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Carl M. Thomas and David M. Schultz

Abstract

Fronts can be computed from gridded datasets such as numerical model output and reanalyses, resulting in automated surface frontal charts and climatologies. Defining automated fronts requires quantities (e.g., potential temperature, equivalent potential temperature, wind shifts) and kinematic functions (e.g., gradient, thermal front parameter, and frontogenesis). Which are the most appropriate to use in different applications remains an open question. This question is investigated using two quantities (potential temperature and equivalent potential temperature) and three functions (magnitude of the horizontal gradient, thermal front parameter, and frontogenesis) from both the context of real-time surface analysis and climatologies from 38 years of reanalyses. The strengths of potential temperature to identify fronts are that it represents the thermal gradients and its direct association with the kinematics and dynamics of fronts. Although climatologies using potential temperature show features associated with extratropical cyclones in the storm tracks, climatologies using equivalent potential temperature include moisture gradients within air masses, most notably at low latitudes that are unrelated to the traditional definition of a front, but may be representative of a broader definition of an airmass boundary. These results help to explain previously published frontal climatologies featuring maxima of fronts in the subtropics and tropics. The best function depends upon the purpose of the analysis, but Petterssen frontogenesis is attractive, both for real-time analysis and long-term climatologies, in part because of its link to the kinematics and dynamics of fronts. Finally, this study challenges the conventional definition of a front as an airmass boundary and suggests that a new, dynamically based definition would be useful for some applications.

Open access
Carl M. Thomas and David M. Schultz

Abstract

Climatologies of fronts, airmass boundaries, and airstream boundaries can be calculated using automated approaches on gridded data. Such approaches may require choices to define a front, including a quantity (or quantities) to diagnose the front, a mathematical function(s) that operates upon the quantity to produce a diagnostic field, a level(s) at which the field is calculated, and a minimum threshold(s) in the magnitude of the field. To understand how resulting climatologies depend upon these choices using a consistent dataset, ERA-Interim reanalyses from 1979 to 2016 are used to construct global monthly climatologies for various definitions of fronts and airstream boundaries from potential temperature, equivalent potential temperature, water vapor mixing ratio, and wind, including gradients, thermal front parameter, frontogenesis, and asymptotic contraction rate at the surface and 850 hPa. Maps of automated fronts are similar to manual analyses when about 10% of the map is identified as a front. Definitions of fronts that use potential temperature or frontogenesis produce climatologies similar to those of manually analyzed fronts with maxima along the major storm tracks and their seasonal migrations. In contrast, definitions that use equivalent potential temperature or the thermal front parameter produce fewer fronts at higher latitudes and more fronts at lower latitudes, more akin to airmass boundaries than fronts. Although surface fronts defined by thermodynamic quantities are more infrequent over the oceans than at 850 hPa, they are more frequent when using metrics that include the wind field (e.g., frontogenesis, asymptotic contraction rate).

Open access
Carl J. Schreck III, Stephen Bennett, Jason M. Cordeira, Jake Crouch, Jenny Dissen, Andrea L. Lang, David Margolin, Adam O’Shay, Jared Rennie, Thomas Ian Schneider, and Michael J. Ventrice

Abstract

Day-to-day volatility in natural gas markets is driven largely by variability in heating demand, which is in turn dominated by cool-season temperature anomalies over the northeastern quadrant of the United States (“Midwest–East”). Energy traders rely on temperature forecasts at horizons of 2–4 weeks to anticipate those fluctuations in demand. Forecasts from dynamical models are widely available, so the markets react quickly to changes in the model predictions. Traders often work with meteorologists who leverage teleconnections from the tropics and the Arctic to improve upon the model forecasts. This study demonstrates how natural gas prices react to Midwest–East temperatures using the anomalous winters of 2011/12 and 2013/14. These examples also illustrate how energy meteorologists use teleconnections from the Arctic and the tropics to forecast heating demand.

Winter 2011/12 was exceptionally warm, consistent with the positive Arctic Oscillation (AO). March 2012 was a fitting exclamation point on the winter as it featured the largest warm anomaly for the United States above the twentieth-century climatology of any month since 1895. The resulting lack of heating demand led to record surpluses of natural gas storage and spurred prices downward to an 11-yr low in April 2012. In sharp contrast, winter 2013/14 was unusually cold. An anomalous Alaskan ridge led to cold air being transported from Siberia into the United States, despite the AO generally being positive. The ensuing swell in heating demand exhausted the surplus natural gas inventory, and prices rose to their highest levels since the beginning of the global recession in 2008.

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