Studies in Long-Term Noise Statistics Regional Climate Sensitivity and Predictability. Final Report (2003) Page: 4 of 6
This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided to Digital Library by the UNT Libraries Government Documents Department.
The following text was automatically extracted from the image on this page using optical character recognition software:
ocean GCM data with observed wind and surface flux, assimilated sea level
height, and longwave radiation data among others. It appears that the nodel
statistics are quite reasonable for identifying physical processes that are
sensitive to climate changes. These model data sets appear to be good
proxies for the observational record.
The statistics of background noise or natural variability derived frcm
coupled general circulation models (CGCvs) also seen to be reasonable. A
detailed comparison of model statistics against those of observational data
and other models is yet to he shown.
(3) The CSEDF analysis of several physical variables in global and regional
domains reveals that same physical processes are good and reliable indicators
of climatic changes whether natural or forced. For instance, the temporal
evolution history of the Arctic Oscillation (AO) exhibits a strong trend,
which can easily be detected from observational data. The signal-to-noise
ratio (SNR) for this physical process is extremely high with a very tight
confidence band. Another example includes the annual cycles of the 200 nb
temperature and the geopotential height fields. These physical processes
also show remarkable trends, which inply the amplification of the annual
cycles. This amplification is most likely related to greenhouse warming.
The idea of identifying physical processes that are sensitive to climatic
changes certainly deserves attention and has a high potential of being
accurate indicators of regional and global climate changes. More
importantly, changing physical processes, such as floods and droughts, are
what affect the life on earth more strongly than global temperature change.
(4) Statements on the detectability and predictability of climate change signals
are typically made based on one physical variable, say surface temperature.
Uncertainty is inherent in such statements because neither the signal nor the
natural variability of the adopted physical variable is completely known.
One way to reduce the uncertainty associated with climate change problems is
to use several physical variables in which an identical signal is
investigated. If the identical signal is seen in many physical variables,
_the confidence level will rise. The idea of detecting and predicting climate
change signals in multivariate data is realizable as the compilation of large
data sets accelerates.
One critical difficulty in a multivariate prediction and detection study is
that a physically and dynamically consistent signal should be found in a
multivariate data space. Namely, how would a signal in the surface
temperature variable appear in the surface wind variable or 200 nb
geopotential height variable, etc. A signal in a nultivariate space can be
generated using a CC;v4. Using ~O3CMs is still c-mputationally intensive and
expensive. In light of using a sensitive physical process as an indicator of
climatic change, a OCXM simulation should still be analyzed to extract the
consistent multivariate pattern of such a physical process. It appears that
Here’s what’s next.
This report can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Report.
Kim, K. Studies in Long-Term Noise Statistics Regional Climate Sensitivity and Predictability. Final Report (2003), report, August 19, 2002; Florida. (digital.library.unt.edu/ark:/67531/metadc777328/m1/4/: accessed September 24, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.