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The complex refractive index (CRI) is one of the key parameter driving aerosol spectral optical properties and direct radiative effects (DRE). Its value and spectral variation under different conditions, such as anthropogenic− and biogenic−dominated environments and anthropogenic−biogenic mixing situations, remains not fully understood. As a consequence, oversimplified representations of aerosol optical properties are generally used in climate models. Therefore, measurements of aerosol CRI in different environments and their inclusion in models are needed. The field observations from the ACROSS campaign, performed in June-July 2022 in the Ile de France region, are used in this study to deepen the knowledge of aerosol optical properties, aiming to improve the aerosol representation in the CHIMERE model and provide the best constraint for DRE simulations. Measurements obtained both at the Paris city center and the Rambouilllet rural forest sites during ACROSS are considered, in order to explore the CRI variability from anthropogenic−dominated to biogenic−dominated environments, including anthropogenic−biogenic mixing situations. The CRI retrievals at seven different wavelengths, performed by combining the Mie theory with optical and size distribution measurements, are representative of different atmospheric conditions, aerosol loadings as well as type and chemical compositions. In fact, the June-July 2022 period was characterized by highly diversified weather conditions: 1) two strong heatwaves, promoting SOA build-up and favoring the export of the Paris pollution plume towards the forest site; 2) Saharan dust events transported from the upper atmosphere to the ground; 3) biomass burning episode; 4) periods with reduced anthropogenic influence. The CRI retrievals under these different conditions and their link to particulate chemical composition is investigated. Hence, the CRI dataset presented here constitutes a unique dataset from which models can benefit to validate and constrain simulations and DRE estimations, under both urban and biogenic emissions influence. These data, in conjunction with those from the aircraft observations during ACROSS, are used to initialize and perform sensitivity studies on the aerosol DRE, using the CHIMERE−WRF coupled model, the OPTSIM model for the aerosol optical properties and the Rapid Radiative Transfer Model for GCMs (RRTMG).Keywords: Complex refractive index, direct radiative effect, aerosol mixing, urban, forest

ACROSS (Atmospheric ChemistRy Of the Suburban foreSt) is an integrative, innovative, multi-scale project within the “Make Our Planet Great Again” (MOPGA) initiative designed to advance understanding of the fate of the photochemical processing of urban and biogenic air mass mixtures in the Paris region. An ACROSS hypothesis is that the anthropogenic-biogenic air mass mixing leads to changes in the production of oxygenated volatile organic compounds (VOCs) whose properties alter their importance in incorporation into secondary organic aerosols (SOA) and their roles in production of ozone and other relevant secondary species. A likely important factor is NOx transport to suburban biogenic environments and the resulting modification of key chemical processes.The key highlight of ACROSS was an intensive, multi-platform measurement campaign that took place in the summer of 2022. The campaign included measurements from multi-instrumented ground sites located in the urban, rural, and semi-rural Paris region including a 40-meter tower and from the ground in the Rambouillet suburban forest southwest of Paris, and airborne regional observations following the urban plume downwind of Paris and areas within 150 km (coordinated with other projects in the framework of the PANAME initiative). As the analysis proceeds, the data collected from this campaign will provide a unique snapshot of the properties that are characteristic of combinations of urban and biogenic air masses around one of the largest European megacities. This new knowledge will contribute to advancement of our understanding at the process level and will lead to the ability to better represent such complex systems in numerical models, ultimately resulting in improved capability to predict the impacts on air quality, regional climate, and global climate change.This presentation consists of a summary of the campaign organization and measurements. Some preliminary findings from the analysis of the observations in the early stages are shown as a complement to other, more focused evaluations that are given at this meeting.

The international ACROSS (Atmospheric ChemistRy Of the Suburban forest) intensive field measurement campaign (supported by the Make Our Planet Great Again initiative) took place from June 13 to July 25 2022 in and around the Ile-de-France during an exceptional hot (more than 3°C above the climatological mean for France) and dry summer season. This work will provide a broad classification of weather conditions that occurred during the campaign, useful for further analysis of observations and simulations. Weather scenarios will be analysed in terms of synoptic weather situations, relating transport patterns (regional advection to the Ile-de-France region, plumes from Paris agglomeration), temperature and relative humidity evolution. During the June-July 2022 period, several distinct weather and pollution patterns occurred, among which: (1) two strong heatwaves, with large photochemical activity promoting secondary pollutants build-up (O3, SOA), (2) advection of relatively clean oceanic air masses, and (3) Saharan dust and intense forest fire events with dust and fire aerosol advection to the Ile-de-France region. The analysis is based on meteorological data from several models (GFS, WRF, ARPEGE, AROME) and observations, pollution forecasts from the French Prev’Air system (http://www2.prevair.org/), and from the AirParif Esmeralda platform (http://www.esmeralda-web.fr/accueil/) that have been used for the airborne campaign support. In addition, dedicated simulations with the CHIMERE model (see companion abstract by Di Antonio et al. for this session), and major pollutant observations by air quality networks, from satellites, or performed within ACROSS will be used to analyse how meteorological conditions and weather patterns impacted the spatial distributions of major primary and secondary chemical species. Especially tracers of anthropogenic and biogenic emissions and photochemical activity (O3, PM2.5, OA, BC, NOx, BVOC) will be analysed. Keywords: Meteorology, pollutant distributions, transport patterns, ACROSS, MOPGA

Organic aerosol (OA) from natural or anthropogenic origin can be directly emitted into the atmosphere (primary organic aerosols, POA) or formed by secondary processes via the oxidation of volatile organic compounds (VOC). However, the formation pathways and their chemical composition of these secondary organic aerosols (SOA), which may contribute up to 90% of the OA mass, are not well understood to date, which is problematic due to the relevance of SOA on climate. To address this issue, this study uses a tracer-based approach to identify and quantify the contribution of different anthropogenic/biogenic VOCs precursors to the SOA formation. To do so, we combine experiments in a large scale atmospheric simulations chamber, CESAM (which means Multiphase Atmospheric Experimental Simulation Chamber), and field measurements during the ACROSS (Atmospheric ChemistRy Of the Suburban foreSt) campaign conducted in the Paris area in summer 2022. This approach provides both a mechanistic study of the oxidation of targeted VOCs in simulated and controlled rural/urban atmospheres and the identification of targeted tracers in the real atmosphere, to quantify their concentrations in ambient air.The ACROSS dataset consists in atmospheric samples of submicron aerosols collected twice a day (day and night) in the urban area of Paris and the Rambouillet forest on the south-west of Paris, as well as samples collected onboard the Safire ATR-42 research aircraft on low-level flights targeting the evolution and dilution of the Paris urban plume. The CESAM chamber dataset consists in samples of SOA generated by the OH oxidation of toluene/ m-xylene in various conditions.Filters are analyzed to provide with the chemical composition at the molecular scale by SFE-GC-MS (Supercritical Fluid Extraction Gas Chromatography- Mass Spectrometry) and UPLC-QTOF-MS (Ultra Performance Liquid Chromatography Time of Flight Mass Spectrometry). The organic mass and chemical speciation are obtained by aerosol mass spectrometry and the organic carbon (OC) concentrations by thermal-optical analysis.The first results of the ambient samples of Paris revealed that the OC concentration varied between 0.69 ± 0.07 and 9.48 ± 0.51 µgC/m3, which correspond to the 28% and 53% of the total mass of the submicron aerosols, for background and polluted (fire) conditions, respectively. These diverse conditions are favorable to trace the compounds identified during the simulation chamber experiments, such as benzoic acid, succinic acid, and 2-methyl-4-nitrophenol. These attempts will be presented and discussed in order to determine the contribution of specific precursors to SOA formation.

Reliable secular time series of essential climatic variables are a fundamental element for the assessment of vulnerability, impact and adaptation to climate change. Here, we implement a readily portable procedure for building an upgradable long‐term homogeneous climate dataset using monthly and daily observations of temperature and precipitation over a given area of interest, exemplified here with Abruzzo, a region in Central Italy characterized by complex orography. We process the dataset according to a preliminary ranking of stations based on data quantity and quality, and we exploit the Climatol algorithm for inhomogeneity correction. The corrected time series show trends in broad agreement with external databases (CRU, Berkeley Earth, E‐OBS), and highlight the importance of relying on a local network for a better representation of gradients and variability over the territory. We estimate that maximum (TX) and minimum temperature (TN) increased by ~1.6 and ~2.2°C/century, respectively, over the period 1930–2019, while in the recent decades 1980–2019 we found an accelerated trend of ~5.7 and ~3.9°C/century. Precipitation (RR) decreased by ~10%/century in 1930–2019, while it has been increasing at a rate of ~26%/century in 1980–2019. The Köppen–Geiger climate classification is sensitive to the increase of precipitation in the recent decades, which is attributable to decreased summer precipitation overcompensated by more rain in late spring and early autumn. The cold climate types are retreating upwards along the slopes of the mountain ranges. Over the period 1980–2019, extreme values are also displaying significant trends. Every 2 years, there is one less frost day (TN 25°C) in the Apennines area, while there is one more tropical night (TN >20°C) in the Adriatic coastal area. Precipitation extremes are increasing, especially along the coast, with rain accumulated in the rainiest days increasing at a rate of 1–2%/year.

Hydrogeological hazard and its related risk prediction is becoming increasingly important in the context of climate change. Since extreme meteorological events, such as drought and intense rainfall, are expected to increase, the continuous update of the Early Warning Systems (EWSs) is particularly challenging, in the context of Civil Protection activities. The new regulations concerning the organization of the Civil Protection distributed Service strongly reiterates the role of the collaboration with the scientific community, in order to ensure the EWS adaptation to deal with environmental changes. Scientists are called to convert up-do-date research findings to products available to end-users. On the other hand, civil protection should encourage scientific collaborations, with the aim of providing useful and user-friendly instruments to its operators, to increase the effectiveness of risk prediction and early intervention. In this context, the World Meteorological Organization recommends as sample products should be readily available for potential customers. From this conception, the rainfall-triggered landslides prediction system presented in this work was set-up by the Centre of Excellence CETEMPS for the Abruzzo Region Civil Protection institutional activities. The landslides forecasting system is based on the use of the Cetemps Hydrological Model (CHyM), coupled with different meteorological observations (gauges network, weather radar or satellites) and forecasts from limited area models. The landslide hazard is then given at hourly basis over the whole region, as well as, selected areas at risk, though the use of a stress index based on different thresholds.