Data from: Model-data assimilation of multiple phenological observations to constrain and predict leaf area index

Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002–2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.

我们对叶片物候的精准模拟能力不足，是生态系统碳循环模型不确定性的主要来源之一。本研究评估了通过观测数据持续更新冠层状态变量，是否有助于提升物候事件的预测精度。我们采用集合调整卡尔曼滤波（ensemble adjustment Kalman filter, EAKF），针对美国威斯康星州威洛克里克流域一处成熟、林龄均匀的北方落叶硬阔林，利用2002—2005年的塔架式光合有效辐射（photosynthetically active radiation, PAR）观测数据与中分辨率成像光谱仪（moderate resolution imaging spectrometer, MODIS）数据，对叶面积指数（leaf area index, LAI）与叶片扩展的预测结果进行更新。本研究以生态系统人口动力学模型第二版（ecosystem demography model version 2, ED2）作为预测模型，驱动数据为离线气候资料。EAKF可成功融合观测信息与模型预测结果，二者的权重由各自的不确定性决定。最终得到的估算结果，相较于参数化模型预测结果，更准确地重现了观测到的春秋季叶片物候周期。研究结果表明：春季时，观测数据在确定正确的芽萌动日期中发挥主要作用，此后模型可实现良好的预测效果；但要精准模拟秋季叶片衰老过程，则需要持续通过观测数据更新模型。尽管春季预测的二氧化碳净生态系统交换量（net ecosystem exchange, NEE）早于塔架观测值与未同化观测数据的模型预测结果，但整体而言，相较于仅使用模型的预测结果，同化后的预测结果更贴合观测到的NEE变化。本研究结果证实，状态数据同化可有效模拟植物叶片物候的演化过程，并提升森林生态系统二氧化碳净交换量的模型预测精度。