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Using Data Assimilation to Reconstruct Paleoclimate for East Asia Since the 14th Century

Abstract Number:

2181 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Eric Sun (1), Hsin-Cheng Huang (1), Kuan-hui Elaine Lin (2), Wan-Ling Tseng (3)

Institutions:

(1) Academia Sinica, Taiwan, (2) National Taiwan Normal University, Taiwan, (3) National Taiwan University, Taiwan

Co-Author(s):

Hsin-Cheng Huang  
Academia Sinica
Kuan-hui Elaine Lin  
National Taiwan Normal University
Wan-Ling Tseng  
National Taiwan University

First Author:

Eric Sun  
Academia Sinica

Presenting Author:

Eric Sun  
N/A

Abstract Text:

In this study, we utilize the Reconstructed East Asian Climate Historical Encoded Series (REACHES) data derived from Chinese historical documents to reconstruct temperature in East Asia since the 14th century. The REACHES temperature indices exhibit bias due to missing values, primarily representing normal weather. To address this, we employ simple kriging to impute the missing data, with the mean of the underlying spatial process set to zero. To enhance temperature reconstruction accuracy, we propose a data assimilation approach that combines the kriged REACHES temperature data with the Last Millennium Ensemble (LME) reanalysis data. Our approach first estimates the temperature distribution by applying regularized maximum likelihood, incorporating a fused lasso penalty within a nonstationary time series model based on the LME data. The resulting distribution serves as the prior, which is subsequently updated to obtain refined temperatures based on the REACHES data using the Kalman filter and smoother. Our approach, which integrates historical records, climate model, and statistical techniques, sheds light on past temperature variations and refines historical temperature estimates.

Keywords:

Bayesian inference|Fused lasso|Simple kriging|Penalized maximum likelihood|Kalman filter|

Sponsors:

Section on Statistics and the Environment

Tracks:

Climate and Meteorology

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