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Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review

期刊

GEOSCIENTIFIC MODEL DEVELOPMENT
卷 14, 期 9, 页码 5863-5889

出版社

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/gmd-14-5863-2021

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  1. Natural Sciences and Engineering Research Council of Canada
  2. (Canada Graduate Scholarships -Doctoral Program)

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The review summarizes the current methods of terrestrial biogeochemical cycle validation utilized by CMIP6 participants and concurrent community model comparison studies, focusing on evaluation dimensions, observation-based reference datasets, and model performance metrics. To ensure objective and thorough validations for the seventh phase of CMIP (CMIP7), a standard validation protocol employing a broad suite of certainty-weighted observation-based reference datasets, targeted model performance metrics, and comparisons across a range of spatiotemporal scales is recommended.
The vital role of terrestrial biogeochemical cycles in influencing global climate change is explored by modelling groups internationally through land surface models (LSMs) coupled to atmospheric and oceanic components within Earth system models (ESMs). The sixth phase of the Coupled Model Intercomparison Project (CMIP6) provided an opportunity to compare ESM output by providing common forcings and experimental protocols. Despite these common experimental protocols, a variety of terrestrial biogeochemical cycle validation approaches were adopted by CMIP6 participants, leading to ambiguous model performance assessment and uncertainty attribution across ESMs. In this review we summarize current methods of terrestrial biogeochemical cycle validation utilized by CMIP6 participants and concurrent community model comparison studies. We focus on variables including the dimensions of evaluations, observation-based reference datasets, and metrics of model performance. To ensure objective and thorough validations for the seventh phase of CMIP (CMIP7), we recommend the use of a standard validation protocol employing a broad suite of certainty-weighted observation-based reference datasets, targeted model performance metrics, and comparisons across a range of spatiotemporal scales.

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