C. Provide opportunities for involving a broader set of mathematicians.
All fields of the mathematical sciences can be profitably incorporated in one aspect
or another of the investigation of HESs. The richness of the techniques and approaches
is one defining characteristic of the complexity of these systems, and only through a
diversified approach will we be able to develop a sound understanding, leading to
believable predictions.
D. Require that both data and models should be kept open source and made fully
public, and encourage citizen science.
A primary recommendation of our working group is to call for both data and models
to be kept open source and made fully public. Progress in model development is more
efficient and rapid when the code is made available to other researchers and modelers.
Keeping models and data proprietary or secret may be beneficial to commercial
interests, but should not be the case when problems facing humanity are at stake.
When a model remains proprietary, there are fewer possibilities for application of the
model or reproduction of results. Wider use of a given model can uncover existing
problems, and lead to model innovations and even new kinds of solutions. When data
and models are open, the research community can check both model design and the
accuracy and the realism of the model. So many of the problems facing humanity, and
in particular those associated with sustainability, do not stop at national or regional
frontiers. International sharing of data should be especially encouraged. Having access
to everyone’s data helps to develop, improve, calibrate and validate models and to
compare them with reality. Inevitably, progress is much slower when data and models
are limited to a few hands and eyes. The common good of humanity calls for this
information to be shared.
Once a model has been developed and passed basic tests, it should be made
available for other scientists to experiment and add/change the model. Such
“Community HES models” would accelerate HES model progress and provide decision
makers better tools with which to make decisions. If the model(s) are sufficiently robust
that they can be run on a PC, invite public volunteers (Citizen Science,
http://www.nsf.gov/news/special_reports/science_nation/citizenscience.jsp)) to run the
models, explore their limits and modify them, and report their results to a central web
site. This will allow us to analyze the many-members’ ensemble results and to develop
the best statistical and dynamic approaches to estimation of risk and uncertainty.
The benefits of open sharing of models are again exemplified by the history of
climate modeling. The improvement in the forecast performance of NWP models in all
the major centers is one of the most remarkable scientific achievements of the last half-
century. As the meteorologist Lorenz discovered, even if a NWP model was perfect and
if the initial conditions were also essentially perfectly known, the presence of
atmospheric instabilities would make it impossible to predict the evolution of weather for
more than two weeks. The discovery that if a flow is chaotic, then there is a limit of
predictability, had a tremendous scientific impact, but was at that time only of academic
interest, since even the 1-2 day forecasts were quite poor. Now, 50 years later, forecasts
routinely remain accurate for 10 days, especially during the winter season, both in the
Northern and the Southern Hemispheres. One reason why progress has accelerated is
that different operational centers have developed their own models and methods of data
assimilation, their research approaches were always public and Meteorology has always