Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Abstract

Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (N d ) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent N d observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and airquality, weather, and climate predictions.air quality | indirect effect | weather prediction | stratiform cloud | microphysics A mbient aerosols are important air pollutants with direct impacts on human health (1). They also play important roles in Earth's weather and climate systems through their direct (2), semi-direct (3), and indirect effects (4) on radiative transfer and clouds. Their role is dependent on their size, number, phase, and composition distributions, which vary significantly in space and time. There remain large uncertainties in predictions of aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal (5-9). These uncertainties in aerosol distributions lead to large uncertainties in weather and air-quality predictions and in estimates of health and climate-change impacts (10).Constraining ambient aerosol distributions with current Earthobserving systems is a difficult task. The most common approach is to assimilate satellite retrievals of aerosol optical depth (AOD) (11, 12), a quantity that represents total aerosol mass and composition in the atmospheric column. The requirement of cloudfree conditions for a successful retrieval and the remaining challenges of relating AOD to aerosol mass, size, and composition limit the utility of AOD retrievals (by themselves) in constraining aerosol mass and composition (13,14). These shortcomings are particularly true in regions of persistent marine stratocumulus, such as the southeast Pacific off the coast of Chile and Peru, where aerosol-cloud interactions are important to the energy balance (15), and limitations in current observing and modeling capabilities adversely impact regional and global weather and climate predictions (16). A typical MODIS AOD scene in this region ( Fig.

Satellite Retrievals of Aerosol Optical Depth over a Subtropical Urban Area: The Role of Stratification and Surface Reflectance

“…The potential bias in MODIS AOD discussed here over large arid or semi-arid regions could introduce spurious values of AOD in assimilation experiments and potentially affect the determination of aerosol sources, radiative forcing, and air quality calculations (e.g., Huneeus et al, 2012;Saide et al, 2012;Schwartz et al, 2012) Evidently, there is a need for further validation of satellite borne instruments against in situ measurements and sunphotometers over SSA. Specifically, we expect to reestablish Santiago as an AERONET site.…”

“…We attempt to investigate the applicability of MODIS AOD to assess the aerosol loading in the boundary layer for Santiago de Chile (33.5°S 70.6°W, 500 m.a.s.l), where health concerns (e.g., Valdés et al, 2012) and potentially cloud and climate impacts (e.g., Saide et al, 2012) require an improved characterization of atmospheric particulates. Moreover, we chose Santiago because it is located under the prevailing subsiding regime imposed by the South Eastern Pacific anticyclone, which contributes to both a well-defined and very stable boundary layer (e.g., Muñoz and Undurraga, 2010;Saide et al, 2011), and a large number of clear days for successful AOD retrievals.…”

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

“…We present two case studies, which correspond to the use of AOD (Saide et al, 2013) and cloud number droplet satellite retrievals (N d ) (Saide et al, 2012a). The WRF-Chem configuration is based on Saide et al (2012b).…”

“…5.2. Saide et al (2012a) developed the adjoint of the mixing/activation parameterization for the activation of aerosols into cloud droplets of WRF-Chem and, using 3D-Var data assimilation of MODIS data, they improved aerosol simulated concentrations. The important result in that work was the ability to improve aerosol simulations using the assimilation of cloud droplet number concentration data, which is only possible due to the coupled nature of WRF-Chem that integrates aerosol indirect effects into the forecasts.…”

“…In order to overcome this limitation, a novel data assimilation approach was developed to use cloud satellite retrievals to provide constraints on belowcloud aerosols (Saide et al, 2012a). The method consists in using the online coupling and aerosol-cloud interactions within WRF-Chem to provide cloud droplet number (N d ) estimates, which are compared to satellite retrievals through the data assimilation framework.…”