Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America
Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America
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通过卫星和基于过程的模型估算的北美地区初级生产总值的高峰生长季节模式和极端气候驱动的响应
DOI：
10.1016/j.agrformet.2020.108292
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发表时间：
2021-03
影响因子：
6.2
通讯作者：
Pierre Friedlingstein
中科院分区：
文献类型：
--
作者：
Wei He;Weimin Ju;Fei Jiang;Nicholas Parazoo;Pierre Gentine;Wu Xiaocui;Zhang Chunhua;Zhu Jiawen;Nicolas Viovy;Atul K. Jain;Stephen Sitch;Pierre Friedlingstein
CO2季节峰值吸收和气候极端效应的代表性对于准确估计陆地碳通量的年量级和年际变化具有重要意义，但不同卫星模式和基于过程（PB）的模式之间的一致性仍然知之甚少。在这里，我们调查了这些问题在北美的基础上，一个大的合奏国家的最先进的总初级生产力（GPP）模型，包括两个太阳能诱导叶绿素荧光（SIF）为基础的模型（WECANN和GOPT），三个遥感驱动的光利用效率（LUE）模型，和10 PB模型。我们发现，这两个基于SIF的GPP估计是双边一致的空间格局的生长高峰期GPP（GPPPGS，在美国玉米带地区的最大吸收）和气候极端驱动的响应。三个LUE模式的模拟结果显示了相对一致的GPPG和气候极端驱动的响应的空间格局，这与基于SIF的估计和基于卫星的度量一致。与基于SIF和LUE的估计明显不同，PB模型的模拟表现出明显的差异，并且大多数未能合理地复制GPPPGS的空间格局。此外，卫星模型和PB模型在捕捉极端气候对GPP的影响方面具有相当的能力，但不同模型之间的影响程度存在明显差异，前者在定位极端气候引起的GPP变化方面优于后者。我们讨论了这种差异的可能来源，在国家的最先进的模型，重点是PB模型。改进关键变量（如叶面积指数）的参数化和更好地表征环境压力，可以使PB模型更可靠地估计大尺度陆地GPP，从而有助于准确评估全球碳收支，更好地了解气候变化对陆地碳循环的影响。我们的研究提供了一个基线，以改善大规模的估计陆地GPP。
Representations of the seasonal peak uptake of CO2and climate extremes effects have important implications for accurately estimating annual magnitude and inter-annual variations of terrestrial carbon fluxes, however the consistency of such representations among different satellite models and process-based (PB) models remain poorly known. Here we investigated these issues over North America based on a large ensemble of state-of-the-art gross primary production (GPP) models, including two solar-induced chlorophyll fluorescence (SIF)-based models (WECANN and GOPT), three remote sensing driven light-use efficiency (LUE) models, and 10 PB models. We found that the two SIF-based GPP estimates were bilaterally consistent in spatial patterns of peak growing season GPP (GPPPGS; with the largest uptake at the Corn-Belt area in the United States) and climate extremes-driven responses. The simulations from three LUE models showed relatively consistent spatial patterns of GPPPGSand climate extremes-driven responses, which agreed well with SIF-based estimates and satellite based metrics. Obviously differed from SIF and LUE based estimates, the simulations from PB models exhibited noticeable divergences and mostly failed to reasonably replicate the spatial pattern of GPPPGS. In addition, satellite models and PB models were comparably able to capture the effects of climate extremes on GPP, but showing obvious divergences in the magnitude of impacts among different models, and the former outperformed the latter in locating GPP changes caused by climate extremes. We discussed the possible origins of such discrepancies in state-of-the-art models with focus on PB models. Improving the parameterizations of critical variables (e.g. leaf area index) and better characterizing environmental stresses could lead to more robust estimates of large-scale terrestrial GPP with PB models, thus serving for accurately assessing global carbon budget and better understanding the impacts of climate change on the terrestrial carbon cycle. Our study offers a baseline for improving large-scale estimation of terrestrial GPP.
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影响因子：
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