Press Release

Media Contact: Lynne Roeder, 509.372.4331

A key uncertainty in computer simulations of climate change is the broad range of cloud-climate feedback processes. Natural phenomena that occur in cloud lifecycles present a difficult challenge to scientists studying how dynamic cloud conditions affect the sun's incoming and Earth's outgoing energy and, in the longer term, our climate. Scientists sponsored by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Program focus on this challenge, with the goal of reducing uncertainty and improving the representation of clouds and radiative feedback processes in climate models.

In August, a series of 18 articles authored by members of the ARM Cloud Parameterization and Modeling (CPM) Working Group will appear in a special section of the Journal of Geophysical Research print edition (JGR, Vol. 110, No. D15). Under the heading "Toward Reducing Cloud-Climate Feedback Uncertainties in Atmospheric General Circulation Models," the collection represents recent research by the CPM Working Group to understand and improve the representation of clouds in models by using observations at process levels.

"Understanding cloud feedback is like running a marathon as opposed to a sprint," said Dr. Anthony Del Genio, a representative of the CPM Working Group. "The papers in this issue are getting modelers out of the blocks by documenting new approaches to data-model comparison that should be used widely in the future."

Among the science highlights in the special issue are:

  • First-ever applications of Single Column Models to climatically significant time periods, made possible by use of ARM data, to provide accurate long-term information about how large-scale wind and temperature fields drive cloud formation and lifecycle;
  • Conclusion that thick midlatitude storm clouds, considered the best understood of all cloud systems, are overpredicted by all climate models without exception; several ARM studies point to the need for models to predict variability of vertical motions inside model gridboxes to simulate this aspect of clouds;
  • First physical approach to the parameterization of cloud variations in gridboxes where deep convection (storm clouds) is occurring;
  • Use of ARM data to parameterize cloud variations inside gridboxes for low-level clouds as a function of the state of the environment;
  • First observational inference of midlatitude cloud feedbacks in climate change using ARM data in combination with a Single Column Model and a global climate model; and
  • First simulations of observed weather events by global climate models.

One of the papers assesses the current status of cloud simulations by global climate modelers in both the United States and Europe. The other 17 papers can be categorized into four groups: a case study of cloud simulations from March 2000; developments of cloud parameterization algorithms using ARM data; evaluation of modeled cloud processes against ARM measurements; and research results concerning measurements of clouds. Links to the various articles, published in previous versions of the journal between March and June 2005, are available via the online reference list, found at http://www.agu.org/journals/ss/ARMCPM1/.

"For a long time, it's been claimed that observations, process models, and weather models will have a beneficial effect on global climate models, but it has been extremely difficult to show the links among these various pieces and demonstrate the direct impact of high resolution data on climate model physics," said Dr. Thomas Ackerman, Chief Scientist for the ARM Program. "This collection of papers really represents one of the first serious attempts to confront climate models with process-level information."

The CPM Working Group addresses the critical issue of relating observations and data analysis to climate model development and evaluation. Their research principally involves (1) single column models to represent the vertical profile evolution of temperature, water vapor, and clouds averaged over a single grid-box of a global climate model, and (2) high-resolution cloud resolving models to represent convective and cloud processes over a limited area. Results from these studies are then used to improve cloud parameterizations in global climate models.

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Additional information about ARM Program Science and the ARM Climate Research Facility
is available at www.arm.gov.