开发和评估空气污染与土地生态系统互动模型(iMAPLE)1.0 版

IF 4 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Geoscientific Model Development Pub Date : 2024-06-12 DOI:10.5194/gmd-17-4621-2024
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, C. Gong, Hui Zheng, Hong Liao
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引用次数: 0

摘要

摘要陆地生态系统是大气成分的重要来源和吸收汇。反过来,空气污染物也会影响生态系统与大气之间碳和水通量的交换率。然而,这些生物地球化学过程通常没有在地球系统模型中得到很好的呈现,从而限制了从区域到全球尺度的陆地生态系统与空气污染物之间相互作用的探索。在此,我们开发并验证了空气污染与陆地生态系统互动模型(iMAPLE),将耶鲁互动陆地生物圈模型升级为基于过程的水循环、火灾排放、湿地甲烷(CH4)排放和基于性状的臭氧(O3)破坏。在 iMAPLE 中,土壤水分和温度是根据土壤层中的水分和能量平衡动态计算的。火灾排放取决于干燥度、闪电、人口和燃料负荷。湿地 CH4 通过氧化、沸腾、扩散和植物介导的迁移产生并消耗。基于性状的方案将不同植物功能类型(PFTs)对 O3 的敏感性与单位面积的叶片质量统一起来。验证结果表明,在 201 个通量塔站点的六个植物功能类型中,总初级生产力(GPP)的相关系数(R)为 0.59-0.86,蒸散量(ET)的相关系数(R)为 0.57-0.84,44 个站点的甲烷排放量的平均相关系数(R)为 0.68。模拟的土壤水分和温度与再分析数据相匹配,R 值高达 0.86 以上,归一化平均偏差(NMBs)低至 7%以内,从而根据卫星观测数据对 2001-2013 年全球 GPP(R 值=0.92,NMB=1.3%)和蒸散发(R 值=0.93,NMB=-10.4%)进行了合理模拟。该模型预测全球每年的焚烧面积为 5.071 亿公顷,接近 1997-2016 年的观测值 4.754 亿公顷,空间 R 值为 0.66。据估计,2000-2014 年期间湿地的 CH4 排放量为 153.45 Tg [CH4]/yr-1,接近多模型平均值 148 Tg [CH4]/yr-1。该模型还显示了 GPP 和蒸散发对漫射辐射变化的合理响应,并得出 O3 对全球 GPP 的平均损害为 2.9%。iMAPLE 为研究陆地生态系统与空气污染物之间的相互作用提供了先进的工具。
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Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
Abstract. Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and the atmosphere. However, these biogeochemical processes are usually not well presented in Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive Terrestrial Biosphere Model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and trait-based ozone (O3) damage. Within iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59–0.86 for gross primary productivity (GPP) and 0.57–0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with high R above 0.86 and low normalized mean biases (NMBs) within 7 %, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3 %) and ET (R=0.93, NMB=-10.4 %) against satellite-based observations for 2001–2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997–2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr−1 during 2000–2014, close to the multi-model mean of 148 Tg [CH4] yr−1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation and yields mean O3 damage of 2.9 % to global GPP. iMAPLE provides an advanced tool for studying the interactions between land ecosystems and air pollutants.
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来源期刊
Geoscientific Model Development
Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
8.60
自引率
9.80%
发文量
352
审稿时长
6-12 weeks
期刊介绍: Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.
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