Articles | Volume 17, issue 5
https://doi.org/10.5194/acp-17-3687-2017
https://doi.org/10.5194/acp-17-3687-2017
Research article
 | 
16 Mar 2017
Research article |  | 16 Mar 2017

A new statistical approach to improve the satellite-based estimation of the radiative forcing by aerosol–cloud interactions

Piyushkumar N. Patel, Johannes Quaas, and Raj Kumar

Related authors

A remote sensing algorithm for vertically resolved cloud condensation nuclei number concentrations from airborne and spaceborne lidar observations
Piyushkumar N. Patel, Jonathan H. Jiang, Ritesh Gautam, Harish Gadhavi, Olga Kalashnikova, Michael J. Garay, Lan Gao, Feng Xu, and Ali Omar
Atmos. Chem. Phys., 24, 2861–2883, https://doi.org/10.5194/acp-24-2861-2024,https://doi.org/10.5194/acp-24-2861-2024, 2024
Short summary

Related subject area

Subject: Aerosols | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
The role of refractive indices in measuring mineral dust with high-spectral-resolution infrared satellite sounders: application to the Gobi Desert
Perla Alalam, Fabrice Ducos, and Hervé Herbin
Atmos. Chem. Phys., 24, 12277–12294, https://doi.org/10.5194/acp-24-12277-2024,https://doi.org/10.5194/acp-24-12277-2024, 2024
Short summary
Influence of covariance of aerosol and meteorology on co-located precipitating and non-precipitating clouds over the Indo-Gangetic Plain
Nabia Gulistan, Khan Alam, and Yangang Liu
Atmos. Chem. Phys., 24, 11333–11349, https://doi.org/10.5194/acp-24-11333-2024,https://doi.org/10.5194/acp-24-11333-2024, 2024
Short summary
Lidar estimates of birch pollen number, mass and related CCN concentrations
Maria Filioglou, Petri Tiitta, Xiaoxia Shang, Ari Leskinen, Pasi Ahola, Sanna Pätsi, Annika Saarto, Ville Vakkari, Uula Isopahkala, and Mika Komppula
EGUsphere, https://doi.org/10.5194/egusphere-2024-3032,https://doi.org/10.5194/egusphere-2024-3032, 2024
Short summary
Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa
Myungje Choi, Alexei Lyapustin, Gregory L. Schuster, Sujung Go, Yujie Wang, Sergey Korkin, Ralph Kahn, Jeffrey S. Reid, Edward J. Hyer, Thomas F. Eck, Mian Chin, David J. Diner, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, and Hans Moosmüller
Atmos. Chem. Phys., 24, 10543–10565, https://doi.org/10.5194/acp-24-10543-2024,https://doi.org/10.5194/acp-24-10543-2024, 2024
Short summary
Fluorescence properties of long-range transported smoke: Insights from five-channel lidar observations over Moscow during the 2023 wildfire season
Igor Veselovskii, Mikhail Korenskiy, Nikita Kasianik, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, and Thierry Podvin
EGUsphere, https://doi.org/10.5194/egusphere-2024-2874,https://doi.org/10.5194/egusphere-2024-2874, 2024
Short summary

Cited articles

Bellouin, N., Jones, A., Haywood, J., and Christopher, S. A.: Updated estimate of aerosol direct Radiative forcing from satellite observations and comparison against the centre climate model, J. Geophys. Res. Atmos., 113, D10205, https://doi.org/10.1029/2007JD009385, 2008.
Bellouin, N., Quaas, J., Morcrette, J. J., and Boucher, O.: Estimates of aerosol radiative forcing from the MACC re-analysis, Atmos. Chem. Phys., 13, 2045–2062, https://doi.org/10.5194/acp-13-2045-2013, 2013.
Bennartz, R.: Global assessment of marine boundary layer cloud droplet number concentration from satellite, J. Geophys. Res., 112, D02201, https://doi.org/10.1029/2006jd007547, 2007.
Bennartz, R. and Rausch, J.: Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1130, in review, 2017.
Brenguier, J.-L., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J., and Fouquart, Y.: Radiative Properties of Boundary Layer Clouds: Droplet Effective Radius versus Number Concentration, J. Atmos. Sci., 57, 803–821, 2000.
Download
Short summary
Radiative forcing by aerosol–cloud interactions (RFaci) remains highly uncertain and difficult to quantify on the basis of current knowledge. The present study reassesses the estimated RFaci by using a new statistical fitting approach, which improves the quantification of RFaci with less uncertainty. The present work helps to improve the parameterisation of RFaci in the present climate model.
Altmetrics
Final-revised paper
Preprint