Boya Zhou, Wenjia Cai, Ziqi Zhu, Han Wang, Sandy P. Harrison, I. Colin Prentice
{"title":"A General Model for the Seasonal to Decadal Dynamics of Leaf Area","authors":"Boya Zhou, Wenjia Cai, Ziqi Zhu, Han Wang, Sandy P. Harrison, I. Colin Prentice","doi":"10.1111/gcb.70125","DOIUrl":null,"url":null,"abstract":"<p>Leaf phenology, represented at the ecosystem scale by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO<sub>2</sub>, energy, and water between the land and atmosphere. Robust simulation of leaf phenology is thus important for both dynamic global vegetation models (DGVMs) and land-surface representations in climate and Earth System models. There is no general agreement on how leaf phenology should be modeled. However, a recent theoretical advance posits a universal relationship between the time course of “steady-state” gross primary production (GPP) and LAI—that is, the mutually consistent LAI and GPP that would pertain if weather conditions were held constant. This theory embodies the concept that leaves should be displayed when their presence is most beneficial to plants, combined with the reciprocal relationship of LAI and GPP via (a) the Beer's law dependence of GPP on LAI, and (b) the requirement for GPP to support the allocation of carbon to leaves. Here we develop a global prognostic LAI model, combining this theoretical approach with a parameter-sparse terrestrial GPP model (the P model) that achieves a good fit to GPP derived from flux towers in all biomes and a scheme based on the P model that predicts seasonal maximum LAI as the lesser of an energy-limited rate (maximizing GPP) and a water-limited rate (maximizing the use of available precipitation). The exponential moving average method is used to represent the time lag between leaf allocation and modeled steady-state LAI. The model captures satellite-derived LAI dynamics across biomes at both site and global levels. Since this model outperforms the 15 DGVMs used in the TRENDY project, it could provide a basis for improved representation of leaf-area dynamics in vegetation and climate models.</p>","PeriodicalId":175,"journal":{"name":"Global Change Biology","volume":"31 3","pages":""},"PeriodicalIF":10.8000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcb.70125","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Change Biology","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/gcb.70125","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIODIVERSITY CONSERVATION","Score":null,"Total":0}
引用次数: 0
Abstract
Leaf phenology, represented at the ecosystem scale by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO2, energy, and water between the land and atmosphere. Robust simulation of leaf phenology is thus important for both dynamic global vegetation models (DGVMs) and land-surface representations in climate and Earth System models. There is no general agreement on how leaf phenology should be modeled. However, a recent theoretical advance posits a universal relationship between the time course of “steady-state” gross primary production (GPP) and LAI—that is, the mutually consistent LAI and GPP that would pertain if weather conditions were held constant. This theory embodies the concept that leaves should be displayed when their presence is most beneficial to plants, combined with the reciprocal relationship of LAI and GPP via (a) the Beer's law dependence of GPP on LAI, and (b) the requirement for GPP to support the allocation of carbon to leaves. Here we develop a global prognostic LAI model, combining this theoretical approach with a parameter-sparse terrestrial GPP model (the P model) that achieves a good fit to GPP derived from flux towers in all biomes and a scheme based on the P model that predicts seasonal maximum LAI as the lesser of an energy-limited rate (maximizing GPP) and a water-limited rate (maximizing the use of available precipitation). The exponential moving average method is used to represent the time lag between leaf allocation and modeled steady-state LAI. The model captures satellite-derived LAI dynamics across biomes at both site and global levels. Since this model outperforms the 15 DGVMs used in the TRENDY project, it could provide a basis for improved representation of leaf-area dynamics in vegetation and climate models.
期刊介绍:
Global Change Biology is an environmental change journal committed to shaping the future and addressing the world's most pressing challenges, including sustainability, climate change, environmental protection, food and water safety, and global health.
Dedicated to fostering a profound understanding of the impacts of global change on biological systems and offering innovative solutions, the journal publishes a diverse range of content, including primary research articles, technical advances, research reviews, reports, opinions, perspectives, commentaries, and letters. Starting with the 2024 volume, Global Change Biology will transition to an online-only format, enhancing accessibility and contributing to the evolution of scholarly communication.