Gang Tang PhD Researcher, University of Melbourne, Australia

Title: Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data

Abstract: Reliable, robust, and consistent data are essential foundations for analysis of carbon cycle feedbacks. Here, we consider the data from multiple Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We identify a mass conservation issue in the reported carbon and nitrogen data, with a few exceptions for specific models and reporting levels. The accumulated mass imbalance in the reported data can amount to hundreds of gigatons of carbon or nitrogen by the end of the simulated period, largely exceeding the total carbon–nitrogen pool size changes over the same period. Nitrogen mass imbalance is evident across all reported organic and inorganic pools, with mineral nitrogen exhibiting the most significant cumulative mass imbalance. Due to a lack of detail in the reported data, we cannot uniquely identify the cause of this imbalance. However, we postulate that the carbon mass imbalance primarily arises from missing fluxes in the reported data and inconsistencies between these data and the definitions provided by the C4MIP protocol (e.g., land-use and fire emissions), rather than from an underlying mass conservation issue in the models themselves. Our findings suggest that future CMIP reporting protocols should consider incorporating mass conservation into their data validation processes so that such issues are caught before users have to deal with them, rather than forcing all users to handle this issue in their own way. In addition, attention from model groups to the detailed diagnostic request and definitions, along with their own quality control, will also help to avoid such issues in future. Given that no additional CMIP6 data are currently being published and none are expected in the future, we recommend that data users that rely on a closed carbon–nitrogen cycle address potential flux imbalances by using the workarounds provided in this study.

Andrew Wood Project Scientist and Research Professor, Colorado School of Mines, United States

Title: Co-developing future local-scale hydroclimate projections supporting water security studies in the continental US

Abstract: Over the last 15 years, 21st century climate projections from the Coupled Model Intercomparison Projects (CMIP) have been downscaled and used to drive hydrologic scenarios across the Contiguous United States (CONUS). The resulting datasets provide input to federal and state agency planning, guidance and policy, for water resources applications from watershed to regional scales, and for the climate-water research community. The advent of CMIP6 spurred a new effort to develop new, updated hydrologic modeling for future hydroclimate impact projections, a multi-agency collaboration that integrates Earth Science researchers with stakeholders from US federal water, climate and energy agencies. This work uses the process-oriented SUMMA land/hydrology model and mizuRoute channel routing model, which have been calibrated for CONUS and adjoining watersheds at a USGS HUC12 and MERIT-Hydro watershed resolution, a contrast to earlier grid-based land modelling approaches. Several hundred CMIP6 climate scenarios are now being downscaled to drive future hydrologic assessments that are tailored to water agency planning needs. Overall, this work required creating a new strategy for continental-scale process-based hydrological modelling, which in prior efforts has been undermined by poor calibration. Notable innovations include a powerful new parameter estimation approach based on machine-learning (ML) emulators; creating extended large-sample catchment datasets for model calibration and validation; creating a new CONUS multi-decadal high-resolution surface meteorological forcing dataset using ML methods; and the use of water management guided performance metrics to inform model training and evaluation. This presentation summarizes this CMIP6 hydroclimate dataset initiative and highlights the critical role of integrated researcher-stakeholder engagement in achieving fit-for-purpose and actionable large-domain hydrology outcomes. 

Ezequiel Cimadevilla PhD Candidate, Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Astrofísica de Andalucía, Spain.

Title: The IPCC Interactive Atlas DataLab: Online reusability for climate change assessment

Abstract: The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) introduced the Interactive Atlas as a novel tool for exploring regional climate change through spatial and temporal analyses. To enhance the accessibility, transparency, and reusability of Atlas products, we developed the IPCC Interactive Atlas DataLab, a cloud-enabled, notebook-based platform designed to operationalise FAIR data principles. The DataLab provides direct access to the gridded monthly datasets underpinning the Atlas, including harmonised outputs from CMIP and CORDEX ensembles, bias-adjusted indices, and metadata-compliant NetCDF files. It supports remote access and next-to-data computing through integrated infrastructures, allowing anyone with an Internet connection to use it. By complementing the Interactive Atlas, the DataLab enables reproducible and user-friendly access to authoritative climate projections and offers a practical framework for leveraging CMIP data in global and regional climate change assessments.