Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations
Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations
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DOI：
10.1029/2010jg001566
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发表时间：
2011-09-03
影响因子：
3.7
通讯作者：
Williams, Christopher
中科院分区：
文献类型：
--
作者：
Jung, Martin;Reichstein, Markus;Williams, Christopher
我们使用机器学习技术模型树集成（MTE）将FLUXNET对二氧化碳，水和能量通量的观测升级到全球范围。我们训练MTE预测站点级总初级生产力（GPP），陆地生态系统呼吸（TER），净生态系统交换（NEE），潜热（LE），和显热（H）的基础上遥感指数，气候和气象数据，以及土地利用信息。我们应用经过训练的MTEs以0.5度x 0.5度的空间分辨率和1982年至2008年的每月时间分辨率生成全球通量场。交叉验证分析显示，MTE在预测NEE站点通量变化方面表现良好，建模效率（MEf）在0.64和0.84之间，除了NEE（MEf = 0.32）。在预测季节性模式方面也表现良好（MEf在0.84和0.89之间，除了NEE（0.64））。相比之下，月异常的预测没有那么强（MEf在0.29和0.52之间）。改进对扰动和滞后环境影响的核算，沿着改进对训练数据集中错误的定性，将最有助于进一步减少不确定性。我们对LE（158 +/- 7 J x 10（18）yr（-1））、H（164 +/- 15 J x 10（18）yr（-1））和GPP（119 +/- 6 Pg C yr（-1））的全球估计值与独立估计值相似。我们的全球TER估计值（96 +/- 6 Pg C yr（-1））可能被低估了5- 10%。碳通量年际变化的热点区域发生在半干旱半湿润地区，并受到水分供应的控制。总的来说，GPP对NEE的年际变化比TER更重要。我们的经验得出的通量可用于陆地表面过程模型的校准和评估，以及生物圈的探索性和诊断性评估。
We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.