Drought and pluvial extremes are defined as deviations from typical climatology; however, the background climatology can shift over time in a non-stationary climate, impacting interpretations of extremes. This study evaluated changes in meteorological drought and pluvial extremes by merging tree-ring reconstructions, observations, and climate-model simulations spanning 850 – 2100 CE across North America to determine whether the Industrial era and projected future lie outside the range of natural climate variability. Our results found widespread and spatially consistent exacerbation of both extremes, especially summer drought and winter pluvials, with west and south drying, the northeast wetting trends, and increased interannual variability across the east and north. Our study underscores climate change has already shifted precipitation climatology beyond pre-Industrial climatology and is projected to further intensify ongoing shifts.

There have been numerous calls to promote reproducible research. This growing awareness coincides with major advances in data/code sharing technologies. Yet authors, journals, institutions, and funders still need to act to advance more reproducible research. Here, we suggest to view reproducibility as a continuum that includes the 1) availability of data, models, code, and directions to use the digital artifacts, 2) replication of results, and 3) reproducibility of findings. We present a simple survey tool to assess where a peer-reviewed journal article lies on the continuum. We use the tool to assess 360 random sampled articles of the 1,989 articles published in 2017 in six well-regarded hydrology and water resources journals. 49% of sampled articles had some materials available online, but just 5.6% made available all the data, models, code, and directions. For 1.6% of articles, we generated results that replicated some or all of the published results. Assessments took 5 to 14 minutes per article to determine the availability of digital artifacts and 25 to 86 minutes to replicate results (25-75% range). The availability of data, models, code, and directions differed by journal and journal policy towards data availability. From the 360 article sample, we estimate that 0.6% to 6.8% of all articles published in the six journals in 2017 can be replicated using their published artifacts (95% confidence interval). These results suggest several practices to improve the reproducibility of published research. First, authors should provide directions to use their data, models, and code in addition to the digital artifacts. Second, on author submission, journals should use a tool like ours to assess the submission’s position on the reproducibility continuum. Third, journals should formulate policies that require authors to state the intended reproducibility of their work and place relevant information in an easy-to-find article location. Fourth, journals, institutions, and funders should highlight work whose digital artifacts, results, and findings are available, replicable, and reproducible.

Each year, seasonal bird migration leads to an immense redistribution of species occurrence and abundances, with pervasive, though unclear, consequences for patterns of multi-faceted avian diversity. Here, we uncover stark disparities in spatiotemporal variation between avian taxonomic and functional diversity across the continental US. In the eastern US, the temporal patterns of taxonomic and functional diversity are diametrically opposed, with functional richness highest in winter despite seasonal loss of species, and the remaining most abundant species amassed in a few regions of the functional space that likely reflect the resources available in winter. In contrast, in the western US, both species and functional richness are high during the breeding season, and species' abundances are regularly distributed and widely spread across the functional space. We anticipate that the uncovered complexity of spatiotemporal associations among avian diversity measures will be the catalyst for adopting an explicitly temporal framework for multi-faceted biodiversity analysis.