The sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions)or model configurations is recognized as an important issue for air quality modelling applications in support of air qualityplans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https:// fairm ode. jrc. ec. europa. eu/) adedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitudeand variability of air quality model responses when studying emission scenarios/projections by assessing the changes of modeloutput in response to emission changes. This work is based on several air quality models that are used to support model usersand developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, andprovide an analysis of the variability of O3and PM concentrations due to emission reduction scenarios. The key novel feature,in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urbanscale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolutepotency. The results show that there is a larger variability of concentration changes between models, when the emission reductionscenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between modelsof absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarlyperturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficientthan the sum of single precursor emission reductions both for O3and PM. In particular for ozone, model responses, in terms oflinearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact ofmodel’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of morein-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.

This study examines the impact of light absorption from biomass burning (BB) brown carbon (BrC) in the Mediterranean basin from local and distant fire incidents during a typical fire season in August 2019 and under severe fire activity in August 2021. The approaches of Saleh et al. (2014) and Wang et al. (2018) are used to describe the BrC absorption within the WRF-Chem model. Focusing on three regions in the Mediterranean (around the islands of Sicily, Malta, and Crete) that are most affected by BB activity, BrC absorption approximates 5 Mm−1 in OC concentrations up to 3 μg m−3 and can approach 15 Mm−1 in extreme conditions (up to 10 μg m−3). When photochemical bleaching is considered, BrC undergoes almost immediate bleaching upon emission due to high levels of OH radical in the Mediterranean atmosphere during summertime, decreasing light absorption between 56% and 75% under both average and extreme BB conditions. Cloud formation is facilitated above the PBL due to moisture increase induced by BrC at the area of fire events, while transported drier and warmer air masses tend to dissipate cloud formation further away from the BB source. The impact of BrC absorption on irradiances is small (up to −6 W m−2 in extreme conditions) and is often overlapped by the absorption from water vapour variations. BrC direct radiative effect (DRE) is estimated at 0.04 W m−2 (∼10% of BC) in average and 0.18 W m−2 in extreme BB activity under clear sky. Under all sky, low-level clouds dissipation in 2019 with average BB emissions enhances DRE (at 0.15 W m−2), while the higher clouds dissipation in 2021 limits DRE (at 0.11 W m−2) resulting in lower DRE despite the extreme BB conditions.

The representation of boundary layer clouds during marine Cold-Air Outbreaks (CAO) remains a great challenge for weather prediction models. Recent studies have shown that the representation of the transition from closed stratocumulus clouds to convective cumulus open cells largely depends on microphysical and precipitation processes, which secondary ice production (SIP) may strongly modulate. In this study we use the Weather Research and Forecasting model to investigate the impact of the most well-known SIP mechanisms (Hallett-Mossop, mechanical break-up upon collisions between ice particles and drop-shattering) on a CAO case observed north of the United Kingdom in 2013. While Hallett-Mossop is the only SIP process extensively implemented in atmospheric models, our results indicate that the other two SIP mechanisms are also favored in the examined conditions. Activation of drop-shattering and especially collisional break-up can result in enhanced riming, ice depositional growth and/or ice aggregation. The first two processes quicken liquid depletion in the stratocumulus cloud, while along with aggregation, they enhance precipitation. The increased precipitation results in enhanced evaporation/sublimation in the sub-cloud layer, promoting boundary-layer decoupling, which further accelerates the onset of the stratocumulus break-up. However, the strong sensitivity to the expression of terminal velocity of the precipitating particles and the rimed fraction of cloud ice/snow suggests that the robust implementation of SIP to improve CAO predictions requires data from a large number of CAO events.