Xueli Huo, Andrew M. Fox, Hamid Dashti, Charles Devine, William Gallery, William K. Smith, Brett Raczka, Jeffrey L. Anderson, Alistair Rogers, David J. P. Moore
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引用次数: 0
摘要
要准确预测北极-北方地区对快速变化的气候的响应,碳吸收和碳储存的模型表示至关重要。在北极和北方地区,对碳吸收和储存模型预测有显著影响的陆地模型对陆地植被覆盖率(LAI)和地上生物量的估算存在很大差异,因此正确评估模型对总初级生产力(GPP)的估算具有挑战性。为了了解并纠正社区土地模型(CLM)中的 LAI 和地上生物量偏差,我们在包括阿拉斯加和加拿大西部在内的实验区域,使用集合调整卡尔曼滤波器(EAKF)将 8 天中分辨率成像分光仪(MODIS)的 LAI 观测数据和年地上生物量的机器学习产品同化到社区土地模型中。将 LAI 和地上生物量同化后,这些模型估计值分别减少了 58% 和 72%。地上生物量的变化与区域和地点层面对冠顶高度的独立估算一致。国际陆地模型基准系统评估表明,数据同化显著提高了陆地模型模拟碳循环和水文循环的性能,以及表现 LAI 与其他变量之间功能关系的性能。为了进一步减少 LAI 偏差校正后剩余的 GPP 偏差,我们对 CLM 进行了重新参数化,以考虑低温对光合作用的抑制。包含新参数化的 LAI 偏差校正模型与模型基准的一致性最好。将数据同化与模型参数化相结合,为评估 LSM 中的光合作用过程提供了一个有用的框架。
Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region
Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.
期刊介绍:
JGR-Biogeosciences focuses on biogeosciences of the Earth system in the past, present, and future and the extension of this research to planetary studies. The emerging field of biogeosciences spans the intellectual interface between biology and the geosciences and attempts to understand the functions of the Earth system across multiple spatial and temporal scales. Studies in biogeosciences may use multiple lines of evidence drawn from diverse fields to gain a holistic understanding of terrestrial, freshwater, and marine ecosystems and extreme environments. Specific topics within the scope of the section include process-based theoretical, experimental, and field studies of biogeochemistry, biogeophysics, atmosphere-, land-, and ocean-ecosystem interactions, biomineralization, life in extreme environments, astrobiology, microbial processes, geomicrobiology, and evolutionary geobiology