How Do Ocean Warm Anomalies Favor the Aggregation of Deep Convective Clouds?

Abstract

We investigate the role of a warm sea-surface temperature (SST) anomaly (hot-spot of typically 3 K to 5 K) on the aggregation of convection using cloud resolving simulations in a non-rotating framework. It is well known that SST gradients can spatially organize convection. Even with uniform SST, the spontaneous self-aggregation of convection is possible above a critical SST (here 295 K), arising mainly from radiative feedbacks. We investigate how a circular hot-spot helps organize convection, and how self-aggregation feedbacks modulate this organization. The hot-spot significantly accelerates aggregation, particularly for warmer/larger hot-spots, and extends the range of SSTs for which aggregation occurs, however at cold SST (290 K) the aggregated cluster disaggregates if we remove the hot-spot. Large convective instability over the hot-spot leads to stronger convection and generates a large-scale circulation which forces the subsidence drying outside the hot-spot. Indeed, convection over the hot-spot brings the atmosphere towards a warmer temperature. The warmer temperatures are imprinted over the whole domain by gravity waves and subsidence warming. The initial transient warming and concomitant subsidence drying suppress convection outside the hot-spot, thus driving the aggregation. The hot-spot induced large-scale circulation can enforce the aggregation even without radiative feedbacks for hot-spots sufficiently large/warm. The strength of the large-scale circulation, which defines the speed of aggregation, is a function of the hot-spot fractional area. At equilibrium, once the aggregation is well established, the moist convective region with upward mid-tropospheric motion, centered over the hot-spot, has an area surprisingly independent of the hot-spot size.

Self‐Aggregation of Convective Clouds With Interactive Sea Surface Temperature

“…For sink = 30 W m −2 , the domain mean SST has a very modest increase after aggregation as the sink of energy is very close to the surface energy imbalance when aggregated (40 W/m 2 ). The simulation with sink = 60 W/m 2 starts cooling after aggregation but does not disaggregate with cooling (not shown) as the aggregation is favored even at cold SST = 295 K in our simulations (Shamekh et al, 2019).…”

“…To follow the progress of aggregation, we use column relative humidity CRH (Shamekh et al, 2019; Wing & Cronin, 2016). where q v , sat is the saturation water vapor specific humidity, ρ is the air density, and the vertical integration is done over the troposphere.…”

“…Introducing an SST anomaly can dictate the preferred location of convection (Tompkins, 2001) and thus impact the self-aggregation. When a circular SST anomaly (constant in time) is imposed, the aggregation process is significantly accelerated (Shamekh et al, 2019), due to the large-scale circulation that develops in response to the stronger upward mass flux over the warm region. Consistently, Back and Bretherton (2009) show that a boundary layer divergent flow forms in response to an SST gradient, which can re-enforce deep convection.…”

“…Worth to mention that all the studies mentioned so far used fixed homogeneous surface temperature. Using a nonhomogeneous SST (constant in time but inhomogeneous in space) or an interactive SST (evolving in time) can also change the occurrence of self‐aggregation and the dominating feedback (Hohenegger & Stevens, 2016; Liu & Moncrieff, 2008; Müller & Hohenegger, 2020; Shamekh et al, 2019; Tompkins, 2001). Introducing an SST anomaly can dictate the preferred location of convection (Tompkins, 2001) and thus impact the self‐aggregation.…”

Clouds and Radiation in a Mock‐Walker Circulation

“…The initial results from the RCE Model Intercomparison Project (RCEMIP; Wing et al, 2020) show a wide range of variability in the temperature, humidity, and clouds among the models. Adding the extra constraint of an overturning circulation forced by a prescribed gradient of SST, similar to the recent work of Shamekh et al (2020) and Müller and Hohenegger (2020) would provide a context within which the wide range of results from RCEMIP could be reexamined and expanded upon.…”