Christopher R. Williams, University of Colorado Boulder (Principal Investigator)
Kaitlyn Loftus, Columbia University (Co-Investigator)
Marcus van Lier-Walqui, Columbia University (Co-Investigator)

Rain formation in warm boundary-layer (WBL) clouds plays an important role in Earth’s radiative balance and hydrological cycle, yet the associated microphysics and radiation processes, along with their interactions, are poorly represented in the world’s leading Earth System Models (ESMs) submitted to the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP). Across modeling centers running different numerical codes, CMIP models show compensating biases relative to satellite observations. These model biases include precipitation that is “too frequent, too light” and tropical clouds that are “too few, too bright”.  In multiple CMIP models, improving warm rain characteristics requires degrading shortwave cloud radiative properties, and vice versa, which suggests insufficient model structural flexibility. We hypothesize that these longstanding ESM WBL biases stem from common assumptions representing warm rain processes and that identifying and improving their structural errors will lead to improved atmospheric system predictability. 

This proposed research aims to evaluate the physical basis of common ESM warm rain parameterization assumptions at fine resolutions using a combination of US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) observations and process-based modeling spanning a hierarchy of complexities, and, if needed, propose alternative representations supported by an observation-modeling bridge. In coordination with ARM field campaign observations, this proposed research has three research objectives:

 

1. Use simplified 0-dimensional box and 1-dimensional column models with advanced (i.e., Lagrangian super-droplet method) and parameterized microphysics, supported by comprehensive 3-dimensional large eddy simulations (LES), to contextualize common ESM structural assumptions from a process-based perspective.  

 

2. Develop the ability to use ARM observations from the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) and Eastern North Atlantic (ENA) field campaigns to inform, at a process-level, the microphysical regimes in campaign data (e.g., by performing radar- and lidar-based drop size distribution retrievals). 

 

3. Evaluate common ESM assumptions with simplified models initialized by ARM observations to identify structural errors in ESMs.

 

By analysis of ARM EPCAPE and ENA field campaign observations, in concert with process-level models, this proposed research aims to improve atmospheric system predictability by breaking the deadlock created by the common use of deficient structural assumptions in ESMs that represent WBL clouds.