This paper analyzes the changes Americans perceive to be taking place in their local weather and tests a series of hypotheses about why they hold these perceptions. Using data from annual nationwide surveys of the American public taken from 2008 to 2011, coupled with geographically specific measures of temperature and precipitation changes over that same period, the authors evaluate the relationship between perceptions of weather changes and actual changes in local weather. In addition, the survey data include measures of individual-level characteristics (age, education level, gender, and income) as well as cultural worldview and political ideology. Rival hypotheses about the origins of Americans' perceptions of weather change are tested, and it is found that actual weather changes are less predictive of perceived changes in local temperatures, but better predictors of perceived flooding and droughts. Cultural biases and political ideology also shape perceptions of changes in local weather. Overall, the analysis herein indicates that beliefs about changes in local temperatures have been more heavily politicized than is true for beliefs about local precipitation patterns. Therefore, risk communications linking changes in local patterns of precipitation to broader changes in the climate are more likely to penetrate identity-protective cognitions about climate.

MetPy is an open-source, Python-based package for meteorology, providing domain specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross-sections, Skew-T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general roadmap for future planned development for MetPy is also discussed.

Tropical cyclone (TC) activity over the southeast Indian Ocean has been studied far less than other TC basins, such as the North Atlantic and northwest Pacific. The authors examine the interannual TC variability of the northwest Australian (NWAUS) subbasin (08-358S, 1058-1358E), using an Australian TC dataset for the 39-yr period of . Thirteen TC metrics are assessed, with emphasis on annual TC frequencies and total TC days.Major findings are that for the NWAUS subbasin, there are annual means of 5.6 TCs and 42.4 TC days, with corresponding small standard deviations of 2.3 storms and 20.0 days. For intense TCs (WMO category 3 and higher), the annual mean TC frequency is 3.0, with a standard deviation of 1.6, and the annual average intense TC days is 7.6 days, with a standard deviation of 4.5 days. There are no significant linear trends in either mean annual TC frequencies or TC days. Notably, all 13 variability metrics show no trends over the 39-yr period and are less dependent upon standard El Niñ o-Southern Oscillation (ENSO) variables than many other TC basins, including the rest of the Australian region basin. The largest correlations with TC frequency were geopotential heights for June-August at 925 hPa over the South Atlantic Ocean (r 5 20.65) and for April-June at 700 hPa over North America (20.64). For TC days the largest correlations are geopotential heights for July-September at 1000 hPa over the South Atlantic Ocean (20.7) and for April-June at 850 hPa over North America (20.58). Last, wavelet analyses of annual TC frequencies and TC days reveal periodicities at ENSO and decadal time scales. However, the TC dataset is too short for conclusive evidence of multidecadal periodicities.Given the large correlations revealed by this study, developing and testing of a multivariate seasonal TC prediction scheme has commenced, with lead times up to 6 months.

The substantial impact of Lake‐effect snow in the Laurentian Great Lakes has led to interest in the impact of climate change on snowfall in the region. A recent assessment of Lake Michigan snowfall revealed a marked decrease in November snowfall since the 1950s, associated with a warming‐induced reduction in the fraction of precipitation days occurring as snowfall. Herein, in order to identify the trend contribution from Lake‐effect snowfall, snow days from the November 1950–2012 study period are classified as primarily System or Lake‐effect, with additional options of Insignificant, Both, Remnant, and Unclear. The classification is based on the snowfall distribution and visual map inspection for synoptic‐scale forcing, and results are compared with an objective classification based on clustering of daily snowfall. The regional snowfall patterns of Lake‐effect and System snow days are markedly different, with Lake‐effect snow days exhibiting a clear Lake‐effect signature downwind of Lake Michigan. The larger‐scale environments also differ, with much colder conditions in the Great Lakes and higher sea‐level pressure in the Great Plains on Lake‐effect days. With snowfall projected onto the classifications, the decrease of snowfall east of the lake is attributable to both reductions in Lake‐effect and System snowfall, while System snowfall changes are dominant west of the lake. The trends are consistent with the sensitivity to regional temperature, as well as an increasing prevalence of rain reports (within other sub‐regions) during snow days. Results based on the objective classification are largely congruent.