Understanding the Roles of Cloud Microphysics and Land Surface Coupling Feedbacks in Multi-Scale Predictions of Central US Summer Hydroclimate

The climate sensitivities of the water cycle on regional scales are highly uncertain. Inspired by recent advances in solving the zeroth order problem of explicitly representing rainfall by organized summer storm systems in global climate models, this project seeks to understand next-order uncertainties linked to microphysics and land surface interactions. The primary analysis tool is the next-generation “Super-Parameterized” Community Earth System Model (SPCESM), which naturally bridges the meso-synoptic atmospheric scale gap by explicitly resolving two atmospheric scale regimes (planetary and cloud-resolving) through a heterogeneous multiscale grid. Superparameterized hindcast simulations of the Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign (April-May 2011) will be used to investigate two issues, validated against high quality data from the Atmospheric Radiation Measurement (ARM) Facility's Southern Great Plains (SGP) site and against MC3E aircraft data. The first goal is to understand how uncertain assumptions about microphysics affect the propagation and structure of Central US storms in superparameterized global simulations. Applying field and ARM validation data will help optimize uncertain microphysical parameters in SPCESM. Sensitivity tests will probe questions such as the following: What is the role of buffering by non-local feedbacks on the microphysical sensitivities of explicitly simulated midlatitude convective systems? Do higher-order microphysics improve the partitioning between suspended/falling liquid/ice condensate? Does evaporative cooling of condensate linked to mesoscale storm organization play a critical role in enabling long-range propagating mid-latitude storms or is advection of potential vorticity the dominant controlling factor? The second problem is to understand the effects of explicitly resolved deep convection on land surface coupling energetics in superparameterized simulations. Results will be validated against soil moisture and surface flux observations from the ARM sites. Sensitivity tests will deconstruct the nonlinear complexities in land-atmosphere coupling feedbacks to address the following questions: How exotic is a negative soil-moisture-rainfall feedback when convection is explicitly resolved? How critical is explicitly resolved land heterogeneity for realistically representing mesoscale land-convection interactions in the context of regional and global hydrology? Are proposed irrigation-driven feedbacks on the Southwest Monsoon robust to a realistic representation of the mechanisms that drive convective precipitation in the Central US?

Research Highlights:

Deep Learning to Represent Subgrid Processes in Climate Models Highlight Presentation