Process-based model analyses are often used to estimate changes in soil organic carbon (SOC), particularly at regional to continental scales. However, uncertainties are rarely evaluated, and so it is difficult to determine how much confidence can be placed in the results. Our objective was to quantify uncertainties across multiple scales in a processbased model analysis, and provide 95% confidence intervals for the estimates. Specifically, we used the Century ecosystem model to estimate changes in SOC stocks for US croplands during the 1990s, addressing uncertainties in model inputs, structure and scaling of results from point locations to regions and the entire country. Overall, SOC stocks increased in US croplands by 14.6 Tg C yr À1 from 1990 to 1995 and 17.5 Tg C yr À1 during 1995 to 2000, and uncertainties were AE 22% and AE 16% for the two time periods, respectively. Uncertainties were inversely related to spatial scale, with median uncertainties at the regional scale estimated at AE 118% and AE 114% during the early and latter part of 1990s, and even higher at the site scale with estimates at AE 739% and AE 674% for the time periods, respectively. This relationship appeared to be driven by the amount of the SOC stock change; changes in stocks that exceeded 200 Gg C yr À1 represented a threshold where uncertainties were always lower than AE 100%. Consequently, the amount of uncertainty in estimates derived from process-based models will partly depend on the level of SOC accumulation or loss. In general, the majority of uncertainty was associated with model structure in this application, and so attaining higher levels of precision in the estimates will largely depend on improving the model algorithms and parameterization, as well as increasing the number of measurement sites used to evaluate the structural uncertainty.

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Using data archived in the Coordinated Enhanced Observing Period (CEOP) project, this study presents an initial evaluation of the prediction skill of five General Circulation Models (GCMs) and three Land Surface Models (LSMs). Comparisons between observations and the GCMs show that all the models are able to produce an afternoon peak in precipitation, but other major features are not well produced, including the total amount of precipitation, onset time of the afternoon peak, the early-evening low (around 1800 LST), and the partition between convective and stratiform rainfall. The ratios of evaporation to precipitation differ among the GCMs. Evaporation in some of the GCMs is even greater than precipitation, perhaps due to the model spin-up effect. In terms of the surface radiation budget, the GCMs generally over-predict downward shortwave radiation and under-predict downward longwave radiation; further investigations of the causes of these trends require cloudiness observations. In terms of the surface energy budget, the GCMs generally over-predict nighttime downward sensible heat fluxes Corresponding author: Kun Yang, Dr., River Lab, Department of Civil Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan. E-mail: yangk@hydra.t.u-tokyo.ac.jp ( 2007, Meteorological Society of Japan and under-predict diurnal ranges of surface-air temperature difference, as heat transfer resistances are under-predicted. Finally, three offline LSMs driven by identical forcing are evaluated, and we note that the reproduction of surface temperature is not a sufficient condition for a LSM to reproduce surface energy partition.

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