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Cloud droplet susceptibility to aerosol particles is higher in low-level clouds than previously thought. This finding impacts estimates of aerosol indirect radiative forcing and highlights model deficiencies.

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Area of Science:

  • Atmospheric Science
  • Climate Science
  • Aerosol Science

Background:

  • Cloud droplet number concentration is sensitive to cloud condensation nuclei (CCN) number.
  • This susceptibility influences aerosol-cloud interactions and radiative forcing, a key uncertainty in climate change projections.
  • Low-level stratiform clouds are significant contributors to Earth's radiative balance.

Purpose of the Study:

  • To investigate the susceptibility of cloud droplet number to CCN number in low-level stratiform clouds.
  • To compare in situ observations with satellite-derived estimates and Earth system model simulations.
  • To quantify aerosol indirect radiative forcing based on observational data.

Main Methods:

  • Utilized long-term in situ observations (3-10 years) of aerosols and clouds at three high-latitude sites.
  • Estimated aerosol indirect radiative forcing from observational data.
  • Evaluated four Earth system models against the observational data for susceptibility and underlying physics.

Main Results:

  • In situ observations revealed higher cloud droplet susceptibility in low-level stratiform clouds compared to satellite data.
  • Estimated aerosol indirect radiative forcing of -1.16 W m⁻² based on observations, aligning with higher-end satellite estimates.
  • Significant inter-model variability in susceptibility was found, with underlying physics often deviating from observations.

Conclusions:

  • Observed cloud droplet susceptibility suggests a stronger aerosol-cloud interaction effect than often represented in models.
  • The study provides a robust observational constraint for aerosol indirect radiative forcing.
  • Discrepancies highlight the need to improve sub-grid-scale updraught velocities and aerosol size distribution representations in Earth system models.