C01: Influence of spatial heterogeniety and temporal evolution of surface properties on radiative energy fluxes in the coupled atmosphere-sea ice-ocean system
The spatial heterogeneity and temporal evolution of surface properties of the Arctic Ocean influence the radiative energy transfer through the coupled compartments (atmosphere, sea ice, open ocean) of the Arctic climate system. Radiative effects of interactions between these components are not well studied, however, they may play an important role in the Arctic climate system. For example, temporal changes of radiative energy fluxes during the transition period between the onset of sea ice melting and freeze-up are critical, also because these processes are not well represented in the models and, thus, may cause significant uncertainties in projections of the Arctic climate system. Furthermore, the effects of spatially heterogeneous surface conditions, in particular in case of clouds, are not well investigated, although clouds are an important player in Arctic amplification. Therefore, this project will observe the inter-annual and seasonal changes of solar and thermal-infrared radiative flux densities within and through the compartments of the coupled atmosphere-sea ice-ocean system during different sea ice regimes as a function of spatially heterogeneous surface properties (e.g., albedo, temperature, sea ice, and snow thickness). These characteristics and further, more specific surface features (e.g., sea ice types, melt ponds, leads, loe size distributions) as well as the transfer of radiative energy fluxes through the compartments of the system, will be investigated on different spatial and temporal scales by (i) in–situ observations over the full annual cycle below and above the sea ice during MOSAiC, (ii) aircraft measurements on regional and seasonal scales during MOSAiC and the HALO-(AC)³ campaign and (iii) multi-year satellite observations. Based on these sources, we will quantify the influence of the heterogeneity of the surface properties on (i) radiative flux densities in the atmosphere and ocean compartments, (ii) atmospheric cloud radiative forcing (CRF), and (iii) sea ice-ocean interface interactions. Transfer functions quantifying the transition of solar and thermal infrared radiative flux densities between the system compartments will be derived and parameterised. Furthermore, we will continue to improve surface albedo parameterisations established during phase I (e.g., for HIRHAM-NAOSIM) by including additional factors (e.g., cloud cover, surface temperature, melt pond coverage, snow depth). We will analyse airborne data from the previous ACLOUD, PAMARCMiP, and AFLUX campaigns, and collect new measurements during the planned MOSAiC and HALO-(AC)³ observations. In addition we will use satellite data (MERIS, Sentinel-3) in our analysis.
The spatial heterogeneity and temporal evolution of surface properties (sea ice types, snow, open ocean, melt ponds) have a major impact on radiative energy fluxes in the coupled Arctic climate system.
To investigate this hypothesis, the following speciic questions will be answered:
- How strong is the influence of spatial heterogeneities of surface properties on the radiative energy fluxes in the two ocean and atmosphere compartments, and how does it depend on spatial scales?
- How is the temporal evolution of effects of sea ice development (melt, freeze-up) on radiative energy fluxes in different regions and ice regimes?
- Which of the two surface parameters, temperature or albedo, has the stronger impact on the local changes of the cloud radiative forcing (CRF) depending on season and region?
Achievements phase I
In C01 the surface albedo parameterisation scheme of the coupled HIRHAM-NAOSIM model was validated and improved (Jäkel et al., 2019). The scheme needed an adaption with respect to the angular dependent illumination and snow property changes (threshold temperatures describing the transition between dry and melting snow/ice) (Jäkel et al., 2019). In addition, a new spectral-to-broadband
conversion for MEdium Resolution Imaging Spectrometer (MERIS) satellite data was derived (Pohl et al., 2019). Several snow types have been implemented into the radiative transfer model SCIATRAN (http://www.iup.uni-bremen.de/sciatran/). It was shown, that the near-field effects in radiative transfer can be neglected, which means that common radiative transfer models, usually applied for atmosphere, can be used for snow layers (Pohl et al., 2020). It was also shown, that three dimensional solar radiative effects on radiative forcing need to be considered only for spatial scales of surface heterogeneity of less than 3 km. Also, a new 3D backward Monte Carlo radiative transfer model (LEIPSIC) was developed (Sun et al., 2020).
Role within (AC)³
Prof. Dr. Manfred Wendisch
University of Leipzig
Leipzig Institute for Meteorology (LIM)
Dr. Gunnar Spreen
University of Bremen
Institute of Environmental Physics (IUP)
Dr. Marcel Nicolaus
Alfred-Wegener-Institute Helmholtz Center for Polar and Marine Research (AWI)
Am Handelshafen 12
Carlsen, T., Birnbaum, G., Ehrlich, A., Helm, V., Jäkel, E., Schäfer, M., and Wendisch, M., 2020: Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica, Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020.
Mei, L., Rozanov, V., Jäkel, E., Cheng, X., Vountas, M., and Burrows, J. P., 2020: The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation, Cryosphere Discuss., https://doi.org/10.5194/tc-2020-270, in review.
Mei, L., Rozanov, V., Pohl, C., Vountas, M., and Burrows, J. P., 2020: The retrieval of snow properties from SLSTR/Sentinel-3 – part 1: method description and sensitivity study, Cryosphere Discuss., https://doi.org/10.5194/tc-2020-269, in review.
Hartmann, M., Adachi, K., Eppers, O., Haas, C., Herber, A., Holzinger, R., Hünerbein, A., Jäkel, E., Jentzsch, C., van Pinxteren, M., Wex, H., and Stratmann, F. ,2020. Wintertime airborne measurements of ice nucleating particles in the high Arctic: a hint to a marine, biogenic source for Ice Nucleating Particles. Geophys. Res. Lett., 47, e2020GL087770. https://doi.org/10.1029/2020GL087770
Stapf, J., Ehrlich, A., Jäkel, E., Lüpkes, C., and Wendisch, M., 2020: Reassessment of shortwave surface cloud radiative forcing in the Arctic: consideration of surface-albedo–cloud interactions, Atmos. Chem. Phys., 20, 9895–9914, https://doi.org/10.5194/acp-20-9895-2020.
Donth, T., Jäkel, E., Ehrlich, A., Heinold, B., Schacht, J., Herber, A., Zanatta, M., and Wendisch, M., 2020: Combining atmospheric and snow radiative transfer models to assess the solar radiative effects of black carbon in the Arctic, Atmos. Chem. Phys., 20, 8139–8156, https://doi.org/10.5194/acp-20-8139-2020.
Nakoudi, K.; Ritter, C.; Böckmann, C.; Kunkel, D.; Eppers, O.; Rozanov, V.; Mei, L.; Pefanis, V.; Jäkel, E.; Herber, A.; Maturilli, M.; Neuber, R. ,2020. Does the Intra-Arctic Modification of Long-Range Transported Aerosol Affect the Local Radiative Budget? (A Case Study). Remote Sens., 12, 2112, https://www.mdpi.com/2072-4292/12/13/2112.
C. Pohl, V. V. Rozanov, L. Mei, J. P. Burrows, G. Heygster, G. Spreen, 2020: Implementation of an ice crystal single-scattering propertydatabase in the radiative transfer model SCIATRAN, J. Quant. Spectrosc. Radiat. Transfer, doi: https://doi.org/10.1016/j.jqsrt.2020.107118
Ruiz-Donoso, E., Ehrlich, A., Schäfer, M., Jäkel, E., Schemann, V., Crewell, S., Mech, M., Kulla, B. S., Kliesch, L.-L., Neuber, R., and Wendisch, M., 2020: Small-scale structure of thermodynamic phase in Arctic mixed-phase clouds observed by airborne remote sensing during a cold air outbreak and a warm air advection event, Atmos. Chem. Phys., 20, 5487–5511, https://doi.org/10.5194/acp-20-5487-2020.
Pohl, C., L. Istomina, S. Tietsche, E. Jäkel, J. Stapf, G. Spreen, and G. Heygster, 2020: Broadband albedo of Arctic sea ice from MERIS optical data, The Cryosphere, 165-182, https://doi.org/10.5194/tc-14-165-2020
Pohl, C., V. Rozanov, M. Wendisch, G. Spreen, and G. Heygster, 2020: Impact of the near-field effects on radiative transfer simulations of the bidirectional reflectance factor and albedo of a densly packed snow layer, J. Quant. Spectrosc. Radiat. Transfer, 241, 106704, doi:10.1016/j.jqsrt.2019.106704
Sun, B., E. Jäkel, M. Schäfer, and M. Wendisch, 2020: A Biased Sampling Approach to Accelerate Backward Monte Carlo Atmospheric Radiative Transfer Simulations and its Application to Arctic Heterogeneous Cloud and Surface Conditions, Journal of Quantitative Spectroscopy & Radiative Transfer, Volume 240, January 2020, 106690, https://doi.org/10.1016/j.jqsrt.2019.106690
Ehrlich, A., M. Wendisch, C. Lüpkes, M. Buschmann, H. Bozem, D. Chechin, H.-C. Clemen, R. Dupuy, O. Eppers, J. Hartmann, A. Herber, E. Jäkel, E. Järvinen, O. Jourdan, U. Kästner, L.-L. Kliesch, F. Köllner, M. Mech, S. Mertes, R. Neuber, E. Ruiz-Donoso, M. Schnaiter, J. Schneider, J. Stapf, and M. Zanatta, 2019: A comprehensive in situ and remote sensing data set from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign, Earth Syst. Sci. Data, https://doi.org/10.5194/essd-11-1853-2019
Seidel, J., 2019: Abhängigkeit der arktischen Oberflächenalbedo vom Meereisanteil, Bachelor Thesis, University of Leipzig
Jäkel, E., J. Stapf, M. Wendisch, M. Nicolaus, W. Dorn, and A. Rinke, 2019: Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns, The Cryosphere, 13, 1695-1708, doi:10.5194/tc-13-1695-2019
Wendisch, M., A. Macke, A. Ehrlich, C. Lüpkes, M. Mech, D. Chechin, K. Dethloff, C. Barrientos, H. Bozem, M. Brückner, H.-C. Clemen, S. Crewell, T. Donth, R. Dupuy, C. Dusny, K. Ebell, U. Egerer, R. Engelmann, C. Engler, O. Eppers, M. Gehrmann, X. Gong, M. Gottschalk, C. Gourbeyre, H. Griesche, J. Hartmann, M. Hartmann, B. Heinold, A. Herber, H. Herrmann, G. Heygster, P. Hoor, S. Jafariserajehlou, E. Jäkel, E. Järvinen, O. Jourdan, U. Kästner, S. Kecorius, E.M. Knudsen, F. Köllner, J. Kretzschmar, L. Lelli, D. Leroy, M. Maturilli, L. Mei, S. Mertes, G. Mioche, R. Neuber, M. Nicolaus, T. Nomokonova, J. Notholt, M. Palm, M. van Pinxteren, J. Quaas, P. Richter, E. Ruiz-Donoso, M. Schäfer, K. Schmieder, M. Schnaiter, J. Schneider, A. Schwarzenböck, P. Seifert, M.D. Shupe, H. Siebert, G. Spreen, J. Stapf, F. Stratmann, T. Vogl, A. Welti, H. Wex, A. Wiedensohler, M. Zanatta, S. Zeppenfeld, 2019: The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multi-Platform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, Bull. Amer. Meteor. Soc., 100 (5), 841–871, doi:10.1175/BAMS-D-18-0072.1
Knudsen, E.M., B. Heinold, S. Dahlke, H. Bozem, S. Crewell, I. V. Gorodetskaya, G. Heygster, D. Kunkel, M. Maturilli, M. Mech, C. Viceto, A. Rinke, H. Schmithüsen, A. Ehrlich, A. Macke, C. Lüpkes, M. Wendisch, 2018: Meteorological conditions during the ACLOUD/PASCAL field campaign near Svalbard in early summer 2017, Atmos. Chem. Phys., 18, 17995-18022, doi:10.5194/acp-18-17995-2018
Pithan, F., G. Svensson, R. Caballero, D. Chechin, T.W. Cronin, A.M.L. Ekman, R. Neggers, M.D. Shupe, A. Solomon, M. Tjernström, and M. Wendisch, 2018: Role of air-mass transformations in exchange between the Arctic and mid-latitudes, Nature Geoscience, doi:10.1038/s41561-018-0234-1
Malinka, A., E. Zege, L. Istomina, G. Heygster, G. Spreen, D. Perovich, and C. Polashenski, 2018: Reflective properties of melt ponds on sea ice. The Cryosphere, doi:10.5194/tc-12-1921-2018