{"title":"温带草原的峰值降水日期支配着峰值绿度时间","authors":"Wenrui Bai, Huanjiong Wang, Quansheng Ge","doi":"10.1002/joc.70027","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Peak greenness timing (PGT), indicating the timings of seasonal peak canopy structure, plays a key role in the terrestrial ecosystem carbon cycle. We assumed that the peak dates of climatic factors would have a greater impact on PGT than preseason climates because the potential maximum gross primary productivity would only be achieved when PGT matches seasonal peak resource availability. To test this hypothesis, we selected the Mongolian grassland as the study area and extracted the PGT using the MODIS EVI dataset. We simultaneously identified the peak dates of temperature, precipitation and radiation from the ERA5-land dataset. Subsequently, we compared the performance of two multiple linear regression models with peak dates of climatic factors or preseason climates as independent variables to simulate PGT. Finally, we compared the standardised regression coefficients of independent variables for each pixel to identify the dominant factor of PGT in different regions. The results showed that the regression model based on peak dates of climatic factors could explain more interannual variance of PGT in 71.0% of the study area than the model based on preseason climates. Although the dominant factor controlling PGT varied across regions with different background climates, the peak date of precipitation (PDP) was identified as the dominant factor in nearly half of the study area, followed by temperature and radiation. The explanatory power of PDP on PGT was stronger in more arid regions. Our research provides new insights into the drivers of PGT in temperate grasslands and enhances our understanding of how plant phenology adapts to seasonal climate variations.</p>\n </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"45 12","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Peak Date of Precipitation Dominates Peak Greenness Timing of Temperate Grassland\",\"authors\":\"Wenrui Bai, Huanjiong Wang, Quansheng Ge\",\"doi\":\"10.1002/joc.70027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Peak greenness timing (PGT), indicating the timings of seasonal peak canopy structure, plays a key role in the terrestrial ecosystem carbon cycle. We assumed that the peak dates of climatic factors would have a greater impact on PGT than preseason climates because the potential maximum gross primary productivity would only be achieved when PGT matches seasonal peak resource availability. To test this hypothesis, we selected the Mongolian grassland as the study area and extracted the PGT using the MODIS EVI dataset. We simultaneously identified the peak dates of temperature, precipitation and radiation from the ERA5-land dataset. Subsequently, we compared the performance of two multiple linear regression models with peak dates of climatic factors or preseason climates as independent variables to simulate PGT. Finally, we compared the standardised regression coefficients of independent variables for each pixel to identify the dominant factor of PGT in different regions. The results showed that the regression model based on peak dates of climatic factors could explain more interannual variance of PGT in 71.0% of the study area than the model based on preseason climates. Although the dominant factor controlling PGT varied across regions with different background climates, the peak date of precipitation (PDP) was identified as the dominant factor in nearly half of the study area, followed by temperature and radiation. The explanatory power of PDP on PGT was stronger in more arid regions. Our research provides new insights into the drivers of PGT in temperate grasslands and enhances our understanding of how plant phenology adapts to seasonal climate variations.</p>\\n </div>\",\"PeriodicalId\":13779,\"journal\":{\"name\":\"International Journal of Climatology\",\"volume\":\"45 12\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Climatology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.70027\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Climatology","FirstCategoryId":"89","ListUrlMain":"https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.70027","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
峰值绿度时序(Peak green time, PGT)在陆地生态系统碳循环中起着关键作用,反映了季节性峰值冠层结构的时序。我们假设气候因子的峰值日期对PGT的影响比季前气候更大,因为只有当PGT匹配季节峰值资源可用性时,潜在的最大总初级生产力才会实现。为了验证这一假设,我们选择蒙古草原作为研究区,利用MODIS EVI数据集提取PGT。我们同时从ERA5-land数据集中确定了温度、降水和辐射的峰值日期。随后,我们比较了以气候因子峰值日期和季前气候为自变量的两个多元线性回归模型对PGT的模拟效果。最后,比较各像元自变量的标准化回归系数,确定不同区域PGT的主导因素。结果表明,在71.0%的研究区,基于气候因子峰值日期的回归模型比基于季前气候的回归模型更能解释PGT的年际变化。虽然不同背景气候的区域对PGT的主导因子存在差异,但在近一半的研究区,降水峰值日期是主导因子,其次是温度和辐射。在干旱地区,PDP对PGT的解释力更强。我们的研究为温带草原PGT的驱动因素提供了新的见解,并增强了我们对植物物候如何适应季节气候变化的理解。
Peak Date of Precipitation Dominates Peak Greenness Timing of Temperate Grassland
Peak greenness timing (PGT), indicating the timings of seasonal peak canopy structure, plays a key role in the terrestrial ecosystem carbon cycle. We assumed that the peak dates of climatic factors would have a greater impact on PGT than preseason climates because the potential maximum gross primary productivity would only be achieved when PGT matches seasonal peak resource availability. To test this hypothesis, we selected the Mongolian grassland as the study area and extracted the PGT using the MODIS EVI dataset. We simultaneously identified the peak dates of temperature, precipitation and radiation from the ERA5-land dataset. Subsequently, we compared the performance of two multiple linear regression models with peak dates of climatic factors or preseason climates as independent variables to simulate PGT. Finally, we compared the standardised regression coefficients of independent variables for each pixel to identify the dominant factor of PGT in different regions. The results showed that the regression model based on peak dates of climatic factors could explain more interannual variance of PGT in 71.0% of the study area than the model based on preseason climates. Although the dominant factor controlling PGT varied across regions with different background climates, the peak date of precipitation (PDP) was identified as the dominant factor in nearly half of the study area, followed by temperature and radiation. The explanatory power of PDP on PGT was stronger in more arid regions. Our research provides new insights into the drivers of PGT in temperate grasslands and enhances our understanding of how plant phenology adapts to seasonal climate variations.
期刊介绍:
The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions