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

Invited Session: Spatial patterns of climate
Reduced Space Approach to Objective Analyses of Historical Climate Data Sets: Progress and
Problems
Speaker: Alexey Kaplan
Alexey Kaplan
Lamont-Doherty Earth Observatory of Columbia University
alexeyk@ldeo.columbia.edu
Observed historical climate fields are characterized by comparatively precise data and
good coverage in the last few decades and poor observational coverage in the earlier period.
<strong>The</strong> technique of the reduced space objective analysis of such fields (i.e. estimating them in
projections onto a low-dimensional space spanned by the leading patterns of the signal
variability) is presented in the context of more traditional approaches to objective data analysis.
Various versions of space reduction and covariance estimation techniques are reviewed.
Outstanding problems and possible ways to overcome them are discussed.
Challenges of regional climate modeling and validation
Speaker: Douglas Nychka
Douglas Nychka, ari Kaufman, Stephen Sain and Linda Mearns
National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307-3000
nychka@ucar.edu
As attention shifts from broad global summaries of climate change to more specific
regional results there is a need for statistics to analyze observations and model output that
have significant variability and also to quantify the uncertainty in regional projections. This talk
will survey some work on interpreting regional climate experiments. In large multi-model
studies one challenge is to understand the contributions of different global and regional model
combinations to the simulated climate. This is difficult because the runs tend to be short in
length and with a limited number of ensemble members. We suggest some spatial models for
the climate fields based on sparse approximations to the covariance matrix and derive an AN
OVA like decomposition for the fields. <strong>The</strong> decomposition into main effects and interactions
helps to isolate the effects of different models. <strong>The</strong> spatial models provide a rigorous
framework for assessing statistical significance and comparing simulations to observed
climate. This approach is illustrated for output from the PRUDENCE program and we also
discuss the newer NARCCAP experiments for regional climate of North America.
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