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Atmospheric general circulation models used for climate simulation and weather forecasting require the fluxes of radiation, heat, water vapor, and momentum across the land-atmosphere interface to be specified. These fluxes are calculated by submodels called land surface parameterizations. Over the last 20 years, these parameterizations have evolved from simple, unrealistic schemes into credible representations of the global soil-vegetation-atmosphere transfer system as advances in plant physiological and hydrological research, advances in satellite data interpretation, and the results of largescale field experiments have been exploited. Some modern schemes incorporate biogeochemical and ecological knowledge and, when coupled with advanced climate and ocean models, will be capable of modeling the biological and physical responses of the Earth system to global change, for example, increasing atmospheric carbon dioxide.Until the early 1980s, global atmospheric general circulation models (AGCMs) incorporated very simple land surface parameterizations (LSPs) to estimate the exchanges of energy, heat, and momentum between the land surface and the atmosphere. These have since evolved into a family of schemes that can realistically describe a comprehensive range of land-atmosphere interactions. These advanced schemes will be needed to understand the response of the biosphere and the climate system to global change, for example, increasing atmospheric CO 2 (1-3).Three generations of models have taken us from the early LSPs to where we stand now. The first, developed in the late 1960s and 1970s, was based on simple aerodynamic bulk transfer formulas and often uniform prescriptions of surface parameters (albedo, aerodynamic roughness, and soil moisture availability) over the continents (4). In the early 1980s, a second generation of models explicitly recognized the effects of vegetation in the calculation of the surface energy balance (5, 6). At the same time, global, spatially varying data of land surface properties were assembled from ecological and geographical surveys published in the scientific literature (7). The latest (third generation) models use modern theories relating photosynthesis and plant water relations to provide a consistent description of energy exchange, evapotranspiration, and carbon exchange by plants (8-10). Some are beginning to incorporate treatments of nutrient dynamics and biogeography, so that vegetation systems can move in response to climate shifts. A series of largescale field experiments have been executed to validate the process models and scaling assumptions involved in land-atmosphere schemes (3). These experiments have also accelerated the development of methods for translating satellite data into global surface parameter sets for the models. Theoretical Background and the First-Generation ModelsIt has been understood for nearly 200 years that the continents and the atmosphere exchange energy, water, and carbon with each other. However, it was not until the late 1960s with the construct...

A new three-dimensional cloud resolving model (CRM) has been developed to study the statistical properties of cumulus convection. The model was applied to simulate a 28-day evolution of clouds over the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site during the summer 1997 Intensive Observation Period. The model was forced by the large-scale advective tendencies and surface fluxes derived from the observations. The sensitivity of the results to the domain dimensionality and size, horizontal grid resolution, and parameterization of microphysics has been tested. In addition, the sensitivity to perturbed initial conditions has also been tested using a 20-member ensemble of runs. The model captures rather well the observed temporal evolution of the precipitable water and precipitation rate, although it severely underestimates the shaded cloud fraction possibly because of an inability to account for the lateral advection of clouds over the ARM site. The ensemble runs reveal that the uncertainty of the simulated precipitable water due to the fundamental uncertainty of the initial conditions can be as large as 25% of the mean values. Even though the precipitation rates averaged over the whole simulation period were virtually identical among the ensemble members, the timing uncertainty of the onset and reaching the precipitation maximum can be as long as one full day. Despite the predictability limitations, the mean simulation statistics are found to be almost insensitive to the uncertainty of the initial conditions. The overall effects of the third spatial dimension are found to be minor for simulated mean fields and scalar fluxes but are quite considerable for velocity and scalar variances. Neither changes in a rather wide range of the domain size nor the horizontal grid resolution have any significant impact on the simulations. Although a rather strong sensitivity of the mean hydrometeor profiles and, consequently, cloud fraction to the microphysics parameters is found, the effects on the predicted mean temperature and humidity profiles are shown to be modest. It is found that the spread among the time series of the simulated cloud fraction, precipitable water, and surface precipitation rate due to changes in the microphysics parameters is within the uncertainty of the ensemble runs. This suggests that correlation of the CRM simulations to the observed long time series of the aforementioned parameters cannot be generally used to validate the microphysics scheme.

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

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