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Groundwater is an important source of drinking and irrigation water. Dating groundwater informs its vulnerability to contamination and aids in calibrating flow models. Here, we report measurements of multiple age tracers ( 14 C, 3 H, 39 Ar, and 85 Kr) and parameters relevant to dissolved inorganic carbon (DIC) from 17 wells in California’s San Joaquin Valley (SJV), an agricultural region that is heavily reliant on groundwater. We find evidence for a major mid-20th century shift in groundwater DIC input from mostly closed- to mostly open-system carbonate dissolution, which we suggest is driven by input of anthropogenic carbonate soil amendments. Crucially, enhanced open-system dissolution, in which DIC equilibrates with soil CO 2 , fundamentally affects the initial 14 C activity of recently recharged groundwater. Conventional 14 C dating of deeper SJV groundwater, assuming an open system, substantially overestimates residence time and thereby underestimates susceptibility to modern contamination. Because carbonate soil amendments are ubiquitous, other groundwater-reliant agricultural regions may be similarly affected. 

Abstract Stocks and fluxes of soil inorganic carbon have long been ignored in the context of coastal carbon sequestration, and their implications for the climate cooling effect of blue carbon ecosystems are complex. Here, we investigate the role of soil inorganic carbon in five salt marshes along the northern coast of the European Wadden Sea, one of the world's largest intertidal areas, harboring ~ 20% of European salt‐marsh area. We demonstrate a substantial contribution of inorganic carbon (average: 29%; range: 7–57%) to the total soil carbon stock of the top 1 m. Notably, inorganic exceeded organic carbon stocks in one of the studied sites; a finding that we ascribe to site geomorphic features, such as proximity to marine calcium carbonate sources and hydrodynamic exposure. Contrary to our hypothesis that inorganic carbon stocks would decline along the successional gradient from tidal flat to high marsh, as carbonate deposits would progressively dissolve in increasingly organic‐rich rooted sediments, our findings demonstrate the opposite pattern—an increase in inorganic carbon stocks along the successional gradient. This suggests that the dissolution of calcium carbonates in the root zone is counterbalanced by other processes, such as trapping of sedimentary carbonates by marsh vegetation and calcium carbonate precipitation in anaerobic subsoils. In the context of blue carbon, it will be critical to develop an improved understanding of these plant‐ and microbiota‐mediated processes in calcium carbonate cycling. 

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making. 

Abstract Methane (CH₄) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre‐industrial times. Wetlands account for a large share of global CH₄emissions, yet the magnitude and factors controlling CH₄fluxes in tidal wetlands remain uncertain. We synthesized CH₄flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH₄emissions. This effort included creating an open‐source database of chamber‐based GHG fluxes (https://doi.org/10.25573/serc.14227085). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH₄m⁻² year⁻¹, with a median of 3.9 g CH₄m⁻² year⁻¹, and only 25% of sites exceeding 18 g CH₄m⁻² year⁻¹. The highest fluxes were observed at fresh‐oligohaline sites with daily maximum temperature normals (MATmax) above 25.6°C. These were followed by frequently inundated low and mid‐fresh‐oligohaline marshes with MATmax ≤25.6°C, and mesohaline sites with MATmax >19°C. Quantile regressions of paired chamber CH₄flux and porewater biogeochemistry revealed that the 90th percentile of fluxes fell below 5 ± 3 nmol m⁻² s⁻¹at sulfate concentrations >4.7 ± 0.6 mM, porewater salinity >21 ± 2 psu, or surface water salinity >15 ± 3 psu. Across sites, salinity was the dominant predictor of annual CH₄fluxes, while within sites, temperature, gross primary productivity (GPP), and tidal height controlled variability at diel and seasonal scales. At the diel scale, GPP preceded temperature in importance for predicting CH₄flux changes, while the opposite was observed at the seasonal scale. Water levels influenced the timing and pathway of diel CH₄fluxes, with pulsed releases of stored CH₄at low to rising tide. This study provides data and methods to improve tidal marsh CH₄emission estimates, support blue carbon assessments, and refine national and global GHG inventories. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.