Search

The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.

This article describes the Integrated Forecasting System aerosol scheme (IFS-AER) used operationally in the IFS cycle 47R1, which was operated by the European Centre for Medium Range Weather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). It represents an update of the Rémy et al. (2019) article, which described cycle 45R1 of IFS-AER in detail. Here, we detail only the parameterisations of sources and sinks that have been updated since cycle 45R1, as well as recent changes in the configuration used operationally within CAMS. Compared to cycle 45R1, a greater integration of aerosol and chemistry has been achieved. Primary aerosol sources have been updated, with the implementation of new dust and sea salt aerosol emission schemes. New dry and wet deposition parameterisations have also been implemented. Sulfate production rates are now provided by the global chemistry component of IFS. This paper aims to describe most of the updates that have been implemented since cycle 45R1, not just the ones that are used operationally in cycle 47R1; components that are not used operationally will be clearly flagged. Cycle 47R1 of IFS-AER has been evaluated against a wide range of surface and total column observations. The final simulated products, such as particulate matter (PM) and aerosol optical depth (AOD), generally show a significant improvement in skill scores compared to results obtained with cycle 45R1. Similarly, the simulated surface concentration of sulfate, organic matter and sea salt aerosol are improved by cycle 47R1 compared to cycle 45R1. Some biases persist, such as the surface concentrations of nitrate and organic matter being simulated too high. The new wet and dry deposition schemes that have been implemented into cycle 47R1 have a mostly positive impact on simulated AOD, PM and speciated aerosol surface concentration.

The Integrated Forecasting System (IFS) of ECMWF is core of the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including reactive gases, as well as aerosol and greenhouse gases. The CAMS global model consists of the aerosol model of the IFS, IFS-AER, which is a sectional-bulk scheme, while the chemistry scheme is based on a CB05-based carbon-bond mechanism, with the option to couple this to BASCOE-based stratospheric chemistry. The composition model is updated regularly, aligned with updates of ECMWF’s operational meteorological model. Here we report on updates planned for the operational version after next, referred to as CY49R1. This concerns revisions on a large range of topics, as developed over the recent years, and therefore impacting many aspects of chemistry and aerosol composition in troposphere and stratosphere. Main aspects concern:A review of the representation of polar stratospheric clouds and of their impact on stratospheric ozone, An extension of IFS-AER to represent stratospheric sulfate aerosols, coupled with CB05 and BASCOE precursor gases, An upgrade of gas-particle partitioning through the implementation of EQSAM4Clim in the IFS, Computation of aerosol, cloud and rain pH, and use of the update pH values in aqueous chemistry, A combined representation of aerosols and chemistry deposition processes (wet and dry), Update of aerosol optics, including a simple representation of dust asphericity and hygroscopic growth, Update of PM diagnostic output In this contribution we provide an overview of expected changes with emphasis on changes in composition modeling aspects. We will present their expected impact on key atmospheric composition aspects, including air quality performance across major pollution regions across the world, aerosol optical depth, dust, and stratospheric composition products.

Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near real time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and PMAp Aerosol Optical Depth (AOD). Given the potential end-of-lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/TERRA over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, Arabian Peninsula and few places in Asia in the July-August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements with the largest bias reduction over Europe, desert, and maritime sites.

Aerosol forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System aerosol module (IFS-AER) for the years 2016–2019 (cycles 41r1–46r1) are compared to vertical profiles of particle backscatter from the Deutscher Wetterdienst (DWD) ceilometer network. The system has been developed in the Copernicus Atmosphere Monitoring Service (CAMS) and its precursors. The focus of this article is to evaluate the realism of the vertical aerosol distribution from 0.4 to 8 km above ground, coded in the shape, bias and temporal variation of the profiles. The common physical quantity, the attenuated backscatter β∗(z), is directly measured and calculated from the model mass mixing ratios of the different particle types using the model's inherent aerosol microphysical properties. Pearson correlation coefficients of daily average simulated and observed vertical profiles between r=0.6–0.8 in summer and 0.7–0.95 in winter indicate that most of the vertical structure is captured. It is governed by larger β∗(z) in the mixing layer and comparably well captured with the successive model versions. The aerosol load tends to be biased high near the surface, underestimated in the mixing layer and realistic at small background values in the undisturbed free troposphere. A seasonal cycle of the bias below 1 km height indicates that aerosol sources and/or lifetimes are overestimated in summer and pollution episodes are not fully resolved in winter. Long-range transport of Saharan dust or fire smoke is captured and timely, only the dispersion to smaller scales is not resolved in detail. Over Germany, β∗(z) values from Saharan dust and sea salt are considerably overestimated. Differences between model and ceilometer profiles are investigated using observed in situ mass concentrations of organic matter (OM), black carbon, SO4, NO3, NH4 and proxies for mineral dust and sea salt near the surface. Accordingly, SO4 and OM sources as well as gas-to-particle partitioning of the NO3–NH4 system are too strong. The top of the mixing layer on average appears too smooth and several hundred meters too low in the model. Finally, a discussion is included of the considerable uncertainties in the observations as well as the conversion from modeled to observed physical quantities and from necessary adaptions of varying resolutions and definitions.

The Integrated Forecasting System (IFS) of ECMWF is core of the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including reactive gases, as well as aerosol and greenhouse gases. Desert dust is simulated globally in three size bins. This system has been extended in an experimental version to prognostically simulate twelve mineralogical components of dust, each of them in three size bins. The chemical composition of dust can be derived from the mineralogical information, which allows for comparison against surface observations, notable of Iron. Each of the dust mineralogical component uses specific optical properties.Four years of dust simulated global mineralogical and chemical composition have been produced. Iron from dust have been compared against observations of surface concentration worldwide and against simulations from the atmospheric iron model intercomparison organized by the Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP). Both evaluations gave satisfactory results. Surface concentration of other dust chemical components have been evaluated against surface observations other US and Europe.Simulation of the dust mineralogy allows for a better representation of the geographical variation in dust absorption, especially depending on the simulated burden of the most absorbing species, hematite and goethite. While this variability cannot yet be represented in the optical properties of the dust species used operationally within CAMS, the climatology of dust mineralogy helped to derive new dust optical properties in the visible part of the spectrum. It also provided a degree of regional information about dust size distribution at emission, which has been implemented in the IFS. These two developments, together with an update of the dust source function, led to a significant improvement in the skill of the IFS system for dust related parameters. They have been included in the next operational upgrade of the operational global CAMS system, cycle 48R1, which is planned in late 2022.

The Integrated Forecasting System (IFS) of ECMWF is used within the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including aerosols as well as reactive trace gases and greenhouse gases. Here we provide a first description and assessment of cloud and aerosol pH as computed in EQSAM4CLIM when implemented in the IFS. The more flexible pH computation is furthermore coupled to the aqueous phase chemistry governing the SO2 in-cloud oxidation, as well as to the wet deposition routine for gases. Currently the IFS describes acidity via the scavenging strong inorganic acids (HNO3, H2SO4) and the contribution from SO2 oxidation and buffering via NH3 using a simplistic description, which results in a pH ranging between 3-5. To provide a more diverse range in pH we have coupled the CB05 chemistry scheme and AER-aerosol components of IFS exploiting EQSAM4Clim-based [H+] calculations by introducing the resulting pH into the aqueous phase chemistry and wet deposition processes. The subsequent representation of aerosol acidity in the solution (aerosol/cloud/rain water) has been evaluated against observations and previous modelling studies using the GEOS-Chem global CTM. The use of aerosol induced acidity in the computation of aqueous phase chemical reaction rates was found to be most important for inorganic soluble gas and aerosol phase species, e.g. SO2, and their subsequent oxidative products, e.g. HNO3. The impact on simulated SO2 concentrations at the surface is significant, through changes to the aqueous phase chemistry and subsequent wet deposition. The impact on PM2.5 is generally small but regionally positive, over Europe, US and China. There is a positive impact on surface O3 with a reduction in the annual mean bias at the surface for Europe, the US and China when compared against observational networks for 2019. Surface SO42-concentrations are generally closer to observations, especially during wintertime over Europe and U.S., while surface NH3 shows only moderate changes. NH3 shows no significant improvement in observed biases against observations, but the impact on simulated surface concentration of NH4+ aerosol is generally positive, particularly in winter. Further improvements here can be expected by improving the coupling with mineral cations (Na+, K+, Ca2+, Mg2+), and by including major organic acids in the aerosol neutralisation reactions. For EQSAM4Clim, the neutralisation reactions and the total liquid water content (of aerosols, fog/cloud, rain) are key for the pH computation and the associated gas/aerosol partitioning, and most critical for NH3. Ammonia forms here the only volatile cation (NH4+), those presence in the aerosol phase critically depends on water and mineral cations.In summary, the production efficacy of sulphate and ammonium aerosol is critically dependent on an accurate representation of acidity in aqueous droplets and aerosol species at global scale. This development is not only expected to bring a much improved constraint on the modelling of surface concentrations of sulfur and nitrogen, together with its deposition. Also the cloud and rain water pH itself are within reach as new products of the CAMS global service.

The Copernicus Atmosphere Monitoring Service (CAMS) produces operationally global 5-day forecast of atmospheric composition and the weather using ECMWF’s Integrated Forecasting System (IFS) since 2015.Beginning with a system upgrade in June 2018 (45r1), the ozone and aerosol fields have been used in the radiation scheme to account for their radiative impact in the global CAMS forecasts. This approach replaced an aerosol and ozone climatology, which had been used before and which is still used in ECMWF's operational high-resolution medium-range NWP forecasts. The CAMS forecast system, which runs at a resolution of about 40 km, is applied here as a test-bed to explore the importance of aerosol direct feedback in an operational configuration, which can guide developments on composition-weather feedbacks for ECMWF's medium-range, monthly and seasonal forecasts. The CAMS prognostic aerosol simulations and the assimilation of different AOD retrievals (MODIS, VIIRS, S5P) have been substantially further modified in several upgrades of the CAMS operational system since 2018.We will discuss the changes and improvements of temperature forecast errors focusing on the impact of changing the aerosol simulation and AOD assimilation in the recent cycles. These changes were introduced to improve the realism of the aerosol forecasts, which is a key CAMS forecast product, and not developed specifically considering their impact on NWP.We will show NWP scores, evaluation with synop-observations and satellite radiation products to demonstrate the impact of the prognostic aerosols. We will also compare the results of the NWP forecast of the CAMS suite, with the NWP scores of ECMWF high resolution forecast run ay 9 km spatial resolution globally. We will further demonstrate that the consistent updates of both the climatological and prognostic aerosol fields are an important prerequisite for a sound assessment of the importance of prognostic aerosol in NWP applications.

The Copernicus Atmosphere Monitoring Service (CAMS) provides global analyses and forecasts of atmospheric composition, relying on the Integrated Forecasting System (IFS) of ECMWF. The CAMS global model consists of the aerosol model of the IFS, IFS-AER, which is a sectional-bulk scheme, while the chemistry scheme is based on a CB05-based carbon-bond mechanism, with the option to couple this to BASCOE-based stratospheric chemistry.The composition model is updated regularly, aligned with updates of ECMWF’s operational meteorological model. Here we report on updates planned for the next operational version, referred to as CY48R1. This concern revisions on a large range of topics, as developed over the recent years, and therefore impacting many aspects of chemistry and aerosol composition in troposphere and stratosphere. Main aspects concern:Isoprene oxidation has been redefined, resulting in increased OH recycling and including a first model description of glyoxal as well as basic aromatics chemistry. NOy chemistry has been updated to include HONO and the longer-lived species CH3O2NO2. A coupling with secondary organic aerosol formation has been established, and the coupling of secondary inorganic aerosol has been revisited. the option to use the BASCOE-based stratospheric chemistry for operational application is actively considered. Dry and wet deposition parameterizations have been revisited by including a GEOS-Chem based deposition parameterization using tile fraction specific deposition velocities. Aerosol dust emissions, and their optical properties have been revised. The emissions handling has been updated, along with updates of the CAMS global emission inventories themselves. The updates of the composition model and its emissions are tested in combination with updates to the tracer transport and data assimilation aspects, and the optimal configuration will be selected for operational application.In this contribution we provide an overview of expected changes with emphasis on changes in composition modeling aspects. We will present the subsequent impacts on key atmospheric composition aspects, including air quality performance for major pollution regions across the world, aerosol optical depth, dust, and stratospheric composition products.

The European Centre for Medium-Range Weather Forecasts (ECMWF) operates the Integrated Forecasting System aerosol module (IFS-AER) to provide daily global analysis and forecast of aerosols for the Copernicus Atmosphere Monitoring Service (CAMS). New estimates of sea salt aerosol emissions have been implemented in the IFS-AER using a new parameterization of whitecap fraction as a function of wind speed and sea surface temperature. The effect of whitecap fraction simulated by old and new parameterizations has been evaluated by comparing the IFS-AER new sea salt aerosol characteristics to those of aerosol retrievals. The new parameterization brought a significant improvement as compared to the two parameterizations of sea salt aerosol emissions previously implemented in the IFS-AER. Likewise, the simulated sea salt aerosol optical depth and surface concentration are significantly improved, as compared against ground and remote sensing products.

Ammonium nitrate is a major aerosol constituent over many land regions and contributes to air pollution episodes, ecosystem destruction, regional haze, and aerosol-induced climate forcing. Many climate models that represent ammonium nitrate assume that the ammonium-sulphate-nitrate chemistry reaches thermodynamic equilibrium instantaneously without considering kinetic limitations on condensation rates. The Met Office's Unified Model (UM) is employed to investigate the sensitivity of ammonium nitrate concentrations to the nitric acid uptake coefficient (γ) in a newly-developed nitrate scheme in which first order condensation theory is utilised to limit the rate at which thermodynamic equilibrium is attained. Two values of γ representing fast (γ = 0.193) and slow (γ = 0.001) uptake rates are tested in 20-year global UM integrations. The global burden of nitrate associated with ammonium in the “fast” simulation (0.11 Tg[N]) is twice as great as in the “slow” simulation (0.05 Tg[N]), while the top-of-the-atmosphere radiative impact of representing nitrate is −0.19 Wm−2 in the “fast” simulation and −0.07 Wm−2 in the “slow” simulation. In general, the “fast” simulation exhibits better spatial correlation with observed nitrate concentrations while the “slow” simulation better resolves the magnitude of concentrations. Local near-surface nitrate concentrations are found to be highly correlated with seasonal ammonia emissions suggesting that ammonia is the predominant limiting factor controlling nitrate prevalence. This study highlights the high sensitivity of ammonium nitrate concentrations to nitric acid uptake rates and provides a mechanism for reducing nitrate concentration biases in climate model simulations. The new UM nitrate scheme represents a step-change in aerosol modelling capability in the UK across weather and climate timescales.

The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

The COVID-19 pandemic struck China in January 2020 and the rest of the world from February 2020 onwards. Public authorities enforced several kinds of lockdowns in order to limit the spread of the pandemic and reduce its impact on the health system: at the height of the first wave of the pandemic, more than one human in two was subjected to a lockdown, with associated disruption in local and international travel, industry, tourism etc. These lockdowns had a profound effect on anthropogenic emissions of aerosol, trace gases and greenhouse gases; in this work we focus on aerosols and a selection of trace gases.The Integrated Forecasting System (IFS) of ECMWF is core of the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including reactive gases, as well as aerosol and greenhouse gases. In this work, we use two emission reduction scenario with an experimental version of the IFS in its CAMS configuration: a global and a European one. Global simulations of aerosols were carried out with these two scenarii and compared to a reference simulation without any COVID-19 impact, and to worldwide observations of PM2.5, AOD and trace gases.The simulated PM2.5 using the global emission reduction scenario were found to reproduce quite accurately the observed evolution over China, India and United States. Over Europe, the simulated PM2.5 using the European reduction scenario were closer to observations and appeared more realistic. India was the only place where a significant impact on AOD and on temperature and radiation from the COVID-19 lockdowns was simulated. These simulations also provided information on how the aerosol speciation was altered by the COVID-19 lockdowns: over Europe and the U.S., most of the decrease in surface aerosols was simulated to come from nitrate aerosols. Over the U.S., this matched well with observations of speciated aerosols at surface.

The Integrated Forecasting System (IFS) of ECMWF is used within the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including aerosols as well as reactive trace gases and greenhouse gases.The aerosol model of the IFS, IFS-AER, is a simple sectional-bulk scheme that forecasts seven species: dust, sea-salt, black carbon, organic matter, sulfate, and since July 2019, nitrate and ammonium. The main developments that have been recently carried out, tested and are now contemplated for implementation in the next operational version (known as cycle 48r1) are presented here.The dry deposition velocities are computed as a function of roughness length, particle size and surface friction velocity, while wet deposition depends mainly on the precipitation fluxes. The parameterizations of both dry and wet deposition have been upgraded with more recent schemes, which have been shown to improve the simulated deposition fluxes for several aerosol species. The impact of this upgrade on the skill scores of simulated aerosol optical depth (AOD) and surface particulate matter concentrations against a range of observations is very positive.The simulated surface concentration of nitrate and ammonium are frequently strongly overestimated over Europe and the United States in the current version of the IFS. Nitrate, ammonium, and their precursors nitric acid and ammonia, were evaluated against a range of ground and remote data and it was found that the recently-implemented gas-particle partitioning scheme is too efficient in producing nitrate and ammonium particles.A series of small-scale changes, such as adjusting nitrate dry deposition velocity, direct particulate sulphate emission, and limiting nitrate/ammonium production by the concentration of mineral cations, have been implemented and shown to be effective in improving the simulated surface concentration of nitrate and ammonium.The representation of secondary organic aerosol (SOA) in the IFS has been overhauled with the introduction of a new SOA species, distinct from primary organic matter, with anthropogenic and biogenic components. The implementation of this new species leads to a significant improvement of the simulated surface concentration of organic carbon. An evaluation of simulated SOA concentrations at the surface against climatological values derived from observations using Positive Matrix Factorisation (PMF) techniques also shows a reasonable agreement.

We present interactive stratospheric aerosol simulations with the ICBG system, a global tropospheric-stratospheric combined aerosol-chemistry model which is an extension to the ECMWF Integrated Forecasting System (IFS), and is developed as part of the Copernicus Atmosphere Monitoring Service (CAMS). ICBG is the result of the merging of two existing CAMS configurations of the IFS:The IFS-GLOMAP tropospheric-stratospheric aerosol microphysics system, which has the GLOMAP-mode aerosol scheme configured for forecast-cycling experiments within the IFS, The IFS-CB05-BASCOE tropospheric (CB05) – stratospheric (BASCOE) chemistry scheme, which is also an established configuration of the IFS within CAMS. During the first phase of CAMS, the stratospheric chemistry scheme IFS-BASCOE was extended to include the stratospheric sulphur chemistry from the UM-UKCA model, with sulphuric acid production rates from IFS-BASCOE passed each timestep to the aerosol scheme IFS-GLOMAP for aerosol particle nucleation and condensation. The aerosol surface area densities (SAD) simulated by IFS-GLOMAP simulated are similarly passed each timestep to the stratospheric chemistry scheme IFS-BASCOE for heterogeneous reactions. In a recent progression of this strato-tropospheric modelling system, the climatology for meteoric smoke particles (MSP) used in UM-UKCA has also been implemented. Thus the simulated stratospheric aerosol layer comprises not only pure sulphuric particles nucleated homogeneously but also meteoric-sulphuric particles formed from the MSPs.We evaluate the simulated stratosphere aerosol layer in quiescent conditions, comparing it to SAGE-II measurements from the 1998-2002 period. The simulated stratospheric sulfate burden, aerosol extinction, stratospheric aerosol optical depth (sAOD) and surface area density (SAD) agree well with the SAGE-II retrievals. We also show results from ICBG simulations of the volcanic aerosol cloud from a large-magnitude tropical eruption (Pinatubo, June 1991, VEI6) and a medium-magnitude eruption at a northern mid-latitude (Raikoke, June 2019, VEI4).

The widespread presence of meteoric smoke particles (MSPs) within a distinct class of stratospheric aerosol particles has become clear from in-situ measurements in the Arctic, Antarctic and at mid-latitudes. We apply an adapted version of the interactive stratosphere aerosol configuration of the composition-climate model UM-UKCA, to predict the global distribution of meteoric-sulphuric particles nucleated heterogeneously on MSP cores. We compare the UM-UKCA results to new MSP-sulphuric simulations with the European stratosphere-troposphere chemistry-aerosol modelling system IFS-CB05-BASCOE-GLOMAP.The simulations show a strong seasonal cycle in meteoric-sulphuric particle abundance results from the winter-time source of MSPs transported down into the stratosphere in the polar vortex. Coagulation during downward transport sees high latitude MSP concentrations reduce from ~500 per cm3 at 40km to ~20 per cm3 at 25km, the uppermost extent of the stratospheric aerosol particle layer (the Junge layer). Once within the Junge layer's supersaturated environment, meteoric-sulphuric particles form readily on the MSP cores, growing to 50-70nm dry-diameter (Dp) at 20-25km. Further inter-particle coagulation between these non-volatile particles reduces their number to 1-5 per cc at 15-20km, particle sizes there larger, at Dp ~100nm.The model predicts meteoric-sulphurics in high-latitude winter comprise >90% of Dp>10nm particles above 25km, reducing to ~40% at 20km, and ~10% at 15km. These non-volatile particle fractions are slightly less than measured from high-altitude aircraft in the lowermost Arctic stratosphere (Curtius et al., 2005; Weigel et al., 2014), and consistent with mid-latitude aircraft measurements of lower stratospheric aerosol composition (Murphy et al., 1998), total particle concentrations also matching in-situ balloon measurements from Wyoming (Campbell and Deshler, 2014). The MSP-sulphuric interactions also improve agreement with SAGE-II observed stratospheric aerosol extinction in the quiescent 1998-2002 period. Simulations with a factor-8-elevated MSP input form more Dp>10nm meteoric-sulphurics, but the increased number sees fewer growing to Dp ~100nm, the increased MSPs reducing the stratospheric aerosol layer’s light extinction.

CO is an abundant tropospheric pollutant that originates from numerous emission sources. Large CO fluxes are emitted during intense Biomass Burning (BB) events over relatively short periods of a few days, which combined with the tropospheric lifetime of a month, act as a marker for air-masses influenced by burning events. Once lifted out the boundary layer, air masses influenced by BB undergo chemical processing which can be assessed by subsequent changes in tropospheric ozone. Increases in ozone and aerosol in the Free Troposphere influence photolysis at lower levels impact surface air-quality. Therefore, capturing this feedback is a necessary step towards determining tropospheric lifetimes of greenhouse gases and pollutants, which affects the fraction of transport out of the burning regions.Here we present results from the Integrated Forecasting System (IFS) of ECMWF, which is the core of the Copernicus Atmosphere Monitoring Service (CAMS). We perform simulations with three independent chemistry modules (modified CB05, MOZART, MOCAGE), including variable photolysis schemes and variable approaches for coupling tropospheric aerosol. We choose the simulation year of 2019 corresponding with the FIREXAQ measurement campaign which occurred over California. We subsequently assess the ability of IFS in terms of (i) the representation of the transport of air masses effected by large BB emissions, (ii) the ability towards capturing chemical processing which occurs in such plumes and (iii) using large discrepancies in the simulated tropospheric profiles to imply deficiencies in BB emission estimates.

The Integrated Forecasting System (IFS) of ECMWF is the core of the Copernicus Atmosphere Monitoring Service (CAMS) which provides global analyses and forecasts of atmospheric composition, namely reactive gases, aerosol and greenhouse gases. With respect to the atmospheric chemistry component, the operational system currently relies on a modified version of the CB05 chemistry scheme for the troposphere, combined with the Cariolle scheme to describe stratospheric ozone. In an alternative, more recent configuration also stratospheric ozone chemistry is included based on the BASCOE chemistry module. Alternative atmospheric chemistry modules which can be employed are based on MOZART and MOCAGE chemistry. Recently, further revisions to the modified CB05 tropospheric chemistry scheme have been developed, focusing both on inorganic and organic chemistry, with the aim of improving the quality of existing air-quality products, and the development of new products. On major update is a revision of the isoprene oxidation scheme based on those employed in existing chemistry transport models, as well as inclusion of the basic chemistry describing C8 and C9 aromatics degradation. An example of a new product derived from these updates include a description of global distribution of glyoxal, while this also resulted in an improved modeling of OH recycling particularly over tropical forests. Also we support improved secondary organic aerosol formation due to gaseous anthropogenic, biogenic and biomass burning sources.In this contribution we provide an overview of these revisions, and provide a first quantification of their uncertainties, by comparing products to observations and to those from alternative chemistry modules.

Aerosol-induced absorption of shortwave radiation can modify the climate through local atmospheric heating, which affects lapse rates, precipitation, and cloud formation. Presently, the total amount of aerosol absorption is poorly constrained, and the main absorbing aerosol species (black carbon (BC), organic aerosols (OA), and mineral dust) are diversely quantified in global climate models. As part of the third phase of the Aerosol Comparisons between Observations and Models (AeroCom) intercomparison initiative (AeroCom phase III), we here document the distribution and magnitude of aerosol absorption in current global aerosol models and quantify the sources of intermodel spread, highlighting the difficulties of attributing absorption to different species. In total, 15 models have provided total present-day absorption at 550 nm (using year 2010 emissions), 11 of which have provided absorption per absorbing species. The multi-model global annual mean total absorption aerosol optical depth (AAOD) is 0.0054 (0.0020 to 0.0098; 550 nm), with the range given as the minimum and maximum model values. This is 28 % higher compared to the 0.0042 (0.0021 to 0.0076) multi-model mean in AeroCom phase II (using year 2000 emissions), but the difference is within 1 standard deviation, which, in this study, is 0.0023 (0.0019 in Phase II). Of the summed component AAOD, 60 % (range 36 %–84 %) is estimated to be due to BC, 31 % (12 %–49 %) is due to dust, and 11 % (0 %–24 %) is due to OA; however, the components are not independent in terms of their absorbing efficiency. In models with internal mixtures of absorbing aerosols, a major challenge is the lack of a common and simple method to attribute absorption to the different absorbing species. Therefore, when possible, the models with internally mixed aerosols in the present study have performed simulations using the same method for estimating absorption due to BC, OA, and dust, namely by removing it and comparing runs with and without the absorbing species. We discuss the challenges of attributing absorption to different species; we compare burden, refractive indices, and density; and we contrast models with internal mixing to models with external mixing. The model mean BC mass absorption coefficient (MAC) value is 10.1 (3.1 to 17.7) m2 g−1 (550 nm), and the model mean BC AAOD is 0.0030 (0.0007 to 0.0077). The difference in lifetime (and burden) in the models explains as much of the BC AAOD spread as the difference in BC MAC values. The difference in the spectral dependency between the models is striking. Several models have an absorption Ångstrøm exponent (AAE) close to 1, which likely is too low given current knowledge of spectral aerosol optical properties. Most models do not account for brown carbon and underestimate the spectral dependency for OA.

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

As part of the Copernicus Atmosphere Monitoring Service (CAMS), operated by ECMWF on behalf of the European Commission, global analyses and forecasts of atmospheric composition have been produced operationally since 2015. These were built on many years of previous work under the GEMS and MACC projects, which began producing regular forecasts in 2007.Since the transition to an operational service, there have continued to be many new developments and improvements to the system in five major upgrades, including increased horizontal and vertical resolution, updated emissions and paramterisations, additional species such as nitrate aerosol, as well as updates to the underlying meteorological model and data assimilation. The components of this system (aerosols, gas-phase chemistry, meteorology and the ocean) are also now coupled more tightly via active feedbacks then ever before.In this interactive presentation, we will demonstrate the impact of a number of these developments on the performance of the resulting global air quality forecasts, alongside the continuing evolution of our approaches to assessing model improvement against independent in-situ and remote-sensing observations from a variety of platforms.Because the continuing evolution of an operational system can make the analysis of long-term trends problematic, we will also contrast this with the CAMS global reanalysis product, which (while not using the very latest version of the model) do provide a consistent long-term dataset from 2003 onwards.

The Copernicus Atmosphere Monitoring Service (CAMS) produces operationally global 5-day forecast of atmospheric composition and the weather using ECMWF’s Integrated Forecasting System (IFS) since 2015.Beginning with a system upgrade in June 2018 (45r1), the ozone and aerosol fields have been used in the radiation scheme to account for their radiative impact in the global CAMS forecasts. This approach replaced an aerosol and ozone climatology, which had been used before and which is still used in ECMWF's operational high-resolution medium-range NWP forecasts. The CAMS forecast system, which runs at a resolution of about 40 km, is applied here as a test-bed to explore the importance of aerosol direct feedback in an operational configuration, which can guide developments on composition-weather feedbacks for ECMWF's medium-range, monthly and seasonal forecasts.We will discuss the changes and improvements of temperature forecast errors (i) using typical NWP scores and (ii) by applying an event based approach that focuses on episodes of high aerosol burdens such as the transport of Sahara dust to Europe during the heatwave in June 2019. In more detail we will show to what extent the realism of the prognostic aerosol fields influences the temperature response by considering aerosol forecast which were, or were not, improved by data assimilation of aerosol optical depth at the start of the forecast. We will further demonstrate that the consistent updates of both the climatological and prognostic aerosol fields are an important prerequisite for a making progress.

This article describes the IFS-AER aerosol module used operationally in the Integrated Forecasting System (IFS) cycle 45R1, operated by the European Centre for Medium RangeWeather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). We describe the different parameterizations for aerosol sources, sinks and its chemical production in IFS-AER, as well as how the aerosols are integrated in the larger atmospheric composition forecasting system. The focus is on the entire 45R1 code-base, including some components that are not used operationally, in which case this will be clearly specified. This paper is an update to the Morcrette et al. (2009) article that described aerosol forecasts at ECMWF, using the cycle 32R2 of the IFS. Between cycles 32R2 and 45R1, a number of source and sink processes have been reviewed and/or added, increasing notably the complexity of IFS-AER. A greater integration with the tropospheric chemistry scheme of the IFS has been achieved, for the sulphur cycle as well as for nitrate production. Two new species, nitrate and ammonium, have also been included in the forecasting system. Global budgets and aerosol optical depth (AOD) fields are shown, as well as an evaluation of the simulated Particulate Matter (PM) and AOD against observations, showing an increase in skill from cycle 40R2, used in the CAMS interim Reanalysis (CAMSiRA), to cycle 45R1.

Asian Dust is a seasonal meteorological phenomenon which affects East Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three one-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to air quality over China. Data used were the MODIS Dark Target and the Deep Blue Aerosol Optical Depth. In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative for the availability of independent Aerosol Optical Depth (AOD) data from two established ground-based networks (AERONET and CARSNET), which could be used to evaluate experiments. Particulate Matter (PM) data from the China Environmental Protection Agency (CEPA) were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert-dust events and to improve the forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better that the unconstrained experiment, generally showing smaller mean normalized bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts well into the forecast. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a big role in delivering improved forecasts of Asian Dust.

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

A unique 4-week ship cruise from Guadeloupe to Cabo Verde in April–May 2013 see part 1, Rittmeister et al. (2017) is used for an in-depth comparison of dust profiles observed with a polarization/Raman lidar aboard the German research vessel Meteor over the remote tropical Atlantic and respective dust forecasts of a regional (SKIRON) and two global atmospheric (dust) transport models (NMMB/BSC-Dust, MACC/CAMS). New options of model–observation comparisons are presented. We analyze how well the modeled fine dust (submicrometer particles) and coarse dust contributions to light extinction and mass concentration match respective lidar observations, and to what extent models, adjusted to aerosol optical thickness observations, are able to reproduce the observed layering and mixing of dust and non-dust (mostly marine) aerosol components over the remote tropical Atlantic. Based on the coherent set of dust profiles at well-defined distances from Africa (without any disturbance by anthropogenic aerosol sources over the ocean), we investigate how accurately the models handle dust removal at distances of 1500 km to more than 5000 km west of the Saharan dust source regions. It was found that (a) dust predictions are of acceptable quality for the first several days after dust emission up to 2000 km west of the African continent, (b) the removal of dust from the atmosphere is too strong for large transport paths in the global models, and (c) the simulated fine-to-coarse dust ratio (in terms of mass concentration and light extinction) is too high in the models compared to the observations. This deviation occurs initially close to the dust sources and then increases with distance from Africa and thus points to an overestimation of fine dust emission in the models. We thank the R/V Meteor team and German Weather Service (DWD) for their support during the cruise M96. We appreciate the effort of AERONET MAN to equip research vessels with sun photometers for atmospheric research. We are grateful to Angela Benedetti (MACC/CAMS model; European Centre for Medium-Range Weather Forecasts, Reading, UK) for all her fruitful comments and suggestion during the initial phase of paper preparation. The SKIRON model operations were supported by the European Commission through the Seventh Framework Programme MARINA platform (Marine Renewable Integrated Application Platform, grant agreement 241402). NMMB/BSC-Dust model simulations were performed in the MareNostrum supercomputer hosted by BSC. Sara Basart and Oriol Jorba acknowledge the CICYT project (CGL2016-75725-R) of the Spanish Government and the AXA Research Fund. The authors also acknowledge support through ACTRIS-2 under grant agreement no. 654109 from the European Union’s Horizon 2020 Research and Innovation Programme.

Aerosols affect the atmosphere through direct interaction with short-wave and long-wave radiation and the microphysical properties of clouds. In this paper we report in detail on several mechanisms by which the short-term impact of dust on surface radiative fluxes can affect the dust loading of the atmosphere via modification of boundary-layer meteorology. This in turn affects the aerosol radiative forcing itself. Examples of these feedbacks between dust and boundary layer meteorology were observed during a series of dust storms in the Sahara and the eastern Mediterranean in April 2012. These case studies have been analysed using the Monitoring Atmospheric Composition and Climate – Interim Implementation (MACC-II) system. The radiative fluxes in the short-wave and long-wave spectra were both significantly affected by the prognostic aerosol–radiation interaction, which in turn impacted the meteorological simulation. Reduced incoming solar radiation below the aerosol layers led to a decrease in maximum surface temperatures and to a more stable thermal stratification of the lower atmosphere. This in turn forced weaker surface wind speeds and eventually smaller dust emissions. Moreover, we also observed a secondary impact of the aerosol radiative forcing, whereby horizontal gradients of surface temperature were increased at the edge of the dust plume, which led to local increases of surface wind speeds due to the thermal wind effect. The differentiated impact of the aerosol layer on surface pressure also contributed to the increase in surface wind speed and dust production in the same area. Enhanced long-wave radiative fluxes by the dust mass were associated with opposite processes. Less stable thermal stratification at night, brought mainly by higher minimum temperatures at the surface, caused stronger surface winds. At the edge of the dust storm, weaker horizontal temperature and pressure gradients forced lower winds and reduced dust production. Regarding dust emissions, short-wave radiative forcing had a larger impact than long-wave radiative forcing, corroborating several previous studies. For surface temperature, short-wave and long-wave contribution were close in intensity. These feedbacks were amplified when using data assimilation to build the aerosol analysis of the MACC-II global system. This led to an improvement in the short-term forecasts of thermal radiative fluxes and surface temperatures.

The El Niño–Southern Oscillation (ENSO) not only affects meteorological fields but also has a large impact on atmospheric composition. Atmospheric composition fields from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis are used to identify the ENSO signal in tropospheric ozone, carbon monoxide, nitrogen oxide and smoke aerosols, concentrating on the months October to December. During El Niño years, all of these fields have increased concentrations over maritime South East Asia in October. The MACC Composition Integrated Forecasting System (C-IFS) model is used to quantify the relative magnitude of dynamically induced and emission-driven changes in the atmospheric composition fields. While changes in tropospheric ozone are a combination of dynamically induced and emission-driven changes, the changes in carbon monoxide, nitrogen oxides and smoke aerosols are almost entirely emission-driven in the MACC model. The ozone changes continue into December, i.e. after the end of the Indonesian fire season while changes in the other fields are confined to the fire season.

Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modeling enterprise. One method to estimate these emissions is to convert Fire Radiative Power (FRP) analysis to dry matter burnt and emissions of smoke constituents using land cover dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments on-board of the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. We develop a method to correct this bias in daily FRP observations from any Low Earth Orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, e.g., GFAS in case of a default of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It removes the bias in daily average FRP observations from Terra and Aqua nearly entirely. Errors are larger for Terra than for Aqua, are generally relatively small at a global scale, but can be important at a local scale. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass burning fire emission estimates from the Global Fire Assimilation System (GFAS). GFAS converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence where observed FRP from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an overestimation of fire duration, which in turn translates into an overestimation of emissions from fires. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

A new global reanalysis data set of atmospheric composition (AC) for the period 2003–2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

Biomass burning is the second largest global source of anthropogenic aerosols, and South America is one of the major source regions. In the dry season, the atmosphere of the Amazon basin features a remarkable haze, with layers containing high loadings of smoke. Aerosols with different degrees of ageing, are encountered in the boundary layer and the free troposphere. The South American Biomass Burning Analysis (SAMBBA) was an intensive observation campaign in September-October 2012 that involved measurements of the Amazonian atmosphere using the Facility for Airborne Measurements (FAAM) BAe-146 research aircraft.

COBEL-ISBA, a boundary-layer 1D numerical model, has been developed for the very-short-term forecasting of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature, specific humidity and liquid water content. As fog forecasting is a threshold problem, the model is strongly nonlinear. A new assimilation method based on a genetic selection particle filter was tested to produce the initial conditions. The particle filter was adapted for a deterministic forecast and to take into account the time dimension by minimizing the error on a time window. A simplified particle filter was also used to determine the initial conditions in the soil. The filter was tested with two sets of simulated observations. In all cases, the initial conditions produced by this algorithm were of considerably better quality than the ones obtained with a Best Linear Unbiased Estimator (BLUE). The forecast of the control variables and of fog events was also improved. When comparing scores with the ones obtained with an ensemble Kalman filter (EnKF), the particle filter showed better performances for most of the cases. The ensemble size impacted the frequency of filter collapse and the quality of the initial temperature and specific humidity profiles in the lower part of the domain. Copyright © 2011 Royal Meteorological Society

A sodar was deployed at Roissy–Charles de Gaulle airport near Paris, France, in 2008 with the aim of improving the forecast of low visibility conditions there. During the winter of 2008–2009, an experiment was conducted that showed that the sodar can effectively detect and locate the top of fog layers which is signaled by a strong peak of acoustic reflectivity. The peak is generated by turbulence activity in the inversion layer that contrasts sharply with the low reflectivity recorded in the fog layer below. A specific version of the 1D-forecast model deployed at Roissy for low visibility conditions (COBEL-ISBA) was developed in which fogs’ thicknesses are initialized by the sodar measurements rather than the information derived from the down-welling IR fluxes observed on the site. It was tested on data archived during the winters of 2008–2009 and 2009–2010 and compared to the version of the model presently operational. The results show a significant improvement—dissipation times of fogs are better predicted.