Prediction of net primary productivity in the middle-to-high latitudes of Eurasia based on snow and soil temperature

Abstract

Net primary productivity (NPP) is the net accumulation of organic matter by vegetation through photosynthesis and serves as a key indicator for exploring vegetation responses to climate change. Considering the remote and local impacts of soil heat capacities on vegetation growth through pathways of atmospheric circulation and land–atmosphere interaction, this paper develops a statistical prediction model for NPP from April to June (AMJ) across the middle-to-high latitudes of Eurasia. The model introduces two physically meaningful predictors: the snow water equivalent (SWE) from February to March (FM) over central Europe and the FM local soil temperature (ST). The positive phase of FM SWE triggers anomalous eastward-propagating Rossby waves, leading to an anomalous low-pressure system and cooling in the middle-to-high latitudes of Eurasia. This effect persists into spring through snow feedback to the atmosphere and affects subsequent NPP changes. The ST is closely related to the AMJ temperature and precipitation. With positive ST anomalies, the AMJ temperature and precipitation exhibit an east–west dipole anomaly distribution in this region. The single-factor prediction scheme using ST as the predictor is much better than using SWE as the predictor. Independent validation results from 2009 to 2014 demonstrate that the ST scheme alone has good predictive performance for the spatial distribution and interannual variability of NPP. The predictive skills of the multi-factor prediction schemes can be improved by about 13 % if the ST predictor is included. The findings confirm that local ST is a predictor that must be included for NPP prediction.摘要净初级生产力 (NPP) 是植被通过光合作用积累机物质的净效益, 是探索植被对气候变化响应的关键指标. 考虑到陆面热力异常通过陆气相互作用和大尺度环流对植被生长的影响, 本文研制了欧亚中高纬地区4–6月NPP预测模型. 该模型引入了两个具有明确物理意义的预测因子: 欧洲中部2–3月的雪水当量 (SWE) 和2–3月局地土壤温度 (ST). SWE的正异常会触发异常东传的Rossby波, 导致下游出现位势高度负异常并引起降温. 通过雪和温度的正反馈, 这种异常低温持续到春季并使得NPP下降. ST与随后季节的温度和降水密切相关, 当ST出现正异常时, 该地区的4–6月温度和降水呈现出东西向偶极子异常分布.对比各方案的预测效果发现, ST比SWE有更好的预测能力. 2009–2014年独立后报结果显示, ST方案对NPP的空间分布和年际变率都有很好的预测效果. 交叉检验的结果显示, 多因子方案中引入ST后能提高模型13 %的预测技巧.