Convective Aggregation and Indices Examined from CERES Cloud Object Data

Abstract

Convective aggregation is a self‐aggregation phenomenon appearing in idealized radiative‐convective equilibrium simulations under constant, uniform sea surface temperature (SST). To gain an understanding of observed convective aggregation or organization, three metrics, i.e., simple convective aggregation index (SCAI), modified SCAI (MCAI), and convective organization potential (COP), are evaluated with cloud object data from CERES. MCAI is related to object sizes through a modified inter‐object distance (IOD). It is found that large‐size object groups are less aggregated according to SCAI but more organized according to COP, compared to small‐size object groups. The opposite sensitivities to object‐group size can be explained by the dominant roles of the IOD in SCAI and the sum of object radii in COP as object‐group sizes increase. However, large‐size object groups are slightly more aggregated than small‐size ones according to MCAI. Both SCAI and MCAI increase with the number of cloud objects (N) in an object group but COP has a weak dependency on N. Further sorting by object‐group total area shows that sensitivity of MCAI to object‐group area agrees with that of SCAI for small‐area ranges but with that of COP for large‐area ranges, which is related to the weak sensitivity of the modified IOD to object‐group area, as compared to that of the original IOD. Finally, the three metrics show the similar contrasts between continental and oceanic convection and the same weak sensitivity to SST. The latter suggests that self‐aggregation is weaker at higher SSTs than at lower SSTs, in contrast to the findings of many simulations.

A Relationship Between ITCZ Organization and Subtropical Humidity

“…Quantifying organization is not straightforward (see, e.g., Figure 1 in Brueck et al, 2020), as evidenced by the various organization indices that have been proposed recently (Brune et al, 2018; Kadoya & Masunaga, 2018; Retsch et al, 2020; Tobin et al, 2012; Tompkins & Semie, 2017; White et al, 2018). Different indices emphasize distinct aspects of organization and do not necessarily agree (Pscheidt et al, 2019; Xu et al, 2019). Most organization indices have been developed to quantify convective organization at the mesoscale and not at the scale of the ITCZ.…”

A New Organization Metric for Synoptic Scale Tropical Convective Aggregation

“…𝐿 × 1000 (4) where N and N max are the number of aggregates and the maximum number of aggregates in the domain, respectively, L is the domain's characteristic length, and D 0 is the geometric mean of distances between all available pairs of aggregates (called "order-zero diameter"). Later, Xu et al (2019) proposed the modified SCAI (MCAI) whereby D 0 was replaced by D 1 ("order-one diameter", the arithmetic mean of distances), and the aggregate size was taken into account in the distance term (i.e., "inter-object distance"; defined as D 2 by combining them).…”

“…The basic idea of SCAI is that more organized scenes are composed of fewer convective systems that are closer to each other. Subsequently, White et al (2018) and Xu et al (2019) argued that the size of each convective system should be an important element in the definition of organization metrics, and suggested the presumably superior "convective organization potential" (COP), and "modified SCAI" (MCAI) metrics, respectively. Independently, Tompkins & Semie (2017) developed an organization metric that compares the horizontal distribution of convective clouds to a purely random distribution (I org ), while Kadoya & Masunaga (2018) introduced the Morphological Index of Convective Aggregation (MICA) metric based on the area fraction occupied by convective clouds.…”

Sensitivity of the Horizontal Scale of Convective Self‐Aggregation to Sea Surface Temperature in Radiative Convective Equilibrium Experiments Using a Global Nonhydrostatic Model

“…In observations, CSA is often defined by appropriate geometric indices within a square region with sides measuring about 1,000 km, using satellite data (Stein et al., 2017; Tobin et al., 2012; Xu et al., 2019). These observational studies show that with an increase in the degree of CSA, there is an increase in aridity, a decrease in cloud cover, and an increase in radiative cooling.…”