Assimilating continental mean temperatures to reconstruct the climate of the late pre-industrial period
Research output: Contribution to journal › Article › peer-review
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.
|Early online date||4 Aug 2015|
|Publication status||Published - 1 Jun 2016|
- Climate of the past, Climate modelling, Data assimilation, PAGES 2K