Long-Lead Seasonal Forecasts—Where Do We Stand?

Abstract

The National Weather Service intends to begin routinely issuing long-lead forecasts of 3-month mean U. S. temperature and precipitation by the beginning of 1995. The ability to produce useful forecasts for certain seasons and regions at projection times of up to 1 yr is attributed to advances in data observing and processing, computercapability, and physical understanding-particularly, for tropical ocean-atmosphere phenomena. Because much of the skill of the forecasts comes from anomalies of tropical SST related to ENSO, we highlight here long-lead forecasts of the tropical Pacific SST itself, which have higher skill than the U.S forecasts that are made largely on their basis. The performance of five ENSO prediction systems is examined: Two are dynamical [the Cane-Zebiak simple coupled model of Lamont-Doherty Earth Observatory and the nonsimple coupled model of the National Centersfor Environmental Prediction (NCEP)]; one is a hybrid coupled model (the Scripps Institution for Oceanogra-phy-Max Planck Institute for Meteorology system with a full ocean general circulation model and a statistical atmosphere); and two are statistical (canonical correlation analysis and constructed analogs, used at the Climate Prediction Center of NCEP). With increasing physical understanding, dynamically based forecasts have the potential to become more skillful than purely statistical ones. Currently , however, the two approaches deliver roughly equally skillful forecasts, and the simplest model performs about as well as the more comprehensive models. At a lead time of 6 months (defined here as the time between the end of the latest observed period and the beginning of the predictand period), the SST forecasts have an overall correlation skill in the 0.60s for 1982-93, which easily outperforms persistence and is regarded as useful. Skill for extra-tropical surface climate is this high only in limited regions for certain seasons. Both types of forecasts are not much better than local higher-order autoregressive controls. However, continual progress is being made in understanding relations among global oceanic and atmospheric climate-scale anomaly fields. It is important that more real-time forecasts be made before we rush to judgement. Performance in the real-time setting is the "Climate Prediction Center NCEP/NWS/NOAA ultimate test of the utility of a long-lead forecast. The National Weather Service's plan to implement new operational long-lead seasonal forecast products demonstrates its effectiveness in identifying and transferring "cutting edge" technologies from theory to applications. This could not have been accomplished without close ties with, and the active cooperation of, the academic and research communities.

Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

“…The quality of predictions of the ENSO state has improved over the decades beginning with the first ones in the 1980s based on simplified coupled ocean-atmosphere physics (e.g., Cane et al 1986) and advancing into the twenty-first century with comprehensive coupled general circulation models and sophisticated data assimilation techniques to set the initial conditions. Evaluations of the skill of ENSO predictions have been presented periodically along this long developmental path (e.g., Barnston et al 1994Barnston et al , 1999Barnston et al , 2012Tippett and Barnston 2008;Tippett et al 2012;L'Heureux et al 2016;Tippett et al 2017). …”

Climate prediction for Brazil's Nordeste by empirical and numerical modeling methods

“…Imperative for a comparison of forecast performance is the consistency in predictand and a sufficiently long prediction period common to the two approaches. In such a comparison for the seasonal forecasting of equatorial Pacific SST, Barnston et al (1994) found that numerical modelling did not surpass the performance of empirically based prediction. Half a decade later, Barnston et al (1999) confirmed the earlier assessment and expressed reservation on whether numerical modelling would ever outperform empirical methods, consistent with perceptions conveyed by other authors (Webster et al, 1998;Anderson et al, 1999;Bamzai and Shukla, 1999;Lorenz, 2007).…”

“…Prediction throughout the tropics on the basis of the empirical diagnostics and numerical modelling has been appraised in a sequence of reviews over the past decades (Hastenrath, 1985(Hastenrath, , 1986(Hastenrath, , 1990(Hastenrath, , 1995a(Hastenrath, ,b, 2002(Hastenrath, , 2006aBarnston et al, 1994Barnston et al, , 1999Palmer and Anderson, 1994;Carson, 1998;Latif et al, 1998, Anderson et al, 1999Nobre et al, 2006). Comparison of performance of the empirical and numerical modelling approaches is called for.…”

Impact of El Niño–southern oscillation on Indian foodgrain production

“…The pervasiveness of the ENSO relationship with Indian climate and the prospect of forecasting ENSO events with useful skill at longer lead-time (Barnston et al, 1994;Latif et al, 1994;Chen et al, 1995) suggests an unprecedented opportunity to predict Indian foodgrain production. Where an influence of ENSO on foodgrain production can be demonstrated, the ENSO prediction may be used either to mitigate the impacts of adverse conditions or to take advantage of favourable conditions.…”