The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

Relationship Between Ensemble Mean Square and Ensemble Mean Skill in Four Climate Models
Youmin Tang
University of Northern British Columbia, Canada
ytang@unbc.ca
Hai Lin
RPN, Meteorological Service of Canada
Andrew Moore
Uni. of California at Santa Cruz
In this study, we investigated the reliability of ensemble prediction of the El Nino and
Southern Oscillation (ENSO) and the Arctic Oscillation (AO), using four different climate
models and various ensemble schemes. Several important issues related to climate
predictability, including reliability measures and dominant precursors that control the reliability,
were addressed. It was found that the ensemble mean (EM) square is a useful measure for the
reliability of both the ENSO and the AO dynamical prediction.<strong>The</strong> relationship between EM^{2}
and the prediction skill depends on the measure of skill. When correlation-based measures are
used, the prediction skill is likely to be a linear function of EM^{2}, i.e., the larger the EM^{2}
the higher skill the prediction; whereas when MSE-based (mean square of error) metrics are
used, a ``triangular relationship'' is suggested between them, such that when EM^{2} is large,
the prediction is likely to be reliable whereas when EM^{2} is small, the prediction skill is highly
variable.
In contrast to ensemble numerical weather predictions (NWP), the ensemble spread in the
ensemble prediction of these climate models was found to have little connection with the
prediction skill. This is probably due to a small variation of ensemble spread in the climate
models which may be associated with the intrinsic nature of ensemble climate predictions. <strong>The</strong>
predictability of these models can be characterized fairly well using a Gaussian framework with
constant variances.
A Methodology in Predicting the Influence of Climatic
Herminia C. Tanguilig
Don Mariano Marcos Memorial State University
hermtang@yahoo.com
Weather interacts with components of agricultural production system. An understanding of
these interactions is essential in formulating crop production strategies.On one hand, it is
difficult to establish quantitatively this complex interaction with some reasonable degree of
reliability. <strong>The</strong> most feasible approach is to conduct long-term experiments involving
continuous cropping and subject to analysis to generate rational or statistical relations.On the
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