Clouds are a key component of the Earth’s climate system; they strongly modulate its radiation balance, by reflecting the incoming solar radiation and trapping the longwave radiation emitted from the Earth. Cloud processes control precipitation and the hydrological cycle, which have a direct impact on the supply of freshwater that affects ecosystems and agriculture. Despite their importance, the treatment of clouds in climate models carries large uncertainties that directly affect climate model predictions, which are further are hampered by the large range of scales of interaction between various components and numerous processes present in clouds that are poorly understood (Seinfeld et al., PNAS, 2016).
Especially uncertain and important are the impacts of aerosols on mixed-phase clouds, given the many processes that occur involving liquid water and ice. Mixed-phase clouds are also the most frequently occurring of clouds in the troposphere, responsible for much of global precipitation and a strong modulator of cloud radiative forcing. An accurate description of mixed-phase clouds in models requires at a minimum a robust quantification of the concentration of liquid water droplets and water ice crystals. Both form on preexisting (seed) particles, termed ice nuclei (IN) and cloud condensation nuclei (CCN), respectively. Observed ice crystal number concentrations (ICNC) in clouds however can be orders of magnitude higher than expected from observed IN concentrations, from the action of Secondary Ice Processes (SIP), where ice crystals (formed upon IN) collide with other cloud droplets/crystals and splinter into many (secondary) ice crystals. These processes are expected to occur in many types of orographic clouds and affect their precipitation rates and properties (e.g., Georgakaki et al., ACPD, 2021).
To provide a better understanding to the key processes involved in the formation and evolution of mixed-phase clouds, we plan to organize an instrumented campaign in the frame of PANACEA and the ERC project PyroTRACH. To this end, we have identified the highest aerosol-cloud Aerosol-in-situ ACTRIS/PANACEA station in Greece, the Helmos Hellenic Atmospheric Aerosol & Climate Change station (or simply Helmos Mt) operated by the National Center for Scientific research “Demokritos” (NCSR-Demokritos). The Aroania (or Helmos) mountainous region is a unique and optimal location for targeted studies of aerosol-cloud interactions. Aroania, situated in the Achaea Prefecture of Greece and is the 3rd highest mountain in the Peloponnese (summit at 2340 m) and hosts a NCSR-Demokritos monitoring station situated at 2314 m. A unique characteristic of the Helmos High Altitude Monitoring Station (2314 m, 42°N 05' 30'', 34°E 14' 25'') is that it is a typical free tropospheric background site with very low influence from the surface polluted layers (Collaud Coen et al., ACP, 2018) and lies in a cross-road of different air masses (continental, Saharan, long-range biomass burning, volcanic, etc.).
A unique component of the CALISHTO experimental Campaign is to track and study a large number of orographic cloud events, influenced by a combination of boundary layer and free tropospheric aerosol, with high spatial and temporal resolution to: (i) Characterise and link the dynamics and microphysics of cloud cycles, (ii) Quantify environmental thermodynamic and kinematic controls on cloud life cycle properties, (iii) Isolate and quantify the impacts of aerosol on clouds at the interface between the Planetary Boundary Layer (PBL) and the free troposphere.
The observations carried out during CALISHTO characterize the (i) dynamics of air masses that drive cloud formation, (ii) sources and types of aerosol upon which cloud droplets and ice crystals form, (iii) cloud microphysical processes, (iv) particle properties (brown carbon content, hygroscopitiy, chemical composition and characteristic tracers, acidity), (v) gas-phase constituents that affect aerosol formation and evolution, and, (vi) bioaerosol concentration and population. Process-level and larger-scale modeling will synthesize the observations and provide a higher-level understanding of the processes controlling cloud formation in the region.