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Douglas C. Stolz, Steven A. Rutledge, Weixin Xu, and Jeffrey R. Pierce

Abstract

This study examines covariability of boundary layer cloud condensation nuclei (CCN) concentrations [estimated using the GEOS 3D chemical transport model (GEOS-Chem)], convective clouds, precipitation, and lightning observed over the central equatorial Indian Ocean (CIO). Three distinct Madden–Julian oscillation (MJO) episodes were observed during the recent Dynamics of the MJO (DYNAMO; 2011/12) field campaign. Coherent relationships between CCN, rainfall, and lightning are apparent in time series from DYNAMO and more lightning located north of the equator is noted, compared to south of the equator. More-polluted environments north of the equator contained deep convective clouds that had stronger radar reflectivities (~2–3 dB) in the mixed-phase region (5–10-km altitude) compared to south of the equator. Following discussion of the MJO episodes that occurred during DYNAMO, 22 cycles of the MJO observed during boreal cold seasons in the years 2004–11 are examined with the aid of TRMM satellite observations. Climatological results suggest that horizontal transport of continental aerosols from proximal landmasses by the large-scale circulation after active MJO convection reinforces the meridional gradient of CCN concentrations in the CIO. Satellite observations depicted comparable aggregate cold cloud feature area in both regions in similar thermodynamic environments, leading to the suggestion that higher CCN concentrations north of the equator act to invigorate convection. Direct comparisons of convective intensity metrics to CCN support the aerosol hypothesis; however, in line with previous studies, it is acknowledged that conditional instability, vertical wind shear, and environmental moisture can modulate the initial development of deep convection over the CIO during select phases of the MJO.

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

Abstract

Prominent achievements made in addressing global precipitation using satellite passive microwave retrievals are often overshadowed by their performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate precipitation measurements. This is especially true over land, with precipitation estimates being based on an observed mean relationship between high-frequency (e.g., 89 GHz) brightness temperature depression (i.e., the ice-scattering signature) and surface precipitation rate. This indirect relationship between the observed (brightness temperatures) and state (precipitation) vectors often leads to inaccurate estimates, with more pronounced biases (e.g., −30% over the United States) observed during extreme events. This study seeks to mitigate these errors by employing previously established relationships between cloud structures and large-scale environments such as CAPE, wind shear, humidity distribution, and aerosol concentrations to form a stronger relationship between precipitation and the scattering signal. The GPM passive microwave operational precipitation retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. The modified algorithm is allowed to use the large-scale environment to filter out a priori states that do not match the general synoptic condition relevant to the observation and thus reduces the difference between the assumed and observed variability in the ice-to-rain ratio. Using the ground Multi-Radar Multi-Sensor (MRMS) network over the United States, the results demonstrate outstanding potential in improving the accuracy of heavy precipitation over land. It is found that individual synoptic parameters can remove 20%–30% of existing bias and up to 50% when combined, while preserving the overall performance of the algorithm.

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Andrey Y. Shcherbina, Miles A. Sundermeyer, Eric Kunze, Eric D’Asaro, Gualtiero Badin, Daniel Birch, Anne-Marie E. G. Brunner-Suzuki, Jörn Callies, Brandy T. Kuebel Cervantes, Mariona Claret, Brian Concannon, Jeffrey Early, Raffaele Ferrari, Louis Goodman, Ramsey R. Harcourt, Jody M. Klymak, Craig M. Lee, M.-Pascale Lelong, Murray D. Levine, Ren-Chieh Lien, Amala Mahadevan, James C. McWilliams, M. Jeroen Molemaker, Sonaljit Mukherjee, Jonathan D. Nash, Tamay Özgökmen, Stephen D. Pierce, Sanjiv Ramachandran, Roger M. Samelson, Thomas B. Sanford, R. Kipp Shearman, Eric D. Skyllingstad, K. Shafer Smith, Amit Tandon, John R. Taylor, Eugene A. Terray, Leif N. Thomas, and James R. Ledwell

Abstract

Lateral stirring is a basic oceanographic phenomenon affecting the distribution of physical, chemical, and biological fields. Eddy stirring at scales on the order of 100 km (the mesoscale) is fairly well understood and explicitly represented in modern eddy-resolving numerical models of global ocean circulation. The same cannot be said for smaller-scale stirring processes. Here, the authors describe a major oceanographic field experiment aimed at observing and understanding the processes responsible for stirring at scales of 0.1–10 km. Stirring processes of varying intensity were studied in the Sargasso Sea eddy field approximately 250 km southeast of Cape Hatteras. Lateral variability of water-mass properties, the distribution of microscale turbulence, and the evolution of several patches of inert dye were studied with an array of shipboard, autonomous, and airborne instruments. Observations were made at two sites, characterized by weak and moderate background mesoscale straining, to contrast different regimes of lateral stirring. Analyses to date suggest that, in both cases, the lateral dispersion of natural and deliberately released tracers was O(1) m2 s–1 as found elsewhere, which is faster than might be expected from traditional shear dispersion by persistent mesoscale flow and linear internal waves. These findings point to the possible importance of kilometer-scale stirring by submesoscale eddies and nonlinear internal-wave processes or the need to modify the traditional shear-dispersion paradigm to include higher-order effects. A unique aspect of the Scalable Lateral Mixing and Coherent Turbulence (LatMix) field experiment is the combination of direct measurements of dye dispersion with the concurrent multiscale hydrographic and turbulence observations, enabling evaluation of the underlying mechanisms responsible for the observed dispersion at a new level.

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Annmarie G. Carlton, Joost de Gouw, Jose L. Jimenez, Jesse L. Ambrose, Alexis R. Attwood, Steven Brown, Kirk R. Baker, Charles Brock, Ronald C. Cohen, Sylvia Edgerton, Caroline M. Farkas, Delphine Farmer, Allen H. Goldstein, Lynne Gratz, Alex Guenther, Sherri Hunt, Lyatt Jaeglé, Daniel A. Jaffe, John Mak, Crystal McClure, Athanasios Nenes, Thien Khoi Nguyen, Jeffrey R. Pierce, Suzane de Sa, Noelle E. Selin, Viral Shah, Stephanie Shaw, Paul B. Shepson, Shaojie Song, Jochen Stutz, Jason D. Surratt, Barbara J. Turpin, Carsten Warneke, Rebecca A. Washenfelder, Paul O. Wennberg, and Xianling Zhou

Abstract

The Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indicators of “biogenic” secondary organic aerosol (SOA), once considered part of the natural background, were positively correlated with one or more indicators of anthropogenic pollution; and 3) liquid water dramatically impacted particle scattering while biogenic SOA did not. SAS findings suggest that atmosphere–biosphere interactions modulate ambient pollutant concentrations through complex mechanisms and feedbacks not yet adequately captured in atmospheric models. The SAS dataset, now publicly available, is a powerful constraint to develop predictive capability that enhances model representation of the response and subsequent impacts of changes in atmospheric composition to changes in emissions, chemistry, and meteorology.

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