Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13207-2021
© Author(s) 2021. 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-21-13207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2021
Research article |
| 06 Sep 2021
| 06 Sep 2021
Morning boundary layer conditions for shallow to deep convective cloud evolution during the dry season in the central Amazon
National Institute for Space Research, Cachoeira Paulista, São Paulo, Brazil
Laboratoire d´Aérologie, Université de Toulouse, CNRS, UPS, Toulouse, France
Gilberto Fisch
National Institute for Space Research, Cachoeira Paulista, São Paulo, Brazil
Luiz A. T. Machado
National Institute for Space Research, Cachoeira Paulista, São Paulo, Brazil
Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
Jean-Pierre Chaboureau
Laboratoire d´Aérologie, Université de Toulouse, CNRS, UPS, Toulouse, France
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Thiago S. Biscaro, Luiz A. T. Machado, Scott E. Giangrande, and Michael P. Jensen
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Cited articles
ARM (Atmospheric Radiation Measurement user facility):
Boundary-layer height data with CEIL (CEILPBLHT), updated hourly, 2014-01-01 to 2015-11-30,
ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), compiled
by: Morris, V. and Shi, Y., ARM Data Center [data set],
https://doi.org/10.5439/1095593, last access: 24 July 2020, 2014a. a
ARM (Atmospheric Radiation Measurement user facility):
Eddy Correlation Flux Measurement System (30ECOR), updated hourly, 2014-04-03 to 2015-12-01,
ARM Mobile Facility (MAO) Manacapuru, Amazonas, Brazil, AMF1 (M1), compiled
by: Sullivan, R., Cook, D., and Keeler, E., ARM Data Center [data set], https://doi.org/10.5439/1025039, last access:
24 July 2020, 2014b. a
ARM (Atmospheric Radiation Measurement user facility):
Radiative Flux Analysis (RADFLUX1LONG), updated hourly, 2013-12-23 to 2015-12-01, ARM Mobile
Facility (MAO) Manacapuru, Amazonas, Brazil; AMF1 (M1), compiled by: Long, C., Gaustad,
K., and Riihimaki, L., ARM Data Center [data set], last access: 4 April 2019,
https://doi.org/10.5439/1157585, 2014c. a
ARM (Atmospheric Radiation Measurement): Surface Energy Balance System (SEBS),
2014-01-01 to 2015-12-01, ARM Mobile Facility (MAO) Manacapuru, Amazonas,
Brazil; AMF1 (M1), compiled by: Sullivan, R., Cook, D., and Keeler, E., ARM Data Center [data set], last
access: 14 November 2020, https://doi.org/10.5439/1025274,
2014d. a
ARM (Atmospheric Radiation Measurement user facility):
Mini Sound Detection and Ranging (SODAR), updated hourly, 2014-02-18 to 2015-12-01, ARM
Mobile Facility (MAO) Manacapuru, Amazonas, Brazil; MAOS (S1), compiled by:
Coulter, R., Muradyan, P., and Martin, T., ARM Data Center [data set], last access: 20 October 2020, https://doi.org/10.5439/1150265, 2014e. a
ARM (Atmospheric Radiation Measurement user facility):
Balloon-Borne Sounding System (SONDEWNPN), updated hourly, 2014-01-01 to 2015-12-01, ARM
Mobile Facility (MAO) Manacapuru, Amazonas, Brazil; AMF1 (M1), compiled by: Holdridge, D., Coulter, R., Kyrouac, J., and Keeler, E., ARM Data Center [data set],
last access: 9 July 2020, https://doi.org/10.5439/1021460,
2014f. a
Angevine, W. M., Baltink, H. K., and Bosveld, F. C.: Observations Of The
Morning Transition Of The Convective Boundary Layer, Bound.-Lay. Meteorol.,
101, 209–227, https://doi.org/10.1023/A:1019264716195, 2001. a, b, c, d
Angevine, W. M., Edwards, J. M., Lothon, M., LeMone, M. A., and Osborne, S. R.:
Transition Periods in the Diurnally-Varying Atmospheric Boundary Layer Over
Land, Bound.-Lay. Meteorol., 177, 1–19, https://doi.org/10.1007/s10546-020-00515-y,
2020. a, b
Betts, A., Fisch, G., Von Randow, C., Silva Dias, M., Cohen, J., Da Silva, R.,
and Fitzjarrald, D.: The Amazonian boundary layer and mesoscale circulations, Amazonia and Global Change, Geophysical Monograph Series,
163–181, American Geophysical Union (AGU), https://doi.org/10.1029/2008GM000720,
2009. a
Betts, A. K. and Viterbo, P.: Land-surface, boundary layer, and cloud-field
coupling over the southwestern Amazon in ERA-40, J. Geophys. Res.-Atmos.,
110, D14108, https://doi.org/10.1029/2004JD005702, 2005. a
Biscaro, T. S., Machado, L. A. T., Giangrande, S. E., and Jensen, M. P.: What drives daily precipitation over the central Amazon? Differences observed between wet and dry seasons, Atmos. Chem. Phys., 21, 6735–6754, https://doi.org/10.5194/acp-21-6735-2021, 2021. a, b
Carneiro, R. G. and Fisch, G.: Observational analysis of the daily cycle of the planetary boundary layer in the central Amazon during a non-El Niño year and El Niño year (GoAmazon project 2014/5), Atmos. Chem. Phys., 20, 5547–5558, https://doi.org/10.5194/acp-20-5547-2020, 2020. a, b, c
Carneiro, R. G., Fisch, G., Borges, C. K., and Henkes, A.: Erosion of the
nocturnal boundary layer in the central Amazon during the dry season, Acta
Amazon., 50, 80–89, https://doi.org/10.1590/1809-4392201804453, 2020. a
Chaboureau, J.-P., Guichard, F., Redelsperger, J.-L., and Lafore, J.-P.: The
role of stability and moisture in the diurnal cycle of convection over land,
Q. J. Roy. Meteor. Soc., 130, 3105–3117, https://doi.org/10.1256/qj.03.132, 2004. a
Chakraborty, S., Schiro, K. A., Fu, R., and Neelin, J. D.: On the role of aerosols, humidity, and vertical wind shear in the transition of shallow-to-deep convection at the Green Ocean Amazon 2014/5 site, Atmos. Chem. Phys., 18, 11135–11148, https://doi.org/10.5194/acp-18-11135-2018, 2018. a, b, c, d, e, f
Chen, J., Hagos, S., Xiao, H., Fast, J. D., and Feng, Z.: Characterization of
Surface Heterogeneity-Induced Convection Using Cluster Analysis, Journal of
Geophysical Res.-Atmos., 125, e2020JD032550, https://doi.org/10.1029/2020JD032550, 2020. a
Edwards, J., Basu, S., Bosveld, F., and Holtslag, A.: The impact of radiation
on the GABLS3 large-eddy simulation through the night and during the morning
transition, Bound.-Lay. Meteorol., 152, 189–211, 2014. a
Edwards, J. M., Beljaars, A. C., Holtslag, A. A., and Lock, A. P.:
Representation of Boundary-Layer Processes in Numerical Weather Prediction
and Climate Models, Bound.-Lay. Meteorol., 177, 511–539, 2020. a
Feng, Z. and Giangrande, S.: Merged RWP-WACR-ARSCL Cloud Mask and Cloud Type, Oak Ridge National Lab.(ORNL), Oak Ridge, TN, USA, Atmospheric Radiation Measurement (ARM) Archive,
https://doi.org/10.5439/1462693, 2018. a, b
Fitzjarrald, D. R., Sakai, R. K., Moraes, O. L. L., Cosme de Oliveira, R.,
Acevedo, O. C., Czikowsky, M. J., and Beldini, T.: Spatial and temporal
rainfall variability near the Amazon-Tapajós confluence, J. Geophys. Res.-Biogeo., 113, G00B11, https://doi.org/10.1029/2007JG000596, 2008. a
Giangrande, S. E., Feng, Z., Jensen, M. P., Comstock, J. M., Johnson, K. L., Toto, T., Wang, M., Burleyson, C., Bharadwaj, N., Mei, F., Machado, L. A. T., Manzi, A. O., Xie, S., Tang, S., Silva Dias, M. A. F., de Souza, R. A. F., Schumacher, C., and Martin, S. T.: Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment, Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, 2017. a, b, c, d
Giangrande, S. E., Wang, D., and Mechem, D. B.: Cloud regimes over the Amazon Basin: perspectives from the GoAmazon2014/5 campaign, Atmos. Chem. Phys., 20, 7489–7507, https://doi.org/10.5194/acp-20-7489-2020, 2020. a, b, c, d
Khairoutdinov, M. and Randall, D.: High-resolution simulation of
shallow-to-deep convection transition over land, J. Atmos. Sci., 63,
3421–3436, https://doi.org/10.1175/JAS3810.1, 2006. a
Machado, L. A. T., Calheiros, A. J. P., Biscaro, T., Giangrande, S., Silva Dias, M. A. F., Cecchini, M. A., Albrecht, R., Andreae, M. O., Araujo, W. F., Artaxo, P., Borrmann, S., Braga, R., Burleyson, C., Eichholz, C. W., Fan, J., Feng, Z., Fisch, G. F., Jensen, M. P., Martin, S. T., Pöschl, U., Pöhlker, C., Pöhlker, M. L., Ribaud, J.-F., Rosenfeld, D., Saraiva, J. M. B., Schumacher, C., Thalman, R., Walter, D., and Wendisch, M.: Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA, Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018, 2018. a
Machado, L. A. T., Franco, M. A., Kremper, L. A., Ditas, F., Andreae, M. O., Artaxo, P., Cecchini, M. A., Holanda, B. A., Pöhlker, M. L., Saraiva, I., Wolff, S., Pöschl, U., and Pöhlker, C.: How weather events modify aerosol particle size distributions in the Amazon boundary layer, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-314, in review, 2021. a
Mahrt, L. and Vickers, D.: Contrasting vertical structures of nocturnal
boundary layers, Bound.-Layer. Meteorol., 105, 351–363,
https://doi.org/10.1023/A:1019964720989, 2002. a
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, 2016. a
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., Jardine, K.,
Medeiros, A., Portela, B., de Sá, S. S., Adachi, K., Aiken, A. C., Albrecht,
R., Alexander, L., Andreae, M. O., Barbosa, H. M. J., Buseck, P., Chand, D.,
Comstock, J. M., Day, D. A., Dubey, M., Fan, J., Fast, J., Fisch, G.,
Fortner, E., Giangrande, S., Gilles, M., Goldstein, A. H., Guenther, A.,
Hubbe, J., Jensen, M., Jimenez, J. L., Keutsch, F. N., Kim, S., Kuang, C.,
Laskin, A., McKinney, K., Mei, F., Miller, M., Nascimento, R., Pauliquevis,
T., Pekour, M., Peres, J., Petäjä, T., Pöhlker, C., Pöschl, U., Rizzo,
L., Schmid, B., Shilling, J. E., Dias, M. A. S., Smith, J. N., Tomlinson,
J. M., Tóta, J., and Wendisch, M.: The Green Ocean Amazon Experiment
(GoAmazon2014/5) Observes Pollution Affecting Gases, Aerosols, Clouds, and
Rainfall over the Rain Forest, B. Am. Meteorol. Soc., 98, 981–997,
https://doi.org/10.1175/BAMS-D-15-00221.1, 2017. a, b, c
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. a
Oliveira, M. I., Acevedo, O. C., Sörgel, M., Nascimento, E. L., Manzi, A. O., Oliveira, P. E. S., Brondani, D. V., Tsokankunku, A., and Andreae, M. O.: Planetary boundary layer evolution over the Amazon rainforest in episodes of deep moist convection at the Amazon Tall Tower Observatory, Atmos. Chem. Phys., 20, 15–27, https://doi.org/10.5194/acp-20-15-2020, 2020. a, b, c, d
Pielke, R. A.: Influence of the spatial distribution of vegetation and soils on
the prediction of cumulus convective rainfall, Rev. Geophys., 39, 151–177,
https://doi.org/10.1029/1999RG000072, 2001. a
Poltera, Y., Martucci, G., Collaud Coen, M., Hervo, M., Emmenegger, L., Henne, S., Brunner, D., and Haefele, A.: PathfinderTURB: an automatic boundary layer algorithm. Development, validation and application to study the impact on in situ measurements at the Jungfraujoch, Atmos. Chem. Phys., 17, 10051–10070, https://doi.org/10.5194/acp-17-10051-2017, 2017. a
Schiro, K. A. and Neelin, J. D.: Tropical continental downdraft characteristics: mesoscale systems versus unorganized convection, Atmos. Chem. Phys., 18, 1997–2010, https://doi.org/10.5194/acp-18-1997-2018, 2018. a, b, c, d
Sorbjan, Z.: A numerical study of daily transitions in the convective boundary
layer, Bound.-Lay. Meteorol., 123, 365–383, 2007. a
Tang, S., Xie, S., Zhang, Y., Zhang, M., Schumacher, C., Upton, H., Jensen, M. P., Johnson, K. L., Wang, M., Ahlgrimm, M., Feng, Z., Minnis, P., and Thieman, M.: Large-scale vertical velocity, diabatic heating and drying profiles associated with seasonal and diurnal variations of convective systems observed in the GoAmazon2014/5 experiment, Atmos. Chem. Phys., 16, 14249–14264, https://doi.org/10.5194/acp-16-14249-2016, 2016. a, b, c
Tennekes, H.: A Model for the Dynamics of the Inversion Above a Convective
Boundary Layer, J. Atmos. Sci., 30, 558–567,
https://doi.org/10.1175/1520-0469(1973)030<0558:AMFTDO>2.0.CO;2, 1973. a
Wang, D., Giangrande, S. E., Bartholomew, M. J., Hardin, J., Feng, Z., Thalman, R., and Machado, L. A. T.: The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment, Atmos. Chem. Phys., 18, 9121–9145, https://doi.org/10.5194/acp-18-9121-2018, 2018. a
Wilson, J. W., Crook, N. A., Mueller, C. K., Sun, J., and Dixon, M.: Nowcasting
thunderstorms: A status report, B. Am. Meteorol. Soc., 79, 2079–2100,
https://doi.org/10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2, 1998. a
Wu, C.-M., Stevens, B., and Arakawa, A.: What controls the transition from
shallow to deep convection?, J. Atmos. Sci., 66, 1793–1806,
https://doi.org/10.1175/2008JAS2945.1, 2009. a
Zhuang, Y., Fu, R., Marengo, J. A., and Wang, H.: Seasonal variation of
shallow-to-deep convection transition and its link to the environmental
conditions over the Central Amazon, J. Geophys. Res.-Atmos., 122, 2649–2666,
https://doi.org/10.1002/2016JD025993, 2017. a, b, c, d, e, f, g, h, i, j, k, l, m
Short summary
The Amazonian boundary layer is investigated during the dry season in order to better understand the processes that occur between night and day until the stage where shallow cumulus clouds become deep. Observations show that shallow to deep clouds are characterized by a shorter morning transition stage (e.g., the time needed to eliminate the stable boundary layer inversion), while higher humidity above the boundary layer favors the evolution from shallow to deep cumulus clouds.
The Amazonian boundary layer is investigated during the dry season in order to better understand...
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