Studies of regional-scale climate variability and change: Hidden Markov models and coupled ocean-atmosphere modes

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In this project we developed further a twin approach to the study of regional-scale climate variability and change. The two approaches involved probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs). We thus made progress in identifying the predictable modes of climate variability and investigating their impacts on the regional scale. In previous work sponsored by DOE’s Climate Change Prediction Program (CCPP), we had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale seasonal predictions of general circulation models (GCMs). Using ... continued below

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Ghil, M.; Kravtsov, S.; Robertson, A. W. & Smyth, P. October 14, 2008.

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Description

In this project we developed further a twin approach to the study of regional-scale climate variability and change. The two approaches involved probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs). We thus made progress in identifying the predictable modes of climate variability and investigating their impacts on the regional scale. In previous work sponsored by DOE’s Climate Change Prediction Program (CCPP), we had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale seasonal predictions of general circulation models (GCMs). Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influence large-scale atmospheric circulation patterns on interannual and longer time scales; similar patterns were found in a hybrid coupled ocean–atmosphere–sea-ice model. In this continuation project, we built on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean–atmosphere modes. Our main project results consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM simulations, called empirical mode reduction (EMR); and observational studies of decadal and multi-decadal natural climate variability, informed by ICM simulations. A particularly timely by-product of this work is an extensive study of clustering of cyclone tracks in the extratropical Atlantic and the western Tropical Pacific, with potential applications to predicting landfall.

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  • Report No.: Final Report
  • Grant Number: FG02-04ER63881
  • DOI: 10.2172/940218 | External Link
  • Office of Scientific & Technical Information Report Number: 940218
  • Archival Resource Key: ark:/67531/metadc902894

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • October 14, 2008

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • July 13, 2017, 1:59 p.m.

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Ghil, M.; Kravtsov, S.; Robertson, A. W. & Smyth, P. Studies of regional-scale climate variability and change: Hidden Markov models and coupled ocean-atmosphere modes, report, October 14, 2008; United States. (digital.library.unt.edu/ark:/67531/metadc902894/: accessed November 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.