The circulation of water through the Earth climate system helps sustain life. The hydrological cycle is linked to the energy cycle and to biogeochemical processes including the carbon cycle. Simulating the various processes that interact to form the hydrological cycle is a daunting task for climate models. In particular, over land, interactions between precipitation and the vegetation/soil system determine the partitioning of water into various storage reservoirs and the subsequent release of water vapor to the atmosphere. Successful simulation of these interactions by the land surface component of a climate model requires detailed representation of processes such as interception, throughfall, canopy drip, snow accumulation and ablation, infiltration, surface and subsurface runoff, soil moisture, and the partitioning of evapotranspiration (ET) between canopy evaporation, transpiration, and soil evaporation. Depending on the capabilities of the model, the water cycle components may interact with and affect the simulation of biogeochemical processes such as the carbon and nitrogen cycles, dust and trace gas emissions, water and carbon isotopes, and vegetation dynamics.
 The Community Land Model version 3 (CLM3) represents land surface processes within the context of a global climate simulation [Oleson et al., 2004]. Dickinson et al.  described the climate statistics of CLM3 when coupled to the Community Climate System Model (CCSM3) [Collins et al., 2006]. Hack et al.  provided an analysis of selected features of the land hydrological cycle. Bonan and Levis  evaluated global plant biogeography and net primary production from CLM3 when coupled to a dynamic global vegetation model (DGVM). Lawrence et al.  examined the impact of changes in CLM3 hydrological parameterizations on partitioning of ET and its effect on the timescales of ET response to precipitation events, interseasonal soil moisture storage, soil moisture memory, and land-atmosphere coupling. Qian et al.  evaluated CLM3's performance in simulating soil moisture content, runoff, and river discharge when forced by observed precipitation, temperature and other atmospheric data. Although the simulation of land surface climate by CLM3 is in many ways adequate [Dickinson et al., 2006], most of the unsatisfactory aspects of the simulated climate described in these studies can be traced directly to a deficient simulation of the hydrological cycle.
 A poor simulation of the hydrological cycle in the Amazon basin is in part due to insufficient precipitation from the atmospheric model but is exacerbated by unrealistic partitioning of ET and deficiencies in runoff and soil water storage. This is also evident in off-line simulations forced with observed precipitation [Qian et al., 2006]. The simulated present-day climate is biased warm and dry with lower runoff than observed [Dickinson et al., 2006; Lawrence et al., 2007; Hack et al., 2006]. In particular, these studies indicate that the simulated ET is dominated by soil and canopy evaporation instead of by transpiration as observed. The deficiencies result in a poor simulation of vegetation biogeography with much less cover of broadleaf evergreen trees and more of deciduous trees than observed [Bonan and Levis, 2006]. On a global scale, forest cover is underestimated compared to observations in favor of grasses because of dry soils. Lawrence and Chase  have noted that because of the unrealistic partitioning of ET, improved surface data sets of leaf and stem area index and plant functional type were unable to rectify temperature and precipitation biases in the coupled modeling system. Other hydrology-related problems in the model include low gross primary production (GPP) [Bonan and Levis, 2006] and poor simulation of the magnitude and seasonality of runoff and soil water storage in regions with frozen soil [Niu and Yang, 2006].
 As a community model, CLM has benefited from a number of scientists willing to scrutinize its scientific contents, offer constructive criticism, and improve its performance. Several new parameterizations designed to address the specific deficiencies in CLM3 have been proposed [Niu et al., 2005; Niu and Yang, 2006; Niu et al., 2007; Thornton and Zimmermann, 2007; Lawrence and Chase, 2007; Lawrence et al., 2007]. Validation and sensitivity testing of the individual parameterizations have been addressed by the respective authors. While these individual parameterizations have clearly been shown to be beneficial in alleviating specific biases in the model, how they might interact with each other and the net effect on the simulation of the hydrological cycle have not previously been examined. This paper describes the implementation of these parameterizations and reports on the aggregated effects on the hydrology of CLM on a global scale. We show that in general the new parameterizations result in a more realistic depiction of the hydrologic cycle. We also demonstrate that the improved hydrology translates into better simulation of global GPP and present-day vegetation biogeography. On the other hand, the analysis of the new model presented here is somewhat limited by the lack of observed data at global scales (e.g., surface energy partitioning). Thus, Stöckli et al.  examine the performance of the new model at local scales by making use of a network of long-term ground-based ecosystem observations (FLUXNET [Baldocchi et al., 2001]). These flux tower sites span a wide range of ecosystems and climate zones and are used to examine in more detail the model parameterizations of heat, water and carbon exchanges.