Land-gas coupling method for improving climate mode carbon-water flux simulation capability, storage medium and computing equipment

Abstract

The invention discloses a land-gas coupling method for improving climate mode carbon-water flux simulation capability, a storage medium and computing equipment, and belongs to the technical field of earth system simulation and land-gas interaction.The method comprises the following steps that a climate mode ECHAM and a dynamic vegetation mode iMAPLE which are to be coupled are obtained; real-time simulation is carried out in the ECHAM to obtain a meteorological field, and the meteorological field is transmitted to the iMAPLE; land surface ecological process calculation is carried out in the iMAPLE, and a calculation result is sent back to the ECHAM; and an ECHAM-iMAPLE online land-gas coupling scheme is established, a long-time test is carried out, and the carbon-water flux simulation capability is evaluated. According to the method, the adopted iMAPLE mode considers more detailed vegetation biological physical and chemical processes and parameterization schemes, the bi-directionally coupled ECHAM6-iMAPLE mode remarkably improves the carbon-water flux simulation capability, and bi-directional feedback of an atmosphere-ecosystem is more comprehensively quantified.

Description

TECHNICAL FIELD

[0001] The present application relates to a land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models, a storage medium and a computing device, belonging to the technical field of earth system simulation and land-atmosphere interaction. BACKGROUND

[0002] Land-atmosphere interaction is a key component of the climate system. The terrestrial ecosystem and the climate system interact through energy exchange, water vapor transport, and carbon and gas exchange, thereby regulating regional and global climate patterns. Meteorological conditions such as temperature, humidity, and precipitation directly affect the growth of vegetation, while the terrestrial ecosystem influences the atmosphere through physical processes and biochemical cycles. As an important carbon sink in the global carbon cycle, the terrestrial ecosystem absorbs about 120 billion tons of carbon per year through photosynthesis. At the same time, vegetation and soil respiration also release carbon dioxide into the atmosphere. These processes collectively regulate the carbon cycle between the atmosphere and the ecosystem. In addition, terrestrial vegetation regulates the surface water and energy balance through growth dynamics and phenological changes, driving the redistribution of water and heat fluxes (sensible heat flux and latent heat flux). Changes in these fluxes affect atmospheric circulation, thereby changing precipitation patterns and cloud formation.

[0003] Research on land-atmosphere interaction can be carried out using ground and near-ground observations, remote sensing technology, numerical simulation, etc. The ground and near-ground observation method has limitations in spatial representation and is difficult to fully cover complex underlying surfaces, involving high site construction, maintenance, and labor costs. The time resolution of remote sensing technology is limited by the satellite revisit period, and the data stability is easily disturbed by weather conditions such as clouds and rain, resulting in greater uncertainty. Numerical simulation, such as climate models, is a key tool for understanding and quantifying land-atmosphere interaction at global or regional scales, and can be run and output at any spatiotemporal resolution, thereby simulating and studying land-atmosphere exchange processes and feedback mechanisms at global or regional scales.

[0004] Although existing climate models can simulate and predict carbon and water fluxes, their accuracy still needs to be improved, and some key biogeochemical processes have not been fully characterized. Therefore, improving the current land-atmosphere coupling scheme is of great scientific significance to improve the accuracy of carbon and water flux simulation and deepen the research on land-atmosphere interaction mechanisms. SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the limitations in the prior art and provide a land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models, a storage medium and a computing device.

[0006] To solve the above technical problems, the present application adopts the following technical solutions:

[0007] The application provides a land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate model, comprising:

[0008] obtaining a climate model ECHAM and a dynamic vegetation model iMAPLE to be coupled;

[0009] carrying out real-time simulation in an integral main program of the climate model ECHAM to obtain a meteorological field;

[0010] transferring the meteorological field simulated by the climate model ECHAM in real time to the dynamic vegetation model iMAPLE;

[0011] carrying out land surface ecological process calculation in the dynamic vegetation model iMAPLE and transferring the calculation result of the land surface ecological process back to the integral main program of the climate model ECHAM;

[0012] establishing an ECHAM-iMAPLE online land-atmosphere coupling scheme based on the data interaction of the climate model ECHAM and the dynamic vegetation model iMAPLE, and carrying out carbon and water flux simulation in a preset time period; the carbon and water flux includes total primary productivity, leaf area index, water use efficiency, evapotranspiration, soil temperature and soil moisture.

[0013] Preferably, the meteorological field is obtained by weather forecast of the climate model ECHAM, including surface air temperature, atmospheric pressure, surface wind speed, atmospheric humidity, shortwave radiation and precipitation.

[0014] Preferably, the meteorological field simulated by the climate model ECHAM in real time is transferred to the dynamic vegetation model iMAPLE, comprising:

[0015] converting the meteorological field simulated by the climate model ECHAM in real time from a spectral space format into a grid format and transferring to the dynamic vegetation model iMAPLE.

[0016] Preferably, the calculation result of the land surface ecological process is transferred back to the integral main program of the climate model ECHAM, comprising:

[0017] converting the calculation result of the land surface ecological process from a grid format into a spectral space format and transferring back to the integral main program of the climate model ECHAM.

[0018] Preferably, the carbon and water flux simulation comprises:

[0019] collecting precipitation data, constructing a water balance equation, decomposing the precipitation simulated by the climate model ECHAM into evapotranspiration runoff and land water storage change and representing as: ​ ;

[0020] where evap is calculated as follows:

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] where is the vegetation transpiration, is the canopy evaporation, is the surface evaporation, denotes the air density, denotes the dry air specific heat capacity, denotes the leaf transpiration coefficient, denotes the saturated vapor pressure at leaf temperature, denotes the canopy air vapor pressure, is the humidity constant, denotes the latent heat flux from the wetted leaf surface to the canopy air, denotes the surface latent heat coefficient, denotes the surface saturated vapor pressure, denotes the surface relative humidity;

[0026] runoff is calculated as follows:

[0027] ,

[0028] ,

[0029] ,

[0030] where is the surface runoff, is the subsurface runoff, denotes the soil surface layer infiltration, denotes the soil subsurface infiltration, denotes the topographic slope coefficient, denotes the soil bottom layer water electrical conductivity;

[0031] land water storage is calculated as follows:

[0032] ,

[0033] where for the surface water content, for the snow water equivalent, for the soil water content, denotes the number of soil types;

[0034] land storage water change is equal to the difference between the land storage water at the beginning of the time step and the land storage water at the end of the time step.

[0035] Preferably, in the carbon and water flux simulation, the soil temperature and soil moisture simulation is calculated as follows:

[0036] ,

[0037] ,

[0038] wherein, denotes the soil moisture, is calculated according to the Clapp-Hornberger curve parameterization, denotes the hydraulic conductivity, is an empirical parameter depending on the soil type, is the volumetric water content of the soil, denotes the soil temperature, denotes the specific heat capacity of the soil, denotes the specific heat, denotes the soil depth.

[0039] Preferably, in the carbon and water flux simulation, the gross primary productivity and the leaf area index simulation is calculated as follows:

[0040] ,

[0041] ,

[0042] , for plants, , , , for plants, , , ,

[0043] wherein, is the gross primary productivity, denotes the leaf area index, represents the photosynthesis of the total leaf, represents the number of canopy layers, represents the phenology factor, depends on the vegetation carbon content, represents the carboxylation of ribulose-1,5-bisphosphate by Rubisco, represents the ability of the Calvin cycle and the thylakoid reactions to regenerate ribulose-1,5-bisphosphate with the support of electron transport, represents the synthesis of starch and sucrose in the regeneration of inorganic phosphate for photosynthetic phosphorylation in the plant, the ability to regenerate phosphoenolpyruvate in the plant, represents the leaf-specific light absorption, represents the incident photosynthetically active radiation, represents the internal quantum efficiency, represents the partial pressure of oxygen inside the leaf, represents the partial pressure of CO2 inside the leaf, represents the CO2 compensation point, represents the maximum carboxylation capacity, and represents the Michaelis constant for Rubisco carboxylation and oxygenation, represents the ambient air pressure, represents the Oleson constant.

[0044] Preferably, in the carbon-water flux simulation, the water use efficiency is simulated as follows:

[0045] ,

[0046] wherein, represents the water use efficiency.

[0047] The present application also provides a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions which, when executed by a computing device, cause the computing device to perform any of the improved land-atmosphere coupling methods for simulating carbon-water fluxes in climate modes according to the above.

[0048] The present application also provides a computing device including one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the improved land-atmosphere coupling methods for simulating carbon-water fluxes in climate modes according to the above.

[0049] The present application achieves the beneficial effects:

[0050] The application can significantly improve the simulation capability of carbon and water flux by coupling the climate model ECHAM and the dynamic vegetation model iMAPLE, and introducing an improved land-air coupling scheme in ECHAM, and the simulated carbon and water flux in space is more matched with satellite observation compared with the previous scheme. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a flowchart of an improved land-air coupling method for improving the simulation capability of carbon and water flux of a climate model provided by the application;

[0052] Figure 2 is a comparison chart of observation values and carbon and water fluxes simulated by different land-air coupling schemes provided in the embodiment of the application. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the application clearer, the application will be further described in detail below with reference to the embodiments and drawings. Herein, the illustrative embodiments of the application and the descriptions thereof are used to explain the application, but not as a limitation of the application.

[0054] It should be noted that, in order to avoid the application being obscured by unnecessary details, only the structures and / or processing steps closely related to the scheme according to the application are shown in the drawings, and other details not closely related to the application are omitted.

[0055] It should be emphasized that the term “comprise / comprising” is used herein to indicate the presence of a feature, element, step or component, but not to exclude the presence or addition of one or more other features, elements, steps or components.

[0056] It should be noted that, if not otherwise specified, the term “connection” herein can not only mean direct connection, but also indirect connection with an intermediate.

[0057] Hereinafter, the embodiments of the application will be described with reference to the drawings. In the drawings, the same reference signs represent the same or similar components, or the same or similar steps.

[0058] It should be emphasized that the step labels mentioned hereinafter are not a limitation of the order of the steps, but it should be understood that the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0059] The embodiment of the application provides an improved land-air coupling method for improving the simulation capability of carbon and water flux of a climate model, referring to Figure 1 , comprising the following steps:

[0060] S1, obtaining a climate model ECHAM and a dynamic vegetation model iMAPLE to be coupled;

[0061] In the embodiment, the climate model ECHAM is a global climate model developed by the Max Planck Institute for Meteorology in Germany, which is used to simulate atmospheric dynamics, radiation transfer and physical processes, and the core includes atmospheric dynamics, radiation transfer, cloud-aerosol physical process modules. The land-atmosphere coupling scheme in the original ECHAM is ECHAM-JSBACH, which includes modules such as vegetation canopy absorption of solar radiation, leaf photosynthesis, and distribution and transport of carbon in vegetation and soil in JSBACH. JSBACH shares the same memory processing, parallelization, time stepping and calendar structure with ECHAM, and is called step by step in the integration main program of ECHAM.

[0062] In the embodiment, the dynamic vegetation model iMAPLE (Modeling of Air Pollution and Land Ecosystems) is a model that takes the carbon-water process of the terrestrial biosphere and the exchange of near-surface energy as the core, takes the simulation of land vegetation growth and phenology dynamics as the basis, and takes the simulation of the interaction between the ecosystem and atmospheric chemistry as the characteristic. The research results of iMAPLE are published in journals such as Nature Geoscience.

[0063] S2, obtaining a meteorological field by real-time simulation in the integration main program of the climate model ECHAM;

[0064] In the embodiment, the meteorological field is obtained by weather forecasting by ECHAM, including surface air temperature, atmospheric pressure, surface wind speed, atmospheric humidity, shortwave radiation and precipitation, etc.

[0065] S3, establishing an iMAPLE_interface interface in the integration main program of the climate model ECHAM to transfer the meteorological field simulated by ECHAM in real time to the dynamic vegetation model iMAPLE;

[0066] In the embodiment, the iMAPLE_interface interface is used to convert the meteorological field simulated by ECHAM in real time from spectral space format to grid format, transfer the grid format meteorological field simulated by ECHAM in real time to the dynamic vegetation model iMAPLE, convert the calculation results of the iMAPLE land surface ecological process from grid format to spectral space format, and transfer the calculation results of the iMAPLE land surface ecological process in spectral space format back to the integration main program of the climate model ECHAM.

[0067] S4, performing land surface ecological process calculation in the dynamic vegetation model iMAPLE, transmitting the land surface ecological process calculation result back to the integral main program of the climate model ECHAM, establishing an ECHAM-iMAPLE online land-air coupling scheme based on the data interaction of the climate model ECHAM and the dynamic vegetation model iMAPLE, and performing carbon and water flux simulation; the carbon and water flux includes total primary productivity , leaf area index , water use efficiency , evapotranspiration , soil temperature and soil moisture .

[0068] In the embodiment, the land surface ecological process calculation result is simulated by the dynamic vegetation model iMAPLE, including total primary productivity, leaf area index, water use efficiency, soil temperature, soil moisture and evapotranspiration.

[0069] In the embodiment, the carbon and water flux simulation in the online land-air coupling scheme includes:

[0070] Collecting precipitation data, constructing a water balance equation, decomposing the precipitation simulated by the climate model ECHAM into evapotranspiration , runoff and land water storage change , and representing as:

[0071] .

[0072] Specifically, the evapotranspiration can be decomposed into vegetation transpiration , canopy evaporation and surface evaporation , and represented as:

[0073] ,

[0074] ,

[0075] ,

[0076] ,

[0077] wherein represents air density, represents dry air specific heat capacity, represents leaf transpiration coefficient, represents saturated vapor pressure at leaf temperature, represents canopy air vapor pressure, is humidity constant,​ represents the latent heat flux from the wetted leaf surface to the canopy air, represents the surface latent heat coefficient, represents the surface saturation vapor pressure, represents the surface relative humidity;

[0078] Based on the above equations, the latent heat flux , and are calculated, respectively, and the evapotranspiration is obtained by summing them up.

[0079] The runoff can be decomposed into the surface runoff and the subsurface runoff , which are represented as:

[0080] ,

[0081] ,

[0082] ,

[0083] where represents the soil surface infiltration, represents the soil subsurface infiltration, represents the terrain slope coefficient, represents the soil bottom water conductivity.

[0084] Based on the above equations, the surface runoff and the subsurface runoff are calculated, respectively, and the runoff is obtained by summing them up.

[0085] The terrestrial water storage can be decomposed into the surface water content , the snow water equivalent and the soil water content , which are represented as:

[0086] ,

[0087] where represents the number of soil types.

[0088] Based on the above equations, the terrestrial water storage is calculated, and the change of the terrestrial water storage is equal to the difference between the total terrestrial water storage at the beginning of the time step and the total terrestrial water storage at the end of the time step . Further, the error for the water budget balance is calculated based on the above results, which is represented as:

[0089] ,

[0090] When a water budget imbalance occurs, the water budget is balanced by adjusting runoff.

[0091] In this embodiment, the soil moisture is calculated using the soil water content, as follows:

[0092] ,

[0093] where, is the soil moisture, is calculated parametrically from the Clapp-Hornberger curve, is the hydraulic conductivity, is an empirical parameter depending on the soil type, is the volumetric water content of the soil.

[0094] In this embodiment, the soil temperature is calculated, as follows:

[0095] ,

[0096] where is the soil temperature, is the specific heat capacity of the soil, is the specific heat, is the soil depth.

[0097] In this embodiment, the biophysical calculation process is performed separately for C3 and C4 plants according to their different photosynthetic pathways. C3 plants are the most common and basic type of plant, which directly fix CO2 into three-carbon compounds during photosynthesis, but the CO2 absorption efficiency is significantly reduced in high-temperature and drought environments, and light respiration rises. C4 plants first concentrate CO2 into four-carbon compounds through carboxylase, and then supply it to the Calvin cycle, effectively suppressing light respiration and being more efficient in high-temperature environments. The leaf photosynthetic rate in the model is as follows:

[0098] , For C3 plants, , , , For C4 plants, , , ,

[0099] wherein represents the photosynthesis of the total leaf, represents the carbon fixation ability of the ribulose-1,5-bisphosphate carboxylase, represents the ability of the Calvin cycle and the thylakoid reaction to regenerate ribulose-1,5-bisphosphate with the support of electron transport, represents the synthesis of starch and sucrose in the plant, represents the ability of the plant to regenerate phosphoenolpyruvate (PEP) for photosynthetic phosphorylation, represents the leaf-specific light absorption, represents the incident photosynthetically active radiation, represents the internal quantum efficiency, represents the oxygen partial pressure inside the leaf, represents the CO2 partial pressure inside the leaf, represents the CO2 compensation point, represents the maximum carboxylation capacity, and represents the Michaelis constant of Rubisco carboxylation and oxidation, represents the ambient air pressure, represents the Oleson constant.

[0100] Further, the total primary productivity is calculated based on the total leaf photosynthesis , and the specific equation is as follows:

[0101] ,

[0102] ,

[0103] wherein, represents the leaf area index, represents the number of canopy layers, represents the phenological factor, depends on the carbon content of the vegetation.

[0104] Further, the water use efficiency is calculated using the calculated total primary productivity and the evapotranspiration , and the specific equation is as follows:

[0105] .

[0106] S5, using the above-mentioned ECHAM-iMAPLE online land-atmosphere coupling scheme, a long-term carbon and water flux simulation test is carried out to verify the stability of the method, and according to the test results, the online land-atmosphere coupling scheme is evaluated for the simulation capability of carbon and water flux.​

[0107] The following specific implementation case verifies the land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models proposed in this invention.

[0108] This implementation example uses data products from the Moderate Resolution Imaging Spectroradiometer (MODIS). Data, based on ground flux tower observations and machine learning extrapolation (FLUXCOM) and Data, along with soil temperature and moisture data from the data assimilation-based atmospheric reanalysis product (MERRA2), were used to assess carbon and water fluxes and water use efficiency in the improved land-atmosphere coupling scheme. Data observed by the FLUXCOM website and Calculations show that carbon flux includes total primary productivity. Leaf area index Water use efficiency evaporation Soil temperature and soil moisture The time range is 2005-2014, and the time resolution is 1 month.

[0109] Figure 2 Monthly time-series plots of multi-year averages of total primary productivity, leaf area index, and evapotranspiration from multi-source data and different land-atmosphere coupling schemes. Figure 2 It can be seen that the ECHAM-iMAPLE scheme better simulates the seasonal variation of carbon flux, especially in total primary productivity (TPP). Figure 2 (a) and evapotranspiration ( Figure 2 The simulation in Figure (c) of the ECHAM-iMAPLE scheme is significantly superior to that of the ECHAM-JSBACH scheme. The total primary productivity curve of the ECHAM-iMAPLE scheme closely matches the seasonal variation trajectory of the FLUXCOM observation data, especially the peak during the growing season (spring and summer) and the trough in winter, indicating that the ECHAM-iMAPLE scheme can more accurately capture the intensity and duration of vegetation photosynthesis. In contrast, the total primary productivity simulated by the ECHAM-JSBACH scheme may have systematic biases. For example, its growing season starts earlier and the peak is too low. This bias directly affects the assessment of the seasonal intensity of terrestrial carbon sinks. Figure 2The correlation coefficient between the simulated leaf area index by ECHAM-iMAPLE and MODIS remote sensing data is 0.95, which is higher than that of ECHAM-JSBACH (0.94) in (b) of FIG. 1. The simulated evapotranspiration by ECHAM-iMAPLE is also very close to the observation. This shows that the scheme can truly reflect the seasonal variation of water loss by transpiration and soil evaporation of vegetation, and better capture the core carbon-water coupling mechanism in the physiological process of vegetation.

[0110] Table 1 shows the comparison of key carbon flux parameters based on FLUXCOM observation data, MODIS remote sensing data and original land-atmosphere coupling scheme (ECHAM-JSBACH) simulation results and improved land-atmosphere coupling scheme (ECHAM-iMAPLE) simulation results, including total primary productivity and leaf area index . The results show that the global total / average of total primary productivity and leaf area index simulated by ECHAM-iMAPLE scheme is reasonable, specifically, the global total primary productivity of ECHAM-iMAPLE scheme is 126.87 Pg C / year, the correlation coefficient with the observation is 0.78, and the root mean square error is 1.35. In contrast, the global total primary productivity simulated by ECHAM-JSBACH scheme is 134.24 Pg C / year, which is obviously overestimated, the correlation coefficient is lower (0.77), and the root mean square error is higher (1.64). The global average of leaf area index simulated by ECHAM-iMAPLE scheme is 1.08, and the correlation coefficient with MODIS remote sensing data is 0.86, which is slightly higher than that of ECHAM-JSBACH scheme.

[0111] Table 1 Comparison of key carbon flux parameters

[0112] Table 2 compares the global average and statistical parameters of evapotranspiration and water use efficiency of FLUXCOM observation data and different land-atmosphere coupling schemes. Compared with the simulation results of ECHAM-JSBACH scheme, the global average of evapotranspiration simulated by ECHAM-iMAPLE scheme is closer to the observation, and the correlation coefficient is 0.86, which is slightly higher than that of ECHAM-JSBACH scheme. ECHAM-iMAPLE scheme better captures the global variation of water use efficiency, and the correlation coefficient with FLUXCOM site observation data is 0.52, which is significantly improved compared with the original scheme. While the root mean square error of ECHAM-JSBACH scheme simulation results and FLUXCOM site observation data is as high as 0.95.

[0113] Table 2 Comparison of global average and statistical parameters of evapotranspiration and water use efficiency

[0114] Table 3 is the global average and statistical parameter difference of soil temperature and soil moisture of MERRA2 reanalysis data and different land-atmosphere coupling schemes. It can be seen that the improved scheme significantly reduces the simulation bias of soil temperature and soil moisture. The correlation coefficients of soil temperature and soil moisture simulated by ECHAM-iMAPLE are 0.99 and 0.76, respectively, the simulation value is closer to the MERRA2 reanalysis data, and the root mean square error is lower. The global average of soil temperature simulated by ECHAM-iMAPLE is 288.37℃, which is only 0.04℃ different from the global average of MERRA2. In terms of soil moisture, the global average of ECHAM-iMAPLE is 0.23m 3 / m 3 , which is almost consistent with the observation, the correlation coefficient is 0.76, and the root mean square error is only 0.04, and the simulation effect is significantly improved. The simulation improvement of soil temperature and soil moisture is mainly due to the optimization of ECHAM-iMAPLE scheme in the calculation of surface energy balance, so as to more accurately simulate the heat exchange process of soil-atmosphere interface. In the simulation of soil moisture, by introducing a more accurate soil moisture dynamic model (Noah-MP), the accuracy of soil moisture simulation is improved, and the global weighted average value is less deviated from MERRA2 data.

[0115] Table 3 Global average and statistical parameter comparison of soil temperature and soil moisture

[0116] Based on the above inventive concept, the present application also provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions which, when executed by a computing device, cause the computing device to perform any one of the methods for improving the carbon and water flux simulation capability of climate model land-atmosphere coupling method according to the above.

[0117] Based on the above inventive concept, the present application also provides a computing device comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise instructions for performing any one of the methods for improving the carbon and water flux simulation capability of climate model land-atmosphere coupling method according to the above.

[0118] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0119] The present application is described in reference to the flowchart and / or block diagrams of the methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0120] These computer program instructions can 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 instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0121] These computer program instructions can 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 flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0122] The above description is only preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the technical principles of the present application, can make a number of improvements and variations, these improvements and variations should be considered as the protection scope of the present application.

Claims

1. A land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models, characterized in that, include: Obtain the climate model ECHAM and the dynamic vegetation model iMAPLE to be coupled; The meteorological field is obtained by real-time simulation in the main integration program of the climate model ECHAM. The meteorological field simulated in real time by the climate model ECHAM is transmitted to the dynamic vegetation model iMAPLE. Land surface ecological processes are calculated in the iMAPLE dynamic vegetation model, and the results of the land surface ecological process calculations are transmitted back to the main integration program of the ECHAM climate model. Based on the data interaction between the climate model ECHAM and the dynamic vegetation model iMAPLE, an online land-atmosphere coupling scheme of ECHAM-iMAPLE is established to simulate carbon and water fluxes within a preset time period. The carbon and water fluxes include total primary productivity, leaf area index, water use efficiency, evapotranspiration, soil temperature, and soil moisture.

2. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 1, characterized in that, The meteorological field was obtained from weather forecasts using the ECHAM climate model, including surface air temperature, atmospheric pressure, surface wind speed, atmospheric humidity, shortwave radiation, and precipitation.

3. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 1, characterized in that, The process of transferring the real-time simulated meteorological field from the ECHAM climate model to the iMAPLE dynamic vegetation model includes: The meteorological field simulated in real time by ECHAM is converted from spectral space format to grid format and then transmitted to the dynamic vegetation model iMAPLE.

4. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 1, characterized in that, The calculation results of the land surface ecological processes are transferred back to the main integration program of the climate model ECHAM, including: The calculation results of the land surface ecological processes are converted from grid format to spectral space format and then transmitted back to the main integration program of the climate model ECHAM.

5. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 1, characterized in that, The carbon flux simulation includes: Collect precipitation data, construct a water budget balance equation, and apply the precipitation data simulated by the ECHAM climate model. Decomposed into evaporation ,run-off and changes in terrestrial water storage , is represented as: ； Among them, evaporation The calculation is as follows: ， ， ， ， in, For vegetation transpiration, For canopy evaporation, Evaporation from the ground surface Indicates air density, This indicates the specific heat capacity of dry air. Indicates the leaf transpiration coefficient. This represents the saturated vapor pressure at the blade temperature. Indicates the vapor pressure of the canopy air. It is a humidity constant. This represents the latent heat flux from the moistened leaf surface to the canopy air. Indicates the latent heat coefficient of the Earth's surface. Represents the saturated vapor pressure at the Earth's surface. Indicates the relative humidity of the Earth's surface; run-off The calculation is as follows: ， ， ， in, It is surface runoff. It is underground runoff. This indicates that water has seeped into the soil surface. Indicates soil infiltration. Indicates the slope coefficient of the terrain. Indicates the electrical conductivity of water in the bottom layer of the soil; Land water storage The calculation is as follows: ， in, This refers to the surface moisture content. In terms of snow water equivalent, Soil moisture content, Indicates the number of soil types; Changes in terrestrial water storage It equals the difference between the land water storage at the beginning of the time step and the land water storage at the end of the time step.

6. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 5, characterized in that, In the carbon-water flux simulation, soil temperature and soil moisture were calculated as follows: ， ， in, Indicates soil moisture. Calculated based on the Clapp-Hornberger curve parameterization. Represents the hydraulic conductivity coefficient. It is an empirical parameter that depends on the soil type. It is the soil volumetric water content. Indicates soil temperature, Indicates the specific heat capacity of soil. Indicates specific heat. Indicates soil depth.

7. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 5, characterized in that, In the carbon flux simulation, the total primary productivity and leaf area index were calculated as follows: ， ， ， for plant, ， ， ， for plant, ， ， ， in, For total primary productivity, Indicates leaf area index, This represents the total photosynthesis of the leaves. Indicates the number of canopy layers. Indicates phenological factors, Depends on vegetation carbon content, This indicates the carbon fixation catalytic ability of ribulose-1,5-bisphosphate carboxylase. This indicates the ability of the Calvin cycle and thylakoid reactions to regenerate ribulose-1,5-bisphosphate with electron transport support. This indicates that starch and sucrose are synthesized in... Regeneration of inorganic phosphate in plants for photophosphorylation The ability of plants to regenerate phosphoenolpyruvate This indicates the leaf-specific absorbance. Indicates the incident photosynthetically active radiation. Indicates internal quantum efficiency. This indicates the partial pressure of oxygen inside the leaf. This indicates the partial pressure of CO2 inside the blade. Indicates the CO2 compensation point. Indicates maximum carboxylation capacity. and Michaelis constants representing the carboxylation and oxidation of Rubisco. Indicates ambient air pressure. This represents the Oleson constant.

8. The land-atmosphere coupling method for improving the carbon and water flux simulation capability of climate models according to claim 7, characterized in that, In the carbon flux simulation, the water use efficiency was calculated as follows: ， in, This indicates water use efficiency.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the land-atmosphere coupling methods for improving the carbon and water flux simulation capabilities of climate models according to claims 1 to 8.

10. A computing device, characterized in that, It includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the land-atmosphere coupling methods for improving the carbon and water flux simulation capabilities of climate models according to claims 1 to 8.

Images