An Assessment of the CFS Real-Time Seasonal Forecasts

Abstract

Abstract This study assesses the real-time seasonal forecasts for 2005–08 with the current National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The forecasts are compared with retrospective forecasts (or hindcasts) for 1981–2004 to examine the consistency of the forecast system, and with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with observed sea surface temperatures (SSTs) to contrast the realized skill against the potential predictability due to the specification of the observed sea surface temperatures. The analysis focuses on the forecasts of SSTs, 2-m surface air temperature (T2M), and precipitation. The CFS forecasts maintained a good level of prediction skill for SSTs in the tropical Pacific, the western Indian Ocean, and the northern Atlantic. The SST forecast skill is within the range of hindcast skill levels calculated with 4-yr windows, which can vary greatly associated with the interannual El Niño–Southern Oscillation (ENSO) variability. Overall, the SST forecast skill over the globe is comparable to the average of the hindcast skill. For the tropical eastern Pacific, however, the forecast skill at lead times longer than 2 months is less than the average hindcast skill due to the relatively weaker ENSO variability during the forecast period (2005–08). The forecasts and hindcasts show a similar level of precipitation skill over most of the globe. For T2M, the spatial distribution of skill differs substantially between the forecasts and hindcasts. In particular, the T2M skill of the forecasts for the Northern Hemisphere during its warm seasons is lower than that of the hindcasts. Comparison with the AMIP simulations shows similar levels of precipitation skill over the tropical Pacific. Over the tropical Indian Ocean, the CFS forecasts show a substantially higher level of skill than the AMIP simulations for a large part of the period. This conforms with the results from previous studies that while interannual variability in the tropical Pacific atmosphere is slaved to the underlying SST anomalies, specification of SSTs (as for the AMIP simulations) in the Indian Ocean may lead to incorrect simulation of the atmospheric variability. Over the tropical Atlantic, the precipitation skill of both the CFS forecasts and AMIP simulations is low, suggesting that SSTs have less control over the atmospheric anomalies and the predictability is low. The analysis reveals several deficiencies in the current CFS that need to be corrected for improved seasonal forecasting. For example, the CFS tends to consistently forecast larger ENSO amplitude and delayed transition between the ENSO phases. Forecasts of T2M also have a strong cold bias in Northern Hemisphere mid- to high latitudes during warm seasons. This error is due to initial soil moisture anomalies, which appear to be too wet compared with two other observational analyses. The strong impacts of soil moisture on the seasonal forecasts, and large discrepancies among the soil moisture analyses, call for more accurate specification of soil moisture. Furthermore, average forecast SST and T2M anomalies for 2005–08 show a cold bias over the entire globe, indicating that the model is unable to maintain the observed long-term warming trend.