More reliable coastal SST forecasts from the North American multimodel ensemble
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The skill of monthly sea surface temperature (SST) anomaly predictions for large marine ecosystems (LMEs) in coastal regions of the United States and Canada is assessed using simulations from the climate models in the North American Multimodel Ensemble (NMME). The forecasts based on the full ensemble are generally more skillful than predictions from even the best single model. The improvement in skill is particularly noteworthy for probability forecasts that categorize SST anomalies into upper (warm) and lower (cold) terciles. The ensemble provides a better estimate of the full range of forecast values than any individual model, thereby correcting for the systematic over-confidence (under-dispersion) of predictions from an individual model. Probability forecasts, including tercile predictions from the NMME, are used frequently in seasonal forecasts for atmospheric variables and may have many uses in marine resource management.
KeywordsSeasonal prediction SST anomaly Coastal ecosystems Climate models Multimodel ensemble forecast
We thank the NOAA Climate Program Office (CPO) for providing funding for this research. DT was funded by a Special Early-Stage Exploration and Development grant from NOAA’s office of oceanic and atmospheric research (OAR) with additional support from NOAA’s National Marine Fisheries Service.
- Anderson DLT et al (2003) Comparison of the ECMWF seasonal forecast system 1 and 2, including the relative performance for the 1997/8 El Nino. Tech. Memo. 404. ECMWF, Reading, pp 93Google Scholar
- Boyer TP et al (2013) World ocean database 2013. NOAA Atlas NESDIS 72. Levitus S, Ed Mishonov A (eds) Silver Spring, pp 209. doi: 10.7289/V5NZ85MT
- Delworth TL et al (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19:644–667Google Scholar
- Infanti JM, Kirtman BP (2016) Prediction and predictability of land and atmosphere initialized CCSM4 climate forecasts over North America. J Geophys Res: Atmos 121(21):12690–12701Google Scholar
- Jacox MG, Alexander MA, Hervieux G, Stock CA (2017) On the skill of seasonal sea surface temperature forecasts in the California current system and its connection to ENSO variability. Clim Dyn. doi: 10.1007/s00382-017-3608-y
- Jolliffe IT, Stephenson DB (2003) Forecast verification: a practitioner’s guide in atmospheric science. Wiley, Chichester, p 240Google Scholar
- Vernieres G, Keppenne C, Rienecker MM, Jacob J, Kovach R (2012) The GEOS-ODAS, description and evaluation. NASA technical report series on global modeling and data assimilation, NASA/TM–2012–104606, vol 30Google Scholar
- Wilks DS (1995) Statistical methods in the atmospheric sciences. Academic Press, Dublin, p 467Google Scholar