Speaker website: http://www.atmos.washington.edu/~hakim/

Title: Estimating and Predicting Climate Signals

Abstract:
Climate variability involves low-frequency signals that are typically
recovered for the past from statistical analysis of proxy data, and
projected into the future from integration of numerical models from
arbitrarily chosen initial states. Combining models and observations to
constrain estimates of both past and future low-frequency climate
signals is an emerging discipline, and the subject of this talk. First,
I describe an efficient method for estimating low-frequency climate
signals from noisy proxy measurements. Second, I propose a modeling
framework, motivated by Shannon's Noisy Channel Coding Theorem and the
dimensional reduction of balance models, where information is carried
by a small number of distinguished variables.

Host: Dan Chavas (drchavas@mit.edu)