B02: Remote sensing of aerosols and their properties in the Arctic from satellite observations
PIs: Hartmut Bösch, Marco Vountas (former PI: John P. Burrows)
The quantification of the impacts of aerosols in the Arctic requires an understanding of the seasonally dependent long-range transport of pollution from lower latitudes, ice and snow melt, local aerosol sources, dry and wet deposition of aerosol particles, and aerosol-cloud interactions. However, the sign and magnitude of Arctic aerosol radiative forcing during the period of Arctic amplification is not adequately understood. For instance, global models have difficulties in simulating low-altitude Arctic mixed-phase clouds (Pithan et al., 2016). Part of this difficulty is because the subsets of the aerosol population, which act as Cloud Condensation Nuclei (CCN) and Ice Nucleating Particles (INP), are not sufficiently well represented. The number of ground-based measurements of AOT in the Arctic is small and the coverage is intrinsically sparse. Thus, improved knowledge of Arctic aerosols and their radiative effects is required to understand their changes and their impact on the Arctic climate during the period of Arctic amplification. During the polar day, the retrieval of AOT from the measurements of passive remote sensing instrumentation on polar-orbiting satellites provides potentially a high spatial resolution aerosol data product having broad coverage and high temporal sampling. The scientific objectives of this project address the need to quantify the change in Aerosol Optical Thickness (AOT) (Mei et al., 2020c,b; Vountas et al., 2020), the aerosol types, and their composition during the period of Arctic amplification.
In phase II of (AC)³, the importance of volcanic eruptions, which reach the stratosphere, on the stratospheric and total AOT, and their impact on the AOT trends were identified. To resolve this issue for the period from 1981 to 2020 (i) the NOAA AVHRR total AOT dataset (merged with our own AOT retrievals over water using the XBAER, eXtensible Bremen AErosol Retrieval algorithm applied to MERIS and OLCI data, for more details see below) was optimized by filtering clouds and ice/snow over the ocean. We have observed small but statistically significant positive trends for this dataset; (ii) a new stratospheric AOT dataset was generated by merging aerosol extinction, retrieved from passive remote sensing limb measurements and the active remote sensing measurements of CALIOP above the ocean in the Arctic; (iii) a tropospheric AOT dataset was generated by subtracting the stratospheric AOT from the total AOT above the ocean in the Arctic. These spatially resolved and temporally sampled datasets were analyzed and their changes and trends were investigated. In addition, a total AOT dataset was created over snow and ice-covered Arctic surfaces from 2003 to 2012. The dataset was retrieved using the AEROSNOW retrieval algorithm developed at IUP. Significant differences between the AOT simulated by a chemical transport model (GEOS-Chem, e.g., (Bey et al., 2001)) and the AEROSNOW AOT retrievals were observed during episodes of biomass burning. Active satellite remote sensing of aerosol by CALIOP was used to validate the passive satellite remote sensing AOT data products, used and retrieved in this study.
Hypothesis:
The regional trends of Aerosol Optical Thickness (AOT) in the Arctic are driven by changing emissions of aerosols and their precursors and by subarctic biomass burning during the period of Arctic amplification.
In phase III we will answer the following questions related to the hypothesis:
- How well are the observed changes in AOT, retrieved from satellite observations, reproduced by atmospheric models, and what are the reasons for the differences?
- Will the recently observed small positive trend in AOT above the ocean in the Arctic continue in the period 2020 to 2025 and which mechanisms drive this increase?
- How can the observed differences between modeled and observed AOT be explained during summer biomass burn episodes, and is there a significant correlation between such episodes and phytoplankton dynamics in the Arctic?
Project B02 addresses key goals of (AC)³ by investigating the changes in AOT and corresponding radiation fluxes using satellite observations over the Arctic. It is one of the (AC)³ projects, which investigates the behavior of AOT from the local to the pan-Arctic scale. The B02 research addresses two of the (AC)³ Strategic Questions (SQs). It contributes to SQ1, which focuses on the causes of Arctic amplification, by determining the changes in total, stratospheric, and tropospheric AOT and assessing their impact on Arctic amplification and vice versa; SQ3, which investigates the evolution of AOT during the Arctic amplification, by comparing the AOT in the Arctic, retrieved from observations and climate models, which enabled the accuracy of AOT simulations and projections of climate models to be assessed.
Achievements phase I
B02 exploits satellite data for detection of changes in Arctic aersosol. This is quite challenging and different approaches are needed from different surface types those spectral surface reflectance (SSR) is also of interest. Over the Arctic open waters a first long-term record of Aerosol Optical Thickness (AOT) covering a period of more than 35 years shows a significant increase of AOT over the Fram Strait during haze season, and over the Chuchki Sea during September. The record also indicate a significant increase of AOT over the northeast passage during July and September. Improved retrievals for dark to moderately bright surfaces, such as snow/ice-free land and ocean (Jafariserajehlou et al., 2019) were developed. Progress has also been made in the field of AOT/SSR retrievals over bright surfaces (snow/ice covered areas) with a novel retrieval, which benefits from improved knowledge of aerosol typing and SSR treatment.
Role within (AC)³
Members
Dr. Marco Vountas
Principal Investigator
University of Bremen
Institute of Environmental Physics (IUP)
Otto-Hahn-Allee 1
28359 Bremen
Prof. Dr. Hartmut Bösch
Principal Investigator
University of Bremen
Institute of Environmental Physics (IUP)
Otto-Hahn-Allee 1
28359 Bremen
Basudev Swain
PhD
University of Bremen
Institute of Environmental Physics (IUP)
Otto-Hahn-Allee 1
28359 Bremen
Former Members
Dr. Soheila Jafariserajehlou
PhD (in phase I)
University of Bremen
Institute of Environmental Physics (IUP)
Otto-Hahn-Allee 1
28359 Bremen
Dr. Luca Lelli
Postdoc (in phase I)
University of Bremen
Institute for Environmental Physics (IUP)
Otto-Hahn-Allee 1
28359 Bremen
Dr. Linlu Mei
Senior Scientist (in phase I & II)
University of Bremen
Institute of Environmental Physics (IUP)
Otto-Hahn-Allee 1
28359 Bremen
Prof. Dr. John P. Burrows
Principal Investigator
University of Bremen
Institute of Environmental Physics
Otto-Hahn-Allee 1
28334 Bremen
Publications
2024
Malasani, C. R., Swain, B., Patel, A., Pulipatti, Y., Anchan. N.L., Sharma, A., Vountas, M., Liu, P., Gunthe, S.S., 2024, Modeling of mercury deposition in India: evaluating emission inventories and anthropogenic impacts, Environ. Sci.: Processes Impacts,26, 1999-2009, https://doi.org/10.1039/D4EM00324A
Anchan, N. L., Swain, B., Sharma, A., Singh, A., Malasani, C. R., Chandrasekharan, A., U. Kumar, N. Ojha, P. Liu, M. Vountas, S. S. Gunthe, 2024. Assessing the variability of aerosol optical depth over India in response to future scenarios: Implications for carbonaceous aerosols. J. Geophys. Res. Atmos., 129, e2024JD040846. https://doi.org/10.1029/2024JD040846
Swain, B., Vountas, M., Singh, A., Anchan, N. L., Deroubaix, A., Lelli, L., Ziegler, Y., Gunthe, S. S., Bösch, H., and Burrows, J. P., 2024: Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer, Atmos. Chem. Phys., 24, 5671–5693, https://doi.org/10.5194/acp-24-5671-2024.
Swain, B., Vountas, M., Deroubaix, A., Lelli, L., Ziegler, Y., Jafariserajehlou, S., Gunthe, S. S., Herber, A., Ritter, C., Bösch, H., and Burrows, J. P., 2024: Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations, Atmos. Meas. Tech., 17, 359–375, https://doi.org/10.5194/amt-17-359-2024.
2023
Mei, L., Rozanov, V., Rozanov, A., and Burrows, J. P.: SCIATRAN software package (V4.6), 2023: update and further development of aerosol, clouds, surface reflectance databases and models, Geosci. Model Dev., 16, 1511–1536, https://doi.org/10.5194/gmd-16-1511-2023.
Wendisch, M.; Brückner, M.; Crewell, S.; Ehrlich, A.; Notholt, J.; Lüpkes, C.; Macke, A.; Burrows, J. P.; Rinke, A.; Quaas, J.; Maturilli, M.; Schemann, V.; Shupe, M. D.; Akansu, E. F.; Barrientos-Velasco, C.; Bärfuss, K.; Blechschmidt, A.-M.; Block, K.; Bougoudis, I.; Bozem, H.; Böckmann, C.; Bracher, A.; Bresson, H.; Bretschneider, L.; Buschmann, M.; Chechin, D. G.; Chylik, J.; Dahlke, S.; Deneke, H.; Dethloff, K.; Donth, T.; Dorn, W.; Dupuy, R.; Ebell, K.; Egerer, U.; Engelmann, R.; Eppers, O.; Gerdes, R.; Gierens, R.; Gorodetskaya, I. V.; Gottschalk, M.; Griesche, H.; Gryanik, V. M.; Handorf, D.; Harm-Altstädter, B.; Hartmann, J.; Hartmann, M.; Heinold, B.; Herber, A.; Herrmann, H.; Heygster, G.; Höschel, I.; Hofmann, Z.; Hölemann, J.; Hünerbein, A.; Jafariserajehlou, S.; Jäkel, E.; Jacobi, C.; Janout, M.; Jansen, F.; Jourdan, O.; Jurányi, Z.; Kalesse-Los, H.; Kanzow, T.; Käthner, R.; Kliesch, L. L.; Klingebiel, M.; Knudsen, E. M.; Kovács, T.; Körtke, W.; Krampe, D.; Kretzschmar, J.; Kreyling, D.; Kulla, B.; Kunkel, D.; Lampert, A.; Lauer, M.; Lelli, L.; von Lerber, A.; Linke, O.; Löhnert, U.; Lonardi, M.; Losa, S. N.; Losch, M.; Maahn, M.; Mech, M.; Mei, L.; Mertes, S.; Metzner, E.; Mewes, D.; Michaelis, J.; Mioche, G.; Moser, M.; Nakoudi, K.; Neggers, R.; Neuber, R.; Nomokonova, T.; Oelker, J.; Papakonstantinou-Presvelou, I.; Pätzold, F.; Pefanis, V.; Pohl, C.; van Pinxteren, M.; Radovan, A.; Rhein, M.; Rex, M.; Richter, A.; Risse, N.; Ritter, C.; Rostosky, P.; Rozanov, V. V.; Donoso, E. R.; Saavedra-Garfias, P.; Salzmann, M.; Schacht, J.; Schäfer, M.; Schneider, J.; Schnierstein, N.; Seifert, P.; Seo, S.; Siebert, H.; Soppa, M. A.; Spreen, G.; Stachlewska, I. S.; Stapf, J.; Stratmann, F.; Tegen, I.; Viceto, C.; Voigt, C.; Vountas, M.; Walbröl, A.; Walter, M.; Wehner, B.; Wex, H.; Willmes, S.; Zanatta, M. & Zeppenfeld, S., 2023: Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)³ Project, Bull. Am. Meteorol. Soc., American Meteorological Society, 104, E208–E242, https://doi.org/10.1175/bams-d-21-0218.1
Mei, L.; Burrows, J.; Zhao, X.; Vountas, M.; Rozanov, V.; Guo, H.; Li, X.; Nakoudi, K. & Ritter, C., 2023: Changes in the Aerosol optical thickness above the ocean in the Arctic observed from space during 1981 and 2020: datasets, trends and origins, Bull. Am. Meteorol. Soc., [submitted to Bull. Am. Meteorol. Soc.]
Lelli, L.; Vountas, M.; Khosravi, N. & Burrows, J. P., 2023: Satellite remote sensing of regional and seasonal Arctic cooling showing a multi-decadal trend towards brighter and more liquid clouds, Atmos. Chem. Phys., 23, 2579-2611, https://doi.org/10.5194/acp-23-2579-2023
2022
L. Mei, V. Rozanov, Z. Jiao, J. P. Burrows, 2022, A new snow bidirectional reflectance distribution function model in spectral regions from UV to SWIR: Model development and application to ground-based, aircraft and satellite observations, ISPRS J. Photogramm. Remote Sens., 188, 269-285, https://doi.org/10.1016/j.isprsjprs.2022.04.010
2021
Jafariserajehlou, S., 2021: Aerosol, Surface and Cloud retrieval using passive remote sensing over the Arctic, Dissertation, Universität Bremen, http://dx.doi.org/10.26092/elib/1170
Mei, L., Rozanov, V., Jäkel, E., Cheng, X., Vountas, M., and Burrows, J. P., 2021: The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation, Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021.
Mei, L., Rozanov, V., Pohl, C., Vountas, M., and Burrows, J. P., 2021: The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study, Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021.
Jafariserajehlou, S., Rozanov, V. V., Vountas, M., Gatebe, C. K., and Burrows, J. P., 2021: Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic, Atmos. Meas. Tech., 14, 369–389, https://doi.org/10.5194/amt-14-369-2021.
2020
M. Vountas, K. Belinska, V. V. Rozanov, L. Lelli, L. Mei, S. Jafariserajehlou, J. P. Burrows, 2020: Retrieval of aerosol optical thickness and surface parameters based on multi-spectral and multi-viewing space-borne measurements, J. Quant. Spectro. Rad. Trans., Volume 256, 107311, ISSN 0022-4073, https://doi.org/10.1016/j.jqsrt.2020.107311.
Mei, L., Rozanov, V., Burrows, J. P., 2020: A fast and accurate radiative transfer model for aerosol remote sensing, J. Quant. Spectrosc. Radiat. Transfer, 256, 107270, https://doi.org/10.1016/j.jqsrt.2020.107270
L. Mei, V. Rozanov, Ch. Ritter, B. Heinold, Z. Jiao, M. Vountas, J. P.Burrows, 2020, Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model, IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2020.2972339
L. Mei, S. Vandenbussche, V. Rozanov, E. Proestakis, V. Amiridis, S. Callewaert, M. Vountas, J. P.Burrows, 2020, On the retrieval of aerosol optical depth over cryosphere using passive remote sensing, Remote Sensing of Enviroment, 241, 111731, https://doi.org/10.1016/j.rse.2020.111731.
2019
Ding, A., Z. Jiao, Y. Dong, X. Zhang, J.I. Peltoniemi, L. Mei, J. Guo, S. Yin, L. Cui, Y. Chang, and R. Xie, 2019: Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved Ross-Roujean BRDF Model, Remote Sensing, 11, 1611, doi:10.3390/rs11131611
Mei, L., V.V. Rozanov, H. Jethva, K.G. Meyer, L. Lelli, M. Vountas, and J.P. Burrows, 2019: Extending XBAER algorithm to aerosol and cloud condition, accepted for publication in IEEE Transactions on Geoscience and Remote Sensing, doi:10.1109/TGRS.2019.2919910
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
Mei, L.L., V. Rozanov, R. Christoph, H. Bernd, Z.T. Jiao, M. Vountas, and J.P. Burrows, 2019: Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model, submitted to IEEE Transactions on Geoscience and Remote Sensing (under review)
Mei, L., J. Strandgren, V. Rozanov, M. Vountas, J. P. Burrows, and Y. J. Wang, 2019: Study of satellite retrieved aerosol optical depth spatial resolution effect on particulate matter concentration prediction, Int. J. Remote Sens., 40 (18), 7084-7112, doi:10.1080/01431161.2019.1601279
Jafariserajehlou, S., L. Mei, M. Vountas, V. Rozanov, J.P. Burrows, and R. Hollmann, 2019: A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements, Atmos. Meas. Tech., 12, 1059-1076, doi:10.5194/amt-12-1059-2019
Jiao, Z., A. Ding, A. Kokhanovsky, C. Schaaf, F. Bréon, Y. Dong, Z. Wang, Y. Liu, X. Zhang, S. Yin, L. Cui, L. Mei, Y. Chang, 2019: Development of a Snow Kernel to Better Model the Anisotropic Reflectance of Pure Snow into a Kernel-Driven BRDF Model Framework, Remote Sensing Environment, 221, 198-209, doi:10.1016/j.rse.2018.11.001
2018
Che, Y., L. Mei, Y. Xue, J. Guang, L. She, and Y. Li, Y., 2018: Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms – Part 1: Global Evaluation. Remote Sens., 10, 1414, doi:10.3390/rs10091414
Mei, L., V. Rozanov, M. Vountas, J.P. Burrows, and A. Richter, 2018: XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation, Atmospheric Chemistry and Physics, 18 (4), 2511–2523, doi:10.5194/acp-18-2511-2018
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., V. Rozanov, M. Vountas, J. P. Burrows, R. C. Levy, W. Lotz, 2017: Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results, Rem. Sens. Environ., 197, 125-140, doi:10.1016/j.rse.2016.11.015
She, L., Mei, L., Xue, Y. Che, Y., and Guang, J., 2017: SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm, Remote Sens., 9, 253, doi:10.3390/rs9030253
Wendisch, M., M. Brückner, J. P. Burrows, S. Crewell, K. Dethloff, K. Ebell, Ch. Lüpkes, A. Macke, J. Notholt, J. Quaas, A. Rinke, and I. Tegen, 2017: Understanding causes and effects of rapid warming in the Arctic. Eos, 98, doi:10.1029/2017EO064803
Project Poster
Phase III Evaluation poster 2023
Phase II Evaluation poster 2019
Phase I Evaluation poster 2015
