Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Climate Variability & Predictability (CVP) logo

A Collaborative Multi-Model Study: Understanding AMOC Variability Mechanisms and their Impacts on Decadal Prediction

We propose a collaborative study between GFDL, NCAR, and WHOI to greatly advance our understanding of simulated AMOC variability, the impact of that variability on the atmosphere (and climate), and the relevance of that variability to our ability to make decadal climate predictions. Our work is motivated by the role that AMOC is thought to play in decadal climate variability and prediction, and by the critical need to improve our understanding of mechanisms and assessing the fidelity and robustness of simulated AMOC variability against limited observations. A major facet of this proposal is the synergy achieved through the coordinated efforts between the three institutions involved, building upon our existing, strong collaborations. In particular, the development of common metrics and the coordinated design and analysis of focused, sensitivity experiments using suites of models from NCAR and GFDL, the two leading U.S. climate modeling centers, and WHOI’s contribution in analysis of mechanisms and climate impacts are critical aspects of the proposed work. This coordination and synergy will provide an accelerated pathway to assessing robustness of model results and underlying mechanisms that, we hope, will lead to improved decadal prediction capabilities. Our goals include investigating impacts of model resolution, parameterizations, biases, and mean states on AMOC variability; determining the impact of ocean eddies on simulated AMOC and its variability; investigating AMOC variability and mechanisms in the recent past; improving our understanding of how particular physical processes and climate state information may give rise to predictive skill related to AMOC variability and evaluating how model differences in simulating AMOC variability affect related decadal predictability. We will use our findings to evaluate the realism of proposed mechanisms and assess the applicability of our results to other IPCC AR5 models.

Relevance: The proposed research is of high relevance to the NOAA CPO. Specifically, we seek to improve scientific understanding of the changing climate system and its impacts through evaluating and advancing climate prediction methodologies used in decadal climate prediction. Thus, we directly address one of the objectives of NOAA’s five-year climate goals outlined in NOAA’s Next Generation Strategic Plan (NGSP), namely improved scientific understanding of the changing climate system and its impacts. Furthermore, we will access the past, current, and future states of the climate system with a focus on AMOC through reconstruction of its behavior during the past century as well as through potential prediction of its future states. These efforts largely address another NGSP objective, i.e., assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions. Our proposed research will advance core capabilities in understanding and modeling and predictions and projections, both aimed at understanding and advancing decadal prediction capabilities. Relevance to the Competition: This proposal directly addresses one of the ESS program solicitation areas, namely AMOC – Mechanisms and Decadal Predictability. In particular, we seek to improve our understanding of the AMOC variability mechanisms, their model dependencies, and their effects on decadal predictability and prediction through focused multi-model analyses and experimentation. As requested in the solicitation, in addition to in-depth analyses of the GFDL and NCAR models, we will make use of the outputs from the other IPCC AR5 models.

Scroll to Top