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生态系统碳循环和氮循环的研究随着人类的不断发展和进步，我们的生活方式已经发生了很大的变化。
然而，这些变化也对我们周围的环境造成了巨大的影响。
其中，碳循环和氮循环是生态系统中两个非常重要的环节。
本文将详细探讨这两个过程的研究。
生态系统碳循环生态系统碳循环是指有机质生物合成的主要来源——二氧化碳和水在生态系统中的转化和循环。
生态系统中的碳循环是一个非常复杂的过程，涉及到生物、大气、地球等多个领域。
首先介绍碳循环的一个重要部分——光合作用。
通过光合作用，植物能够将二氧化碳和水转化为有机质和氧气。
同时，植物的生长和呼吸也会产生二氧化碳。
这些二氧化碳会被其他生物吸收和利用，或者直接回到大气中。
除了光合作用之外，碳在生态系统中的循环还包括了生物的死亡和分解、火灾、人类活动等。
有机物的分解不仅会产生二氧化碳，还会释放出有机气体、甲烷等温室气体。
而森林采伐、燃煤等人类活动也会增加大气中的二氧化碳含量，使得碳的循环变得更加复杂。
为了更好地掌握碳循环的规律，科学家们从不同的角度对其进行研究。
例如，他们会对大气中的碳含量进行测量，并进行模型模拟分析。
他们还会研究地球化学过程、生态系统结构和功能等方面，以了解碳在生态系统中的转化和循环。
生态系统氮循环氮是生命活动必不可少的元素之一。
然而，氮在大气中的气态只是一种双原子分子——氮气，植物和动物需要的是氨、硝酸盐等化合物。
因此，生态系统中氮的循环和转化比较复杂，涉及多种生物学和地球化学过程。
氮的循环过程大致可以分为以下几个步骤：氮固定、氨化作用、硝化作用、脱氮作用等。
其中，氮固定指的是将氮气转化为植物可以吸收和利用的形式，如氨和硝酸盐。
植物通过根部摄入氮化合物，并将其转化为有机质，然后被食草动物和食肉动物摄入利用。
与碳循环类似，氮循环也和人类活动密切相关。
例如，过度施肥会使得土壤中的氮含量增加，甚至导致水体富营养化问题；固氮作用受到工业污染和大气中氮氧化物的增多影响等等。
为了更好地理解氮循环，科学家们会从不同的角度对其进行研究。
生命科学和地球科学中的碳循环和氮循环地球上的生命和环境受到了严重的威胁，碳循环和氮循环在其中扮演了重要的角色。
这两个循环是地球科学和生命科学中的重要问题之一。
碳循环碳循环是指碳在地球大气、水体和土壤中的运动。
它是地球上的重要化学循环之一。
碳是地球上最丰富的元素之一，它在地球上的存量大于所有生物体中的其他成分的总和。
碳循环是生物系统和地球系统之间的关键交互之一。
碳循环可以分为两个部分：生物循环和地球化学循环。
生物循环包括光合作用和呼吸作用。
在光合作用中，植物通过吸收二氧化碳和水分，利用阳光的能量将其合成有机物质并释放氧气。
在呼吸作用中，生物细胞中的有机物质被氧气氧化，释放出二氧化碳和水。
光合作用和呼吸作用是地球生态系统中最重要的过程之一，它们可以循环利用碳并维持生态平衡。
地球化学循环是指碳在地球上的地球化学环境中的运动。
它包括大气和水体之间的气体交换、生物残体沉积和泥炭沼泽的形成、地球表面化学作用和岩石圈之间的碳的转移等。
碳循环对人类的贡献非常重要，例如，全球变暖和海洋酸化都与碳循环有关。
氮循环氮循环是指氮在生物系统和地球系统中的运动。
氮是生成生物有机物的关键元素。
氮循环由一系列化学和生物学过程组成，包括固氮、氧化还原作用和细菌分解等。
氮循环对大气、土壤和水体化学、物理和生物特性的影响十分显著。
氮在空气中占据78%的体积，但是氮气是一种极度稳定的分子，只有通过固氮作用才能被生物利用。
固氮作用是将氮转换成有机化合物的过程。
在固氮过程中，许多氮分子被细菌和蓝绿藻等生物转化为真菌和腐生动物的有机物。
在氧化还原作用中，一些氮物质会发生化学反应，转换成可被生物利用的形式。
这个过程还包括岩石侵蚀、火山作用、天然气和石油的转化等自然现象。
细菌分解是将有机物质分解为无机氮的过程。
这个过程在生态系统的动植物死亡、腐烂和生态系统的成长过程中扮演了重要的角色。
总的来说，氮循环是影响全球气候、土壤品质和生态系统健康的关键化学过程。
碳循环知识：碳循环与氮循环——生态系统的复杂相互作用生态系统中的碳循环和氮循环是复杂的相互作用。
碳循环是指地球大气中二氧化碳、有机碳和无机碳等不同形式的碳在地球系统和生态系统中的传输和转换过程。
而氮循环则是指氮元素在生物圈和地球圈之间不断循环和转化的过程。
这两个循环对于维护地球生态平衡具有重要作用。
碳循环的过程非常复杂，它包括几种不同类型的碳转化和流动。
这些类型包括光合作用、呼吸作用、去沉淀、除湿和地球化学过程等。
光合作用是指植物和海洋中的浮游生物通过使用光能和二氧化碳将成分分离并合成有机碳的过程。
这个过程是碳循环的起点，为高食物链中的其他生物提供了能量来源。
呼吸作用是指生物组织或细胞分解有机物质产生水和二氧化碳的过程。
这种呼吸作用通常发生在动物或者其他微生物体内的细胞中，是碳循环中的一个重要组成部分。
这个过程释放的二氧化碳进入大气层并继续循环。
去沉淀和除湿是指通过空气流动将固态和液态碳从地球表面分离出来的过程。
这个过程也涉及到沉积物和土壤中的有机碳和无机碳流动。
在不同地理位置，地表上的碳沈积带动了全球气温、降雨量和冰川等情况的变化。
地球化学过程旨在合成深埋在地下的有机碳和无机碳。
这个过程需要时间在地下被压力、热和化学反应逐渐形成，期间还会影响地球内部的运动。
相比较于碳循环而言，氮循环相对简单，大部分都发生在土壤和植物根系中。
氮元素在生物圈中以氨、硝酸盐及硝酸气等形式存在。
氮元素是蛋白质、核酸等生物大分子的基本成分，因此它在生命过程中发挥着不可或缺的作用。
不过，这些化合物对于土壤生态系统和环境都具有一定的危害性。
氮元素在土壤中的固氮作用是氮循环的核心，其通过在植物根部生长的固氮细菌分解空气中的氮气，将氮元素转化为其它盐基形式。
进而植物就可以通过吸收根部的盐基化合物使氮元素被有效利用。
氮循环的核心也能够通过一些微生物将氨、硝化和反硝化等过程加速循环，从而通过生物转化作用实现氮素之间相互转化的过程。
碳氮循环变化特征及驱动机制概述说明以及解释1. 引言1.1 概述碳和氮是地球生态系统中两个重要的元素循环。
碳氮循环的变化特征及其驱动机制对于理解全球变化、生物地球化学过程和环境保护具有重要意义。
本文旨在综述碳氮循环的变化特征及其驱动机制，并分析其变化模式和影响因素。
1.2 文章结构本文共分为以下几个部分：第一部分为引言，主要介绍研究背景、目的和文章结构；第二部分将详细讨论碳氮循环的变化特征，包括碳循环与氮循环的不同方面；第三部分将探讨碳氮循环的驱动机制，包括外部驱动因素和内部驱动因素；第四部分将进行变化模式与影响因素分析，探究碳氮循环的具体模式和主要影响因素；最后一部分是结论，总结目前研究成果并展望未来研究方向。
1.3 目的研究人员对于碳氮循环变化特征及其驱动机制进行了广泛研究。
本文的主要目的是通过对相关研究成果的整理和分析，深入了解碳氮循环的变化特征以及推动其发生变化的驱动机制。
同时，本文力求提供对于未来研究方向的展望和建议，以促进更好地理解和保护碳氮循环。
2. 碳氮循环变化特征：2.1 碳循环变化特征：碳是地球上最常见的元素之一，它在生物圈、大气圈、海洋和陆地等多个系统中循环。
碳的变化特征主要包括不同储存库之间的交换以及碳吸收和释放过程的速率。
其中，全球二氧化碳（CO2）浓度呈增长趋势，主要原因是人类活动导致的燃烧排放和森林砍伐等行为释放了大量CO2。
此外，碳还通过植物光合作用、生物降解和土壤有机质分解等方式进入陆地生态系统，并通过呼吸、腐解和溶解等途径返回大气和水体。
2.2 氮循环变化特征：氮是构成细胞核酸、氨基酸和蛋白质等生物分子的重要元素，对维持生态系统功能至关重要。
与碳循环相似，氮也在不同储存库之间进行交换，并通过一系列微生物介导的转化过程在不同形式之间进行转换。
全球氮输入主要来源于农业施肥和化石燃料燃烧释放的氮氧化物。
然而，过量的氮输入会导致水体富营养化和土壤酸化等环境问题。
综上所述，碳和氮在生态系统中的循环变化特征受到了多种因素的影响，包括自然因素和人类活动。
碳、氮、磷循环的过程碳、氮、磷循环是生物地球系统中重要的物质循环过程，其中碳循环描述了碳在地球大气、陆地和海洋之间的相互转移和交换；氮循环描述了氮在大气、土壤、植物和动物之间的循环；磷循环描述了磷在土壤、植物、动物和水体之间的循环。
碳循环的过程：1. 碳固定：通过光合作用，植物使用二氧化碳（CO2）从大气中固定碳，并将其转化为有机物。
这些有机物进一步被用于植物的生长和发展。
2. 呼吸和分解：植物和动物通过呼吸将有机物中的碳释放为二氧化碳，返回到大气中。
此外，分解作用将有机物分解为二氧化碳，进一步增加了大气中的碳含量。
3. 碳储存：部分固定的碳被保存在植物和土壤中，并可以长期储存。
这些碳可以作为植物和土壤有机质的一部分，或者转化为煤、石油和天然气等化石燃料，被埋藏在地下。
氮循环的过程：1. 氮固定：氮气（N2）通过闪电活动、细菌和蓝藻等生物固定为氨（NH3）或硝酸盐（NO3-）。
植物和其他生物可以利用这些氮源合成蛋白质和其他氮化合物。
2. 氮循环：植物吸收土壤中的氨或硝酸盐，将其合成为有机物。
动物通过食物链摄取植物中的氮化合物，将其转化为自身组织中的蛋白质。
当植物和动物死亡时，氮会通过分解作用释放到土壤中，再次被植物吸收和利用。
3. 氮损失：氮还可以通过硝化作用和反硝化作用释放到大气中。
硝化作用将氨氧化为硝酸盐，而反硝化作用将硝酸盐还原为氮气。
磷循环的过程：1. 磷释放：磷以无机形式存在于岩石和土壤中，通过风化和侵蚀作用，磷释放到土壤中。
2. 磷吸收：植物通过根部吸收土壤中的磷，将其转化为有机物并用于生长和发展。
动物通过摄食植物或其他动物获取磷。
3. 磷循环：当植物和动物死亡时，磷经过分解作用释放到土壤中。
这些有机物也可能进一步转化为磷酸盐，被吸附在岩石和土壤中，形成新的磷矿物。
4. 磷溶解：磷酸盐可以通过溶解作用从岩石和土壤中释放出来，进入水体。
这些磷酸盐可以被水生生物吸收，形成食物链和海洋沉淀物，也可以长期沉积在海底形成磷矿床。
土壤碳氮循环的微生物过程及调控内容土壤碳氮循环是指土壤中碳和氮元素的循环过程，其中微生物起着至关重要的作用。
微生物通过分解有机物质、固氮、硝化和反硝化等过程，参与了土壤碳氮循环的各个环节。
微生物通过分解有机物质将有机碳转化为无机碳。
有机物质是土壤中碳的主要来源，微生物通过分解有机物质将其转化为二氧化碳和水，释放出能量。
这个过程被称为呼吸作用，是土壤中碳循环的重要环节。
微生物还能通过固氮将氮元素转化为可利用的形式。
固氮是指将空气中的氮气转化为氨或亚硝酸盐等化合物，这些化合物可以被植物吸收利用。
固氮作用是土壤中氮循环的重要环节，微生物通过固氮作用为植物提供了可利用的氮源。
微生物还能通过硝化和反硝化过程参与土壤中氮的循环。
硝化是指将氨氮转化为硝酸盐，反硝化则是将硝酸盐还原为氮气。
这两个过程是氮循环中的重要环节，微生物通过硝化和反硝化过程调节土壤中氮的含量和形态。
微生物在土壤碳氮循环中的作用是不可替代的，但是它们的活动受到许多因素的影响。
土壤温度、湿度、pH值、有机物质含量等因素都会影响微生物的生长和代谢活动。
因此，调控这些因素可以促进微生物的活动，提高土壤碳氮循环的效率。
微生物在土壤碳氮循环中扮演着重要的角色，通过分解有机物质、固氮、硝化和反硝化等过程参与了土壤中碳氮元素的循环。
调控土壤环境因素可以促进微生物的活动，提高土壤碳氮循环的效率，从而为农业生产和生态环境保护做出贡献。
生物地化循环名词解释
生物地化循环是指地球上生物体与环境之间相互作用的过程，
其中包括了物质的循环和能量的流动。
以下是几个与生物地化循环
相关的名词解释：
1. 碳循环，碳是生命体中的基本元素，碳循环是指碳在地球上
的循环过程。
它包括了碳的吸收、储存、释放和转化等过程，包括
植物通过光合作用吸收二氧化碳，动物通过呼吸将碳释放到大气中，以及有机物的分解和矿化等。
2. 氮循环，氮是生命体中的重要元素，氮循环是指氮在生物体
和环境之间的转化过程。
氮循环包括氮的固定（将氮气转化为可被
生物利用的形式）、氮的硝化（将氨转化为硝酸盐）、氮的还原
（将硝酸盐还原为氨）以及氮的脱氮（将氮气释放到大气中）等过程。
3. 水循环，水循环是指地球上水分在不同形式之间循环的过程。
它包括蒸发（水从地表蒸发成水蒸气）、凝结（水蒸气变为液态水）、降水（水从大气中以雨、雪、露水等形式降落到地表）以及
地表径流、地下水补给等过程。
4. 磷循环，磷是生物体中的重要元素，磷循环是指磷在生物体和环境之间的转化过程。
磷循环包括磷的吸收（植物通过根系吸收磷）、磷的再循环（生物体死亡后磷被分解并释放到环境中）、磷的沉积和磷的溶解等过程。
5. 硫循环，硫是生物体中的重要元素，硫循环是指硫在生物体和环境之间的转化过程。
硫循环包括硫的吸收（植物通过根系吸收硫）、硫的再循环（生物体死亡后硫被分解并释放到环境中）、硫的氧化和硫的还原等过程。
以上是生物地化循环中的一些重要名词解释。
生物地化循环是维持地球生态平衡的重要过程，通过这些循环，地球上的物质得以循环利用，能量得以流动，保持了生物多样性和生态系统的稳定。
碳循环和氮循环过程中微生物的生态功能碳循环和氮循环是生物圈中的两条关键循环路径，微生物在其中起着至关重要的作用。
微生物的生态功能既包括分解有机物、维持土壤结构稳定等基本功能，也包括特定的生态过程，如甲烷氧化、硝化反应等。
碳循环是生物圈中碳元素的流转过程，其中微生物主要参与有机物分解、二氧化碳固定和甲烷氧化等。
有机物分解始于死亡的生物体，营养物成为其他生物的基础。
微生物在有机物分解中扮演活性碳池和被动碳池两种角色。
活性碳池包括微生物体内的有机物，被动碳池则是残留的类似木质素的难分解有机物。
微生物通过有机物的分解使其成为二氧化碳等简单有机物，进而逐渐游离到大气中。
特别地，自然湿地和农田中的甲烷可以通过微生物的甲烷氧化途径被消耗，从而减少对温室气体的贡献。
氮循环则主要涉及氮元素在生物圈中的不同形态之间的转化。
氮的有机形态包括蛋白质、核酸、氨基酸等，而无机形态主要包括氨氮、硝态氮等。
微生物在氮循环中起到了很重要的作用，例如，氨氧化微生物可以将氨氮转化为硝态氮，而硝化微生物则将硝态氮还原为氮气和甲烷。
此外，微生物还在固氮作用中起到了关键作用，将大气中的氮转化为能被其他生物利用的氨基化合物，从而促进了生物圈的生产力和生态系统的营养循环。
除此以外，微生物在土壤结构稳定、陆地生态系统碳收支平衡、和海洋生态系统的维持等方面也发挥了重要作用。
例如，微生物可以形成土壤胶体，维持了土壤结构的稳定性。
它们还是土壤有机碳存储和循环的关键参与者，影响着直接和间接的碳收支平衡。
此外，微生物在土壤、水体和海洋等系统中，还通过形成生态网络来调控不同微生物的相互关系，维持整个生态系统的稳定性。
总的来说，微生物在碳循环和氮循环过程中发挥着至关重要的作用，其生态功能的多样性和复杂性也值得深入研究。
只有加深对于地球生态系统微观领域的理解，才能更好地指导环境管理和生态保护。
生态系统中碳循环与氮循环的作用及关系研究碳和氮是生态系统中最为重要的元素之一，它们对生态系统的稳定性和可持续发展具有重要的影响。
在生态系统中，碳和氮元素之间的相互作用非常复杂，需要进行深入的研究。
碳循环是指碳在生态系统中的流动和储存方式。
它包括陆地和水体生态系统中的碳固定、碳储存和碳释放。
碳的来源主要是生物体的呼吸和光合作用，而碳的固定则是通过光合作用和海洋中的生物作用。
碳的释放则是由于生物体的呼吸和有机物的降解所产生的。
碳循环的作用在于维持生态系统中的生物多样性和生态平衡。
在地球上，大气中的二氧化碳是非常重要的温室气体之一，它可以对地球的气温产生直接的影响。
而生态系统中的碳循环则可以通过吸收和储存大气中的二氧化碳来缓解温室效应的问题。
另外，碳在生态系统中还具有调节水分循环、维持土壤肥力和提高植物抗逆性等方面的作用。
氮循环是指氮在生态系统中流动和转化的过程。
它包括了陆地生态系统和水体生态系统中的氮吸收、固定、转化和释放等环节。
氮的来源主要是生物体的氨气呼吸和大气中的固氮作用。
氮的固定则是通过土壤中的微生物作用和植物根系生产的根瘤菌来完成的。
氮的转化则是指将不同形态的氮化合物相互转换，例如将氨转化为硝酸盐。
氮的释放则是由于有机物的分解和生物体的排泄所产生的。
氮循环的作用在于维持生态系统中生物的正常生长和发育。
在生态系统中，氮是生物体的重要成分之一，它是蛋白质、核酸和其他重要生物分子的基本组成部分。
另外，氮还可以促进植物的生长和提高作物的产量。
在水体生态系统中，氮循环还可以影响水体的营养状况和生态环境。
碳循环和氮循环之间存在着密切的关系。
首先，碳循环和氮循环都是通过生物体的参与实现的。
植物在进行光合作用时会吸收二氧化碳并释放氧气，而植物根部的微生物则可以进行氮的固定和转化。
其次，在生态系统中，碳和氮互相影响并且通过一系列的转化来进行相互调节。
为了维持生态系统中的稳定，碳和氮之间的转化和配合是必要的。
CO2emissions from land-use change affected more by nitrogen cycle,than by the choice of land-cover dataA T U L K.J A I N*,P R A S A N T H M E I Y A P P A N*,Y A N G S O N G*and JOANNA I.HOUSE†*Department of Atmospheric Sciences,University of Illinois,Urbana,IL61801,USA,†Department of Geography,Cabot Institute, University of Bristol,Bristol,BS81SS,UKAbstractThe high uncertainty in land-based CO2fluxes estimates is thought to be mainly due to uncertainty in not only quan-tifying historical changes among forests,croplands,and grassland,but also due to different processes included in calculation methods.Inclusion of a nitrogen(N)cycle in models is fairly recent and strongly affects carbon(C)fluxes. In this study,for thefirst time,we use a model with C and N dynamics with three distinct historical reconstructions of land-use and land-use change(LULUC)to quantify LULUC emissions and uncertainty that includes the integrated effects of not only climate and CO2but also N.The modeled global average emissions including N dynamics for the 1980s,1990s,and2000–2005were1.8Æ0.2,1.7Æ0.2,and1.4Æ0.2GtC yrÀ1,respectively,(mean and range across LULUC data sets).The emissions from tropics were0.8Æ0.2,0.8Æ0.2,and0.7Æ0.3GtC yrÀ1,and the non tropics were1.1Æ0.5,0.9Æ0.2,and0.7Æ0.1GtC yrÀpared to previous studies that did not include N dynamics, modeled net LULUC emissions were higher,particularly in the non tropics.In the model,N limitation reduces regrowth rates of vegetation in temperate areas resulting in higher net emissions.Our results indicate that exclusion of N dynamics leads to an underestimation of LULUC emissions by around70%in the non tropics,10%in the tropics, and40%globally in the1990s.The differences due to inclusion/exclusion of the N cycle of0.1GtC yrÀ1in the tro-pics,0.6GtC yrÀ1in the non tropics,and0.7GtC yrÀ1globally(mean across land-cover data sets)in the1990s were greater than differences due to the land-cover data in the non tropics and globally(0.2GtC yrÀ1).While land-cover information is improving with satellite and inventory data,this study indicates the importance of accounting for different processes,in particular the N cycle.Keywords:carbon cycle,carbon emissions,land-use change,model,nitrogen cycleReceived28February2013and accepted18March2013IntroductionLand-use and land-use change(LULUC)refers to car-bon(C)fluxes from the land due to human activity:that resulting from the use or management of land within one type of land cover(e.g.,forest management for wood harvest)and changes in land-cover type (e.g.,deforestation,afforestation,conversion of grass-lands to pastureland).In total,LULUC was responsible for approximately11%of all anthropogenic CO2emis-sions(7.8Æ0.4GtC yrÀ1fossil fuel;1.0Æ0.5GtC yrÀ1 LULUC)in the decade2000–2009(Le Qu e r e et al.,2012). The land and the ocean each take up about30%of all anthropogenic C emissions(Denman et al.,2007;Le Qu e r e et al.,2009,2012).The land takes up C from the atmosphere due to natural processes,affected by envi-ronmental change such as CO2and N fertilization effects,and climate change(e.g.,longer growing seasons in northern extratropical forests)(Denman et al.,2007).The atmospheric measurements of[CO2]combined with O2:N ratios suggest that the land is cur-rently acting as a net sink of CO2despite large-scale tropical deforestation(Denman et al.,2007;Raupach, 2011).Both the IPCC(Denman et al.,2007)and the Global Carbon Project(Le Qu e r e et al.,2012)calculate land sink due to the natural response of ecosystems to environmental change as the residual from other better constrainedflux terms and LULUC emissions calcu-lated by models(2.5Æ0.8GtC yrÀ1,Le Qu e r e et al., 2012).Thus,this term is often known as the‘residual terrestrialflux’.Uncertainties in LULUC emissions propagate into uncertainties in the residual terrestrial uptake calculations,making these two terms the most uncertain in the C budget(Denman et al.,2007; Le Qu e r e et al.,2012).Estimates of theflux of C from LULUC vary widely among different model estimates(Houghton et al., 2012).According to the most recent IPCC assessment (Denman et al.,2007),C emissions due to LULUC for the1990s had a range of0.5–2.7GtC yrÀ1,with a med-ian value of1.6GtC yrÀ1based on two results:the Houghton(2003)book-keeping model and data based on the2005global Forest Resources Assessment(FRA)Correspondence:Atul K.Jain,tel.+217-333-2128,fax+217-244-1752,e-mail:jain1@©2013John Wiley&Sons Ltd2893 Global Change Biology(2013)19,2893–2906,doi:10.1111/gcb.12207of the Food and Agricultural Organization(FAO,2006), and the tropical satellite study of DeFries et al.(2002) also using the Houghton book-keeping model.With improvements in data on land-cover change and bio-mass,and better understanding,information and mod-eling of different land processes,the mean estimate has been revised downwards and the range across results is reduced despite the much larger number of modeled estimates now published.A recent intercomparison study of many published estimates reported a mean, standard deviation,and range across13process-based vegetation models and book-keeping models of 1.1Æ0.2GtC yrÀ1(full range0.75–1.50GtC yrÀ1)for the1990s(Houghton et al.,2012).The authors of the in-tercomparison used the limited amount of literature assessing uncertainty in LULUC emission estimates, along with expert judgment to suggest an uncertainty ofÆ0.5GtC yrÀ1.It is widely acknowledged that a key uncertainty in LULUC emissions stems from uncertainties in estimat-ing historical changes in areal coverage among forests, croplands,and grassland,though the uncertainties have significantly narrowed with time mainly due to improved data from satellites and inventories (Goldewijk&Ramankutty,2004;Lepers et al.,2005; Ramankutty et al.,2007;Houghton,2010;Hurtt et al., 2011;Verburg et al.,2011).Further uncertainty stems from incomplete understanding of all the processes affecting the netflux of C from LULUC,different approaches adopted to calculate emissions,and data-related uncertainties.Several previous intercomparison studies(e.g.,Ramankutty et al.,2007;Ito et al.,2008; Houghton et al.,2012)have evaluated the overall range of uncertainty associated with estimates of netflux of C resulting from LULUC.However,complex linkages among the various contributing factors have made it difficult to quantify and attribute the resulting uncer-tainties to each of its sources.In an earlier study,Jain&Yang(2005)quantified the uncertainties resulting from using two different,but commonly used land-use change data sets(Ramankutty &Foley,1999;and Houghton&Hackler,2001)to drive the C cycle component of a land-surface model,the Inte-grated Science Assessment Model(ISAM)for the time period1765–1990.Differences in the rates of changes in cropland area between the two data sets contributed significantly to uncertainty in estimated Cfluxes,and it was argued that further refinement of land-use data sets using ground and satellite-based measurements was required.The Jain&Yang(2005)study was useful in explaining and quantifying the uncertainty due to LULUC on Cflux as a part of wider studies on estimat-ing LULUC-related uncertainties(Ramankutty et al., 2007;Piao et al.,2008;Ricciuto et al.,2008).In recent years,several LULUC data sets have been updated.Improvements have primarily taken place on three aspects:using historical inventory data with higher level of spatial detail;integrating multiple and advanced high-resolution satellite estimates;an improved methodology to downscale inventory data to grid cell level.Three of the most commonly used data sets were harmonized using a globally consistent meth-odology by Meiyappan&Jain(2012):(i)The HYDE (Historical Database of the Global Environment)spa-tially explicit data set(Klein Goldewijk et al.,2010, 2011),which is the basis of the Hurtt et al.(2011)data set supplied for Earth System Models being used in the upcoming IPCC Fifth Assessment Report;(ii)The spa-tially explicit RF data set(Ramankutty&Foley,1999), updated to include pasture conversions and revised cropland estimates(Ramankutty et al.,2008);and(iii) The Houghton data set(Houghton&Hackler,2001) updated with FAO(2006)forest area data(Houghton, 2008;the version that was used by Meiyappan&Jain, 2012)and more recently with FAO(2010)data which substantially revised down deforestation rates for the 1990s.The effects of inclusion of different processes in cal-culating LULUCfluxes have been explored with vari-ous process-based global vegetation models.Several studies have shown that emissions from LULUC activi-ties are different when considering the fertilization effects of changing[CO2]on ecosystem C balance (Churkina et al.,2007;Pongratz et al.,2009;Arora& Boer,2010).Most process models now include the effects of climate and CO2on vegetation,but few include the effects of nitrogen(N).N is a limiting nutrient for plant growth in mid-and high-latitude regions(Vitousek&Howarth,1991).In trop-ical regions,N is not considered a limiting nutrient, because the warmer and wetter tropical climate enhances N mineralization in soils(Vitousek&Howarth,1991; Cleveland&Townsend,2006)and biological Nfixation is high(Yang et al.,2009).The N cycle is rapidly changing due to human activity(Galloway et al.,2004,2008;Can-field et al.,2010).Enhanced N in the atmosphere can act as a pollutant or have a fertilization effect on plants(Reay et al.,2008).Climate,CO2,and N all interact to alter plant growth(Jain et al.,2009)and decomposition,thus affect-ing both the C lost when vegetation is removed,and the rate of C accumulation in regrowing vegetation and soils (Mathers et al.,2006).A recent modeling study by Zaehle et al.(2011)indi-cates that anthropogenic N inputs account for about a fifth of the C sequestered by terrestrial ecosystem between1996and2005.Churkina et al.(2007)estimated a C uptake of0.75–2.21GtC yrÀ1during the1990s by regrowing forest in response to enhanced N deposition.©2013John Wiley&Sons Ltd,Global Change Biology,19,2893–29062894 A.K.JAIN et al.The wide ranges in their study arise from assumptions made about proportions and age of regrowing forests. However,neither study included the effects of LULUC. Yang et al.(2010)modeled for thefirst time the effect of including a fully coupled N cycle(in ISAM)on global LULUC.ISAM results indicated that the contribution of N deposition to C uptake was about 0.13GtC yrÀ1in regrowing secondary forests,and 0.31GtC yrÀ1in all ecosystem types.Consideration of full N dynamics limited C uptake due to N limitation in regrowing forests in northern temperate regions in particular.The study was very sensitive to land transi-tions in tropical regions.While N is not a limiting nutri-ent in primary tropical forests,the results suggested strong N limitation in the secondary forests of tropical regions,because land-use change activities(harvesting, burning)remove large amounts of N from the system. N removal due to LULUC constrained the fertilizing effects of N deposition and atmospheric CO2in some regions,but less in others depending on climatic condi-tions emphasizing the need to consider the interactive effects of all three drivers(climate,CO2,N)on net LULUCflux.In this article,we build upon our previous studies to provide revised estimates of C emissions from histori-cal LULUC looking for thefirst time at the effects of N under different LULUC scenarios.This study presents several crucial updates on multiple fronts,in particular: (i)We use a fully coupled Carbon-Nitrogen(C–N)cycle component of the ISAM(Yang et al.,2009),very few of the current generation of global vegetation models include a N cycle component,and only ISAM has been applied specifically to estimate LULUC emissions;(ii) The study incorporates the impact of N limitation and N deposition on the C sink associated with secondary forest regrowth including the effects of wood harvest activities(Yang et al.,2010);(iii)The estimates have been extended until the year2010where possible;and (iv)We use three historical reconstructions of LULUC (Meiyappan&Jain,2012)based on new and updated data sets(Klein Goldewijk et al.,2011;updated esti-mates based on Ramankutty&Foley(1999)and Rama-nkutty et al.(2008);and,Houghton,2008).In addition, all the three reconstructed data sets include the effects of urban land expansion(Klein Goldewijk et al.,2010) and wood harvest(Hurtt et al.,2011).Materials and methodsOverview of the ISAM C–N modelThe C–N cycle component of the ISAM is used to assess the C emissions from LULUC.The structure,parameterization,and performance of ISAM has been previously discussed in detail (Jain&Yang,2005;Jain et al.,2009;Yang et al.,2009).Here,we provide an overview.The model calculates C and Nfluxes between vegetation and the atmosphere,above and below ground litter,and soil organic matter compartments of the terrestrial biosphere at0.5°x0.5°spatial resolution.The mod-eled C cycle accounts for important feedback processes, including impact of increasing atmospheric[CO2]on Net Primary Productivity(NPP);impact of temperature and precipitation changes on photosynthesis,autotrophic and het-erotrophic respiration;and the effect of N deposition on C uptake by plants.The modeled N cycle accounts for major processes as described in Yang et al.(2009).In addition,the model accounts for both symbiotic and non symbiotic biologi-cal Nfixation.The performance of ISAM and its N cycle has been extensively calibrated and evaluated usingfield mea-surements(Jain et al.,2009;Yang et al.,2009).Each0.5°x0.5°grid cell contains at least one of the18land-cover types(Yang et al.,2010),of which10are forest land-cover types,five are herbaceous land-cover types and the other three being cropland,pastureland,and urban land. ISAM accounts forfive climatic types of primary forest(tropi-cal evergreen,tropical deciduous,temperate evergreen, temperate deciduous,and boreal)and their corresponding ‘secondary forests’.The model accounts separately for forest regrowth following agricultural abandonment and wood har-vest,and this is what we refer to as a‘secondary forest’(Yang et al.,2010).The land conversions in the model are carried out based on the method described in Meiyappan&Jain(2012).We start with a map of potential natural vegetation at0.5°x0.5°reso-lution,which is indicative of the land cover that would have existed if human activities were absent.We then advance in time(starting from1765to2010),by superimposing the year-to-year cropland,pastureland,wood harvest,and urban land maps,respectively.We define rules,specific to each land-disturbance activity(cropland,pastureland,wood harvest, and urban land),for replacing natural vegetation.In general, following cropland and pastureland expansion,the natural vegetations present in a grid cell are removed proportional to its area and demand for cropland/pastureland.Upon aban-donment(reduction in cropland/pastureland area between two consecutive years),the land recovers back to the domi-nant potential natural vegetation in the grid cell.Wood is preferentially harvested from primary forest,and secondary (regrowing)forest is used when the extent of primary forest is less than the demand.Urban land expansion usually occurs at the expense of cropland abandonment and in other cases from natural vegetations.The resulting land-cover maps for the period2000–2005are compared with remote sensing-based land-cover maps(500m resolution MODIS data–Friedl et al., 2010)spanning the same period.Discrepancies in forest area between satellite data and model estimates are used to accord-ingly adjust the land-disturbance activity specific rules(for each grid cell)to increase(or decrease)the proportions at which forest was cleared(or regrown)historically following expansion(or abandonment)of agricultural activity,such that rerunning the model with adjusted rules results in land-cover maps whose forest distribution closely agrees with remote sensing observations for recent years.This calibration©2013John Wiley&Sons Ltd,Global Change Biology,19,2893–2906CO2EMISSIONS FROM L AND-USE CHANGE2895implicitly accounts for uncertainty in potential vegetation map,rule-based assumptions,and spatial heterogeneity in land-use dynamics.Thus,the three reconstructions start with a common potential natural vegetation map and end with a map whose forest distribution are consistent with satellite estimates,but the pathway they follow between the starting and ending point is constrained by the land-use data sets used.Emissions of C due to LULUC are calculated as described in Jain&Yang(2005).In brief,upon removal of natural vege-tation from a grid cell,a specified fraction of vegetation bio-mass is transferred to litter reservoirs,effectively representing plant material left on the ground following deforestation activ-ities(Yang et al.,2009).The remaining vegetation materials are either burned to clear the land for agriculture,which releases C and N(in gaseous and/or mineral form)contained in the burned plant material;or are transferred as C and N to wood and/or fuel product reservoirs and subsequently released at three different rates depending on the assigned product categories.LULUC dataThe three historical LULUC reconstructions(ISAM-HYDE, ISAM-RF,and ISAM-HH)were based on cropland and pastureland area change in the three updated historical land-use change data sets:(i)HYDE3.1(Klein Goldewijk et al., 2011);(ii)RF(Ramankutty&Foley,1999)including new pas-tureland estimates and updated cropland estimates based on and Ramankutty et al.(2008);and(iii)Houghton&Hackler (2001)deforestation estimates updated in Houghton(2008) with revised deforestation rates from FAO(2006),respectively. The HYDE and RF data sets are both based on FAOSTAT agri-cultural statistics including data on change in agricultural land area(FAO,2009),which is available from1960,making assumptions on the change in other land cover(e.g.,forest)to meet agricultural demand.The Houghton(2008)data set is based primarily on FAO FRA area change and biomass data (FAO,2006)making assumptions about change in other land cover(e.g.,croplands,pasture)to account for forest area change,supported by FAOSTAT data.A variety of other his-torical information is used to estimate land-use transitions prior to the availability of FAO data in each data set.A com-mon spatially explicit data set for wood harvest based on FAO data(Hurtt et al.,2011)and urban land extent(Klein Goldewijk et al.,2010)was applied to all three reconstructions. ISAM-HYDE,ISAM-RF,and ISAM-HH estimates start from the year1765and extend until2010,2007,and2005,respec-tively.All three reconstructions start with a common land-cover map during1765and follow different pathways as determined by the land-use change data sets to attain forest area distributions close to satellite estimates of forests for recent years.The sum of non forested land-cover types(herba-ceous vegetation,cropland,pastureland,and urban land) matches satellite estimates.However,there are discrepancies between the land-use data sets and satellite estimates in the extent of individual herbaceous land-cover types.Model simulations performedThe ISAM was initialized with an atmospheric[CO2]of 278ppmv,representative of approximate conditions in the starting year(1765AD)of the model simulation,to allow veg-etation and soil C pools to reach an initial steady state.Dur-ing the time period of1765–2010,net C exchanges between atmosphere and terrestrial ecosystems are calculated based on observed changes in climate(updated estimates based on Mitchell&Jones,2005),atmospheric[CO2](Meinshausen et al.,2011),wet and dry atmospheric N deposition rates (Galloway et al.,2004),and three distinct historical recon-structions of LULUC as harmonized in Meiyappan&Jain (2012).Two separate model runs are carried out to calculate the contribution of LULUC to the terrestrial Cfluxes(Table1). In thefirst model run(A1),atmospheric[CO2],climate,and N deposition rates are varied with time based on prescribed values and the LULUC is assumed to be zero over time.In the second model run(A2),atmospheric[CO2],climate,N deposition rates,and LULUC are varied with time.The second model run(A2)was performed for each of the three historical LULUC reconstruction used in this study.The emissions due to LULUC are estimated by subtracting C fluxes calculated infirst model run(A1)from those in the second model run(A2).With this approach we captured the interactive effects of CO2,climate,and N limitation on LULUC emissions.We carried out two additional model runs(B1and B2)to study the impact of excluding the interactive effects of N limi-tation on LULUC emissions(Table1).Both experiments B1 and B2are similar to A1and A2,respectively,but they did not include the effects of N nd is always assumed to have sufficient N for plant growth.Subtracting Cfluxes calcu-lated in experiment B1from that of B2provides an estimate of LULUC emissions that only includes the interactive effects ofTable1Design of the simulation experiments.Tick mark(✔)indicates the environmental factor was varied with time.Cross mark (✗)indicates that the environmental factor was held constant at initial value.Inclusion of N deposition is irrelevant(denoted by‘–’) when N dynamics is inactive in the modelExperiment CO2Climate N deposition Land-use and land-use change(LULUC)N dynamics A1✔✔✔✗ActiveA2✔✔✔✔ActiveB1✔✔–✗InactiveB2✔✔–✔Inactive©2013John Wiley&Sons Ltd,Global Change Biology,19,2893–2906 2896 A.K.JAIN et al.CO 2and climate.This (B2-B1)model experiment is analogous to the majority of other model approaches to calculating the LULUC flux in models that include only climate and CO 2effects (e.g.,McGuire et al.,2001;Pongratz et al.,2009;Piao et al.,2009;Van Minnen et al.,2009;Arora &Boer,2010nonin-teractive runs;Stocker et al.,2011).The difference between the two sets of experiments (A2–A1)and (B2–B1)is an indicator of the effect of additionally considering N cycle effects and its interactions with CO 2and climate on LULUC fluxes.We did not look at the effects of N on LULUC alone (i.e.,excluding cli-mate and CO 2effects)as the study attempts the best quantifi-cation of LULUC including all possible drivers and processes,and to assess the possible uncertainty in LULUC estimates by failing to account for N effects.To summarize,the following three estimates are calculated based on the four experiments (Table 1):1LULUC flux including N effect =A2(D climate,CO 2,N,LULUC)ÀA1(D climate,CO 2,N).2LULUC flux excluding N effect =B2(D climate,CO 2,LULUC)ÀB1(D climate,CO 2).3Effect of N on LULUC flux =(A2ÀA1)À(B2ÀB1).ResultsGlobal net LULUC emissions based on different land-cover reconstructionsLarge interannual variations in global net C emissions from LULUC are observed in the model runs based on each of the three data sets,for the period 1900–2010(Fig.1).These variations are mainly induced by the effects of interannual variations in climate on LULUC fluxes.In particular,soil respiration,decomposition of slash and litter,and NPP in growing vegetation are affected by changes in temperature and precipitation inboth the runs subject to LULUC (A2and B2)and those not subject to LULUC (A1and B1)(e.g.,McGuire et al.,2001;Jain &Yang,2005).Because the natural vegetation responds to climate drivers the same way in A1and A2,the flux shown here (A2–A1)reflects the combined effect of LULUC and climate variability (in addition to CO 2and N)on the land affected by LULUC only.From 1900to 2005,the global cumulative net emis-sions from LULUC were 178,160,and 163GtC for ISAM-HYDE,ISAM-RF,and ISAM-HH,respectively.The ISAM-HYDE estimated global total C emissions for the time period 1900–2010were 180GtC.(All data in this section are from model runs including the N dynamics unless otherwise stated).All three estimated emission tra-jectories show substantially different trends over the period 1900–1960,although they all have a mean value of approximately 1.5GtC yr À1(Fig.1).The net emis-sions based on all three data sets peaked in the 1950s,with ISAM-HH reaching its peak slightly later than the other two data sets.This result from rapid deforestation due to expansion of agriculture in the tropics around the early 1950s followed by a rapid reduction in the rates of deforestation around the late 1950s and early 1960s,with less of a reduction based on ISAM-HH data.Emissions estimates based on ISAM-HH data are very different from those based on ISAM-RF and ISAM-HYDE in the 1960s.Emissions over the last three dec-ades then follow similar trends based on all three data sets;an increase from 1970to 1990and a decline since 1990.The mean decadal net emissions based on ISAM-HYDE data are higher during the 1980s and lower dur-ing the 1990s and 2000s compared to other two data sets,which show similar emissions during the 1980s and 1990s (Table 2).Thus,the decline in emissions from the 1980s to the 2000s is much more pronounced in ISAM-HYDE.The reasons can be found looking at the rate of conversion of land types in the underlying harmonized data sets (Fig.2).ISAM-HYDE shows a sharp decrease in the expansion rates of both cropland and pastureland between 1980and 2005(Fig.2a –d),and a sharp decrease in deforested area (Fig.2e)which is offset to a lesser extent each decade by a declining expansion of the area of secondary forest regrowth (Fig.2f)(partly reforestation on abandoned agricul-tural land and partly conversion of ‘natural’forests to secondary regrowth forests after wood harvest).In contrast,ISAM-RF and ISAM-HH data show an increase in conversion into cropland (Fig.2a and b)and a decrease in conversion of forests to pastures (Fig.2c).Both ISAM-RF and ISAM-HH show an increase in the expansion rate of secondary forest regrowth from the 1980s to the 2000s partly offsetting the loss of primary forest area (Fig.2e andf).Fig.1ISAM-estimated global land-use and land-use change (LULUC)emissions for the period 1900–2010(GtC yr À1)based on Integrated Science Assessment Model-Historical Database of the Global Environment (ISAM-HYDE),ISAM-RF and ISAM-HH data sets.Estimates based on ISAM-RF and ISAM-HH esti-mates extend until year 2007and 2005,respectively.©2013John Wiley &Sons Ltd,Global Change Biology ,19,2893–2906CO 2EMISSIONS FROM L AND-USE CHANGE 2897Emissions based on ISAM-HH data become higher than the other two estimates from 2000to 2005(Fig.1and Table 2)because the conversion of forests to crop-lands and pastures (Fig.2a and c),and hence the over-all area of deforestation (Fig.2e)is higher.Regional differences in LULUC emissionsThere are substantial differences in regional estimates of LULUC emissions between the model results based on the different data sets (Table 2).Except for Tropical America,Eurasia,and China,there is no consistent trend exhibited among the three estimates.These three regions show a generally decreasing trend from between the 1980s to the end of the data set for all three data sets,with the decline being much more pro-nounced in ISAM-HYDE than that in ISAM-RF and ISAM-HH.LULUC emissions based on ISAM-HYDE have decreased substantially over the last three decades for the tropics (30%decline)and non tropics (50%).In con-trast,the estimated emissions based on ISAM-RF show very little change in the tropics and a smaller decrease in the non tropics (30%)between 2000and 2005com-pared to both the 1980s and 1990s,which were very similar.ISAM-HH shows very little change in the tro-pics,and a small increase from the 1980s to the 1990s then a similar decline again to the 2000s (2000–2005average)in the non tropics.Over the last three decades,net emission estimates based on ISAM-HH data are higher for tropical regions and lower for nontropical regions compared to net emission estimates based on other two data sets (Table 2).This is because ISAM-HH data shows much higher deforestation rates for agricultural land in tropi-cal regions (especially in Tropical America)(Fig.2a,c and e).In nontropical regions,the ISAM-HH data set (based on forest statistics)has lower conversion of for-ests to croplands than the other two data sets,and assumes no clearing of forests for pastureland (forest clearing would have been assumed converted to crop-land or secondary forests).The other two data sets (based on agricultural statistics),derived based on a rule-based approach to clear vegetation,have a fraction of pastureland expansion at the expense of forests (Meiyappan &Jain,2012)(Fig.2c).Houghton (2008)(which forms the basis for ISAM-HH)assumes that the expansion of pasture area in North America,China and Pacific Developed regions occurred in the 1950s,and therefore has negligible impact on C emissions for recent years.On the other hand,ISAM-HYDE and ISAM-RF indicate that in the non tropics,forest area was converted to pastures over the last three decades (Fig.2c).T a b l e 2R e g i o n a l b r e a k d o w n o f d e c a d a l m e a n n e t l a n d -u s e a n d l a n d -u s e c h a n g e (L U L U C )e m i s s i o n s (G t C y r À1)f o r t h e 1980s ,1990s a n d 2000s b a s e d o n I n t e g r a t e d S c i e n c eA s s e s s m e n t M o d e l -H i s t o r i c a l D a t a b a s e o f t h e G l o b a l E n v i r o n m e n t (I S A M -H Y D E ),I S A M -R F ,a n d I S A M -H H d a t a s e t sR e g i o n /g l o b a l1980s1990s 2000sI S A M -H Y D EI S A M -R FI S A M -H H M e a n &r a n g e I S A M -H Y D E I S A M -R F I S A M -H H M e a n &r a n g e I S A M -H Y D E *I S A M -R F †I S A M -H H‡M e a n &r a n g e T r o p i c a l A m e r i c a 0.260.330.590.39Æ0.170.200.340.640.39Æ0.220.140.240.460.28Æ0.16T r o p i c a l A f r i c a 0.01À0.030.110.04Æ0.070.04À0.030.110.04Æ0.070.03À0.040.090.03Æ0.06T r o p i c a l A s i a 0.340.350.400.37Æ0.030.310.340.380.34Æ0.030.250.430.530.41Æ0.14T r o p i c s t o t a l 0.610.651.110.79Æ0.250.560.651.130.78Æ0.290.430.631.080.71Æ0.33N o r t h A m e r i c a 0.300.280.190.25Æ0.060.270.280.210.25Æ0.030.250.230.280.25Æ0.03E u r a s i a 0.710.600.290.53Æ0.210.470.620.340.48Æ0.140.390.460.220.36Æ0.12C h i n a 0.590.150.080.27Æ0.260.190.140.070.13Æ0.060.120.090.060.09Æ0.03O c e a n i a 0.000.030.020.02Æ0.010.000.050.080.04Æ0.040.02À0.080.01À0.02Æ0.5N o n t r o p i c s t o t a l 1.611.060.561.08Æ0.520.921.090.700.90Æ0.190.800.690.570.69Æ0.12G l o b a l2.211.701.721.88Æ0.261.481.741.831.68Æ0.181.221.331.651.40Æ0.21*A v e r a g e f o r t h e p e r i o d 2000–2009.†A v e r a g e f o r t h e p e r i o d 2000–2007.‡A v e r a g e f o r t h e p e r i o d 2000–2005.©2013John Wiley &Sons Ltd,Global Change Biology ,19,2893–29062898 A.K.JAIN et al.。
