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Carbonyl sulfide (COS), a trace gas in our atmosphere that leads to the formation of aerosols in the stratosphere, is largely taken up by terrestrial ecosystems. Quantifying the biosphere uptake of COS could provide a useful quantity to estimate gross primary productivity (GPP). Some COS sources and sinks still contain large uncertainties, and several top-down estimates of the COS budget point to an underestimation of sources, especially in the tropics. We extended the inverse model TM5-4DVAR to assimilate Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite data, in addition to National Oceanic and Atmospheric Administration (NOAA) surface data as used in a previous study. To resolve possible discrepancies among the two observational data sets, a bias correction scheme is necessary and implemented. A set of inversions is presented that explores the influence of the different measurement streams and the settings of the prior fluxes. To evaluate the performance of the inverse system, the HIAPER Pole-to-Pole Observations (HIPPO) aircraft observations and NOAA airborne profiles are used. All inversions reduce the COS biosphere uptake from a prior value of 1053 GgS a1 to much smaller values, depending on the inversion settings. These large adjustments of the biosphere uptake often turn parts of Amazonia into a COS source. Only inversions that exclusively use MIPAS observations, or strongly reduce the prior errors on the biosphere flux, maintain the Amazon as a COS sink. Inclusion of MIPAS data in the inversion leads to a better separation of land and ocean fluxes. Over the Amazon, these inversions reduce the biosphere uptake from roughly 300 to 100 GgS a1, indicating a strongly overestimated prior uptake in this region. Although a recent study also reported reduced COS uptake over the Amazon, we emphasise that a careful construction of prior fluxes and their associated errors remains important. For instance, an inversion that gives large freedo

In this paper, we propose improved nitrogen dioxide (NO2) to nitrogen oxide (NOx) scaling factors for several data-driven methods that are used for the estimation of NOx power plant emissions from satellite observations of NO2. The scaling factors are deduced from high-resolution simulations of power plant plumes with the MicroHH large-eddy simulation model with a simplified chemistry and then applied to Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) NO2 satellite observations over the Matimba/Medupi power stations in South Africa. We show that due to the non-linear chemistry the optimal NO2 to NOx scaling factors depend on both the method employed and the specific segments of the plume from which emission estimate is derived. The scaling factors derived from the MicroHH simulations in this study are substantially (more than 50%) higher than the typical values used in the literature with actual NO2 observations. The results highlight the challenge in appropriately accounting for the conversion from NO2 to NOx when estimating point source emissions from satellite NO2 observations.

In this paper, we propose improved nitrogen dioxide (NO2) to nitrogen oxide (NOx) scaling factors for several data-driven methods that are used for the estimation of NOx power plant emissions from satellite observations of NO2. The scaling factors are deduced from high-resolution simulations of power plant plumes with the MicroHH large-eddy simulation model with a simplified chemistry and then applied to Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) NO2 satellite observations over the Matimba/Medupi power stations in South Africa. We show that due to the non-linear chemistry the optimal NO2 to NOx scaling factors depend on both the method employed and the specific segments of the plume from which emission estimate is derived. The scaling factors derived from the MicroHH simulations in this study are substantially (more than 50%) higher than the typical values used in the literature with actual NO2 observations. The results highlight the challenge in appropriately accounting for the conversion from NO2 to NOx when estimating point source emissions from satellite NO2 observations.

Carbonyl sulfide (COS), a trace gas in our atmosphere that leads to the formation of aerosols in the stratosphere, is largely taken up by terrestrial ecosystems. Quantifying the biosphere uptake of COS could provide a useful quantity to estimate gross primary productivity (GPP). Some COS sources and sinks still contain large uncertainties, and several top-down estimates of the COS budget point to an underestimation of sources, especially in the tropics. We extended the inverse model TM5-4DVAR to assimilate Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite data, in addition to National Oceanic and Atmospheric Administration (NOAA) surface data as used in a previous study. To resolve possible discrepancies among the two observational data sets, a bias correction scheme is necessary and implemented. A set of inversions is presented that explores the influence of the different measurement streams and the settings of the prior fluxes. To evaluate the performance of the inverse system, the HIAPER Pole-to-Pole Observations (HIPPO) aircraft observations and NOAA airborne profiles are used. All inversions reduce the COS biosphere uptake from a prior value of 1053 GgS a1 to much smaller values, depending on the inversion settings. These large adjustments of the biosphere uptake often turn parts of Amazonia into a COS source. Only inversions that exclusively use MIPAS observations, or strongly reduce the prior errors on the biosphere flux, maintain the Amazon as a COS sink. Inclusion of MIPAS data in the inversion leads to a better separation of land and ocean fluxes. Over the Amazon, these inversions reduce the biosphere uptake from roughly 300 to 100 GgS a1, indicating a strongly overestimated prior uptake in this region. Although a recent study also reported reduced COS uptake over the Amazon, we emphasise that a careful construction of prior fluxes and their associated errors remains important. For instance, an inversion that gives large freedom t

Carbonyl sulfide (COS) is a useful tracer to estimate gross primary production (GPP) because it shares part of the uptake pathway with CO2. COS is taken up in plants through hydrolysis, catalyzed by the enzyme carbonic anhydrase (CA), but is not released. The Simple Biosphere model version 4 (SiB4) simulates COS leaf uptake using a conductance approach. SiB4 applies the temperature response of the RuBisCo enzyme (used for photosynthesis) to simulate the COS leaf uptake, but the CA enzyme might respond differently to temperature. We introduce a new temperature response function for CA in SiB4, based on enzyme kinetics with an optimum temperature. Moreover, we determine Ball–Woodrow–Berry (BWB) model parameters for stomatal conductance (gs) using observation-based estimates of COS flux, GPP, and gs along with meteorological measurements in an evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF). We find that CA has optimum temperatures of 20 ∘C (ENF) and 36 ∘C (DBF), which is lower than that of RuBisCo (45 ∘C), suggesting that canopy temperature changes can critically affect CA's catalyzation activity. Optimized values for the BWB offset parameter are similar to the original value (0.010 ± 0.003 mol m−2 s−1), and optimized values for the BWB slope parameter (ENF: 16.4, DBF: 11.4) are higher than the original value (9.0) at both sites. The optimization reduces prior errors on all parameters by more than 50 % at both stations. We apply the optimized gi and gs parameters in SiB4 site simulations, thereby improving the timing and peak of COS assimilation. In addition, we show that SiB4 underestimates the leaf humidity stress under conditions where high vapor pressure deficit (VPD) should limit gs in the afternoon, thereby overestimating gs. Furthermore, global COS biosphere sinks with optimized parameters show smaller COS uptake in regions where the air temperature is over 25 ∘C, mostly in the tropics, and larger uptake in regions where the temperature i

This paper provides a description of ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer, or the exchange of gases, moisture, heat, and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow the assimilation of various streams of observations (fluxes, mixing ratios at multiple heights, etc.) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework allows the retrieval of parameters in an objective manner and enables the estimation of information that is difficult to obtain directly by observations, for example, free tropospheric mixing ratios or stomatal conductances. Furthermore, it allows the estimation of possible biases in observations. Modelling the carbon cycle at the ecosystem level is one of the main intended fields of application. The physical model around which the framework is constructed is relatively simple yet contains the core physics to simulate the essentials of a well-mixed boundary layer and of the land–atmosphere exchange. The model includes an explicit description of the atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of the best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, which is used for minimisation of this function. An implemented Monte Carlo way of running ICLASS can further help to handle non-linearity and provides posterior statistics on the estimated parameters. The paper provides a technical description of the framework, includes a validation of the adjoint code, in addition to tests for

Aerosol formation acts as a sink for gas-phase atmospheric species that controls their atmospheric lifetime and environmental effects. To investigate aerosol formation and evolution in the Netherlands, a hybrid positive matrix factorization (PMF) analysis has been conducted using observations from May, June, and September 2021 collected in a rural site of Cabauw in Central Netherlands. The hybrid input matrix consists of the full organic mass spectrum acquired from a time-of-flight aerosol chemical speciation monitor (ToF-ACSM), ACSM species concentrations, and binned particle size distribution concentrations from a scanning mobility particle sizer (SMPS). These hybrid PMF analyses discerned six factors that describe aerosol composition variations: four size-driven factors that are related to new particle formation and growth (F6, F5, F4, and F3), and two bulk factors driven by composition, not size (F2, F1). The smallest-diameter size factor (F6) contains ammonium sulfate and organics, and typically occurs during the daytime. Newly formed particles, represented by F6, are correlated with wind from the southwesterly-westerly, northerly, and easterly sectors that transport sulfur oxides (SOx), ammonia (NH3), and organic precursors to Cabauw. As the particles grow from F6 to F3, nitrate plays an increasing role, and the particle loading diurnal cycle shifts from daytime to a nighttime maximum. The inorganic ion balance and organics composition in the bulk atmosphere affects the chemical composition variation across factors and seasons. Changing ammonium-sulfate-nitrate equilibrium shifts inorganic species among factors, and greater organics availability makes secondary organic aerosol (SOA) more influential in summertime aerosol growth, principally due to volatility differences produced by seasonal variation in photooxidation and temperature.

Carbonyl sulfide (COS), a trace gas in our atmosphere that leads to the formation of aerosols in the stratosphere, is taken up by terrestrial ecosystems. Quantifying the biosphere uptake of (COS) could provide a useful quantity to estimate Gross Primary Productivity. Some COS sources and sinks still contain large uncertainties, and several top down estimates of the COS budget point to an underestimation of sources especially in the tropics. We extended the inverse model TM5-4DVAR to assimilate MIPAS satellite data, in addition to NOAA surface data as used in a previous study. To resolve possible discrepancies among the two observational datasets, a bias correction scheme was implemented. A set of inversions is presented that explores the influence of the different measurement instruments and the settings of the prior fluxes. To evaluate the performance of the inverse system, the HIAPER Pole-to-Pole Observations (HIPPO) aircraft observations and NOAA airborne profiles are used. All inversions reduce the (COS) biosphere uptake from a prior value of 1053 GgS a-1 to much smaller values, depending on the inversion settings. These large adjustments of the biosphere uptake often turn parts of the Amazonia into a (COS) source. Only inversions that exclusively use MIPAS observations, or strongly reduce the prior errors on the biosphere flux maintain the Amazonia as a COS sink. Assimilating both NOAA surface data and MIPAS data requires a small bias correction for MIPAS data, mostly at higher latitudes, to correct for inconsistencies in the observational data and/or transport model errors. Analysis of the error reduction and posterior correlation between land and ocean fluxes indicates that co-assimilation of NOAA surface observations and MIPAS data better constrains the (COS) budget than assimilation of one individual dataset alone. Our inversions with bias corrections reduce the global biosphere uptake to respectively 570 and 687 GgS a-1, depending on the prior biosphere er

Dry deposition to the surface is one of the main removal pathways of tropospheric ozone (O₃). We quantified for the first time the impact of O₃ deposition to the Arctic sea ice on the planetary boundary layer (PBL) O₃ concentration and budget using year-round flux and concentration observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign and simulations with a single-column atmospheric chemistry and meteorological model (SCM). Based on eddy-covariance O₃ surface flux observations, we find a median surface resistance on the order of 20,000 s m¯¹, resulting in a dry deposition velocity of approximately 0.005 cm s¯¹. This surface resistance is up to an order of magnitude larger than traditionally used values in many atmospheric chemistry and transport models. The SCM is able to accurately represent the yearly cycle, with maxima above 40 ppb in the winter and minima around 15 ppb at the end of summer. However, the observed springtime ozone depletion events are not captured by the SCM. In winter, the modelled PBL O₃ budget is governed by dry deposition at the surface mostly compensated by downward turbulent transport of O₃ towards the surface. Advection, which is accounted for implicitly by nudging to reanalysis data, poses a substantial, mostly negative, contribution to the simulated PBL O₃ budget in summer. During episodes with low wind speed (<5 m s¯¹) and shallow PBL (<50 m), the 7-day mean dry deposition removal rate can reach up to 1.0 ppb h¯¹. Our study highlights the importance of an accurate description of dry deposition to Arctic sea ice in models to quantify the current and future O₃ sink in the Arctic, impacting the tropospheric O₃ budget, which has been modified in the last century largely due to anthropogenic activities.

Aerosol formation acts as a sink for gas-phase atmospheric species that controls their atmospheric lifetime and environmental effects. To investigate aerosol formation and evolution in the Netherlands, a hybrid positive matrix factorization (PMF) analysis was conducted using observations from May, June, and September 2021 collected in the rural site of Cabauw in the central part of the Netherlands. The hybrid input matrix consists of the full organic mass spectrum acquired from a time-of-flight aerosol chemical speciation monitor (ToF-ACSM), ACSM inorganic species concentrations, and binned particle size distribution concentrations from a scanning mobility particle sizer (SMPS). These hybrid PMF analyses discerned four factors that describe aerosol composition variations: two size-driven factors that are related to new particle formation (NPF) and growth (F4 and F3), as well as two bulk factors driven by composition, not size (F2 and F1). The distribution of chemical species across these factors shows that different compounds are responsible for nucleation and growth of new particles. The smallest-diameter size factor (F4) contains ammonium sulfate and organics and typically peaks during the daytime. Newly formed particles, represented by F4, are mainly correlated with wind from the southwesterly-westerly and easterly sectors that transport sulfur oxides (SOx), ammonia (NH3), and organic precursors to Cabauw. As the particles grow from F4 to F3 and to bulk factors, nitrate and organics play an increasing role, and the particle loading diurnal cycle shifts from daytime to a nighttime maximum. Greater organics availability makes secondary organic aerosol (SOA) more influential in summertime aerosol growth, principally due to volatility differences produced by seasonal variation in photooxidation and temperature.

This paper provides a description of ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer, or the exchange of gases, moisture, heat, and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow the assimilation of various streams of observations (fluxes, mixing ratios at multiple heights, etc.) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework allows the retrieval of parameters in an objective manner and enables the estimation of information that is difficult to obtain directly by observations, for example, free tropospheric mixing ratios or stomatal conductances. Furthermore, it allows the estimation of possible biases in observations. Modelling the carbon cycle at the ecosystem level is one of the main intended fields of application. The physical model around which the framework is constructed is relatively simple yet contains the core physics to simulate the essentials of a well-mixed boundary layer and of the land-atmosphere exchange. The model includes an explicit description of the atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of the best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, which is used for minimisation of this function. An implemented Monte Carlo way of running ICLASS can further help to handle non-linearity and provides posterior statistics on the estimated parameters. The paper provides a technical description of the framework, includes a validation of the adjoint code, in addition to tests for

Dry deposition to the surface is one of the main removal pathways of tropospheric ozone (O3). We quantified for the first time the impact of O3 deposition to the Arctic sea ice on the planetary boundary layer (PBL) O3 concentration and budget using year-round flux and concentration observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign and simulations with a single-column atmospheric chemistry and meteorological model (SCM). Based on eddy-covariance O3 surface flux observations, we find a median surface resistance on the order of 20,000 s m−1, resulting in a dry deposition velocity of approximately 0.005 cm s−1. This surface resistance is up to an order of magnitude larger than traditionally used values in many atmospheric chemistry and transport models. The SCM is able to accurately represent the yearly cycle, with maxima above 40 ppb in the winter and minima around 15 ppb at the end of summer. However, the observed springtime ozone depletion events are not captured by the SCM. In winter, the modelled PBL O3 budget is governed by dry deposition at the surface mostly compensated by downward turbulent transport of O3 towards the surface. Advection, which is accounted for implicitly by nudging to reanalysis data, poses a substantial, mostly negative, contribution to the simulated PBL O3 budget in summer. During episodes with low wind speed (

In recent years, the Amazon forest has experienced several major droughts (2010, 2015/16) and floods (2012, 2014, 2021). Extreme events represent a threat to the Amazons important functions, but these perturbations also provide valuable insights into the underlying mechanisms. Here we studied the most recent massive drought and flood events in detail, and quantified their severity and spatiotemporal extent relative to a multi-year baseline. First, we describe the anomalous hydrological status of these events, by bringing together a large variety of data sets, including in-situ observations and reanalysis products for precipitation, discharge, vapor pressure deficit and soil moisture. During the strong El Niño conditions following the dry season of 2015, the precipitation fell below its climatological values. This was soon reflected in low discharge rates and soil moisture levels, persisting far into the year 2016 for some regions. In contrast, we find anomalously high precipitation over the northern Amazon during the first months of 2021, resulting in high discharge rates, and rising river levels that have led to massive floods in downstream regions. Finally, we quantified the impact of the 2015/16 drought on vegetation using the inverse model CarbonTacker South America (CT-SAM) and remote sensing proxies for photosynthesis. To address the uncertainty in prior emission estimates, we have used a range of different biosphere models (SiBCASA, SiB4), including a biosphere model linked to a detailed hydrological model (PCR-GLOBWB). For the fire flux we used multiple data sets (GFAS, SiBCASA-GFED4), including a modified version based on CO inversions performed with the TM5-4DVAR system. We find that photosynthesis was reduced during the 2015 drought, especially in the drier, southern part of the Amazon. This was followed by a recovery in the first months of 2016, but during the subsequent dry season a secondary impact on photosynthesis was found. The inv

A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15% to 30% in summer and 40% to 50% in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10% with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32±5.3% and 28.3±3.9%, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1±8.4% and 101±21%, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by 1/452±14% and reducing NOx emissions by 15.5±4.1%. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10% in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowin

Collection of analysis and plotting scripts and model in- and output needed to reproduce the simulations, figures and analysis for the manuscript "Efficacy of high-resolution satellite observations in inverse modeling of carbon monoxide emissions" authored by Nüß et al. See the included README for more information on what each file includes.

Dry deposition is an important ozone sink that impacts ecosystem carbon and water cycling. Ozone dry deposition in forests is regulated by vertical transport, stomatal uptake, and non-stomatal processes including chemical removal. However, accurate descriptions of these processes in deposition parameterizations are hindered by sparse observational constraints on individual sink terms. Here we quantify the contribution of canopy-atmosphere turbulent exchange and chemical ozone removal by soil-emitted nitric oxide (NO) to ozone deposition in a North-Italian broadleaf deciduous forest. We apply a multi-layer canopy exchange model to interpret campaign observations of nitrogen oxides (NOx = NO + NO2) and ozone exchange above and inside the forest canopy. Two state-of-science parameterizations of in-canopy vertical diffusivity, based on above-canopy wind speed or stability, do not reproduce the observed exchange suppressed by canopy-top radiative heating, resulting in overestimated dry deposition velocities of 10%–19% during daytime. Applying observation-derived vertical diffusivities in our simulations largely resolves this overestimation. Soil emissions are an important NOx source despite the observed high background NOx levels. Soil NOx emissions decrease the gradient between canopy and surface layer NOx mixing ratios, which suppresses simulated NOx deposition by 80% compared to a sensitivity simulation without soil emissions. However, a sensitivity analysis shows that the enhanced chemical ozone sink by reaction with soil-emitted NO is offset by increased vertical ozone transport from aloft and suppressed dry deposition. Our results highlight the need for targeted observations of non-stomatal ozone removal and turbulence-resolving deposition simulations to improve quantification and model representation of forest ozone deposition.

Inglese

Understanding how multiphase processes affect the iron-containing aerosol cycle is key to predicting ocean biogeochemistry changes and hence the feedback effects on climate. For this work, the EC-Earth Earth system model in its climate-chemistry configuration is used to simulate the global atmospheric oxalate (OXL), sulfate (SO42-), and iron (Fe) cycles after incorporating a comprehensive representation of the multiphase chemistry in cloud droplets and aerosol water. The model considers a detailed gas-phase chemistry scheme, all major aerosol components, and the partitioning of gases in aerosol and atmospheric water phases. The dissolution of Fe-containing aerosols accounts kinetically for the solution's acidity, oxalic acid, and irradiation. Aerosol acidity is explicitly calculated in the model, both for accumulation and coarse modes, accounting for thermodynamic processes involving inorganic and crustal species from sea salt and dust. Simulations for present-day conditions (2000-2014) have been carried out with both EC-Earth and the atmospheric composition component of the model in standalone mode driven by meteorological fields from ECMWF's ERA-Interim reanalysis. The calculated global budgets are presented and the links between the (1) aqueous-phase processes, (2) aerosol dissolution, and (3) atmospheric composition are demonstrated and quantified. The model results are supported by comparison to available observations. We obtain an average global OXL net chemical production of 12.615 ± 0.064 Tg yr-1 in EC-Earth, with glyoxal being by far the most important precursor of oxalic acid. In comparison to the ERA-Interim simulation, differences in atmospheric dynamics and the simulated weaker oxidizing capacity in EC-Earth overall result in a ∼ 30 % lower OXL source. On the other hand, the more explicit representation of the aqueous-phase chemistry in EC-Earth compared to the previous versions of the model leads to an overall ∼ 20 % higher sulfate production, but this

Carbonyl sulfide (COS) has the potential to be used as a climate diagnostic due to its close coupling to the biospheric uptake of CO2 and its role in the formation of stratospheric aerosol. The current understanding of the COS budget, however, lacks COS sources, which have previously been allocated to the tropical ocean. This paper presents a first attempt at global inverse modelling of COS within the 4-dimensional variational data-assimilation system of the TM5 chemistry transport model (TM5-4DVAR) and a comparison of the results with various COS observations. We focus on the global COS budget, including COS production from its precursors carbon disulfide (CS2) and dimethyl sulfide (DMS). To this end, we implemented COS uptake by soil and vegetation from an updated biosphere model (Simple Biosphere Model – SiB4). In the calculation of these fluxes, a fixed atmospheric mole fraction of 500 pmol mol−1 was assumed. We also used new inventories for anthropogenic and biomass burning emissions. The model framework is capable of closing the COS budget by optimizing for missing emissions using NOAA observations in the period 2000–2012. The addition of 432 Gg a−1 (as S equivalents) of COS is required to obtain a good fit with NOAA observations. This missing source shows few year-to-year variations but considerable seasonal variations. We found that the missing sources are likely located in the tropical regions, and an overestimated biospheric sink in the tropics cannot be ruled out due to missing observations in the tropical continental boundary layer. Moreover, high latitudes in the Northern Hemisphere require extra COS uptake or reduced emissions. HIPPO (HIAPER Pole-to-Pole Observations) aircraft observations, NOAA airborne profiles from an ongoing monitoring programme and several satellite data sources are used to evaluate the optimized model results. This evaluation indicates that COS mole fractions in the free troposphere remain underestimated after optimizat

This dataset contains supporting files, figures and datasets for the method of sulfur isotope measurements of carbonyl sulfide and ambient air measurements in the Netherlands during 2020 and 2021.

Dry deposition is an important sink of tropospheric ozone that affects surface concentrations and impacts crop yields, the land carbon sink, and the terrestrial water cycle. Dry deposition pathways include plant uptake via stomata and non-stomatal removal by soils, leaf surfaces, and chemical reactions. Observational studies indicate that ozone deposition exhibits substantial temporal variability that is not reproduced by atmospheric chemistry models due to a simplified representation of vegetation uptake processes in these models. In this study, we explore the importance of stomatal and non-stomatal uptake processes in driving ozone dry deposition variability on diurnal to seasonal timescales. Specifically, we compare two land surface ozone uptake parameterizations - a commonly applied big leaf parameterization W89;Currency sign and a multi-layer model (MLC-CHEM) constrained with observations - to multi-year ozone flux observations at two European measurement sites (Ispra, Italy, and Hyytiälä, Finland). We find that W89 cannot reproduce the diurnal cycle in ozone deposition due to a misrepresentation of stomatal and non-stomatal sinks at our two study sites, while MLC-CHEM accurately reproduces the different sink pathways. Evaluation of non-stomatal uptake further corroborates the previously found important roles of wet leaf uptake in the morning under humid conditions and soil uptake during warm conditions. The misrepresentation of stomatal versus non-stomatal uptake in W89 results in an overestimation of growing season cumulative ozone uptake (CUO), a metric for assessments of vegetation ozone damage, by 18% (Ispra) and 28% (Hyytiälä), while MLC-CHEM reproduces CUO within 7% of the observation-inferred values. Our results indicate the need to accurately describe the partitioning of the ozone atmosphere-biosphere flux over the in-canopy stomatal and non-stomatal loss pathways to provide more confidence in atmospheric chemistry model simulations of surface ozone mi

Carbonyl sulfide (COS) has the potential to be used as a climate diagnostic due to its close coupling to the biospheric uptake of CO2 and its role in the formation of stratospheric aerosol. The current understanding of the COS budget, however, lacks COS sources, which have previously been allocated to the tropical ocean. This paper presents a first attempt at global inverse modelling of COS within the 4-dimensional variational data-assimilation system of the TM5 chemistry transport model (TM5-4DVAR) and a comparison of the results with various COS observations. We focus on the global COS budget, including COS production from its precursors carbon disulfide (CS2) and dimethyl sulfide (DMS). To this end, we implemented COS uptake by soil and vegetation from an updated biosphere model (Simple Biosphere Model – SiB4). In the calculation of these fluxes, a fixed atmospheric mole fraction of 500 pmol mol−1 was assumed. We also used new inventories for anthropogenic and biomass burning emissions. The model framework is capable of closing the COS budget by optimizing for missing emissions using NOAA observations in the period 2000–2012. The addition of 432 Gg a−1 (as S equivalents) of COS is required to obtain a good fit with NOAA observations. This missing source shows few year-to-year variations but considerable seasonal variations. We found that the missing sources are likely located in the tropical regions, and an overestimated biospheric sink in the tropics cannot be ruled out due to missing observations in the tropical continental boundary layer. Moreover, high latitudes in the Northern Hemisphere require extra COS uptake or reduced emissions. HIPPO (HIAPER Pole-to-Pole Observations) aircraft observations, NOAA airborne profiles from an ongoing monitoring programme and several satellite data sources are used to evaluate the optimized model results. This evaluation indicates that COS mole fractions in the free troposphere remain underestimated after optimization. Assim

Variations in the atmospheric oxidative capacity, largely determined by variations in the hydroxyl radical (OH), form a key uncertainty in many greenhouse and other pollutant budgets, such as that of methane (CH4). Methyl chloroform (MCF) is an often-adopted tracer to indirectly put observational constraints on large-scale variations in OH.We investigated the budget of MCF in a 4DVAR inversion using the atmospheric transport model TM5, for the period 1998-2018, with the objective to derive information on large-scale, interannual variations in atmospheric OH concentrations. While our main inversion did not fully converge, we did derive interannual variations in the global oxidation of MCF that bring simulated mole fractions of MCF within 1 %-2% of the assimilated observations from the NOAA-GMD surface network at most sites. Additionally, the posterior simulations better reproduce aircraft observations used for independent validation compared to the prior simulations. The derived OH variations showed robustness with respect to the prior MCF emissions and the prior OH distribution over the 1998 to 2008 period. Although we find a rapid 8% increase in global mean OH concentrations between 2010 and 2012 that quickly declines afterwards, the derived interannual variations were typically small (<3 %/yr), with no significant long-term trend in global mean OH concentrations. The inverse system found strong adjustments to the latitudinal distribution of OH, relative to widely used prior distributions, with systematic increases in tropical and decreases in extra-tropical OH concentrations (both up to 30 %). These spatial adjustments were driven by intrahemispheric biases in simulated MCF mole fractions, which have not been identified in previous studies. Given the large amplitude of these adjustments, which exceeds spread between literature estimates, and a residual bias in the MCF intrahemispheric gradients, we suggest a reversal in the extratropical ocean sink of MCF in respo

Dry deposition is an important removal mechanism for tropospheric ozone (O3). Currently, O3 deposition to oceans in atmospheric chemistry and transport models (ACTMs) is generally represented using constant surface uptake resistances. This occurs despite the role of solubility, waterside turbulence and O3 reacting with ocean water reactants such as iodide resulting in substantial spatiotemporal variability in O3 deposition and concentrations in marine boundary layers. We hypothesize that O3 deposition to the Arctic Ocean, having a relatively low reactivity, is overestimated in current models with consequences for the tropospheric concentrations, lifetime and long-range transport of O3. We investigate the impact of the representation of oceanic O3 deposition to the simulated magnitude and spatiotemporal variability in Arctic surface O3. We have integrated the Coupled Ocean-Atmosphere Response Experiment Gas transfer algorithm (COAREG) into the mesoscale meteorology and atmospheric chemistry model Polar-WRF-Chem (WRF) which introduces a dependence of O3 deposition on physical and biogeochemical drivers of oceanic O3 deposition. Also, we reduced the O3 deposition to sea ice and snow. Here, we evaluate WRF and CAMS reanalysis data against hourly averaged surface O3 observations at 25 sites (latitudes > 60° N). This is the first time such a coupled modeling system has been evaluated against hourly observations at pan-Arctic sites to study the sensitivity of the magnitude and temporal variability in Arctic surface O3 on the deposition scheme. We find that it is important to nudge WRF to the ECMWF ERA5 reanalysis data to ensure adequate meteorological conditions to evaluate surface O3. We show that the mechanistic representation of O3 deposition over oceans and reduced snow/ice deposition improves simulated Arctic O3 mixing ratios both in magnitude and temporal variability compared to the constant resistance approach. Using COAREG, O3 deposition velocities are in the order

In Colombia, industrialization and a shift towards intensified agriculture have led to increased emissions of air pollutants. However, the baseline state of air quality in Colombia is relatively unknown. In this study we aim to assess the baseline state of air quality in Colombia with a focus on the spatial and temporal variability in emissions and atmospheric burden of nitrogen oxides (NOx = NO + NO2) and evaluate surface NOx , ozone (O3) and carbon monoxide (CO) mixing ratios. We quantify the magnitude and spatial distribution of the four major NOx sources (lightning, anthropogenic activities, soil biogenic emissions and biomass burning) by integrating global NOx emission inventories into the mesoscale meteorology and atmospheric chemistry model, namely Weather Research and Forecasting (WRF) coupled with Chemistry (collectively WRF-Chem), at a similar resolution ( 25 km) to the Emission Database for Global Atmospheric Research (EDGAR) anthropogenic emission inventory and the Ozone Monitoring Instrument (OMI) remote sensing observations. The model indicates the largest contribution by lightning emissions (1258 GgNyr1), even after already significantly reducing the emissions, followed by anthropogenic (933 GgNyr1), soil biogenic (187 GgNyr1) and biomass burning emissions (104 GgNyr1). The comparison with OMI remote sensing observations indicated a mean bias of tropospheric NO2 columns over the whole domain (WRF-Chem minus OMI) of 0.02 (90% CI: [0:43, 0.70])1015 molecules cm2, which is < 5% of the mean column. However, the simulated NO2 columns are overestimated and underestimated in regions where lightning and biomass burning emissions dominate, respectively. WRFChem was unable to capture NOx and CO urban pollutant mixing ratios, neither in timing nor in magnitude. Yet, WRFChem was able to simulate the urban diurnal cycle of O3 satisfactorily but with a systematic overestimation of 10 parts per billion (ppb) due to the equally large underestimation of NO mixing rati

This work documents and evaluates the tropospheric gas-phase chemical mechanism MOGUNTIA in the three-dimensional chemistry transport model TM5-MP. Compared to the modified CB05 (mCB05) chemical mechanism previously used in the model, MOGUNTIA includes a detailed representation of the light hydrocarbons (C1-C4) and isoprene, along with a simplified chemistry representation of terpenes and aromatics. Another feature implemented in TM5-MP for this work is the use of the Rosenbrock solver in the chemistry code, which can replace the classical Euler backward integration method of the model. Global budgets of ozone (O3), carbon monoxide (CO), hydroxyl radicals (OH), nitrogen oxides (NOx), and volatile organic compounds (VOCs) are analyzed, and their mixing ratios are compared with a series of surface, aircraft, and satellite observations for the year 2006. Both mechanisms appear to be able to satisfactorily represent observed mixing ratios of important trace gases, with the MOGUNTIA chemistry configuration yielding lower biases than mCB05 compared to measurements in most of the cases. However, the two chemical mechanisms fail to reproduce the observed mixing ratios of light VOCs, indicating insufficient primary emission source strengths, oxidation that is too fast, and/or a low bias in the secondary contribution to C2-C3 organics via VOC atmospheric oxidation. Relative computational memory and time requirements of the different model configurations are also compared and discussed. Overall, the MOGUNTIA scheme simulates a large suite of oxygenated VOCs that are observed in the atmosphere at significant levels. This significantly expands the possible applications of TM5-MP.

The triple oxygen isotope signature Δ17O in atmospheric CO2, also known as its “17O excess,” has been proposed as a tracer for gross primary production (the gross uptake of CO2 by vegetation through photosynthesis). We present the first global 3-D model simulations for Δ17O in atmospheric CO2 together with a detailed model description and sensitivity analyses. In our 3-D model framework we include the stratospheric source of Δ17O in CO2 and the surface sinks from vegetation, soils, ocean, biomass burning, and fossil fuel combustion. The effect of oxidation of atmospheric CO on Δ17O in CO2 is also included in our model. We estimate that the global mean Δ17O (defined as Δ17O = ln(δ17O+1)−λRL·ln(δ18O+1) with λRL = 0.5229) of CO2 in the lowest 500 m of the atmosphere is 39.6 per meg, which is ∼20 per meg lower than estimates from existing box models. We compare our model results with a measured stratospheric Δ17O in CO2 profile from Sodankylä (Finland), which shows good agreement. In addition, we compare our model results with tropospheric measurements of Δ17O in CO2 from Göttingen (Germany) and Taipei (Taiwan), which shows some agreement but we also find substantial discrepancies that are subsequently discussed. Finally, we show model results for Zotino (Russia), Mauna Loa (United States), Manaus (Brazil), and South Pole, which we propose as possible locations for future measurements of Δ17O in tropospheric CO2 that can help to further increase our understanding of the global budget of Δ17O in atmospheric CO2.

Ozone (O3) is a secondary air pollutant that negatively affects human and ecosystem health. Ozone simulations with regional air quality models suffer from unexplained biases over Europe, and uncertainties in the emissions of ozone precursor group nitrogen oxides (NOx= NO+NO) contribute to these biases. The goal of this study is to use NO2 column observations from the Ozone Monitoring Instrument (OMI) satellite sensor to infer top-down NOx emissions in the regional Weather Research and Forecasting model with coupled chemistry (WRF-Chem) and to evaluate the impact on simulated surface O3 with in situ observations. We first perform a simulation for July 2015 over Europe and evaluate its performance against in situ observations from the AirBase network. The spatial distribution of mean ozone concentrations is reproduced satisfactorily. However, the simulated maximum daily 8h ozone concentration (MDA8 O3) is underestimated (mean bias error of -14.2μgm-3), and its spread is too low. We subsequently derive satellite-constrained surface NOx emissions using a mass balance approach based on the relative difference between OMI and WRF-Chem NO2 columns. The method accounts for feedbacks through OH, NO2's dominant daytime oxidant. Our optimized European NOx emissions amount to 0.50TgN (for July 2015), which is 0.18TgN higher than the bottom-up emissions (which lacked agricultural soil NOx emissions). Much of the increases occur across Europe, in regions where agricultural soil NOx emissions dominate. Our best estimate of soil NOx emissions in July 2015 is 0.1TgN, much higher than the bottom-up 0.02TgN natural soil NOx emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN). A simulation with satellite-updated NOx emissions reduces the systematic bias between WRF-Chem and OMI NO2 (slopeCombining double low line0.98, r2Combining double low line0.84) and reduces the low bias against independent surface NO2 measurements by 1.1μgm-3 (-56%). Following these

This work reports on the current status of the global modeling of iron (Fe) deposition fluxes and atmospheric concentrations and the analyses of the differences between models, as well as between models and observations. A total of four global 3-D chemistry transport (CTMs) and general circulation (GCMs) models participated in this intercomparison, in the framework of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) Working Group 38, “The Atmospheric Input of Chemicals to the Ocean”. The global total Fe (TFe) emission strength in the models is equal to ∼72 Tg Fe yr−1 (38–134 Tg Fe yr−1) from mineral dust sources and around 2.1 Tg Fe yr−1 (1.8–2.7 Tg Fe yr−1) from combustion processes (the sum of anthropogenic combustion/biomass burning and wildfires). The mean global labile Fe (LFe) source strength in the models, considering both the primary emissions and the atmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1, accounting for both mineral dust and combustion aerosols. The mean global deposition fluxes into the global ocean are estimated to be in the range of 10–30 and 0.2–0.4 Tg Fe yr−1 for TFe and LFe, respectively, which roughly corresponds to a respective 15 and 0.3 Tg Fe yr−1 for the multi-model ensemble model mean. The model intercomparison analysis indicates that the representation of the atmospheric Fe cycle varies among models, in terms of both the magnitude of natural and combustion Fe emissions as well as the complexity of atmospheric processing parameterizations of Fe-containing aerosols. The model comparison with aerosol Fe observations over oceanic regions indicates that most models overestimate surface level TFe mass concentrations near dust source regions and tend to underestimate the low concentrations observed in remote ocean regions. All models are able to simulate the tendency of higher Fe concentrations near and downwind from the dust source regions, with the mean normal

This work reports on the current status of the global modeling of iron (Fe) deposition fluxes and atmospheric concentrations and the analyses of the differences between models, as well as between models and observations. A total of four global 3-D chemistry transport (CTMs) and general circulation (GCMs) models participated in this intercomparison, in the framework of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) Working Group 38, “The Atmospheric Input of Chemicals to the Ocean”. The global total Fe (TFe) emission strength in the models is equal to ∼72 Tg Fe yr−1 (38–134 Tg Fe yr−1) from mineral dust sources and around 2.1 Tg Fe yr−1 (1.8–2.7 Tg Fe yr−1) from combustion processes (the sum of anthropogenic combustion/biomass burning and wildfires). The mean global labile Fe (LFe) source strength in the models, considering both the primary emissions and the atmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1, accounting for both mineral dust and combustion aerosols. The mean global deposition fluxes into the global ocean are estimated to be in the range of 10–30 and 0.2–0.4 Tg Fe yr−1 for TFe and LFe, respectively, which roughly corresponds to a respective 15 and 0.3 Tg Fe yr−1 for the multi-model ensemble model mean. The model intercomparison analysis indicates that the representation of the atmospheric Fe cycle varies among models, in terms of both the magnitude of natural and combustion Fe emissions as well as the complexity of atmospheric processing parameterizations of Fe-containing aerosols. The model comparison with aerosol Fe observations over oceanic regions indicates that most models overestimate surface level TFe mass concentrations near dust source regions and tend to underestimate the low concentrations observed in remote ocean regions. All models are able to simulate the tendency of higher Fe concentrations near and downwind from the dust source regions, with the mean normal

We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation > 0.75, bias < ± ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (1x1 in this case) optimization is possible with a dense observation net

We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation > 0.75, bias < ± ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (1x1 in this case) optimization is possible with a dense observation network.

We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation > 0.75, bias < ± ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (1x1 in this case) optimization is possible with a dense observation net

This work reports on the current status of the global modeling of iron (Fe) deposition fluxes and atmospheric concentrations and the analyses of the differences between models, as well as between models and observations. A total of four global 3-D chemistry transport (CTMs) and general circulation (GCMs) models participated in this intercomparison, in the framework of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) Working Group 38, “The Atmospheric Input of Chemicals to the Ocean”. The global total Fe (TFe) emission strength in the models is equal to ∼72 Tg Fe yr−1 (38–134 Tg Fe yr−1) from mineral dust sources and around 2.1 Tg Fe yr−1 (1.8–2.7 Tg Fe yr−1) from combustion processes (the sum of anthropogenic combustion/biomass burning and wildfires). The mean global labile Fe (LFe) source strength in the models, considering both the primary emissions and the atmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1, accounting for both mineral dust and combustion aerosols. The mean global deposition fluxes into the global ocean are estimated to be in the range of 10–30 and 0.2–0.4 Tg Fe yr−1 for TFe and LFe, respectively, which roughly corresponds to a respective 15 and 0.3 Tg Fe yr−1 for the multi-model ensemble model mean. The model intercomparison analysis indicates that the representation of the atmospheric Fe cycle varies among models, in terms of both the magnitude of natural and combustion Fe emissions as well as the complexity of atmospheric processing parameterizations of Fe-containing aerosols. The model comparison with aerosol Fe observations over oceanic regions indicates that most models overestimate surface level TFe mass concentrations near dust source regions and tend to underestimate the low concentrations observed in remote ocean regions. All models are able to simulate the tendency of higher Fe concentrations near and downwind from the dust source regions, with the m