Kuribayashi Masatoshi, Noh Nam-Jin, Saitoh Taku M, Ito Akihiko, Wakazuki Yasutaka, Muraoka Hiroyuki
Nagano Environmental Conservation Research Institute, 2054-120 Kitago, Nagano, 381-0075, Japan.
River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan.
Int J Biometeorol. 2017 Jun;61(6):989-1001. doi: 10.1007/s00484-016-1278-9. Epub 2016 Dec 6.
Accurate projection of carbon budget in forest ecosystems under future climate and atmospheric carbon dioxide (CO) concentration is important to evaluate the function of terrestrial ecosystems, which serve as a major sink of atmospheric CO. In this study, we examined the effects of spatial resolution of meteorological data on the accuracies of ecosystem model simulation for canopy phenology and carbon budget such as gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) of a deciduous forest in Japan. Then, we simulated the future (around 2085) changes in canopy phenology and carbon budget of the forest by incorporating high-resolution meteorological data downscaled by a regional climate model. The ecosystem model overestimated GPP and ER when we inputted low-resolution data, which have warming biases over mountainous landscape. But, it reproduced canopy phenology and carbon budget well, when we inputted high-resolution data. Under the future climate, earlier leaf expansion and delayed leaf fall by about 10 days compared with the present state was simulated, and also, GPP, ER and NEP were estimated to increase by 25.2%, 23.7% and 35.4%, respectively. Sensitivity analysis showed that the increase of NEP in June and October would be mainly caused by rising temperature, whereas that in July and August would be largely attributable to CO fertilization. This study suggests that the downscaling of future climate data enable us to project more reliable carbon budget of forest ecosystem in mountainous landscape than the low-resolution simulation due to the better predictions of leaf expansion and shedding.
准确预测未来气候和大气二氧化碳(CO₂)浓度下森林生态系统的碳收支,对于评估作为大气CO₂主要汇的陆地生态系统功能至关重要。在本研究中,我们研究了气象数据的空间分辨率对日本落叶林冠层物候和碳收支(如总初级生产力(GPP)、生态系统呼吸(ER)和净生态系统生产力(NEP))的生态系统模型模拟准确性的影响。然后,我们通过纳入由区域气候模型降尺度得到的高分辨率气象数据,模拟了该森林未来(约2085年)冠层物候和碳收支的变化。当输入低分辨率数据时,生态系统模型高估了GPP和ER,这些低分辨率数据在山区存在变暖偏差。但是,当输入高分辨率数据时,该模型能很好地再现冠层物候和碳收支。在未来气候条件下,模拟结果显示与当前状态相比,叶片展叶提前、落叶延迟约10天,并且GPP、ER和NEP预计分别增加25.2%、23.7%和35.4%。敏感性分析表明,6月和10月NEP的增加主要由温度升高引起,而7月和8月的增加则主要归因于CO₂施肥效应。本研究表明,与低分辨率模拟相比,未来气候数据的降尺度处理能够使我们更可靠地预测山区森林生态系统的碳收支,因为其对叶片展叶和落叶的预测更好。