Scientists used data from the DOE GOAmazon field campaign to evaluate a new formulation for entrainment in deep convective systems.
Deep convective clouds can develop in isolation or can aggregate into large-scale organized storms known as mesoscale convective systems (MCS). In the tropics, MCSs play a substantial role in the redistribution of heat and momentum, as well as being responsible for a substantial fraction of precipitation. Climate and numerical weather prediction models have trouble representing the organization of MCSs. In part, this difficulty is because most convective parameterizations are built around a set of assumptions, known as the entraining plume model, which govern how boundary layer air is entrained into the convective updraft. The parameters in the entraining plume model that control the sensitivity of the convective system to the amount of drier environmental air entrained are not well constrained by observations, which affects the simulation of tropical climate and contributes to the uncertainty in climate projections. Scientists used data from the DOE Atmospheric Radiation Measurement (ARM) field campaign in the Amazon basin (GOAmazon 2014/15) to study the characteristics of isolated and organized convection and evaluate a new formulation for entrainment that is more directly constrained by observations.
Observations from the DOE GOAmazon2014/15 field campaign suggest that a new formulation of entrainment that constrains the inflow of environmental air based on weighting of radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between buoyancy and precipitation in both mesoscale and smaller-scale convective systems. The new deep-inflow framework emphasizes layer-mean properties of the inflow rather than assuming local complete mixing, as in the entrainment plume model. The results presented here suggest that two of the key challenges faced by climate model convective parameterizations — representing the effects of MCS convection and sensitivity to poorly constrained entrainment — may be linked and identifies a potential path forward. The deep-inflow formulation potentially eliminates a leading parameter sensitivity in conventional parameterizations, while showing predictive capability for the probability and magnitude of precipitation.
A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multi instrument evidence from the Green Ocean Amazon field campaign (2014-2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep inflow mixing yields a strong precipitation–buoyancy relations across the tropics. Deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.
Contacts (BER PM)
ARM Program Manager
RGCM Program Manager
University of California and NASA Jet Propulsion Laboratory
This study was funded by: K.A.S., J.D.N., and F.A. were supported in part by the Office of Biological and Environmental Research in the Office of Science of the US Department of Energy (DOE) Grant DE-SC0011074, National Science Foundation Grant AGS-1505198, National Oceanic and Atmospheric Administration Grant NA14OAR4310274, and a Dissertation Year Fellowship from the University of California, Los Angeles (to K.A.S.). Part of this work was performed at the Jet Propulsion Laboratory. The DOE ARM User Facility field campaign data were essential to this work. S.E.G. of Brookhaven Science Associates, LLC, is supported under Contract DE-SC0012704 with the US DOE.
Schiro, K.A., F. Ahmed, S.E. Giangrande, and J. David Neelin. "Goamazon2014/5 Campaign Points to Deep-Inflow Approach to Deep Convection Across Scales." Proceedings of the National Academy of Sciences, USA 115(18), 4577-4582 (2018). [DOI: 10.1073/pnas.1719842115]
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