As an important cause of global warming, CO2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO2 data as the accuracy control conditions to obtain high accuracy XCO2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist.
HASM; WRF-CHEM; GOSAT XCO2; XCO2 simulation
Global warming has drawn worldwide attention. As an important greenhouse gas, atmospheric CO
A large number of ground-based observatories have been built around the world that use methods such as bottle sampling, spectroscopy, and eddy covariance to obtain information about CO
Another viable method to obtain the high resolution spatial and temporal distributions of CO
Based on the High Accuracy Surface Modeling (HASM) developed from the fundamental theorem of surfaces, the model simulation results are used as the driving fields, and the observational data as the optimal control conditions to obtain more accurate CO
The study area is located in the North China, between 34.3° N~43.5° N and 111° E~121.9° E (see Fig. 1). This region incorporates the Beijing-Tianjin-Hebei region, which is a key economic zone in northern China, and Shandong Province, which is a large economically important province with the second largest population in China. In addition, part of Henan Province (which has the third largest population), Shanxi Province, Liaoning Province, and Inner Mongolia, which have a variety of underlying surfaces, including grass, forests, farmland, and cities, are also included in the study area.Land cover of the study region derived from ESA (European Space Agency) global land cover dataset. The red dots are the locations of the available GOSAT XCO
GOSAT (Greenhouse gases Observing SATellite) was jointly designed by the Ministry of the Environment (MOE), the National Institute for Environment Studies (NIES), and the Japan Aerospace Exploration Agency (JAXA), and was successfully launched on January 23, 2009 by the Japanese Space Agency. This satellite is the first to be used to specifically monitor the concentrations of CO
In this study, the column-averaged dry-air fractions of the CO
The WDCGG (World Data Centre for Greenhouse Gases) is one of the data archiving and service centers under the GAW (Global Atmosphere Watch) program of WMO (World Meteorological Organization) that collects all observational data of greenhouse gases. Such as CO
The WRF-CHEM (Weather Research and Forecasting-Chemistry) is a regional air quality model developed by the National Oceanic and Atmospheric Administration (NOAA) for biomass combustion, anthropogenic emissions, chemical vapor schemes, and aerosol solutions; the model includes a trace gas transport option and a subroutine to calculate the plume lifting (Simpson et al. [
The WRF (Weather Research and Forecasting) model is a new generation of mesoscale weather forecasting model and was codeveloped by NCAR and NCEP. This model has a fully compressible non-static equilibrium mode and contains a wealth of physical parameterization options (Wei et al. [
The greenhouse gas module of WRF-CHEM was developed by the Max Planck Institute for Biogeochemistry (Beck et al. [
In the greenhouse gas module, the Vegetation Photosynthesis Respiration Model (VPRM) is a key elemebt used to estimate the net ecosystem exchange (NEE), including the light-driven gross ecosystem exchange and the ecosystem respiration term driven by temperature.NEE=−λ×Tscale×Pscale×Wscale×EVI×11+PAR/PAR0×PAR+α×T+β
In which λ(μmol CO
We use a 1°× 1 reanalysis produce, i.e., the ERA-Interim data, with time intervals of 6 h as downloaded from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the initial field and boundary conditions for WRF. The total surface CO
The input of the VPRM module includes four kinds of data. The temperature at 2 m and the downward shortwave flux at the ground surface can be provided by the coupled WRF. LSWI and EVI are calculated from the Terra MODIS satellite level-3 land product (MYD09A1: MODIS/Aqua Surface Reflectance 8-Day L3 Global 500 m SIN Grid V006) via the VPRM-Preprocessor tool.
The WRF-CHEM utilized is WRF-CHEM version 3.6, which is used to model from February to May 2015. During the study period, simulations were performed four times. Each simulation has a 6-h spin up for its meteorology and a 1-month run time period for the CO
Based on the fundamental theorem of surface, a surface is uniquely determined by the first and second fundamental coefficients. The first fundamental coefficients of a surface describe the geometric properties of the surface, by which we can calculate the lengths of the curves, the angles of the tangent vectors, the areas of regions, and the geodesics on the surface. These geometric properties and objects are called the intrinsic geometric properties and are only determined via the first fundamental coefficients of a surface, depending on measurements that we can conduct while staying on the surface itself (Toponogov [
Suppose a surface z can be represented by a function of x and y; that is, z = f(x, y). The first fundamental coefficients, E, F, and G, and the second fundamental coefficients, L, M, and N, are defined as follow:E=1+fx2F=fx⋅fyG=1+fy2L=fxx1+fx2+fy2M=fxy1+fx2+fy2N=fyy1+fx2+fy2
In which f
A similar definition applies for f
Based on previous research, an equation set called the Gauss equation was found to relate the intrinsic curvature of the surface to the derivatives of the Gauss map, namely, the first fundamental coefficients (Eqs. 6-8) and the second fundamental coefficients (Eq. 9-11) satisfy the following equation set:fxx=Γ111fx+Γ112fy+LE+G−1fxy=Γ121fx+Γ122fy+ME+G−1fyy=Γ221fx+Γ222fy+NE+G−1whereΓ111=GEx−2FFx+FEy2EG−F2Γ121=GEx−FGx2EG−F2Γ221=2GFy−GGx−FGy2EG−F2Γ112=2EFx−EEy−FEx2EG−F2Γ122=EGx−FEy2EG−F2Γ222=EGy−2FFy+FGx2EG−F2
Γ111, Γ121, Γ221, Γ112, Γ122, and Γ222 are the second type of Christoffel symbols and rely only on the first fundamental coefficients, E, F, and G, and their derivatives. In the process of solution of the Eqs. 12-14, the central difference method is used to displace the partial derivatives.
We mark the first, second, and third equations of Gauss equation set as a, b, and c. Then, HASM abc can be expressed as a constrainted least-squares approximation.minABC⋅zn−1−dqhn2S⋅zn−1=kwhere the second equation of Eq. 21 is the constraint equation representing the sampling points information. A, B, and C are the coefficient matrix of the discrete equation form of the Gauss equation. d, q, and p are found on the right-hand side of the Gauss equation. S denotes the sampling matrix, and k denotes the sampling vector.
Equation 21 is a least-squares problem constrained by terrestrial sampling. The purpose of Eq. 21 is to confine the overall simulation error to a minimum value, while keeping the simulated value equal to the sample value at the sampling point. Taking full advantage of the sampling information is also an effective way to ensure that the iterative formulation of HASM approaches the best simulation result.
HASM can also be transferred into an unconstrained least-squares approximation:ATBTCTλ⋅STABCλ⋅SZn+1=ATBTCTλ⋅STdnqnpnλ⋅kwhere λ denotes the weight of the sampling point values, which refers the relative importances of the sampling points in the simulated field. λ could be a real number or a vector, depending on whether all sampling points are equally important or if each point has its own weight.
In the existing research, HASM have been applied for the study of soil properties (Shi et al., [
Figure 4 shows the comparison between the WRF-CHEM CO
Comparison between WRF-CHEM and GOSAT
Based on the information of the dates and longitudes/latitudes in the GOSAT retrieval dataset, we extract the simulation data for the same dates and positions to compare between the simulation and GOSAT XCO2. Note that the original WRF-CHEM output is a layered CO
The basic statistics of the XCO
From Fig. 4 and Table 1, WRF-CHEM has a certain degree of ability to simulate regional CO
Taking the simulated XCO
Comparisons of the three methods and the GOSAT retrieval data for each month are shown in Figs. 5, 6, 7, and 8. The fitting equations of HASM present the highest R
Table 2 shows the monthly mean absolute error (MAE) and root mean square error (RMSE) values of the three methods and indicates that HASM perform better than the classic interpolation methods. This is because that the HASM used in this paper is not a typical spatial interpolation method to construct new data points using only a finite set of known data points. The output of HASM normally contains information from both finite known data points and an approximate field. To some extent, the HASM used in this paper is more of a data fusion method.
The monthly XCO
With vegetation growth in the terrestrial ecosystems and the end of heat use in April, the difference of the north-south distribution of XCO
According to the fundamental theorem of surfaces, a surface is determined by the first and the second fundamental coefficients. In this paper, HASM is applied to obtain high precision simulated XCO
Compared with the previous research of HASM, the few observed data are used in this study. Meanwhile, the accuracy of the WRF-CHEM simulation field is limited. These factors will transmit biases into the outputs of HASM. Therefore, increasing the number of observation points and improving the driving field accuracies are important methods to obtain more accurate CO
This work was supported by the National Natural Science Foundation of China (41590844), the National Natural Science Foundation of China (41421001), and the Innovation Project of LREIS (O88RA600YA).
We are grateful for the CO
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By Yu Liu; Tianxiang Yue; Lili Zhang; Na Zhao; Miaomiao Zhao and Yi Liu