Identifying Robust Cloud Feedbacks in Observations and Models

Average longwave (LW), shortwave (SW), and net cloud feedbacks as
simulated by global climate


For more than 30 years, scientists have known that the inability to predict how clouds will respond to a climate change hinders a confident prediction of the magnitude of global warming resulting from a given increase in greenhouse gases. As a result, we are not able to confidently identify the magnitude of carbon emission reductions necessary to avoid dangerous anthropogenic interference in the climate system. Thus it is imperative to perform research aimed at reducing the uncertainty range associated with the response of clouds to a warming of the planet, also known as the “cloud feedback”.

To reduce the substantial uncertainties associated with long-term climate-change, a team of researchers at Lawrence Livermore National Laboratory and the University of California at Los Angeles are working to reduce these uncertainties by identifying robust cloud feedbacks in today’s climate models and constraining them with available observations. The team scrutinizes the results from simulations of future climate made by the most recent climate models assessed by the Intergovernmental Panel on Climate Change to answer a variety of questions:

Which cloud types matter for cloud feedback?

Cloud feedbacks are extremely variable between different climate models. However, it is not always clear what is the relative contribution of cloud types from various regions to the global mean cloud feedback and its inter-model spread. Researchers in this project have developed novel techniques to separate the contribution of different cloud types and have found that cloud feedbacks are not the result of a single cloud type but that we must consider the feedbacks from many cloud types including low clouds, high clouds, mid-latitude clouds and polar clouds.

What aspects of cloud feedback can be constrained with observations we have today?

A key aspect of the project is the identification of cloud feedbacks where similarities are found in simulations of both current-climate variability and of projected climate change (so called “timescale invariant feedbacks”). For example, if fluctuations of clouds with day-to-day or season-to-season variations of temperature are similar to those shown over climate change time-scales, we could use observations from the current climate to constrain cloud feedbacks. Researchers are working to identify which cloud types exhibit time-scale invariance as well as the observations that can quantitatively constrain these feedbacks.

What physical processes contribute to cloud feedback and what feedbacks are correct?

Our confidence in any given cloud feedback is dependent on our ability to understand the physical processes from which the feedbacks result and our confidence in those processes. Researchers are working towards identifying the physical mechanisms of various feedbacks simulated by complex climate models and critiquing their realism. One technique to accomplish this is the application of more realistic models of cloud processes to the changes in the large-scale environment predicted by global climate models.

This Cloud Feedback project is supported by the Regional and Global Model Analysis (RGMA) Program of the U.S. Department of Energy’s Office of Science Biological and Environmental Research (BER).