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Aerosols - Cloud Interactions

Figure 7.16 | Schematic depicting the myriad aerosol–cloud–precipitation related processes occurring within a typical GCM grid box. The schematic conveys the importance of considering aerosol–cloud–precipitation processes as part of an interactive system encompassing a large range of spatiotemporal scales. Cloud types include low-level stratocumu- lus and cumulus where research focuses on aerosol activation, mixing between cloudy and environmental air, droplet coalescence and scavenging which results in cloud processing of aerosol particles, and new particle production near clouds; cirrus clouds where a key issue is ice nucleation through homogeneous and heterogeneous freezing; and deep convective clouds where some of the key questions relate to aerosol influences on liquid, ice, and liquid–ice pathways for precipitation formation, cold pool formation and scavenging. These processes influence the shortwave and longwave cloud radiative effect and hence climate. Primary processes that affect aerosol–cloud interactions are labelled in blue while secondary processes that result from and influence aerosol–cloud interactions are in grey. 

      The aerosol-cloud interactions (aci) in AR5 is corresponding to the first indirect aerosol forcing in AR4. Since AR4, great improvements have been made in aerosol-cloud interaction studies due to:

 

  • A great diversity of aerosol-cloud interactions in global models;

 

Figure 7.16 (above) illustrates the series of aerosol-cloud interactions (including related processes involving precipitation) parameterized in GCMs. Refer to the figure caption for more details in their mutual relationship and logics behind this setup.

 

One thing needs to be pointed out. Thanks to the advances in cloud process modeling, it has now been recognized that in warm clouds, many aerosol-cloud interactions cancel with each other (a buffering effect). As a result, the net radiative forcing appears to be smaller than that coming from the idealized albedo and lifetime effect model used in AR4 [Stevens & Feingold, 2009]. And the entire system appears to be less susceptible to aerosol and other perturbations. Some of these compensating processes are, for example, interplay between the droplet size distribution and mixing processes that determine cloud macrostructure (e.g. cloud cover, thickness etc.) and the dependence of precipitation development in stratiform clouds on the vertical thermodynamic structure of the clouds.

 

In addition, due to the inherent difficulty of separating the cloud albedo effect from other aerosol-cloud interactions, this indirect aerosol forcing is combined with other aerosol-cloud interactions and is collectively assessed as effective radiative forcing due to aerosol-cloud interactions (ERFaci) in AR5.

 

  • improved observational capability to distinguish aerosol particles from cloud droplets, thanks to advanced remote-sensing instrumentation(e.g. development of spectral polarization or multi-angular sensors, as well as active sensors);

 

Previously, observation-based studies on aerosol-cloud interactions suffered from issues like: measurements classified as ‘cloud-free’ may not be so; and measured aerosols from the vicinity of clouds (high humidity) likely have properties from aerosols in cloud-free spots (relatively low humidity), due to for instance humidification effect.

 

  • increased usage of regional model to assess regional scale aci;

 

  • more widespread use of fine-scale process model to better understand how turbulent mixing, cloud and meso-scale circulations may counteract aerosol perturbations (refer to discussions on cloud process models in “cloud modeling”).

 

      For a more quantitative assessment see "Radiative Forcing and Effective Radiative Forcing by Anthropogenic Aerosols".

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