Dr Simon Michel

Climate models and theoretical evidence show that the ocean drives climate from sub-decadal to centennial timescales through a variety of processes and their interactions. The range of direct climate observations, however, is too short to understand the exact role of the ocean in shaping observed and future climate variability on top of anthropogenic climate change. In the present study, we use a large set of paleoclimate records combined with a random forest algorithm to reconstruct a gridded dataset of sea surface temperatures since 850 C.E. to provide a better framework for the study of ocean surface variability. In line with modeling and paleodata studies, our reconstruction suggests that natural climate forcings have importantly influenced the last millennium climate variability. Our reconstruction also suggests that North Atlantic SST multidecadal variability influences Pacific SST on decadal timescales. However, the latter result is shown to be strongly dependent on background climate conditions. This new reconstruction offers a useful resource for testing the capabilities of climate models to reproduce the linkages between Atlantic and Pacific as well as the response to external forcings.

Past studies show that coupled model biases in European blocking and North Atlantic eddy-driven jet variability decrease as one increases the horizontal resolution in the atmospheric and oceanic model components, but it remains unclear if atmospheric or oceanic resolution plays the greater role, and why. Here, following recent work by Schemm et al., we leverage a large multi-model ensemble to show that a coupled model’s ability to simulate extreme Gulf Stream precipitation is tightly linked to its simulated frequency of European blocking and northern jet excursions. Furthermore, the reduced biases in blocking and jet variability are consistent with better resolved precipitation extrema in high-resolution models. Analysis supports a hypothesis that models which simulate more extreme precipitation can generate more strongly poleward propagating cyclones and more intense anticyclonic anomalies due to the stronger latent heat release occurring during extreme events. By contrast, typical North Atlantic SST biases are found to share only a weak or negligible relationship with blocking and jet biases. Finally, while previous studies have used a comparison between coupled models and models run with prescribed SSTs to argue for the role of ocean resolution, we emphasise here that models run with prescribed SSTs experience greatly reduced precipitation extremes due to their excessive thermal damping, making it unclear if such a comparison is meaningful. Instead, we speculate that most of the reduction in coupled model biases may actually be due to increased atmospheric resolution leading to better resolved convection.

Gaining knowledge of the past and present variations of the Atlantic Meridional Overturning Circulation (AMOC) is crucial for the development of accurate future climate projections. The short range covered by direct AMOC observations, inconsistent paleoclimate records, and scattered hydrographic data are insufficient to realistically reconstruct the AMOC strength since 1900. An AMOC proxy index based on sea surface temperatures suggests that the AMOC has declined by 15% since the late 19th century but this index received extensive scientific criticism. Here, we use a deep learning algorithm and climate model simulations to accurately reconstruct the AMOC strength between 20°N and 60°N since 1900. In contrast with the existing indices, our reconstructions are well in agreement with AMOC strength variations simulated by climate models and direct observations at 26.5°N. Our novel set of AMOC reconstructions contribute to a larger confidence in 21st century AMOC decline projections from climate models.