is expressed as follows:
wherein,represents the solubility of carbon dioxide in seawater, +.>、/>Represents the equilibrium constant of carbon dioxide in seawater,represent the fugacity constant, < >>Indicate temperature,/->Indicating atmospheric pressure, ++>Represents the data of the partial pressure of the simulated carbon dioxide,representing the ocean total alkalinity parameter;
the method for constructing the ocean total alkalinity parameterized model comprises the following steps of:
based on ocean salinity data, performing polynomial fitting on ocean total alkalinity, and obtaining an ocean total alkalinity parameterized model through the relationship between balance parameter and fitting precision, wherein the model is expressed as follows:
wherein,indicating total sea alkalinity->Representing marine salinity data, < > 10 >>、/>、/>、/>Representing a constant;
the carbon dioxide partial pressure difference between the sea-gas interfaces is obtained by the following calculation method:
the simulated sea carbon flux model is expressed as follows:
wherein,representing the gas transmission coefficient, +.>，/>Represents the solubility of carbon dioxide in seawater, +.>Represents the partial pressure difference of carbon dioxide between sea-gas interfaces, +.>Represents the wind speed at the sea level of 10 meters, +.>Representation->Coefficient of->Data representing partial pressure of carbon dioxide of surface sea water, +.>Partial pressure data of carbon dioxide representing the sea surface atmosphere, +.>Represents sea gas carbon flux data.
3. A computer readable storage medium storing a computer program, which when executed by a processor implements the method of claim 1.
4. A marine hydrocarbon flux reconstruction device based on remote sensing data assimilation comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the method of claim 1 when executing the computer program.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217098A (en) * | 2014-07-14 | 2014-12-17 | 浙江大学 | Sea-area carbon budget computing method based on combination of sailing section monitoring and satellite remote sensing |
| WO2017110889A1 (en) * | 2015-12-25 | 2017-06-29 | 国立大学法人東京大学 | Precise method of measuring carbonate-based parameters of sea water, and measuring device for use in said method |
| CN108896501A (en) * | 2018-07-07 | 2018-11-27 | 中国科学院南京地理与湖泊研究所 | A kind of remote sensing estimation method of the effective Sea―going flux of river dissolved organic carbon |
| CN109375286A (en) * | 2018-09-12 | 2019-02-22 | 广州海洋地质调查局 | A kind of self-operated measuring unit and method of Air-sea heat fluxes |
| CN114547885A (en) * | 2022-02-21 | 2022-05-27 | 清华大学 | Method, device, equipment and storage medium for quantitative inversion of carbon emission |
| CN115290512A (en) * | 2022-07-15 | 2022-11-04 | 大连海事大学 | Water color remote sensing method and device for estimating concentration of organic carbon in ocean surface particles |
| CN116626155A (en) * | 2023-04-04 | 2023-08-22 | 厦门大学 | Online observation method for carbon dioxide flux of water-gas interface in estuary/bay area |
| CN116825220A (en) * | 2023-08-25 | 2023-09-29 | 青岛海洋地质研究所 | Marine organic carbon degradation process evaluation method based on continuity distribution function |
| CN117078276A (en) * | 2023-07-17 | 2023-11-17 | 深圳市北研生态环境科技有限公司 | Marine carbon sink data investigation system and method |
| CN117251989A (en) * | 2023-08-22 | 2023-12-19 | 中国科学院新疆生态与地理研究所 | Meteorological site carbon water flux information determination method, system and electronic equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2751209C (en) * | 2009-02-02 | 2018-08-21 | Planetary Emissions Management | System of systems for monitoring greenhouse gas fluxes |
-
2024
- 2024-01-18 CN CN202410074905.7A patent/CN117592316B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104217098A (en) * | 2014-07-14 | 2014-12-17 | 浙江大学 | Sea-area carbon budget computing method based on combination of sailing section monitoring and satellite remote sensing |
| WO2017110889A1 (en) * | 2015-12-25 | 2017-06-29 | 国立大学法人東京大学 | Precise method of measuring carbonate-based parameters of sea water, and measuring device for use in said method |
| CN108896501A (en) * | 2018-07-07 | 2018-11-27 | 中国科学院南京地理与湖泊研究所 | A kind of remote sensing estimation method of the effective Sea―going flux of river dissolved organic carbon |
| CN109375286A (en) * | 2018-09-12 | 2019-02-22 | 广州海洋地质调查局 | A kind of self-operated measuring unit and method of Air-sea heat fluxes |
| CN114547885A (en) * | 2022-02-21 | 2022-05-27 | 清华大学 | Method, device, equipment and storage medium for quantitative inversion of carbon emission |
| CN115290512A (en) * | 2022-07-15 | 2022-11-04 | 大连海事大学 | Water color remote sensing method and device for estimating concentration of organic carbon in ocean surface particles |
| CN116626155A (en) * | 2023-04-04 | 2023-08-22 | 厦门大学 | Online observation method for carbon dioxide flux of water-gas interface in estuary/bay area |
| CN117078276A (en) * | 2023-07-17 | 2023-11-17 | 深圳市北研生态环境科技有限公司 | Marine carbon sink data investigation system and method |
| CN117251989A (en) * | 2023-08-22 | 2023-12-19 | 中国科学院新疆生态与地理研究所 | Meteorological site carbon water flux information determination method, system and electronic equipment |
| CN116825220A (en) * | 2023-08-25 | 2023-09-29 | 青岛海洋地质研究所 | Marine organic carbon degradation process evaluation method based on continuity distribution function |
Non-Patent Citations (3)
| Title |
|---|
| MONITORING OF SINKING FLUX OF OCEAN PARTICULATE ORGANIC CARBON USING REMOTE SENSING METHODS;Tao Zui.etc;《IEEE》;20161231;全文 * |
| 利用卫星遥感技术估算区域性海-气CO_2通量研究综述;许苏清;陈立奇;;极地研究;20150915(03);全文 * |
| 海洋颗粒有机碳浓度水色遥感研究进展;王桂芬;曹文熙;殷建平;周雯;孙兆华;杨倩;;热带海洋学报;20121115(06);全文 * |
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