气候变暖对陆地生态系统碳循环的影响

作为全球变化的主要表现之一,气候变暖对全球陆地生态系统碳循环的影响巨大,揭示这一作用对于精确理解碳循环的过程和相关政策的制定具有重要的指导意义。该文综述了此领域近十几年来的主要研究工作,总结了陆地生态系统碳循环对气候变暖响应的主要内部机制及其过程,简述了相关模型的发展及其主要应用,并指出以往研究中存在的主要问题以及未来研究的主要方向。在气候变暖条件下,陆地生态系统碳循环的变化主要体现在以下几个方面:1)低纬度地区生态系统NPP一般表现为降低,而在中高纬度地区通常表现为增加,而在全球尺度上表现为NPP增加;2)土壤呼吸作用增强,但经过一段时间后表现出一定的适应性;3)高纬度地区的生态系统植被碳库表现为增加趋势,低纬度地区生态系统植被碳库变化不大,或略微降低,在全球尺度上表现为植被碳库增加;4)地表凋落物的产量和分解速率增加;5)土壤有机碳分解加速,进而减少土壤碳储存,同时植被碳库向土壤碳库的流动增加从而增加土壤碳库,这两种作用在不同生态系统的比重不同,在全球尺度上表现为土壤碳库的减少;6)尽管不同生态系统表现各异,总体上全球陆地生态系统表现为一个弱碳源。生物物理模型、生物地理模型和生物地球化学模型陆续被开发出来用于研究工作,并取得了一定的成果,但是研究结果仍然存在很大的不确定性。在未来的数年甚至是数十年间,气候变暖与全球变化的其它表现间的协同影响将是下一步的研究重点,气候变暖和陆地生态系统间的双向反馈作用机制是进行更准确研究的理论基础,生态系统结构和功能对气候变化的适应性是准确理解和预测未来气候情景下陆地生态系统碳循环的前提。     

陆地生态系统碳循环模型研究概述

陆地碳循环研究是全球变化研究中的一个重要组成部分,而碳循环模型已成为目前研究陆地碳循环的必要手段.本文针对有关碳循环研究方面的进展,介绍了陆地碳循环模型的基本结构、碳循环过程中涉及的两个基本模型以及目前陆地生态系统碳循环模型的两大类型,并通过对现有主要陆地生态系统碳收支模式的分析,指出了未来陆地碳循环模型的研究方向可能是发展基于动态植被的生物物理模型.这种耦合模型也可能是地球系统模式的重要组成部分.     

利用大气二氧化碳和羰基硫浓度评估陆地生态系统模型碳通量模拟的不确定性

生态系统模型是模拟全球陆地生态系统碳循环的重要工具,但是其在全球不同区域的模拟存在很大的不确定性。如何评估陆地生态系统模型的不确定性是一项重要的研究。以北美地区为例,利用8个高塔观测站点同步获取的大气CO_2和羰基硫(OCS)浓度数据,结合WRF-STILT大气粒子扩散模型,评估了CASA-GFED3、SiB3和SiBCASA三种陆地生态系统模型模拟总初级生产力(GPP)和净生态系统CO_2交换(NEE)通量的不确定性。结果表明,SiB3模型能很好地模拟北美陆地生态系统GPP和NEE的季节变化时相和幅度,在3种模型中具有最佳的模拟能力;CASA-GFED3模型模拟的NEE季节变化较为理想、但对生长季GPP的模拟存在较大的误差,SiBCASA模型在模拟冬季晚期和春季早期的NEE和GPP时表现较不理想。研究证明了大气CO_2和OCS在评估陆地生态系统模型碳通量模拟的不确定性中的作用,为利用大气CO_2和OCS观测数据优化计算陆地生态系统光合和呼吸碳通量提供了理论支撑。     

陆地生态系统碳水循环的相互作用及其模拟

回顾了近年来陆地生态系统碳循环与水循环相互作用及模拟方面的进展,指出了今后该领域研究的重点和发展方向。陆地生态系统碳水循环是两个相互耦合的生态学过程,二者及其相互作用均受气候、大气成分和人类活动的影响,并对气候系统具有强烈的反馈作用,因而成为当前全球变化研究的热点。近年来,国内外开展了大量观测和模拟研究,分析了碳循环和水循环在不同时空尺度上的相互作用及其对环境因子和土地利用/覆被变化的响应,发现土壤水分条件对陆地生态系统碳循环的主要分量（光合和呼吸）均具有显著作用,但作用的强度在不同的生态系统存在差异。精确模拟土壤水分动态及其对碳循环的影响是陆地生态系统碳收支估算的基础,碳循环和水循环的耦合模拟是生态和水文模型发展的方向。目前,大部分模型在模拟土壤水分动态时,未考虑地形对土壤水分水平移动的影响,土壤水分对土壤异养呼吸影响的模拟也多采用经验性模型,制约了碳收支模拟的精度,需要加以解决。     

地-气间碳通量气候响应的模拟 I.近百年来气候变化

利用一个简单的计算陆地生态系统净碳通量的数学模型 ,根据最新重建的 1 90 1～ 1 995年间全球陆地 0 .5× 0 .5度的气候资料 ,对近百年来的主要气候变化特征进行了分析 ,并对碳通量的时间变化和空间分布情况进行了模拟。结果表明 ,2 0世纪 4 0～ 70年代中期的气候条件 (温度下降而降水增加 )最有利于陆地生态系统的净碳吸收 ,而此后的情况 (温度增加而降水减少 )则不利于生态系统的净碳累计。近百年来的气候变化可能对陆地生态系统净吸收大气二氧化碳有重要贡献。对模型的不足也做了讨论。     

陆地生态系统类型转变与碳循环

 土地利用变化引起的陆地生态系统类型转变对于全球碳循环有着极其重要的作用。　通过总结国内外有关森林砍伐以及森林、草地转变成农田对于碳循环的影响,阐述了可能引起全球“未知汇”现象的重要原因,强调未来中国陆地生态系统碳循环研究应充分重视陆地生态系统类型转变对于全球碳循环的影响研究,包括研究陆地生态系统的不同发展阶段(自然与退化生态系统)、利用方式的改变(森林转化为人工林或农田,草地转化为农田、退耕还林草等)所引起的碳库类型转换的增汇机理及其对全球变化响应,并指出了建立统一观测方法与规范的陆地生态系统碳通量观测网     

大尺度森林碳循环过程模拟模型综述

森林生态系统碳循环是全球陆地生态系统碳循环的重要组成部分,而碳循环模型已经成为研究森林碳循环的必要手段。森林碳循环模型可以分为统计模型和过程模型,其中过程模型以其完整的理论框架、严谨的结构分析和清晰的过程机理,逐渐占据了主导地位。从地球化学过程模型、陆面物理过程模型和生物过程模型等3个方面综述区域尺度到全球尺度(本文称为大尺度)森林碳循环过程模型研究进展,论述了各类模型的主要特征、优缺点以及应用现状,探讨了森林碳循环模拟研究中存在的问题,并讨论了森林碳循环过程模型的主流研究方向。可为不同空间尺度下森林生态系统碳循环模拟模型的选择提供参考,以及为森林碳循环研究提供借鉴。     

长江河口典型湿地碳库动态研究方法

湿地生态系统在维持碳平衡和全球气候调节中起着重要作用。长江河口湿地生态系统是国际公认的生态过程敏感区,选择长江河口典型湿地——崇明东滩作为长期碳循环观测和实验模拟的研究对象,针对长江河口湿地生态系统碳库动态研究具体的科学问题,从"土壤-植被-大气"连续体(SPAC)碳流过程监测、涡度相关法碳通量监测、植物生长控制实验(开顶式气候室,OTC)、"湿地碳循环-气候变化"生态过程模型构建、多方数据源校正等方面,总结了全球气候变化条件下长江河口湿地碳库动态的研究方法。旨在了解长江河口湿地生态系统碳库动态特征,探寻湿地碳平衡的主导控制因子,评估中国东部沿海湿地碳库对全球气候变化的响应。进而为提出长江河口湿地碳储备功能的保育策略提供理论依据。     

陆地碳循环研究中的模型方法

陆地碳循环是全球变化研究中的重要内容,碳循环模型已成为研究陆地碳循环的必要方法．其中气候变化、大气ＣＯ２浓度上升以及人类活动引起的土地利用和土地覆盖变化导致陆地生态系统在结构、功能、组成和分布等方面的变化及其反馈关系对陆地碳循环的影响是模型模拟的关键问题．生物地理模型和生物地球化学模型是碳循环模型的两大类型,建模方法、模型性质、特点和应用范围各异．碳循环模型的发展方向是综合两类模型的特点,建立全球动态碳循环模型．     

The response of ecosystem carbon and nitrogen pools to experimental warming in grasslands: a meta-analysis

草地生态系统碳氮库对增温响应的整合分析 陆地生态系统碳氮耦合过程有可能改变全球碳循环对气候变化的敏感性。然而,碳氮的交互作用对陆地生态系统碳固存的贡献仍不明确。本研究采用Meta分析的方法量化了野外增温条件下草地碳氮储量的变化,并且进一步评估了3个主要的碳氮耦合过程(生态系统氮总量的变化,氮在植被和土壤之间的重新分配,植被与土壤碳氮比的变化)对草地碳固存的相对贡献。增温使得土壤、凋落物 和植被的碳氮比增加,并导致约2%的氮从土壤转移到植被和凋落物中。增温提高了植被和凋落物的碳储量(111.2 g m⁻²),而降低了土壤的碳储量(30.0 g m⁻²),由此可见,增温提高了整个草地生态系统的碳储量。碳氮比的变化是温度升高条件下草地碳储量增加的主要贡献者,氮的重新分配次之。相反,氮总量的减少则降低了生态系统的碳储量。这些结果表明,温度升高对草地生态系统碳氮储量的变化及其耦合过程具有显著的影响,建议生态模型考虑碳氮循环的交互作用,以便更准确地预测未来陆地碳储量的变化。     

气候变化对我国干旱/半干旱区小麦生产影响的模拟研究

利用随机天气模型,将气候模式对大气中CO₂倍增时预测的气候情景与CERES-小麦模式相连接,研究了气候变化对我国冬小麦和春小麦生产的可能影响。并对水分、温度、CO₂综合对小麦的作用进行初步模拟分析。所得结论为:①气候变化后小麦发育将加快,生育期缩短,春小麦生育期缩短的绝对数和相对数均小于冬小麦。②北方十个站点小麦生产的最适水分条件在不同站点、不同气候情景下都有所不同。最适水分条件变幅在40%～80%。③在不考虑CO₂对小麦影响的情况下,由于热量充足,只要水分条件适宜,未来我国北方干旱、半干旱地区小麦产量整体都有增产趋势。如果考虑CO₂,增产效果更加明显。     

Food security in 2044: How do we control the fungal threat?

Plant fungal pathogens place considerable strain on agricultural productivity and threaten global food security. In recent decades, advances in crop breeding, farming practice and the agrochemical industry have allowed crop yields to keep pace with food demand. In this opinion article, we speculate on which recent technological advances will allow us to maintain this situation into the future. We take inspiration that it is 25 y since the first plant disease resistance genes were cloned, and imagine if and how agricultural control of pathogens will be achieved by the year 2044. We examine which technologies are best poised to make the jump from lab bench to field application, and propose that future control measures will likely depend on effective integrated disease management.     

How do elevated [CO2], warming, and reduced precipitation interact to affect soil moisture and LAI in an old field ecosystem?

Soil moisture content and leaf area index (LAI) are properties that will be particularly important in mediating whole system responses to the combined effects of elevated atmospheric [CO₂], warming and altered precipitation. Warming and drying will likely reduce soil moisture, and this effect may be exacerbated when these factors are combined. However, elevated [CO₂] may increase soil moisture contents and when combined with warming and drying may partially compensate for their effects. The response of LAI to elevated [CO₂] and warming will be closely tied to soil moisture status and may mitigate or exacerbate the effects of global change on soil moisture. Using open-top chambers (4-m diameter), the interactive effects of elevated [CO₂], warming, and differential irrigation on soil moisture availability were examined in the OCCAM (Old-Field Community Climate and Atmospheric Manipulation) experiment at Oak Ridge National Laboratory in eastern Tennessee. Warming consistently reduced soil moisture contents and this effect was exacerbated by reduced irrigation. However, elevated [CO₂] mitigated the effects of warming and drying on soil moisture. LAI was determined using an AccuPAR ceptometer and both the leaf area duration (LAD) and canopy size were increased by irrigation and elevated [CO₂]. Changes in LAI were closely linked to soil moisture status. The climate of the southeastern United States is predicted to be warmer and drier in the future, and this research suggests that although elevated [CO₂] will ameliorate the effects of warming and drying, losses of soil moisture will cause declines in the LAI of old field ecosystems in the future.     

长江中游城市群碳效率时空演化特征——基于三阶段SBM-DEA模型

长江中游城市群作为我国区域发展格局的中坚力量,其碳效率的提升对助力我国实现“双碳”目标意义重大。采用三阶段SBM-DEA模型测度2006—2019年长江中游城市群28市的碳效率并进行环境变量影响分析,以探究长江中游城市群碳效率的影响路径;进而运用核密度估计、中心-标准差椭圆等方法分析碳效率的时空演化特征,以探明各区域碳效率差异并给出相应提升策略。研究结果表明：(1)长江中游城市群整体碳效率水平不高,但呈现出逐年波动上升的趋势;(2)长江中游城市群碳效率呈现出“武汉城市圈>环长株潭城市群>环鄱阳湖城市群”的空间分异格局,效率中心整体向东北方向移动,标准差椭圆长轴标准差整体缩小,短轴标准差相对稳定;(3)长江中游城市群碳效率存在正向空间溢出效应,城镇化进程、产业结构和科技支撑强度是影响碳效率的重要因素。     

A dynamic assessment of water scarcity risk in the Lower Brahmaputra River Basin: An integrated approach

Many international river basins are likely to experience increasing water scarcity over the coming decades. This water scarcity is not rooted only in the limitation of resources, i.e. the shortage in the availability of freshwater relative to water demand, but also on social factors (e.g. flawed water planning and management approaches, institutional incapability to provide water services, unsustainable economic policies). Therefore, the assessment of water scarcity risks is not limited to the assessment of physical water supply and demand, but it requires also consideration of several socio-economic factors. In this study, we provide a comprehensive dynamic assessment of water scarcity risks for the Lower Brahmaputra river basin, a region where the hydrological impact of climate change is expected to be particularly strong and population pressure is high. The basin area of Brahmaputra River lies among four different countries: China, India, Bangladesh and Bhutan. For water scarcity assessment, we propose a novel integration of different approaches: (i) the assessment of water scarcity risk, considering complex social-ecological system; (ii) the analysis of dynamic behaviour of the system; (iii) exploration of participatory approach in which limited number of stakeholders identify the most relevant issues with reference to water scarcity risks and provide their preferences for the aggregation of risk assessment indicators. Results show that water scarcity risk is expected to slightly increase and to fluctuate remarkably as a function of the hazard signal. Social indicators show trends that can at least partially compensate the increasing trend of the drought index. The results of this study are intended to be used for contributing to planned adaptation of water resources systems, in Lower Brahmaputra River Basin.     

区域生态保护修复碳汇潜力评估方法与应用——基于第一批山水林田湖草生态保护修复工程的研究

山水林田湖草生态保护修复工程是生态系统恢复的有效措施，借助生态保护与修复提升生态系统固碳潜力，无疑是土地利用碳减排的新路径。基于山水林田湖草沙综合整治视角，从生态系统的格局和质量两个方面评估了第一批山水林田湖草生态保护修复工程的实施效果，并借助InVEST模型定量化地分析了工程实施前后的生态系统碳汇能力。结果如下：(1)山水林田湖草生态保护修复试点工程加速了各类生态系统间的相互转化，主要表现为城镇生态系统的增加、农田生态系统的减少；工程区植被覆盖度整体提高，NDVI值平均水平不断上升、高值区逐步扩大，劣质、低质生态系统改造成果显著，陆地生态系统质量有效提升。(2)试点区生态系统碳汇能力和潜力得到有效改善，工程累积增加碳汇面积22.68%,其中工程实施前期增加碳汇面积18.06%,中后期增加面积4.62%;工程实施后2018年碳汇总量增加32.74 Tg, 2020年碳汇总量增加31.28 Tg,年均碳汇潜力的提升约1.24%;工程在增加生态系统质与量、碳汇潜力的巩固与提升上具有显著成效。分析结果表明，生态保护修复是实现“双碳”目标的必然选项，这也是生态保护修复在实现“双碳”目标中的基本...     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

基于InVEST模型的疏勒河流域碳储量时空变化研究

研究区域土地利用方式与生态系统服务碳储量的关系，对于区域生态系统保护及经济社会可持续发展具有重要意义。利用InVEST模型碳储量模块和CA-Markov模型，探究并预测疏勒河流域1990-2015及2015-2040年流域生态系统碳储量时空变化特征及其与土地利用方式之间的关系。结果表明：疏勒河流域1990、1995、2000、2005、2010、2015年碳储量分别为7.994×10⁸、7.996×10⁸、7.998×10⁸、8.038×10⁸、8.064×10⁸、8.071×10⁸t，呈逐年增加趋势，累计增加7.7×10⁶t。土地利用类型变化是导致生态系统碳储量变化的主要因素，未利用地向耕地和草地转化有利于碳储量增加，而草地向耕地和未利用地的转化则导致碳储量减少。疏勒河流域碳储量存在显著的空间格局，碳储量较高区域呈现"北部点状-中部带状-南部点状片状"特征，这种分布格局与流域土地利用类型紧密联系。预测表明至2040年疏勒河流域碳储量为9.128×10⁸t，较2015年增加13.1%，主要原因是草地、耕地和林地面积较大幅度增长，提高了流域内的碳储量。     

The dendroclimatic value of oak stable isotopes

Tree-ring stable oxygen and carbon isotope ratios (δ¹⁸O and δ¹³C) are an important archive for climate reconstructions. However, it remains unclear whether the polyvinyl acetate emulsion, often used for the preservation and fixation of wood samples, influences δ¹⁸O and δ¹³C signals. Further uncertainties are associated with the possible effects of geographical origin and cambial age of historical samples. Here, we present annually-resolved and absolutely-dated δ¹⁸O and δ¹³C measurements of 21 living oaks (Quercus robur and Q. petraea) from the Czech Republic. We find that the δ¹⁸O and δ¹³C signals in the extracted alpha-cellulose are not affected by polyvinyl acetate treatment. Covering the entire 20^th century and reaching until 2018 CE, our dataset reveals spatial and temporal coherency within and between the individual δ¹⁸O and δ¹³C chronologies of different oak species, sample locations, and tree ages. Highly significant (p < 0.01) Pearson’s correlation coefficients of the site-specific δ¹³C and δ¹⁸O chronologies range from 0.48–0.77 and 0.36–0.56, respectively. The isotopic inter-series correlations of Q. robur and Q. petraea from the same site are 0.75 and 0.43 for the mean δ¹³C and δ¹⁸O values, respectively. Significant (p < 0.01) correlations of 0.49 and 0.84 are found for δ¹³C and δ¹⁸O, respectively, when all measurements from all sampling locations and tree ages are included. Our study shows that non-pooled oak δ¹⁸O and δ¹³C measurements from both species, different locations, and diverse tree ages can be combined into robust isotopic chronologies for climate reconstructions.