Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making - Kaiyu Guan; Rongzhu Qin; Zewei Ma; Lei Zhao; Licheng Liu; Sheng Wang; Trevor F. Keenan; Mengqi Jia; Debjani Sihi; Eric Potash; Senthold Asseng; Jingyun Tang; et al. (Global Change Biology, 2026)
Ecosystem models are increasingly central to the decision‐making for environmental policy, conservation planning, and climate‐related investments. Yet, the growing reliance on Multi‐Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision‐making‐relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter‐model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks—MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co‐design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision‐relevant information. Building upon the past success and lessons from the existing MIPs‐MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter‐dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs‐MMEs. We highlighted the under‐recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision‐making.