Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008–2015

• Published in: Climate dynamics 2017-12, Vol.49 (11-12), p.4141-4156
• Main Authors: Min, Young-Mi, Kryjov, Vladimir N., Oh, Sang Myeong, Lee, Hyun-Ju
• Format: Article
• Language: English
• Subjects: Atmospheric temperature; Bias; Climate and economics; Climatology; Earth and Environmental Science; Earth Sciences; El Nino; El Nino events; El Nino phenomena; El Nino-Southern Oscillation event; Evaluation; Forecasts and trends; Geophysics/Geodesy; Lead; Mathematical models; Mean temperatures; Oceanography; Precipitation; Precipitation (Meteorology); Precipitation forecasting; Real time; Regions; Seasons; Southern Oscillation; Temperature; Temperature effects; Tropical environments; Weather forecasting; Winter
• ISSN: 0930-7575; 1432-0894
• DOI: 10.1007/s00382-017-3576-2

This paper assesses the real-time 1-month lead forecasts of 3-month (seasonal) mean temperature and precipitation on a monthly basis issued by the Asia-Pacific Economic Cooperation Climate Center (APCC) for 2008–2015 (8 years, 96 forecasts). It shows the current level of the APCC operational multi-model prediction system performance. The skill of the APCC forecasts strongly depends on seasons and regions that it is higher for the tropics and boreal winter than for the extratropics and boreal summer due to direct effects and remote teleconnections from boundary forcings. There is a negative relationship between the forecast skill and its interseasonal variability for both variables and the forecast skill for precipitation is more seasonally and regionally dependent than that for temperature. The APCC operational probabilistic forecasts during this period show a cold bias (underforecasting of above-normal temperature and overforecasting of below-normal temperature) underestimating a long-term warming trend. A wet bias is evident for precipitation, particularly in the extratropical regions. The skill of both temperature and precipitation forecasts strongly depends upon the ENSO strength. Particularly, the highest forecast skill noted in 2015/2016 boreal winter is associated with the strong forcing of an extreme El Nino event. Meanwhile, the relatively low skill is associated with the transition and/or continuous ENSO-neutral phases of 2012–2014. As a result the skill of real-time forecast for boreal winter season is higher than that of hindcast. However, on average, the level of forecast skill during the period 2008–2015 is similar to that of hindcast.