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

e to adopt a different calibrated language to report subjective probabilities to that used to
report likelihoods, but whether this could be accepted at this stage is a moot point.
Invited Session: Seasonal to Decadal Forecast
Slow Modes of Climate Variability and Seasonal Prediction
Speaker: Carsten S. Frederiksen
Carsten S. Frederiksen
Bureau of Meteorology Research Centre, Melbourne, Australia
c.frederiksen@bom.gov.au
Xiaogu Zheng
National Institute of Water and Atmospheric Research, Wellington, New Zealand
<strong>The</strong> provision of skillful seasonal forecasts of temperature, rainfall and other climate
variables has obvious potential benefits for countries whose economic activity is dependent on
agriculture, tourism and other climate-sensitive industries. At extra-tropical latitudes, a
substantial component of interannual variability of seasonal mean climate fields arises from
variability within the season (i.e. intraseasonal variability). This 'intraseasonal' component is
mainly contributed to by weather variability with time scale longer than the deterministic
prediction period (about ten days) and therefore it is essentially unpredictable on seasonal, or
longer, timescales. After removing this component from seasonal mean fields, the residual
component is more likely to be associated with slowly varying external forcings (e.g. sea
surface temperatures) and from slowly varying (interannual/supra-annual, slower than
intraseasonal time scale) internal atmospheric variability and therefore, it is more potentially
predictable at the long range. This component is referred to commonly as the 'slow' or
'potentially predictable' component.
Recently, the authors developed a methodology for estimating, from monthly mean data,
spatial patterns, or modes, of the slow and intraseasonal components. This methodology
provides a way to better identify and understand the sources of predictive skill as well as the
sources of uncertainty in climate variability. Here, we illustrate this by applying the
methodology to an analysis of New Zealand rainfall variability and the Southern Hemisphere
and Northern Hemisphere 500hPa geopotential height field, often used to characterize the
general atmospheric circulation. In particular, we show how the information obtained can be
used to develop improved statistical seasonal forecasts of these two variables. <strong>The</strong>
methodology for constructing the forecast schemes is based on determining predictors for the
principal component time series of the dominant slow modes and then using these to construct
a forecast of the climate field as a linear combination of the slow modes. We compare the skill
of our statistical schemes with dynamical seasonal forecast models and show how our
methodology can be used to analyse the sources of model predictive skill.
5