Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget

Author:

Griesche Hannes JaschaORCID,Barrientos-Velasco CarolaORCID,Deneke HartwigORCID,Hünerbein AnjaORCID,Seifert PatricORCID,Macke AndreasORCID

Abstract

Abstract. Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m−2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

Funder

Deutsche Forschungsgemeinschaft

Bundesministerium für Bildung und Forschung

Horizon 2020

Publisher

Copernicus GmbH

Subject

Atmospheric Science

Reference82 articles.

1. Achtert, P., O'Connor, E. J., Brooks, I. M., Sotiropoulou, G., Shupe, M. D., Pospichal, B., Brooks, B. J., and Tjernström, M.: Properties of Arctic liquid and mixed-phase clouds from shipborne Cloudnet observations during ACSE 2014, Atmos. Chem. Phys., 20, 14983–15002, https://doi.org/10.5194/acp-20-14983-2020, 2020. a, b

2. Anderson, G., Clough, S., Kneizys, F., Chetwynd, J., and Shettle, E.: AFGL atmospheric constituent profiles (0–120 km), techreport, AIR FORCE Geophysics Lab Hanscom AFB MA, https://apps.dtic.mil/dtic/tr/fulltext/u2/a175173.pdf (last access: 24 September 2020), 1986. a

3. Barker, H. W., Stephens, G., Partain, P., Bergman, J., Bonnel, B., Campana, K., Clothiaux, E., Clough, S., Cusack, S., Delamere, J., Edwards, J., Evans, K., Fouquart, Y., Freidenreich, S., Galin, V., Hou, Y., Kato, S., Li, J., EJ, M., and Yang, F.: Assessing 1D Atmospheric Solar Radiative Transfer Models: Interpretation and Handling of Unresolved Clouds, J. Climate, 16, 2676–2699, https://doi.org/10.1175/1520-0442(2003)016<2676:ADASRT>2.0.CO;2, 2003. a

4. Barker, H. W., Kato, S., and Wehr, T.: Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data, Surv. Geophys., 33, 657–676, https://doi.org/10.1007/s10712-011-9164-9, 2012. a

5. Barlakas, V., Deneke, H., and Macke, A.: The sub-adiabatic model as a concept for evaluating the representation and radiative effects of low-level clouds in a high-resolution atmospheric model, Atmos. Chem. Phys., 20, 303–322, https://doi.org/10.5194/acp-20-303-2020, 2020. a

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