Lawrence Livermore National Laboratory
models have a hard time representing clouds accurately because they lack the
spatial resolution necessary to accurately simulate the billowy air masses.
Lawrence Livermore National Laboratory (LLNL) scientists and international
collaborators have developed a new tool that will help scientists better
represent the clouds observed in the sky in climate models.
observations from satellites infer the properties of clouds from the radiation
field (reflection of sunlight back into space, or thermal emission of the planet).
However, to accurately use satellite data in climate model assessment, a tool
is required that allows an apples-to-apples comparison between the clouds
simulated in a climate model and the cloud properties retrieved from
models are becoming more interactive and are taking into account the radiation
data from the satellite observations and is an important part of the process of
making better climate models,” says the Laboratory’s Stephen Klein, who
along with LLNL’s Yuying Zhang and other collaborators have developed the
Cloud-Feedback-Model Intercomparison Project Observation Simulator Package
models have been improving and refining their representations of clouds and
COSP will play an important role in furthering this improvement,” Klein
models struggle to represent clouds accurately because the models lack the
spatial resolution to fully represent clouds. Global climate models typically
have a 100-km resolution while meteorological models have a 20-km range.
However, to accurately represent clouds as seen in satellite measurements, the
scale would need to be from the 500-m resolution to 1-km range.
those small scales are not practical for weather or global climate
models,” Klein says. “Our tool will better connect with what the
satellites observe—how many clouds, their levels, and their reflectivity.”
COSP is now used worldwide by most of the major models for climate and weather
prediction, and it will play an important role in the evaluation of models that
will be reviewed by the next report of the Intergovernmental Panel on Climate
Change, Klein says.
COSP allows for a meaningful comparison between model-simulated clouds and
corresponding satellite observations. In other words, what would a satellite
see if the atmosphere had the clouds of a climate model?
is an important and necessary development because modeled clouds cannot be
directly compared with observational data; the model representation of clouds
is not directly equivalent to what satellites are able to see,” Klein
explains. “The COSP eliminates significant ambiguities in the direct
comparison of model simulations with satellite retrievals.”
includes a down-scaler that allows for large-scale climate models to estimate
the clouds at the satellite-scale. The tool also allows modelers to diagnose
how well models are able to simulate clouds as well as how climate change
alters clouds. The tool already has revealed climate model limitations such as too
many optically thick clouds, too few mid-level clouds and an overestimate of
the frequency of precipitation. Additionally, COSP has shown that climate
change leads to an increase in optical thickness and increases the altitude of
high clouds and decreases the amount of low and mid-level clouds.