Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds

Area of Science:

• Atmospheric science
• Climate modeling
• Arctic research

Background:

• Mixed-phase clouds are crucial for Arctic warming but are poorly represented in climate models.
• Observational constraints on cloud phase and vertical structure are limited.
• Global climate models often struggle to accurately parameterize cloud processes.

Purpose of the Study:

• Investigate the vertical structure of Arctic clouds using satellite-derived metrics.
• Identify and correct model errors related to ice nucleation in mixed-phase clouds.
• Evaluate cloud feedbacks in climate models under Arctic-specific conditions.

Main Methods:

• Utilized two satellite-derived cloud phase metrics to analyze Arctic clouds.
• Employed two global climate models with the Community Atmosphere Model version 6 (CAM6).
• Adjusted model microphysical variables to create Arctic-constrained simulations.

Main Results:

• Identified a model error limiting ice nucleation.
• Observed model overestimation of summer cloud fraction, with variable winter/spring representation.
• Linked modeled cloud phase metrics and low-level liquid cloud changes to longwave cloud feedback.

Conclusions:

• Mixed-phase cloud processes are key mediators of Arctic climate change.
• Wintertime and springtime cloud responses to warming are significantly modified by mixed-phase processes.
• Improving mixed-phase cloud parameterization is essential for accurate Arctic climate projections.