From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool

Abstract

Supporting a transition to net-zero carbon (C) emissions is a key component of international action to avoid dangerous climate change. Science has outlined potential routes to net-zero, which include using nature-based solutions to grow C sinks and diminish sources linked to land use and land use change. However, decision-makers are challenged by ongoing climate change and the complexity of the biosphere, interacting with socio-economic constraints. Decision-makers need science-based, but easy to use, tools to understand the current and potential future states of the terrestrial C-cycle, and its sensitivity to their decisions. These tools must provide clear uncertainty estimates to help take account of risks, must be flexible enough to be updated as new data become available, and simple enough to be deployed broadly. We argue that model-data fusion approaches, combining the systemic ecological theory embedded in intermediate complexity models with an ever-expanding collection of ecosystem observations from field and remote sensing campaigns, provide the scientific means to address each of these challenges and therefore facilitate management decisions as we face an uncertain future.