On the suitability of data assimilation products to quantifying variability and teleconnections across scales

Data assimilation techniques, such as the Kalman Filter, have enabled the development of complete climate field reconstructions over the last millennium, commonly referred to as paleoclimate reanalysis. These techniques effectively integrate paleoclimate data, facilitating the understanding and attribution of past climate events. The resulting spatio-temporal fields are invaluable for studying teleconnections and exploring dynamical links between variables and locations in the past. However, when the observation network is sparse, or proxies exhibit high levels of non-climatic noise, the Kalman Filter tends to revert to the prior. These limitations often result in paleoclimate data assimilation products underestimating variability in earlier periods and overestimating spatial coherence compared to modern observations, reducing their reliability. We thus investigate the timescale-dependent variance and the spatio-temporal covariance of different paleoclimate data assimilation products: ModE-RA, LMR, and PHYDA, and relate differences primarily to the methodology and prior assumptions. The results from the data assimilation products were further assessed against instrumental data and CMIP6 pre-industrial control and fully forced simulations.