Impact of changes in the Arctic Ocean freshwater budgets on AMOC strength
Dr. Alexandra Jahn
INSTAAR and ATOC
The Arctic is changing rapidly, but what do these changes mean for the global climate? One way that Arctic changes can potentially impact global climate is through changes in the amount and phase of freshwater exported from the Arctic to the North Atlantic. There it can impact the deep water formation in the North Atlantic, and ultimately the strength of the Atlantic meridional overturning circulation (AMOC). In this talk, I will show how simulated changes in the Arctic freshwater budget impact AMOC strength, in climate simulations for the 21st to 23rd century and for a previous warm period, the Pliocene, which occurred 3 million years ago. In particular, I will show that in simulations with the Community Earth System Model (CESM) for the 21st to 23rd century, the maximum strength of the AMOC decreases proportionally to the applied CO2 forcing (Jahn and Holland, 2013). This weakening of the overturning is caused by a reduction or shut down of North Atlantic deep convection due to a surface freshening originating in the Arctic Ocean. For the Pliocene, I will show how changes in the representation of three small Arctic gateways to the Pacific and Atlantic Ocean (Bering Strait, Northwest Passage, Nares Strait) change the magnitude and phase of the simulated Arctic freshwater export in the CESM, which leads to changes in the AMOC strength (Otto-Bliesner et al., 2016). By closing all of the small gateways, in agreement with a recent new PRISM4 reconstruction, the Arctic freshwater export decreases and the AMOC strength increases. This improves the agreement of the simulated Pliocene climate in the North Atlantic with proxy reconstructions.
Dr. Alexandra Jahn
INSTAAR and ATOC
•Jahn, A., and M. M. Holland (2013), Implications of Arctic sea ice changes for North Atlantic deep convection and the meridional overturning circulation in CCSM4-CMIP5 simulations, Geophys. Res. Lett., 40, 1206–1211, doi:10.1002/grl.50183.
•Otto-Bliesner, B. L., A. Jahn, R. Feng,E. C. Brady, A. Hu, and M. Löfverström (2016), Amplified North Atlantic warming in the late Pliocene by changes in Arctic gateways, Geophys. Res. Lett., 44, doi:10.1002/2016GL071805.
CSTPR Noontime Seminar:
Climate Change Politics and Machine Learning
by Justin Farrell
Abstract: Drawing on large-scale computational data and methods, this research demonstrates how polarization efforts are influenced by a patterned network of political and financial actors. These dynamics, which have been notoriously difficult to quantify, are illustrated here with a computational analysis of climate change politics in the United States. The comprehensive data include all individual and organizational actors in the climate change countermovement (164 organizations), as well as all written and verbal texts produced by this network between 1993–2013 (40,785 texts, more than 39 million words). Two main findings emerge. First, that organizations with corporate funding were more likely to have written and disseminated texts meant to polarize the climate change issue. Second, and more importantly, that corporate funding influences the actual thematic content of these polarization efforts, and the discursive prevalence of that thematic content over time. These findings provide new, and comprehensive, confirmation of dynamics long thought to be at the root of climate change politics and discourse. Beyond the specifics of climate change, this paper has important implications for understanding ideological polarization more generally, and the increasing role of private funding in determining why certain polarizing themes are created and amplified. Lastly, the paper suggests that future studies build on the novel approach taken here that integrates largescale textual analysis with social networks.
Biography: Justin Farrell is Assistant Professor of Sociology at Yale University. He studies culture, environment, and social movements using a mixture of methods from large-scale computational text analysis, qualitative fieldwork, network science, and machine learning.