Confronting the challenge of modeling cloud and precipitation microphysics
van Lier-Walqui, Marcus — Columbia University
Morrison, Hugh Clifton — UCAR
Area of research
The state of the art in cloud microphysics modeling is reviewed and remaining challenges, which produce biases in weather and climate forecasts, are identified. It is argued that current approaches are unlikely to adequately address these challenges, and new approaches are needed that build upon recent advancements in observations, modeling, and statistical inference.
In the atmosphere, microphysics — the small‐scale processes affecting cloud and precipitation — are a critical part of Earth's weather and climate, but remain a leading source of errors in numerical forecasts even as microphysics models become more complex. New approaches are needed to move beyond current challenges, including increased laboratory studies, advanced Lagrangian microphysics schemes, and Bayesian approaches to quantifying uncertainties.
Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricate shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a “particle‐based” approach that addresses several limitations of traditional bulk and bin approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics are represented in cloud, weather, and climate models.