Interactions Between Clouds and Wind-Driven Surface Heat Exchanges over Land
Active Dates | 8/15/2021-8/14/2024 |
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Program Area | Atmospheric System Research |
Project Description
Interactions Between Clouds and Wind-Driven Surface Heat Exchanges over Land
Ian N Williams, Iowa State University (Principal Investigator)
William A Gallus Jr., Iowa State University (Co-Investigator)
Due to their multiscale nature, wind-driven surface heat fluxes remain a significant source of model error and an opportunity for improvement in climate prediction. It is recently recognized that large variations in land surface heat fluxes can be driven by a variety of convectively-induced circulations; here defined to include surface wind gusts from large eddies in sheared environments of low-level jets, secondary circulations such as horizontal convective rolls and cells, circulations surrounding boundary-layer clouds, and convective cold pools and gust fronts. The current generation of global climate models lacks realistic representation of the interactions between these circulations and the land surface, and relies on an empirical parameterization of the effects of convectively-induced winds on surface heat fluxes. As a controlling factor in evapotranspiration, surface wind affects the water cycle, and deficiencies in its parameterization continue to limit the predictive capability of global climate models.
This project will use the long record (over 20 years) of continuous ground-based observations at the DOE Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, and AmeriFlux sites, to explore potential improvements to the parameterization of wind-driven surface heat fluxes in the DOE Energy Exascale Earth System Model (E3SM). A nonlinear analysis of observations will be used to develop predictive relationships between surface wind speed, subgrid-scale circulations, and turbulence characteristics; as turbulence and cumulus regimes undergo transitions from large-eddies to rolls and cells, and from shallow cumulus to congestus and precipitating clouds. The analysis will address three questions critical for advancing the parameterization of wind-driven heat fluxes and their feedback on continental warm boundary layer clouds: (1) How is surface wind speed influenced by boundary layer cumulus and convectively-induced circulations over land? (2) How do surface heat and momentum fluxes respond to boundary layer cumulus and convectively-induced circulations across spatial and temporal scales? (3) What influences do soil moisture and vegetation properties have on wind-driven heat fluxes and feedback between surface heat fluxes and clouds?
Novel observationally-constrained numerical experiments will be used to understand how convectively-induced surface heat fluxes can be parameterized across spatial scales, and to extract causal understanding of the drivers of surface heat fluxes from high spatial and temporal resolution measurements. The proposed research will bring together a unique combination of high-quality surface and boundary layer profiling measurements, across varied land surface types having well-constrained characteristics, to address a significant model deficiency that hampers our ability to predict land surface and climate change.
Ian N Williams, Iowa State University (Principal Investigator)
William A Gallus Jr., Iowa State University (Co-Investigator)
Due to their multiscale nature, wind-driven surface heat fluxes remain a significant source of model error and an opportunity for improvement in climate prediction. It is recently recognized that large variations in land surface heat fluxes can be driven by a variety of convectively-induced circulations; here defined to include surface wind gusts from large eddies in sheared environments of low-level jets, secondary circulations such as horizontal convective rolls and cells, circulations surrounding boundary-layer clouds, and convective cold pools and gust fronts. The current generation of global climate models lacks realistic representation of the interactions between these circulations and the land surface, and relies on an empirical parameterization of the effects of convectively-induced winds on surface heat fluxes. As a controlling factor in evapotranspiration, surface wind affects the water cycle, and deficiencies in its parameterization continue to limit the predictive capability of global climate models.
This project will use the long record (over 20 years) of continuous ground-based observations at the DOE Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, and AmeriFlux sites, to explore potential improvements to the parameterization of wind-driven surface heat fluxes in the DOE Energy Exascale Earth System Model (E3SM). A nonlinear analysis of observations will be used to develop predictive relationships between surface wind speed, subgrid-scale circulations, and turbulence characteristics; as turbulence and cumulus regimes undergo transitions from large-eddies to rolls and cells, and from shallow cumulus to congestus and precipitating clouds. The analysis will address three questions critical for advancing the parameterization of wind-driven heat fluxes and their feedback on continental warm boundary layer clouds: (1) How is surface wind speed influenced by boundary layer cumulus and convectively-induced circulations over land? (2) How do surface heat and momentum fluxes respond to boundary layer cumulus and convectively-induced circulations across spatial and temporal scales? (3) What influences do soil moisture and vegetation properties have on wind-driven heat fluxes and feedback between surface heat fluxes and clouds?
Novel observationally-constrained numerical experiments will be used to understand how convectively-induced surface heat fluxes can be parameterized across spatial scales, and to extract causal understanding of the drivers of surface heat fluxes from high spatial and temporal resolution measurements. The proposed research will bring together a unique combination of high-quality surface and boundary layer profiling measurements, across varied land surface types having well-constrained characteristics, to address a significant model deficiency that hampers our ability to predict land surface and climate change.
Award Recipient(s)
- Iowa State University of Science and Technology (PI: Williams, Ian)