Paleoclimate Reconstruction with Iterative Data Assimilation: An Observing System Simulation Experiment with an Intermediate AGCM
Knowledge of the past climate conditions is invaluable to understand the climate system. Recently, data assimilation (DA) has been applied to reconstruct paleoclimate, in which proxy data such as tree-ring width and isotopic composition in ice sheets are used. DA has long been used for forecasting the weather and is a well-established method. However, the DA algorithms used for weather forecasts cannot be directly applied to paleoclimate due to the different temporal resolution, spatial extent, and type of information contained within the observation data. Especially, the temporal resolution of proxy data is significantly lower (typically annual) than the present-day observations used for weather forecasts. Therefore, DA applied to paleoclimate is only loosely linked to the methods used in the more mature field of weather forecasting. Up until now, several DA methods have been proposed for paleoclimate, and successfully reconstructed the paleoclimate. The most popular method is “offline-DA,” which does not cycle the analysis to the next DA step. This is because the effect of the previous DA update is thought to be lost well before the DA step due to the chaotic nature of the atmosphere and the temporally sparse observation. On the other hand, many studies show predictability longer than decades in the climate system, suggesting that we may use a flow-dependent error covariance matrix for paleoclimate reconstruction. Therefore, we explored a potential use of an iterative-DA method proposed by Kalnay and Yang for paleoclimate reconstruction. We performed an observing system simulation experiment using an intermediate AGCM known as the SPEEDY model. We will show the benefit and limitation of the iterative-DA method in the presentation.
Dr. Atsushi Okazaki RIKEN