Abstract
Abstract. Radiosonde observations collected during the GoAmazon2014/5 campaign are analyzed to identify the primary thermodynamic regimes accompanying different modes of convection over the Amazon. This analysis identifies five thermodynamic regimes that are consistent with traditional Amazon calendar definitions of seasonal shifts, which include one wet, one transitional, and three dry season regimes based on a k-means cluster analysis. A multisensor ground-based approach is used to project associated bulk cloud and precipitation properties onto these regimes. This is done to assess the propensity for each regime to be associated with different characteristic cloud frequency, cloud types, and precipitation properties. Additional emphasis is given to those regimes that promote deep convective precipitation and organized convective systems. Overall, we find reduced cloud cover and precipitation rates to be associated with the three dry regimes and those with the highest convective inhibition. While approximately 15 % of the dataset is designated as organized convection, these events are predominantly contained within the transitional regime.
Highlights
A primary source of uncertainty in global climate or Earth system model (GCM; ESM) predictions of possible climate change is the representation of cloud processes and associated cloud feedbacks that regulate Earth’s energy and water cycles (e.g., Klein and Del Genio, 2006; Del Genio, 2012)
If convection is initiated for a given regime, what is the likelihood that the convection is nonprecipitating, is isolated, or develops into a widespread precipitation event? In Fig. 13, we break down the likelihood that precipitation events observed during GoAmazon2014/5 fall under nonprecipitating (NULL), isolated precipitating convection (ISO), or wide deeper convective (WDC) events. Among those WDC events, we identify those events having mature-stage mesoscale convective systems (MCSs) characteristics (i.e., MCS is a subset of the WDC events)
To provide information on the potential controls for clouds experienced over the Amazon Basin, a cluster analysis was performed on routine radiosondes launched during GoAmazon2014/5
Summary
A primary source of uncertainty in global climate or Earth system model (GCM; ESM) predictions of possible climate change is the representation of cloud processes and associated cloud feedbacks that regulate Earth’s energy and water cycles (e.g., Klein and Del Genio, 2006; Del Genio, 2012). We classify the primary thermodynamic regimes that are associated with the cloud observations over Manaus using a k-means cluster analysis applied to the morning radiosonde launches collected during the GoAmazon2014/5 campaign. This is done to isolate the potential controls of large-scale conditions on convective regimes. The primary datasets were from the routine ARM radiosonde launches during the campaign at the main AMF field site downwind of the city of Manaus, Brazil, and near Manacapuru, Brazil These radiosondes provide the thermodynamic quantities of interest and act as the basis for regime clustering methods Additional details on these products during GoAmazon2014/5 are found in Tang et al (2016)
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