[1] This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 18 results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.48-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niñ o-Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden-Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.48C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.

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

[1] The augmented Noah land surface model described in the first part of the two-part series was evaluated here over global river basins. Across various climate zones, global-scale tests can reveal a model's weaknesses and strengths that a local-scale testing cannot. In addition, global-scale tests are more challenging than local-and catchment-scale tests. Given constant model parameters (e. g., runoff parameters) across global river basins, global-scale tests are more stringent. We assessed model performance against various satellite and ground-based observations over global river basins through six experiments that mimic a transition from the original Noah LSM to the fully augmented version. The model shows transitional improvements in modeling runoff, soil moisture, snow, and skin temperature, despite considerable increase in computational time by the fully augmented Noah-MP version compared to the original Noah LSM. The dynamic vegetation model favorably captures seasonal and spatial variability of leaf area index and green vegetation fraction. We also conducted 36 ensemble experiments with 36 combinations of optional schemes for runoff, leaf dynamics, stomatal resistance, and the b factor. Runoff schemes play a dominant and different role in controlling soil moisture and its relationship with evapotranspiration compared to ecological processes such as the b factor, vegetation dynamics, and stomatal resistance. The 36-member ensemble mean of runoff performs better than any single member over the world's 50 largest river basins, suggesting a great potential of land-based ensemble simulations for climate prediction.

[1] This paper develops a simple TOPMODEL-based runoff parameterization (hereinafter SIMTOP) for use in global climate models (GCMs) that improves the runoff production and the partitioning of runoff between surface and subsurface components. SIMTOP simplifies the TOPMODEL runoff formulations in two ways: (1) SIMTOP represents the discrete distribution of the topographic index as an exponential function, not as a three-parameter gamma distribution; this change improves the parameterization of the fractional saturated area, especially in mountainous regions. (2) SIMTOP treats subsurface runoff as a product of an exponential function of the water table depth and a single coefficient, not as a product of several parameters that depend on topography and soil properties; this change facilitates applying TOPMODEL-based runoff schemes on global scale. SIMTOP is incorporated into the National Center for Atmospheric Research (NCAR) Community Land Model version 2.0 (CLM 2.0). SIMTOP is validated at a watershed scale using data from the Sleepers River watershed in Vermont, USA. It is also validated on a global scale using the monthly runoff data from the University of New Hampshire Global Runoff Data Center (UNH-GRDC). SIMTOP performs favorably when compared to the baseline runoff formulation used in CLM2.0. Realistic simulations can be obtained using two distinct saturated hydraulic conductivity (K sat ) profiles. These profiles include (1) exponential decay of K sat with depth (as is typically done in TOPMODEL-based runoff schemes) and (2) the definition of K sat using the soil texture profile data (as is typically done in climate models) and the concordant reduction of the gravitational drainage from the bottom of the soil column.