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

affect climate variability at that spatial scale. An extended record is required for a cleaner
separation of naturally occurring variability from changes that are linked to the recent
anthropogenic influence on climate. Additionally, to raise confidence in regional climate
predictions, it is important to test if hypothesized links between forcings and response are
indeed systematic over time, and possibly even the same over multiple time scales. Climate
field reconstructions (CFRs) based on high-resolution proxy networks provide a way of
extending the often short instrumental record. Significant progress has been made in recent
years in understanding the properties and skills of reconstructions on a global or hemispheric
mean scale. In preparation for assessments of future regional scale climate and its
predictability, it is now crucial to investigate how much of regional climate is driven by external
forcing and what components are simply due to internal, unforced variability. Solar activity
changes over decadal to millennial time scales and short lived, interannual volcanic effects on
climate offer our only way of testing such links. Successful preservation of the forced
structures in climate response to external forcing is therefore crucial, particularly given the fact
that during the short calibration period natural forcing factors were confounded by increasing
anthropogenic disturbance. This contribution employs coupled climate model output to test
how much of externally forced climate signals are preserved in field reconstructions, and how
much information could be lost or compromised by the short calibration or underlying
assumptions of stationarity.
Reconstruction of the Past 100-Year Weather Variation in Europe
Speaker: Youmin Chen
Youmin Chen, Galina Churkina and Martin Heimann
Max-Planck-Institute for Biogeochemistry
ychen@bgc-jena.mpg.de
<strong>The</strong> historical climate is reconstructed in the aspects of temperature, precipitation,
radiation, humidity, vapor pressure deficit, and so on, which will be used for ecosystem
modeling as the climate forcing. <strong>The</strong> daily variations are based on REMO daily dataset from
1971 to 2000; the monthly summaries are based on the CRU dataset from 1901 to 2002.
Canonical correlation analysis (CCA) is used for monthly anomaly reconstruction from CRU to
REMO datasets; and the weather generator-like technique is used for reproducing daily
variability. We employ auto-regression method to generate time series based on the time
coefficient of principle component (PC) so that spatial relationship between spatial grids and
different variables will be preserved through PC projection. One of the augments is the
selection of the distribution of the random number, which is expected to have the similar
distribution as the residuals after the auto-regression estimation. <strong>The</strong> reconstruction result has
the characters that monthly data is consistent with CRU dataset and daily variability is
consistent with REMO dataset; and that the relationships between grids and variables are
preserved. That is obviously important for reconstruction of the variables in a specific domain,
as the ecosystem modeling often required.
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