Are researchers closing in on a general theory of convective cloud systems?
Weve all had
the experience of getting rained on from one small cloud even though blue
sky is plainly visible all around. The reason is that rain can come from
an individual cumulus cloud just as easily as from a massive cloud system
that hangs over an entire region.
Researchers, however, have yet to figure out the precise mechanisms that
drive the behavior of either individual clouds or the larger-scale systems
that comprise them. Cloud-scale models can yield some insight on what
makes a single cloud tick, but on a global scale the collective behavior
of clouds is still a challenge. Thats partly because climate models,
which are fundamental for research into regional or global climate patterns,
lack the resolution to accurately capture events that occur on scales
of less than about 250 kilometers (155 miles). As a result, they have
to estimate, or parameterize, the behavior of a group of clouds that each
may be as small as 10 kilometers (6.2 miles)
The small scale is very important, explains Mitch Moncrieff
of the Mesoscale and Microscale Meteorology Division. At small scales,
theres an awful lot of instability and huge exchanges of energy.
Convective rainfall is intense.
Mitch and his colleagues at MMM, including Wojciech Grabowski, Changhai
Liu, and Piotr Smolarkiewicz, are working to fill that gap by studying
the evolution of cloud systems ranging in size from about one kilometer
(0.6 mile) to thousands of kilometers. They are looking at natural processes
that span widely differing timescales: raindrops can develop in seconds,
for example, whereas intraseasonal oscillations in the tropics may last
A major focus of the research is understanding the difference between
events that occur seemingly at random (localized showers and thunderstorms
caused by convection, as opposed to steady rain over large areas) and
events that proceed in an organized way (the formation of clusters of
cumulus cloud systems that can span many hundreds of miles). A computer
model may parameterize cumulus clouds by specifying some number of them
in a given area without trying to identify each specific cloud. But the
techniques used to parameterize each clouds random behavior may
be missing some aspect that connects the cloud features.
Mitch and his colleagues are finding that convective systems on both large
and small scales appear to be governed by similar natural lawsan
understanding that could shed light on the physics of entire cloud systems.
If the finding holds up, it would indicate that clouds, which often seem
to be acting randomly, actually are behaving in a coherentand partially
predictablefashion. For example, large organized systems are often
born from localized showers and thunderstorms.
The finding could also mark an important step toward creating a general
theory of precipitating systems. As Mitch puts it, constructing such a
theory of cloud systems and how to parameterize them is the Holy
of precipitation prediction.
The implications go beyond rainfall. Clouds have profound effects on long-term
climate trends because they reflect and absorb sunlight and transport
water vapor, which is the most important of the greenhouse gases that
warm the atmosphere. Quantifying the evolution of cloud systems is critical
for understanding the cycling of water and energy in the atmosphere, as
well as for making accurate numerical weather forecasts. (Part of the
reason that weather prediction models cannot forecast precipitation accurately
is because they fail to model the formation of cloud systems.)
So far, Mitch has focused largely on clouds in the tropics, where convection
is the dominant atmospheric process. Understanding tropical convection
will help weather prediction in midlatitudes because the effects of tropical
convection ripple across to midlatitudes within a few days. If we
are ever to predict
midlatitude weather at its deterministic limit of about two weeks, we
must understand tropical convection,
What is learned about the tropics,
in other words, can be applied to the entire earth.
The long shadow of a few showers
Scientists have long understood the basics of convection: warm, moist
air bubbles up through colder air aloft. Its the complex interplay
of convection with the atmosphere around it, sometimes at a great distance,
that remains a challenge. For example, tropical storms over the Indian
Ocean and western Pacific Ocean may be related to the timing of larger-scale
pulses of wind and storminess that cross this region from west to east
every 30 to 60 days. This cycle, known as the Madden-Julian Oscillation
(discovered by NCARs Rol Madden and Paul Julian, formerly of NCAR),
in turn affects
the evolution of clouds and weather across large parts of the Americas
and the Atlantic.
When working with regional and high-resolution global weather prediction
models, researchers face another challenge: the models do not correctly
handle the growth of large storm systems. As a storm system grows big
enough to exceed the size of a single grid cell in the model, it may jump
over an artificial threshold: instead of parameterizing the growing system,
the model begins to resolve it. Weve shown that surrogate
and distorted cloud systems may develop, generating a new kind of uncertainty
in modern numerical weather prediction models, Mitch says.
To better understand precipitating convection and clouds, Mitch and his
colleagues are drawing on a variety of disciplines, including mathematics,
cloud physics, fluid dynamics, and numerical models. Their advanced cloud-system-resolving
models calculate cloud systems directly in a process called superparameterization.
This new approach was developed by comparing the models and parameterizations
with data collected during major field experiments, such as the Tropical
Ocean and Global Atmosphere Programs Coupled Ocean-Atmosphere Response
Experiment (TOGA COARE) and NASAs Tropical Rainfall Measuring Mission
While it may not be possible to anticipate the location of a single rain
cloud in a partly sunny sky, MMMs findings should lead to increasingly
detailed estimates of the timing and general location of storms in the
next few years.
One of the ultimate goals of our research, Mitch says, is
to provide a foundation for more accurate and longer-range weather forecasting.
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