Method for estimating high-resolution atmosphere carbon dioxide concentration in land area of China

Abstract

The invention relates to a method for estimating high-resolution atmosphere carbon dioxide concentration in a land area of China, which comprises the following steps of: (1) calculating a vegetation index, a surface water index and a vegetation phenology index by using satellite data, and generating a vegetation data file; (2) generating a carbon dioxide background field file and a meteorological background field file by using a CAMS global greenhouse gas concentration data set and a fifth-generation atmosphere reanalysis data set of the European mid-term weather forecast center; (3) generating a human-derived carbon dioxide emission data file by using a multi-scale emission list of the Xinghua University; (4) constructing an optimized atmospheric carbon dioxide simulation system; and (5) inputting the vegetation data file, the carbon dioxide background field and meteorological background field file and the man-made source carbon dioxide emission data file into the optimized atmospheric carbon dioxide simulation system, and operating to obtain a high-resolution atmospheric carbon dioxide concentration estimation result in the land area of China. The method can significantly improve the ability to estimate the carbon dioxide concentration of the atmosphere in the land area of China.

Description

Technical Field

[0001] The present invention relates to a method for producing data in the field of ecological environment and atmospheric science, and in particular to a method for estimating high-resolution atmospheric carbon dioxide concentration in land areas of China. Background Art

[0002] A series of problems caused by climate change have had a significant impact on human life and development, and have attracted the attention of governments and the scientific community (Fang et al., 2011). Carbon dioxide in the atmosphere, as the main greenhouse gas, has a huge impact on global climate change (Zimov et al., 2006). Compared with pre-industrial levels, the concentration of atmospheric carbon dioxide has increased by more than 50% (Friedlingstein et al., 2023). In order to control carbon dioxide emissions and thus slow down global warming, countries around the world have been making various efforts for many years. Therefore, it is crucial to formulate reasonable strategies to control carbon dioxide emissions and accurately estimate the concentration of carbon dioxide in the atmosphere and its temporal and spatial distribution (Deng et al., 2013).

[0003] At present, the estimation of atmospheric carbon dioxide mainly adopts the means of site observation, satellite remote sensing and numerical model simulation (Piao Shilong et al., 2022). Ground observation can obtain relatively accurate atmospheric carbon dioxide concentration values ​​with high temporal resolution and good continuity, but due to the limited spatial distribution, ground observation is difficult to reflect the regional distribution characteristics of a large range (Wang et al., 2020). Compared with site observation, satellite remote sensing has a high coverage density and can provide regional carbon dioxide distribution information, but due to its specific orbital mode and observation period, it is impossible to conduct continuous high-temporal resolution observations in the same area (Zhao et al., 2023). Numerical models can simulate regional and even global atmospheric carbon dioxide in a time-continuous manner, becoming an effective tool for studying the distribution and spatiotemporal changes of carbon dioxide concentration in the atmosphere.

[0004] Since global numerical models cannot simulate with high spatial resolution and it is difficult to consider the influence of small and medium-scale weather processes, there is often a large uncertainty in the estimation of atmospheric carbon dioxide concentration in a specific region (Geels et al., 2007). In contrast, regional models can simulate with higher temporal and spatial resolution, and at the same time, they can more fully consider small and medium-scale weather systems, and can more accurately describe the emission and transmission process of carbon dioxide in the atmosphere of a specific region. At present, there are many regional models that can estimate atmospheric carbon dioxide concentration, such as RAMS-SIB2 (Denning et al., 2003), RAMS-SIB3 (Corbin et al., 2010), RAMS-CMAQ (Xing et al., 2013), WRF-CMAQ (Li et al., 2017), WRF-Chem (Ahmadov et al., 2009), etc.

[0005] In regional models, the WRF-Chem model calculates biogenic carbon dioxide concentration based on the VPRM model. It is widely used in atmospheric carbon dioxide related research because it does not have too many complex parameters and the simulation results are accurate and reliable. Even in areas with complex terrain, WRF-Chem has been proven to estimate atmospheric carbon dioxide concentrations consistent with observations (Pillai, 2011). However, the model uses a simple linear formula to calculate ecosystem respiration, which also leads to certain errors in the carbon dioxide absorbed / released by terrestrial ecosystems in the model (Hu et al., 2020). In addition, the use of reasonable parameters in different study areas plays a vital role in the accuracy of model estimates of atmospheric carbon dioxide concentrations (Mahadevan et al., 2008). Most previous studies on atmospheric carbon dioxide in China's land areas directly used parameters obtained by predecessors based on foreign stations for simulation (Dong et al., 2021; Li et al., 2020), or performed parameter fitting for local specific underlying surface types (Dayalu et al., 2018), and the calculation of ecosystem respiration in the WRF-Chem model used was a simple linear formula. Therefore, to accurately estimate the atmospheric carbon dioxide concentration in China's land area, a more complete ecosystem respiration calculation formula and calculation parameters applicable to the region are needed.

[0006] The WRF-Chem model's estimation of atmospheric carbon dioxide depends on the meteorological field (temperature, relative humidity, wind field, etc.) simulated by the WRF model and downward shortwave radiation. Studies have found that the meteorological field and photosynthetically active radiation directly simulated by the model have large errors (Ahmadov et al., 2007; Dayalu et al., 2018). The Nudging method is a method to improve the simulation of the model's meteorological field. Its core idea is to make the model's results close to the observational data by adding a false tendency term proportional to the difference between the forecast and the observation to the forecast equation. Many studies have shown that the use of the Nudging method can effectively improve the WRF model's simulation ability for the meteorological field (Ma et al., 2016; Mai et al., 2017), but there are few studies that use the Nudging method, especially observational Nudging, to improve the meteorological field simulation results in the estimation of atmospheric carbon dioxide. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for estimating the high-resolution atmospheric carbon dioxide concentration in the land area of ​​China with higher accuracy.

[0008] To solve the above problems, the present invention provides a method for estimating high-resolution atmospheric carbon dioxide concentration in China's land area, comprising the following steps:

[0009] ⑴ Use satellite data to calculate vegetation index, surface moisture index, and vegetation phenology index, and distribute the calculated data together with land type data to the numerical model grid to generate the vegetation data file required for the operation of the numerical model;

[0010] (2) The CAMS global greenhouse gas concentration dataset from the European Centre for Medium-Range Weather Forecasts is used as the original carbon dioxide background field, and the fifth-generation atmospheric reanalysis dataset (ERA5) from the European Centre for Medium-Range Weather Forecasts is used as the original meteorological background field. The data of the original carbon dioxide background field and the original meteorological background field are interpolated onto the numerical model grid using the inverse distance weighted method to generate carbon dioxide background field and meteorological background field files;

[0011] ⑶ The multi-scale emission inventory (MEIC) from Tsinghua University is used as the anthropogenic emission inventory data. The anthropogenic emission inventory data with monthly resolution is first converted to hourly resolution by linear interpolation, and then the anthropogenic emission inventory data is interpolated onto the numerical model grid by the inverse distance weighting method to generate anthropogenic carbon dioxide emission data files;

[0012] (4) By improving the calculation formulas of photosynthesis and respiration of terrestrial ecosystems and improving the three-dimensional temperature field calculated by numerical models, an optimized atmospheric carbon dioxide simulation system is constructed; among which:

[0013] ① In the WRF-Chem model, the calculation formula for photosynthesis in terrestrial ecosystems is:

[0014]

[0015] PAR new =0.77×PAR model +7.36

[0016] Where: GEE is photosynthesis of terrestrial ecosystems, unit: μmol m -2 s -1 ; λ is the maximum light energy utilization rate, unit: μmol CO2 m -2 s -1 / μmol PAR mm -2 s -1 PAR stands for photosynthetically active radiation; PAR0 is the photosynthetically active radiation when photosynthesis reaches half-saturation value, unit: μmol mm -2 s -1 ; T scale is the temperature influence index; P scale represents vegetation phenology index; W scale1 LSWI is the photosynthesis water stress index; max The maximum value of the multi-year surface moisture index; PAR newis the corrected light and effective radiation, in μmolm -2 s -1 PAR model Light and effective radiation calculated for the original numerical model;

[0017] ②The calculation formula for terrestrial ecosystem respiration is:

[0018] R=β+α1×T′+α2×T′ 2 +γ×EVI+k1×W scale2 +k2×W scale2 ×T′+k3×W scale2 ×T′ 2

[0019]

[0020] When T a ≥T c Time: T′=T a

[0021] When T a <T c Time: T′=T c -T m ×(T c -T a )

[0022] Where: T' is the temperature of the improved pair, unit: ℃; W scale2 is the respiration water stress index; β, α1, α2, γ, k1, k2, and k3 are calculated parameters applicable to different underlying vegetation types in China's land area, obtained by fitting the productivity observation data of different vegetation types in China for many years; LSWI min is the minimum value of the multi-year surface moisture index; T a is the atmospheric temperature at 2 m, in °C; T c is the low temperature threshold, unit is °C; T m The atmospheric temperature is lower than T c The empirical coefficient of

[0023] ③ Improve the three-dimensional temperature field calculated by the numerical model:

[0024] The 2m temperature data observed at the station are assimilated by the observation Nudging method to improve the 2m temperature estimated by the model; the high-level temperature estimated by the model is made close to the high-level temperature in the ERA5 reanalysis data of the European Center for Medium-Range Weather Forecasts by the grid point Nudging method, thereby improving the high-level temperature estimated by the model; finally, the three-dimensional temperature field calculated by the improved numerical model is obtained;

[0025] (5) Input the vegetation data file, carbon dioxide background field and meteorological background field file, and anthropogenic carbon dioxide emission data file into the optimized atmospheric carbon dioxide simulation system and run it to obtain the high-resolution atmospheric carbon dioxide concentration estimation results for China's land area.

[0026] In the step (1), the vegetation index, surface moisture index and vegetation phenology index are calculated using MODIS satellite data according to the following formula:

[0027]

[0028] Where: EVI represents the enhanced vegetation index, ranging from -1 to 1; ρ mir represents the reflectivity in the near-infrared band; ρ red Represents the reflectivity of red light band; ρ blue represents the reflectance of the blue light band; G is the gain factor, which is 2.5; C1 and C2 are the correction parameters for atmospheric correction of red light and blue light, which are 6 and 7.5 respectively; L is the soil correction parameter, which is 1; LSWI represents the surface moisture index, which ranges from -1 to 1; ρ swir Represents the reflectivity of the short-wave infrared band; P scale Represents the vegetation phenology index.

[0029] The numerical model grid in step (1) refers to the Lambert projection, with a resolution of 12×12 km, a grid number of 354×415, and a center point of 36.461°N, 102.53°E.

[0030] The value ranges of λ and PAR0 in step ① are shown in Table 1:

[0031] Table 1 Photosynthesis calculation parameters

[0032]

[0033] The value ranges of β, α1, α2, γ, k1, k2, and k3 in step ② are shown in Table 2:

[0034] Table 2 Respiration calculation parameters

[0035]

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] 1. Based on the mesoscale numerical model WRF-Chem, the present invention constructs an optimized atmospheric carbon dioxide simulation system by improving the calculation formula of vegetation respiration, fitting calculation parameters suitable for China's land area, providing temperature fields and photosynthetically active radiation closer to actual observations for model calculations, and adopting a more refined atmospheric carbon dioxide background field.

[0038] 2. The present invention combines observational Nudging and grid Nudging methods to improve the meteorological field simulated by the model, and further uses multi-year station observation data to correct the photosynthetically active radiation in the model, thereby improving the model's ability to simulate atmospheric carbon dioxide in China's land area from multiple angles. Compared with other methods, the present invention can obtain three-dimensional atmospheric carbon dioxide concentration estimation results with high temporal and spatial resolution, effectively improving the ability to estimate atmospheric carbon dioxide concentration in China's land area. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0040] Figure 1 It is a flow chart of the present invention.

[0041] Figure 2 The hourly 2-meter temperature, light and effective radiation time series estimated by the present invention at different stations in July 2016 are compared with the observations. In the figure, OBS represents observation, CTL is the control test, indicating the model result before optimization, and VPRM_C is the estimation result of the present invention. (a) is the 2-meter temperature time series estimated by the observation of Haibei Station and the VPRM_C test in July 2016, (b) is the 2-meter temperature time series estimated by the observation of Huzhong Station and the VPRM_C test in July 2016, (c) is the photosynthetically active radiation time series estimated by the observation, control test and VPRM_C test of Haibei Station in July 2016, (d) is the photosynthetically active radiation time series estimated by the observation, control test and VPRM_C test of Huzhong Station in July 2016.

[0042] Figure 3 The estimated daily average 2-meter temperature, light and effective radiation at different stations in July 2016 are compared with the observations. (a) is the daily average 2-meter temperature estimated by the observations at Haibei Station and the VPRM_C test in July 2016, (b) is the daily average 2-meter temperature estimated by the observations at Huzhong Station and the VPRM_C test in July 2016, (c) is the daily average photosynthetically active radiation estimated by the observations at Haibei Station, the control test and the VPRM_C test in July 2016, (d) is the daily average photosynthetically active radiation estimated by the observations at Huzhong Station, the control test and the VPRM_C test in July 2016.

[0043] Figure 4The hourly net ecosystem exchange time series estimated by the present invention at different stations in July 2016 were compared with the observations. (a) is the net ecosystem exchange time series estimated from the observations, control experiments and VPRM_C experiments at Haibei Station in July 2016, (b) is the net ecosystem exchange time series estimated from the observations, control experiments and VPRM_C experiments at Huzhong Station in July 2016, (c) is the daily average net ecosystem exchange estimated from the observations, control experiments and VPRM_C experiments at Haibei Station in July 2016, (d) is the daily average net ecosystem exchange estimated from the observations, control experiments and VPRM_C experiments at Huzhong Station in July 2016.

[0044] Figure 5 Time series of daily atmospheric carbon dioxide concentrations estimated from observations at the Waliguan station, the control experiment, and the VPRM_C experiment in July 2016.

[0045] Figure 6 Comparison of the average atmospheric carbon dioxide column concentration distribution in July 2016 estimated by the present invention with observation and reanalysis data. (a) is the average atmospheric carbon dioxide column concentration distribution in July 2016 observed by satellite, (b) is the average atmospheric carbon dioxide column concentration distribution in July 2016 from the CarbonTracker reanalysis dataset, (c) is the average atmospheric carbon dioxide column concentration distribution in July 2016 estimated by the control experiment, and (d) is the average atmospheric carbon dioxide column concentration distribution in July 2016 estimated by the VPRM_C experiment. DETAILED DESCRIPTION

[0046] like Figure 1 As shown, a method for estimating high-resolution atmospheric carbon dioxide concentration in the land area of ​​China includes the following steps:

[0047] ⑴ Use satellite data to calculate vegetation index, surface moisture index, and vegetation phenology index, and distribute the calculated data together with land type data to the numerical model grid to generate the vegetation data file required for the operation of the numerical model.

[0048] Among them: Vegetation index, surface moisture index and vegetation phenology index are calculated using MODIS satellite data according to the following formula:

[0049]

[0050]

[0051] Where: EVI represents the enhanced vegetation index, ranging from -1 to 1; ρ nir represents the reflectivity in the near-infrared band; ρ red Represents the reflectivity of red light band; ρ bluerepresents the reflectance of the blue light band; G is the gain factor, which is 2.5; C1 and C2 are the correction parameters for atmospheric correction of red light and blue light, which are 6 and 7.5 respectively; L is the soil correction parameter, which is 1; LSWI represents the surface moisture index, which ranges from -1 to 1; ρ swir Represents the reflectivity of the short-wave infrared band; P scale Represents the vegetation phenology index.

[0052] Land type data uses MODIS satellite observation data.

[0053] The numerical model grid refers to the Lambert projection with a resolution of 12×12km, a grid number of 354×415, and a center point of 36.461°N, 102.53°E.

[0054] The vegetation score is obtained by calculating the proportion of underlying surface types in each numerical model grid. Finally, the vegetation index, surface moisture index, vegetation phenology index and vegetation score are integrated to generate the vegetation data file required for the operation of the numerical model.

[0055] (2) The CAMS global greenhouse gas concentration dataset from the European Centre for Medium-Range Weather Forecasts is used as the original carbon dioxide background field, and the fifth-generation atmospheric reanalysis dataset (ERA5) from the European Centre for Medium-Range Weather Forecasts is used as the original meteorological background field. The data of the original carbon dioxide background field and the original meteorological background field are interpolated onto the numerical model grid through the inverse distance weighted method to generate the carbon dioxide background field and meteorological background field files.

[0056] ⑶ The Multi-scale Emission Inventory (MEIC) from Tsinghua University is used as the anthropogenic emission inventory data. The monthly resolution anthropogenic emission inventory data is first converted to hourly resolution through linear interpolation, and then the anthropogenic emission inventory data is interpolated to the numerical model grid through the inverse distance weighted method to generate anthropogenic carbon dioxide emission data file.

[0057] (4) By improving the calculation formulas of photosynthesis and respiration of terrestrial ecosystems and improving the three-dimensional temperature field calculated by numerical models, an optimized atmospheric carbon dioxide simulation system is constructed; among which:

[0058] ① In the WRF-Chem model, the calculation formula for photosynthesis in terrestrial ecosystems is:

[0059]

[0060] PAR new =0.77×PAR model +7.36 Where: GEE is photosynthesis of terrestrial ecosystems, unit: μmol m -2 S -1; λ is the maximum light energy utilization rate, unit: μmol CO2 m -2 s -1 / μmol PAR mm -2 s -1 PAR stands for photosynthetically active radiation; PAR0 is the photosynthetically active radiation when photosynthesis reaches half-saturation value, unit: μmol mm -2 s -1 ; T scale is the temperature influence index; P scale represents vegetation phenology index; W scale1 LSWI is the photosynthesis water stress index; max The maximum value of the multi-year surface moisture index; PAR new is the corrected light and effective radiation, in μmolm -2 s -1 PAR model Light and effective radiation calculated for the original numerical model.

[0061] In the present invention, the light and effective radiation observed at the site for many years are used to correct the light and effective radiation calculated by the numerical model, with the aim of providing the numerical model with light and effective radiation that are closer to the actual observations for calculating the carbon dioxide concentration.

[0062] In the present invention, λ and PAR0 are obtained by fitting the observation data of productivity sites of different vegetation types in China for many years, as shown in Table 1:

[0063] Table 1 Photosynthesis calculation parameters

[0064]

[0065] ②In the original WRF-Chem model, the calculation formula for terrestrial ecosystem respiration is:

[0066] R=α×T+β

[0067] Where: R is the respiration of terrestrial ecosystems, unit: μmol m -2 s -1 ; α and β are parameters obtained by fitting the station observation data; T is the air temperature at 2m, unit ℃.

[0068] After improving the original formula, the present invention obtains the terrestrial ecosystem respiration calculation formula as follows:

[0069] R=β+α1×T′+α2×T′ 2 +γ×EVI+k1×W scale2 +k2×W scale2 ×T′+k3×W scale2 ×T′ 2

[0070]

[0071] When T a ≥T c Time: T′=T a

[0072] When T a <T c Time: T′=T c -T m ×(T c -T a )

[0073] Where: T' is the improved temperature, unit: ℃; W scale2 is the respiration water stress index; β, α1, α2, γ, k1, k2, and k3 are the calculation parameters applicable to different underlying vegetation types in China's land area obtained by fitting the productivity observation data of different vegetation types in China for many years, as shown in Table 2; LSWI min is the minimum value of the multi-year surface moisture index; T a is the atmospheric temperature at 2 m, in °C; T c is the low temperature threshold, unit is ℃; T m The atmospheric temperature is lower than T c The empirical coefficient of .

[0074] Table 2 Respiration calculation parameters

[0075]

[0076] T of different underlying vegetation types c and T m As shown in Table 3:

[0077] Table 3 Temperature calculation parameters

[0078]

[0079] ③ Improve the three-dimensional temperature field calculated by the numerical model:

[0080] In the WRF-Chem model, the three-dimensional temperature field is used to calculate the carbon dioxide concentration absorbed / released by the terrestrial ecosystem and the diffusion and transmission of the total atmospheric carbon dioxide concentration. Therefore, the 2m temperature estimated by the model is improved by assimilating the 2m temperature data observed at the site through the observation Nudging method; the high-level temperature estimated by the model is made close to the high-level temperature in the ERA5 reanalysis data of the European Center for Medium-Range Weather Forecasts through the grid Nudging method, thereby improving the high-level temperature estimated by the model; and finally the three-dimensional temperature field calculated by the improved numerical model is obtained.

[0081] (5) Input the vegetation data file, carbon dioxide background field and meteorological background field file, and anthropogenic carbon dioxide emission data file into the optimized atmospheric carbon dioxide simulation system and run it to obtain the high-resolution atmospheric carbon dioxide concentration estimation results for China's land area.

[0082] In order to verify the optimization effect of the present invention on the estimation of atmospheric carbon dioxide concentration in China's land area, a group of control experiments (CTL, simulated by the unoptimized model) and a group of optimization experiments (VPRM_C, simulated by the optimized model of the present invention) were designed to estimate the atmospheric carbon dioxide concentration in China's land area in July 2016. The detailed experimental design is shown in Table 4:

[0083] Table 4 Experimental design table

[0084]

[0085] The simulation time of the two groups of experiments was from June 20, 2016 to July 31, 2016. The first 10 days were used as spin-up time to balance the internal elements of the model. The results from July 1 to 31 were used for analysis. The present invention is based on WRF-Chemv4.1.3. The carbon dioxide background field is derived from the 1.4°×0.7° CAMS global greenhouse gas concentration data set of the European Center for Medium-Term Weather Forecasts, and the meteorological background field is derived from the 0.25°×0.25° fifth generation atmospheric reanalysis data set (ERA5) of the European Center for Medium-Term Weather Forecasts. The model adopts single-layer nesting, with a horizontal resolution of 12km, a grid of 354×415, 48 layers in the vertical direction, and an integration step of 40s. The selected model parameterization schemes are as follows: Morrison2 microphysical process scheme, RRTM longwave radiation scheme, Dudhia shortwave radiation scheme, Noah land surface process scheme, YSU planetary boundary layer scheme, and New Grell cumulus parameterization scheme.

[0086] Accurate 2m temperature, light and effective radiation are crucial for estimating carbon dioxide concentration. The hourly 2m temperature, light and effective radiation time series estimated from site observations and experiments in July 2016 are shown in Figure 2. Figure 2 As shown in Figure 2, the optimized experiment simulated the 2m temperature at different stations, which was very close to the observation ( Figure 2 ab), the correlation coefficient (R, the higher the correlation between the data) of Haibei station reached 0.95, and the root mean square error (RMSE, the lower the better, indicating that the data difference is smaller) reached 1.82℃, and the R of Huzhong station was 0.92, and the RMSE was 3.12℃. For photosynthetically active radiation, compared with the control experiment, the optimization experiment effectively improved the simulation ability of light and effective radiation at different stations ( Figure 2cd), the RMSE at Haibei station decreased by 15%, and the RMSE at Huzhong station decreased by 28%. Subsequently, the simulated daily average 2m temperature, light and effective radiation of the present invention were further tested. The results showed that both groups of experiments could simulate the daily variation characteristics of 2m temperature, light and effective radiation close to the observed ones at different stations ( Figure 3 ), and the optimization experiment showed better simulation ability.

[0087] my country is rich in ecological resources, and net ecosystem exchange (NEE) can significantly affect the atmospheric carbon dioxide concentration. The net ecosystem exchange estimated by the present invention was tested. For the hourly NEE in July 2016, the optimization experiment significantly improved the model's ability to estimate NEE at different stations, among which the improvement of nighttime respiration at Haibei Station was particularly obvious ( Figure 4 The NNE value in a is greater than 0), and the R with the observation is 0.85, and the RMSE is 4.74 μmol m -2 s -1 , effectively improving the estimated NEE value during the day at Huzhong Station ( Figure 4 (b) The R value is less than 0, and the RMSE decreases by 45%. Similarly, for the daily average NEE in July 2016, the optimization experiment can obtain more realistic daily variation estimation results, with R reaching 0.98 at Haibei Station and RMSE reduced by 46%, and R reaching 0.98 at Huzhong Station and RMSE reduced by 74%.

[0088] In order to verify the atmospheric carbon dioxide concentration estimated by the present invention, the hourly carbon dioxide concentration time series of ground stations in July 2016 was calculated ( Figure 5 ) and the distribution of carbon dioxide column concentration ( Figure 6 At the Waliguan site, the ground CO2 concentration estimated by the optimization experiment is more consistent with the observed value ( Figure 5 )R is 0.7, and RMSE is 4.21ppm. The carbon dioxide column concentration distribution can reflect the distribution of carbon dioxide in the entire atmosphere. Compared with the carbon dioxide column concentration distribution observed by satellite ( Figure 6 a), both sets of tests can simulate the distribution of carbon dioxide column concentration, but the results of the optimization test are closer to the observed values ​​overall ( Figure 6 d), while the control experiment overestimated CO2 concentrations in most regions ( Figure 6 c), especially in the Yangtze River Delta and southeastern Tibet. Compared with the CarbonTracker reanalysis data ( Figure 6 b), the present invention provides a more detailed distribution of carbon dioxide concentration.

[0089] In summary, the present invention effectively improves the 2m air temperature, photosynthetically active radiation and net ecosystem exchange results calculated by the model, and significantly enhances the ability to estimate the atmospheric carbon dioxide concentration in China's land area in all aspects.

Claims

1. A method for estimating high-resolution atmospheric carbon dioxide concentration over the land area of ​​China, comprising the following steps: ⑴ Use satellite data to calculate vegetation index, surface moisture index, and vegetation phenology index, and distribute the calculated data together with land type data to the numerical model grid to generate the vegetation data file required for the operation of the numerical model; (2) The CAMS global greenhouse gas concentration dataset from the European Centre for Medium-Range Weather Forecasts is used as the original carbon dioxide background field, and the fifth-generation atmospheric reanalysis dataset from the European Centre for Medium-Range Weather Forecasts is used as the original meteorological background field. The data of the original carbon dioxide background field and the original meteorological background field are interpolated onto the numerical model grid using the inverse distance weighted method to generate carbon dioxide background field and meteorological background field files; (3) The multi-scale emission inventory from Tsinghua University is used as the anthropogenic emission inventory data. The monthly resolution anthropogenic emission inventory data is first converted to hourly resolution by linear interpolation, and then the anthropogenic emission inventory data is interpolated onto the numerical model grid by the inverse distance weighting method to generate anthropogenic carbon dioxide emission data files; (4) By improving the calculation formulas of photosynthesis and respiration of terrestrial ecosystems and improving the three-dimensional temperature field calculated by numerical models, an optimized atmospheric carbon dioxide simulation system is constructed; among which: ① In the WRF-Chem model, the calculation formula for photosynthesis in terrestrial ecosystems is: ABOUT new =0.77×PAR model +7.36 Where: GEE is photosynthesis of terrestrial ecosystems, unit: μmol m -2 s -1 ; λ is the maximum light energy utilization rate, unit: μmolCO2 m -2 s -1 / μmol PAR mm -2 s -1 PAR stands for photosynthetically active radiation; PAR0 is the photosynthetically active radiation when photosynthesis reaches half-saturation value, unit: μmol mm -2 s -1 ; T scale is the temperature influence index; P scale represents vegetation phenology index; W scale1 LSWI is the photosynthesis water stress index; max The maximum value of the multi-year surface moisture index; PAR new is the corrected light and effective radiation, in μmolm -2 s -1 PAR model Light and effective radiation calculated for the original numerical model; ②The calculation formula for terrestrial ecosystem respiration is: R=β+α1×T′+α2×T′ 2 +γ×EVI+k1×W scale +k2×W scale2 ×T′+k3×W scale2 ×T′ 2 When T a ≥T c Time: T′=T a When T a <T c Time: T′=T c -T m ×(T c -T a ) Where: T' is the improved temperature, unit: ℃; W scale2 is the respiration water stress index; β, α1, α2, γ, k1, k2, and k3 are the calculation parameters applicable to different underlying vegetation types in China's land area obtained by fitting the productivity station observation data of different vegetation types in China for many years; LSWI min is the minimum value of the multi-year surface moisture index; T a is the atmospheric temperature at 2 m, in °C; T c is the low temperature threshold, unit is °C; T m The atmospheric temperature is lower than T c The empirical coefficient of ③ Improve the three-dimensional temperature field calculated by the numerical model: The 2m temperature data observed at the station are assimilated by the observation Nudging method to improve the 2m temperature estimated by the model; the high-level temperature estimated by the model is made close to the high-level temperature in the ERA5 reanalysis data of the European Center for Medium-Range Weather Forecasts by the grid point Nudging method, thereby improving the high-level temperature estimated by the model; finally, the three-dimensional temperature field calculated by the improved numerical model is obtained; (5) The vegetation data file, CO2 background field and meteorological background field file, and anthropogenic CO2 emission data file are input into the optimized atmospheric CO2 simulation system and run, thus obtaining the high-resolution atmospheric CO2 concentration estimation results for the land area of ​​China.

2. A method for estimating high-resolution atmospheric carbon dioxide concentration in China's land area according to claim 1, characterized in that: In the step (1), the vegetation index, surface moisture index and vegetation phenology index are calculated using MODIS satellite data according to the following formula: Where: EVI represents the enhanced vegetation index, ranging from -1 to 1; ρ nir represents the reflectivity in the near-infrared band; ρ red Represents the reflectivity of red light band; ρ blue represents the reflectance of the blue light band; G is the gain factor, which is 2.5; C1 and C2 are the correction parameters for atmospheric correction of red light and blue light, which are 6 and 7.5 respectively; L is the soil correction parameter, which is 1; LSWI represents the surface moisture index, which ranges from -1 to 1; ρ swir Represents the reflectivity of the short-wave infrared band; P scale Represents the vegetation phenology index.

3. The method for estimating high-resolution atmospheric carbon dioxide concentration in China's land area according to claim 1, characterized in that: The numerical model grid in step (1) refers to the Lambert projection, with a resolution of 12×12 km, a grid number of 354×415, and a center point of 36.461°N, 102.53°E.

4. The method for estimating high-resolution atmospheric carbon dioxide concentration in China's land area according to claim 1, characterized in that: The value ranges of λ and PAR0 in step ① are shown in Table 1: Table 1 Photosynthesis calculation parameters 5. The method for estimating high-resolution atmospheric carbon dioxide concentration in China's land area according to claim 1, characterized in that: The value ranges of β, α1, α2, γ, k1, k2, and k3 in step ② are shown in Table 2: Table 2 Respiration calculation parameters

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