Coupled ocean–atmosphere modeling and predictions
Miller, Arthur J.
Jensen, Tommy G.
Pezzi, Luciano Ponzi
Pierce, David W.
MetadataShow full item record
KeywordClimate modeling; Climate predictability; Decadal climate variability; El Niño Southern Oscillation; ENSO; Global warming; Monsoons; Ocean-atmospherel and interactions; Regional climate downscaling
Key aspects of the current state of the ability of global and regional climate models to represent dynamical processes and precipitation variations are summarized. Interannual, decadal, and global-warming timescales, wherein the influence of the oceans is relevant and the potential for predictability is highest, are emphasized. Oceanic influences on climate occur throughout the ocean and extend over land to affect many types of climate variations, including monsoons, the El Niño Southern Oscillation, decadal oscillations, and the response to greenhouse gas emissions. The fundamental ideas of coupling between the ocean-atmosphere-land system are explained for these modes in both global and regional contexts. Global coupled climate models are needed to represent and understand the complicated processes involved and allow us to make predictions over land and sea. Regional coupled climate models are needed to enhance our interpretation of the fine-scale response. The mechanisms by which large-scale, low-frequency variations can influence shorter timescale variations and drive regionalscale effects are also discussed. In this light of these processes, the prospects for practical climate predictability are also presented.
Author Posting. © The Authors, 2017. This article is posted here by permission of Sears Foundation for Marine Research for personal use, not for redistribution. The definitive version was published in Journal of Marine Research 75 (2017): 361-402, doi:10.1357/002224017821836770.
Suggested CitationJournal of Marine Research 75 (2017): 361-402
Showing items related by title, author, creator and subject.
Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System Wilkin, John L.; Rosenfeld, Leslie K.; Allen, Arthur; Baltes, Rebecca; Baptista, Antonio; He, Ruoying; Hogan, Patrick; Kurapov, Alexander; Mehra, Avichal; Quintrell, Josie; Schwab, David; Signell, Richard P.; Smith, Jane (2017-04-19)This paper outlines strategies that would advance coastal ocean modeling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the U.S. Integrated Ocean ...
Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on atmospheric, ocean and wave models : uncertainties and probability distribution areas Rixen, Michel; Ferreira-Coelho, E.; Signell, Richard P. (Elsevier B.V., 2007-02-20)Despite numerous and regular improvements in underlying models, surface drift prediction in the ocean remains a challenging task because of our yet limited understanding of all processes involved. Hence, deterministic ...
The CBLAST-Hurricane program and the next-generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction Chen, Shuyi S.; Zhao, Wei; Donelan, Mark A.; Price, James F.; Walsh, Edward J. (American Meteorological Society, 2007-03)The record-setting 2005 hurricane season has highlighted the urgent need for a better understanding of the factors that contribute to hurricane intensity, and for the development of corresponding advanced hurricane ...