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

model has been applied to the series of the Siberian high index and demonstrated its ability for
fitting and forecasting.Besides, the relationship between the Siberian high in winter and the
East-Asian summer monsoon was studied. <strong>The</strong> synchronization between the Siberian high in
winter and the East-Asian summer monsoon existed before 1925, after that the variations of
both the Siberian high in winter and the East-Asian summer monsoon were quite different, the
East-Asian summer monsoon became weaker.
Long-term Probabilistic Forecast and T* Distribution
Shao-Hang Chu
US Environmental Protection Agency
chu.shao-hang@epa.gov
Models are great tools to test ideas. <strong>The</strong>ir usefulness, however, depends on their ability to
simulate the current reality and predict the future. In this study, I show that a statistical model
based on a new t*-distribution of station temporal data is capable of predicting the probability
of any future outcome to exceed a specific value using only the currently available sample
statistics assuming a normal random variable. In an air quality management application, the
model has demonstrated categorically an average success rate of over 80% both in simulating
the current ozone non-attainment areas and forecasting the rate of future violation of the
8-hour ozone National Ambient Air Quality Standards in the U.S. for up to 12 years. While the
predictability of deterministic climate models is still limited by large uncertainties, the
probabilistic forecast by this model provides a promising alternative in assessing the climate
impact on environment for decades.
Spatial Drought Reconstruction Based On Point-By-Point Regression
Edward R. Cook
Tree-Ring Laboratory, Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, NY 10964
drdendro@ldeo.columbia.edu
<strong>The</strong> paleoclimate reconstruction of climate fields is often considered a joint space-time
estimation problem where the predictand climate field, typically on a regular grid, is
reconstructed simultaneously from an irregular predictor network of paleoclimate proxies. In
dendroclimatology, there is a rich history of doing this as far back as 1971. In so doing, sea
level pressure, temperature, precipitation, and drought fields have been successfully
reconstructed from tree-ring networks using a variety of reduced space multivariate regression
methods. While such methods can work well, they are also difficult to diagnose because the
network of predictors is contributing jointly to the reconstruction of the climate field in a
potentially very complicated way. Thus, why a reduced space multivariate method might fail to
skillfully reconstruct certain areas of a climate field might be impossible to identify and correct
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