A new coupled global NCEP Reanalysis for the period 1979-present is now available, at much higher temporal and spatial resolution, for climate studies. T he first reanalysis at NCEP (all acronyms are defined in the appendix), conducted in the 1990s, resulted in the NCEP-NCAR reanalysis (Kalnay et al. 1996), or R1 for brevity, and ultimately covered many years, from 1948 to the present (Kistler et al. 2001). It is still being executed at NCEP, to the benefit of countless users for monthly, and even daily, updates of the current state of the atmosphere. At the same time, other reanalyses were being conducted, namely, ERA-15 (Gibson et al. 1997) was executed for a more limited period (1979-93) at the ECMWF, COLA conducted a short reanalysis covering the May 1982-November 1983 period (Paolino et al. 1995), and NASA GSFC conducted a reanalysis covering the 1980-94 period (Schubert et al. 1997). The general purpose of conducting reanalyses is to produce multiyear global state-of-the-art gridded representations of atmospheric states, generated by a constant model and a constant data assimilation system. To use the same model and data assimilation over a very long period was the great advance during the 1990s, because gridded datasets available before 1995 had been created in real time by ever-changing models and analysis methods, even by hand analyses prior to about 1965. The hope was that a reanalysis,

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We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 onetier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the

NCEP's newly developed second-generation operational seasonal forecast system aims at a seamless suite of forecasts and provides much more comprehensive datasets for users.n April 2000, a new dynamical seasonal prediction system was introduced at the National Centers for Environmental Prediction (NCEP; the acronyms used in this paper are summarized in the appendix). The transition to the new system was hastened by a computer fire in September 1999 and subsequent changeover from a Cray C90 to an IBM-SP computer system. This article will be a reference for people who are using the NCEP numerical seasonal forecast products.The first-generation dynamical seasonal prediction model was based on the notion that the seasonal predictability in the Northern Hemisphere extratropics

Composites based on observations and model outputs from the CLIVAR drought experiments were used to examine the impact of El Nino Southern Oscillation (ENSO) and the Atlantic multi-decadal oscillation (AMO) on drought over the United States. The experiments were performed by forcing an AGCM with prescribed sea surface temperature anomalies (SSTAs) superimposed on the monthly mean SST climatology. Four model outputs from the NCEP GFS, NASA NSIPP1, GFDL AM2.1 and LDEO/NCAR CCM3 were analyzed in this study. The impact of ENSO on drought over the United States is concentrated over the Southwest, the Great Plains and the lower Colorado River Basin with cold (warm) ENSO events in favor of drought (wet spells). Over the East Coast and the Southeast, the impact of ENSO is small because the precipitation responses to ENSO are opposite in sign for winter and summer. For these areas, a prolonged ENSO from winter to summer does not favor persistent drought or wet spell. The direct influence of the AMO on drought is small. The major influence of the AMO is to modulate the impact of ENSO on drought. The influence is large when the SSTAs in the tropical Pacific and in the North Atlantic are opposite in phase. A cold ENSO event in a positive AMO phase favors drought over the Southwest, the Great Plains and the Colorado River basin. A warm ENSO event in a negative AMO phase has the opposite impact. The ENSO influence on drought is much weaker when the SSTAs in the tropical Pacific and in the North Atlantic are in phase.