Stable oxygen and hydrogen isotopes in water (H218O and HDO) are useful natural tracers for hydrologic cycles [e.g., Gat, 1996]. Because their concentration is sensitive to phase changes of water during its circulation, geographical and temporal variations of isotopic ratios emerge in land surface reservoirs such as rivers and groundwater. In order to understand, explain, and ultimately use observed variations in the reservoirs for assessing the hydrologic cycle, the relation between atmospheric processes and isotopic information in water vapor and precipitation has been intensively studied [e.g., Craig and Gordon, 1965; Ehhalt, 1974; Jouzel, 1986; Gedzelman and Arnold, 1994; Webster and Heymsfield, 2003; Worden et al., 2007].
 Various empirical methods to explain the distribution of isotope ratio have been used since the classical “isotopic effects” was proposed (e.g., temperature effect [Dansgaard, 1964]). Bowen and Revenaugh  showed that the monthly climatology precipitation isotope ratios can be reasonably well explained by a multivariate regression relationship with several meteorological and geographical variables. However, the accuracy of this multivariate relationship depends highly on the number of available observations, and much of the interannual variability is not captured by simple predictors (N. Buenning and D. Noone, Role of local and nonlocal processes in the seasonal cycle and interannual variability of the isotopic composition of precipitation deduced through observations and models, submitted to Journal of Geophysical Research, 2008). Since the observations are scarce, particularly for vapor isotopes and for both vapor and precipitation isotopes at time scales shorter than a month, the robustness of regression approaches still require further verification. In particular, simple regression models fail to capture the aspects of the isotope signal associated with atmospheric transport, and are thus ultimately limited. Perhaps more importantly, the physical mechanisms behind these empirical approaches need to be understood more explicitly.
 In contrast to observational studies, isotope-incorporated atmospheric general circulation models (AGCM) [Joussaume et al., 1984; Jouzel et al., 1987; Hoffmann et al., 1998; Mathieu et al., 2002; Noone and Simmonds, 2002; Schmidt et al., 2005; Lee et al., 2007] provide a more physical approach to understanding isotope distributions since they combine physical processes associated with the change in isotope ratio with the dynamic and moist thermodynamic processes of the atmosphere. These models simulate the three-dimensional structure of vapor isotope distribution with explicit consideration of complex phase changes of water associated with the moist physical processes in the global atmosphere. The resulting simulations show good agreement with climatological distribution of precipitation isotopes, but their temporal variability does not agree well with the observations [Hoffmann et al., 2000]. The reason for this poor isotope simulation is partly due to the inferior representation of atmospheric circulation by the AGCMs forced only by the observed sea surface temperature, and is also associated with the AGCMs' ability to simulate variability in the hydrologic cycle.
 Yoshimura et al. [2003, 2004] successfully reproduced the daily to interannual variations of precipitation isotopes over the globe using a simpler model in which the observed circulation was prescribed from atmospheric Reanalysis. They concluded that the isotopes can be used to evaluate the atmospheric moisture transport in models and that the isotopic AGCMs would be capable of simulating day-to-day isotopic variations in precipitation more accurately if the large-scale circulation fields are more accurately simulated. This finding also indicates that by constraining the isotopic fields the simulation of water vapor transport can be improved, but this issue leaves for future studies. Furthermore, vapor isotopes observed by satellites quantified the re-evaporation of tropical rainfall [Worden et al., 2007], and the isotope simulations clarified that there is a need for evaporation of rain to remoisten the lower troposphere in AGCMs [Noone, 2003].
 Recently, Yoshimura and Kanamitsu  used a spectral nudging technique for global downscaling of global Reanalysis. In this method, small-scale detail is generated by the high-resolution global model, whose large-scale circulations is constrained by the coarse resolution global atmospheric Reanalysis. The technique can be regarded as an economical alternative to computationally demanding high-resolution data assimilation. In this current study we apply the global spectral nudging technique not for a downscaling purpose, but for providing dynamical constraints to the water isotope circulations. It is expected that multidecadal and three dimensional distributions of isotopic species that are consistent with observed atmospheric circulation can be obtained. We used version of the Scripps global spectral model with water isotopes-incorporated (IsoGSM), which was newly developed from the up-to-date version of the Scripps Experimental Climate Prediction Center's (ECPC) GSM [Kanamitsu et al., 2002a]. As an atmospheric analysis, the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Reanalysis 2 (R2) [Kanamitsu et al., 2002b] was used to constrain the meteorology.
 This study has two main aims. The first is to make a long-term and three-dimensional data set of stable water isotopes, which is thermo-dynamically consistent with observed long and short-term atmospheric circulations. The results aid in understanding the mechanisms controlling of the global distributions and temporal variations of isotopes in a similar manner to that understanding atmospheric circulation on various time scales has benefited from Reanalysis products. The second aim is to make a reference isotopic variability analysis based on the model forced by observed atmospheric circulation. This analysis can be used to measure the a priori quality of the model performance for future studies involving the assimilation of isotopic data. This aim comes with an additional interest to establish the potential for improvement in the analysis of atmospheric circulation by the introduction of isotopes in a full data assimilation.
 Section 2 describes the new isotopic AGCM, the nudging method, and the simulation specification to make the isotopic data set. In section 3 the simulated isotope distribution is verified against observations and compared with other isotopic AGCMs. Improvements in the representations of the isotopic interannual variability are described in section 4. A summary and conclusions follow in section 5.