Curriculum vitae of the LOTOS-EUROS (v2.0) chemistry transport model

The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model's history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model's most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description. With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS–EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS–EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model's performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data. This includes the use of different sources of land use characterization (roughness length and vegetation), detailing of emissions in space and time, and efficient coupling to meteorology from different meteorological models

Global Atmospheric δ13CH4 and CH4 Trends for 2000–2020 from the Atmospheric Transport Model TM5 Using CH4 from Carbon Tracker Europe–CH4 Inversions

This study investigates atmospheric δ13CH4 trends, as produced by a global atmospheric transport model using CH4 inversions from CarbonTracker-Europe CH4 for 2000–2020, and compares them to observations. The CH4 inversions include the grouping of the emissions both by δ13CH4 isotopic signatures and process type to investigate the effect, and to estimate the CH4 magnitudes and model CH4 and δ13CH4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH4 trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30∘ N–60∘ N are less than 50% of those from priors. Estimated global CH4 rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60∘ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed δ13CH4 trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors

The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom : 1990-2019

Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD

The global methane budget 2000–2017

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning

Source backtracking for dust storm emission inversion using an adjoint method: Case study of Northeast China

Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are constructed empirically; hence, a partially unrepresentative covariance is unavoidable. Concerning its complex parameterization, dust emissions are a typical example where the uncertainty could be induced from many underlying inputs, e.g., information on soil composition and moisture, land cover and erosive wind velocity, and these can hardly be taken into account together. This paper describes how an adjoint model can be used to detect errors in the emission uncertainty assumptions. This adjoint-based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources in case large observation-minus-simulation residues remain after assimilation based on empirical background covariance. The method follows an application of a data assimilation emission inversion for an extreme severe dust storm over East Asia &lt;span classCombining double low line"cit"idCombining double low line"xref_paren.1"&gt;(&lt;a hrefCombining double low line"#bib1.bibx31"&gt;Jin et al.&lt;/a&gt;, &lt;a hrefCombining double low line"#bib1.bibx31"&gt;2019&lt;/a&gt;&lt;a hrefCombining double low line"#bib1.bibx31"&gt;b&lt;/a&gt;)&lt;/span&gt;. The assimilation system successfully resolved observation-minus-simulation errors using satellite AOD observations in most of the dust-affected regions. However, a large underestimation of dust in Northeast China remained despite the fact that the assimilated measurements indicated severe dust plumes there. An adjoint implementation of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads. The backward modeling points to the Horqin desert as the source region, which was indicated as a non-source region by the existing emission scheme. The reference emission and uncertainty are then reconstructed over the Horqin desert by assuming higher surface erodibility. After the emission reconstruction, the emission inversion is performed again, and the posterior dust simulations and reality are now in much closer harmony. Based on our results, it is advised that emission sources in dust transport models include the Horqin desert as a more active source region. </p

Machine learning for observation bias correction with application to dust storm data assimilation

Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diverge from reality since the data assimilation itself cannot distinguish whether the differences between model simulations and observations are due to the biased observations or model deficiencies. Unfortunately, modeling of observation biases or baselines which show strong spatiotemporal variability is a challenging task. In this study, we report how data-driven machine learning can be used to perform observation bias correction for data assimilation through a real application, which is the dust emission inversion using PM10 observations. PM10 observations are considered unbiased; however, a bias correction is necessary if they are used as a proxy for dust during dust storms since they actually represent a sum of dust particles and non-dust aerosols. Two observation bias correction methods have been designed in order to use PM10 measurements as proxy for the dust storm loads under severe dust conditions. The first one is the conventional chemistry transport model (CTM) that simulates life cycles of non-dust aerosols. The other one is the machine-learning model that describes the relations between the regular PM10 and other air quality measurements. The latter is trained by learning using 2 years of historical samples. The machine-learning-based non-dust model is shown to be in better agreement with observations compared to the CTM. The dust emission inversion tests have been performed, through assimilating either the raw measurements or the bias-corrected dust observations using either the CTM or machine-learning model. The emission field, surface dust concentration, and forecast skill are evaluated. The worst case is when we directly assimilate the original observations. The forecasts driven by the a posteriori emission in this case even result in larger errors than the reference prediction. This shows the necessities of bias correction in data assimilation. The best results are obtained when using the machine-learning model for bias correction, with the existing measurements used more precisely and the resulting forecasts close to reality.Mathematical Physic

Dust Emission Inversion Using Himawari-8 AODs Over East Asia: An Extreme Dust Event in May 2017

Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a comparison with AErosol RObotic NETwork AODs. This study focuses on extreme dust events only when dust aerosols are dominant; promising results are obtained in AOD assimilation experiments during a case in May 2017. The dust emission fields that drive the simulation model are strongly improved by the inverse modeling, and consequently, the simulated dust concentrations are in better agreements with the observed AOD as well as ground-based observations of PM 10 . However, some satellite AODs show significant inconsistence with the simulations and the PM 10 and AErosol RObotic NETwork observations, which might arise from retrieval errors over a partially clouded scene. The data assimilation procedure therefore includes a screening method to exclude these observations in order to avoid unrealistic results. A dust mask screening method is designed, which is based on selecting only those observations where the deterministic model produces a substantial amount of dust. This screen algorithm is tested to give more accurate result compared to the traditional method based on background covariance in the case study. Note that our screen method would exclude valuable information in case the model is not able to simulate the dust plume shape correctly; hence, applications in related studies require inspections of simulations and observations by user. Mathematical Physic

Source backtracking for dust storm emission inversion using an adjoint method: Case study of Northeast China

Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are constructed empirically; hence, a partially unrepresentative covariance is unavoidable. Concerning its complex parameterization, dust emissions are a typical example where the uncertainty could be induced from many underlying inputs, e.g., information on soil composition and moisture, land cover and erosive wind velocity, and these can hardly be taken into account together. This paper describes how an adjoint model can be used to detect errors in the emission uncertainty assumptions. This adjoint-based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources in case large observation-minus-simulation residues remain after assimilation based on empirical background covariance. The method follows an application of a data assimilation emission inversion for an extreme severe dust storm over East Asia &lt;span classCombining double low line"cit"idCombining double low line"xref_paren.1"&gt;(&lt;a hrefCombining double low line"#bib1.bibx31"&gt;Jin et al.&lt;/a&gt;, &lt;a hrefCombining double low line"#bib1.bibx31"&gt;2019&lt;/a&gt;&lt;a hrefCombining double low line"#bib1.bibx31"&gt;b&lt;/a&gt;)&lt;/span&gt;. The assimilation system successfully resolved observation-minus-simulation errors using satellite AOD observations in most of the dust-affected regions. However, a large underestimation of dust in Northeast China remained despite the fact that the assimilated measurements indicated severe dust plumes there. An adjoint implementation of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads. The backward modeling points to the Horqin desert as the source region, which was indicated as a non-source region by the existing emission scheme. The reference emission and uncertainty are then reconstructed over the Horqin desert by assuming higher surface erodibility. After the emission reconstruction, the emission inversion is performed again, and the posterior dust simulations and reality are now in much closer harmony. Based on our results, it is advised that emission sources in dust transport models include the Horqin desert as a more active source region. Mathematical Physic

Position correction in dust storm forecasting using LOTOS-EUROS v2.1: Grid-distorted data assimilation v1.0

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations.Mathematical Physic

Bureau international du Travail, Amélioration et harmonisation des systèmes de sécurité sociale en Afrique

Euzéby Alain. Bureau international du Travail, Amélioration et harmonisation des systèmes de sécurité sociale en Afrique. In: Tiers-Monde, tome 19, n°73, 1978. Environnement et aménagement en Afrique (sous la direction de Jacques Bugnicourt) sous la direction de Jacques Bugnicourt. p. 216