the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Jul 2025
CMIP7 Data Request: Land and Land Ice Priorities and Opportunities
Abstract. The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community’s demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land-atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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- RC1: 'Comment on egusphere-2025-3207', Qing Bao, 09 Oct 2025 reply
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Review Comments: Accepted subject to minor revisions
This manuscript provides a comprehensive overview of the priorities and opportunities pertaining to Land and Land Ice in the CMIP7 Data Request, delivering valuable insights for guiding the development of Earth system models (ESMs) and advancing climate research. To further strengthen its scientific rigor and alignment with emerging technical paradigms (e.g., artificial intelligence [AI] and big data), the following targeted revisions and expansions are recommended—with a specific focus on comparisons with CMIP6 and the enhancements enabled by AI and big data technologies.
1. Clarify Key Differences Between CMIP7 and CMIP6 in Land and Land Ice Themes, and Their Underlying Drivers
While the manuscript acknowledges advancements from CMIP6, it lacks a systematic comparison of the substantive differences between CMIP7 and CMIP6 in three critical areas: scientific focus, variable design, and technical scope. Equally important, the motivations driving these changes (e.g., new scientific insights, evolving climate research needs, or technical advancements) are not fully elaborated. Strengthening this section will contextualize the innovations of CMIP7, highlight its unique added value, and help readers better understand the rationale for updating the Land and Land Ice themes.
2. Enhance Alignment of CMIP7 Land and Land Ice Themes with AI and Big Data Requirements
A critical gap in the manuscript is its lack of discussion on how CMIP7 can support AI/big data-driven climate research. This omission is notable given the growing role of machine learning in advancing climate science—including applications in process parameterization, uncertainty quantification, and impact prediction. To address this, the manuscript may explore how CMIP7’s variable design, data formatting, or metadata standards can be optimized to facilitate AI/ML workflows .
These revisions will strengthen the manuscript’s relevance to both traditional climate science and emerging AI/big data paradigms, ultimately ensuring that CMIP7’s Land and Land Ice data maximizes its scientific impact and societal value.