@article{9137, abstract = {Pools of air cooled by partial rain evaporation span up to several hundreds of kilometers in nature and typically last less than 1 day, ultimately losing their identity to the large-scale flow. These fundamentally differ in character from the radiatively-driven dry pools defining convective aggregation. Advancement in remote sensing and in computer capabilities has promoted exploration of how precipitation-induced cold pool processes modify the convective spectrum and life cycle. This contribution surveys current understanding of such cold pools over the tropical and subtropical oceans. In shallow convection with low rain rates, the cold pools moisten, preserving the near-surface equivalent potential temperature or increasing it if the surface moisture fluxes cannot ventilate beyond the new surface layer; both conditions indicate downdraft origin air from within the boundary layer. When rain rates exceed ∼ 2 mm h−1, convective-scale downdrafts can bring down drier air of lower equivalent potential temperature from above the boundary layer. The resulting density currents facilitate the lifting of locally thermodynamically favorable air and can impose an arc-shaped mesoscale cloud organization. This organization allows clouds capable of reaching 4–5 km within otherwise dry environments. These are more commonly observed in the northern hemisphere trade wind regime, where the flow to the intertropical convergence zone is unimpeded by the equator. Their near-surface air properties share much with those shown from cold pools sampled in the equatorial Indian Ocean. Cold pools are most effective at influencing the mesoscale organization when the atmosphere is moist in the lower free troposphere and dry above, suggesting an optimal range of water vapor paths. Outstanding questions on the relationship between cold pools, their accompanying moisture distribution and cloud cover are detailed further. Near-surface water vapor rings are documented in one model inside but near the cold pool edge; these are not consistent with observations, but do improve with smaller horizontal grid spacings.}, author = {Zuidema, Paquita and Torri, Giuseppe and Muller, Caroline J and Chandra, Arunchandra}, issn = {0169-3298}, journal = {Surveys in Geophysics}, keywords = {Geochemistry and Petrology, Geophysics}, number = {6}, pages = {1283--1305}, publisher = {Springer Nature}, title = {{A survey of precipitation-induced atmospheric cold pools over oceans and their interactions with the larger-scale environment}}, doi = {10.1007/s10712-017-9447-x}, volume = {38}, year = {2017}, } @article{9138, abstract = {Convective self-aggregation, the spontaneous organization of initially scattered convection into isolated convective clusters despite spatially homogeneous boundary conditions and forcing, was first recognized and studied in idealized numerical simulations. While there is a rich history of observational work on convective clustering and organization, there have been only a few studies that have analyzed observations to look specifically for processes related to self-aggregation in models. Here we review observational work in both of these categories and motivate the need for more of this work. We acknowledge that self-aggregation may appear to be far-removed from observed convective organization in terms of time scales, initial conditions, initiation processes, and mean state extremes, but we argue that these differences vary greatly across the diverse range of model simulations in the literature and that these comparisons are already offering important insights into real tropical phenomena. Some preliminary new findings are presented, including results showing that a self-aggregation simulation with square geometry has too broad distribution of humidity and is too dry in the driest regions when compared with radiosonde records from Nauru, while an elongated channel simulation has realistic representations of atmospheric humidity and its variability. We discuss recent work increasing our understanding of how organized convection and climate change may interact, and how model discrepancies related to this question are prompting interest in observational comparisons. We also propose possible future directions for observational work related to convective aggregation, including novel satellite approaches and a ground-based observational network.}, author = {Holloway, Christopher E. and Wing, Allison A. and Bony, Sandrine and Muller, Caroline J and Masunaga, Hirohiko and L’Ecuyer, Tristan S. and Turner, David D. and Zuidema, Paquita}, issn = {0169-3298}, journal = {Surveys in Geophysics}, keywords = {Geochemistry and Petrology, Geophysics}, number = {6}, pages = {1199--1236}, publisher = {Springer Nature}, title = {{Observing convective aggregation}}, doi = {10.1007/s10712-017-9419-1}, volume = {38}, year = {2017}, } @article{9139, abstract = {Organized convection in the tropics occurs across a range of spatial and temporal scales and strongly influences cloud cover and humidity. One mode of organization found is “self-aggregation,” in which moist convection spontaneously organizes into one or several isolated clusters despite spatially homogeneous boundary conditions and forcing. Self-aggregation is driven by interactions between clouds, moisture, radiation, surface fluxes, and circulation, and occurs in a wide variety of idealized simulations of radiative–convective equilibrium. Here we provide a review of convective self-aggregation in numerical simulations, including its character, causes, and effects. We describe the evolution of self-aggregation including its time and length scales and the physical mechanisms leading to its triggering and maintenance, and we also discuss possible links to climate and climate change.}, author = {Wing, Allison A. and Emanuel, Kerry and Holloway, Christopher E. and Muller, Caroline J}, issn = {0169-3298}, journal = {Surveys in Geophysics}, keywords = {Geochemistry and Petrology, Geophysics}, number = {6}, pages = {1173--1197}, publisher = {Springer Nature}, title = {{Convective self-aggregation in numerical simulations: A review}}, doi = {10.1007/s10712-017-9408-4}, volume = {38}, year = {2017}, }