@Article{Beadling_BAMS_20260401,
 author		= {Rebecca L. Beadling and Ranjini Swaminathan and Romain Beucher and Ed Blockley and Swen Brands and Birgit Hassler and Dora Heged\H{u}s and Forrest M. Hoffman and Jiwoo Lee and Jared Lewis and Jianhua Lu and Elizaveta Malinina and Brian Medeiros and Enrico Scoccimarro and Jerry Tjiputra and Briony Turner and Duncan Watson-Parris},
 title		= {Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices},
 journal	= BAMS,
 volume		= 107,
 number		= 4,
 pages		= {E813--E835},
 doi		= {10.1175/BAMS-D-25-0079.1},
 day		= 1,
 month		= apr,
 year		= 2026,
 abstract	= {Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space--time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.}
}