Abstract
Abstract. This study offers an alternative presentation regarding how diurnal precipitation is modulated by convective events that developed over the central Amazon during the preceding nighttime period. We use data collected during the Observations and Modelling of the Green Ocean Amazon (GoAmazon 2014/2015) field campaign that took place from 1 January 2014 to 30 November 2015 in the central Amazon. Local surface-based observations of cloud occurrence, soil temperature, surface fluxes, and planetary boundary layer characteristics are coupled with satellite data to identify the physical mechanisms that control the diurnal rainfall in central Amazon during the wet and dry seasons. This is accomplished through evaluation of the atmospheric properties during the nocturnal periods preceding raining and non-raining events. Comparisons between these non-raining and raining transitions are presented for the wet (January to April) and dry (June to September) seasons. The results suggest that wet-season diurnal precipitation is modulated by nighttime cloud coverage and local influences such as heating induced turbulence, whereas the dry-season rain events are controlled by large-scale circulations.
Highlights
As a key component of the atmospheric system, convective cloud processes and their inadequate model representations in tropical regions introduce significant uncertainty in numerical weather and climate predictions (Betts and Jakob, 2002; Dai, 2006)
We present an alternative approach on how to visualize the potential controls on the daytime diurnal cycle of precipitation by isolating nighttime, previous-day influences on convection in the central Amazon
Our analysis is based on a starting hypothesis that nighttime cloudiness delays surface solar heating on the following day during the wet season; this contrasts with the dry season that suggests a smaller cloud coverage during those periods
Summary
As a key component of the atmospheric system, convective cloud processes and their inadequate model representations in tropical regions introduce significant uncertainty in numerical weather and climate predictions (Betts and Jakob, 2002; Dai, 2006). The tropical diurnal precipitation cycle has been studied for decades using various numerical models (Bechtold et al, 2004; Sato et al, 2009; Stratton and Stirling, 2012) and observational techniques (Itterly et al, 2016; Machado et al, 2002; Oliveira et al, 2016). Despite these efforts, there remain several unresolved issues related to the representation of tropical precipitation in large-scale atmospheric models, including (a) an incorrect phasing of the diurnal cycle of precipitation over land that favors models triggering precipitation too early in the day (Gentine et al, 2013); (b) the poor positioning and potential doubling of the Intertropical Convergence Zone (Hwang and Frierson, 2013); and (c) the underestimation of rainfall over the Amazon forest (Huntingford et al, 2004). Cloudresolving models (CRMs), on the other hand, can capture qualitative aspects of the convective diurnal cycle, they are subject to model resolution and subgrid-scale process representation
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