Predicting Coupled Ocean-Atmosphere Modes with a Climate Modeling Hierarchy -- Final Report

• Main Authors: Michael Ghil, UCLA, Andrew W. Robertson, IRI, Columbia Univ., Sergey Kravtsov, U. of Wisconsin, Milwaukee, Padhraic Smyth, UC Irvine
• Language: unknown
• Published: 2008
• Subjects: 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; CLIMATE MODELS; CLIMATES; COMPUTERS; FEEDING; HYPOTHESIS; SIMULATION; Pacific; North Atlantic
• Online Access: http://www.osti.gov/servlets/purl/889817; https://www.osti.gov/biblio/889817; https://doi.org/10.2172/889817

The goal of the project was to determine midlatitude climate predictability associated with tropical-extratropical interactions on interannual-to-interdecadal time scales. Our strategy was to develop and test a hierarchy of climate models, bringing together large GCM-based climate models with simple fluid-dynamical coupled ocean-ice-atmosphere models, through the use of advanced probabilistic network (PN) models. PN models were used to develop a new diagnostic methodology for analyzing coupled ocean-atmosphere interactions in large climate simulations made with the NCAR Parallel Climate Model (PCM), and to make these tools user-friendly and available to other researchers. We focused on interactions between the tropics and extratropics through atmospheric teleconnections (the Hadley cell, Rossby waves and nonlinear circulation regimes) over both the North Atlantic and North Pacific, and the ocean’s thermohaline circulation (THC) in the Atlantic. We tested the hypothesis that variations in the strength of the THC alter sea surface temperatures in the tropical Atlantic, and that the latter influence the atmosphere in high latitudes through an atmospheric teleconnection, feeding back onto the THC. The PN model framework was used to mediate between the understanding gained with simplified primitive equations models and multi-century simulations made with the PCM. The project team is interdisciplinary and built on an existing synergy between atmospheric and ocean scientists at UCLA, computer scientists at UCI, and climate researchers at the IRI.