English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. The main uncertainties regarding the estimation of changes in the Earth'senergy budget are related to the role of atmospheric aerosols. These changesare caused by aerosol–radiation (ARIs) and aerosol–cloud interactions (ACIs),which heavily depend on aerosol properties. Since the 1980s, manyinternational modeling initiatives have studied atmospheric aerosols andtheir climate effects. Phase 3 of the Air Quality Modelling EvaluationInternational Initiative (AQMEII) focuses on evaluating and intercomparingregional and linked global/regional modeling systems by collaborating withthe Task Force on the Hemispheric Transport of Air Pollution Phase 2 (HTAP2)initiative. Within this framework, the main aim of this work is theassessment of the representation of aerosol optical depth (AOD) and theAngstrom exponent (AE) in AQMEII Phase 3 simulations over Europe. Theevaluation was made using remote-sensing data from the Moderate ResolutionImaging Spectroradiometer (MODIS) sensors aboard the Terra and Aquaplatforms, and the instruments belonging to the ground-based AerosolRobotic Network (AERONET) and the Maritime Aerosol Network (MAN). Overall,the skills of AQMEII simulations when representing AOD (mean absolute errorsfrom 0.05 to 0.30) produced lower errors than for the AE (mean absoluteerrors from 0.30 to 1). Regardless of the models or the emissions used,models were skillful at representing the low and mean AOD values observed(below 0.5). However, high values (around 1.0) were overpredicted for biomassburning episodes, due to an underestimation in the common fires' emissions,and were overestimated for coarse particles – principally desert dust – relatedto the boundary conditions. Despite this behavior, the spatial and temporalvariability of AOD was better represented by all the models than AEvariability, which was strongly underestimated in all the simulations.Noticeably, the impact of the model selection when representing aerosoloptical properties is higher than the use of different emission inventories.On the other hand, the influence of ARIs and ACIs has a little visible impactcompared to the impact of the model used.

Aerosol optical properties over Europe: an evaluation of the AQMEII Phase 3 simulations against satellite observations