13 Novembre – Thesis defense - Simon Michel
14 h Full videoconferencing
Characterising last millennium climate variations using new statistical methods: Paleoclimate reconstructions and integration within a general circulation climate model.
Climate variability strongly impacts our lives in many ways. The observed growth in global temperatures, forced by anthropogenic greenhouse gases emissions, has experienced of period of hiatus between 1998 and 2012. Causes at the origin of such a sequence are subject to intensive scientific controversies, notably concerning the relative roles of climate variability intrinsic to the climate system and external radiative forcings. In order to address this questions, it is necessary to better understand decadal modes of variability resulting from the global scale organisation of the climate system. However, the only 150 years of instrumental observations are not enough to rigorously caractérise their nature, variability and dynamics.
This thesis aims at improving our knowledge of multidecadal climate variability by producing reconstructions of climate indices and grids over the last millennium. For doing so, up-to-date statistical methods are used, namely machine learning technics, which are applied to paleoclimate data coming from natural archives (tree rings, ice cores..). The recently developed PAGES 2k database provides more than 700 of these records, and will thus be abundantly used for training the proposed statistical models.
Objective mathematical metrics show that a non linear technique, namely the random forest, generally produces more robust results than the usual linear technics. We thus use the random forest method to reconstruct variations of the preferential mode of North Atlantic sea surface temperatures (SST) de l'Atlantique Nord, namely the Atlantic Multidecadal Variability (AMV), which is notably related to the Atlantic Meridional overturning circulation (AMOC). This reconstruction suggests that changes in this circulation, which occured at the end of the 12th century, has probably been the catalyser of an early onset of the little ice age, a relatively cold period of the last millennium. The strong volcanic activities from the 13th, 15th and 19th centuries have nevertheless been identified as the main causes of the little ice age extension, which reached its climax during the 19th century. Moreover, according to dynamical systems theory, recent changes in the AMV variability constitute an early warning signal of an income tipping point in the AMOC.
A gridded SST reconstruction over the last millennium has also been produced using the random forest method. This reconstruction shows that positive phases of AMV are likely to drive, after about a decade, a negative phase of the preferential mode of Pacific SST, namely the Interdecadal Pacific Variability. Since observation show that the 1998-2012 hiatus period exactly match with a long term negative phase of the AMV, these results suggest that the AMV is one of the main drivers of such hiatus periods. In line with the assumption of an AMOC weakening deduced from spectral properties of the reconstructed AMV this grid also suggest that the AMOC has reached an unprecedented low level of intensity, since at least 850 C.E.
Finally, the same gridded reconstruction has been assimilated using SST nudging method within the IPSL-CM5A2-LR general circulation model. While the simulation fairly reproduce the observed temperatures and paleo-temperatures, the nudging method seems to be less efficient than more complex data assimilation technics, such as Kalman filtering. Moreover, the simulated AMOC in this reanalyses does not correspond to the one obtained from statistical reconstructions, which might be due to the fact that salinity largely influence ocean surface layers densities, and thus to force oceanic convection and the AMOC. Assimilating surface salinity data thus seems essential to correctly reproduce last millennium AMOC variability using a general circulation model.