Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia

Fu, Yue; Liao, Hong; Tian, Xiangjun; Gao, Hao; Cai, Zucong; Han, Ruqi

doi:10.1016/j.accre.2020.03.001

Cited by 15 publications

(10 citation statements)

References 52 publications

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“…The simulation of CO 2 in the model is conducted as a separate tracer simulation and can be "tagged" by its source type or region, whose concentrations are determined by atmospheric transport (horizontal and vertical advection and diffusion) and eight types of CO 2 emission inventories [49]. This model has been widely utilized to investigate the spatio-temporal variation of CO 2 concentration [50][51][52], and the CO 2 sources and sinks inversion studies [53][54][55][56]. The abovementioned studies have proven that the model can capture the variations of atmospheric CO 2 reasonably well for both seasonal and interannual scales.…”

Section: Model and Numerical Simulationmentioning

confidence: 99%

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Gao

Liao

et al. 2021

Remote Sensing

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Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region.

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Section: Model and Numerical Simulationmentioning

confidence: 99%

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Gao

Liao

et al. 2021

Remote Sensing

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“…Simulated XCO2 is also compared with TCCON Hefei site observations, and a very good agreement is found with MB of -0.79 ppmv and NMB of -0.2%. In general, recent atmospheric inverse modelling studies (Fu et al, 2019a;Wang et al, 2019;Xie et al, 2018) report the simulation bias of XCO2 as 0.5-2 ppmv with posterior flux inputs. The WRF-VPRM model has demonstrated good agreement with the observations as a process-based model though our evaluation.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Machine-learning technique has also been employed to upscale site observations to regional-scale (Yao et al, 2018;Zhu et al, 2014), but the estimations of carbon budget and dynamics retain large uncertainty due to the diversity of biomass among sites and suffer from coarse grid resolution. These pilot studies have shed light on improving the understanding of spatiotemporal characteristics of CO2 in China with modelling or observational methods, but an integrated investigation with both modelling and observations at fine-scale is urgently needed to expand diagnostic understanding of localized and regional transport, flux, and concentration of CO2 to inform emission management and climate adaption policies (Fu et al, 2019a;Niu et al, 2017;Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Analysis of CO<sub>2</sub> spatiotemporal variations in China using tower data and a weather-biosphere-online-coupled model, WRF-VPRM

Dong¹,

Yue²,

Jiang³

et al. 2020

Preprint

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Abstract. Dynamics of CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from terrestrial flux and the influence of atmospheric mixing. In this study we apply the Weather Research and Forecasting model coupled with Vegetation Photosynthesis and Respiration Model (WRF-VPRM) in China to characterize CO2 dynamics with tower data collected at a background site Lin’an (30.30° N, 119.75° E). The online coupled weather-biosphere WRF-VPRM simulations are able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. Simulations are conducted for three years (2016–2018) with fine grid resolution (20 km) to detail the spatiotemporal variations of CO2 fluxes and concentrations. This is the first attempt to apply the weather-biosphere model for a multi-year simulation with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the dynamics of CO2 in China. We find that the spatial distribution of CO2 is determined by anthropogenic emissions, while its seasonality (with maximum concentrations in April 15 ppmv higher than minimums in August) is dominated by terrestrial flux and background CO2. Observations and simulations reveal a consistent increasing trend in column-averaged CO2 (XCO2) of 0.6 %/yr resulting from anthropogenic emission growth and biosphere uptake. WRF-VPRM successfully reproduces ground-based measurements of surface CO2 concentration with mean bias of −0.79 ppmv (−0.20 %) and satellite derived XCO2 with mean bias of 0.76 ppmv (0.19 %). The model-simulated seasonality is also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and Orbiting Carbon Observatory-2 (OCO-2) satellite data, respectively. However, evaluation against Lin'an tower data reveals uncertainty within the model for simulating the intensity and diurnal variation of terrestrial flux, which contributes to overestimation by ~5.35 ppmv (1.26 %). Lin'an tower observations also reveal a strong correlation (−0.85) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmospheric CO2.

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“…However, as atmospheric CO 2 can be significantly affected by atmospheric circulation and regional surface CO 2 fluxes, it is difficult to monitor changes in the regional carbon cycle solely based on observations. The chemical transport model (CTM) has been used to identify for the drivers of atmospheric CO 2 variations by separating the influences of regional sources and sinks on CO 2 variations [ 19 , 24 , 25 ]. Using CTM simulations, Fu et al [ 25 ] estimated that terrestrial CO 2 flux and fossil fuel CO 2 emissions account for up to 14 and 17 % of the interannual variations of atmospheric CO 2 over East Asia, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The chemical transport model (CTM) has been used to identify for the drivers of atmospheric CO 2 variations by separating the influences of regional sources and sinks on CO 2 variations [ 19 , 24 , 25 ]. Using CTM simulations, Fu et al [ 25 ] estimated that terrestrial CO 2 flux and fossil fuel CO 2 emissions account for up to 14 and 17 % of the interannual variations of atmospheric CO 2 over East Asia, respectively. Yun et al [ 19 ] showed that the observed increasing seasonal difference in atmospheric CO 2 in South Korea results from enhanced terrestrial carbon uptake.…”

Section: Introductionmentioning

confidence: 99%

Contributions of economic growth, terrestrial sinks, and atmospheric transport to the increasing atmospheric CO2 concentrations over the Korean Peninsula

Yun

Jeong

2021

Carbon Balance Manage

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Background Understanding a carbon budget from a national perspective is essential for establishing effective plans to reduce atmospheric CO2 growth. The national characteristics of carbon budgets are reflected in atmospheric CO2 variations; however, separating regional influences on atmospheric signals is challenging owing to atmospheric CO2 transport. Therefore, in this study, we examined the characteristics of atmospheric CO2 variations over South and North Korea during 2000–2016 and unveiled the causes of their regional differences in the increasing rate of atmospheric CO2 concentrations by utilizing atmospheric transport modeling. Results The atmospheric CO2 concentration in South Korea is rising by 2.32 ppm year− 1, which is more than the globally-averaged increase rate of 2.05 ppm year− 1. Atmospheric transport modeling indicates that the increase in domestic fossil energy supply to support manufacturing export-led economic growth leads to an increase of 0.12 ppm year− 1 in atmospheric CO2 in South Korea. Although enhancements of terrestrial carbon uptake estimated from both inverse modeling and process-based models have decreased atmospheric CO2 by up to 0.02 ppm year− 1, this decrease is insufficient to offset anthropogenic CO2 increases. Meanwhile, atmospheric CO2 in North Korea is also increasing by 2.23 ppm year− 1, despite a decrease in national CO2 emissions close to carbon neutrality. The great increases estimated in both South Korea and North Korea are associated with changes in atmospheric transport, including increasing emitted and transported CO2 from China, which have increased the national atmospheric CO2 concentrations by 2.23 ppm year− 1 and 2.27 ppm year− 1, respectively. Conclusions This study discovered that economic activity is the determinant of regional differences in increasing atmospheric CO2 in the Korea Peninsula. However, from a global perspective, changes in transported CO2 are a major driver of rising atmospheric CO2 over this region, yielding an increase rate higher than the global mean value. Our findings suggest that accurately separating the contributions of atmospheric transport and regional sources to the increasing atmospheric CO2 concentrations is important for developing effective strategies to achieve carbon neutrality at the national level.

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Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia

Cited by 15 publications

References 52 publications

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Analysis of CO<sub>2</sub> spatiotemporal variations in China using tower data and a weather-biosphere-online-coupled model, WRF-VPRM

Contributions of economic growth, terrestrial sinks, and atmospheric transport to the increasing atmospheric CO2 concentrations over the Korean Peninsula

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Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia

Cited by 15 publications

References 52 publications

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

Analysis of CO&lt;sub&gt;2&lt;/sub&gt; spatiotemporal variations in China using tower data and a weather-biosphere-online-coupled model, WRF-VPRM

Contributions of economic growth, terrestrial sinks, and atmospheric transport to the increasing atmospheric CO2 concentrations over the Korean Peninsula

Contact Info

Product

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Analysis of CO<sub>2</sub> spatiotemporal variations in China using tower data and a weather-biosphere-online-coupled model, WRF-VPRM