Analysis of CO<sub>2</sub> spatio-temporal variations in China using a weather–biosphere online coupled model

Abstract

Abstract. The dynamics of atmospheric CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from the terrestrial flux and the influence of atmospheric mixing. In this study we apply the WRF-Chem model configured with the Vegetation Photosynthesis and Respiration Model (VPRM) option for biomass fluxes in China to characterize the dynamics of CO2 in the atmosphere. The online coupled WRF-Chem model is able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. We apply WRF-Chem for a multi-year simulation (2016–2018) with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the spatio-temporal variations of CO2 fluxes and concentrations in China. We find that the spatial distribution of CO2 was dominated by anthropogenic emissions, while its seasonality (with maxima in April 15 ppmv higher than minima in August) was dominated by the terrestrial flux and background CO2. Observations and simulations revealed a consistent increasing trend in column-averaged CO2 (XCO2) of 2.46 ppmv (0.6 % yr−1) resulting from anthropogenic emission growth and biosphere uptake. WRF-Chem successfully reproduced ground-based measurements of surface CO2 concentration with a mean bias of −0.79 ppmv and satellite-derived XCO2 with a mean bias of 0.76 ppmv. The model-simulated seasonality was also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and satellite data, respectively. Tower observations from a background site at Lin'an (30.30∘ N, 119.75∘ E) revealed a strong correlation (−0.98) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmospheric CO2.

Deriving Full‐Coverage and Fine‐Scale XCO2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output

“…Spatially, ∆XCO 2 was higher in South China (+0.58 ppm), Central China (+0.44 ppm), and East China (+0.41 ppm), but lower in Northwest China (−0.20 ppm; Table S9 and Figure S7 in Supporting Information ). Compared to LGB XCO 2 , the CT XCO 2 was overestimated (and underestimated) in mountain ranges (and desert areas; Figure S7 in Supporting Information ), as also reported in a previous study (Dong et al., 2021). The spatiotemporal patterns of ∆XCO 2 could be attributed to the performance of the LGB and CT models in assimilating the OCO‐2 XCO 2 data.…”

Characterizing Average Seasonal, Synoptic, and Finer Variability in Orbiting Carbon Observatory‐2 XCO2 Across North America and Adjacent Ocean Basins

“…Dong et al. ( 2021 ) used OCO‐2 v9 data integrated onto a weather‐biosphere‐online‐coupled model WRF‐Chem and CarbonTracker 2019 grids (20 km grid and 1° × 1° grids, respectively) for validation of simulated XCO 2 . Byrne et al.…”

“…For example, Hu et al (2020) evaluated biases in monthly mean high-resolution WRF-VPRM model-simulated XCO 2 to time-matched OCO-2 v9 XCO 2 data pairs aggregated in 1° × 1° grid boxes. Dong et al (2021) used OCO-2 v9 data integrated onto a weather-biosphere-online-coupled model WRF-Chem and CarbonTracker 2019 grids (20 km grid and 1° × 1° grids, respectively) for validation of simulated XCO 2 . Byrne et al (2021) used OCO-2 v10 XCO 2 to optimize fluxes from the NASA Carbon Monitoring System-Flux (CMS-Flux) inversion at 2° × 2.5° spatial resolution.…”

Implementation of Improved Parameterization of Terrestrial Flux in WRF‐VPRM Improves the Simulation of Nighttime CO2 Peaks and a Daytime CO2 Band Ahead of a Cold Front

“…EVI is derived from the MOD09A1 C6 500 m land surface reflectance data set (Zhang et al., 2016a, 2016b, 2017, 2018) and aggregated to model grids. This version of VPRM (Mahadevan et al., 2008) and the corresponding parameters previously calibrated off‐line using eddy covariance tower data over North America (Hilton et al., 2016) were implemented into WRF‐VPRM to examine spatiotemporal variation of CO 2 over the contiguous U.S. (Hu, Crowell, Wang, Zhang, et al., 2020) and China (Li et al., 2020, Dong et al., 2021). While Equation is a light‐use efficiency algorithm commonly used to simulate GEE (Dong et al., 2015; Wagle et al., 2014; Zhang et al., 2016a, 2016b, 2017, 2018), respiration (Equation ), is considerably simpler than parameterizations of ecosystem respiration in more complex, process‐based models.…”