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Reduce
major systematic errors and biases in GCMs used for climate
variability prediction and climate change projection
Climate variability
prediction includes subseasonal-to-decadal timescale prediction
and simulations. Beyond decadal timescales, simulations
must use uncertain forcings (e.g. for anthropogenic CO2)
and are described as projections. Systematic errors and
biases of concern are those that exist in multiple climate
models. Major errors are those which have a large impact
on climate simulations. Established examples include: representation
of stratocumulus, eastern ocean boundary SST, excessive
cold tongue, double ITCZ, weak tropical variability, storm
track placement and variations, and mid-latitude air-sea
interaction.
Use
process studies to quantify climatically important processes
and to provide guidance for extending long-term in situ and
satellite observations
In addition to their
primary function of improving understanding of key processes,
process studies should be designed to leave a continuing legacy
for the overall climate observational record. This legacy can
include: (a) guidance for placement of long-term sparse observing
platforms; (b) calibration of satellite products, enabling extension
of the climate record into the past through existing satellite
data.
Ensure
that process studies lead to climate model improvement
Many field programs
explore climatically important processes, combining in situ and
remote observations on a variety of scales. Combined with process
models, these can develop understanding of the process. Our goal
is to ensure that this understanding translates into improved
climate GCMs. That may involve: consultation on process study
design, recommendation for supplemental modeling and/or field
activities, and guidance for climate process teams.
Facilitate
collaborations with other national and international partners
such as international CLIVAR, GEWEX, OCCC.
We welcome and value
the opportunity to collaborate with other programs on modeling
and observational activities of mutual interest and recognize
that these opportunities can result in leveraged resources and
added scientific benefits.
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