Twentieth century ENSO characteristics in the IPCC database
- 1k Downloads
In this paper, we assess and compare to observations the spatial characteristics of the twentieth Century ENSO SST variability simulated by 23 models of the IPCC-AR4/CMIP3 database. The analysis is confined to the SST anomalies along the equatorial Pacific and is based on the use of a non-linear neural classification algorithm, the Self-Organizing Maps. Systematic biases include a larger than observed proportion for modelled ENSO maximum variability occurring in the Western Pacific. No clear relationship is found between this bias and the characteristics of the modelled mean state bias in the equatorial Pacific. This bias is mainly related to a misrepresentation of both El Niño and La Niña termination phases for most of the models. In contrast, the onset phase is quite well simulated. Modelled El Niño and La Niña peak phases display an asymmetric bias. Whereas the main bias of the modelled El Niño peak is to exhibit a maximum in the western Pacific, the simulated La Niña bias mainly occurs in the central Pacific. In addition, some models are able to capture the observed El Niño peak characteristics while none of them realistically simulate La Niña peaks. It also arises that the models closest to the observations score unevenly in reproducing the different phases, preventing an accurate classification of the models quality to reproduce the overall ENSO-like variability.
We acknowledge the modelling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model output, and the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The multi-model data archive is supported by the Office of Science, US Department of Energy. We also acknowledge the modeling group of INGV-SXG. SOM Toolbox is Copyright (C) 2000–2005 by Esa Alhoniemi, Johan Himberg, Juha Parhankangas and Juha Vesanto and freely available at http://www.cis.hut.fi/projects/somtoolbox/. The authors are grateful to the reviewers which comments helped improving the original manuscript. Authors are thankful to Éric Guilyardi, Aymeric Chazottes, Sylvie Thiria, and Julien Brajard for stimulating discussions. J. L. was founded by the european project claris (http://www.claris-eu.org), vartrop team/locean/cnrs, and aci-fns French Program under the project mc2 and M. L. thanks the LEFE project.
- Kohonen T (1981) Construction of similarity diagrams for phonemes by a self-organizing algorithm. Report TKK-F-A463. Helsinki University of Technology, EspooGoogle Scholar
- Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, HeidelbergGoogle Scholar
- Leloup J, Lachkar Z, Boulanger JP, Thiria S (2007) Detecting decadal changes in ENSO using neural networks. Clim Dyn 28(2–3):147–162Google Scholar
- Tozuka T, Yamagata T (2003) Annual ENSO. J Phys Oceanogr 33(8):1564–1578Google Scholar
- Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (1999) Self-organizing map in Matlab: the SOM toolbox. In: Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November 16–17, pp 35–40Google Scholar
- Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOM toolbox for Matlab 5. Report A57, Helsinki University of Technology, Finland (http://www.cis.hut.fi/projects/somtoolbox/)