skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Evaluation of the Multi-scale Modeling Framework Using Data from the Atmospheric Radiation Measurement Program

Abstract

One of the goals of the Atmospheric Radiation Measurement (ARM) program is to provide long-term observations for evaluating and improving cloud and radiation treatment in global climate models. Unfortunately, the traditional parametric approach of diagnosing cloud and radiation properties for gridcells that are tens to hundreds kilometers across from large-scale model fields is not well suited for comparison with time series of ground based observations at selected locations. A recently emerging approach called a multi-scale modeling framework (MMF) has shown promise to bridge the scale gap. The MMF consists of a two-dimensional or small three-dimensional cloud resolving model (CRM) embedded into each grid column of the Community Atmospheric Model (CAM), thereby computing cloud properties at a scale that is more consistent with observations. We present a comparison of data from two ARM sites, one at the Southern Great Plains (SGP) in Oklahoma and one at Nauru Island in the Tropical Western Pacific (TWP) region, with output from both the CAM and MMF. Two sets of one year long simulations are considered: one using climatological sea surface temperatures (SST) and another using 1999 SST. Each set includes a run with the MMF as well as the CAM run with traditional ormore » standard cloud and radiation treatment. Time series of cloud fraction, precipitation intensity, and downwelling solar radiation flux at the surface are statistically analyzed. For the TWP site, nearly all parameters of frequency distributions of these variables from the MMF run are shown to be more consistent with observation than those from the CAM run. This change is attributed to the improved representation of convective clouds in the MMF compared to the conventional climate model. For the SGP, the MMF shows little to no improvement in predicting the same quantities. Possible causes of this lack of improvement are discussed.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
882371
Report Number(s):
PNNL-SA-43480
Journal ID: ISSN 0894-8755; JLCLEL; TRN: US200614%%66
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Climate, 19(9):1716-1729; Journal Volume: 19; Journal Issue: 9
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CLIMATE MODELS; CLOUDS; DOWNWELLING; EVALUATION; ISLANDS; NAURU; OKLAHOMA; PRECIPITATION; RADIATIONS; SEAS; SIMULATION; SOLAR RADIATION; clouds; climate; precipitation

Citation Formats

Ovtchinnikov, Mikhail, Ackerman, Thomas P., Marchand, Roger T., and Khairoutdinov, Marat. Evaluation of the Multi-scale Modeling Framework Using Data from the Atmospheric Radiation Measurement Program. United States: N. p., 2006. Web. doi:10.1175/JCLI3699.1.
Ovtchinnikov, Mikhail, Ackerman, Thomas P., Marchand, Roger T., & Khairoutdinov, Marat. Evaluation of the Multi-scale Modeling Framework Using Data from the Atmospheric Radiation Measurement Program. United States. doi:10.1175/JCLI3699.1.
Ovtchinnikov, Mikhail, Ackerman, Thomas P., Marchand, Roger T., and Khairoutdinov, Marat. Mon . "Evaluation of the Multi-scale Modeling Framework Using Data from the Atmospheric Radiation Measurement Program". United States. doi:10.1175/JCLI3699.1.
@article{osti_882371,
title = {Evaluation of the Multi-scale Modeling Framework Using Data from the Atmospheric Radiation Measurement Program},
author = {Ovtchinnikov, Mikhail and Ackerman, Thomas P. and Marchand, Roger T. and Khairoutdinov, Marat},
abstractNote = {One of the goals of the Atmospheric Radiation Measurement (ARM) program is to provide long-term observations for evaluating and improving cloud and radiation treatment in global climate models. Unfortunately, the traditional parametric approach of diagnosing cloud and radiation properties for gridcells that are tens to hundreds kilometers across from large-scale model fields is not well suited for comparison with time series of ground based observations at selected locations. A recently emerging approach called a multi-scale modeling framework (MMF) has shown promise to bridge the scale gap. The MMF consists of a two-dimensional or small three-dimensional cloud resolving model (CRM) embedded into each grid column of the Community Atmospheric Model (CAM), thereby computing cloud properties at a scale that is more consistent with observations. We present a comparison of data from two ARM sites, one at the Southern Great Plains (SGP) in Oklahoma and one at Nauru Island in the Tropical Western Pacific (TWP) region, with output from both the CAM and MMF. Two sets of one year long simulations are considered: one using climatological sea surface temperatures (SST) and another using 1999 SST. Each set includes a run with the MMF as well as the CAM run with traditional or standard cloud and radiation treatment. Time series of cloud fraction, precipitation intensity, and downwelling solar radiation flux at the surface are statistically analyzed. For the TWP site, nearly all parameters of frequency distributions of these variables from the MMF run are shown to be more consistent with observation than those from the CAM run. This change is attributed to the improved representation of convective clouds in the MMF compared to the conventional climate model. For the SGP, the MMF shows little to no improvement in predicting the same quantities. Possible causes of this lack of improvement are discussed.},
doi = {10.1175/JCLI3699.1},
journal = {Journal of Climate, 19(9):1716-1729},
number = 9,
volume = 19,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2006},
month = {Mon May 01 00:00:00 EDT 2006}
}
  • One of the goals of the Atmospheric Radiation Measurement (ARM) program was to provide long-term observations for evaluation of cloud and radiation treatment in global climate models. Unfortunately, traditional parametric approach of diagnosing cloud and radiation properties from large-scale model fields is not well suited for comparison with observed time series at selected locations. A recently emerging approach called the multi-scale modeling framework (MMF) has shown promise to bridge the gap. MMF consists of a two-dimensional cloud system resolving model (CSRM) embedded into each CAM grid column of the Community Atmospheric Model (CAM), thereby computing cloud properties at a scalemore » that is more consistent with observations. Because the approach is computationally expensive only limited simulations have been carried out. In this presentation, we will present a comparison of data from two ARM sites, one at the Southern Great Plains (SGP) in Oklahoma and one at Nauru island in the Tropical Western Pacific (TWP) region, with output from both CAM and MMF. Two sets of one year long simulations are considered: one using climatological sea surface temperatures (SST) and another using 1999 SST. Each set includes a run with MMF as well as CAM run with traditional or standard cloud and radiation treatment. Time series of cloud fraction, precipitation intensity, and downwelling solar radiation flux at the surface are statistically analyzed. For the TWP site, nearly all parameters of frequency distributions of these variables from MMF run are shown to be more consistent with observation than those from CAM run. For the SGP, the improvements are marginal.« less
  • Radiative heating associated with the variability of water vapor and clouds in the atmosphere is a principal driver of tropical circulation. Models must produce cloud and radiative heating rate profiles with realistic horizontal, vertical, and diurnal variability in order to produce realistic tropical circulations and cloud feedbacks. A recent study has indicated that the inability of many models to simulate realistic representations of the Madden-Julian Oscillation (MJO) may be caused by systematic diabatic heating profile errors. One of the primary difficulties in producing accurate heating rate profiles within a large-scale general circulation model (GCM) is the sub-grid scale nature ofmore » cloud processes and their interactions with radiation. A new approach to climate modeling, the Multi-Scale Modeling Framework (MMF), reduces the need for sub-grid scale cloud parameterizations by replacing the cloud and radiation parameterizations of a GCM with a 2-D cloud system resolving model. The long time series of cloud radar observations at the ARM tropical sites provide an unprecedented dataset for directly calculating radiative heating rate profiles with high temporal and vertical resolution. In this study, we compare radiative heating rate profiles calculated from ARM cloud observations at the Nauru and Manus sites to the model output from the MMF and its parent model, the NCAR Community Atmosphere Model (CAM 3.0). During the study period, the Nauru site was experiencing suppressed conditions while the Manus site had more active convection, leading to very different average radiative heating rate profiles at the two sites. We examine the differences in the observations and model output during these two meteorological regimes. Features of the average heating rates as well as the details of the diurnal cycle are examined. Initial results indicate that differences in the cloud amounts and cloud properties produced in the two models due to their different treatment of cloud processes lead to large differences in the average heating rate profiles. Both sets of model results fail to capture some of the structure of the observed heating because their vertical resolution is too coarse to fully resolve shallow boundary layer clouds and the observed mid-level cloud feature near the freezing level. The differences in the resulting radiative heating rate profiles may have important impacts on the model dynamics.« less
  • Vertical profiles of hydrometeor occurrence from the Multiscale Modeling Framework (MMF) climate model are compared with profiles observed by a vertically pointing millimeter wavelength cloud-radar (located in the U.S. Southern Great Plains) as a function of the largescale atmospheric state. The atmospheric state is determined by classifying (or clustering) the large-scale (synoptic) fields produced by the MMF and a numerical weather prediction model using a neural network approach. The comparison shows that for cold frontal and post-cold frontal conditions the MMF produces profiles of hydrometeor occurrence that compare favorably with radar observations, while for warm frontal conditions the model tendsmore » to produce hydrometeor fractions that are too large with too much cloud (non-precipitating hydrometeors) above 7 km and too much precipitating hydrometeor coverage below 7 km. We also find that the MMF has difficulty capturing the formation of low clouds and that for all atmospheric states that occur during June, July, and August, the MMF produces too much high and thin cloud, especially above 10 km.« less
  • Water circulation in Puget Sound, a large complex estuary system in the Pacific Northwest coastal ocean of the United States, is governed by multiple spatially and temporally varying forcings from tides, atmosphere (wind, heating/cooling, precipitation/evaporation, pressure), and river inflows. In addition, the hydrodynamic response is affected strongly by geomorphic features, such as fjord-like bathymetry and complex shoreline features, resulting in many distinguishing characteristics in its main and sub-basins. To better understand the details of circulation features in Puget Sound and to assist with proposed nearshore restoration actions for improving water quality and the ecological health of Puget Sound, a high-resolutionmore » (around 50 m in estuaries and tide flats) hydrodynamic model for the entire Puget Sound was needed. Here, a threedimensional circulation model of Puget Sound using an unstructured-grid finite volume coastal ocean model is presented. The model was constructed with sufficient resolution in the nearshore region to address the complex coastline, multi-tidal channels, and tide flats. Model open boundaries were extended to the entrance of the Strait of Juan de Fuca and the northern end of the Strait of Georgia to account for the influences of ocean water intrusion from the Strait of Juan de Fuca and the Fraser River plume from the Strait of Georgia, respectively. Comparisons of model results, observed data, and associated error statistics for tidal elevation, velocity, temperature, and salinity indicate that the model is capable of simulating the general circulation patterns on the scale of a large estuarine system as well as detailed hydrodynamics in the nearshore tide flats. Tidal characteristics, temperature/salinity stratification, mean circulation, and river plumes in estuaries with tide flats are discussed.« less
  • Existing estimates of methane (CH 4) fluxes from North American wetlands vary widely in both magnitude and distribution. In light of these differences, this study uses atmospheric CH 4 observations from the US and Canada to analyze seven different bottom-up, wetland CH 4 estimates reported in a recent model comparison project. We first use synthetic data to explore whether wetland CH 4 fluxes are detectable at atmospheric observation sites. We find that the observation network can detect aggregate wetland fluxes from both eastern and western Canada but generally not from the US. Based upon these results, we then use realmore » data and inverse modeling results to analyze the magnitude, seasonality, and spatial distribution of each model estimate. The magnitude of Canadian fluxes in many models is larger than indicated by atmospheric observations. Many models predict a seasonality that is narrower than implied by inverse modeling results, possibly indicating an oversensitivity to air or soil temperatures. The LPJ-Bern and SDGVM models have a geographic distribution that is most consistent with atmospheric observations, depending upon the region and season. Lastly, these models utilize land cover maps or dynamic modeling to estimate wetland coverage while most other models rely primarily on remote sensing inundation data.« less