Scientists from several institutions and with different research backgrounds have worked together to develop a prototype modular land model for weather forecasting and climate studies. This model is now available for public use and further development.C limate and weather forecasting models require the energy, water, and momentum fluxes across the land-atmosphere interface to be specified. Various land surface parameterizations (LSPs), ranging from the simple bucket-type LSP in the 1960s to the current soil-vegetation-atmosphere interactive LSP, have been developed in the past four decades to calculate these fluxes. The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS) has demonstrated that, even with the same atmospheric forcing data and similar land surface parameters, different LSPs still give significantly different surface fluxes and soil wetness, partly because of the differences in the formulations of individual processes and coding architectures in participant models . On the other hand, most LSPs share many common components, suggesting the need to develop a publicly available common land model with a modular structure that could facilitate the exploration of new issues, less repetition of past efforts, and sharing of improvements and refinements contributed by different groups.The Common Land Model (CLM) effort dates back to the mid-1990s and has evolved through various workshops and e-mail correspondence. The initial motivation was to provide a framework for a truly community-developed land component of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). Interest in applying CLM came from the Goddard Space Flight Center (GSFC) Data Assimilation Office (DAO), which was implementing the Mosaic model (Koster and Suarez 1992), and the Center for Ocean-Land-Atmosphere Studies (COLA) scientists, who were revising their Simplified Simple Biosphere Model (SSiB) (Xue et al. 1991). We also established ties to groups performing carbon cycle and ecological modeling.In developing CLM, we attempted to combine the best features of three existing successful and relatively

A global biofuels program will lead to intense pressures on land supply and can increase greenhouse gas emissions from land-use changes. Using linked economic and terrestrial biogeochemistry models, we examined direct and indirect effects of possible land-use changes from an expanded global cellulosic bioenergy program on greenhouse gas emissions over the 21st century. Our model predicts that indirect land use will be responsible for substantially more carbon loss (up to twice as much) than direct land use; however, because of predicted increases in fertilizer use, nitrous oxide emissions will be more important than carbon losses themselves in terms of warming potential. A global greenhouse gas emissions policy that protects forests and encourages best practices for nitrogen fertilizer use can dramatically reduce emissions associated with biofuels production.

The impact of carbon-nitrogen dynamics in terrestrial ecosystems on the interaction between the carbon cycle and climate is studied using an earth system model of intermediate complexity, the MIT Integrated Global Systems Model (IGSM). Numerical simulations were carried out with two versions of the IGSM's Terrestrial Ecosystems Model, one with and one without carbon-nitrogen dynamics.Simulations show that consideration of carbon-nitrogen interactions not only limits the effect of CO 2 fertilization but also changes the sign of the feedback between the climate and terrestrial carbon cycle. In the absence of carbon-nitrogen interactions, surface warming significantly reduces carbon sequestration in both vegetation and soil by increasing respiration and decomposition (a positive feedback). If plant carbon uptake, however, is assumed to be nitrogen limited, an increase in decomposition leads to an increase in nitrogen availability stimulating plant growth. The resulting increase in carbon uptake by vegetation exceeds carbon loss from the soil, leading to enhanced carbon sequestration (a negative feedback). Under very strong surface warming, however, terrestrial ecosystems become a carbon source whether or not carbonnitrogen interactions are considered.Overall, for small or moderate increases in surface temperatures, consideration of carbon-nitrogen interactions result in a larger increase in atmospheric CO 2 concentration in the simulations with prescribed carbon emissions. This suggests that models that ignore terrestrial carbon-nitrogen dynamics will underestimate reductions in carbon emissions required to achieve atmospheric CO 2 stabilization at a given level. At the same time, compensation between climate-related changes in the terrestrial and oceanic carbon uptakes significantly reduces uncertainty in projected CO 2 concentration.

Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra-and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models' snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.

This study quantifies mean annual and monthly fluxes of Earth's water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.