评估地球系统模型中植被建模的机器学习框架

IF 4.4 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Advances in Modeling Earth Systems Pub Date : 2024-07-19 DOI:10.1029/2023MS004097
Ranjini Swaminathan, Tristan Quaife, Richard Allan
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引用次数: 0

摘要

植被总初级生产力(GPP)是陆地生物圈中最大的碳通量,它反过来又负责封存 25%-30% 的人为二氧化碳排放。因此,建立 GPP 模型的能力对于计算碳预算和了解气候反馈至关重要。地球系统模式(ESM)有能力模拟全球升温潜能值(GPP),但其各自的估算结果差异很大,导致了很大的不确定性。我们介绍了一种机器学习(ML)方法,用于研究造成 ESM 模拟的 GPP 数量差异的两个关键因素:不同大气驱动因素的相对重要性和对陆地表面过程表示的差异。我们介绍了开发可解释 ML 框架的不同步骤,包括算法选择、参数调整、训练和评估。我们的研究结果表明,ESM 在很大程度上同意文献中提到的对 GPP 起作用的物理气候驱动因素,例如地中海地区的干旱变量或北极地区的辐射和温度。然而,由于模型并不一定就哪个变量与全球升温潜能值最相关达成一致,因此确实存在差异。我们还探讨了将 GPP 差异归因于气候影响与过程差异的距离测量法,并举例说明了我们的方法在哪些地方有效(南亚、地中海),在哪些地方无效(北美东部)。
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A Machine Learning Framework to Evaluate Vegetation Modeling in Earth System Models

Vegetation gross primary productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering 25%–30% of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth system models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a machine learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable ML framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don't necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean) and where they are inconclusive (Eastern North America).

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来源期刊
Journal of Advances in Modeling Earth Systems
Journal of Advances in Modeling Earth Systems METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
11.40
自引率
11.80%
发文量
241
审稿时长
>12 weeks
期刊介绍: The Journal of Advances in Modeling Earth Systems (JAMES) is committed to advancing the science of Earth systems modeling by offering high-quality scientific research through online availability and open access licensing. JAMES invites authors and readers from the international Earth systems modeling community. Open access. Articles are available free of charge for everyone with Internet access to view and download. Formal peer review. Supplemental material, such as code samples, images, and visualizations, is published at no additional charge. No additional charge for color figures. Modest page charges to cover production costs. Articles published in high-quality full text PDF, HTML, and XML. Internal and external reference linking, DOI registration, and forward linking via CrossRef.
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