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Long-lead ENSO predictability from CMIP5 decadal hindcasts

Gonzalez, P. L. M. and Goddard, L. (2016) Long-lead ENSO predictability from CMIP5 decadal hindcasts. Climate Dynamics, 46 (9). pp. 3127-3147. ISSN 0930-7575

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To link to this item DOI: 10.1007/s00382-015-2757-0


Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3--4Â years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system's ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:85684


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