The fundamental task in climate variability research is to eke
out structure from climate signals. Ideally we want a causal
connection between a physical process and the structure of the
signal. Sometimes we have to settle for a correlation between
these. The challenge is that the data is often poorly
constrained and/or sparse. Even though many data gathering
campaigns are taking place or are being planned, the very high
dimensional state space of the system makes the prospects of
climate variability analysis from data alone impractical.
Progress in the analysis is possible by the use of models and
data. Data assimilation is one such strategy. In this talk we
will describe the methodology, illustrate some of its
challenges, and highlight some of the ways our group has
proposed to improving the methodology.