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www.clim-past-discuss.net/7/2835/2011/
doi:10.5194/cpd-7-2835-2011
© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
A novel approach to climate reconstructions using Ensemble Kalman Filtering
1Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
2Oeschger Centre and Institute of Geography, University of Bern, Bern, Switzerland
*now at: CSIRO Marine and Atmospheric Research, Aspendale, Australia
Abstract. Data assimilation is a promising approach to obtain climate reconstructions that are both consistent with observations of the past and with our understanding of the physics of the climate system as represented in the climate model used. Here, we investigate the use of Ensemble Square Root Filtering (EnSRF) – a technique used in weather forecasting – for climate reconstructions. We constrain an ensemble of 29 simulations from an atmosphere-only general circulation model (GCM) with 37 pseudo-proxy time series. Assimilating spatially sparse information with low temporal resolution (semi-annual) improves the representation of not only surface quantities such as temperature and precipitation, but also upper-air features such as the intensity of the northern stratospheric polar vortex or the strength of the northern subtropical jet. Given the sparsity of the assimilated information and the limited size of the ensemble used, a localisation procedure is crucial to reduce "overcorrection" of climate variables far away from the assimilated information.
Discussion Paper (PDF, 1585 KB) Interactive Discussion (Closed, 9 Comments) Manuscript under review for CP
Citation: Bhend, J., Franke, J., Folini, D., Wild, M., and Brönnimann, S.: A novel approach to climate reconstructions using Ensemble Kalman Filtering, Clim. Past Discuss., 7, 2835-2862, doi:10.5194/cpd-7-2835-2011, 2011. Bibtex EndNote Reference Manager XML
