Rechercher des projets européens

Quantifying aerosol-cloud-climate effects by regime (QUAERERE)
Date du début: 1 oct. 2012, Date de fin: 30 sept. 2017 PROJET  TERMINÉ 

"Global climate change is widely considered one of the main concerns of humankind. However, predictions are highly uncertain, with no substantial improvement since more than three decades. They are hampered by the huge uncertainty of climate forcing, which is dominated by the uncertainty in anthropogenic aerosol-cloud-climate effects (“aerosol indirect effects”). The goal of QUAERERE (Latin for researching) is a reliable, observations-based, global quantification of these effects, which would also imply a constraint on climate sensitivity and thus climate predictions. This goal is now reachable combining recent advances in different disciplines: (i) a decade-long satellite dataset involving retrievals of the relevant quantities is now available, complemented by a complete aerosol dataset from a new reanalysis; (ii) on the basis of high-resolved numerical weather pre­diction models, which include parameterisations of aerosol cycles and cloud-precipitation microphysics, cloud-system resolving simulations at a regional scale are now possible; reliable simulations beyond idealised cases are thus possible. These tools are complemented by comprehensive global climate models and reference ob­servations from ground-based sites.The problem in aerosol-cloud-climate effects is in its complexity: Various processes counteract each other, and large spatiotemporal variability of clouds buffers the forcing effects. QUAERERE proposes a two-fold “divide-and-conquer” approach to this complex problem: (i) aerosol-cloud-climate effects will be investigated by regime; this allows to circumvent the problem of aerosol-cloud-climate ef­fects being buffered when averaging over different regimes; and (ii) by investigating individual terms con­tributing to the aerosol-cloud-climate effects separately; this allows to analyse individual statistical relation­ship in satellite observations and model results consistently, and to perform model sensitivity studies for cause-effect attribution."

Details