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The Institute of Global Environment and Society, Inc. (IGES) has
established a center of excellence dedicated to basic research
on the Earth's current climate: the Center for
Ocean-Land-Atmosphere Studies (COLA), located in Calverton,
Maryland. By consolidating several research grants from three
different federal agencies (NSF, NOAA and NASA), a single,
multi-agency, multi-year research project was developed to
create a critical mass of scientists working together as a team
at COLA on the basic problem of the predictability of the
present climate. With continuing multi-agency support, COLA has
become a national center of excellence for research on climate
variability and predictability.
COLA is a unique institution, which allows earth scientists from
several disciplines to work closely together on
interdisciplinary research related to variability and
predictability of Earth's climate on seasonal to decadal time
scales. The scientific premise for research at COLA is that
there is a predictable element of the Earth's current climate
that makes it possible to accurately forecast climate
variations. While the chaotic nature of the global atmosphere is
known to impose a limit on the predictability of the state of
the climate at a given instant, the hypothesis behind COLA's
research suggests that there is predictability in the midst of
chaos, and that accurate climate forecasts with lead times
longer than the inherent limit of deterministic predictability
are possible.
In order to critically examine the hypothesis, COLA scientists
utilize numerical models of the Earth's global atmosphere, world
oceans and land surface biosphere in numerical predictability
experiments and experimental predictions, and use advanced
techniques for analysis of observational and model data. By
seeking to always use the best available climate model, COLA
scientists remain on the leading edge of research
advancements. By tightly coupling the predictability research
and experimental predictions for real observed climate
situations, COLA scientists find that the predictability results
provide guidance for improving the experimental forecast, and
the prediction results can be applied directly to experimental
design for studying predictability and improving prediction
models.
Website: "http://grads.iges.org/cola.html"
[Summary provided by COLA.]
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