Low-latency forecasting framework for assessing ENSO impacts on terrestrial carbon cycle

Increasing wetland CH₄ emissions are projected to have significant implications for keeping global warming below 2 °C. However, to better understand the future dynamics of wetland CH₄ emissions, improved deployment of observational systems (e.g., aircraft and flux towers) is required. One avenue to allow targeted field observations is to improve subseasonal-to-seasonal (S2S) CH₄ forecasting. Here, we present a workflow that enables low-latency (<4-week lag) ecosystem model carbon cycle products associated with the United States of America Interagency Greenhouse Gas (GHG) Center. The workflow increases accessibility to the LPJwsl v2.0 dynamic global vegetation model by providing near-real-time CH₄, CO₂, and other carbon cycle products to science users, allowing interaction with the codebase from a user-friendly website front-end integrated with Amazon Web Services computing resources. In the immediate future, with this increase in accessibility and decreased latency in carbon cycle products, analyses related to the onset of the 2023 El Niño mode of ENSO can be rapidly implemented to improve our understanding of carbon cycle dynamics. To demonstrate the utility of the near-real-time carbon cycle products, we couple 9-month GMAO GEOS climate forecast data with MERRA2 S2S reanalysis data to forecast LPJ carbon products until the end of the 2024 calendar year. LPJ S2S carbon forecasts are verified against historic ENSO anomalies for skillfulness. We highlight regions and periods forecasted to have anomalously higher or lower CH₄ emissions during the late 2023 and early 2024 strong El Niño cycle. S2S CH₄ forecasts enable the pre-positioning of in situ measurement networks to improve the coverage of CH₄ observations. By migrating institutional, high-performance computing processes to a cloud-ecosystem framework, we provide increased access to carbon cycle products on a near-real-time basis.