Hurricanes pose a substantial risk to New England forest carbon stocks.

Nature-based climate solutions (NCS) are championed as a primary tool to mitigate climate change, especially in forested regions capable of storing and sequestering vast amounts of carbon. New England is one of the most heavily forested regions in the United States (>75% forested by land area), and forest carbon is a significant component of climate mitigation policies. Large infrequent disturbances, such as hurricanes, are a major source of uncertainty and risk for policies relying on forest carbon for climate mitigation, especially as climate change is projected to alter the intensity and extent of hurricanes. To date, most research into disturbance impacts on forest carbon stocks has focused on fire. Here, we show that a single hurricane in the region can down between 121 and 250 MMTCO2e or 4.6%-9.4% of the total aboveground forest carbon, much greater than the carbon sequestered annually by New England's forests (16 MMTCO2e year-1). However, emissions from hurricanes are not instantaneous; it takes approximately 19 years for downed carbon to become a net emission and 100 years for 90% of the downed carbon to be emitted. Reconstructing hurricanes with the HURRECON and EXPOS models across a range of historical and projected wind speeds, we find that an 8% and 16% increase in hurricane wind speeds leads to a 10.7- and 24.8-fold increase in the extent of high-severity damaged areas (widespread tree mortality). Increased wind speed also leads to unprecedented geographical shifts in damage, both inland and northward, into heavily forested regions traditionally less affected by hurricanes. Given that a single hurricane can emit the equivalent of 10+ years of carbon sequestered by forests in New England, the status of these forests as a durable carbon sink is uncertain. Understanding the risks to forest carbon stocks from disturbances is necessary for decision-makers relying on forests as a NCS.

The End-to-End Provenance Project.

Data provenance is a machine-readable summary of the collection and computational history of a dataset. Data provenance confers or adds value to a dataset, helps reproduce computational analyses, or validates scientific conclusions. The people of the End-to-End Provenance Project are a community of professionals who have developed software tools to collect and use data provenance.

If these data could talk.

In the last few decades, data-driven methods have come to dominate many fields of scientific inquiry. Open data and open-source software have enabled the rapid implementation of novel methods to manage and analyze the growing flood of data. However, it has become apparent that many scientific fields exhibit distressingly low rates of reproducibility. Although there are many dimensions to this issue, we believe that there is a lack of formalism used when describing end-to-end published results, from the data source to the analysis to the final published results. Even when authors do their best to make their research and data accessible, this lack of formalism reduces the clarity and efficiency of reporting, which contributes to issues of reproducibility. Data provenance aids both reproducibility through systematic and formal records of the relationships among data sources, processes, datasets, publications and researchers.