Modeled and observed properties related to the direct aerosol radiative effect...

Doherty, S., Saide Peralta, P. Zuidema, Y. Shinozuka, Guajardo Ferrada, H. Gordon, M. Mallet, K. G. Meyer, D. Painemal, S. Howell, S. Freitag, A. Dobracki, J. Podolske, S. Burton, R. Ferrare, C. Howes, P. Nabat, G. Carmichael, A. da Silva, K. Pistone, I. Chang, L. Gao, R. Wood, and J. Redemann (2021), Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the Southeast Atlantic, Atmos. Chem. Phys., doi:10.5194/acp-2021-333 (submitted).

Biomass burning smoke is advected over the southeast Atlantic Ocean between July and October of each year.

This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly, in both magnitude and sign. The NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project deployed for field campaigns off the west coast of Africa in three consecutive years (Sept., 2016; Aug., 2017; and Oct., 2018) with the goal of better characterizing this plume as a function of the monthly evolution, by measuring the parameters necessary to calculate the direct aerosol radiative effect.

Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UMUKCA). The focus is on comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect, and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20% of the monthly mean at plume altitudes. When compared to the observations, in all models the simulated plume is too vertically diffuse, has smaller vertical gradients, and, in two of the models (GEOS and UM- UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate under-estimates in modeled plume concentrations, leading in general to under-estimates in mid- visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradient sinsingle scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2018) and September (2017). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4%) uncertainty in integrated solar absorption by the aerosol and therefore in the aerosol direct radiative effect. Model biases in cloud fraction, and therefore the scene albedo below the plume, vary significantly across the four models. The opticalthickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stra to cumulus region and is under-estimated in the GEOS model; UM-UKCA simulates significantly too-high cloud optical thickness. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a sub-set of five comparison gridboxes. A significant finding is that the observed gridbox-average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five gridboxes, whereas DARE using the gridbox-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo, but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significantcontribution.

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Atmospheric Composition