Matthew D. LaPlante

Abrupt snowmelt, triggered by rain-on-snow events or “snow-eater heat waves,” can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850–2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

Scientific and journalistic investigations are complementary pursuits, involving a search for truth through the collection and analysis of evidence, good-faith endeavors of objectivity, and a commitment to transparency and reproducibility. Yet scientists do not often embrace journalistic methods of storytelling, and journalists rarely engage in scientific research. Described here through three recent experiences, the Research-based Reporting (ReBaR) framework for scientific storytelling is introduced as a model for concurrent journalistic and scientific investigation with publishing outcomes in peer-reviewed and lay press, and in which there are symbiotic benefits to both parts of the process.

The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed.

Annual wheat yields have steadily risen over the past century, but harvests remain highly variable and dependent on myriad weather conditions during a long growing season. In Kansas, for example, the 2014 crop year brought the lowest average yield in decades at 28 bushels per acre, while in 2016 farmers in the Wheat State, as Kansas is often called, enjoyed a historic high of 57 bushels per acre. It is broadly known that remote forces like El Niño–Southern Oscillation contribute to meteorological outcomes across North America, including in the wheat-growing regions of the U.S. Midwest, but the differential imprints of ENSO phases and flavors have not been well explored as leading indicators for harvest outcomes in highly specific agricultural regions, such as the more than 7 million acres upon which wheat is grown in Kansas. Here, we demonstrate a strong, steady, and long-term association between a simple “wheat yield index” and sea surface temperature anomalies, more than a year earlier, in the East Pacific, potentially offering insights into forthcoming harvest yields several seasons before planting commences.

The corresponding author for this article

Precipitation in the Southern Plains of the United States is relatively well depicted by the Community Earth System Model (CESM). However, despite its ability to capture seasonal mean precipitation anomalies, CESM consistently underestimates extreme pluvial and drought events, rendering it an insufficient tool for extending simulation lead times for exceptional events, such as the abnormally dry May 2011, which helped drive Texas into its worst period of drought in more than a century, and the abnormally wet May 2015, which led to widespread flooding in that state. Ensemble‐based regional climate experiments are completed for the two extreme years using Weather Research and Forecasting model (WRF) and downscaled from CESM. WRF simulations are at convection‐permitting grid resolution for improved physical representation of simulated precipitation over the Southern Great Plains. By integrating convection‐permitting models (CPMs) into each individual member of a CESM ocean data assimilation ensemble, this study demonstrates that high‐resolution dynamical downscaling can improve model skillfulness at capturing these two events and is thus a potentially useful tool for forecasting extremely high and extremely low precipitation events at subseasonal or even seasonal lead times.

The record-setting winter of 2022–2023 came as an answer to both figurative and literal prayers for political leaders, policy makers, and water managers reliant on snowpacks in the Upper Colorado River Basin, a vital source of water for tens of millions of people across the Western United States. But this “drought-busting” winter was not well-predicted, in part because while interannual patterns of tropical ocean temperatures have a well-known relationship to precipitation patterns across much of the American West, the Upper Colorado is part of a liminal region where these connections tend to be comparatively weak. Using historical sea surface temperature and snowpack records, and leveraging a long-term cross-basin relationship to extend the timeline for evaluation, this analysis demonstrates that the 2022–2023 winter did not present in accordance with other high-snowpack winters in this region, and that the associative pattern of surface temperatures in the tropical Pacific, and snow water equivalent in the regions that stored and supplied most of the water to the Colorado River during the 2022–2023 winter, was not substantially different from a historically incoherent arrangement of long-term correlation. These findings suggest that stochastic variability plays an outsized role in influencing water availability in this region, even in extreme years, reinforcing the importance of other trends to inform water policy and management.