World Data Centre For Greenhouse Gases Research Articles

Estimating ground-level CH4 concentrations inferred from Sentinel-5P

ABSTRACT Methane (CH4) is an important greenhouse gas; however, there is a lack of large-scale studies on ground-level CH4 concentrations. We estimated global ground-level CH4 concentrations based on the CH4 columns from the Copernicus Sentinel-5 precursor satellite (S5P) and vertical profiles of CH4 concentrations simulated from the Atmospheric Chemical Transport Model (GEOS-Chem). The proposed approach had achieved a high predictive accuracy for monthly ground-level CH4 concentrations, with a correlation coefficient of 0.93 (p < 0.01) and RMSE of 29.93 ppb between the estimated CH4 concentrations and those of ground measurements from the World Data Centre for Greenhouse Gases (WDCGG). Compared with the S5P CH4 columns, the estimated ground-level CH4 concentration has a close spatial relationship with emissions. The high CH4 concentrations occurred in eastern China, northern India, western Russia, eastern U.S., and central Europe. Furthermore, the estimated ground-level CH4 concentrations could reflect the seasonal variations of the observations, with correlation coefficients from 0.14 to 0.92. Our findings highlight the importance of satellite observations on atmospheric CH4 in understanding the spatial and temporal emissions.

Spatiotemporal variability of near-surface CO2 and its affecting factors over Mongolia

We investigate the spatiotemporal variability of near-surface CO2 concentrations in Mongolia from 2010 to 2019 and the factors affecting it over four climate zones of Mongolia based on the Köppen-Geiger climate classification system, including arid desert climate (BWh), arid steppe climate (BSk), dry climate (Dw), and polar frost climate (ET). Initially, we validate the near-surface CO2 datasets obtained from the Greenhouse Gases Observing Satellite (GOSAT) using ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) and found good agreement. The results showed that CO2 concentrations over Mongolia steadily increased from 389.48 ppmv in 2010 to 409.72 ppmv in 2019, with an annual growth rate of 2.24 ppmv/year. Spatially, the southeastern Gobi desert region has the highest annual average CO2 concentration, while the northwestern Alpine and Meadow steppe region exhibits the most significant growth rate. Additionally, significant monthly and seasonal variations were observed in each climate zone, with CO2 levels decreasing to a minimum in summer and reaching a maximum in spring. Furthermore, our findings revealed a negative correlation between CO2 concentrations and vegetation parameters (NDVI, GPP, and LAI) during summer when photosynthesis is at its peak, while a positive correlation was observed during spring and autumn when the capacity for carbon sequestration is lower. Understanding CO2 concentrations in different climate zones and the uptake capacity of vegetation may help improve estimates of carbon sequestration in ecosystems such as deserts, steppes and forests.

Accuracy Verification of Satellite Products and Temporal and Spatial Distribution Analysis and Prediction of the CH4 Concentration in China

In this study, the spatiotemporal variations in CH4 concentrations in China from 2003 to 2021 are investigated, and their trends are forecasted over the next decade. Based on the seventh edition standard product retrieved by the atmospheric infrared detector (AIRS) at an altitude of 500 hPa, we verified monthly CH4 products using observational data provided by the World Data Center for Greenhouse Gases (WDCGG) from six ground stations in and around China. The correlation coefficients (R values) between the two data sets ranged from 0.68 to 0.92, signifying the ability of AIRS inversion data to represent temporal and spatial changes in surface CH4 concentrations. Additionally, China was classified into three regions (steps) based on terrain, and the changes in CH4 concentrations were assessed from three perspectives: spatial distribution, interannual variation, and seasonal variation. The results revealed that the CH4 concentration decreased with elevation along a topographic gradient, with high-value areas located in the first and second steps, corresponding to the eastern Qinghai–Tibet Plateau, northern Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region. Over 19 years, the average increase in CH4 concentration has ranged from 65 to 175 ppb. In addition, the CH4 concentrations were higher during summer and autumn and lower during spring and winter. Finally, a SARIMA model was used to predict the near-surface CH4 concentration trend in China over the next ten years, which indicated a continued seasonal increase.

Spatiotemporal Analysis of XCO2 and Its Relationship to Urban and Green Areas of China’s Major Southern Cities from Remote Sensing and WRF-Chem Modeling Data from 2010 to 2019

Monitoring CO2 concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate for the sparse and uneven spatial distribution of ground observation stations, allowing for the collection of a wide range of CO2 concentration data. However, satellite monitoring’s spatial coverage remains limited. This study fills the knowledge gaps of column-averaged dry-air mole fraction of CO2 (XCO2) products retrieved from the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory Satellite (OCO-2) based on the normalized output of atmospheric chemical models, WRF-Chem, in Southern China during 2010–2019. Hefei (HF)/Total Carbon Column Observing Network (TCCON), Lulin (LLN)/World Data Centre for Greenhouse Gases (WDCGG) station observations were used to validate the results of void filling with an acceptable accuracy for spatiotemporal analysis (R = 0.96, R2 = 0.92, RMSE = 2.44 ppm). Compared to the IDW (inverse distance weighting) and Kriging (ordinary Kriging) interpolation methods, this method has a higher validation accuracy. In addition, spatiotemporal distributions of CO2, as well as the sensitivity of CO2 concentration to the urban built-up areas and urban green space areas in China’s major southern cities during 2010–2019, are discussed. The approximate annual average concentrations have gradually increased from 388.56 to 414.72 ppm, with an annual growth rate of 6.73%, and the seasonal cycle presents a maximum in spring and a minimum in summer or autumn from 2010 to 2019. CO2 concentrations have a strong positive correlation with the impervious area to city area ratio, while anomaly values of the impervious area to urban green area ratio occurred in individual cities. The experimental findings demonstrate the viability of the study hypothesis that combines remote sensing data with the WRF-Chem model to produce a local area dataset with high spatial resolution and an extracted urban unit from statistical data.

A long-term global XCO2 dataset: Ensemble of satellite products

Many satellites such as SCIAMACHY (scanning imaging absorption spectrometer for atmospheric cartography), GOSAT-1/2 (greenhouse gases observing satellite), OCO-2/3 (orbiting carbon observatory) and TanSat (CarbonSat, Tan means “carbon” in Chinese) provide key observations of atmospheric CO2 concentration and different XCO2 retrieval algorithms have been developed for these satellite measurements. However, limited by the revisit period and scanning swath of the satellites, the effective daily observation coverage of satellites mentioned above is very small (<1%), which is a great challenge for high-resolution mapping of global XCO2. In this paper, we re-evaluated the uncertainty of each XCO2 pixel with 0.5° × 0.5° spatial resolution in the global based on season, AOD (aerosol optical depth), surface albedo, uncertainty parameters of SCIAMACHY, GOSAT-1/2, OCO-2/3 and TanSat XCO2 products by TCCON (Total Carbon Column Observing Network) data. Then, a 30-day time window was used to smooth XCO2 datasets from satellite observations and filled in the missing values according to CarbonTracker. Finally, we ensembled global XCO2 datasets using maximum likelihood estimation (MLE) method and optimal interpolation (OI) based on the re-evaluated uncertainty. The ensemble XCO2 dataset at 0.5° × 0.5° spatial resolution for every three hours from January 2003 to August 2020 was generated. Compared to TCCON and WDCGG (World Data Centre for Greenhouse Gases) measurements separately on the global scale, we obtained the correlation coefficient R = 0.96, the Root Mean Square Error (RMSE) of 2.62 ppm for TCCON and R = 0.82, the RMSE of 6.75 ppm for WDCGG, respectively. The RMSE of the ensemble dataset was 6 ppm lower than that of the satellites' XCO2 used for fusion compared with WDCGG and the coverage is greatly improved at a higher spatial and temporal resolution, proving the feasibility and high precision of the method.

Spatiotemporal Investigation of Near-Surface CO2 and Its Affecting Factors Over Asia

In this work, we extracted the near-surface CO2 concentration from the Greenhous gases Observing SATellite (GOSAT) and the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker model datasets for a temporal period of 8 years from 2010 to 2017 to study the spatiotemporal distribution of near-surface CO2 and the factors affecting it over five regions of Asia including Central Asia, East Asia, South Asia, Southeast Asia, and West Asia. The near-surface CO2 datasets from both satellite and model were first validated against the ground-based CO2 observations obtained from the World Data Center for Greenhouse Gases (WDCGG) stations located in Asia to confirm their applicability and the results showed a good agreement between the datasets with significant correlations. The results from the time-series analyses showed a gradual increase in the near-surface CO2 with significant monthly and seasonal variations over all the regions. To study the factors affecting the spatial distribution of near-surface CO2, we investigated the relationship of near-surface CO2 with the anthropogenic CO2 emissions, terrestrial ecosystem, and winds. The results showed that over Asia, the anthropogenic CO2 emissions and winds primarily controlled the spatial distribution of near-surface CO2. However, in the areas where anthropogenic emissions were lower, the terrestrial ecosystem and winds affected the near- surface CO2 distribution. To study the factors controlling the temporal distribution of near-surface CO2, the relationship of near-surface CO2 with vegetation, precipitation, and relative humidity was investigated. The results showed an inverse relationship between near-surface CO2 and NDVI, precipitation, and relative humidity over monsoon-influenced regions, i.e., East Asia, South Asia, and Southeast Asia. However, a positive relation of near-surface CO2 was observed with precipitation and relative humidity over arid and semi-arid regions, i.e., Central Asia and West Asia. The results were also verified by determining the correlations among these variables.

Applicability of Machine Learning Model to Simulate Atmospheric CO₂ Variability

Carbon dioxide (CO₂) is the most important greenhouse gas influencing the Earth&#x2019;s climate; therefore, accurate modeling of its variability has paramount significance. In this regard, we have performed the simulation of CO₂ residue (i.e., detrended depersonalized CO₂) based on input of meteorological parameters (temperature, humidity, pressure, and wind), El Ni&#x00F1;o index, sea surface temperature, and normalized difference vegetation index in a machine learning (ML) model. Long-term observations available from the World Data Centre for Greenhouse Gases (WDCGG) and the National Oceanic and Atmospheric Administration (NOAA) have been used for training and validation of ML model. The model successfully reproduced 72&#x0025; of observed variability in CO₂ residue with an error of 0.45 ppmv over Mauna Loa (19.54&#x00B0;N; &#x2212;155.58&#x00B0;E). The cumulative temperature anomaly is found to play a key role in the simulation of CO₂ residue over Mauna Loa. Evaluation reveals a reasonably good agreement between modeled and observed CO₂ residue (<inline-formula> <tex-math notation="LaTeX">$R^{2} = 0.20$ </tex-math></inline-formula>&#x2013;0.55 and root-mean-square error (RMSE) &#x003D; 20&#x0025;&#x2013;60&#x0025;) over regional sites and for global mean CO₂. However, the model shows a limitation in capturing spikes likely caused by strong local influences. Inclusion of additional input parameters, representing local anthropogenic influences, is recommended to further improve the model performance over regional sites. Our study demonstrates the potential of ML modeling for the simulations of CO₂ variability to complement the computationally expensive climate models.

Increasing the maturity of measurements of essential climate variables (ECVs) at Italian atmospheric WMO/GAW observatories by implementing automated data elaboration chains

In the framework of the National Project of Interest NextData, we developed automatic procedures for the flagging and formatting of trace gases, atmospheric aerosols and meteorological data to be submitted to the World Data Centers (WDCs) of the Global Atmosphere Watch program of the World Meteorological Organization (WMO/GAW). In particular, the atmospheric Essential Climate Variables (ECVs) covered in this work are observations of near-surface trace gas concentrations, aerosol properties and meteorological variables, which are under the umbrella of the World Data Center for Greenhouse Gases (WDCGG), the World Data Center for Reactive Gases, and the World Data Center for Aerosol (WDCRG and WDCA). We developed an overarching processing chain to create a number of data products (data files and reports) starting from the raw data, finally contributing to increase the maturity of these measurements. To this aim, we implemented specific routines for data filtering, flagging, format harmonization, and creation of data products, useful for detecting instrumental problems, particular atmospheric events and quick data dissemination towards stakeholders or citizens. Currently, the automatic data processing is active for a subset of ECVs at 5 measurement sites in Italy. The system represents a valuable tool to facilitate data originators towards a more efficient data production. Our effort is expected to accelerate the process of data submission to WMO/GAW or to other reference data centers or repositories. Moreover, the adoption of automatic procedures for data flagging and data correction allows to keep track of the process that led to the final validated data, and makes data evaluation and revisions more efficient by improving the traceability of the data production process.

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

The study on how the variations in CO2 sources and sinks can affect the CO2 concentration over East Asia would be useful to provide information for policymaker concerning carbon emission reduction. In this study, a nested-grid version of global chemical transport model (GEOS-Chem) is employed to assess the impacts of variations in meteorological parameters, terrestrial fluxes, fossil fuel emissions, and biomass burning on inter-annual variations of CO2 concentrations over East Asia in 2004–2012. Simulated CO2 concentrations are compared with observations at 14 surface stations from the World Data Centre for Greenhouse Gases (WDCGG) and satellite-derived CO2 column density (XCO2) from the Gases Observing SATellite (GOSAT). The comparison shows that the simulated CO2 column density is generally higher than that of GOSAT by 1.33 × 10−6 (annual mean point by point biases averaged over East Asia). The model reasonably captures the temporal variations of CO2 concentrations observed at the ground-based stations, but it is likely to underestimate the peaks-to-troughs amplitude of the seasonal cycle by 50% or more. The simulated surface CO2 concentration in East Asia exhibits the largest inter-annual variation in December-January–February (DJF). The regional mean absolute deviation (MAD) values over East Asia are within (4.4–5.0) × 10−6 for all seasons. Model sensitivity simulations indicate that the inter-annual variations of surface CO2 concentrations are mainly driven by variations of meteorological parameters, and partly modulated by the inter-annual variations of terrestrial fluxes and fossil fuel emissions in local regions. The variations of the terrestrial fluxes and fossil fuel emissions may account for ∼28% of the inter-annual variation of surface CO2 concentration in southern China. The inter-annual variations of the peaks-to-troughs amplitude are dependent on variations of meteorological parameters, terrestrial fluxes and fossil fuel emissions in local regions. The influence of biomass burning emissions is relatively weak.

Assessing Lagrangian inverse modelling of urban anthropogenic CO2 fluxes using in situ aircraft and ground-based measurements in the Tokyo area

BackgroundIn order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG).ResultsWe produce gridded estimates of the CO2 emissions and calculate the averages for different areas within the Kanto plain where Tokyo is located. Using these inversions as reference we investigate the impact of perturbing different elements in the inversion system. We modified the observations amount and location (surface only sparse vs. including aircraft CO2 observations), the background representation, the wind data used to drive the transport model, the prior emissions magnitude and time resolution and error parameters of the inverse model.ConclusionsOptimized fluxes were consistent with other estimates for the unperturbed simulations. Inclusion of CONTRAIL measurements resulted in significant differences in the magnitude of the retrieved fluxes, 13% on average for the whole domain and of up to 21% for the spatiotemporal cells with the highest fluxes. Changes in the background yielded differences in the retrieved fluxes of up to 50% and more. Simulated biases in the modelled transport cause differences in the retrieved fluxes of up to 30% similar to those obtained using different meteorological winds to advect the Lagrangian trajectories. Perturbations to the prior inventory can impact the fluxes by ~ 10% or more depending on the assumptions on the error covariances. All of these factors can cause significant differences in the estimated flux, and highlight the challenges in estimating regional CO2 fluxes from atmospheric observations.

Long-Term Trends of Atmospheric CH4 Concentration across China from 2002 to 2016

Spatiotemporal variations of atmospheric CH4 from 2002 to 2016 across China were detected, based on the Atmospheric Infrared Sounder (AIRS) sixth-layer CH4 concentration. The CH4 concentration showed good consistency with the ground measurements of surface CH4 concentration from the World Data Centre for Greenhouse Gases (WDCGG) (R2 = 0.83, p &lt; 0.01), indicating that the remotely-sensed CH4 reflected the spatial and temporal variations of surface CH4 concentration. Across China, three hotspots of CH4 concentration were found in northern Xinjiang, the northeast of Inner Mongolia/Heilongjiang, and the Norgay plateau in northwest Sichuan. The CH4 concentration showed obviously seasonal variations, with the maximum CH4 concentration occurring in summer, followed by the autumn, winter, and spring. Furthermore, the CH4 concentration showed significantly increasing trends across China, with the rate of increase ranging from ~0.29 to 0.62 ppb·month−1, which would bring a 0.0019~0.014 mK potential rise in surface temperature response over China. In particular, the most rapidly increasing rates occurred in the Qinghai-Tibet plateau, while relatively low rates occurred in southeast China.

Three-dimensional methane distribution simulated with FLEXPART 8-CTM-1.1 constrained with observation data

Abstract. A Lagrangian particle dispersion model, the FLEXible PARTicle dispersion chemical transport model (FLEXPART CTM), is used to simulate global three-dimensional fields of trace gas abundance. These fields are constrained with surface observation data through nudging, a data assimilation method, which relaxes model fields to observed values. Such fields are of interest to a variety of applications, such as inverse modelling, satellite retrievals, radiative forcing models and estimating global growth rates of greenhouse gases. Here, we apply this method to methane using 6 million model particles filling the global model domain. For each particle, methane mass tendencies due to emissions (based on several inventories) and loss by reaction with OH, Cl and O(1D), as well as observation data nudging were calculated. Model particles were transported by mean, turbulent and convective transport driven by 1∘×1∘ ERA-Interim meteorology. Nudging is applied at 79 surface stations, which are mostly included in the World Data Centre for Greenhouse Gases (WDCGG) database or the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION) in Siberia. For simulations of 1 year (2013), we perform a sensitivity analysis to show how nudging settings affect modelled concentration fields. These are evaluated with a set of independent surface observations and with vertical profiles in North America from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL), and in Siberia from the Airborne Extensive Regional Observations in SIBeria (YAK-AEROSIB) and the National Institute for Environmental Studies (NIES). FLEXPART CTM results are also compared to simulations from the global Eulerian chemistry Transport Model version 5 (TM5) based on optimized fluxes. Results show that nudging strongly improves modelled methane near the surface, not only at the nudging locations but also at independent stations. Mean bias at all surface locations could be reduced from over 20 to less than 5 ppb through nudging. Near the surface, FLEXPART CTM, including nudging, appears better able to capture methane molar mixing ratios than TM5 with optimized fluxes, based on a larger bias of over 13 ppb in TM5 simulations. The vertical profiles indicate that nudging affects model methane at high altitudes, yet leads to little improvement in the model results there. Averaged from 19 aircraft profile locations in North America and Siberia, root mean square error (RMSE) changes only from 16.3 to 15.7 ppb through nudging, while the mean absolute bias increases from 5.3 to 8.2 ppb. The performance for vertical profiles is thereby similar to TM5 simulations based on TM5 optimized fluxes where we found a bias of 5 ppb and RMSE of 15.9 ppb. With this rather simple model setup, we thus provide three-dimensional methane fields suitable for use as boundary conditions in regional inverse modelling as a priori information for satellite retrievals and for more accurate estimation of mean mixing ratios and growth rates. The method is also applicable to other long-lived trace gases.

CMAQ simulation of atmospheric CO2 concentration in East Asia: Comparison with GOSAT observations and ground measurements

Satellite observations are widely used in global CO2 assimilations, but their quality for use in regional assimilation systems has not yet been thoroughly determined. Validation of satellite observations and model simulations of CO2 is crucial for carbon flux inversions. In this study, we focus on evaluating the uncertainties of model simulations and satellite observations. The atmospheric CO2 distribution in East Asia during 2012 was simulated using a regional chemical transport model (RAMS-CMAQ) and compared with both CO2 column density (XCO2) from the Gases Observing SATellite (GOSAT) and CO2 concentrations from the World Data Centre for Greenhouse Gases (WDCGG). The results indicate that simulated XCO2 is generally lower than GOSAT XCO2 by 1.19 ppm on average, and their monthly differences vary from 0.05 to 2.84 ppm, with the corresponding correlation coefficients ranging between 0.1 and 0.67. CMAQ simulations are good to capture the CO2 variation as ground-based observations, and their correlation coefficients are from 0.62 to 0.93, but the average value of CMAQ simulation is 2.4 ppm higher than ground-based observation. Thus, we inferred that the GOSAT retrievals may overestimate XCO2, which is consistent with the validation of GOSAT XCO2 using Total Carbon Column Observing Network measurements. The near-surface CO2 concentration was obviously overestimated in GOSAT XCO2. Compared with the relatively small difference between CMAQ and GOSAT XCO2, the large difference in CO2 near surface or their vertical profiles indicates more improvements are needed to reduce the uncertainties in both satellite observations and model simulations.

A 15-year record of CO emissions constrained by MOPITT CO observations

Abstract. Long-term measurements from satellites and surface stations have demonstrated a decreasing trend of tropospheric carbon monoxide (CO) in the Northern Hemisphere over the past decade. Likely explanations for this decrease include changes in anthropogenic, fires, and/or biogenic emissions or changes in the primary chemical sink hydroxyl radical (OH). Using remotely sensed CO measurements from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument, in situ methyl chloroform (MCF) measurements from the World Data Centre for Greenhouse Gases (WDCGG) and the adjoint of the GEOS-Chem model, we estimate the change in global CO emissions from 2001 to 2015. We show that the loss rate of MCF varied by 0.2 % in the past 15 years, indicating that changes in global OH distributions do not explain the recent decrease in CO. Our two-step inversion approach for estimating CO emissions is intended to mitigate the effect of bias errors in the MOPITT data as well as model errors in transport and chemistry, which are the primary factors contributing to the uncertainties when quantifying CO emissions using these remotely sensed data. Our results confirm that the decreasing trend of tropospheric CO in the Northern Hemisphere is due to decreasing CO emissions from anthropogenic and biomass burning sources. In particular, we find decreasing CO emissions from the United States and China in the past 15 years, and unchanged anthropogenic CO emissions from Europe since 2008. We find decreasing trends of biomass burning CO emissions from boreal North America, boreal Asia and South America, but little change over Africa. In contrast to prior results, we find that a positive trend in CO emissions is likely for India and southeast Asia.

Global consistency check of AIRS and IASI total CO2 column concentrations using WDCGG ground-based measurements

This article describes a global consistency check of CO2 satellite retrieval products from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) using statistical analysis and data from the World Data Centre for Greenhouse Gases (WDCGG). We use the correlation coefficient (r), relative difference (RD), root mean square errors (RMSE), and mean bias error (MBE) as evaluation indicators for this study. Statistical results show that a linear positive correlation between AIRS/IASI and WDCGG data occurs for most regions around the world. Temporal and spatial variations of these statistical quantities reflect obvious differences between satellite-derived and ground-based data based on geographic position, especially for stations near areas of intense human activities in the Northern Hemisphere. It is noteworthy that there appears to be a very weak correlation between AIRS/IASI data and ten groundbased observation stations in Europe, Asia, and North America. These results indicate that retrieval products from the two satellite-based instruments studied should be used with great caution.

Improved simulation of tropospheric ozone by a global-multi-regional two-way coupling model system

Abstract. Small-scale nonlinear chemical and physical processes over pollution source regions affect the tropospheric ozone (O3), but these processes are not captured by current global chemical transport models (CTMs) and chemistry–climate models that are limited by coarse horizontal resolutions (100–500 km, typically 200 km). These models tend to contain large (and mostly positive) tropospheric O3 biases in the Northern Hemisphere. Here we use the recently built two-way coupling system of the GEOS-Chem CTM to simulate the regional and global tropospheric O3 in 2009. The system couples the global model (at 2.5° long. × 2° lat.) and its three nested models (at 0.667° long. × 0.5° lat.) covering Asia, North America and Europe, respectively. Specifically, the nested models take lateral boundary conditions (LBCs) from the global model, better capture small-scale processes and feed back to modify the global model simulation within the nested domains, with a subsequent effect on their LBCs. Compared to the global model alone, the two-way coupled system better simulates the tropospheric O3 both within and outside the nested domains, as found by evaluation against a suite of ground (1420 sites from the World Data Centre for Greenhouse Gases (WDCGG), the United States National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory Global Monitoring Division (GMD), the Chemical Coordination Centre of European Monitoring and Evaluation Programme (EMEP), and the United States Environmental Protection Agency Air Quality System (AQS)), aircraft (the High-performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) and Measurement of Ozone and Water Vapor by Airbus In- Service Aircraft (MOZAIC)) and satellite measurements (two Ozone Monitoring Instrument (OMI) products). The two-way coupled simulation enhances the correlation in day-to-day variation of afternoon mean surface O3 with the ground measurements from 0.53 to 0.68, and it reduces the mean model bias from 10.8 to 6.7 ppb. Regionally, the coupled system reduces the bias by 4.6 ppb over Europe, 3.9 ppb over North America and 3.1 ppb over other regions. The two-way coupling brings O3 vertical profiles much closer to the HIPPO (for remote areas) and MOZAIC (for polluted regions) data, reducing the tropospheric (0–9 km) mean bias by 3–10 ppb at most MOZAIC sites and by 5.3 ppb for HIPPO profiles. The two-way coupled simulation also reduces the global tropospheric column ozone by 3.0 DU (9.5 %, annual mean), bringing them closer to the OMI data in all seasons. Additionally, the two-way coupled simulation also reduces the global tropospheric mean hydroxyl radical by 5 % with improved estimates of methyl chloroform and methane lifetimes. Simulation improvements are more significant in the Northern Hemisphere, and are mainly driven by improved representation of spatial inhomogeneity in chemistry/emissions. Within the nested domains, the two-way coupled simulation reduces surface ozone biases relative to typical GEOS-Chem one-way nested simulations, due to much improved LBCs. The bias reduction is 1–7 times the bias reduction from the global to the one-way nested simulation. Improving model representations of small-scale processes is important for understanding the global and regional tropospheric chemistry.

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland.

Observations and modeling of air quality trends over 1990–2010 across the Northern Hemisphere: China, the United States and Europe

Abstract. Trends in air quality across the Northern Hemisphere over a 21-year period (1990–2010) were simulated using the Community Multiscale Air Quality (CMAQ) multiscale chemical transport model driven by meteorology from Weather Research and Forecasting (WRF) simulations and internally consistent historical emission inventories obtained from EDGAR. Thorough comparison with several ground observation networks mostly over Europe and North America was conducted to evaluate the model performance as well as the ability of CMAQ to reproduce the observed trends in air quality over the past 2 decades in three regions: eastern China, the continental United States and Europe. The model successfully reproduced the observed decreasing trends in SO2, NO2, 8 h O3 maxima, SO42− and elemental carbon (EC) in the US and Europe. However, the model fails to reproduce the decreasing trends in NO3− in the US, potentially pointing to uncertainties of NH3 emissions. The model failed to capture the 6-year trends of SO2 and NO2 in CN-API (China – Air Pollution Index) from 2005 to 2010, but reproduced the observed pattern of O3 trends shown in three World Data Centre for Greenhouse Gases (WDCGG) sites over eastern Asia. Due to the coarse spatial resolution employed in these calculations, predicted SO2 and NO2 concentrations are underestimated relative to all urban networks, i.e., US-AQS (US – Air Quality System; normalized mean bias (NMB) = −38% and −48%), EU-AIRBASE (European Air quality data Base; NMB = −18 and −54%) and CN-API (NMB = −36 and −68%). Conversely, at the rural network EU-EMEP (European Monitoring and Evaluation Programme), SO2 is overestimated (NMB from 4 to 150%) while NO2 is simulated well (NMB within ±15%) in all seasons. Correlations between simulated and observed O3 wintertime daily 8 h maxima (DM8) are poor compared to other seasons for all networks. Better correlation between simulated and observed SO42− was found compared to that for SO2. Underestimation of summer SO42− in the US may be associated with the uncertainty in precipitation and associated wet scavenging representation in the model. The model exhibits worse performance for NO3− predictions, particularly in summer, due to high uncertainties in the gas/particle partitioning of NO3− as well as seasonal variations of NH3 emissions. There are high correlations (R &gt; 0.5) between observed and simulated EC, although the model underestimates the EC concentration by 65% due to the coarse grid resolution as well as uncertainties in the PM speciation profile associated with EC emissions. The almost linear response seen in the trajectory of modeled O3 changes in eastern China over the past 2 decades suggests that control strategies that focus on combined control of NOx and volatile organic compound (VOC) emissions with a ratio of 0.46 may provide the most effective means for O3 reductions for the region devoid of nonlinear response potentially associated with NOx or VOC limitation resulting from alternate strategies. The response of O3 is more sensitive to changes in NOx emissions in the eastern US because the relative abundance of biogenic VOC emissions tends to reduce the effectiveness of VOC controls. Increasing NH3 levels offset the relative effectiveness of NOx controls in reducing the relative fraction of aerosol NO3− formed from declining NOx emissions in the eastern US, while the control effectiveness was assured by the simultaneous control of NH3 emission in Europe.

A correlation analysis of monthly mean CO2 retrieved from the Atmospheric Infrared Sounder with surface station measurements

As one of the major greenhouse gases, atmospheric carbon dioxide (CO2) concentrations have been monitored by both top-down satellite observations and air sampling systems on surface stations. The Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua low Earth orbit (LEO) satellite is a high-resolution infrared sounder that has been in operation for more than 10 years. The World Data Centre for Greenhouse Gases (WDCGG) archives and provides data on CO2 and other greenhouse gases measured mainly from surface stations. In this article, we focus on the correlation between the two different sources of CO2 data and the influencing factors. In general, we find that a linear positive correlation occurs at most stations. However, the variation in the correlation coefficient is large, especially for stations in the Northern Hemisphere. The station’s location, including its latitude, longitude, and altitude, is an important influencing factor because it determines how much its CO2 measurements are influenced by human activities. We also use root mean square difference (RMSD) and bias as evaluation indicators and find that they have similar trends like correlation coefficients.

Clarification of Methane Emission Sources Using WDCGG Data: Case Study of Anmyeon-do Observatory, Korea

Methane concentrations have been monitored at the Anmyeon-do Observatory, Korea, since 1999. In recent years, the methane concentration has increased, but the sources of this increase have yet to be identified. This study was designed to identify the major source contributing to the increase by using World Data Centre for Greenhouse Gases (WDCGG) data and the Greenhouse Gases Emission Presumption (GEP) method. The data were collected at Anmyeon-do between 2003 and 2009 (except 2008), and the analyses showed that the increase in methane concentration originated mainly in rice paddies around the observation point. The annual averagemethane concentration at Anmyeon-do was 1894 ppb, of which 100-103 ppb (5.3-5.4%) was shown to originate mainly from rice paddies. The seasonal fluctuation in methane concentration from May to October estimated by the GEP method was compared with experimental data of previous research conducted on rice paddies. The close match obtained through this comparison shows that the GEP method is effective. The difference in methane concentration was also analyzed in terms of land use and land cover. It was shown that although rice paddies account for only 14.7% of the area surveyed, they accounted for between 69 and 90% of the total increase in methane concentration. These results confirm that rice paddies are the main source of the increase in methane concentration observed at Anmyeon-do.