Geoscience Reference
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rain, especially in regions of complex topography. Even with the increased resolu-
tion, for some applications such as catchment-scale water resource management,
resolution of less than a few kilometers may be required. In this respect, the
downscaling techniques using regional climate models and macroscale hydrology
models will be important.
Improve model physics. While increasing resolution will reduce model uncertainty
and improve the geophysical fluid dynamic aspects of climate models, the major
culprit of model uncertainty is still the inadequacy of representation of the physical
processes that determine the forcing functions of the model. If a model is driven by
erroneous forcing functions, no matter how well the flow fields can be simulated, it
will not give the right answers. Improvement of physical representation in models is
therefore paramount and should be focused on processes that are key drivers of the
Earth's hydrologic cycles. These include cumulus heating in the tropics, aerosol-
cloud-radiative processes, fluxes at the air-sea and air-land interfaces, land surface
and vegetation processes. Improving model physics is an extremely difficult and
tedious endeavor, because the physics of climate is very complex and interwoven.
Improving a physical process in a stand-alone model does not necessarily mean that it
will give better performance in a coupled model. Likewise, improving one part of the
system does not always lead to improvement of other parts. Hence the process of
improving model physics can be very arduous, calling for multiple tests and valida-
tion with observations under a variety of conditions. Substantial improvements are
not likely to come in the short term, but sustained organized efforts by the scientific
community are required. In some sense, we have exhausted much of the reliable
information that can be derived from current climate models. Unless model physics
improvement is taken seriously, model uncertainties will remain unacceptably large.
Improve data for model validation. One major stumbling block for model improve-
ment is the lack of detailed data suitable for model validation and improvement.
Given the vast amount of data obtained from ground-based and satellite atmo-
spheric and oceanic observations, field campaigns, and special measurement plat-
forms, it may seem a bit puzzling that there is still a shortage of data for model
validation. The reason is that for model physics improvement very specialized data
with high spatial and temporal resolutions, directly relevant to the model para-
meters, are required. These data are often not direct observables in the climate
system, but are quantities derived from the observables and therefore have large
uncertainties themselves. Often, they require special intensive observation plat-
forms, which for practical purposes can only be carried out over a short period in
field campaigns. To be sure, besides new data from future field campaigns, there are
data that can be extracted from the vast satellite and operational historical database,
as well as various enhanced observational sites to be used for model validation and
physics improvement. There is a need for coordinating and extracting global,
regional, and site data from various sources, and making them available for
model improvement and prediction.
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