Articles | Volume 20, issue 16
https://doi.org/10.5194/acp-20-10073-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-10073-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Aug 2020
Research article |
| 28 Aug 2020
| 28 Aug 2020
Radiative heating rate profiles over the southeast Atlantic Ocean during the 2016 and 2017 biomass burning seasons
Allison B. Marquardt Collow
CORRESPONDING AUTHOR
Universities Space Research Association, Columbia, Maryland, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, Maryland, USA
Mark A. Miller
Department of Environmental Sciences, Rutgers University, New
Brunswick, New Jersey, USA
Lynne C. Trabachino
Institute for Earth, Ocean, and Atmospheric Sciences, Rutgers
University, New Brunswick, New Jersey, USA
Michael P. Jensen
Environmental and Climate Sciences Department, Brookhaven National
Laboratory, Upton, New York, USA
Meng Wang
Environmental and Climate Sciences Department, Brookhaven National
Laboratory, Upton, New York, USA
Related authors
Caterina Mogno, Peter R. Colarco, Allison B. Collow, Sampa Das, Sarah A. Strode, Vanessa Valenti, Michael E. Manyin, Qing Liang, Luke Oman, Stephen D. Steenrod, and K. Emma Knowland
EGUsphere, https://doi.org/10.5194/egusphere-2025-2354, https://doi.org/10.5194/egusphere-2025-2354, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We investigated a climate model's ability to simulate atmospheric aerosols focusing on the relationship between mass and optical properties, by comparing predictions with observations. Our analysis revealed that model errors in aerosol scattering primarily stem from inaccurate particle mass concentrations and relative humidity, rather than flawed optical property assumptions in the model. These findings point out improvements for enhancing the accuracy for aerosols representation in our model.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Allison B. Marquardt Collow, Virginie Buchard, Peter R. Colarco, Arlindo M. da Silva, Ravi Govindaraju, Edward P. Nowottnick, Sharon Burton, Richard Ferrare, Chris Hostetler, and Luke Ziemba
Atmos. Chem. Phys., 22, 16091–16109, https://doi.org/10.5194/acp-22-16091-2022, https://doi.org/10.5194/acp-22-16091-2022, 2022
Short summary
Short summary
Biomass burning aerosol impacts aspects of the atmosphere and Earth system through radiative forcing, serving as cloud condensation nuclei, and air quality. Despite its importance, the representation of biomass burning aerosol is not always accurate in models. Field campaign observations from CAMP2Ex are used to evaluate the mass and extinction of aerosols in the GEOS model. Notable biases in the model illuminate areas of future development with GEOS and the underlying GOCART aerosol module.
Dié Wang, Roni Kobrosly, Tao Zhang, Tamanna Subba, Susan van den Heever, Siddhant Gupta, and Michael Jensen
Atmos. Chem. Phys., 25, 9295–9314, https://doi.org/10.5194/acp-25-9295-2025, https://doi.org/10.5194/acp-25-9295-2025, 2025
Short summary
Short summary
We aim to understand how tiny particles in the air, called aerosols, affect rain clouds in the Houston–Galveston area. More aerosols generally do not make these clouds grow much taller, with an average height increase of about 1 km. However, their effects on rainfall strength and cloud expansion are less certain. Clouds influenced by sea breezes show a stronger aerosol impact, possibly due to factors that are unaccounted for like vertical winds in near-surface layers.
Tamanna Subba, Michael P. Jensen, Min Deng, Scott E. Giangrande, Mark C. Harvey, Ashish Singh, Die Wang, Maria Zawadowicz, and Chongai Kuang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2659, https://doi.org/10.5194/egusphere-2025-2659, 2025
Short summary
Short summary
This study highlights how sea breeze circulations influence aerosol concentrations and radiative effects in Southern Texas region. Using TRacking Aerosol Convection Interactions Experiment field campaign observations and model simulations, we show that sea breeze–aerosol interactions significantly impact cloud-relevant aerosols and regional air quality. These findings improve understanding of mesoscale controls on aerosols in complex coastal urban environments.
Caterina Mogno, Peter R. Colarco, Allison B. Collow, Sampa Das, Sarah A. Strode, Vanessa Valenti, Michael E. Manyin, Qing Liang, Luke Oman, Stephen D. Steenrod, and K. Emma Knowland
EGUsphere, https://doi.org/10.5194/egusphere-2025-2354, https://doi.org/10.5194/egusphere-2025-2354, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We investigated a climate model's ability to simulate atmospheric aerosols focusing on the relationship between mass and optical properties, by comparing predictions with observations. Our analysis revealed that model errors in aerosol scattering primarily stem from inaccurate particle mass concentrations and relative humidity, rather than flawed optical property assumptions in the model. These findings point out improvements for enhancing the accuracy for aerosols representation in our model.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Kaiden Sookdar, Scott Edward Giangrande, John Rausch, Lihong Ma, Meng Wang, Dié Wang, Michael Jensen, Ching-Shu Hung, and J. Christine Chiu
EGUsphere, https://doi.org/10.5194/egusphere-2025-694, https://doi.org/10.5194/egusphere-2025-694, 2025
Short summary
Short summary
Photometer observations of stratocumulus cloud properties are evaluated for a multiyear archive. Retrievals for cloud optical depth, cloud droplet effective radius, and liquid water path show solid agreement with collocated references. Continental stratocumulus clouds sorted by cloud thickness indicate double the cloud optical depth and liquid water path of their marine counterparts, while exhibiting similar bulk cloud droplet effective radius.
Siddhant Gupta, Dié Wang, Scott E. Giangrande, Thiago S. Biscaro, and Michael P. Jensen
Atmos. Chem. Phys., 24, 4487–4510, https://doi.org/10.5194/acp-24-4487-2024, https://doi.org/10.5194/acp-24-4487-2024, 2024
Short summary
Short summary
We examine the lifecycle of isolated deep convective clouds (DCCs) in the Amazon rainforest. Weather radar echoes from the DCCs are tracked to evaluate their lifecycle. The DCC size and intensity increase, reach a peak, and then decrease over the DCC lifetime. Vertical profiles of air motion and mass transport from different seasons are examined to understand the transport of energy and momentum within DCC cores and to address the deficiencies in simulating DCCs using weather and climate models.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Allison B. Marquardt Collow, Virginie Buchard, Peter R. Colarco, Arlindo M. da Silva, Ravi Govindaraju, Edward P. Nowottnick, Sharon Burton, Richard Ferrare, Chris Hostetler, and Luke Ziemba
Atmos. Chem. Phys., 22, 16091–16109, https://doi.org/10.5194/acp-22-16091-2022, https://doi.org/10.5194/acp-22-16091-2022, 2022
Short summary
Short summary
Biomass burning aerosol impacts aspects of the atmosphere and Earth system through radiative forcing, serving as cloud condensation nuclei, and air quality. Despite its importance, the representation of biomass burning aerosol is not always accurate in models. Field campaign observations from CAMP2Ex are used to evaluate the mass and extinction of aerosols in the GEOS model. Notable biases in the model illuminate areas of future development with GEOS and the underlying GOCART aerosol module.
Michael P. Jensen, Virendra P. Ghate, Dié Wang, Diana K. Apoznanski, Mary J. Bartholomew, Scott E. Giangrande, Karen L. Johnson, and Mandana M. Thieman
Atmos. Chem. Phys., 21, 14557–14571, https://doi.org/10.5194/acp-21-14557-2021, https://doi.org/10.5194/acp-21-14557-2021, 2021
Short summary
Short summary
This work compares the large-scale meteorology, cloud, aerosol, precipitation, and thermodynamics of closed- and open-cell cloud organizations using long-term observations from the astern North Atlantic. Open-cell cases are associated with cold-air outbreaks and occur in deeper boundary layers, with stronger winds and higher rain rates compared to closed-cell cases. These results offer important benchmarks for model representation of boundary layer clouds in this climatically important region.
Yang Wang, Guangjie Zheng, Michael P. Jensen, Daniel A. Knopf, Alexander Laskin, Alyssa A. Matthews, David Mechem, Fan Mei, Ryan Moffet, Arthur J. Sedlacek, John E. Shilling, Stephen Springston, Amy Sullivan, Jason Tomlinson, Daniel Veghte, Rodney Weber, Robert Wood, Maria A. Zawadowicz, and Jian Wang
Atmos. Chem. Phys., 21, 11079–11098, https://doi.org/10.5194/acp-21-11079-2021, https://doi.org/10.5194/acp-21-11079-2021, 2021
Short summary
Short summary
This paper reports the vertical profiles of trace gas and aerosol properties over the eastern North Atlantic, a region of persistent but diverse subtropical marine boundary layer (MBL) clouds. We examined the key processes that drive the cloud condensation nuclei (CCN) population and how it varies with season and synoptic conditions. This study helps improve the model representation of the aerosol processes in the remote MBL, reducing the simulated aerosol indirect effects.
Thiago S. Biscaro, Luiz A. T. Machado, Scott E. Giangrande, and Michael P. Jensen
Atmos. Chem. Phys., 21, 6735–6754, https://doi.org/10.5194/acp-21-6735-2021, https://doi.org/10.5194/acp-21-6735-2021, 2021
Short summary
Short summary
This study suggests that there are two distinct modes driving diurnal precipitating convective clouds over the central Amazon. In the wet season, local factors such as turbulence and nighttime cloud coverage are the main controls of daily precipitation, while dry-season daily precipitation is modulated primarily by the mesoscale convective pattern. The results imply that models and parameterizations must consider different formulations based on the seasonal cycle to correctly resolve convection.
Cited articles
Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan,
V., and Welton, E. J.: Reduction of Tropical Cloudiness by Soot, Science,
288, 1042–1047, https://doi.org/10.1126/science.288.5468.1042, 2000.
Adebiyi, A. A. and Zuidema, P.: The role of the southern African easterly jet
in modifying the southeast Atlantic aerosol and cloud environments, Q. J.
Roy. Meteor. Soc., 142, 1574–1589, https://doi.org/10.1002/qj.2765, 2016.
Adebiyi, A. A., Zuidema, P., and Abel, S. J.: The Convolution of Dynamics and
Moisture with the Presence of Shortwave Absorbing Aerosols over the
Southeast Atlantic, J. Climate, 28, 1997–2024,
https://doi.org/10.1175/JCLI-D-14-00352.1, 2015.
Atmospheric Radiation Measurement (ARM) Climate Research Facility:
Interpolated Sonde (INTERPOLATEDSONDE), 2016-08-01 to 2016-10-30, ARM Mobile
Facility (ASI) Airport Site, Ascension Island, South Atlantic Ocean;
Supplemental Site (S1), compiled by: Giangrande, S. and Toto, T., Atmospheric
Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak
Ridge, Tennessee, USA,
https://doi.org/10.5439/1095316,
2016a.
Atmospheric Radiation Measurement (ARM) Climate Research Facility:
Interpolated Sonde (INTERPOLATEDSONDE), 2017-08-01 to 2017-10-30, ARM Mobile
Facility (ASI) Airport Site, Ascension Island, South Atlantic Ocean;
Supplemental Site (S1), compiled by: Giangrande, S. and Toto, T., Atmospheric
Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak
Ridge, Tennessee, USA,
https://doi.org/10.5439/1095316,
2016b.
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of
tropical cloud feedback uncertainties in climate models, Geophys. Res.
Lett., 32, L20806, https://doi.org/10.1029/2005GL023851, 2005.
Bosilovich, M. G., Akella, S., Coy, L., Cullather, R., Draper, C., Gelaro,
R., Kovach, R., Liu, Q., Molod, A., Norris, P., Wargan, K., Chao, W.,
Reichle, R., Takacs, L., Vikhliaev, Y., Bloom, S., Collow, A., Firth, S.,
Labow, G., Partyka, G., Pawson, S., Reale, O., Schubert, S., and Suarez, M.:
MERRA-2: Initial Evaluation of the Climate, NASA/TM–2015–104606, 43, available at:
https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf (last access: 25 August 2020), 2015.
Bretherton, C. S. and Wyant, M. C.: Moisture Transport,
Lower-Tropospheric Stability, and Decoupling of Cloud-Topped Boundary
Layers. J. Atmos. Sci., 54, 148–167,
https://doi.org/10.1175/1520-0469(1997)054<0148:MTLTSA>2.0.CO;2, 1997.
Brown, H., Liu, X., Feng, Y., Jiang, Y., Wu, M., Lu, Z., Wu, C., Murphy, S.,
and Pokhrel, R.: Radiative effect and climate impacts of brown carbon with
the Community Atmosphere Model (CAM5), Atmos. Chem. Phys., 18, 17745–17768,
https://doi.org/10.5194/acp-18-17745-2018, 2018.
Buchard, V., Randles, C. A., da Silva, A. M. Darmenov, A.,Colarco, P. R.,
Govindaraju, R., Ferrare, R., Hair, J.,Beyersdorf, A. J., Ziemba, L. D., and
Yu, H.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and
Case Studies, J. Climate, 30, 6851–6872,
https://doi.org/10.1175/JCLI-D-16-0613.1, 2017.
Chang, I. and Christopher, S. A.: The impact of seasonalities on direct
radiative effects and radiative heating rates of absorbing aerosols above
clouds, Q. J. Roy. Meteor. Soc., 143, 1395–1405, https://doi.org/10.1002/qj.3012, 2017.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R.
T., Miller, M. A., and Martner, B. E.: Objective determination of cloud
heights and radar reflectivities using a combination of active remote
sensors at the ARM CART sites, J. Appl. Meteorol., 39, 645–665,
https://doi.org/10.1175/1520-0450(2000)039<0645:ODOCHA>2.0.CO;2, 2000.
Collow, A. B. M. and Miller, M. A.: The Seasonal Cycle of the Radiation Budget
and Cloud Radiative Effect in the Amazon Rain Forest of Brazil, J.
Climate, 29, 7703–7722, https://doi.org/10.1175/JCLI-D-16-0089.1, 2016.
Darmenov, A. S. and da Silva, A.: The Quick Fire Emissions Dataset
(QFED) – Documentation of versions 2.1, 2.2 and 2.4, Tech. Rep. Ser. on
Global Modeling and Data Assimilation, 38, NASA/TM–2015–104606, Greenbelt,
MD, USA, 183 pp., 2015.
Das, S., Harshvardhan, H., Bian, H., Chin, M., Curci, G., Protonotariou,
A.P., Mielonen, T., Zhang, K., Wang, H., and Liu, X.: Biomass burning aerosol
transport and vertical distribution over the South African-Atlantic region,
J. Geophys. Res.-Atmos., 122, 6391–6415,
https://doi.org/10.1002/2016JD026421, 2017.
de Graaf, M., Schulte, R., Peers, F., Waquet, F., Tilstra, L. G., and Stammes, P.: Comparison of south-east Atlantic aerosol direct radiative effect over clouds from SCIAMACHY, POLDER and OMI–MODIS, Atmos. Chem. Phys., 20, 6707–6723, https://doi.org/10.5194/acp-20-6707-2020, 2020.
Diamond, M. S., Dobracki, A., Freitag, S., Small Griswold, J. D., Heikkila, A., Howell, S. G., Kacarab, M. E., Podolske, J. R., Saide, P. E., and Wood, R.: Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean, Atmos. Chem. Phys., 18, 14623–14636, https://doi.org/10.5194/acp-18-14623-2018, 2018.
Dolinar, E. K., Dong, X., and Xi, B.: Evaluation and intercomparison of
clouds, precipitation, and radiation budgets in recent reanalyses using
satellite-surface observations, Clim. Dynam., 46, 2123–2144,
https://doi.org/10.1007/s00382-015-2693-z, 2015.
Dunn, M., Johnson, K., and Jensen, M.: The microbase value-added product: A
baseline retrieval of cloud microphysical properties, ARM Climate Research
Facility, DOE/SC-ARM/TR-095, 2011.
Gaustad, K. L., Turner, D. D., and McFarlane, S. A.: MWRRET value-added
product: The retrieval of liquid water path and precipitable water vapor
from microwave radiometer (MWR) data sets, DOE/SC-ARM/TR-081.2, available at: https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-081.2.pdf (last access: 25 August 2020), 2011.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C., Darmenov, A., Bosilovich, M. G.,Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V.,Conaty, A., da
Silva, A., Gu, W., Kim,G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst3_3d_aer_Nv: 3d, 3-Hourly, Instantaneous,
Model-Level, Assimilation, Aerosol Mixing Ratio V5.12.4, Greenbelt, MD, USA,
Goddard Earth Sciences Data and Information Services Center (GES DISC),
https://doi.org/10.5067/LTVB4GPCOTK2, 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_aer_Nx: 2d,1-Hourly, Time-averaged,
Single-Level, Assimilation, Aerosol Diagnostics V5.12.4, Greenbelt, MD, USA,
Goddard Earth Sciences Data and Information Services Center (GES DISC),
https://doi.org/10.5067/KLICLTZ8EM9D, 2015b.
Gordon, H., Field, P. R., Abel, S. J., Dalvi, M., Grosvenor, D. P., Hill, A. A., Johnson, B. T., Miltenberger, A. K., Yoshioka, M., and Carslaw, K. S.: Large simulated radiative effects of smoke in the south-east Atlantic, Atmos. Chem. Phys., 18, 15261–15289, https://doi.org/10.5194/acp-18-15261-2018, 2018.
Huang, D., Zhao, C., Dunn, M., Dong, X., Mace, G. G., Jensen, M. P., Xie, S., and Liu, Y.: An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies, Atmos. Meas. Tech., 5, 1409–1424, https://doi.org/10.5194/amt-5-1409-2012, 2012.
Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and
Clouds: The Software Package OPAC, B. Am. Meteorol. Soc., 79, 831–844,
https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2, 1998.
Holben, B. N., Tanrìe, D., Smirnov, A., Eck, T. F., Slutsker, I., Abuhassan,
N., Newcomb, W. W., Schafer, J. S., Chatenet, B., Lavenu, F., Kaufman, Y.
J., Castle, J. V., Setzer, A., Markham, B., Frouin, D. C. R., Halthore, R.,
Karneli, A., O'Neill, N. T., Pietras, C., Pinker, R. T., Voss, K., and
Zibordi, G.: An emerging ground-based aerosol climatology: Aerosol optical
depth from AERONET, J. Geophys. Res., 106, 12067–12098,
https://doi.org/10.1029/2001JD900014, 2001.
KazemiRad, M. and Miller, M. A.: Summertime Post-Cold-Frontal Marine
Stratocumulus Transition Processes over the Eastern North Atlantic, J.
Atmos. Sci., 77, 2011–2037, https://doi.org/10.1175/JAS-D-19-0167.1, 2020.
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and
Gleckler, P. J.: Are climate model simulations of clouds improving? An
evaluation using the ISCCP simulator, J. Geophys. Res.-Atmos., 118,
1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.
Koontz, A., Flynn, C., Hodges, G., Michalsky, J., and Barnard, J.: Aerosol
optical depth value-added product, ARM Climate Research Facility,
DOE/SC-ARM/TR-129, available at: https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-129.pdf (last access: 25 August 2020), 2013.
Lin, J., Qian, T., and Shinoda, T.: Stratocumulus Clouds in Southeastern
Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models, J. Climate,
27, 3000–3022, https://doi.org/10.1175/JCLI-D-13-00376.1, 2014.
Lu, Z., Liu, X., Zhang, Z., Zhao, C., Meyer, K., Rajapakshe, C., Wu, C.,
Yang, Z., and Penner, J. E.: Biomass smoke from southern Africa can
significantly enhance the brightness of stratocumulus over the southeastern
Atlantic Ocean, P. Natl. Acad. Sci. USA, 115,
2924–2929, https://doi.org/10.1073/pnas.1713703115, 2018.
Mallet, M., Nabat, P., Zuidema, P., Redemann, J., Sayer, A. M., Stengel, M., Schmidt, S., Cochrane, S., Burton, S., Ferrare, R., Meyer, K., Saide, P., Jethva, H., Torres, O., Wood, R., Saint Martin, D., Roehrig, R., Hsu, C., and Formenti, P.: Simulation of the transport, vertical distribution, optical properties and radiative impact of smoke aerosols with the ALADIN regional climate model during the ORACLES-2016 and LASIC experiments, Atmos. Chem. Phys., 19, 4963–4990, https://doi.org/10.5194/acp-19-4963-2019, 2019.
Mather, J. H. and Voyles, J. W.: The ARM Climate Research Facility: A review
of structure and capabilities. B. Am. Meteorol. Soc., 94, 377–392,
https://doi.org/10.1175/BAMS-D-11-00218.1, 2013.
Mather, J. H., McFarlane, S. A., Miller, M. A., and Johnson, K. L.: Cloud
properties and associated radiative heating rates in the tropical western
Pacific, J. Geophys. Res., 112, D05201,
https://doi.org/10.1029/2006JD007555, 2007.
Miller, M. A., Nitschke, K., Ackerman, T. P., Ferrell, W. R., Hickmon, N.,
and Ivery, M.: Chapter 9: The ARM Mobile Facilities, AMS Meteorol. Mono.,
57, 9.1–9.15, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0051.1, 2016.
Noda, A. T. and Satoh, M.: Intermodel variances of subtropical
stratocumulus environments simulated in CMIP5 models, Geophys. Res. Lett.,
41, 7754–7761, https://doi.org/10.1002/2014GL061812, 2014.
Nuijens, L., Medeiros, B., Sandu, I., and Ahlgrimm, M: Observed and modeled
patterns of covariability between low-level cloudiness and the structure of
the trade-wind layer, J. Adv. Model. Earth Sy., 7, 1741–1764,
https://doi.org/10.1002/2015MS000483, 2015.
Peers, F., Bellouin, N., Waquet, F., Ducos, F., Goloub, P., Mollard, J.,
Myhre, G., Skeie, R. B., Takemura, T., Tanré, D., Thieuleux, F., and Zhang,
K.: Comparison of aerosol optical properties above clouds between POLDER and
AeroCom models over the South East Atlantic Ocean during the fire season,
Geophys. Res. Lett., 43, 3991–4000, https://doi.org/10.1002/2016GL068222,
2016.
Pistone, K., Redemann, J., Doherty, S., Zuidema, P., Burton, S., Cairns, B., Cochrane, S., Ferrare, R., Flynn, C., Freitag, S., Howell, S. G., Kacenelenbogen, M., LeBlanc, S., Liu, X., Schmidt, K. S., Sedlacek III, A. J., Segal-Rozenhaimer, M., Shinozuka, Y., Stamnes, S., van Diedenhoven, B., Van Harten, G., and Xu, F.: Intercomparison of biomass burning aerosol optical properties from in situ and remote-sensing instruments in ORACLES-2016, Atmos. Chem. Phys., 19, 9181–9208, https://doi.org/10.5194/acp-19-9181-2019, 2019.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A.,
Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka,
Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980-onward, Part I:
System Description and Data Assimilation Evaluation, J. Climate, 30,
6823–6850, https://doi.org/10.1175/jcli-d-16-0609.1, 2017.
Rapp, A. D.: Cloud responses in AMIP simulations of CMIP5 models in the
southeastern Pacific marine subsidence region, Int. J. Climatol., 35,
2908–2921, https://doi.org/10.1002/joc.4181, 2015.
Shinozuka, Y., Saide, P. E., Ferrada, G. A., Burton, S. P., Ferrare, R., Doherty, S. J., Gordon, H., Longo, K., Mallet, M., Feng, Y., Wang, Q., Cheng, Y., Dobracki, A., Freitag, S., Howell, S. G., LeBlanc, S., Flynn, C., Segal-Rosenhaimer, M., Pistone, K., Podolske, J. R., Stith, E. J., Bennett, J. R., Carmichael, G. R., da Silva, A., Govindaraju, R., Leung, R., Zhang, Y., Pfister, L., Ryoo, J.-M., Redemann, J., Wood, R., and Zuidema, P.: Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-678, in review, 2019.
Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J., Cohen, M. D., and
Ngan, F.: NOAA's HYSPLIT Atmospheric Transport and Dispersion Modeling
System, B. Am. Meteorol. Soc., 96, 2059–2077,
https://doi.org/10.1175/BAMS-D-14-00110.1, 2015.
Stier, P., Schutgens, N. A. J., Bellouin, N., Bian, H., Boucher, O., Chin, M., Ghan, S., Huneeus, N., Kinne, S., Lin, G., Ma, X., Myhre, G., Penner, J. E., Randles, C. A., Samset, B., Schulz, M., Takemura, T., Yu, F., Yu, H., and Zhou, C.: Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study, Atmos. Chem. Phys., 13, 3245–3270, https://doi.org/10.5194/acp-13-3245-2013, 2013.
Toto, T. and Jensen, M.: Interpolated Sounding and Gridded Sounding
Value-Added Products, ARM Climate Research Facility, DOE/SC-ARM-TR-183,
2016.
Zhang, J. and Zuidema, P.: The diurnal cycle of the smoky marine boundary layer observed during August in the remote southeast Atlantic, Atmos. Chem. Phys., 19, 14493–14516, https://doi.org/10.5194/acp-19-14493-2019, 2019.
Zhang, Z., Meyer, K., Yu, H., Platnick, S., Colarco, P., Liu, Z., and Oreopoulos, L.: Shortwave direct radiative effects of above-cloud aerosols over global oceans derived from 8 years of CALIOP and MODIS observations, Atmos. Chem. Phys., 16, 2877–2900, https://doi.org/10.5194/acp-16-2877-2016, 2016.
Zhao, C., Xie, S., Klein, S. A., McCoy, R., Comstock, J., Deng, M., Dunn,
M., Hogan, R., Huang, D., Jensen, M. P., Mace, G. G., McFarlane, S.,
O'Connor, E., Protat, A., Shupe, M., Turner, D. D., and Wang, Z.:
Understanding differences in current ARM ground-based cloud retrievals, J.
Geophys. Res., 117, D10206, https://doi.org/10.1029/2011JD016792.
Zuidema, P., Redemann, J., Haywood, J., Wood, R., Piketh, S., Hipondoka, M.,
and Formenti, P.: Smoke and clouds above the southeast Atlantic: Upcoming
field campaigns probe absorbing aerosol's impact on climate, B. Am. Meteorol.
Soc., 97, 1131–1135, https://doi.org/10.1175/BAMS-D-15-00082.1, 2016.
Zuidema, P., Alvarado, M., Chiu, C., DeSzoeke, S., Fairall, C., Feingold,
G., Freedman, A., Ghan, S., Haywood, J., Kollias, P., Lewis, E., McFarquhar,
G., McComiskey, A., Mechem, D., Onasch, T., Redemann, J., Romps, D., Turner,
D., Wang, H., Wood, R., Yuter, S., and Zhu P.: Layered Atlantic Smoke
Interactions with Clouds (LASIC) Field Campaign Report, edited by:
Stafford, R., ARM Climate Research Facility, DOE/SC-ARM-18-018, 2018a.
Zuidema, P., Sedlacek III, A. J., Flynn, C., Springston, S., Delgadillo, R.,
Zhang, J., Aiken, A. C., Koontz, A., and Muradyan, P.: The Ascension Island
boundary layer in the remote southeast Atlantic is often smoky, Geophys.
Res. Lett., 45, 4456–4465, https://doi.org/10.1002/2017GL076926, 2018b.
Short summary
Uncertainties in marine boundary layer clouds arise in the presence of biomass burning aerosol, as is the case over the southeast Atlantic Ocean. Heating due to this aerosol has the potential to alter the thermodynamic profile as the aerosol is transported across the Atlantic Ocean. Radiation transfer experiments indicate local shortwave aerosol heating is ~2–8 K d−1; however uncertainties in this quantity exist due to the single-scattering albedo and back trajectories of the aerosol plume.
Uncertainties in marine boundary layer clouds arise in the presence of biomass burning aerosol,...
Altmetrics
Final-revised paper
Preprint