Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Hwang, Young Hwan; Schlüter, Stephan; Choudhury, Tanupriya; Um, Jung‐Sup

doi:10.3390/su13126700

Cited by 6 publications

(3 citation statements)

References 49 publications

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“…Atmospheric inverse models are also being used to constrain anthropogenic CO 2 emissions and removals (Chevallier, 2021; Z. Deng et al., 2021; Hwang et al., 2021; Petrescu et al., 2021). On regional scales, estimates of CO 2 emissions and removals derived from atmospheric measurements of XCO 2 are not as source specific as the traditional bottom‐up statistical methods used to compile national inventories, which infer CO 2 emissions from fuel use (e.g., Andrew, 2020), land use change (e.g., Houghton & Nassikas, 2017) and other human activities.…”

Section: The Atmospheric Carbon Cyclementioning

confidence: 99%

How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle?

Crisp

Dolman

Tanhua

et al. 2022

Reviews of Geophysics

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Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid‐ and high‐latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship‐based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean‐atmosphere fluxes and interior ocean carbon storage on policy‐relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management.

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Section: The Atmospheric Carbon Cyclementioning

confidence: 99%

How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle?

Crisp

Dolman

Tanhua

et al. 2022

Reviews of Geophysics

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show abstract

“…Atmospheric inverse models are also being used to constrain anthropogenic CO 2 emissions and removals (Chevallier, 2021; Z. Deng et al, 2021;Hwang et al, 2021;Petrescu et al, 2021). On regional scales, estimates of CO 2 emissions and removals derived from atmospheric measurements of XCO 2 are not as source specific as the traditional bottom-up statistical methods used to compile national inventories, which infer CO 2 emissions from fuel use (e.g., Andrew, 2020), land use change (e.g., Houghton & Nassikas, 2017) and other human activities.…”

Section: Constraining Regional-scale Co 2 Sources and Sinks With Atmo...mentioning

confidence: 99%

How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle?

Crisp

Dolman

Tanhua

et al. 2021

Preprint

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Since the beginning of the industrial age, human activities have increased the atmospheric concentrations of carbon dioxide (CO 2 ) and other greenhouse gases (GHGs) to levels never before seen in human history. These large increases are driving climate change because CO 2 is an efficient greenhouse gas with atmospheric residence times spanning years to millennia (see Box 6.1 of Ciais et al. [2013]). Bottom-up statistical inventories indicate that fossil fuel combustion, industry, agriculture, forestry, and other human activities are now adding more than 11.5 Pg of carbon (Pg C) to the atmosphere each year (Friedlingstein et al., 2019(Friedlingstein et al., , 2020(Friedlingstein et al., , 2021. Direct measurements of CO 2 in the atmosphere and in air bubbles in ice cores (Etheridge et al., 1996) indicate that human activities have increased the globally averaged atmospheric CO 2 dry air mole fraction from less than 277 parts

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“…With a lifetime of hundreds of years and well-known sources, CO 2 essentially disperse after being emitted, and reliable data-product of such atmospheric trace gas can be critical in studies of air pollution dispersion and source apportionment of many atmospheric trace gases [4,5]. In addition, it can potentially provide a second option for evaluating national inventory [6].…”

Section: Introductionmentioning

confidence: 99%

Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level

Custódio

Borrego

Relvas

2022

Toxics

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This study systematically examines the global uncertainties and biases in the carbon dioxide (CO2) mixing ratio provided by the Copernicus Atmosphere Monitoring Service (CAMS). The global greenhouse gas re-analysis (EGG4) data product from the European Centre for Medium-Range Weather Forecasts (ECMWF) was evaluated against ground-based in situ measurements from more than 160 of stations across the world. The evaluation shows that CO2 re-analysis can capture the general features in the tracer distributions, including the CO2 seasonal cycle and its strength at different latitudes, as well as the global CO2 trend. The emissions and natural fluxes of CO2 at the surface are evaluated on a wide range of scales, from diurnal to interannual. The results highlight re-analysis compliance, reproducing biogenic fluxes as well the observed CO2 patterns in remote environments. CAMS consistently reproduces observations at marine and remote regions with low CO2 fluxes and smooth variability. However, the model’s weaknesses were observed in continental areas, regions with complex sources, transport circulations and large CO2 fluxes. A strong variation in the accuracy and bias are displayed among those stations with different flux profiles, with the largest uncertainties in the continental regions with high CO2 anthropogenic fluxes. Displaying biased estimation and root-mean-square error (RMSE) ranging from values below one ppmv up to 70 ppmv, the results reveal a poor response from re-analysis to high CO2 mixing ratio, showing larger uncertainty of the product in the boundaries where the CAMS system misses solving sharp flux variability. The mismatch at regions with high fluxes of anthropogenic emission indicate large uncertainties in inventories and constrained physical parameterizations in the CO2 at boundary conditions. The current study provides a broad uncertainty assessment for the CAMS CO2 product worldwide, suggesting deficiencies and methods that can be used in the future to overcome failures and uncertainties in regional CO2 mixing ratio and flux estimates.

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Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Cited by 6 publications

References 49 publications

How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle?

How Well Do We Understand the Land‐Ocean‐Atmosphere Carbon Cycle?

How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle?

Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level

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