Reassessment of shortwave surface cloud radiative forcing in the Arctic: consideration of surface-albedo–cloud interactions

Abstract

Abstract. The concept of cloud radiative forcing (CRF) is commonly applied to quantify the impact of clouds on the surface radiative energy budget (REB). In the Arctic, specific radiative interactions between microphysical and macrophysical properties of clouds and the surface strongly modify the warming or cooling effect of clouds, complicating the estimate of CRF obtained from observations or models. Clouds tend to increase the broadband surface albedo over snow or sea ice surfaces compared to cloud-free conditions. However, this effect is not adequately considered in the derivation of CRF in the Arctic so far. Therefore, we have quantified the effects caused by surface-albedo–cloud interactions over highly reflective snow or sea ice surfaces on the CRF using radiative transfer simulations and below-cloud airborne observations above the heterogeneous springtime marginal sea ice zone (MIZ) during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. The impact of a modified surface albedo in the presence of clouds, as compared to cloud-free conditions, and its dependence on cloud optical thickness is found to be relevant for the estimation of the shortwave CRF. A method is proposed to consider this surface albedo effect on CRF estimates by continuously retrieving the cloud-free surface albedo from observations under cloudy conditions, using an available snow and ice albedo parameterization. Using ACLOUD data reveals that the estimated average shortwave cooling by clouds almost doubles over snow- and ice-covered surfaces (−62 W m−2 instead of −32 W m−2), if surface-albedo–cloud interactions are considered. As a result, the observed total (shortwave plus longwave) CRF shifted from a warming effect to an almost neutral one. Concerning the seasonal cycle of the surface albedo, it is demonstrated that this effect enhances shortwave cooling in periods when snow dominates the surface and potentially weakens the cooling by optically thin clouds during the summertime melting season. These findings suggest that the surface-albedo–cloud interaction should be considered in global climate models and in long-term studies to obtain a realistic estimate of the shortwave CRF to quantify the role of clouds in Arctic amplification.

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

“…As reported by Stevens et al (2020), the representation of clouds in atmospheric models benefits from higher-resolved simulations. Nevertheless, long-term global simulations at the hectometer scale will not be feasible in the foreseeable future (Schneider et al, 2017), whereas climate projections at the kilometer scale can be achieved (Stevens et al, 2019). It is, therefore, especially important to improve models on such scales to enable them to make realistic simulations.…”

“…Due to much smaller temporal and spatial scales, those observations only have limited suitability for the evaluation of large-scale models. To this end, the use of storm-resolving models with grid sizes on the order of kilometers or large eddy models is necessary, as they are able to better capture features and variability present in those rather smaller-scale observations (Stevens et al, 2019). Due to the relatively large computational effort that is needed for large eddy simulations, they are limited in spatial extent and are often used for comparison with groundbased observations at individual locations in the Arctic (e.g., Loewe et al, 2017;Sotiropoulou et al, 2018;Schemann and Ebell, 2020).…”

“…To avoid the need for large computational resources but still be able to resolve many processes that act on scales that cannot be captured by GCMs, limitedarea simulations with grid sizes on the order of a few kilometers, where (deep) convection does not need to be explicitly parameterized, can offer a good compromise. Simulations at such resolutions on relatively large domains have received increased interest in recent years (Stevens et al, 2019).…”

Influence of Thermodynamic State Changes on Surface Cloud Radiative Forcing in the Arctic: A Comparison of Two Approaches Using Data From AFLUX and SHEBA

“…CRF estimates are analyzed for flight altitudes below 100 m at a flight speed of 50–60 m s −1 resulting in an horizontal resolution of the REB components of 2.5–3 m. Differences between the CRF and REB derived at flight level and the surface were estimated from radiative transfer modeling of atmospheric profiles used in this study and for the typical flight altitude in low‐level sections. Differences are in the range of 1–2 W·m −2 and, thus, less than measurement errors (Ehrlich et al., 2019; Stapf et al., 2020a). As a consequence, they were neglected in the further analysis.…”

“…As discussed by Stapf et al. (2020a), different concepts have been developed to incorporate the difference between cloud‐free and cloudy surface albedo, α cf and α all . Here we consider the radiative transfer‐based CRF, which uses the surface albedo α all and the thermodynamic profile data, both measured in cloudy conditions, to calculate the net irradiances of the cloud‐free reference: …”