Using ARM Observations to Evaluate Process-Interactions in MCS Simulations Across Scales
Active Dates | 8/15/2019-8/14/2024 |
---|---|
Program Area | Atmospheric System Research |
Project Description
Mesoscale convective systems
(MCSs) have pronounced impacts on the earth’s
radiative balance,
the hydrological cycle, and are related to extreme events such as floods and droughts. However, MCSs are poorly represented in state-of-the-art climate models. This is partly due to a lack of understanding of the complex process interactions between MCS kinematics, cloud-physics, and environmental controls on MCSs. Our main objective is to leverage unique Atmospheric Radiation Measurement (ARM) observations from midlatitude and tropical deployments to improve process-level MCS understanding and simulated MCS process interactions across a wide range of scales.
MCS observations from the ARM U.S. Southern Great Plains (SGP), the Green Ocean Amazon (GoAmazon), and Clouds, Aerosols, and Complex Terrain Interactions (CACTI) campaigns will be complemented by large eddy simulations (LES; horizontal grid spacing of ~100 m) of observed MCSs. The spatiotemporal scales and energetics of MCSs and associated processes will be investigated using these LES simulations including microphysics sensitivity tests.
Recent advances in observing MCS process characteristics at ARM supersites allow a unique opportunity to assess at which model horizontal grid spacing (Δx) MCS processes such as up/downdraft core characteristics, the representation of cold pools, and precipitation processes start to converge.
Our overarching question is whether the most salient MCS lifecycle features and subsequent climate-scale implications can be adequately represented without the need for global LES-scale models?
ARM observations are used to evaluate ensemble model sensitivity simulations spanning the hydrostatic (Δx?10 km), terra incognita (Δx~1 km), and large eddy regime to test this hypothesis. Models with different complexity (idealized and realistic), as well as microphysics sensitivity tests, will provide insights into the robustness of our results.
The key outcomes of this proposal are:
Introducing improved model constraints on MCS processes based on novel ARM supersite datasets.
Constrained model simulations that are used to test scaling for various key MCS characteristics and interactions in different environmental regimes.
Enhanced knowledge of the interactions between simulated MCS dynamics, thermodynamics, and microphysics and assessment of possible error cancelation effects.
Accelerated climate model development by identifying computationally efficient model setups that allow capturing energetically and hydrologically important MCS properties in different climate regimes.
These outcomes will benefit ASR’s mission based on a novel use of recent ARM advancements by supporting enhancements in the predictive ability of high-impact weather and water resources in regional and global models.
MCS observations from the ARM U.S. Southern Great Plains (SGP), the Green Ocean Amazon (GoAmazon), and Clouds, Aerosols, and Complex Terrain Interactions (CACTI) campaigns will be complemented by large eddy simulations (LES; horizontal grid spacing of ~100 m) of observed MCSs. The spatiotemporal scales and energetics of MCSs and associated processes will be investigated using these LES simulations including microphysics sensitivity tests.
Recent advances in observing MCS process characteristics at ARM supersites allow a unique opportunity to assess at which model horizontal grid spacing (Δx) MCS processes such as up/downdraft core characteristics, the representation of cold pools, and precipitation processes start to converge.
Our overarching question is whether the most salient MCS lifecycle features and subsequent climate-scale implications can be adequately represented without the need for global LES-scale models?
ARM observations are used to evaluate ensemble model sensitivity simulations spanning the hydrostatic (Δx?10 km), terra incognita (Δx~1 km), and large eddy regime to test this hypothesis. Models with different complexity (idealized and realistic), as well as microphysics sensitivity tests, will provide insights into the robustness of our results.
The key outcomes of this proposal are:
Introducing improved model constraints on MCS processes based on novel ARM supersite datasets.
Constrained model simulations that are used to test scaling for various key MCS characteristics and interactions in different environmental regimes.
Enhanced knowledge of the interactions between simulated MCS dynamics, thermodynamics, and microphysics and assessment of possible error cancelation effects.
Accelerated climate model development by identifying computationally efficient model setups that allow capturing energetically and hydrologically important MCS properties in different climate regimes.
These outcomes will benefit ASR’s mission based on a novel use of recent ARM advancements by supporting enhancements in the predictive ability of high-impact weather and water resources in regional and global models.
Award Recipient(s)
- University Corporation for Atmospheric Research (PI: Prein, Andreas)