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 $\mathrm{0.1}{}^{\circ }\times \mathrm{0.1}{}^{\circ }$ . 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.

The paper shows wind shear impact on PM₁₀ vertical profiles in Kraków, southern Poland. The data used consist of background data for two cold seasons (September 2018 to April 2019 and September 2019 to April 2020) and data for several case studies from November 2019 to March 2020. The data are composed of PM₁₀ measurements, model data, and wind speed and direction data. The background model data come from operational forecast results of the AROME model. PM₁₀ concentration in the vertical profile was measured with a sightseeing balloon. Significant spatial variability of the wind field was found. The case studies represent the conditions with much lower wind speed and a much higher PM₁₀ level than the seasonal average. The inversions were much more frequent than on average too. Wind shear turned out to be the important factor in terms of PM₁₀ vertical profile modification. It is generated due to the relief impact, i.e. the presence of a large valley, blocked on one side with the hills. The analysis of PM₁₀ profiles from all flights allows us to distinguish three vertical zones of potential air pollution hazards within the valley (about 100 m deep) and the city of Kraków: (1) up to about 60 m a.g.l. – the zone where during periods of low wind speed, air pollution is potentially the highest and the duration of such high levels is the longest, i.e. the zone with the worst aerosanitary conditions; (2) about 60–100 m a.g.l. – transitional zone where the large decrease in PM₁₀ levels with height is observed; (3) above 100–120 m a.g.l. – the zone where air quality is significantly better than in zone 1, either due to the increase in the wind speed or due to the wind direction change and advection of different, clean air masses.

Atmospheric methane (CH₄) growth rates reached unprecedented values in the years 2020–2022. To identify the main drivers of this increase, an inverse modeling study estimated regional and sectoral emission changes for 2016–2022. Three inverse estimates based on different sets of atmospheric CH₄ observations (surface observations only, surface and aircraft observations, and GOSAT observations) consistently suggest notable emission increases from 2016–2019 to 2020–2022 in the tropics (15° S–10° N) (10–18 Tg CH₄ yr⁻¹) and in northern low latitudes (10–35° N) (ca. 20 Tg CH₄ yr⁻¹), the latter of which likely contributed to the growth rate surge from 2020. The emission increase in the northern low latitudes is attributed to emissions in South Asia and northern Southeast Asia, which abruptly increased from 2019 to 2020, and elevated emissions continued until 2022. Meanwhile, the tropical emission increase is dominated by Tropical South America and Central Africa, but emissions were continuously increasing before 2019. Agreement was found in the sectoral estimates of the three inversions in the tropics and northern low latitudes, suggesting the largest contribution of biogenic emissions. Uncertainty reductions demonstrate that the flux estimates in Asia are well constrained by surface and aircraft observations. Furthermore, a sensitivity test with the probable reduction of OH radicals showed smaller emissions by up to 2–3 Tg CH₄ yr⁻¹ in each Asian region for 2020, still suggesting notable emission contributions. These results highlight the importance of biogenic emissions in Asian regions for the persistent high growth rate observed during 2020–2022.

We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO₂ fluxes to achieve best fit to atmospheric CO₂ data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO₂ emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO₂ observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to 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 is being 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) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO₂ fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for 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 vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO₂ concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO₂ flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

An inverse study of atmospheric methane (CH₄) estimated regional and sectoral emission contributions to the unprecedented surge of the atmospheric growth rate for 2020–2022. Three inverse analyses, which used only surface observations, surface and aircraft observations, and satellite (GOSAT) observations, consistently suggested notable emission increases in the tropics (15° S–10° N) (10–18 Tg CH₄ yr⁻¹) and in northern low-latitudes (10–35° N) (ca. 20 Tg CH₄ yr⁻¹), the latter of which likely contributed to the growth rate surge from 2020. The emission increase in the northern low-latitudes is attributed to emissions in South Asia (6–7 Tg CH₄ yr⁻¹) and northern Southeast Asia (5 Tg CH₄ yr⁻¹), which abruptly increased from 2019 to 2020, and elevated emissions continued until 2022. Meanwhile, the tropical emission increase is dominated by tropical South America (5–7 Tg CH₄ yr⁻¹) and central Africa (3–6 Tg CH₄ yr⁻¹), but they were continuously increasing before 2019. Agreement was found in sectoral estimates in the tropics and northern low-latitudes, suggesting that biogenic emissions from wetlands, agriculture, and waste are the largest contributors. High-precision surface and aircraft observations imposed constraints that were comparable to or 1.5 times stronger than GOSAT constraints on the flux estimates in South and Southeast Asia. Furthermore, a sensitivity inversion test suggested that the effect of the probable reduction of OH radicals in 2020 might be limited in the Asian regions. These results highlight the importance of biogenic emissions in Asian regions for the persistent high growth rate observed during 2020–2022.