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Negative extreme events in gross primary productivity and their drivers in China during the past three decades

机译:过去三十年来,中国初级生产力及其司机的负面极端事件

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Climate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP) in China and the sub-regions during 1982-2015, using monthly GPP simulated by 12 process-based models (TRENDYv6) and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC year(-1)) in TRENDY models and 2.3% (i.e. 151 TgC year(-1)) in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (- 53 gC m(-2)year(-1)) in TRENDY models and Northeast China (-42 gC m(-2) year(-1)) in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%-50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation) while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.
机译:气候极端对生态系统产生了显着影响,预计将随着未来的全球变暖而增加。然而,只有少数研究都集中在中国生态极端事件及其司机。在这项研究中,我们在1982 - 2015年期间在中国和子地区的初级生产力(GPP)中的负极端事件进行了分析,使用了由12个基于过程的模型(TrendyV6)和基于观察的观察的月度GPP型号(Yao-GPP)。极端被定义为GPP异常的负5百分位数,其使用三维连续算法进一步合并到单个极端事件中。通过考虑1000个最大的极端事件来分析阴性GPP异常的时空模式。结果表明,极端事件的影响减少了2.8%(即208 TGC年(-1))时期的效果和2.3%(即151 TGC年(-1))在姚GPP。极端GPP赤字的热点主要在华北地区( - 53 GC M(-2)年(-2)年(-1))和中国东北(-42 GC M(-2)年(-1)年(-1))在姚明GPP。对于中国整体而言,归因分析表明,极端低降水与40%-50%的极端负面GPP事件有关。中国北部和西方的大多数事件都可以通过气象干旱(即低降水)解释,而中国南部的GPP极端事件与极端温度更加有关,特别是具有寒冷的法术。 GPP被揭示对热/干旱比冷/湿极端事件更敏感。结合中国气候极端的预计变化,中国北方干旱事件和中国南方的极端干旱事件造成的GPP负异常可能更加突出。

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