Search

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.

Motivated by the need to obtain accurate objective analysis of field experimental data to force physical parameterizations in numerical models, this paper first reviews the existing objective analysis methods and interpolation schemes that are used to derive atmospheric wind divergence, vertical velocity, and advective tendencies. Advantages and disadvantages of different methods are discussed. It is shown that considerable uncertainties in the analyzed products can result from the use of different analysis. The paper then describes a hybrid approach to combine the strengths of the regular grid and the line-integral methods, together with a variational constraining procedure for the analysis of field experimental data. In addition to the use of upper-air data, measurements at the surface and at the top of the atmosphere (TOA) are used to constrain the upper-air analysis to conserve column-integrated mass, water, energy, and momentum. Analyses are shown for measurements taken in the Atmospheric Ra...

Using measurements from the Department of Energy’s Atmospheric Radiation Measurement Program, a modified ground-based remote sensing technique is developed and evaluated to study the impacts of the subadiabatic character of continental low-level stratiform clouds on microphysical properties and radiation budgets. Airborne measurements and millimeter-wavelength cloud radar data are used to validate retrieved microphysical properties of three stratus cloud systems occurring in the April 1994 and 1997 intensive observation periods at the Southern Great Plains site. The addition of the observed cloud-top height into the Han and Westwater retrieval scheme eliminates the need to invoke the adiabatic assumption. Thus, the retrieved liquid water content (LWC) profile is represented as the product of an adiabatic LWC profile and a weighting function. Based on in situ measurements, two types of weighting functions are considered in this study: one is associated with a subadiabatic condition involving cloud...

Eleven different single-column models (SCMs) and one cloud ensemble model (CEM) are driven by boundary conditions observed at the Atmospheric Radiation Measurement (ARM) program southern Great Plains site for a 17 day period during the summer of 1995. Comparison of the model simulations reveals common signatures identifiable as products of errors in the boundary conditions. Intermodel differences in the simulated temperature, humidity, cloud, precipitation, and radiative fluxes reflect differences in model resolution or physical parameterizations, although sensitive dependence on initial conditions can also contribute to intermodel differences. All models perform well at times but poorly at others. Although none of the SCM simulations stands out as superior to the others, the simulation by the CEM is in several respects in better agreement with the observations than the simulations by the SCMs. Nudging of the simulated temperature and humidity toward observations generally improves the simulated cloud and radiation fields as well as the simulated temperature and humidity but degrades the precipitation simulation for models with large temperature and humidity biases without nudging. Although some of the intermodel differences have not been explained, others have been identified as model problems that can be or have been corrected as a result of the comparison.

[1] This study represents an effort to develop Single-Column Model (SCM) and Cloud-Resolving Model large-scale forcing data from a sounding array in the high latitudes. An objective variational analysis approach is used to process data collected from the Atmospheric Radiation Measurement Program (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE), which was conducted over the North Slope of Alaska in October 2004. In this method the observed surface and top of atmosphere measurements are used as constraints to adjust the sounding data from M-PACE in order to conserve column-integrated mass, heat, moisture, and momentum. Several important technical and scientific issues related to the data analysis are discussed. It is shown that the analyzed data reasonably describe the dynamic and thermodynamic features of the Arctic cloud systems observed during M-PACE. Uncertainties in the analyzed forcing fields are roughly estimated by examining the sensitivity of those fields to uncertainties in the upper-air data and surface constraints that are used in the analysis. Impacts of the uncertainties in the analyzed forcing data on SCM simulations are discussed. Results from the SCM tests indicate that the bulk features of the observed Arctic cloud systems can be captured qualitatively well using the forcing data derived in thismore » study, and major model errors can be detected despite the uncertainties that exist in the forcing data as illustrated by the sensitivity tests. Lastly, the possibility of using the European Center for Medium-Range Weather Forecasts analysis data to derive the large-scale forcing over the Arctic region is explored.« less

European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and model forecast data are evaluated using observations collected during the Atmospheric Radiation Measurement (ARM) October 2004 Mixed-Phase Arctic Cloud Experiment (M-PACE) at its North Slope of Alaska (NSA) site. It is shown that the ECMWF analysis reasonably represents the dynamic and thermodynamic structures of the large-scale systems that affected the NSA during M-PACE. The model-analyzed near-surface horizontal winds, temperature, and relative humidity also agree well with the M-PACE surface measurements. Given the well-represented large-scale fields, the model shows overall good skill in predicting various cloud types observed during M-PACE; however, the physical properties of single-layer boundary layer clouds are in substantial error. At these times, the model substantially underestimates the liquid water path in these clouds, with the concomitant result that the model largely underpredicts the downwelling longwave radiation at the surface and overpredicts the outgoing longwave radiation at the top of the atmosphere. The model also overestimates the net surface shortwave radiation, mainly because of the underestimation of the surface albedo. The problem in the surface albedo is primarily associated with errors in the surface snow prediction. Principally because of the underestimation of the surface downwelling longwave radiation at themore » times of single-layer boundary layer clouds, the model shows a much larger energy loss (-20.9 W m-2) than the observation (-9.6 W m-2) at the surface during the M-PACE period.« less

[1] This study quantitatively evaluates the overall performance of nine single-column models (SCMs) and four cloud-resolving models (CRMs) in simulating a strong midlatitude frontal cloud system taken from the spring 2000 Cloud Intensive Observational Period at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The evaluation data are an analysis product of constrained variational analysis of the ARM observations and the cloud data collected from the ARM ground active remote sensors (i.e., cloud radar, lidar, and laser ceilometers) and satellite retrievals. Both the selected SCMs and CRMs can typically capture the bulk characteristics of the frontal system and the frontal precipitation. However, there are significant differences in detailed structures of the frontal clouds. Both CRMs and SCMs overestimate high thin cirrus clouds before the main frontal passage. During the passage of a front with strong upward motion, CRMs underestimate middle and low clouds while SCMs overestimate clouds at the levels above 765 hPa. All CRMs and some SCMs also underestimated the middle clouds after the frontal passage. There are also large differences in the model simulations of cloud condensates owing to differences in parameterizations; however, the differences among intercompared models are smaller in the CRMs than the SCMs. In general, the CRM-simulated cloud water and ice are comparable with observations, while most SCMs underestimated cloud water. SCMs show huge biases varying from large overestimates to equally large underestimates of cloud ice. Many of these model biases could be traced to the lack of subgrid-scale dynamical structure in the applied forcing fields and the lack of organized mesoscale hydrometeor advections. Other potential reasons for these model errors are also discussed in the paper.

[1] The large-scale forcing data diagnosed from the European Center for Medium Range Weather Forecast (ECMWF) model for driving Single-Column Models (SCMs) and Cloud System Resolving Models (CSRMs) are compared with forcing data derived using the objective variational analysis constrained by observations collected at the Atmospheric Radiation Measurement program (ARM) Southern Great Plains (SGP) site. The comparison covers the following three different synoptic conditions: a strong precipitation period dominated by subgrid scale processes during the ARM summer 1997 Intensive Operational Period (IOP), a moderate precipitation period dominated by synoptic scale processes during the spring 2000 IOP, and a nonprecipitation period during the fall 2000 IOP. In the study we demonstrate that the differences between the two forcing data sets are considerably large during the strong convective precipitation period, while they are much less during the moderate and nonprecipitation periods. By analyzing the column-integrated heat and moisture budgets we show that errors in the ECMWF-model-derived forcing are closely associated with errors in the model-predicted surface precipitation, which largely reflect deficiencies of model parameterizations. In SCM tests we show that SCM simulations are sensitive to the prescribed large-scale forcing data. The simulation errors are not well correlated between the SCM runs with the two different forcing data sets for all the three cases. Some important SCM simulated fields, such as surface precipitation, tend to follow the ECMWF model simulations rather than the observations when it is forced with the ECMWF forcing, especially for the summer case.

This study examines the feasibility of using numerical weather prediction (NWP) model products to replace radiosondes to develop long-term continuous forcing, instead of just intensive operational periods (IOPs), for single-column models (SCMs) and cloud-resolving models (CRMs). This is motivated by the need for long-term continuous forcing for statistical studies of SCMs/CRMs results. Studies show that SCMs/CRMs results are sensitive to their detailed initial conditions. One way to reduce this sensitivity is through statistical studies of SCMs/CRMs results so that one can filter out those uninteresting random errors and focus on those physically important systematic errors. The long-term forcing can be obtained directly from NWP model analyses, but the forcing is largely affected by model physical parameterizations that are used in the data analysis procedure. To reduce the problem, we propose a combined NWP data analysis and the Atmospheric Radiation Measurement (ARM) Program objective variational analysis approach to derive the long-term continuous forcing. In the combined system, NWP data provide vertical distribution of atmospheric state variables and the variational analysis is used to derive the required large-scale forcing data constrained by the ARM surface and top of the atmosphere (TOA) measurements. We conducted a preliminary study using data from the National Oceanic and Atmospheric Administration (NOAA) mesoscale model Rapid Update Cycle (RUC) analysis during the ARM SCM Summer 97 and Spring 2000 IOPs for this study. Results are compared with those from the ARM operational objective variational analysis (Zhang and Lin 1997; Zhang et al. 2001a,b) and those from the European Center for Medium-range Weather Forecasts (ECMWF) analysis. Impacts of the derived forcing on Community Climate Model Version 3 (CCM3) SCM are also analyzed.

This report describes the ARM (Atmospheric Radiation Measurement) operational variational objective analysis system. It is currently used to process the data collected from the ARM Intensive Operational Periods (IOPs) for driving and evaluating physical parameterizations in climate models. The analysis system was originally developed by Zhang and Lin (1997) at State University of New York at Stony Brook and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. In contrast with previous objective analysis (e.g., Barnes, 1964; O'Brien, 1970; Lin and Johnson, 1996), the ARM objective analysis used the constrained variational analysis method developed by Zhang and Lin (1997), in which the atmospheric state variables are forced to satisfy the conservation of mass, heat, moisture, and momentum through a variational technique. The purpose of this technical report is to provide an overview of the constrained variational analysis method, the architecture of the objective analysis system, along with in-depth information on running the variational analysis codes.

The Atmospheric Radiation Measurement (ARM) Program is a multi-laboratory, interagency program that was created in 1989 with funding from the U.S. Department of Energy (DOE). The ARM Program is part of DOE's effort to resolve scientific uncertainties about global climate change with a specific focus on improving the performance of general circulation models (GCMs) used for climate research and prediction. These improved models will help scientists better understand the influences of human activities on the earth's climate. The objectives of the ARM Program are: (1) to relate observed atmospheric radiation to the temperature and composition of the atmosphere, especially water vapor and clouds, across a wide range of climatologically relevant possibilities, and (2) to develop and test improved parameterizations of radiation and radiative interactions with water vapor, aerosols, and clouds, for use in GCMs.