August 10, 1998
Dr. Aristedes Patrinos
Department of Energy
Biological and Environmental Research ER-70
19901 Germantown Road
Germantown, MD 20874-1290
This letter is in response to your
request that JASON review plans for the Advanced Climate Prediction Initiative.
Specifically, your request was that
The "executive summary" of our response
is captured by the following two observations:
We elaborate on these points in turn.
The rationale for increased computational
Understanding the earth?s climate
is one of today?s most challenging scientific problems. Multiple components
are involved, including the atmosphere, the ocean, the cryosphere, the
land surface, and the biosphere. These vary and interact with one another
on diverse spatial and temporal scales.
Models of the climate embody and
test our understanding of this system. They also allow us to assimilate
observational data ("fill in the gaps"). Ideally, they allow us to forecast
changes in the climate system (both due to natural variability and to anthropogenic
response) and the likely effects of various mitigation strategies.
Fundamental physical considerations
set the spatial and temporal resolutions needed for a realistic climate
model. These, together with the necessity for century-to-millennium scale
runs to quantify the response to anthropogenic forcing or the natural variability
imply, at a minimum, terascale computing resources. ACPI is a step toward
meeting that need.
Successful climate modeling will
find utility in a number of different modes. On short timescales, one may
expect some improvement in numerical weather prediction (NWP), although
this is quite a distinct problem from climate (because of the very different
timescales involved) and most of the improvement is expected from better
observational data. Over somewhat longer intervals, seasonal to interannual
climate prediction will likely be a real benefit. [The improved agricultural
response of Peru to El Niño forecasts during the last decade clearly
demonstrates the benefits possible.] Accurate descriptions on decade-to-century
scales are necessary to evaluate the likely impacts of anthropogenic activities
and the likely efficacy of various responses. And simulations involving
many millennia are required to describe paleoclimates. These diverse needs
imply the need for flexible simulation capability that can be used in new
and imaginative ways.
Many other nations have realized
the importance of sustained and focused investment in climate modeling,
the European Center for Medium-range Weather Forecasting (ECMWF) perhaps
being the outstanding example. These organizations benefit from sustained
funding, state-of-the-art technology, and strong intellectual leadership.
Surprisingly, although the US has led world in climate observations and
in pursuing the science embodied in the models, it lags significantly in
the exploitation of modeling capabilities. Greater computing power will
help to address this deficiency. Among other benefits, it will enable objective
testing of simulations with diverse assumptions and process descriptions.
Organization, management, and
implementation of the ACPI
In considering how additional computing
power might best be applied to climate modeling, it is important to realize
that the science is as yet uncertain, and controversial in parts. Further,
any successful modeling effort will need to closely integrate diverse disciplines,
including computer and computational science, applied mathematics, fluid
dynamics, chemistry, weather, biology, and climatology. There is also the
obvious need to harness the creative energy and talents of the university
The basic question in organizing
the ACPI is whether to simply supply more computational capability to the
diverse US modeling community, or to create a more centralized structure
for a coherent attack on climate simulation. Our inclination is toward
the latter. Strong scientific leadership must be identified early in the
project and charged with managing the computational and scientific resources.
Science must be at the center of the enterprise, with technology as a means
to an end, not an end in itself.
However ACPI is implemented, we urge
a close coupling with NWP. The necessity of continually confronting detailed
observations can only be a bracing experience for the modeling community,
as has been shown by the ECMWF experience. A USCMWF or organization
similar to NCAR but focused on climate are notions that capture many attractive
We believe that the case is strong
for the ACPI proceeding expeditiously. It will address serious deficiencies
in climate computing and will allow climate modelers to efficiently refine
and exploit their models. However, as the benefits of terascale climate
computing are some years off, there are some short term steps that could
be considered, including a reallocation of existing computing resources
or the purchase of a 100-1000 Gflop capacity.
ACPI is only a part of the Global
As noted above, ACPI in and of itself
will not greatly improve our abilities to predict climate. Success will
require that modeling be integrated with observational programs and process
studies. Indeed, it is essential that all three of these elements work
together. Such coordination is rare within the global change research program,
the DOE?s on-going ARM program being a conspicuous exception.
It is encouraging that ACPI includes
a substantial outreach component to make modeling results more widely available.
However, even before ACPI begins, it is important to educate the public
and policy makers about the nature of climate science and the real scientific
uncertainties and deficiencies the field is trying to address (in part
through efforts like ACPI). Among these are:
I hope that these thoughts will be
of use to you and others as you plan and implement the ACPI. Please don?t
hesitate to contact me if you would like any clarification or amplification
of these points.
Dr. Steven E. Koonin
H. Abarbanel, K. Case, F. Dyson, S. Flatte, N. Fortson, E. Frieman,
M. Gregg, M. Goldgerger, W. Happer, R. Jeanloz, W. Munk, W. Nierenberg,
O. Rothaus, M. Ruderman, R. Schwitters, P. Weinberger, F. Zachariasen