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Improving our ability to understand and predict the dynamics of the terrestrial carbon cycle remains a pressing challenge despite a rapidly growing volume and diversity of Earth Observation data. State data assimilation represents a path forward via an iterative cycle of making process-based forecasts and then statistically reconciling these forecasts against numerous ground-based and remotely-sensed data constraints into a “reanalysis” data product that provides full spatiotemporal carbon budgets with robust uncertainty accounting. Here we report on an >100x expansion of the PEcAn+SIPNET reanalysis from 500 sites CONUS, 25 ensemble members, and 2 data constraints to 6400 sites across North America, 100 ensemble members, and 5 data constraints: GEDI and Landtrendr AGB, MODIS LAI, SoilGrids Soil C, and SMAP soil moisture. We also report on an ensemble-based machine learning (ML) downscaling to a 1km product that preserves spatial, temporal, and across-variable covariances and demonstrate the impacts of these covariances on uncertainty accounting (Fig. 1). Synergistically, we use the same ML models to assess what climate, vegetation, and soil variables explain the spatiotemporal variability in different C pools and fluxes. In addition, we review a wide range of ongoing validation activities, comparing the outputs of the reanalysis against withheld data from: Ameriflux and NEON NEE and LE; USFS Forest Inventory biomass, biomass increment, tree rings, soil C, and litter; and NEON soil C and soil respiration. Finally, we touch on ML analyses to diagnose and correct systematic biases and emulator-based recalibration efforts. 

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities. 

Abstract This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma‐separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross‐forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short‐term dissemination and long‐term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process. 

A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.