U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights


Cloud Feedbacks to Surface Warming in the World’s First Global Climate Model to Include Explicit Boundary-Layer Turbulence
Published: November 30, 2018
Posted: May 21, 2019

A computationally ambitious quest to refine the resolution of standard superparameterization produces remarkably familiar cloud feedbacks to warming.

The Science
Most earth system models still rely on imperfect parameterizations of unresolved cloud processes. Cloud SuperParameterization (SP) - embedding thousands of limited-domain cloud-resolving arrays in a global climate model - has proved a promising alternative for deep convection, but its computational cost has been too prohibitive to include the sub-km scales of motion that form low clouds, which are especially critical to climate. Here researchers from a SciDac project spanning UC Irvine, the University of Washington, Stony Brook University and the Pacific Northwest National Laboratory, show results from a new form of SP calculations that avoid parameterizing even sub-km scales by using near-LES resolution, i.e., “ultraparameterization (UP).” Surprisingly, the results show remarkably familiar zonal mean cloud feedbacks to surface warming compared to both standard SP simulations, as well as SP augmented with higher-order closures of the sub-km scale.

The Impact
It is simultaneously reassuring, surprising and troubling to discover an insensitivity of overall cloud radiative feedback across global simulations that make radically different grey-zone resolution choices. The good news is that this may imply strong constraints on circulation and thermodynamics dominate irksome grey zone sensitivities; this may suggest promise for reducing error bars on cloud feedbacks to warming in the convection-permitting era. The bad news is that subjective microphysical tuning choices prove more impactful than intentional turbulence-permitting grid resolution choices. This points to the urgency of a broad problem facing our community - that cloud microphysical parameterizations continue to be unsatisfyingly hard to constrain.

Summary
Global cloud feedbacks to surface warming are analyzed for the first time using UltraParameterization (UP), a new form of superparameterization (SP) that uses near-LES resolution to explicitly resolve even the boundary layer turbulence that forms low clouds. Comparing UP’s response to +4K surface warming against standard SP reveals a remarkably similar cloud radiative response. Some muting of high latitude phase change feedback strength happens with UP but this is due to microphysical tuning choices, not grey zone grid resolution refinement.

Contacts (BER PMs)
Dr. Dorothy Koch
Dorothy.Koch@science.doe.gov

Dr. Michael Pritchard,
University of California, Irvine,
mspritch@uci.edu

Dr. Chris Bretherton,
University of Washington, Seattle
breth@uw.edu

 (PI Contact)
Dr. Michael Pritchard
Department of Earth System Sciences, University of California, Irvine
mspritch@uci.edu

Funding
This research was funded, in full, by the Scientific Discovery through Advanced Computing (SciDac) Program of the Department of Energy.

Publications
Parishani, H., Pritchard, M. S., Bretherton, C. S., Terai, C. R., Wyant, M. C., Khairoutdinov, M., and Singh, B. “Insensitivity of the cloud response to surface warming under radical changes to boundary layer turbulence and cloud microphysics: Results from the Ultraparameterized CAM.” Journal of Advances in Modeling Earth Systems 10, 3139-3158 (2018). [DOI:10.1029/2018MS001409]

Topic Areas:

  • Cross-Cutting: Scientific Computing and SciDAC

Division: SC-23.1 Climate and Environmental Sciences Division, BER

 

BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]

Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]

Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]

Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]

Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]

List all highlights (possible long download time)