A Hybrid Gain Analog Offline EnKF for Paleoclimate Data Assimilation

Author:

Sun Haohao1,Lei Lili12ORCID,Liu Zhengyu3ORCID,Ning Liang4,Tan Zhe‐Min1ORCID

Affiliation:

1. Key Laboratory of Mesoscale Severe Weather Ministry of Education and School of Atmospheric Sciences Nanjing University Nanjing China

2. School of Atmospheric Sciences Frontiers Science Center for Critical Earth Material Cycling Nanjing University Nanjing China

3. Department of Geography The Ohio State University Columbus OH USA

4. National Key Laboratory for Virtual Geographic Environment Ministry of Education and School of Geography Nanjing Normal University Nanjing China

Abstract

AbstractFor Paleoclimate data assimilation (PDA), a hybrid gain analog offline ensemble Kalman filter (HGAOEnKF) is proposed. It keeps the benefits of the analog offline ensemble Kalman filter (AOEnKF) that constructs analog ensembles from existing climate simulations with joint information of the proxies. The analog ensembles can provide more accurate prior ensemble mean and “flow‐dependent” error covariances than randomly sampled ensembles. HGAOEnKF further incorporates the benefits of static prior error covariances that better capture large‐scale error correlations and mitigate sampling errors than the sample prior error covariances, through a hybrid gain approach within an ensemble framework. Observing system simulation experiments are conducted for various data assimilation methods, using ensemble simulations from the Community Earth System Model‐Last Millennium Ensemble Project. Results show that using the static prior error covariances estimated from a sufficiently large sample set is beneficial for the traditional offline ensemble Kalman filter (OEnKF) and AOEnKF. HGAOEnKF method is superior to the OEnKF and AOEnKF with and without static prior error covariances, especially for the reconstruction of extreme events. The advantages of HGAOEnKF over OEnKF and AOEnKF with and without static prior error covariances are persistent with varying sample sizes and presence of model errors.

Publisher

American Geophysical Union (AGU)

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