Members of the U.S. Climate Alliance, a coalition of 24 states committed to achieving the emissions reductions outlined in the 2015 Paris Agreement, are considering policy options for inclusion of forest carbon in climate mitigation plans. These initiatives are generally limited by a lack of relevant data on forest carbon stocks and fluxes past-to-future. Previously, we developed a new forest carbon modeling system that combined high-resolution remote sensing, field data, and ecological modeling to estimate contemporary above-ground forest carbon stocks, and projected future forest carbon sequestration potential for the state of Maryland. Here we extended this work to provide a consistent geospatial approach for monitoring changes in forest carbon stocks over time. Utilizing the same data and modeling system developed previously for planning, we integrated historical input data on weather and disturbance to reconstruct the history of vegetation dynamics and forest above-ground carbon stocks annually over the period 1984–2016 at 30 m resolution and provided an extension to 2023. Statewide, forested land had an average annual net above ground carbon sink of 1.37 TgC yr⁻¹, comparable to prior estimates. However, unlike the prior estimates, there was considerable variation around this mean. The statewide net above ground flux ranged interannually from −0.65 to 2.77 Tg C yr⁻¹. At the county scale, the average annual net above ground flux ranged spatially from 0.01 to 0.13 Tg C yr⁻¹ and spatiotemporally from −0.43 to 0.24 Tg C yr⁻¹. Attribution analyses indicate the primary importance of persistent and regrowing forests, vegetation structure, local disturbance, and rising CO₂ to the mean flux, and the primary importance of weather to the large-scale interannual variability. These results have important implications for state climate mitigation planning, reporting and assessment. With this approach, it is now possible to monitor changes in forest carbon stocks spatiotemporally over policy relevant domains with a consistent framework that is also enabled for future planning.

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