What information on contemporary terrestrial carbon dynamics can an integration of current geospatial data confidently provide?

Abstract. The terrestrial carbon (C) cycle is a key global biogeochemical cycle, linked to climate change and regulation, provisioning and regulating ecosystem services and underpinning human livelihoods. To understand climate risks and to support the development of effective climate mitigation strategies requires rigorous, uncertainty bounded, systemic information on C dynamics and their underpinning ecological processes. Significant effort has been invested into attempting to quantify current C stocks and exchanges of C. These data are often incomplete in space and time, contain poorly  characterised errors, and lack internal consistency checks, making the construction of a reliable global C budget a significant challenge. Here, we ask what information on contemporary terrestrial carbon dynamics can an integration of current geospatial data confidently provide?

We present a global (0.5 × 0.5 deg), multi-decadal (2003–2024) analysis of the terrestrial C-cycle at a monthly time step. Using a Bayesian model-data fusion framework (CARDAMOM) we calibrate a model of terrestrial ecosystems (DALEC) integrating diverse ecological spatio-temporal observations (leaf area, absorbed photosynthetically active radiation, gross primary production, woody biomass, soil C), and forcing (meteorology, atmospheric CO₂, burned area and forest loss). CARDAMOM propagates observational uncertainties through to the retrieved DALEC parameters (i.e. ecosystem properties) and simulates C states and fluxes for each of 55,246 pixels across the vegetated land surface. The resulting analysis is systemic (i.e. consistent between states and fluxes) and can support national and international policy development (e.g., Global Carbon Project).

Our analysis shows that current global multi-decadal datasets are inadequate to provide > 95 % confidence on the sign of net exchange across 91 % of the vegetated land surface. At this confidence level we can identify 0.23 % of vegetated land is a net source, with 0.75 % a net sink and 7.3 % as neutral. Although we note that at 68 % confidence we the area identified as likely a sink increases to 30 % without change to the area identified as neutral or a source. We show that uncertainty in net C exchange is strongly correlated with the dynamics of wood (r = 0.8) and soil organic matter (r = 0.7) but poorly correlated with gross fluxes which have historically been a focus of research such as photosynthesis (r = 0.1). New satellite missions such as BIOMASS have more accurate wood biomass sensing, and so should enhance confidence in sink/source detection in the next five years. Soil C dynamics data remain a major challenge, with research and investment required to provide a global constraint on soil C stock changes.