Studies in Long-Term Noise Statistics Regional Climate Sensitivity and Predictability. Final Report (2003) Page: 1 of 6
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Studies in Long-Term Noise Statistics,
Regional Climate Sensitivity and Predictability:
Final Report (2003)
Kwang-Y. Kim, Principal Investigator
Department of Meteorology, Florida State Unive Wypd f ,arance Granted
SCIENTIFIC GOALS OF THE PROJECT: Mark P Dvorscak
E-mAll m~ek firstname.lastname@example.orgV
The specific goals of the project were: offE-.) n t-w Pvroperty Lgaw
(1) to develop a family of simplified coupled climate moff C ' i ra oFe
results might be useful in benchmarking and comparing with the super models
to be developed under CHAMMP;
(2) to better understand the noise response characteristics of simple and
complex coupled climate systems, which is an integral part of climate change
(3) to address sampling problems arising from various estimation techniques
based on finite length records and spatially sparse observations;
(4) to develop techniques of linear estimation problems such as detection,
estimation and prediction that will ultimately be used to address the
detectability and predictability at the regional scale; and
(5) to apply the developed tools to climate change studies.
RESULTS TO DATE:
(1) The PI developed many different versions of energy balance models (Efils) to
use the for the purpose of studying all the statistical issues of climate
change problems. These simple coupled models were also used for the purpose
of verifying climate statistics of coupled general circulation models (GCMs)
and the uncertainty of climate change statement due to inaccurate model
(2) The capacity of cyclostationary EDFs (CSEDFs) in representing moving and
- temporally evolving spatial patterns makes the CSEDF analysis technique
suitable and attractive for climate change research. This useful tool allows
the PI to cast the climate change problems into more revealing and
interesting forms as discussed below. The computation of CSEOFs as they
apply to climate change problems requires long data sets. At present,
climate models are an only means of generating data sets long enough for
reliable cyclostationary statistics of important physical variables. Model
statistics are, of course, different frcm statistics of observational data.
Therefore, it is important to understand whether model statistics will be
reliable for long term climate change studies. During the report period,
cyclostationary statistics of many model data sets have been examined in
light of their reasonableness and comparability with observational data. The
model data sets include the NCIEP reanalysis data, EX dF reanalysis data,
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Kim, K. Studies in Long-Term Noise Statistics Regional Climate Sensitivity and Predictability. Final Report (2003), report, August 19, 2002; United States. (digital.library.unt.edu/ark:/67531/metadc777328/m1/1/: accessed March 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.