Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Science Pub Date : 2024-06-19 DOI:10.1016/j.jocs.2024.102366
Gautam Bisht , William J. Riley , Richard Tran Mills
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Abstract

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO2, and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO2, and soil moisture) on canopy processes (i.e. net CO2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO2 assimilation and increased air temperature. Net CO2 assimilation increased at higher atmospheric CO2, while decreasing soil moisture resulted in lower leaf water potential.

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为 E3SM 土地模型开发支持异构计算的多层冠层模型
植被冠层的垂直结构创造了微气候。然而,包括能源超大规模地球系统模式(ESM)在内的大多数地球系统模式的陆地部分通常忽略垂直冠层结构,使用单层大叶表示法来模拟陆地与大气之间的水、二氧化碳和能量交换。在这项研究中,我们为 E3SM 陆地模型开发了多层冠层模型,以解决植被冠层造成的微气候问题。本研究开发的模型重新实现了 CLM-ml_v1,以支持由 CPU 和 GPU 组成的异构计算架构,并包括三个额外的基于优化的气孔导度模型。使用可移植、可扩展的科学计算工具包,GPU 的计算速度比 CPU 快 25-50 倍。利用 Ameriflux 美国-密歇根大学生物站的数据,对模型的数值实施与 CLM-ml_v1 进行了为期一个月的模拟验证。通过对五个气孔导度模型进行控制模拟,探索了模型结构的不确定性,这些模型排除和包括了植物流体力学(PHD)对光合作用的控制。在模型中考虑 PHD 时,模拟的显热和潜热通量偏差较小。此外,还进行了六次理想化模拟,研究三个环境变量(即气温、大气二氧化碳和土壤湿度)对冠层过程(即二氧化碳净同化、叶片温度和叶片水势)的影响。气温升高会降低二氧化碳净同化量,气温升高则会增加二氧化碳净同化量。大气二氧化碳含量越高,二氧化碳净同化量越大,而土壤水分减少则导致叶片水势降低。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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