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On the Controls of Daytime Precipitation in the Amazonian Dry Season

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On the Controls of Daytime Precipitation in the Amazonian Dry Season BNL-113652-2017-JA On the Controls of Daytime Precipitation in the Amazonian Dry Season Virendra P. Ghate and Pavlos Kollias Accepted for publication in Journal of Hydrometeorology December 2016 Environmental & Climate Science Dept. Brookhaven National Laboratory U.S. Department of Energy USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23) Notice: This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or any third party’s use or the results of such use of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. 1 On the Controls of Daytime Precipitation in the Amazonian Dry Season 2 3 4 Virendra P. Ghate1 and Pavlos Kollias2,3 5 1. Argonne National Laboratory; 2. Brookhaven National Laboratory, 6 3. State University of New York, Stony Brook 7 8 9 10 11 12 Corresponding Author 13 Virendra P. Ghate 14 9700 South Cass avenue, 15 Lemont IL 60439 16 Email: [email protected] 17 Tel: 630-252-1609 18 19 20 21 22 23 1 24 ABSTRACT 25 The Amazon plays an important role in the global energy and hydrological budgets. The 26 precipitation during the dry season (June to September) plays a critical role in maintaining the 27 extent of the rainforest. The deployment of the Atmospheric Radiation Measurements (ARM)’s 28 first Mobile Facility (AMF-1) in the context of the GO Amazon field campaign at Manacapuru, 29 Brazil provided comprehensive measurements of surface, cloud, precipitation, radiation and 30 thermodynamic properties for two complete dry seasons (2014 and 2015). The precipitation 31 events occurring during the nighttime were associated with propagating storm systems (non-local 32 effects), while the daytime precipitation events were primarily a result of local land-atmosphere 33 interactions. During the two dry seasons, precipitation was recorded at the surface on 106 (43%) 34 days from 158 rain events with 82 daytime precipitation events occurring on 64 days (60.37%). 35 Detailed comparisons between the diurnal cycles of surface and profile properties between days 36 with and without daytime precipitation suggested the increased moisture at low- and mid- levels 37 to be responsible for lowering the lifting condensation level, reducing convective inhibition and 38 entrainment, and thus, triggering the transition from shallow to deep convection. Although the 39 monthly accumulated rainfall decreased during progression of dry season, the contribution of 40 daytime precipitation to it increased, suggesting the decrease to be mainly due to reduction in 41 propagating squall lines. The control of daytime precipitation during the dry season on large- 42 scale moisture advection above the boundary layer and the total rainfall on propagating squall 43 lines suggests that coarse resolution models should be able to accurately simulate the dry season 44 precipitation over the Amazon basin. 45 46 2 47 1. Introduction 48 The Amazon forest stores substantial amount of carbon in its biomass (Saatchi et al. 49 2011; Gloor et al. 2012). Precipitation plays a regulating role of the Amazonian rainforest 50 canopy at various timescales (Aragao et al. 2008; Phillips et al. 2009; Toomey et al. 2011; 51 Saatchi et al. 2013; etc.). The region is a part of the South American Monsoon System (SAMS) 52 that is characterized by two distinct seasons, a wet season from October to May and a dry season 53 from June to September, with regional differences in the duration and onset of the two seasons 54 (Liebmann and Mechoso, 2011; Raia and Cavalcanti, 2008). Rainfall occurs during both wet and 55 dry seasons with the rainfall in the dry season being crucial for maintaining the rain forest 56 (Toomey et al. 2011; Lewis et al. 2011). Most of the Global Climate Models (GCM) simulations 57 part of the Intergovernmental Panel for Climate Change (IPCC) fifth assessment report (AR5) 58 forecast the Amazonian forest to go through severe changes in meteorology and climate in the 59 future, with reduction in its extent primarily due to a drier and longer dry season (Cook et al. 60 2012; Joetzjer et al. 2013). The rainfall in the dry season is affected by many factors including 61 the Sea Surface Temperatures (SSTs) in the tropical Atlantic and Pacific oceans (Grimm, 2003, 62 2004; Misra, 2008; Fernandes et al. 2015; Yoon, 2016), moisture recycling due to evaporation 63 (Juarez et al. 2007; Spracklen et al. 2012), and local effects (Li and Fu, 2004; Tanaka et al. 2014; 64 dos Santos et al. 2014; Fitzjarrald et al. 2008). 65 A number of observational (Harriss et al. 1988; Kaufman et al. 1998; Silva Dias et al. 66 2002) and modeling (Werth and Avisaar, 2002; Anber et al. 2015; Harper et al. 2014; de 67 Goncalves et al. 2013) studies have been conducted with a goal of improving our understanding 68 of the controls of precipitation and the hydrological cycle in the Amazonia. Despite these efforts, 69 considerable disagreement exists in the present climate GCM simulation of the precipitation in 3 70 the Amazonia region. This is partly due to our limited understanding of the processes responsible 71 for causing precipitation during the dry season (Malhi et al. 2009; Mehran et al. 2014). 72 Precipitation occurs at all hours during the dry season with the number of precipitation events 73 decreasing during the progression of the dry season (Fig. 1 and Tanaka et al. 2014). The 74 nighttime precipitation is due to the passage of storm systems that originate at a different region, 75 and hence is due to nonlocal effects (Rickenbach, 2004). However, the daytime precipitation 76 occurs due to local land-atmosphere interactions (Fitzjarrald et al. 2008). Here, observations 77 collected during the Green Ocean Amazon (GO Amazon) field campaign are used to study the 78 controls of daytime (local) precipitation during the Amazon dry season. In particular, the 79 presented study aims to address the following questions: 1) Which factors control the daytime 80 transition from shallow (non-precipitating) to deep (precipitating) convection? and 2) What 81 causes the number of precipitation events to decrease as the dry season progresses? Hence, 82 although the study reports the characteristics (amount, duration, etc) of precipitation events, the 83 focus is on the factors responsible for forming precipitation producing clouds. 84 To answer the above questions, we have used the data collected by the Atmospheric 85 Radiation Measurement (ARM)’s first Mobile Facility (AMF-1) during the GO Amazon field 86 campaign (http://www.arm.gov/sites/amf/mao/). Data collected during the 2014 and 2015 dry 87 season (June to September) are used in this study and are described in section 2. First, the mean 88 diurnal cycle of surface meteorology, thermodynamics, and clouds during the dry season is 89 described in section 3. Subsequently, the differences in the diurnal cycle of days with daytime 90 precipitation and days with cumulus clouds (non-precipitating) are described in section 4. The 91 progression of the daily averaged values of few key parameters during the dry season is studied 4 92 in section 5 in the context of daytime precipitation events. The article is concluded with a 93 summary, conclusion and discussion section. 94 95 2. Data and Instrumentation 96 The AMF-1 was deployed at Manacapuru, Brazil (3.2°S, 60.5°W, 50 m) for the GO 97 Amazon field campaign from January, 2014 to December, 2015. In this study, the dry season is 98 defined from June 1 to September 30 similar to Harper et al. (2014). The AMF-1 instrumentation 99 includes a vertically pointing Doppler cloud radar, lidars, and radiometers. Below we have only 100 described the instruments data from whom were used in this study. A detailed description of the 101 AMF-1 instrumentation can be found in Mather and Voyles (2013). 102 A vertically pointing 94-GHz Doppler cloud radar known as the W-band ARM Cloud 103 Radar (WACR) was part of the AMF-1. The WACR reports the full Doppler spectrum and its 104 first three moments at 2 second temporal and 30 m range resolution. Also part of the AMF-1 was 105 a laser ceilometer that operated at 905 nm wavelength and reported the values of first three cloud 106 base heights at 15 second temporal and 30 m range resolution. The WACR and ceilometer data 107 were combined to produce estimates of noise filtered radar reflectivity, cloud boundaries, and 108 cloud fraction as described by Kollias et al. (2009) at a 5 second temporal and 30 m range 109 resolution.
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