A spurious warming trend in the NMME equatorial Pacific SST hindcasts
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Using seasonal hindcasts of six different models participating in the North American Multimodel Ensemble project, the trend of the predicted sea surface temperature (SST) in the tropical Pacific for 1982–2014 at each lead month and its temporal evolution with respect to the lead month are investigated for all individual models. Since the coupled models are initialized with the observed ocean, atmosphere, land states from observation-based reanalysis, some of them using their own data assimilation process, one would expect that the observed SST trend is reasonably well captured in their seasonal predictions. However, although the observed SST features a weak-cooling trend for the 33-year period with La Niña-like spatial pattern in the tropical central-eastern Pacific all year round, it is demonstrated that all models having a time-dependent realistic concentration of greenhouse gases (GHG) display a warming trend in the equatorial Pacific that amplifies as the lead-time increases. In addition, these models’ behaviors are nearly independent of the starting month of the hindcasts although the growth rates of the trend vary with the lead month. This key characteristic of the forecasted SST trend in the equatorial Pacific is also identified in the NCAR CCSM3 hindcasts that have the GHG concentration for a fixed year. This suggests that a global warming forcing may not play a significant role in generating the spurious warming trend of the coupled models’ SST hindcasts in the tropical Pacific. This model SST trend in the tropical central-eastern Pacific, which is opposite to the observed one, causes a developing El Niño-like warming bias in the forecasted SST with its peak in boreal winter. Its implications for seasonal prediction are discussed.
This research is supported by grants from NSF (AGS-1338427), NOAA (NA14OAR4310160), and NASA (NNX14AM19G), and a grant from the Indian Institute of Tropical Meteorology and the Ministry of Earth Sciences, Government of India (MM/SERP/COLA-GMU_USA/2013/INT-2/002). We acknowledge NOAA MAPP, NSF, NASA, and the DOE that support the NMME-Phase II system, and we thank the climate modeling groups (Environment Canada, NASA, NCAR, NOAA/GFDL, NOAA/NCEP, and University of Miami) for producing and making available their model output. NOAA/NCEP, NOAA/CTB, and NOAA/CPO jointly provided coordinating support and led development of the NMME-Phase II system. We also acknowledge the Extreme Science and Engineering Discovery Environment (XSEDE) for providing the computational resources for the reforecast project. Finally, we thank the editor and five anonymous reviewers for their constructive comments and suggestions.
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