Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources

Abstract

Abstract. The local and regional influence of elevated point sources on summertime aerosol forcing and cloud-aerosol interactions in northeastern North America was investigated using the WRF-Chem community model. The direct effects of aerosols on incoming solar radiation were simulated using existing modules to relate aerosol sizes and chemical composition to aerosol optical properties. Indirect effects were simulated by adding a prognostic treatment of cloud droplet number and adding modules that activate aerosol particles to form cloud droplets, simulate aqueous-phase chemistry, and tie a two-moment treatment of cloud water (cloud water mass and cloud droplet number) to precipitation and an existing radiation scheme. Fully interactive feedbacks thus were created within the modified model, with aerosols affecting cloud droplet number and cloud radiative properties, and clouds altering aerosol size and composition via aqueous processes, wet scavenging, and gas-phase-related photolytic processes. Comparisons of a baseline simulation with observations show that the model captured the general temporal cycle of aerosol optical depths (AODs) and produced clouds of comparable thickness to observations at approximately the proper times and places. The model overpredicted SO 2 mixing ratios and PM 2.5 mass, but reproduced the range of observed SO 2 to sulfate aerosol ratios, suggesting that atmospheric oxidation processes leading to aerosol sulfate formation are captured in the model. The baseline simulation was compared to a sensitivity simulation in which all emissions at model levels above the surface layer were set to zero, thus removing stack emissions. Instantaneous, site-specific differences for aerosol and cloud related properties between the two simulations could be quite large, as removing abovesurface emission sources influenced when and where clouds formed within the modeling domain. When summed spaCorrespondence to: E. G. Chapman (elaine.chapman@pnl.gov) tially over the finest resolution model domain (the extent of which corresponds to the typical size of a single global climate model grid cell) and temporally over a three day analysis period, total rainfall in the sensitivity simulation increased by 31% over that in the baseline simulation. Fewer optically thin clouds, arbitrarily defined as a cloud exhibiting an optical depth less than 1, formed in the sensitivity simulation. Domain-averaged AODs dropped from 0.46 in the baseline simulation to 0.38 in the sensitivity simulation. The overall net effect of additional aerosols attributable to primary particulates and aerosol precursors from point source emissions above the surface was a domain-averaged reduction of 5 W m −2 in mean daytime downwelling shortwave radiation.

Flux and polarisation spectra of water clouds on exoplanets

“…In particular, from Earth studies we know that the cloud particle sizes strongly influence the cloud radiative forcing (see e.g. Chapman et al 2009;Kobayashi & Adachi 2009, and references therein), since they change the way cloud particles scatter and absorb incident sunlight and thermal radiation. Given the huge number of free parameters in systems like this, our aim was not to cover the whole parameter space, but rather to explore the information content of, in particular, the degree of polarisation of starlight that is reflected by a planet, and the spectral and phase angle ranges that would provide this information.…”

“…Knowing the sizes of cloud particles is important for understanding a cloud's influence on a planetary climate, because it determines how a cloud particle scatters and absorbs incident light and thermal radiation (see e.g. Chapman et al 2009, and references therein). How a particle scatters the light depends in particular on the size parameter, i.e.…”

Modelling the Effect of Aerosol Feedbacks on the Regional Meteorology Factors over China

“…Aerosol optical properties calculated based upon volume approximation (Peckham et al, 2011). The aerosolradiative feedback is simulated through coupling with the Goddard shortwave radiation parameterization using aerosol optical depth, single scattering albedo, and asymmetry factor derived from MOSAIC particulates and Mie theory (Ghan et al, 2001;Fast et al, 2006;Gustafson et al, 2007, Chapman et al, 2009) and the Lin et al microphysics parameterization. The aerosol-cloud interation is simulated through coupling with the Lin et al microphysics parameterization (Gustafson et al, 2007;Chapman et al, 2009) that include prognostic treatments of cloud droplet number and activated (cloudphase) aerosol species, aerosol activation and resuspension (Ghan et al, 2001;Zhang et al, 2002), bulk cloud chemistry (Fahey and Pandis, 2001), and in-cloud and below-cloud wet removal of particulates and trace gases (Easter et al, 2004).…”

“…WRF/chem includes a unified significantly coupled mesoscale meteorology chemistry aerosol radiation model, which represents the state-of-thescience online-coupled models that account for many types of feedbacks (Zhang, 2008). WRF/chem has been used in simulating the effect of elevated sources on aerosol forcing and cloud-aerosol interaction in summertime over northeastern North America (Chapman et al, 2009); the three days of studies revealed a domain-averaged reduction of 5 W/m 2 in the mean daytime incoming solar radiation, and the rainfall was increased by 31%. Another study focused on the chemistry-aerosol-cloud-radiation-climate feedbacks over the continental United States (Zhang et al, 2010a).…”

Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

“…Compared to an LES that only simulates individual cloud systems, regional climate models are able to simulate the feedbacks between clouds and aspects of the large-scale circulation and its variability reasonably well. Even though regional models do not describe part of the large-scale feedbacks, they may be considered a good optimal compromise (Bangert et al, 2011;Van den Heever and Cotton, 2007;Chapman et al, 2009;Forkel et al, 2015;Yang et al, 2012). A still often applied cloud microphysics parameterization in numerical weather prediction is a bulk one-moment scheme (Kessler, 1969;Lin et al, 1983) which uses the specific masses for different hydrometeor species as prognostic variables.…”