Dataset Information

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales.

ABSTRACT: 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 multiinstrument 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 relation 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.

SUBMITTER: Schiro KA

PROVIDER: S-EPMC5939085 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales.

Schiro Kathleen A KA Ahmed Fiaz F Giangrande Scott E SE Neelin J David JD

Proceedings of the National Academy of Sciences of the United States of America 20180417 18

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 multiinstrument evidence from the Green Ocean Amazon field campaign (2014-2015; GoAmazon2014/5), that an empirica ...[more]

PMID: 29666237

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Project description:[1] Convective cold pools and the breakdown of nocturnal low-level jets (NLLJs) are key meteorological drivers of dust emission over summertime West Africa, the world's largest dust source. This study is the first to quantify their relative contributions and physical interrelations using objective detection algorithms and an off-line dust emission model applied to convection-permitting simulations from the Met Office Unified Model. The study period covers 25 July to 02 September 2006. All estimates may therefore vary on an interannual basis. The main conclusions are as follows: (a) approximately 40% of the dust emissions are from NLLJs, 40% from cold pools, and 20% from unidentified processes (dry convection, land-sea and mountain circulations); (b) more than half of the cold-pool emissions are linked to a newly identified mechanism where aged cold pools form a jet above the nocturnal stable layer; (c) 50% of the dust emissions occur from 1500 to 0200 LT with a minimum around sunrise and after midday, and 60% of the morning-to-noon emissions occur under clear skies, but only 10% of the afternoon-to-nighttime emissions, suggesting large biases in satellite retrievals; (d) considering precipitation and soil moisture effects, cold-pool emissions are reduced by 15%; and (e) models with parameterized convection show substantially less cold-pool emissions but have larger NLLJ contributions. The results are much more sensitive to whether convection is parameterized or explicit than to the choice of the land-surface characterization, which generally is a large source of uncertainty. This study demonstrates the need of realistically representing moist convection and stable nighttime conditions for dust modeling. Citation: Heinold, B., P. Knippertz, J. H. Marsham, S. Fiedler, N. S. Dixon, K. Schepanski, B. Laurent, and I. Tegen (2013), The role of deep convection and nocturnal low-level jets for dust emission in summertime West Africa: Estimates from convection-permitting simulations, J. Geophys. Res. Atmos., 118, 4385-4400, doi:10.1002/jgrd.50402.

Dataset Information

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales.

Publications

GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales.

Similar Datasets

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