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.

The Toxicology Investigators Consortium 2024 Annual Report - Kim Aldy; Shao Li; Mari Costantini; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; Michael Abesamis; Faiz Ahmed; Salman Ahsan; Peter Akpunonu; Adam Algren; et al. (Journal of Medical Toxicology, 2026)

Earth's record-high greenness and its attributions in 2020 - Yulong Zhang; Jiafu Mao; Ge Sun; Qinfeng Guo; Jeffrey Atkins; Wenhong Li; Mingzhou Jin; Conghe Song; Jingfeng Xiao; Taehee Hwang; Tong Qiu; Lin Meng; et al. (Remote Sensing of Environment, 2025)

Quantifying the long-term changes of terrestrial water storage and their driving factors - Xiaoying Shi; Yaoping Wang; Jiafu Mao; Peter E. Thornton; Daniel M. Ricciuto; Forrest M. Hoffman; Yuefeng Hao (Journal of Hydrology, 2024)

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration - Istem Fer; Anthony K. Gardella; Alexey N. Shiklomanov; Eleanor E. Campbell; Elizabeth M. Cowdery; Martin G. De Kauwe; Ankur Desai; Matthew J. Duveneck; Joshua B. Fisher; Katherine D. Haynes; Forrest M. Hoffman; Miriam R. Johnston; et al. (Global Change Biology, 2021)
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.

The Earth has humans, so why don’t our climate models? - Brian Beckage; Katherine Lacasse; Jonathan M. Winter; Louis J. Gross; Nina Fefferman; Forrest M. Hoffman; Sara S. Metcalf; Travis Franck; Eric Carr; Asim Zia; Ann Kinzig (Climatic Change, 2020)

CTFS ‐Forest GEO : a worldwide network monitoring forests in an era of global change - Kristina J. Anderson‐Teixeira; Stuart J. Davies; Amy C. Bennett; Erika B. Gonzalez‐Akre; Helene C. Muller‐Landau; S. Joseph Wright; Kamariah Abu Salim; Angélica M. Almeyda Zambrano; Alfonso Alonso; Jennifer L. Baltzer; Yves Basset; Norman A. Bourg; et al. (Global Change Biology, 2015)
Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long‐term forest dynamics research sites ( CTFS ‐Forest GEO ) useful for characterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS ‐Forest GEO spans 25°S–61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS ‐Forest GEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 °C), changes in precipitation (up to ±30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m −2 yr −1 and 3.1 g S m −2 yr −1 ), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS ‐Forest GEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS ‐Forest GEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models - JAMES T. RANDERSON; FORREST M. HOFFMAN; PETER E. THORNTON; NATALIE M. MAHOWALD; KEITH LINDSAY; YEN‐HUEI LEE; CYNTHIA D. NEVISON; SCOTT C. DONEY; GORDON BONAN; RETO STÖCKLI; CURTIS COVEY; STEVEN W. RUNNING; et al. (Global Change Biology, 2009)
With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon‐LAnd Model Intercomparison Project (C‐LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) – Carnegie‐Ames‐Stanford Approach′ (CASA′) and carbon–nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1–3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO 2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4 Pg C yr −1 for CASA′ vs. 1.2 Pg C yr −1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988–2004 based on comparison with atmospheric inversion results from TRANSCOM ( r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C‐LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community‐wide platform for model‐data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C‐LAMP are publicly available on the Earth System Grid.

Potential of Multivariate Quantitative Methods for Delineation and Visualization of Ecoregions - William W. Hargrove; Forrest M. Hoffman (Environmental Management, 2004)