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Abstract As the use of artificial intelligence (AI) has grown exponentially across a wide variety of science applications, it has become clear that it is critical to share data and code to facilitate reproducibility and innovation. AMS recently adopted the requirement that all papers include an availability statement. However, there is no requirement to ensure that the data and code are actually freely accessible during and after publication. Studies show that without this requirement, data is openly available in about a third to a half of journal articles. In this work, we surveyed two AMS journals, Artificial Intelligence for the Earth Systems (AIES) and Monthly Weather Review (MWR), and two non-AMS journals. These journals varied in primary topic foci, publisher, and requirement of an availability statement. We examined the extent to which data and code are stated to be available in all four journals, if readers could easily access the data and code, and what common justifications were provided for articles without open data or code. Our analysis found that roughly 75% of all articles that produced data and had an availability statement made at least some of their data openly available. Code was made openly available less frequently in three out of the four journals examined. Access was inhibited to data or code in approximately 15% of availability statement that contained at least one link. Finally, the most common justifications for not making data or code openly available referenced dataset size and restrictions of availability from non-co-author entities. 

In the Great Lakes region, total cold-season snowfall consists of contributions from both lake-effect systems (LES) and non-LES snow events. To enhance understanding of the regional hydroclimatology, this research examined these separate contributions with a focus on the cold seasons (October–March) of 2009/2010, a time period with the number of LES days substantially less than the mean, and 2012/2013, a time period with the number of LES days notably greater than the mean, for the regions surrounding Lakes Erie, Michigan, and Ontario. In general, LES snowfall exhibited a maximum contribution in near-shoreline areas surrounding each lake while non-LES snowfall tended to provide a more widespread distribution throughout the entire study regions with maxima often located in regions of elevated terrain. The percent contribution for LES snowfall to the seasonal snowfall varied spatially near each lake with localized maxima and ranged in magnitudes from 10% to over 70%. Although total LES snowfall amounts tended to be greater during the cold season with the larger number of LES days, the percent of LES snowfall contributing to the total cold-season snowfall was not directly dependent on the number of LES days. The LES snowfall contributions to seasonal totals were found to be generally larger for Lakes Erie and Ontario during the cold season with a greater number of LES days; however, LES contributions were similar or smaller for areas in the vicinity of Lake Michigan during the cold season with a smaller number of LES days.