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Anirban Guha and Gregory A. Lawrence

Abstract

Studies over a period of several decades have resulted in a relatively simple set of equations describing the tidally and width-averaged balances of momentum and salt in a rectangular estuary. The authors rewrite these equations in a fully nondimensional form that yields two nondimensional variables: (i) the estuarine Froude number and (ii) a modified tidal Froude number. The latter is the product of the tidal Froude number and the square root of the estuarine aspect ratio. These two variables are used to define a prognostic estuary classification scheme, which compares favorably with published estuarine data.

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Timothy D. Finnigan, Jason A. Vine, Peter L. Jackson, Susan E. Allen, Gregory A. Lawrence, and Douw G. Steyn

Abstract

Strong gap winds in Howe Sound, British Columbia, are simulated using a small-scale physical model. Model results are presented and compared with observations recorded in Howe Sound during a severe gap wind event in December 1992. Hydraulic theory is utilized to explain along-channel variation in wind. Field observations affirm the findings of the physical modeling with both, indicating the presence and location of controls and hydraulic jumps in the wind layer. Hydraulic behavior is found to change as the synoptic pressure gradient and the flow rate increase. In particular, field results indicate two distinct hydraulic situations: one during relatively weak wind, the other, which is more strongly controlled, during the period of peak wind. An additional comparison is made with output from the computer model hydmod of Jackson and Steyn. Numerical simulations, configured for the conditions present in Howe Sound during the December 1992 event, indicate channel hydraulics (and thus spatial wind speed variation) closely resembling the physical model and field results.

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Chidong Zhang, Aaron F. Levine, Muyin Wang, Chelle Gentemann, Calvin W. Mordy, Edward D. Cokelet, Philip A. Browne, Qiong Yang, Noah Lawrence-Slavas, Christian Meinig, Gregory Smith, Andy Chiodi, Dongxiao Zhang, Phyllis Stabeno, Wanqiu Wang, Hong-Li Ren, K. Andrew Peterson, Silvio N. Figueroa, Michael Steele, Neil P. Barton, Andrew Huang, and Hyun-Cheol Shin

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

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Mary C. Barth, Christopher A. Cantrell, William H. Brune, Steven A. Rutledge, James H. Crawford, Heidi Huntrieser, Lawrence D. Carey, Donald MacGorman, Morris Weisman, Kenneth E. Pickering, Eric Bruning, Bruce Anderson, Eric Apel, Michael Biggerstaff, Teresa Campos, Pedro Campuzano-Jost, Ronald Cohen, John Crounse, Douglas A. Day, Glenn Diskin, Frank Flocke, Alan Fried, Charity Garland, Brian Heikes, Shawn Honomichl, Rebecca Hornbrook, L. Gregory Huey, Jose L. Jimenez, Timothy Lang, Michael Lichtenstern, Tomas Mikoviny, Benjamin Nault, Daniel O’Sullivan, Laura L. Pan, Jeff Peischl, Ilana Pollack, Dirk Richter, Daniel Riemer, Thomas Ryerson, Hans Schlager, Jason St. Clair, James Walega, Petter Weibring, Andrew Weinheimer, Paul Wennberg, Armin Wisthaler, Paul J. Wooldridge, and Conrad Ziegler

Abstract

The Deep Convective Clouds and Chemistry (DC3) field experiment produced an exceptional dataset on thunderstorms, including their dynamical, physical, and electrical structures and their impact on the chemical composition of the troposphere. The field experiment gathered detailed information on the chemical composition of the inflow and outflow regions of midlatitude thunderstorms in northeast Colorado, west Texas to central Oklahoma, and northern Alabama. A unique aspect of the DC3 strategy was to locate and sample the convective outflow a day after active convection in order to measure the chemical transformations within the upper-tropospheric convective plume. These data are being analyzed to investigate transport and dynamics of the storms, scavenging of soluble trace gases and aerosols, production of nitrogen oxides by lightning, relationships between lightning flash rates and storm parameters, chemistry in the upper troposphere that is affected by the convection, and related source characterization of the three sampling regions. DC3 also documented biomass-burning plumes and the interactions of these plumes with deep convection.

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