Cloud-atmosphere impacts on the central Arctic surface energy budget
| Active Dates | 9/1/2020-8/31/2024 |
|---|---|
| Program Area | Atmospheric System Research |
Project Description
M. Shupe, University of Colorado / NOAA (Principal Investigator)
A. Solomon, University of Colorado / NOAA (Co-Principal Investigator)
The Arctic system is changing rapidly, embodied by a declining sea-ice pack. The ice is getting thinner, spatially less extensive, and transitioning towards different physical properties. These rapid transitions are intimately linked with, and a leading sign of, a changing global climate system. They have implications that span the Arctic and the globe, with relevance for Earth’s radiative balance, climate prediction, resource development, energy production, ecosystem health, national security, and many others. Importantly, and in spite of the rapid changes, our models have major deficiencies in representing the Arctic system, its changes, and its linkages with the global system.
Some of the most significant uncertainties related to Arctic change and prediction are associated with complex atmospheric processes and their interactions with the ever evolving central Arctic surface. This project uses the framework of the surface energy budget to examine the many interactions between the atmosphere and the surface that drive, and respond to, the declining sea ice. The project is designed to address two related questions:
How does the partitioning of surface energy budget terms (conductive, turbulent, radiative) over sea ice vary with cloud state, atmospheric stability, season, snow depth and sea-ice thickness?
How can Arctic cloud-precipitation model schemes be optimized to simultaneously and consistently represent cloud radiative effects and net precipitation?
To address these questions, the project will harness a collection of unprecedented observations made by the Atmospheric Radiation Measurement (ARM) user facility and collaborators during the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic ice pack. These observations will be used in coordination with the regional Coupled Arctic Forecast System model to study processes in a way that would not be possible with observations or models alone. The project will assemble a broad collection of observations to characterize all terms of the surface energy budget and its key atmospheric drivers related to the atmospheric boundary layer and clouds. These datasets will serve as the basis for developing a set of inter-connected process relationships that will be used to assess the regional model in a sophisticated way. These process relationships, in combination with a suite of model sensitivity studies using a nested higher-resolution atmospheric model domain, will then be used to examine how key factors such as snow depth, ice thickness, cloud presence, and season modulate the partitioning of energy at the sea-ice surface. A conceptual model for surface energy budget partitioning will be developed. Additionally, observational and modeling tools will also be used to directly evaluate and improve upon the way in which Arctic clouds are modelled, by specifically targeting the concurrent representation of cloud radiative and precipitation processes. The skill with which models represent these processes will largely determine the skill with which they can represent the all-important surface energy budget.
This project is designed to leverage major recent investments in Arctic observations and models to significantly improve our understanding and modeling of emergent processes in the Arctic related to the thinning ice pack. Additionally, the project will develop a unique, process-based methodology for model assessment that is transportable and can be applied to many different models and re-analyses.
A. Solomon, University of Colorado / NOAA (Co-Principal Investigator)
The Arctic system is changing rapidly, embodied by a declining sea-ice pack. The ice is getting thinner, spatially less extensive, and transitioning towards different physical properties. These rapid transitions are intimately linked with, and a leading sign of, a changing global climate system. They have implications that span the Arctic and the globe, with relevance for Earth’s radiative balance, climate prediction, resource development, energy production, ecosystem health, national security, and many others. Importantly, and in spite of the rapid changes, our models have major deficiencies in representing the Arctic system, its changes, and its linkages with the global system.
Some of the most significant uncertainties related to Arctic change and prediction are associated with complex atmospheric processes and their interactions with the ever evolving central Arctic surface. This project uses the framework of the surface energy budget to examine the many interactions between the atmosphere and the surface that drive, and respond to, the declining sea ice. The project is designed to address two related questions:
How does the partitioning of surface energy budget terms (conductive, turbulent, radiative) over sea ice vary with cloud state, atmospheric stability, season, snow depth and sea-ice thickness?
How can Arctic cloud-precipitation model schemes be optimized to simultaneously and consistently represent cloud radiative effects and net precipitation?
To address these questions, the project will harness a collection of unprecedented observations made by the Atmospheric Radiation Measurement (ARM) user facility and collaborators during the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic ice pack. These observations will be used in coordination with the regional Coupled Arctic Forecast System model to study processes in a way that would not be possible with observations or models alone. The project will assemble a broad collection of observations to characterize all terms of the surface energy budget and its key atmospheric drivers related to the atmospheric boundary layer and clouds. These datasets will serve as the basis for developing a set of inter-connected process relationships that will be used to assess the regional model in a sophisticated way. These process relationships, in combination with a suite of model sensitivity studies using a nested higher-resolution atmospheric model domain, will then be used to examine how key factors such as snow depth, ice thickness, cloud presence, and season modulate the partitioning of energy at the sea-ice surface. A conceptual model for surface energy budget partitioning will be developed. Additionally, observational and modeling tools will also be used to directly evaluate and improve upon the way in which Arctic clouds are modelled, by specifically targeting the concurrent representation of cloud radiative and precipitation processes. The skill with which models represent these processes will largely determine the skill with which they can represent the all-important surface energy budget.
This project is designed to leverage major recent investments in Arctic observations and models to significantly improve our understanding and modeling of emergent processes in the Arctic related to the thinning ice pack. Additionally, the project will develop a unique, process-based methodology for model assessment that is transportable and can be applied to many different models and re-analyses.
Award Recipient(s)
- University of Colorado Boulder (PI: Shupe, Matthew)