Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multimodel ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O 3 , NO 2 , SO 2 , CO, PM 10 , PM 2.5 , NO, NH 3 , total NMVOCs (non-methane volatile organic compounds) and PAN+PAN Published by Copernicus Publications on behalf of the European Geosciences Union. V. Marécal et al.:A regional air quality forecasting system over Europe precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations.The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO 2 and PM 10 . The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 µg m −3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30-50 µg m −3 . Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM 10 for winter 2013-1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 µg m −3 . The ensemble median fractional gross error is larger for PM 10 (∼ 0.52) than for ozone and the correlation is lower (∼ 0.35 for PM 10 and ∼ 0.54 for ...

We review the studies carried out during the African Monsoon Multidisciplinary Analysis (AMMA)-EU on the changes of interannual sea surface temperature (SST)-West African monsoon (WAM) covariability at multidecadal timescales, together with the influence of global warming (GW). The results obtained in the AMMA-EU suggest the importance of the background state, modulated by natural and anthropogenic variability, in the appearance of different interannual modes. The lack of reliability of current coupled models in giving a realistic assessment for WAM in the future is also stated.

Abstract. This paper deals with recent improvements to the global chemical transport model of Météo-France MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle) that consists of updates to different aerosol parameterizations. MOCAGE only contains primary aerosol species: desert dust, sea salt, black carbon, organic carbon, and also volcanic ash in the case of large volcanic eruptions. We introduced important changes to the aerosol parameterization concerning emissions, wet deposition and sedimentation. For the emissions, size distribution and wind calculations are modified for desert dust aerosols, and a surface sea temperature dependant source function is introduced for sea salt aerosols. Wet deposition is modified toward a more physically realistic representation by introducing re-evaporation of falling rain and snowfall scavenging and by changing the in-cloud scavenging scheme along with calculations of precipitation cloud cover and rain properties. The sedimentation scheme update includes changes regarding the stability and viscosity calculations. Independent data from satellites (MODIS, SEVIRI), the ground (AERONET, EMEP), and a model inter-comparison project (AeroCom) are compared with MOCAGE simulations and show that the introduced changes brought a significant improvement on aerosol representation, properties and global distribution. Emitted quantities of desert dust and sea salt, as well their lifetimes, moved closer towards values of AeroCom estimates and the multimodel average. When comparing the model simulations with MODIS aerosol optical depth (AOD) observations over the oceans, the updated model configuration shows a decrease in the modified normalized mean bias (MNMB; from 0.42 to 0.10) and a better correlation (from 0.06 to 0.32) in terms of the geographical distribution and the temporal variability. The updates corrected a strong positive MNMB in the sea salt representation at high latitudes (from 0.65 to 0.16), and a negative MNMB in the desert dust representation in the African dust outflow region (from − 1.01 to −0.22). The updates in sedimentation produced a modest difference; the MNMB with MODIS data from 0.10 in the updated configuration went to 0.11 in the updated configuration only without the sedimentation updates. Yet, the updates in the emissions and the wet deposition made a stronger impact on the results; the MNMB was 0.27 and 0.21 in updated configurations only without emission, and only without wet deposition updates, respectively. Also, the lifetime, the extent, and the strength of the episodic aerosol events are better reproduced in the updated configuration. The wet deposition processes and the differences between the various configurations that were tested greatly influence the representation of the episodic events. However, wet deposition is not a continuous process; it has a local and episodic signature and its representation depends strongly on the precipitation regime in the model.

A significant part of the West African monsoon (WAM) interannual variability can be explained by the remote influence of El Niñ o-Southern Oscillation (ENSO). Whereas the WAM occurs in the boreal summer, ENSO events generally peak in late autumn. Statistics show that, in the observations, the WAM is influenced either during the developing phase of ENSO or during the decay of some long-lasting La Niñ a events. The timing of ENSO thus seems essential to the teleconnection process. Composite maps for the developing ENSO illustrate the large-scale mechanisms of the teleconnection. The most robust features are a modulation of the Walker circulation and a Kelvin wave response in the high troposphere.In the Centre National de Recherches Mé té orologiques Coupled Global Climate Model, version 3 (CNRM-CM3), the teleconnection occurs unrealistically at the end of ENSO events. An original sensitivity experiment is presented in which the ocean component is forced with a reanalyzed wind stress over the tropical Pacific. This allows for the reproduction of the observed ENSO chronology in the coupled simulation. In CNRM-CM3, the atmospheric response to ENSO is slower than in the reanalysis data, so the influence on the WAM is delayed by a year.The two principal features of the teleconnection are the timing of ENSO onsets and the time lag of the atmospheric response. Both are assessed separately in 16 twentieth-century simulations of the third phase of the Coupled Model Intercomparison Project (CMIP3). The temporal aspects of the ENSO teleconnection are reproduced with difficulty in state-of-the-art coupled models. Only four models simulate an impact of ENSO on the WAM during the developing phase.

A set of 12 state-of-the-art coupled oceanatmosphere general circulation models (OAGCMs) is explored to assess their ability to simulate the main teleconnections between the West African monsoon (WAM) and the tropical sea surface temperatures (SSTs) at the interannual to multi-decadal time scales. Such teleconnections are indeed responsible for the main modes of precipitation variability observed over West Africa and represent an interesting benchmark for the models that have contributed to the fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC4). The evaluation is based on a maximum covariance analysis (MCA) applied on tropical SSTs and WAM rainfall. To distinguish between interannual and multi-decadal variability, all datasets are partitioned into low-frequency (LF) and high-frequency (HF) components prior to analysis. First applied to HF observations, the MCA reveals two major teleconnections. The first mode highlights the strong influence of the El Niño Southern Oscillation (ENSO). The second mode reveals a relationship between the SST in the Gulf of Guinea and the northward migration of the monsoon rainbelt over the West African continent. When applied to HF outputs of the twentieth century IPCC4 simulations, the MCA provides heterogeneous results. Most simulations show a single dominant Pacific teleconnection, which is, however, of the wrong sign for half of the models. Only one model shows a significant second mode, emphasizing the OAGCMs’ difficulty in simulating the response of the African rainbelt to Atlantic SST anomalies that are not synchronous with Pacific anomalies. The LF modulation of these HF teleconnections is then explored through running correlations between expansion coefficients (ECs) for SSTs and precipitation. The observed time series indicate that both Pacific and Atlantic teleconnections get stronger during the twentieth century. The IPCC4 simulations of the twentieth and twenty-first centuries do not show any significant change in the pattern of the teleconnections, but the dominant ENSO teleconnection also exhibits a significant strengthening, thereby suggesting that the observed trend could be partly a response to the anthropogenic forcing. Finally, the MCA is also applied to the LF data. The first observed mode reveals a well-known inter-hemispheric SST pattern that is strongly related to the multi-decadal variability of the WAM rainfall dominated by the severe drying trend from the 1950s to the 1980s. Whereas recent studies suggest that this drying could be partly caused by anthropogenic forcings, only 5 among the 12 IPCC4 models capture some features of this LF coupled mode. This result suggests the need for a more detailed validation of the WAM variability, including a dynamical interpretation of the SST–rainfall relationships

Abstract. In this study we develop a secondary inorganic aerosol (SIA) module for the MOCAGE chemistry transport model developed at CNRM. The aim is to have a module suitable for running at different model resolutions and for operational applications with reasonable computing times. Based on the ISORROPIA II thermodynamic equilibrium module, the new version of the model is presented and evaluated at both the global and regional scales.The results show high concentrations of secondary inorganic aerosols in the most polluted regions: Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emission reductions in Europe and North America.Using two simulations, one with and the other without secondary inorganic aerosol formation, the global model outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database. The model shows a better agreement with MODIS AOD retrievals in all geographical regions after introducing the new SIA scheme. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA generally gives a better agreement with observations for secondary inorganic aerosol precursors (nitric acid, sulfur dioxide, ammonia), in particular with a reduction of the modified normalized mean bias (MNMB).At the regional scale, over Europe, the model simulation with SIA is compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained from the global simulations. The AIRBASE database was used to compare the model to regulated air quality pollutants: particulate matter, ozone and nitrogen dioxide concentrations. Introduction of the SIA in MOCAGE provides a reduction in the PM 2.5 MNMB of 0.44 on a yearly basis and up to 0.52 for the 3 spring months (March, April, May) when SIAs are at their maximum.

ABSTRACT:The surface ocean explains a significant part of the inter-annual variability of the West African monsoon (WAM). The present paper explores the role of Gulf of Guinea sea surface temperatures (SST): how is the ocean-atmosphere observed relationship reproduced by state-of-the-art coupled models? The 'Atlantic Niño' is the main mode of inter-annual variability in the Gulf of Guinea. SST anomalies are maximum in June-July, and are associated with a convective anomaly in the marine Intertropical Convergence Zone (ITCZ), which spreads over the Guinean coast. In most of the studied CMIP3 simulations, the inter-annual variability of SST is very weak in the Gulf of Guinea, especially along the Guinean Coast. As a consequence, the influence on the monsoon rainfall over the African continent is hardly reproduced. Interestingly, many models exhibit a dipolar response of the marine ITCZ to the Atlantic Niño. In the observations and reanalyses, the absence of any evident shift in the position of the monsoon rainbelt is associated with a collapse of the correlations between Gulf of Guinea SST and Sahel rainfall at the end of the twentieth century. It is suggested that this may be due to the counteracting effects of the Pacific and Atlantic basins over the last decades. In CMIP3 simulations, the Atlantic Niño is often correlated with the El Niño Southern Oscillation (ENSO). However, only one simulation catches the observed evolution of the Pacific-Atlantic relationship at the end of the century.

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.