Global Atmospheric Transport Model Research Articles

China’s methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China’s CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a−1) in 2020 were closer to the official reported figure (53.57 Tg a−1) than to the prior (63.80 Tg a−1), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

Global Gridded Emission Inventory of Organophosphate Flame Retardants from 2010 to 2020.

As a substitute for brominated flame retardants, organophosphate flame retardants (OPFRs) have become a global concern due to their high toxicity and bioaccumulation. To paint an overall picture of OPFRs in the global environment, the present study develops a gridded global emission inventory of OPFRs on a spatial resolution of 1 × 1° from 2010 to 2020. Revealing a 3.31% average annual increase in emissions, totaling 21,324.42 tons. The production process is the primary source, accounting for 55.43% of emissions, with consumption processes making up the rest. Major sources are in Asia, North America, and Europe. The inventory is verified by implementing emission data into a global atmospheric transport model to predict OPFR concentrations in the global environment and comparing modeled concentrations with field sampled data. The results indicate that the inventory is reliable except for the pristine polar region, where the emission inventory and modeled concentrations underestimate OPFR levels in the atmosphere, likely resulting from ignorance of chemical reactions and the secondary derivative of parent OPFRs during their global long-distance atmospheric transport in the model. This comprehensive data set aids in formulating OPFR emission control policies and assessing health risks.

Integration of surface-based and space-based atmospheric CO2 measurements for improving carbon flux estimates using a new developed 3-GAS inversion model

The ongoing necessity and developmental trajectory in atmospheric CO2 assimilation system revolve around enhancing the optimization efficacy of assimilation inversions through the synergistic integration of distinct observational datasets. In this study, we developed an new inversion system-Global Greenhouse-Gases Assimilation System (3-GAS), by coupling the global atmospheric transport model GEOS-Chem with the four-dimensional local ensemble transform Kalman filter (4D-LETKF) algorithm. We compiled an integrated dataset utilizing the widely recognized carbon observation data from ObsPack (Observation Package), GOSAT (The Greenhouse Gases Observing Satellite), and OCO-2 (Orbiting Carbon Observatory-2) to estimate global surface fluxes. The integration of multiple observational data significantly amplifies spatial coverage, enhances the variability in flux inversion, and fosters improvements in localized assimilation efficacy. The spatial distribution of the carbon flux from the combined assimilation emerges as a comprehensive outcome, born from the assimilation with individual observational data. This distribution is primarily influenced by the coverage attributes of the original observations and the meticulous selection of observations during the assimilation process. The validation with surface and aircraft observations reveals that the results of combined assimilation possess composite advantages, which are manifested in alleviating certain intrinsic errors inherent to individual assimilation. The combined assimilation improves the accuracy based on root-mean-square error reduced by 9%, 22% and 33% in comparison to surface-only, GOSAT-only and OCO-2-only assimilation. Eventually, predicated upon preliminary inversion results and regional characteristics, we endeavor to propose a more refined filtration scheme and error determination method for the integrated dataset, as the initiative for the efficient assimilation of multi-source data.

MALTA: A Zonally Averaged Global Atmospheric Transport Model for Long‐Lived Trace Gases

AbstractWe present a two‐dimensional, zonally averaged global model of atmospheric transport named MALTA: Model of Averaged in Longitude Transport in the Atmosphere. It aims to be accessible to a broad community of users, with the primary function of quantifying emissions of greenhouse gases and ozone depleting substances. The model transport is derived from meteorological reanalysis data and flux‐gradient experiments using a three‐dimensional transport model. Atmospheric sinks are prescribed loss frequency fields. The zonally averaged model simulates important large‐scale transport features such as the influence on trace gas concentrations of the quasi‐biennial oscillation and variations in inter‐hemispheric transport rates. Stratosphere‐troposphere exchange is comparable to a three‐dimensional model and inter‐hemispheric transport is faster by up to 0.3 years than typical transport times of three‐dimensional models, depending on the metric used. Validation of the model shows that it can estimate emissions of CFC‐11 from an incorrect a priori emissions field well using three‐dimensional (3D) mole fraction fields generated using a different 3D model than which the flux gradient relationships were derived. The model is open source and is expected to be applicable to a wide range of studies requiring a fast, simple model of atmospheric transport and chemical processes for estimating associated emissions or mole fractions.

Anthropogenic NO x emissions of China, the U.S. and Europe from 2019 to 2022 inferred from TROPOMI observations

Anthropogenic nitrogen oxide (NO x ) emissions are closely associated with human activities. In recent years, global human activity patterns have changed significantly owing to the COVID‐19 epidemic and international energy crisis. However, their effects on NO x emissions are not yet fully understood. In this study, we developed a two-step inversion framework using NO2 observations from the TROPOMI satellite and the GEOS-Chem global atmospheric chemical transport model, and inferred global anthropogenic NO x emissions from 2019 to 2022, focusing on China, the United States (U.S.), and Europe. Our results indicated an 1.68% reduction in NO x emissions in 2020 and a 5.72% rebound in 2021 across all regions. China rebounded faster than the others, surpassing its 2019 levels by July 2020. In 2022, emissions declined in all regions, driven mainly by the Omicron variant, energy shortages, and clean energy policies. Our findings provide valuable insights for the development of effective future emission management strategies.

Atmospheric CO2 inversion reveals the Amazon as a minor carbon source caused by fire emissions, with forest uptake offsetting about half of these emissions

Abstract. Tropical forests such as the Amazonian rainforests play an important role for climate, are large carbon stores and are a treasure of biodiversity. Amazonian forests have been exposed to large-scale deforestation and degradation for many decades. Deforestation declined between 2005 and 2012 but more recently has again increased with similar rates as in 2007–2008. The resulting forest fragments are exposed to substantially elevated temperatures in an already warming world. These temperature and land cover changes are expected to affect the forests, and an important diagnostic of their health and sensitivity to climate variation is their carbon balance. In a recent study based on CO2 atmospheric vertical profile observations between 2010 and 2018, and an air column budgeting technique used to estimate fluxes, we reported the Amazon region as a carbon source to the atmosphere, mainly due to fire emissions. Instead of an air column budgeting technique, we use an inverse of the global atmospheric transport model, TOMCAT, to assimilate CO2 observations from Amazon vertical profiles and global flask measurements. We thus estimate inter- and intra-annual variability in the carbon fluxes, trends over time and controls for the period of 2010–2018. This is the longest period covered by a Bayesian inversion of these atmospheric CO2 profile observations to date. Our analyses indicate that the Amazon is a small net source of carbon to the atmosphere (mean 2010–2018 = 0.13 ± 0.17 Pg C yr−1, where 0.17 is the 1σ uncertainty), with the majority of the emissions coming from the eastern region (77 % of total Amazon emissions). Fire is the primary driver of the Amazonian source (0.26 ± 0.13 Pg C yr−1), while forest carbon uptake removes around half of the fire emissions to the atmosphere (−0.13 ± 0.20 Pg C yr−1). The largest net carbon sink was observed in the western-central Amazon region (72 % of the fire emissions). We find larger carbon emissions during the extreme drought years (such as 2010, 2015 and 2016), correlated with increases in temperature, cumulative water deficit and burned area. Despite the increase in total carbon emissions during drought years, we do not observe a significant trend over time in our carbon total, fire and net biome exchange estimates between 2010 and 2018. Our analysis thus cannot provide clear evidence for a weakening of the carbon uptake by Amazonian tropical forests.

CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations

AbstractMost of China's carbon sink inversion research uses global atmospheric transport models to assimilate natural fluxes, which quantifies the biosphere and ocean carbon budget with a relative coarse spatial resolution and long timescale from a weekly or monthly perspective. Toward high‐resolution inversion of CO2 fluxes, a novel carbon flux forecast model was developed in this study, which was then further used to carry out carbon assimilation based on a regional chemical transport model (CMAQ) at higher spatial (64 × 64 km2) and temporal (1 hr) resolutions. An Ensemble Kalman Smoother was applied as the assimilation algorithm and further extended to assimilate surface CO2 observations. Concentrations and fluxes were simultaneously assimilated as state variables to help reduce the uncertainty in the initial CO2 fields with the joint data assimilation framework (JDAS). In general, the posterior fluxes reproduced the seasonal, daily and hourly variation effectively, demonstrating the ability to fully absorb and utilize observations. Moreover, the influence of the choice of assimilation window on the carbon flux inversion was assessed via sensitivity experiments, revealing 36 hr to be the optimum length. Evaluation of the prior and posterior flux simulations also indicated that JDAS offers reasonable improvements, making it suitable for fine‐scale flux optimization and estimation. In addition, the posterior biosphere estimation in mainland China (−682 TgC yr−1) tends to be the optimal mathematical solution under current sparse observation coverage with daytime photosynthetic uptake, which likely leads to the overestimation of the optimized CO2 sink. This study serves as a basis for future regional and urban assessment.

Impact of lowering fine particulate matter from major emission sources on mortality in Canada: A nationwide causal analysis

Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.

A tool for air pollution scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies

Abstract. Air pollution is a major sustainability challenge – and future anthropogenic precursor and greenhouse gas (GHG) emissions will greatly affect human well-being. While mitigating climate change can reduce air pollution both directly and indirectly, distinct policy levers can affect these two interconnected sustainability issues across a wide range of scenarios. We help to assess such issues by presenting a public Tool for Air Pollution Scenarios (TAPS) that can flexibly assess pollutant emissions from a variety of climate and air quality actions, through the tool's coupling with socioeconomic modeling of climate change mitigation. In this study, we develop and implement TAPS with three components: recent global and fuel-specific anthropogenic emissions inventories, scenarios of emitting activities to 2100 from the MIT Economic Projection and Policy Analysis (EPPA) model, and emissions intensity trends based on recent scenario data from the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) model. An initial application shows that in scenarios with less climate and pollution policy ambition, near-term air quality improvements from existing policies are eclipsed by long-term emissions increases – particularly from industrial processes that combine sharp production growth with less stringent pollution controls in developing regions. Additional climate actions would substantially reduce air pollutant emissions related to fossil fuel (such as sulfur and nitrogen oxides), while further pollution controls would lead to larger reductions for ammonia and organic carbon (OC). Future applications of TAPS could explore diverse regional and global policies that affect these emissions, using pollutant emissions results to drive global atmospheric chemical transport models to study the scenarios' health impacts.

The Emissions of Water Vapour and NOx from Modelled Hydrogen-Fuelled Aircraft and the Impact of NOx Reduction on Climate Compared with Kerosene-Fuelled Aircraft

A kerosene fuelled aircraft was modelled within a performance tool and fuel burn and the emissions of nitrogen oxides (NOx) and water vapour at different stages of flight throughout the mission were estimated. Adaptions were made to engine and aircraft parameters within the performance tool to accommodate a liquid hydrogen fuel over the same given mission. Once an iterative design process had been completed to ensure the aircraft could perform the given mission, the performance tool was again used to calculate total fuel burn. Fuel burn results alongside predicted emission indices were used to estimate the emissions of NOx, water vapour from hydrogen-fuelled aircraft. The use of hydrogen fuel over kerosene fuel in the modelled aircraft resulted in the removal of carbon-based emission species alongside 86% reduction in NOx and 4.3 times increase in water vapour emission. The climate impact of this switch with the reduction in NOx emission was assessed by a 3D global atmospheric chemistry and transport model, STOCHEM-CRI, which found a significant reduction in the concentration of a potent greenhouse gas, ozone, and an oxidizing agent, OH, by up to 6% and 25%, respectively. The reduction of OH levels could induce a positive radiative forcing effect as the lifetime of another important greenhouse gas, methane, is increased. However, the magnitude of this increase is very small (~0.3%), thus the overall impact of the reduction in NOx emissions is likely to have a net negative radiative forcing effect, improving aviation’s impact on the environment. However, further work is warranted on effects of other emission species, specifically water vapour, particulate matter and unburned hydrogen.

Newly identified climatically and environmentally significant high-latitude dust sources

Abstract. Dust particles from high latitudes have a potentially large local, regional, and global significance to climate and the environment as short-lived climate forcers, air pollutants, and nutrient sources. Identifying the locations of local dust sources and their emission, transport, and deposition processes is important for understanding the multiple impacts of high-latitude dust (HLD) on the Earth's systems. Here, we identify, describe, and quantify the source intensity (SI) values, which show the potential of soil surfaces for dust emission scaled to values 0 to 1 concerning globally best productive sources, using the Global Sand and Dust Storms Source Base Map (G-SDS-SBM). This includes 64 HLD sources in our collection for the northern (Alaska, Canada, Denmark, Greenland, Iceland, Svalbard, Sweden, and Russia) and southern (Antarctica and Patagonia) high latitudes. Activity from most of these HLD sources shows seasonal character. It is estimated that high-latitude land areas with higher (SI ≥0.5), very high (SI ≥0.7), and the highest potential (SI ≥0.9) for dust emission cover >1 670 000 km2, >560 000 km2, and >240 000 km2, respectively. In the Arctic HLD region (≥60∘ N), land area with SI ≥0.5 is 5.5 % (1 035 059 km2), area with SI ≥0.7 is 2.3 % (440 804 km2), and area with SI ≥0.9 is 1.1 % (208 701 km2). Minimum SI values in the northern HLD region are about 3 orders of magnitude smaller, indicating that the dust sources of this region greatly depend on weather conditions. Our spatial dust source distribution analysis modeling results showed evidence supporting a northern HLD belt, defined as the area north of 50∘ N, with a “transitional HLD-source area” extending at latitudes 50–58∘ N in Eurasia and 50–55∘ N in Canada and a “cold HLD-source area” including areas north of 60∘ N in Eurasia and north of 58∘ N in Canada, with currently “no dust source” area between the HLD and low-latitude dust (LLD) dust belt, except for British Columbia. Using the global atmospheric transport model SILAM, we estimated that 1.0 % of the global dust emission originated from the high-latitude regions. About 57 % of the dust deposition in snow- and ice-covered Arctic regions was from HLD sources. In the southern HLD region, soil surface conditions are favorable for dust emission during the whole year. Climate change can cause a decrease in the duration of snow cover, retreat of glaciers, and an increase in drought, heatwave intensity, and frequency, leading to the increasing frequency of topsoil conditions favorable for dust emission, which increases the probability of dust storms. Our study provides a step forward to improve the representation of HLD in models and to monitor, quantify, and assess the environmental and climate significance of HLD.

Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case

The global atmospheric chemical transport model has become a key technology for air quality forecast and management. However, precise and rapid air quality simulations and forecast are frequently limited by the model’s computational performance. The gas-phase chemistry module is the most time-consuming module in air quality models because its traditional solution method is dynamically stiff. To reduce the solving time of the gas phase chemical module, we built an emulator based on a deep residual neural network emulator (NN) for Carbon Bond Mechanism Z (CBM-Z) mechanism implemented in Global Nested Air Quality Prediction Modeling System. A global high resolution cross-life multi-species dataset was built and trained to evaluate multi-species concentration changes at a single time step of CBM-Z. The results showed that the emulator could accelerate to approximately 300–750 times while maintaining an accuracy similar to that of CBM-Z module (the average correlation coefficient squared was 0.97) at the global scale. This deep learning-based emulator could adequately represent the stiff kinetics of CBM-Z, which involves 47 species and 132 reactions. The emulated ozone (O3), nitrogen oxides (NOx), and hydroxyl radical (OH) were consistent with those of the original CBM-Z module in different global regions, heights, and time. Our results suggest that data-driven emulations have great potential in the construction of hybrid models with process-based air quality models, particularly at larger scales.

Iron from coal combustion particles dissolves much faster than mineral dust under simulated atmospheric acidic conditions

Abstract. Mineral dust is the largest source of aerosol iron (Fe) to the offshore global ocean, but acidic processing of coal fly ash (CFA) in the atmosphere could be an important source of soluble aerosol Fe. Here, we determined the Fe speciation and dissolution kinetics of CFA from Aberthaw (United Kingdom), Krakow (Poland), and Shandong (China) in solutions which simulate atmospheric acidic processing. In CFA PM10 fractions, 8 %–21.5 % of the total Fe was found to be hematite and goethite (dithionite-extracted Fe), and 2 %–6.5 % was found to be amorphous Fe (ascorbate-extracted Fe), while magnetite (oxalate-extracted Fe) varied from 3 %–22 %. The remaining 50 %–87 % of Fe was associated with other Fe-bearing phases, possibly aluminosilicates. High concentrations of ammonium sulfate ((NH4)2SO4), often found in wet aerosols, increased Fe solubility of CFA up to 7 times at low pH (2–3). The oxalate effect on the Fe dissolution rates at pH 2 varied considerably, depending on the samples, from no impact for Shandong ash to doubled dissolution for Krakow ash. However, this enhancement was suppressed in the presence of high concentrations of (NH4)2SO4. Dissolution of highly reactive (amorphous) Fe was insufficient to explain the high Fe solubility at low pH in CFA, and the modelled dissolution kinetics suggest that other Fe-bearing phases such as magnetite may also dissolve relatively rapidly under acidic conditions. Overall, Fe in CFA dissolved up to 7 times faster than in a Saharan dust precursor sample at pH 2. Based on these laboratory data, we developed a new scheme for the proton- and oxalate-promoted Fe dissolution of CFA, which was implemented into the global atmospheric chemical transport model IMPACT (Integrated Massively Parallel Atmospheric Chemical Transport). The revised model showed a better agreement with observations of Fe solubility in aerosol particles over the Bay of Bengal, due to the initial rapid release of Fe and the suppression of the oxalate-promoted dissolution at low pH. The improved model enabled us to predict sensitivity to a more dynamic range of pH changes, particularly between anthropogenic combustion and biomass burning aerosols.

Globalization-Driven Industry Relocation Significantly Reduces Arctic PAH Contamination.

Industry relocation under globalization has altered the origins and strength of emission sources of many air pollutants. We develop global emission inventories of polycyclic aromatic hydrocarbons (PAHs) embodied in the production and consumption of goods and services. We implement these inventories within a global atmospheric transport model and simulate spatial-temporal changes in atmospheric concentrations of benzo[α]pyrene (BaP), the most toxic congener in unsubstituted PAHs, and depositions across the Arctic subject to global trade and industry relocation. We show that interregional trade and industry relocation dramatically reduce the atmospheric levels and deposition of BaP in the Arctic. The most significant BaP decline occurs in the European and North American Arctic regions due to attenuated sources in the two well-developed continents proximate to the polar region induced by the relocation of high-PAH pollution industries to many developing countries far from the Arctic. Although BaP emissions embodied in industry relocations in China, India, and South and Southeast Asia resulted in increased BaP contamination in the Asian Arctic, such increases in pollution are minor compared to significant BaP reductions occurring in the European and North American Arctic regions. We find that "North-to-South" industry transfer could reduce trade-related BaP contamination by 60% in the Arctic.

Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide

Abstract. Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. To ensure that these models represent the atmosphere adequately, it is important to compare their outputs with measurements. However, ground based measurements of atmospheric composition are typically sparsely distributed and representative of much smaller spatial scales than those resolved in models; thus, direct comparison incurs uncertainty. In this study, we investigate the feasibility of using observations of one or more atmospheric constituents to estimate parameters in chemistry transport models and to explore how these estimates and their uncertainties depend upon representation errors and the level of spatial coverage of the measurements. We apply Gaussian process emulation to explore the model parameter space and use monthly averaged ground-level concentrations of ozone (O3) and carbon monoxide (CO) from across Europe and the US. Using synthetic observations, we find that the estimates of parameters with greatest influence on O3 and CO are unbiased, and the associated parameter uncertainties are low even at low spatial coverage or with high representation error. Using reanalysis data, we find that estimates of the most influential parameter – corresponding to the dry deposition process – are closer to its expected value using both O3 and CO data than using O3 alone. This is remarkable because it shows that while CO is largely unaffected by dry deposition, the additional constraints it provides are valuable for achieving unbiased estimates of the dry deposition parameter. In summary, these findings identify the level of spatial representation error and coverage needed to achieve good parameter estimates and highlight the benefits of using multiple constraints to calibrate atmospheric chemistry transport models.

Quantifying Nitrate Formation Pathways in the Equatorial Pacific Atmosphere from the GEOTRACES Peru-Tahiti Transect

This study aims to better our collective understanding of the oxidative capacity and atmospheric chemistry over the equatorial Pacific. Bulk and size-segregated filter samples were collected during the GEOTRACES Eastern Tropical Pacific transect (4.1° S, 81.9° W to 10.5° S, 152.0° W; October–December 2013) and measured for aerosol concentration and complete isotopic composition of nitrate (δ15N, δ18O, Δ17O where Δ17O = δ17O – 0.52 × δ18O). Combined size-segregated filters produced data similar to that found in bulk filter samples, and notably neither δ15N nor δ18O showed any trends based on aerosol size. Similar to other studies, NO3– is concentrated (>80%) in the coarse size fractions (>1.5 μm). Bulk aerosol concentrations ranged from 6.6 to 89.8 nmol/m3. The bulk δ15N-, δ18O-, and Δ17O-nitrate ranged from −13.1 to −3.2‰, 68.5 to 79.3‰, and 23.5 to 28.4‰, respectively. Higher δ15N values near the coast are best explained by the influence of continental sources; lower δ15N values far from the coast may be associated with chemical fractionation during long-range transport or an oceanic source. Both Δ17O and δ18O are interpreted using kinetic analysis and gas concentrations from a global atmospheric chemical transport model (GEOS-Chem), which showed that nitrate production in this environment is dominated by OH oxidation (60%) and RONO2 hydrolysis (15%). To best match the δ18O and Δ17O observations in this study, the terminal oxygen isotopic values for ozone must be higher than those suggested by available observations and/or halogen-mediated chemistry must be more important than the models currently suggest.

Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

Changes to simulated global atmospheric composition resulting from recent revisions to isoprene oxidation chemistry

Recent revisions to our understanding of the oxidation chemistry of isoprene have been incorporated into a well-established global atmospheric chemistry and transport model, STOCHEM-CRI. These revisions have previously been shown to increase the production and recycling of HOx radicals at lower NOx levels characteristic of the remote troposphere. The main aim of this study is to assess the resultant broader changes to atmospheric composition due to the recent revisions to isoprene oxidation chemistry. The impact of the increased isoprene-related HOx recycling is found to be significant on the reduction of volatile organic compounds (VOCs) lifetime, e.g. a decrease in isoprene's tropospheric burden by ~17%. The analysis of lifetime reduction of the potent greenhouse gas, methane, associated with the increased HOx recycling, suggests its significant lifetime reduction by ~5% in terms of the current literature. The revisions to the isoprene chemistry also reduce the amount of ozone (by up to 10%), but provide a significant increase in NO3 (by up to 30%) over equatorial forested regions, which can alter the oxidizing capacity of the troposphere. The calculated mixing ratios of formic acid are decreased which in turn leads to an increase in the inferred concentrations of Criegee intermediates due to reduced loss through reaction with formic acid (up to 80%) over the dominant isoprene emitting regions.

Assessment of spatio-temporal distribution of CO2 over greater Asia using the WRF–CO2 model

In-depth knowledge of global and regional carbon budget is required for effective policymaking to mitigate the global climate change. However, Asian carbon budget shows large uncertainty due to both lack of sufficient observations and detailed understanding of the existing CO2 observations. A regional air quality model (WRF–CO2) is set up for simulating atmospheric CO2 variations over the greater Asia region (68–124°E, 2°S–45°N) for the period 2010–2012. The WRF–CO2 simulations are compared with observations from nine sites and a global Atmospheric Chemistry Transport Model (ACTM). The comparisons suggest WRF–CO2 simulation is able to capture large scale features in the observed variabilities, with varied ability at fine scales depending on representation of surface fluxes and meteorology around the observation sites. Analysis of CO2 signals from individual flux components suggests that ocean flux has least contribution to the CO2 variation ( 80%). CO2 mixing ratios are found to be maximum in northern hemisphere (NH) winter over East Asia, while they are maximum in NH spring over Indian subcontinent. Observed peak-to-trough seasonal amplitude is lowest (4.5 ppm) for the site Bukit Koto Tabang, Indonesia and highest (29.5 ppm) for Shangdianzi in China. Statistical analysis from monthly mean CO2 time series shows that correlation coefficient and normalised standard deviation with observations, are generally equal or better for the WRF–CO2 than the coarser resolution ACTM. Study of synoptic scale CO2 variations shows that the WRF–CO2 is able to better resolve daytime signatures than those in the night. Year-to-year CO2 variations of seasonal cycle amplitude is highest (~5 ppm) at Nainital, India compared to all other sites.

Mercury Benefits of Climate Policy in China: Addressing the Paris Agreement and the Minamata Convention Simultaneously.

National commitments under the Paris Agreement on climate change interact with other global environmental objectives, such as those of the Minamata Convention on Mercury. We assess how mercury emissions and deposition reductions from national climate policy in China under the Paris Agreement could contribute to the country's commitments under the Minamata Convention. We examine emissions under climate policy scenarios developed using a computable general equilibrium model of China's economy, end-of-pipe control scenarios that meet China's commitments under the Minamata Convention, and these policies in combination, and evaluate deposition using a global atmospheric transport model. We find climate policy in China can provide mercury benefits when implemented with Minamata policy, achieving in the year 2030 approximately 5% additional reduction in mercury emissions and deposition in China when climate policy achieves a 5% reduction per year in carbon intensity (CO2 emissions 9.7 Gt in 2030). This corresponds to 63 Mg additional mercury emissions reductions in 2030 when implemented with Minamata Convention policy, compared to Minamata policy implemented alone. Climate policy provides emissions reductions in sectors not considered under the Minamata Convention, such as residential combustion. This changes the combination of sectors that contribute to emissions reductions.