scholarly journals A regional carbon flux data assimilation system and its preliminary evaluation in East Asia

2014 ◽  
Vol 14 (14) ◽  
pp. 20345-20381
Author(s):  
Z. Peng ◽  
M. Zhang ◽  
X. Kou ◽  
X. Tian ◽  
X. Ma
Keyword(s):  
Carbon Flux ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Preliminary Evaluation ◽  
Co2 Fluxes ◽  
Scaling Factors ◽  
Co2 Concentrations ◽  
Inflation Factor

Abstract. In order to optimize surface CO2 fluxes at finer scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by simultaneously assimilating CO2 concentrations and surface CO2 fluxes into the regional modeling system, CMAQ. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signal-to-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using the ensemble Kalman filter (EnKF) could constrain the CO2 concentrations effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using the ensemble Kalman smoother (EnKS) demonstrated that CFI-CMAQ could in general reproduce true fluxes at finer scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFI-CMAQ performed very well even with a short lag-window (e.g. 3 days).

scholarly journals A regional carbon data assimilation system and its preliminary evaluation in East Asia

2015 ◽  
Vol 15 (2) ◽  
pp. 1087-1104 ◽  
Author(s):  
Z. Peng ◽  
M. Zhang ◽  
X. Kou ◽  
X. Tian ◽  
X. Ma
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Preliminary Evaluation ◽  
Co2 Fluxes ◽  
Scaling Factors ◽  
Multi Scale ◽  
Co2 Concentrations ◽  
Inflation Factor

Abstract. In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signal-to-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using EnKF could constrain the CO2 concentration effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using EnKS demonstrated that CFI-CMAQ could, in general, reproduce true fluxes at grid scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFI-CMAQ performed very well even with a short lag-window (e.g. 3 days).

scholarly journals Measuring Regional Atmospheric CO2 Concentrations in the Lower Troposphere with a Non-Dispersive Infrared Analyzer Mounted on a UAV, Ogata Village, Akita, Japan

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 487 ◽  
Author(s):  
Takashi Chiba ◽  
Yumi Haga ◽  
Makoto Inoue ◽  
Osamu Kiguchi ◽  
Takeshi Nagayoshi ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Lower Troposphere ◽  
Co2 Concentration ◽  
Measuring System ◽  
Japan Meteorological Agency ◽  
Co2 Concentrations ◽  
Monthly Pattern ◽  
Carbon Dioxide Co2 ◽  
Atmospheric Co2 Concentrations

We have developed a simple measuring system prototype that uses an unmanned aerial vehicle (UAV) and a non-dispersive infrared (NDIR) analyzer to detect regional carbon dioxide (CO2) concentrations and obtain vertical CO2 distributions. Here, we report CO2 measurement results for the lower troposphere above Ogata Village, Akita Prefecture, Japan (about 40° N, 140° E, approximately −1 m amsl), obtained with this UAV system. The actual flight observations were conducted at 500, 400, 300, 200, 100, and 10 m above the ground, at least once a month during the daytime from February 2018 to February 2019. The raw CO2 values from the NDIR were calibrated by two different CO2 standard gases and high-purity nitrogen (N2) gas (as a CO2 zero gas; 0 ppm). During the observation period, the maximum CO2 concentration was measured in February 2019 and the minimum in August 2018. In all seasons, CO2 concentrations became higher as the flight altitude was increased. The monthly pattern of observed CO2 changes is similar to that generally observed in the Northern Hemisphere as well as to surface CO2 changes simulated by an atmospheric transport model of the Japan Meteorological Agency. It is highly probable that these changes reflect the vegetation distribution around the study area.

scholarly journals Evaluating a 3-D transport model of atmospheric CO<sub>2</sub> using ground-based, aircraft, and space-borne data

2010 ◽  
Vol 10 (7) ◽  
pp. 18025-18061 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
Y. Yang ◽  
R. M. Yantosca ◽  
S. R. Kawa ◽  
...  
Keyword(s):  
Boundary Layer ◽  
North America ◽  
Seasonal Cycle ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Fluxes ◽  
A Posteriori ◽  
Upper Troposphere ◽  
Co2 Concentrations ◽  
Geographical Regions

Abstract. We evaluate the GEOS-Chem atmospheric transport model (v8-02-01) of CO2 over 2003–2006, driven by GEOS-4 and GEOS-5 meteorology from the NASA Goddard Global Modelling and Assimilation Office, using surface, aircraft and space-borne concentration measurements of CO2. We use an established ensemble Kalman filter to estimate a posteriori biospheric+biomass burning (BS+BB) and oceanic (OC) CO2 fluxes from 22 geographical regions, following the TransCom 3 protocol, using boundary layer CO2 data from a subset of GLOBALVIEW surface sites. Global annual net BS+BB+OC CO2 fluxes over 2004–2006 for GEOS-4 (GEOS-5) meteorology are −4.4±0.9 (−4.2±0.9), −3.9±0.9 (−4.5±0.9), and −5.2±0.9 (−4.9±0.9) Pg C yr−1 , respectively. The regional a posteriori fluxes are broadly consistent in the sign and magnitude of the TransCom-3 study for 1992–1996, but we find larger net sinks over northern and southern continents. We find large departures from our a priori over Europe during summer 2003, over temperate Eurasia during 2004, and over North America during 2005, reflecting an incomplete description of terrestrial carbon dynamics. We find GEOS-4 (GEOS-5) a posteriori CO2 concentrations reproduce the observed surface trend of 1.91–2.43 ppm yr−1, depending on latitude, within 0.15 ppm yr−1 (0.2 ppm yr−1) and the seasonal cycle within 0.2 ppm (0.2 ppm) at all latitudes. We find the a posteriori model reproduces the aircraft vertical profile measurements of CO2 over North America and Siberia generally within 1.5 ppm in the free and upper troposphere but can be biased by up to 4–5 ppm in the boundary layer at the start and end of the growing season. The model has a small negative bias in the free troposphere CO2 trend (1.95–2.19 ppm yr−1) compared to AIRS data which has a trend of 2.21–2.63 ppm yr−1 during 2004–2006, consistent with surface data. Model CO2 concentrations in the upper troposphere, evaluated using CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) aircraft measurements, reproduce the magnitude and phase of the seasonal cycle of CO2 in both hemispheres. We generally find that the GEOS meteorology reproduces much of the observed tropospheric CO2 variability, suggesting that these meteorological fields will help make significant progress in understanding carbon fluxes as more data become available.

scholarly journals Evaluating a 3-D transport model of atmospheric CO<sub>2</sub> using ground-based, aircraft, and space-borne data

2011 ◽  
Vol 11 (6) ◽  
pp. 2789-2803 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
Y. Yang ◽  
R. M. Yantosca ◽  
S. R. Kawa ◽  
...  
Keyword(s):  
Boundary Layer ◽  
North America ◽  
Seasonal Cycle ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Fluxes ◽  
A Posteriori ◽  
Upper Troposphere ◽  
Co2 Concentrations ◽  
Geographical Regions

Abstract. We evaluate the GEOS-Chem atmospheric transport model (v8-02-01) of CO2 over 2003–2006, driven by GEOS-4 and GEOS-5 meteorology from the NASA Goddard Global Modeling and Assimilation Office, using surface, aircraft and space-borne concentration measurements of CO2. We use an established ensemble Kalman Filter to estimate a posteriori biospheric+biomass burning (BS + BB) and oceanic (OC) CO2 fluxes from 22 geographical regions, following the TransCom-3 protocol, using boundary layer CO2 data from a subset of GLOBALVIEW surface sites. Global annual net BS + BB + OC CO2 fluxes over 2004–2006 for GEOS-4 (GEOS-5) meteorology are −4.4 ± 0.9 (−4.2 ± 0.9), −3.9 ± 0.9 (−4.5 ± 0.9), and −5.2 ± 0.9 (−4.9 ± 0.9) PgC yr−1, respectively. After taking into account anthropogenic fossil fuel and bio-fuel emissions, the global annual net CO2 emissions for 2004–2006 are estimated to be 4.0 ± 0.9 (4.2 ± 0.9), 4.8 ± 0.9 (4.2 ± 0.9), and 3.8 ± 0.9 (4.1 ± 0.9) PgC yr−1, respectively. The estimated 3-yr total net emission for GEOS-4 (GEOS-5) meteorology is equal to 12.5 (12.4) PgC, agreeing with other recent top-down estimates (12–13 PgC). The regional a posteriori fluxes are broadly consistent in the sign and magnitude of the TransCom-3 study for 1992–1996, but we find larger net sinks over northern and southern continents. We find large departures from our a priori over Europe during summer 2003, over temperate Eurasia during 2004, and over North America during 2005, reflecting an incomplete description of terrestrial carbon dynamics. We find GEOS-4 (GEOS-5) a posteriori CO2 concentrations reproduce the observed surface trend of 1.91–2.43 ppm yr−1 (parts per million per year), depending on latitude, within 0.15 ppm yr−1 (0.2 ppm yr−1) and the seasonal cycle within 0.2 ppm (0.2 ppm) at all latitudes. We find the a posteriori model reproduces the aircraft vertical profile measurements of CO2 over North America and Siberia generally within 1.5 ppm in the free and upper troposphere but can be biased by up to 4–5 ppm in the boundary layer at the start and end of the growing season. The model has a small negative bias in the free troposphere CO2 trend (1.95–2.19 ppm yr−1) compared to AIRS data which has a trend of 2.21–2.63 ppm yr−1 during 2004–2006, consistent with surface data. Model CO2 concentrations in the upper troposphere, evaluated using CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) aircraft measurements, reproduce the magnitude and phase of the seasonal cycle of CO2 in both hemispheres. We generally find that the GEOS meteorology reproduces much of the observed tropospheric CO2 variability, suggesting that these meteorological fields will help make significant progress in understanding carbon fluxes as more data become available.

scholarly journals Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Kai Wu ◽  
Thomas Lauvaux ◽  
Kenneth J. Davis ◽  
Aijun Deng ◽  
Israel Lopez Coto ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Environment ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Atmospheric Transport ◽  
System Simulation ◽  
Co2 Flux ◽  
Point Sources ◽  
Co2 Fluxes ◽  
The Impact

The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 µmol m–2 s–1 compared to the spatially averaged anthropogenic CO2 emissions of 5 µmol m–2 s–1. The presence of biogenic CO2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with 1 ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates.

scholarly journals Global to local impacts on atmospheric CO2 caused by COVID-19 lockdown

2021 ◽  
Author(s):  
Ning Zeng
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Monitoring Systems ◽  
Carbon Cycle Model ◽  
Safe Level ◽  
Small Change

&lt;p&gt;&lt;span&gt;The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction of fossil fuel CO2 emissions 1,2, but it is unclear how much it would reduce atmospheric CO2 concentration, the main driver of climate change, and whether it can be observed. We estimated that a 7.9% reduction in emissions for 4 months would result in a 0.25 ppm decrease in the Northern Hemisphere CO2, an increment that is within the capability of current CO2 analyzers, but is a few times smaller than natural CO2 variabilities caused by weather and the biosphere such as El Nino. We used a state-of-the-art atmospheric transport model to simulate CO2, driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show a 0.13 ppm decrease in atmospheric column CO2 anomaly averaged over 50S-50N for the period February-April 2020 relative to a 10-year climatology. A similar decrease was observed by the carbon satellite GOSAT3. Using model sensitivity experiments, we further found that COVID, the biosphere and weather contributed 54%, 23%, and 23% respectively. In May 2020, the CO2 anomaly continued to decrease and was 0.36 ppm below climatology, mostly due to the COVID reduction and a biosphere that turned from a relative carbon source to carbon sink, while weather impact fluctuated. This seemingly small change stands out as the largest sub-annual anomaly in the last 10 years. Measurements from global ground stations were analyzed. At city scale, on-road CO2 enhancement measured in Beijing shows reduction of 20-30 ppm, consistent with drastically reduced traffic during the lockdown, while station data suggest that the expected COVID signal of 5-10 ppm was swamped by weather-driven variability on multi-day time scales. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signal on the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment whose impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy and expanded spatiotemporal coverage of our monitoring systems.&lt;/span&gt;&lt;/p&gt;

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Estimating surface CO<sub>2</sub> fluxes from space-borne CO<sub>2</sub> dry air mole fraction observations using an ensemble Kalman Filter

2009 ◽  
Vol 9 (8) ◽  
pp. 2619-2633 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
H. Bösch ◽  
S. Dance
Keyword(s):  
Kalman Filter ◽  
Mole Fraction ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Co2 Flux ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Co2 Fluxes ◽  
Dry Air ◽  
Geographical Regions

Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day lag window, recognising that XCO2 measurements include surface flux information from prior time windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths <0.3; and b) scene-dependent averaging kernels that relate the CO2 profiles to XCO2, accounting for differences between nadir and glint measurements, and the associated scene-dependent observation errors. We show that OCO XCO2 measurements significantly reduce the uncertainties of surface CO2 flux estimates. Glint measurements are generally better at constraining ocean CO2 flux estimates. Nadir XCO2 measurements over the terrestrial tropics are sparse throughout the year because of either clouds or smoke. Glint measurements provide the most effective constraint for estimating tropical terrestrial CO2 fluxes by accurately sampling fresh continental outflow over neighbouring oceans. We also present results from sensitivity experiments that investigate how flux estimates change with 1) bias and unbiased errors, 2) alternative duty cycles, 3) measurement density and correlations, 4) the spatial resolution of estimated flux estimates, and 5) reducing the length of the lag window and the size of the ensemble. At the revision stage of this manuscript, the OCO instrument failed to reach its orbit after it was launched on 24 February 2009. The EnKF formulation presented here is also applicable to GOSAT measurements of CO2 and CH4.

scholarly journals O<sub>2</sub> : CO<sub>2</sub> exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO<sub>2</sub> emissions

2020 ◽  
Vol 20 (9) ◽  
pp. 5293-5308
Author(s):  
Shigeyuki Ishidoya ◽  
Hirofumi Sugawara ◽  
Yukio Terao ◽  
Naoki Kaneyasu ◽  
Nobuyuki Aoki ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Urban Area ◽  
Co2 Emission ◽  
Co2 Flux ◽  
Co2 Exchange ◽  
Co2 Fluxes ◽  
Diurnal Cycles ◽  
Co2 Concentrations ◽  
Human Respiration ◽  
Two Peaks

Abstract. In order to examine O2 consumption and CO2 emission in a megacity, continuous observations of atmospheric O2 and CO2 concentrations, along with CO2 flux, have been carried out simultaneously since March 2016 at the Yoyogi (YYG) site located in the middle of Tokyo, Japan. An average O2 : CO2 exchange ratio for net turbulent O2 and CO2 fluxes (ORF) between the urban area and the overlaying atmosphere was obtained based on an aerodynamic method using the observed O2 and CO2 concentrations. The yearly mean ORF was found to be 1.62, falling within the range of the average OR values of liquid and gas fuels, and the annual average daily mean O2 flux at YYG was estimated to be −16.3 µmol m−2 s−1 based on the ORF and CO2 flux. By using the observed ORF and CO2 flux, along with the inventory-based CO2 emission from human respiration, we estimated the average diurnal cycles of CO2 fluxes from gas and liquid fuel consumption separately for each season. Both the estimated and inventory-based CO2 fluxes from gas fuel consumption showed average diurnal cycles with two peaks, one in the morning and another one in the evening; however, the evening peak of the inventory-based gas consumption was much larger than that estimated from the CO2 flux. This can explain the discrepancy between the observed and inventory-based total CO2 fluxes at YYG. Therefore, simultaneous observations of ORF and CO2 flux are useful in validating CO2 emission inventories from statistical data.

scholarly journals Sensitivity of simulated CO<sub>2</sub> concentration to sub-annual variations in fossil fuel CO<sub>2</sub> emissions

2016 ◽  
Vol 16 (4) ◽  
pp. 1907-1918 ◽  
Author(s):  
Xia Zhang ◽  
Kevin R. Gurney ◽  
Peter Rayner ◽  
David Baker ◽  
Yu-ping Liu
Keyword(s):  
Fossil Fuel ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Vertical Mixing ◽  
Grid Cell ◽  
Co2 Concentration ◽  
Tracer Transport ◽  
Annual Variations ◽  
Atmospheric Co2 Concentrations ◽  
The Impact

Abstract. Recent advances in fossil fuel CO2 (FFCO2) emission inventories enable sensitivity tests of simulated atmospheric CO2 concentrations to sub-annual variations in FFCO2 emissions and what this implies for the interpretation of observed CO2. Six experiments are conducted to investigate the potential impact of three cycles of FFCO2 emission variability (diurnal, weekly and monthly) using a global tracer transport model. Results show an annual FFCO2 rectification varying from −1.35 to +0.13 ppm from the combination of all three cycles. This rectification is driven by a large negative diurnal FFCO2 rectification due to the covariation of diurnal FFCO2 emissions and diurnal vertical mixing, as well as a smaller positive seasonal FFCO2 rectification driven by the covariation of monthly FFCO2 emissions and monthly atmospheric transport. The diurnal FFCO2 emissions are responsible for a diurnal FFCO2 concentration amplitude of up to 9.12 ppm at the grid cell scale. Similarly, the monthly FFCO2 emissions are responsible for a simulated seasonal CO2 amplitude of up to 6.11 ppm at the grid cell scale. The impact of the diurnal FFCO2 emissions, when only sampled in the local afternoon, is also important, causing an increase of +1.13 ppmv at the grid cell scale. The simulated CO2 concentration impacts from the diurnally and seasonally varying FFCO2 emissions are centered over large source regions in the Northern Hemisphere, extending to downwind regions. This study demonstrates the influence of sub-annual variations in FFCO2 emissions on simulated CO2 concentration and suggests that inversion studies must take account of these variations in the affected regions.

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