Ensemble-based data assimilation for the climate of the past millennium

Data assimilation (DA) is an emerging research area in palaeoclimatology. Here, ensemble-based DA schemes are implemented and evaluated for the reconstruction of the climate of some of the key periods from the past millennium. The study is among the first to employ a General Circulation Model for palaeoclimate DA. An off-line and an on-line DA method are first compared, assimilating continental proxy-based temperature reconstructions and using the 17th century as testing period. Both schemes provide simulations that follow the assimilated targets on large scales better than without DA. The on-line scheme has the advantage of temporal consistency of the analysis, and is subsequently used to reconstruct the climate for 1750-1850 AD. The assimilation performs well on large-scale temperatures, but there is no agreement between the DA analysis and reconstructions for regional temperature patterns. Evidence is presented to suggest that this lack of information propagation to smaller spatial scales is likely due to the fact that the Northern Hemisphere continental mean temperatures are not the best predictors for large-scale circulation anomalies, or that the assimilated reconstructions include noise. The lack of regional skill is again found when instrumental data for 1850-1949 AD are assimilated. Based on these results, it is argued that a potential way of improving the performance of DA is the assimilation of temperature reconstructions with higher spatial resolution

Influence of proxy data uncertainty on data assimilation for the past climate

Data assimilation (DA) is an emerging topic in palaeoclimatology and one of the key challenges in this field. Assimilating proxy-based continental mean temperature reconstructions into the MPI-ESM model showed a lack of information propagation to small spatial scales <cite classCombining double low line. Here, we investigate whether this lack of regional skill is due to the methodology or to errors in the assimilated reconstructions. Error separation is fundamental, as it can lead to improvements in DA methods. We address the question by performing a new set of simulations, using two different sets of target data; the proxy-based PAGES 2K reconstructions (DA-P scheme), and the HadCRUT3v instrumental observations (DA-I scheme). Again, we employ ensemble-member selection DA using the MPI-ESM model, and assimilate Northern Hemisphere (NH) continental mean temperatures; the simulated period is 1850-1949 AD. Both DA schemes follow the large-scale target and observed climate variations well, but the assimilation of instrumental data improves the performance. This improvement cannot be seen for Asia, where the limited instrumental coverage leads to errors in the target data and low skill for the DA-I scheme. No skill on small spatial scales is found for either of the two DA schemes, demonstrating that errors in the assimilated data are not the main reason for the unrealistic representation of the regional temperature variability in Europe and the NH. It can thus be concluded that assimilating continental mean temperatures is not ideal for providing skill on small spatial scales. © 2016 Author(s)

Assimilating continental mean temperatures to reconstruct the climate of the late pre-industrial period

An on-line, ensemble-based data assimilation (DA) method is performed to reconstruct the climate for 1750–1850 AD, and the performance is evaluated on large and small spatial scales. We use a low-resolution version of the Max Planck Institute for Meteorology MPI-ESM model and assimilate the PAGES 2K continental mean temperature reconstructions for the Northern Hemisphere (NH). The ensembles are generated sequentially for sub-periods based on the analysis of previous sub-periods. The assimilation has good skill for large-scale temperatures, but there is no agreement between the DA analysis and proxy-based reconstructions for small-scale temperature patterns within Europe or with reconstructions for the North Atlantic Oscillation (NAO) index. To explain the lack of added value in small spatial scales, a maximum covariance analysis (MCA) of links between NH temperature and sea level pressure is performed based on a control simulation with MPI-ESM. For annual values, winter and spring the Northern Annular Mode (NAM) is the pattern that is most closely linked to the NH continental temperatures, while for summer and autumn it is a wave-like pattern. This link is reproduced in the DA for winter, spring and annual means, providing potential for constraining the NAM/NAO phase and in turn regional temperature variability. It is shown that the lack of actual small-scale skill is likely due to the fact that the link might be too weak, as the NH continental mean temperatures are not the best predictors for large-scale circulation anomalies, or that the PAGES 2K temperatures include noise. Both factors can lead to circulation anomalies in the DA analysis that are substantially different from reality, leading to unrealistic representation of small-scale temperature variability. Moreover, we show that even if the true amplitudes of the leading MCA circulation patterns were known, there is still a large amount of unexplained local temperature variance. Based on these results, we argue that assimilating temperature reconstructions with a higher spatial resolution might improve the DA performance. © 2015 Springer-Verlag Berlin Heidelber