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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

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.

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 (

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.

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