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A central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement. Since 1992, ARM has established six permanent ARM Climate Research Facility (ACRF) sites and deployed an ARM Mobile Facility (AMF) in diverse climate regimes around the world (Fig. 1) to perform long-term continuous field measurements. [...] a statistical summary file - including a monthly mean climatology and a monthly climatology of the diurnal cycle- derived from CMBE data will be released in the near future.

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework. To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM. The National Center for Atmospheric Research is sponsored by the National Science Foundation

[...]a relationship has not yet been resolved for lowlevel liquid clouds. [...]the radar reflectivity profile of liquid clouds must be related to the retrieved liquid water content (LWC) with additional assumptions, such as the shape of the droplet spectrum (Frisch et al. 1995), the adiabatic condition, and no hydrometeor fallout (Liao and Sassen 1994; Paluch et al. 1996). [...]the subadiabatic character of low-level stratiform clouds and their related microphysical structures are important in studying radiative energy budgets and cloud longevity.

This paper provides an overview of a new data product, named the Climate Modeling Best Estimate (CMBE) dataset, developed by the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Program in order to better serve the need of climate model developers and encourage greater use of ARM data by modelers. The CMBE dataset contains those quantities that are often used in model evaluation and reflect unique ARM measurements of clouds and radiation (e.g., cloud occurrence, liquid water path, and surface radiative fluxes) from the highest quality data that ARM has for many years. The data are averaged over one hour period, which is comparable to a typical temporal resolution used in climate model output. They are currently available at five ARM Climate Research Facility (ACRF) sites located at the Southern Great Plains, North Slope of Alaska, and Tropic Western Pacific, and can be obtained from the ACRF data archive. The long-term continuous ARM data provide invaluable information to improve our understanding of the interaction between clouds and radiation and a solid observational basis for model validation and improvement. This paper shows some examples to demonstrate its unique values in studies of cloud processes, climate variability and change, and climate modeling. Plans for future enhancements of the CMBE product are also discussed.

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation in the atmosphere. A central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement. Since 1992, ARM has established six permanent ARM Climate Research Facility (ACRF) sites and deployed an ARM Mobile Facility (AMF) in diverse climate regimes around the world (Fig. 1) to perform long-term continuous field measurements. The time record of ACRF data now exceeds a decade at most ACRF fixed sites and ranges from several months to one year for AMF deployments. Billions of measurements are currently stored in millions of data files in the ACRF Data Archive. The long-term continuous ACRF data provide invaluable information to improve our understanding of the interaction between clouds and radiation, and an observational basis for model validation and improvement and climate studies. Given the huge number of data files and current diversity of archived ACRF data structures, however, it can be difficult for an outside user such as a climate modeler to quickly find the ACRF data product(s) that best meets their research needs. The required geophysical quantities may exist in multiple data streams, and over the history of ACRF operations, the measurements could be obtained by a variety of instruments, reviewed with different levels of data quality assurance, or derived using different algorithms. In addition, most ACRF data are stored in daily-based files with a temporal resolution that ranges from a few seconds to a few minutes, which is much finer than that sought by some users. Therefore, it is not as convenient for data users to perform quick comparisons over large spans of data, and this can hamper the use of ACRF data by the climate community. To make ACRF data better serve the needs of climate studies and model development, ARM has developed a data product specifically tailored for use by the climate community. The new data product, named the Climate Modeling Best Estimate (CMBE) dataset, assembles those quantities that are both well observed by ACRF over many years and are often used in model evaluation into one single dataset. The CMBE product consists of hourly averages and thus has temporal resolution comparable to a typical resolution used in climate model output. It also includes standard deviations within the averaged hour and quality control flags for the selected quantities to indicate the temporal variability and data quality. Since its initial release in February 2008, the new data product has quickly drawn the attention of the climate modeling community. It is being used for model evaluation by two major U.S. climate modeling centers, the National Center for Atmospheric Research (NCAR) and the Geophysical Fluid Dynamics Laboratory (GFDL). The purpose of this paper is to provide an overview of CMBE data and a few examples that demonstrate the potential value of CMBE data for climate modeling and in studies of cloud processes and climate variability and change.

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program (www.arm.gov) was created in 1989 to address scientific uncertainties related to global climate change, with a focus on the crucial role of clouds and their influence on the transfer of radiation atmosphere. Here, a central activity is the acquisition of detailed observations of clouds and radiation, as well as related atmospheric variables for climate model evaluation and improvement.

Phillips et al evaluate the parameterizations in general circulation models (GCM). CCPP-ARM Parameterization Testbed (CAPT) is motivated by the experience of GCM developers that it is very difficult to identify particular parameterization deficiencies solely by analyzing a model's climate statistics, which reflect compensating errors resulting from the nonlinear interactions of many different processes. Thus, CAPT is not a panacea for improving climate GCM parameterizations at all time scales, but just one choice from a \"tool kit\" that may also include single-column models, cloud-resolving models, and simplified general circulation models.

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

The goal of this research is to improve our ability to realistically simulate the stable boundary layer (SBL). The scientific objectives are: (1) to characterize features of the evolving SBL structure for a range of meteorological conditions (wind speed and surface cooling), (2) to simulate realistically the transfer of energy between resolved and subgrid scales, and (3) to apply results to improve simulation of dispersion in the SBL. A large-eddy simulation (LES) approach with a dynamic, mixed subgrid-scale (SGS) turbulence model is used. The several SBLs simulated illustrate the key role of mechanical turbulence supported by the geostrophic forcing, and the lesser competing effects of turbulence damping by buoyancy that develops in response to the surface cooling. A forcing effects of surface buoyancy flux and geostrophic wind are strongly correlated with the bulk stability of the resulting SBL. The SGS model allows for backscatter (upscale transfer) of turbulent kinetic energy (TKE) and thermal energy. The TKE backscatter is dominated by the interaction of the streamwise velocity component with the wall-normal shear stress. The thermal backscatter occurs during ejections of cool surface air, associated with the action of coherent structures in the flow. The simulation of episodes of enhanced turbulence is made possible by the inclusion of energy backscatter. These episodes are associated with the breakdown of large-scale wave-like activity. The implications for dispersion in the SBL are demonstrated by releasing marker particles in LES-generated wind fields for an SBL with an enhanced turbulence event. The effect of the enhanced turbulence is to spread the plume over a larger volume in response to (1) mixing due to the increased small scale turbulence and (2) differential advection after the mixing begins due to the presence of a strong vertical gradient in horizontal wind direction. Eddy diffusivities are estimated directly from LES fields. These values agree surprisingly well with estimates from algorithms used in practical dispersion models for the undisturbed SBL. However, the practical models cannot capture effects of enhanced turbulence on eddy diffusion during episodes.