Aerosol indirect effects on the nighttime Arctic Ocean surface from thin, predominantly liquid clouds

Abstract

Abstract. Aerosol indirect effects have potentially large impacts on the Arctic Ocean surface energy budget, but model estimates of regional-scale aerosol indirect effects are highly uncertain and poorly validated by observations. Here we demonstrate a new way to quantitatively estimate aerosol indirect effects on a regional scale from remote sensing observations. In this study, we focus on nighttime, optically thin, predominantly liquid clouds. The method is based on differences in cloud physical and microphysical characteristics in carefully selected clean, average, and aerosol-impacted conditions. The cloud subset of focus covers just ∼ 5 % of cloudy Arctic Ocean regions, warming the Arctic Ocean surface by ∼ 1-1.4 W m −2 regionally during polar night. However, within this cloud subset, aerosol and cloud conditions can be determined with high confidence using CALIPSO and CloudSat data and model output. This cloud subset is generally susceptible to aerosols, with a polar nighttime estimated maximum regionally integrated indirect cooling effect of ∼ −0.11 W m −2 at the Arctic sea ice surface (∼ 8 % of the clean background cloud effect), excluding cloud fraction changes. Aerosol presence is related to reduced precipitation, cloud thickness, and radar reflectivity, and in some cases, an increased likelihood of cloud presence in the liquid phase. These observations are inconsistent with a glaciation indirect effect and are consistent with either a deactivation effect or less-efficient secondary ice formation related to smaller liquid cloud droplets. However, this cloud subset shows large differences in surface and meteorological forcing in shallow and higher-altitude clouds and between sea ice and openocean regions. For example, optically thin, predominantly liquid clouds are much more likely to overlay another cloud over the open ocean, which may reduce aerosol indirect effects on the surface. Also, shallow clouds over open ocean do not appear to respond to aerosols as strongly as clouds over stratified sea ice environments, indicating a larger influence of meteorological forcing over aerosol microphysics in these types of clouds over the rapidly changing Arctic Ocean.

A satellite-based estimate of aerosol-cloud microphysical effects over the Arctic Ocean

“…Clouds below 0.6 km were not assessed due 30 to near-ground uncertainties in the CloudSat and CALIPSO data (de Boer et al, 2009;Liu et al, 2017 Oceanic areas were determined by ETOPO1 Bedrock GMT4 data (Amante and Eakins, 2009). Oceanic clouds were separated into open ocean and sea ice regions following Zamora et al (2017): for each profile, the corresponding monthly fractional sea ice cover was determined from the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, version 2 Peng et al, 2013), and samples associated with > 80% or < 20% monthly sea ice fractions were classified as being over sea ice or open ocean, respectively. 5…”

“…Active lidar signals often get attenuated in clouds. Moreover, active sensors such as CALIPSO cannot always detect dilute aerosols, even in conditions with the highest lidar sensitivity (i.e., above clouds at night (Zamora et al, 2017)). …”

“…Note that emission fluxes in the model rely on inventories of emission factor measurements that partially include thermooptical measurements, which may not always completely differentiate between BC and "brown" or organic carbon (BrC or OC) (Russell et al, 20 2014;Samset et al, 2018). Further details on the model and its configuration can be found in Zamora et al (2017).…”

“…FLEXPART is widely used, and is well-validated for the purpose of studying Arctic smoke and pollution transport (Damoah et al, 2004;Eckhardt et al, 2015;Forster et al, 2001;Paris et al, 2009;Sodemann et al, 2011;Stohl et al, 2002Stohl et al, , 2003Stohl et al, , 2015Zamora et al, 2017). FLEXPART BC is used in this study as a proxy for all combustion aerosols, and the association 25 of high levels of modeled BC with CALIPSO aerosols in general (Zamora et al, 2017) indicates that modeled BC is a fairly good proxy for strong aerosol layers during polar night, even though some local sources of combustion aerosols 6 might not be included in the model.…”

“…Aerosols can influence a number of factors relevant to cloud fraction, including cloud droplet number, phase, lifetime, and probability of precipitation (Albrecht, 1989;Coopman et al, 2016;Girard et al, 2005;Lance et 25 al., 2011;Lindsey and Fromm, 2008;Zamora et al, 2017). However, the regional-scale importance of aerosol microphysical processes on CF has been difficult to constrain from observations and models.…”