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

Oregon Convention Center 

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study, we utilize the Reconstructed East Asian Climate Historical Encoded Series (REACHES) data derived from Chinese historical documents to reconstruct temperature in East Asia since the 14th century.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.

Bayesian inference

Fused lasso

Simple kriging

Penalized maximum likelihood

Kalman filter 

Section on Statistics and the Environment