Helga S. Huntley1 and Gregory J. Hakim2
1 College of Earth and Marine Studies, University of Delaware, Newark, DE
2 Department of Atmospheric Sciences, University of Washington,Seattle, WA
Climate Dynamics, 32, submitted.
The problem of reconstructing past climates from a sparse network of noisy time-averaged observations is considered with a novel ensemble Kalman filter approach. Results for a sparse network of 100 idealized observations for a quasi-geostrophic model of a jet interacting with a mountain reveal that, for a wide range of observation averaging times, analysis errors are reduced by about 50% relative to the control case without assimilation. Results are robust to modest changes to observational error, the number of observations, and an imperfect model. In the limit of small numbers of observations, very small error reduction is found except for the case of an optimally determined network, whose stations have a very large impact on a normalized, per-observation basis. A network of fifteen optimally determined observations reduces analysis errors by 30% relative to the control, as compared to 50% for a randomly chosen network of 100 observations.
keywords: Data assimilation, Paleoclimate, Ensemble Kalman filter, Atmospheric modeling