The NCEP Climate Forecast System

Abstract

The Climate Forecast System (CFS), the fully coupled ocean–land–atmosphere dynamical seasonal prediction system, which became operational at NCEP in August 2004, is described and evaluated in this paper. The CFS provides important advances in operational seasonal prediction on a number of fronts. For the first time in the history of U.S. operational seasonal prediction, a dynamical modeling system has demonstrated a level of skill in forecasting U.S. surface temperature and precipitation that is comparable to the skill of the statistical methods used by the NCEP Climate Prediction Center (CPC). This represents a significant improvement over the previous dynamical modeling system used at NCEP. Furthermore, the skill provided by the CFS spatially and temporally complements the skill provided by the statistical tools. The availability of a dynamical modeling tool with demonstrated skill should result in overall improvement in the operational seasonal forecasts produced by CPC. The atmospheric component of the CFS is a lower-resolution version of the Global Forecast System (GFS) that was the operational global weather prediction model at NCEP during 2003. The ocean component is the GFDL Modular Ocean Model version 3 (MOM3). There are several important improvements inherent in the new CFS relative to the previous dynamical forecast system. These include (i) the atmosphere–ocean coupling spans almost all of the globe (as opposed to the tropical Pacific only); (ii) the CFS is a fully coupled modeling system with no flux correction (as opposed to the previous uncoupled “tier-2” system, which employed multiple bias and flux corrections); and (iii) a set of fully coupled retrospective forecasts covering a 24-yr period (1981–2004), with 15 forecasts per calendar month out to nine months into the future, have been produced with the CFS. These 24 years of fully coupled retrospective forecasts are of paramount importance to the proper calibration (bias correction) of subsequent operational seasonal forecasts. They provide a meaningful a priori estimate of model skill that is critical in determining the utility of the real-time dynamical forecast in the operational framework. The retrospective dataset also provides a wealth of information for researchers to study interactive atmosphere–land–ocean processes.

Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI

“…The concurrent, OSST data, for nonforecast simulations (lead time less than zero), S denotes the Reynolds version 2 data (Reynolds et al 2002); the real-time persisted SST (FSST p ) and hindcast persisted SST (HSST p ) are undamped and then damped with persisted anomalies initialized from anomalies observed the previous month. The evolving anomalous SST (FSST e ) is from one or more of the following models: the NCEP coupled model (Ji et al 1998), the CFS (Saha et al 2006), the LDEO-5 (Chen et al 2004), and/or the statistical constructed analog (CA) (van den Dool 1994Dool , 2007. More details about the FSST e are provided in Camargo and Barnston (2008 …”

“…2 The tropical Pacific SST has been based on one or more of the following: the NCEP coupled ENSO prediction model (Ji et al 1998), the NCEP Climate Forecast System (NCEP-CFS) (Saha et al 2006), the Lamont-Doherty Earth Observatory intermediate model, version 5 (LDEO-5), (Chen et al 2004), and the statistical constructed analog (CA) model (van den Dool 1994Dool , 2007. 3 A model TC needs to exceed simultaneously thresholds for low-level vorticity (850 hPa), surface wind speed, and vertically integrated local temperature anomaly for at least 2 days, and must also have a relative local minimum of sea level pressure, local maximum of temperature anomalies in various levels, and mean wind speed at 850 hPa larger than at 300 hPa.…”

Developments in dynamical seasonal forecasting relevant to agricultural management

“…Given that biases in the simulations made with the state-of-the-art dynamical models used in seasonal forecasting (Palmer et al 2004, Saha et al 2006) are non-negligible, bias correction of the systematic error in the mean is carried out on the DEMETER ensemble simulations. Anomalies for the prediction and the reference values are calculated as the difference between the value for a given season and the corresponding climatology.…”

“…However, in this contribution only dynamical methods will be considered. Palmer et al (2004), Hagedorn et al (2005) and Saha et al (2006), among many others, show and describe results on the seasonal forecasting problem from the meteorological point of view. The present study offers an overview of recent improvements and future devel-opments in seasonal forecasting that are relevant for agricultural management.…”

Simulation of low clouds in the Southeast Pacific by the NCEP GFS: sensitivity to vertical mixing

“…Underprediction of stratus results in overestimation of heat flux into the ocean and may be the primary reason why ocean-atmosphere coupled models show positive SST biases of several degrees off the coast of Peru (Mechoso et al, 1995;de Szoeke et al, 2006). These model difficulties with marine boundary layer clouds in the eastern tropical oceans are evident in the 2003 version of the operational GFS, which has been used since 2003 as the atmospheric component of the Climate Forecast System (CFS) to produce operational climate forecasts at NCEP (Saha et al, 2006). The CFS forecasts have large errors in the simulated mean SST, especially along the South American coast and in the Pacific "equatorial cold tongue" region.…”