B01: Changes of Top-of-Atmosphere reflectance and cloud properties in the Arctic from 1995 to the present using satellite data
PIs: John P. Burrows, Marco Vountas
One important component of the radiation budget at high latitudes is the role of clouds and surface reflectance in the solar spectral region during the polar day. This project is thus motivated by the need to quantify and understand the changes in the top–of–atmosphere (TOA) reflectance and cloud optical properties over the past decades and their role in Arctic Amplification. Optimized retrieval algorithms for the TOA reflectance RTOA and cloud properties (cloud cover, CC, cloud optical thickness (or spectral albedo) COT, and the altitude of cloud boundaries, i.e., cloud top height, CTH and cloud bottom height, CBH) will be developed and optimized for use with the observations from space based hyperspectral and multispectral remote sensing spectrometers and radiometers. A unique data set for RTOA and cloud data products will be produced. The resultant long–term trends of RTOA and cloud parameters, derived from the measurements of the space–based spectrally resolved spectrometers and radiometers flown since 1995, will be analysed and interpreted. The source of data is the fleet of radiometric calibrated spectrometers (GOME and ATSR-2 onboard ERS-2, SCIAMACHY, MERIS and AATSR onboard ENVISAT and GOME-2 and AVHRR-3 on the MetOp-A and MetOp-B) available over the Arctic, between 60°N and 90°N, and the retrieved data products will be compared and consolidated.
The quality of the data products is determined by the accuracy of the algorithms (i.e., model assumptions), the quality (calibration) of the instrument, and the knowledge from auxiliary or a priori information and data used in the algorithm. RTOA will be modeled using the radiative transfer model SCIATRAN, which has been developed at the University of Bremen over the past 25 years and is a world leading radiative transfer model, optimized with respect to its representation of snow, ice and oceanic surfaces at high latitudes and the relevant solar zenith angles etc. In the proposed research, the retrieved data products will be validated by comparison with data from ground–based, aircraft and satellite instrumentation, including the focused campaign observations carried out by partners within research project, B03 and B04, of (AC)³. The latter will be used also for atmospheric corrections, where appropriate. The validation quantifies the uncertainty of the retrieved cloud properties and their trends and thus yields their significance. The results will then be used to quantify the changes in RTOA and cloud data products from the relevant part of the solar spectral region. Iteratively, as part of collaborations with the D and E clusters and their modelling activities, the differences between model and observations will be investigated. Similarly the role of cloud in determining TOA spectral reflectance within the period of Arctic Amplification and the importance feedback will be established.
Hypothesis: Long–term changes of solar reflectance at the TOA provide an early warning of Arctic climate changes at the surface.
In order to test the hypothesis, we will address the following central questions:
- What are the changes in the top–of–atmosphere reflectance in the solar spectral region and the retrieved cloud optical properties, observed from space over the past decades?
- Are the predicted changes in surface and cloud properties in agreement with the identified changes?
Role within (AC)³
- Algorithm development
- Assessment of models
Dr. Marco Vountas
University of Bremen
Institute of Environmental Physics (IUP)
Prof. Dr. John P. Burrows
University of Bremen
Institute of Environmental Physics
Andersen, H., J. Cermak, I. Solodovnik, L. Lelli, and R. Vogt, 2019: Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground and space-based observations, Atmos. Chem. Phys., 19, 4383-4392, doi:10.5194/acp-19-4383-2019
Kokhanovsky, A.A., L. Lelli, F. Ducos, and R. Munro, 2018: A Simple Approximation for the Reflectance of a Thick Cloud in Gaseous Absorption Band and Its Application for the Cloud-Top Height Determination, IEEE Transactions on Geoscience and Remote Sensing, doi:10.1109/TGRS.2018.2883358
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, 2018: The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multi-Platform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, accepted by Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-18-0072.1, in press
Mei, L., V. Rozanov, M. Vountas, J.P. Burrows, 2018: The retrieval of ice cloud parameters from multi-spectral satellite observations of reflectance using a modified XBAER algorithm, Remote Sensing of Environment, 215, 128-144, doi:10.1016/j.rse.2018.06.007
Lelli, L. and Vountas, M., 2018: Chapter 5 – Aerosol and Cloud Bottom Altitude Covariations From Multisensor Spaceborne Measurements, In Remote Sensing of Aerosols, Clouds, and Precipitation, edited by Tanvir Islam, Yongxiang Hu, Alexander Kokhanovsky and Jun Wang, Elsevier, pp 109-127, ISBN 9780128104378, https://doi.org/10.1016/B978-0-12-810437-8.00005-0
Lelli, L., V. V. Rozanov, M. Vountas, J. P. Burrows, 2017: Polarized radiative transfer through terrestrial atmosphere accounting for rotational Raman scattering, J. Quant. Spect. Rad. Trans., 200, 70-89, doi:10.1016/j.jqsrt.2017.05.027
Mei, L., M. Vountas, L. Gómez-Chova, V. Rozanov, M. Jäger, W. Lotz, J. P. Burrows, R. Hollmann, 2017: A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data, Rem. Sens. Environ., 197, 141-160, doi:10.1016/j.rse.2016.11.016