A high-resolution hourly XCO2 dataset for eastern China (2016–2020) based on WRF-Chem simulations and multi-source validation

Abstract. We describe and evaluate a high-resolution real-time air quality forecast system over the Eastern Mediterranean, based on a regional, online coupled atmospheric chemistry and aerosol model. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to perform daily, 3 d forecasts of regulated pollutants (NO2, O3, PM2.5) over the Eastern Mediterranean, applying three nested domains with horizontal resolutions of 50, 10 and 2 km, the latter focusing on Cyprus. Natural (dust, sea-salt, biogenic) emissions are calculated online, while anthropogenic emissions are based on the Emissions Database for Global Atmospheric Research – Hemispheric Transport of Air Pollution (EDGAR-HTAP) global emission inventory. A high spatial (1 km) and temporal (hourly) anthropogenic emission inventory is used for the island of Cyprus in the innermost domain. The model skill in forecasting the concentrations of atmospheric pollutants is evaluated using measurements from a network of nine ground stations in Cyprus and compared with the forecasting skill of the EU Copernicus Atmosphere Monitoring Service (CAMS). The forecast of surface temperature, pressure, and wind speed is found to be accurate, with minor discrepancies between the modelled and observed 10 m wind speed at mountainous and coastal sites attributed to the limited representation of the complex topography of Cyprus. Compared to CAMS, the WRF-Chem model predicts with higher accuracy the NO2 mixing ratios at the residential site with a normalized mean bias (NMB) of 7 % during winter and −44 % during summer, whereas the corresponding biases for CAMS are −81 % and −84 %. Due to the high temporal resolution of the anthropogenic emission inventory, the WRF-Chem model captures more accurately the diurnal profiles of NO2 and O3 mixing ratios at the residential site. Background PM2.5 concentrations influenced by long-range transport are overestimated by the WRF-Chem model during winter (NMB = 54 %), whereas the corresponding NMB for CAMS is 11 %. Our results support the adoption of regional, online coupled air quality models over chemical transport models for real-time air quality forecasts.

Abstract. The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of 1-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations, and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime model–data misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Nevertheless, our results demonstrate the potential of our optimal CO2 atmospheric modeling system to be utilized in atmospheric inversions of CO2 emissions over the Paris metropolitan area. We evaluated the model performances in terms of wind, vertical mixing, and CO2 model–data mismatches, and we developed a filtering algorithm for outliers due to local contamination and unfavorable meteorological conditions. Analysis of model–data misfit indicates that future inversions at the mesoscale should only use afternoon urban CO2 measurements in winter and suburban measurements in summer. Finally, we determined that errors related to CO2 boundary conditions can be overcome by including distant background observations to constrain the boundary inflow or by assimilating CO2 gradients of upwind–downwind stations rather than by assimilating absolute CO2 concentrations.

<p>Atmospheric chemistry models play a major role for relating greenhouse gases and pollutants concentrations to emissions at high temporal resolutions over large areas. On that account, it is fundamental to use up-to-date anthropogenic emissions maps as model inputs. Despite efforts by researchers to create global emission datasets with high temporal resolutions, for countries with no national data, generic activity maps and emission factors are used, thus the accurate representation of the anthropogenic emissions in a local scale still remains challenging. This study presents an improved spatially explicit dataset for anthropogenic emissions of CO2 and NOx over the Middle-East, a region characterized by extensive gas power plants and heavy industries operations. Our dataset was developed by combining a detailed infrastructure map for point sources in the area and it is used to simulate the distribution of CO2 and NO2 using the WRF-Chem mesoscale atmospheric transport chemistry model. Furthermore, the chemistry scheme of the WRF-Chem model in the simulation of CO2 and NO2 plumes is examined, in comparison with satellite observations.<br />   <br />In the framework of the Eastern Mediterranean and Middle East – Climate and Atmosphere Research (EMME-CARE) project, our new detailed infrastructure map for power plants and gas flaring has been implemented in WRF-Chem simulations for the Middle-East region to complete the Emission Database for Global Atmospheric Research (EDGAR) as input. The EDGAR data consists of emissions by various sectors such as power plants, industry, residential, transportation and agriculture. Furthermore, hourly scaling factors have been applied to the anthropogenic emissions according to the electricity consumption of the particular urban areas, taking into account the weekly as well as the monthly variations. The periods under study are January 2021 and June 2021. By comparing the WRF-Chem outputs to TROPOMI satellite observations for NO2, the results show that the addition of point sources was crucial for the detection of some NO2 plumes. Moreover, the WRF-Chem model systematically overestimated the NO2 concentrations in the area with the current EDGAR dataset, therefore we introduced a new relationship between monthly and annual emissions for the Middle-East region. By carrying out WRF-Chem simulations with NO2 acting as a passive tracer it was also possible to examine the impact of the model chemistry in NO2 plumes development. Finally, CO2 was also simulated by the WRF-Chem model as a passive tracer and the results showed a good agrement with XCO2 data observed by the OCO-2 and OCO-3 instruments. </p>

<p>The growing content of greenhouse gases (GHGs) influences the radiation balance of the planet causing the rise of air temperature in lower atmosphere. This circumstance triggers researchers to create and develop the new methods of estimation of anthropogenic CO<sub>2</sub> emissions. One of such method is top-down estimation which is based on measurements and chemical transport modelling. Since the errors of the top-down approach depend on quality of the modelled data it requires validation by complex observations. In current study we investigated the performance of regional numerical weather prediction and chemistry transport model WRF-Chem and CAMS service in simulating spatio-temporal variation of near surface atmospheric CO<sub>2</sub> mixing ratio in March and April 2019 for the Saint-Petersburg area (Russia). To validate the modelled data, we used local observations obtained on Peterhof (St. Petersburg) station. The analysis demonstrates that WRF-Chem model can adequate simulate the transport of CO<sub>2</sub> in near-surface layer with spatial resolution of 3 km. Average difference and correlation coefficient are in range 0.8-1.6% and 0.55-0.72 respectively. It was found that the WRF-Chem modelled data where biogenic and anthropogenic fluxes were considered fit the observation data worse than the WRF-Chem simulation where only anthropogenic emissions were used. It can be linked to the errors of the biogenic flux calculation. However, to prove that investigations for two contrast periods (in summer and winter) are needed. Despite the rude spatial resolution of the CAMS data (approximately 200x400 km) we found that in general the trend of surface atmospheric CO<sub>2</sub> mixing ratio in March and April 2019 for the Saint-Petersburg area from the CAMS dataset fits the observations.</p>

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

BackgroundUnderstanding a carbon budget from a national perspective is essential for establishing effective plans to reduce atmospheric CO2 growth. The national characteristics of carbon budgets are reflected in atmospheric CO2 variations; however, separating regional influences on atmospheric signals is challenging owing to atmospheric CO2 transport. Therefore, in this study, we examined the characteristics of atmospheric CO2 variations over South and North Korea during 2000–2016 and unveiled the causes of their regional differences in the increasing rate of atmospheric CO2 concentrations by utilizing atmospheric transport modeling.ResultsThe atmospheric CO2 concentration in South Korea is rising by 2.32 ppm year− 1, which is more than the globally-averaged increase rate of 2.05 ppm year− 1. Atmospheric transport modeling indicates that the increase in domestic fossil energy supply to support manufacturing export-led economic growth leads to an increase of 0.12 ppm year− 1 in atmospheric CO2 in South Korea. Although enhancements of terrestrial carbon uptake estimated from both inverse modeling and process-based models have decreased atmospheric CO2 by up to 0.02 ppm year− 1, this decrease is insufficient to offset anthropogenic CO2 increases. Meanwhile, atmospheric CO2 in North Korea is also increasing by 2.23 ppm year− 1, despite a decrease in national CO2 emissions close to carbon neutrality. The great increases estimated in both South Korea and North Korea are associated with changes in atmospheric transport, including increasing emitted and transported CO2 from China, which have increased the national atmospheric CO2 concentrations by 2.23 ppm year− 1 and 2.27 ppm year− 1, respectively.ConclusionsThis study discovered that economic activity is the determinant of regional differences in increasing atmospheric CO2 in the Korea Peninsula. However, from a global perspective, changes in transported CO2 are a major driver of rising atmospheric CO2 over this region, yielding an increase rate higher than the global mean value. Our findings suggest that accurately separating the contributions of atmospheric transport and regional sources to the increasing atmospheric CO2 concentrations is important for developing effective strategies to achieve carbon neutrality at the national level.

<p>The <strong>Enviro-HIRLAM</strong> (Environment - HIgh Resolution Limited Area Model) is seamless/ online integrated numerical weather prediction and atmospheric chemical transport modelling system capable to simulate simultaneously meteorology – atmospheric composition on regional to subregional – urban scales.</p><p>The <strong>main areas</strong> of the model research and development include: downscaling/  nesting  for  high  resolutions;  improved  resolving  boundary  and  surface  layers  structures; urbanization and sub-layer processes; improvement of advection schemes; integration of natural and anthropogenic emission inventories; implementation of gas-phase chemistry mechanisms, aerosol dynamics and microphysics, aerosol feedback and interactions mechanisms.</p><p>The <strong>Enviro-components</strong> includes: gas-phase chemistry; aerosol microphysics with nucleation, coagulation, condensation of sulfate, mineral dust, sea-salt, black and organic carbon together  with  aerosols’ dry and wet deposition, sedimentation processes;  parameterisations of urban sublayer with modifications of the interaction soil–biosphere–atmosphere scheme; sulfur cycle mechanism with dimethyl sulfide, sulfur dioxide and sulfate; radiation scheme improved to  account  explicitly  for  aerosol  radiation interactions  for   aerosol  subtypes; aerosol  activation  implemented in condensation-convection scheme with nucleation dependent on aerosol properties and ice-phase processes; locally  mass-conserving  semi-Lagrangian  numerical  advection  scheme; natural and anthropogenic emission inventories.</p><p>The Enviro-HIRLAM utilises extraction and pre-processing of initial/ boundary meteorology-chemistry-aerosol conditions and observations for data assimilation (from ECMWF’s ERA-5 & CAMS), pre-processing of selected emission inventories for anthropogenic and natural emissions. The latest version has been run on CRAY-XC30/40 and Atos BullSequana HPCs machines, and it has been developed through the research and HPC projects such as Enviro-HIRLAM at CSC and Enviro-PEEX & Enviro-PEEX(Plus) at ECMWF, as well as other research projects.</p><p>The <strong>research, development and science education of the modelling system and its applications</strong> will be demonstrated on examples, where the Enviro-HIRLAM is used as a research tool  for studies in domain of the Pan-Eurasian Experiment (PEEX; https://www.atm.helsinki.fi/peex) programme. Examples of such include: aspects of regional-subregional-urban downscaling with focus on metropolitan areas of St.Petersburg and Moscow; influence of dust transport from artificial tailing dumps and Cu-Ni smelters of the Kola Peninsula on pollution of environment and health of population; aerosol feedbacks and interactions at regional scale in the Arctic-boreal domain; evaluation of atmosphere-land-sea surfaces interactions, and in particular, heat-moisture exchange/ regime between these surfaces and for better understanding and forecasting of local meteorology in the Arctic; analysis of urban meteorology and atmospheric pollution with integrated approach to high-resolution numerical modelling; and others. The modelling output provides meteorology-chemistry related input to assessment studies for population and environment as well as can be integrated into GIS environment for further risk/vulnerability/consequences/etc. estimation, and other studies.</p><p>The <strong>science education component</strong> for the model is also realised though short-term visits of young researchers, organization and carrying out research training weeks. The latest face-to-face trainings took place in Apr and Jun 2019 (Helsinki and Tyumen), and online in Nov-Dec 2021 (https://megapolis2021.ru).</p>

<strong class="journal-contentHeaderColor">Abstract.</strong> We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (<span class="inline-formula">CO₂</span>) exchange over Europe at high resolution (0.1 <span class="inline-formula">×</span> 0.2<span class="inline-formula">^∘</span>) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (<span class="inline-formula">0.1×0.2</span><span class="inline-formula">^∘</span>, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to <span class="inline-formula">0.1×0.2</span><span class="inline-formula">^∘</span> using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean <span class="inline-formula">CO₂</span> fluxes are included in our product, based on Jena CarboScope ocean <span class="inline-formula">CO₂</span> fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric <span class="inline-formula">CO₂</span> mole fractions over Europe. We assess the skill of the CTE-HR <span class="inline-formula">CO₂</span> fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric <span class="inline-formula">CO₂</span> mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured <span class="inline-formula">CO₂</span> fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted <span class="inline-formula">CO₂</span>, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of <span class="inline-formula">CO₂</span> (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of <span class="inline-formula">CO₂</span> observations as in CTE. <span id="page580"/>We furthermore compare <span class="inline-formula">CO₂</span> concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates <span class="inline-formula">CO₂</span> concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–<span class="inline-formula">10×</span> coarsened). The remaining 10 % of the simulated <span class="inline-formula">CO₂</span> mole fraction differs by <span class="inline-formula">&gt;2</span> ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric <span class="inline-formula">CO₂</span> modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a <span class="inline-formula">CO₂</span> data assimilation system. The data are available at <a href="https://doi.org/10.18160/20Z1-AYJ2">https://doi.org/10.18160/20Z1-AYJ2</a> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx109">van der Woude</a>, <a href="#bib1.bibx109">2022</a><a href="#bib1.bibx109">a</a>)</span>.

We present the CarbonTracker Europe High-Resolution system that estimates carbon dioxide (CO2) exchange over Europe at high-resolution (0.1 x 0.2°) and in near real-time (about 2 months latency). It includes a dynamic fossil fuel emission model, which uses easily available statistics on economic activity, energy-use, and weather to generate fossil fuel emissions with dynamic time profiles at high spatial and temporal resolution (0.1 x 0.2°, hourly). Hourly net biosphere exchange (NEE) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEE is downscaled to 0.1 x 0.2° using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map, and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. An ocean flux extrapolation and downscaling based on wind speed and temperature for Jena CarboScope ocean CO2 fluxes is included in our product. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the ability of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 drought, (b) to capture the reduction of fossil fuel emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ERA5, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city centre of Amsterdam (The Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and fossil fuel emissions (COVID-19 pandemic in 2020). After transport with TM5, the CTE-HR fluxes have lower root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor-person inversion). RSMEs are close to those of the reanalysis with the data assimilation system CarbonTracker Europe (CTE). This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different sectors in summer and winter. Interestingly, in periods where synoptic scale transport variability dominates CO2 variations, the CTE-HR fluxes perform similar to low-resolution fluxes (5–10x coarsened). The remaining 10 % of simulated CO2 mole fraction differ by > 2ppm between the low-resolution and high-resolution flux representation, and are clearly associated with coherent structures ("plumes") originating from emission hotspots, such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely-populated region like the Amsterdam city centre, our fluxes underestimate the magnitude of measured eddy-covariance fluxes, but capture their substantial diurnal variations in summer- and wintertime well. We conclude that our product is a promising tool to model the European carbon budget at a high-resolution in near real-time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near real-time monitoring and modeling, for example as a-priori flux product in a CO2 data-assimilation system. The data is available at https://doi.org/10.18160/20Z1-AYJ2.

We present the CarbonTracker Europe High-Resolution system that estimates carbon dioxide (CO2) exchange over Europe at high-resolution (0.1 x 0.2°) and in near real-time (about 2 months latency). It includes a dynamic fossil fuel emission model, which uses easily available statistics on economic activity, energy-use, and weather to generate fossil fuel emissions with dynamic time profiles at high spatial and temporal resolution (0.1 x 0.2°, hourly). Hourly net biosphere exchange (NEE) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEE is downscaled to 0.1 x 0.2° using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map, and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. An ocean flux extrapolation and downscaling based on wind speed and temperature for Jena CarboScope ocean CO2 fluxes is included in our product. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the ability of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 drought, (b) to capture the reduction of fossil fuel emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ERA5, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city centre of Amsterdam (The Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and fossil fuel emissions (COVID-19 pandemic in 2020). After transport with TM5, the CTE-HR fluxes have lower root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor-person inversion). RSMEs are close to those of the reanalysis with the data assimilation system CarbonTracker Europe (CTE). This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different sectors in summer and winter. Interestingly, in periods where synoptic scale transport variability dominates CO2 variations, the CTE-HR fluxes perform similar to low-resolution fluxes (5–10x coarsened). The remaining 10 % of simulated CO2 mole fraction differ by > 2ppm between the low-resolution and high-resolution flux representation, and are clearly associated with coherent structures ("plumes") originating from emission hotspots, such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely-populated region like the Amsterdam city centre, our fluxes underestimate the magnitude of measured eddy-covariance fluxes, but capture their substantial diurnal variations in summer- and wintertime well. We conclude that our product is a promising tool to model the European carbon budget at a high-resolution in near real-time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near real-time monitoring and modeling, for example as a-priori flux product in a CO2 data-assimilation system. The data is available at https://doi.org/10.18160/20Z1-AYJ2.

The ocean has long been known to be both a source and sink of climate reactive trace species. Recently, satellite data and general circulation models have been used to simulate air‐sea exchange inventories with high resolution and global coverage. Here, we discuss the differences between a 3‐D global atmospheric CO transport model run with a homogeneous ocean to atmosphere CO flux (35 Tg CO yr−1), and a larger spatially and temporally varying ocean CO flux (153 Tg CO yr−1). Both ocean sources of atmospheric CO are within previous estimates of the ocean to atmosphere flux. The model sensitivity calculations show that the ocean can account for between 5 – 50 % of the total surface level atmospheric CO pool, over large areas of the northern and southern hemisphere oceans. Oceanic CO fluxes to the atmosphere are important in both hemispheres, with a greater relative influence occurring in the southern hemisphere. Seasonal cycle calculations indicate that area‐weighted CO concentrations in the remote southern and northern hemisphere marine troposphere are most heavily influenced by the ocean source during summer months. The main source of CO in surface ocean waters is thought to be photochemical oxidation of organic matter. Any climate related changes in surface radiative fluxes, due to ozone depletion or other processes, may alter surface ocean CO concentrations and by implication atmospheric marine boundary layer CO, OH and O3 abundance.

We estimate the effects of the anthropogenic fugitive, combustion, and industrial dust (AFCID) on winter air quality in China and South Korea for November 2015–March 2016 using the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (KU-CREATE) monthly anthropogenic emission inventory in conjunction with a nested version of GEOS-Chem. Including AFCID emissions in models results in a better agreement with observations and a reduced normalized mean bias of −28% compared to −40% without AFCID. Furthermore, we find that AFCID amounts to winter PM10 concentrations of 17.9 μg m−3 (17%) in eastern China (30−40° N, 112−120° E) with the largest contribution of AFCID to winter PM10 concentrations of up to 45 μg m−3 occurring in eastern China causing a significant impact on air quality to downwind regions. Including AFCID in the model results in an increase of simulated winter PM10 concentrations in South Korea by 3.1 μg m−3 (9%), of which transboundary transport from China accounts for more than 70% of this increased PM10 concentration. Our results indicate that AFCID is an essential factor for winter PM10 concentrations over East Asia and its sources and physical characteristics need to be better quantified to improve PM air quality forecasts.

The Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) is seamless/ online integrated numerical weather prediction and atmospheric chemical transport modelling system. It is capable of simultaneous simulation of meteorology &amp;#8211; atmospheric composition and downscaling/nesting for regional&amp;#8211;subregional&amp;#8211;urban scales. The research and development are focused on: multi-scale modelling up to fine resolution; improving parameterizations describing urban processes, boundary/surface layer structures; implementation of emissions, aerosol/chemistry mechanisms, aerosol feedback and interactions. The Enviro-components includes: gas-phase chemistry; aerosol microphysics and deposition processes; urban sublayer physics parameterisations; direct/indirect/combined aerosol feedbacks due to radiation; locally&amp;#160; mass-conserving&amp;#160; semi-Lagrangian&amp;#160; numerical&amp;#160; advection&amp;#160; scheme; natural and anthropogenic emission inventories. The model has modules for pre-processing of the ECMWF&amp;#8217;s initial/ boundary conditions for meteorology-chemistry-aerosols, observations for data assimilation, and selected emission inventories. The model has been developed through HPC projects such as Enviro-HIRLAM at CSC and Enviro-PEEX(Plus) at ECMWF, as well as other research projects.The research and development of Enviro-HILRAM and its application will be demonstrated on examples, where this model is used as a research tool&amp;#160; for studies in domain of the Pan-Eurasian Experiment (PEEX; https://www.atm.helsinki.fi/peex) programme. The examples include: Integrated modelling for assessment of potential pollution regional atmospheric transport as result of accidental wildfires; Integrated modelling and analysis of influence of land cover changes on regional weather conditions/ patterns; High-resolution integrated urban environmental modelling with integration of the urban large-eddy simulation (PALM model) and meteorological simulations into a seamless modelling chain; Effects of spring air pollution and weather on Covid-19 infection/situation in Finland; Meteorology integration between seamless and trajectory (FLEXPART model) models; and others. The Enviro-HIRLAM model generated output provides valuable input (3D meteorology and atmospheric composition) to assessment studies, and it as can be integrated into GIS environment for further risk/ vulnerability/ consequences/ etc. estimation, and other studies.

Abstract. We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

PM2.5 concentration distribution patterns and influencing meteorological factors in the central and eastern China during 1980–2018