Publications

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2014
Zhang, C, Wang M, Morrison H, Somerville RCJ, Zhang K, Liu X, Li J-LF.  2014.  Investigating ice nucleation in cirrus clouds with an aerosol-enabled Multiscale Modeling Framework. Journal of Advances in Modeling Earth Systems. 6:998-1015.   10.1002/2014MS000343   Abstract

In this study, an aerosol-dependent ice nucleation scheme has been implemented in an aerosol-enabled Multiscale Modeling Framework (PNNL MMF) to study ice formation in upper troposphere cirrus clouds through both homogeneous and heterogeneous nucleation. The MMF model represents cloud scale processes by embedding a cloud-resolving model (CRM) within each vertical column of a GCM grid. By explicitly linking ice nucleation to aerosol number concentration, CRM-scale temperature, relative humidity and vertical velocity, the new MMF model simulates the persistent high ice supersaturation and low ice number concentration (10–100/L) at cirrus temperatures. The new model simulates the observed shift of the ice supersaturation PDF toward higher values at low temperatures following the homogeneous nucleation threshold. The MMF model predicts a higher frequency of midlatitude supersaturation in the Southern Hemisphere and winter hemisphere, which is consistent with previous satellite and in situ observations. It is shown that compared to a conventional GCM, the MMF is a more powerful model to simulate parameters that evolve over short time scales such as supersaturation. Sensitivity tests suggest that the simulated global distribution of ice clouds is sensitive to the ice nucleation scheme and the distribution of sulfate and dust aerosols. Simulations are also performed to test empirical parameters related to auto-conversion of ice crystals to snow. Results show that with a value of 250 μm for the critical diameter, Dcs, that distinguishes ice crystals from snow, the model can produce good agreement with the satellite-retrieved products in terms of cloud ice water path and ice water content, while the total ice water is not sensitive to the specification of Dcs value.

DeFlorio, MJ, Ghan SJ, Singh B, Miller AJ, Cayan DR, Russell LM, Somerville RCJ.  2014.  Semidirect dynamical and radiative effect of North African dust transport on lower tropospheric clouds over the subtropical North Atlantic in CESM 1.0. Journal of Geophysical Research: Atmospheres. 119:2013JD020997.   10.1002/2013JD020997   AbstractWebsite

This study uses a century length preindustrial climate simulation by the Community Earth System Model (CESM 1.0) to explore statistical relationships between dust, clouds, and atmospheric circulation and to suggest a semidirect dynamical mechanism linking subtropical North Atlantic lower tropospheric cloud cover with North African dust transport. The length of the run allows us to account for interannual variability of North African dust emissions and transport in the model. CESM's monthly climatology of both aerosol optical depth and surface dust concentration at Cape Verde and Barbados, respectively, agree well with available observations, as does the aerosol size distribution at Cape Verde. In addition, CESM shows strong seasonal cycles of dust burden and lower tropospheric cloud fraction, with maximum values occurring during boreal summer, when a strong correlation between these two variables exists over the subtropical North Atlantic. Calculations of Estimated Inversion Strength (EIS) and composites of EIS on high and low downstream North African dust months during boreal summer reveal that dust is likely increasing inversion strength over this region due to both solar absorption and reflection. We find no evidence for a microphysical link between dust and lower tropospheric clouds in this region. These results yield new insight over an extensive period of time into the complex relationship between North African dust and North Atlantic lower tropospheric clouds, which has previously been hindered by spatiotemporal constraints of observations. Our findings lay a framework for future analyses using different climate models and submonthly data over regions with different underlying dynamics.

Zhao, Z, Kooperman GJ, Pritchard MS, Russell LM, Somerville RCJ.  2014.  Investigating impacts of forest fires in Alaska and western Canada on regional weather over the northeastern United States using CAM5 global simulations to constrain transport to a WRF-Chem regional domain. Journal of Geophysical Research: Atmospheres. 119:2013JD020973.   10.1002/2013JD020973   AbstractWebsite

An aerosol-enabled globally driven regional modeling system has been developed by coupling the National Center for Atmospheric Research's Community Atmosphere Model version 5 (CAM5) with the Weather Research and Forecasting model with chemistry (WRF-Chem). In this modeling system, aerosol-enabled CAM5, a state-of-the-art global climate model is downscaled to provide coherent meteorological and chemical boundary conditions for regional WRF-Chem simulations. Aerosol particle emissions originating outside the WRF-Chem domain can be a potentially important nonlocal aerosol source. As a test case, the potential impacts of nonlocal forest fire aerosols on regional precipitation and radiation were investigated over the northeastern United States during the summer of 2004. During this period, forest fires in Alaska and western Canada lofted aerosol particles into the midtroposphere, which were advected across the United States. WRF-Chem simulations that included nonlocal biomass burning aerosols had domain-mean aerosol optical depths that were nearly three times higher than those without, which reduced peak downwelling domain-mean shortwave radiation at the surface by ~25 W m−2. In this classic twin experiment design, adding nonlocal fire plume led to near-surface cooling and changes in cloud vertical distribution, while variations in domain-mean cloud liquid water path were negligible. The higher aerosol concentrations in the simulation with the fire plume resulted in a ~10% reduction in domain-mean precipitation coincident with an ~8% decrease in domain-mean CAPE. A suite of simulations was also conducted to explore sensitivities of meteorological feedbacks to the ratio of black carbon to total plume aerosols, as well as to overall plume concentrations. Results from this ensemble revealed that plume-induced near-surface cooling and CAPE reduction occur in a wide range of conditions. The response of moist convection was very complex because of strong thermodynamic internal variability.

2013
Shen, SSP, Velado M, Somerville RCJ, Kooperman GJ.  2013.  Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method. Journal of Geophysical Research: Atmospheres.   10.1002/jgrd.50408   AbstractWebsite

We describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof-of-concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5-day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short- and long-time means, the 5-day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction.