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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.

 

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 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. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.