中亚热带人工针叶林生态系统碳通量拆分差异分析

涡度通量观测可直接获取陆地生态系统与大气之间CO2净交换量(NEE),但深入认识碳循环过程和校验生态系统模型需要不同时间尺度总初级生产力(GPP)和生态系统呼吸(Re)等碳通量数据。利用中国陆地生态系统通量观测与研究网络(ChinaFLUX)中亚热带人工针叶林生态系统2003—2009年的涡度通量和气象观测数据,分析了两种NEE拆分方法对不同时间尺度GPP和Re评估的影响,结果表明:(1)两种拆分方法得到的生态系统碳通量组分(GPP和Re)的季节动态变化一致,都在生长季7、8月份达到峰值;(2)非线性回归模型拆分得到的全年Re和GPP相较于光响应曲线模型分别高出2%—28.6%和1.6%—23%,最大高出317.6 gC·m-2·a-1(2006年),逐月最大差值主要发生在8、9月份;(3)不同时间尺度上,两种方法拆分得到的GPP和Re之间差值的环境响应因子不同。在广泛采用非线性回归模型进行拆分时,如果当月光合有效辐射接近到905mol·m-2·月-1,月平均空气饱和水汽压差接近1.18 kPa时,需要考虑使用光响应曲线模型拆分该月通量,结合两种拆分方法以减小全年的误差。     

基于FORCCHN模型的中国典型森林生态系统碳通量模拟

森林生态系统是陆地碳循环的重要组成部分,其固碳能力显著高于其他陆地生态系统,研究森林生态系统碳通量是认识和理解全球变化对碳循环影响的关键。碳循环模型是研究森林生态系统碳通量有效工具。以长白山温带落叶阔叶林、千烟洲亚热带常绿针叶林、鼎湖山亚热带常绿阔叶林和西双版纳热带雨林等4种中国典型森林生态系统为研究对象,利用涡度相关2003-2012年观测数据,评估FORCCHN模型对生态系统呼吸（ER）,总初级生产力（GPP）,净生态系统生产力（NEP）的模型效果。结果表明：（1） FORCCHN模型能够较好的模拟中国4种典型森林生态系统不同时间尺度的碳通量。落叶阔叶林和常绿针叶林ER和GPP的逐日变化模拟效果较好（ER的相关系数分别为0.94和0.92,GPP的相关系数分别为0.86和0.74）;（2）4种森林生态系统碳通量季节动态模拟值和观测值显著相关（P<0.01）,ER、GPP、NEP的观测值和模拟值的R²分别为0.77-0.93、0.54-0.88和0.15-0.38;模型可以很好地模拟森林生态系统不同季节碳汇（NEP>0）,碳源（NEP<0）的变化规律;（3）4种森林生态系统碳通量模拟值与观测值的年际变化有很好的吻合度,但在数值大小上存在差异,模型高估了常绿阔叶林的ER和GPP,略微低估了其他3种森林生态系统ER和GPP。     

NEE观测误差分布类型对陆地生态系统机理模型参数估计的影响——以长白山温带阔叶红松林为例

基于观测数据的陆地生态系统模型参数估计有助于提高模型的模拟和预测能力,降低模拟不确定性.在已有参数估计研究中,涡度相关技术测定的净生态系统碳交换量(NEE)数据的随机误差通常被假设为服从零均值的正态分布.然而近年来已有研究表明NEE数据的随机误差更服从双指数分布.为探讨NEE观测误差分布类型的不同选择对陆地生态系统机理模型参数估计以及碳通量模拟结果造成的差异,以长白山温带阔叶红松林为研究区域,采用马尔可夫链-蒙特卡罗方法,利用2003～2005年测定的NEE数据对陆地生态系统机理模型CEVSA2的敏感参数进行估计,对比分析了两种误差分布类型(正态分布和双指数分布)的参数估计结果以及碳通量模拟的差异.结果表明,基于正态观测误差模拟的总初级生产力和生态系统呼吸的年总量分别比基于双指数观测误差的模拟结果高61～86 g C m-2 a-1和107～116 g C m-2 a-1,导致前者模拟的NEE年总量较后者低29～47 g C m-2 a-1,特别在生长旺季期间有明显低估.在参数估计研究中,不能忽略观测误差的分布类型以及相应的目标函数的选择,它们的不合理设置可能对参数估计以及模拟结果产生较大影响.     

基于数据同化的哈佛森林地区水、碳通量模拟

模型模拟和站点观测是陆地生态系统水、碳循环研究最主要的两种手段,但各有优势和不足,若二者相互结合,则能更准确地反映生态系统水、碳通量的动态变化.数据同化为模型与观测结合提供了一条有效的途径.本文采用哈佛森林环境监测站相关数据,利用集合卡曼滤波同化算法,将实测叶面积指数(LAI)和遥感LAI同化进入Biome BGC模型中,对该地区水、碳通量进行模拟.结果表明：与未同化模拟相比,将1998、1999和2006年实测LAI数据同化后,模型模拟碳通量(NEE)与通量观测NEE的决定系数(R²)平均提升8.4%;蒸散发(ET)的R²平均提升10.6%;NEE的绝对误差和(SAE)和均方根误差(RMSE)平均下降17.7%和21.2%,ET的SAE和RMSE平均下降26.8%和28.3%.将2000-2004年MODIS LAI 产品与模型同化后,NEE、ET模拟值与观测值间的R²分别提升7.8%和4.7%;NEE的SAE和 RMSE分别下降21.9%和26.3%,ET的SAE和 RMSE分别下降24.5%和25.5%.无论实测LAI还是遥感观测LAI,同化进入模型都能不同程度地提高水碳通量的模拟精度.     

基于模型数据融合的千烟洲亚热带人工林碳水通量模拟

人工林生态系统是我国森林生态系统的重要组成部分,在全球碳平衡中的作用越来越受到重视.利用千烟洲亚热带人工针叶林通量观测站的碳水通量和气象观测数据,通过模型数据融合方法对碳水循环过程模型——SIPNET模型关键参数进行反演,模拟了2004-2009年千烟洲人工林生态系统的碳水通量.结果表明:仅用碳通量观测数据优化模型参数时,净生态系统碳交换量(NEE)模拟效果较好(R2=0.934),而生态系统蒸散(ET)模拟效果较差(R2=0.188);同时用碳水通量观测数据优化时,NEE模拟效果稍差(R2=0.929),但ET模拟效果显著提升(R2=0.824),说明利用碳水通量观测数据同时优化,SIPNET模型才能较好地模拟试验站点碳水通量.在此基础上,开展了人工林生态系统碳通量对降水变化响应的敏感性分析,发现降水量减少对光合作用的影响比对呼吸作用的影响更为强烈,且碳水通量同时参与优化时模型才能较好地模拟碳通量随降水减少而快速降低的趋势,表明如果不能同时利用碳水通量进行参数优化,模型无法正确揭示生态系统碳循环对降水变异的响应.     

川西贡嘎山峨眉冷杉成熟林生态系统CO2通量特征

成熟森林的碳收支对陆地生态系统碳循环研究具有重要意义。目前,我国关于西南亚高山暗针叶林成熟林碳通量的研究还相对较少,尚不明确对碳循环的作用。以涡度相关技术为基础,对川西贡嘎山东坡峨眉冷杉成熟林生态系统尺度的CO_2通量进行长期定位观测。利用2015年6月至2016年5月观测数据,分析了峨眉冷杉成熟林净生态系统CO_2交换量(NEE)、生态系统呼吸(Re)和总生态系统生产力(GPP)的季节变异特征及其源汇状况,并结合环境因子,分析CO_2通量的主要控制因子。结果表明:(1)峨眉冷杉成熟林NEE具有明显的日变化特征,呈现"U"形变化,白天为负值,夜间为正值,中午前后CO_2通量达到最大;各月间日平均NEE变化差异显著,NEE峰值最大出现在2015年6月(-0.64 mg CO_2m~(-2)s~(-1)),峰值最小出现在2016年1月(-0.08 mg CO_2m~(-2)s~(-1));日平均NEE由正值变为负值的时间夏季最早,冬季最晚,NEE由负值变为正值的时间冬季最早,夏季最晚。(2)峨眉冷杉成熟林NEE、Re和GPP具有明显的月变化。2015年6月和12月NEE分别达到最大值(-46.02 g C m~(-2)月~(-1))和最小值(-1.42 g C m~(-2)月~(-1));Re呈现单峰变化,最大和最小值分别出现在2015年6月(84.78 g C m~(-2)月~(-1))和2016年1月(12.82 g C m~(-2)月~(-1));GPP最大值和最小值分别出现在2015年6月(130.81 g C m~(-2)月~(-1))与2016年1月(16.15 g C m~(-2)月~(-1))。(3)空气温度(T_a)、5 cm土壤温度(T_(s5))和光合有效辐射(PAR)是影响峨眉冷杉成熟林CO_2通量的主要环境因子。T_a与CO_2通量呈指数相关(R~2=0.5283,P0.01);白天CO_2通量与PAR显著相关(R~2=0.4373,P0.01);夜晚CO_2通量与T_(s5)显著相关(R~2=0.4717,P0.01)。(4)全年NEE、Re和GPP分别为-241.87、564.81 g C m~(-2)和806.68 g C m~(-2),表明川西贡嘎山峨眉冷杉成熟林具有较强的碳汇功能。     

双集合卡尔曼滤波LAI同化结合BEPS模型的竹林生态系统碳通量模拟

叶面积指数(LAI)是森林生态系统碳循环研究的重要观测数据,也是驱动森林生态系统模型模拟碳循环的重要参数.本文以毛竹林和雷竹林为研究对象,首先利用双集合卡尔曼滤波,同化两种竹林生态系统观测站点2014—2015年MODIS LAI时间序列数据,然后将同化的高质量毛竹LAI和雷竹LAI作为输入数据驱动BEPS模型,模拟两种竹林生态系统总初级生产力(GPP)、净生态系统碳交换量(NEE)和总生态系统呼吸(TER)等碳循环数据,并用通量站实际观测值评价模拟结果;另外,还对比不同质量LAI对碳循环模拟的影响.结果表明: 双集合卡尔曼滤波同化得到的毛竹林和雷竹林LAI与实测LAI之间的相关关系极为显著,R²分别为0.81和0.91,且均方根误差和绝对偏差均较小,极大地提高了MODIS LAI的产品精度;在同化得到的LAI驱动下,BEPS模型模拟的毛竹林GPP、NEE和TER与实际观测值之间的R²分别为0.66、0.47和0.64,雷竹林分别为0.66、0.45和0.73,模拟结果均好于三次样条帽盖算法平滑LAI模拟得到的GPP、NEE和TER,其中,毛竹林、雷竹林NEE的模拟精度提高幅度最大,分别为11.2%和11.8%.     

1980—2013年中国陆地生态系统总初级生产力对干旱的响应特征

干旱事件通过影响陆地生态系统的组成、结构和功能显著改变整个陆地生态系统碳循环。陆地生态系统总初级生产力(GPP)是全球陆地碳通量中最大的组成部分,反映了陆地生态系统的生产力水平。本研究利用基于过程模型模拟的GPP数据(DLM GPP)、基于通量观测升尺度的GPP数据(FLUXCOM GPP)和标准化降水蒸散指数(SPEI),量化分析了1980-2013年中国陆地生态系统GPP和干旱的时空格局,讨论了不同时间尺度上GPP对干旱的响应特征。结果表明:1980-2013年,两种不同GPP数据在中国地区呈现的时间变化趋势的空间分布格局较为一致,上升趋势主要分布在西南地区,下降趋势主要分布在东北大部分地区;中国干旱面积的长期时间变化趋势略有下降,其中干旱化趋势主要位于秦岭淮河以南地区,而西北内陆地区则呈现明显的湿润化趋势;时间尺度上,GPP与SPEI年际变化格局基本吻合,1986、1997、2001和2011年等干旱年份的GPP显著降低;空间尺度上,北方大部分地区的GPP与SPEI呈正相关,南方大部分地区呈负相关,干旱对GPP的影响在半干旱地区表现更加明显;GPP对干旱的响应格局与选取干旱指数的时间尺度密切相关,而且不同方式估算的GPP对干旱响应和敏感度存在差异。因此,未来需进一步改进GPP模型和方法,增加观测站点,提高GPP估算的精确性。     

植被光合呼吸模型在长白山温带阔叶红松林的优化及验证

植被光合呼吸模型(VPRM)关键参数的确定和优化是准确计算生态系统净CO_2交换(NEE)的基础。利用中国通量观测研究联盟(China FLUX)长白山站温带阔叶红松林2005年的通量观测资料,对VPRM的4个参数(最大光能利用率ε_0、光照为半饱和条件下光合有效辐射值PAR0和呼吸参数(α、β))进行优化,并使用2006年的观测资料对参数优化前后的模拟结果进行评估。结果表明:参数优化后,VPRM能够较好地模拟长白山地区2006年植物生长季NEE的变化。对30min NEE模拟的平均误差为-1.81μmol m~(-2)s~(-1),相关系数为0.72,模拟NEE平均日变化的峰值约为观测值的91%,相关系数为0.97。但在植物非生长季模型对森林NEE的模拟效果较差。模型模拟30min NEE的平均误差为0.39μmol m~(-2)s~(-1),相关系数仅为0.10,并且模拟低估NEE平均日变化白天吸收峰值约82%,日变化模拟值与观测值的相关系数为0.50。通过分析不同天气个例,发现模型可以较好地模拟晴天条件下NEE的变化,而对阴雨天NEE的模拟误差较大。该研究有利于提高VPRM模型对温带落叶阔叶林NEE的模拟能力,对进一步改进区域陆地NEE的模拟具有重要意义。     

数据处理方法不确定性对CO_2通量组分估算的影响

基于中国陆地生态系统通量观测研究网络(ChinaFLUX)4个站点(2个森林站和2个草地站)的涡度相关通量观测资料,分析了CO2通量数据处理过程中异常值剔除参数设置、夜间摩擦风速(u*)临界值(u*c)确定及数据插补模型选择对CO2通量组分估算的影响.结果表明:3种数据处理方法均对净生态系统碳交换量(NEE)年总量估算有显著影响,其中u*c确定是影响NEE估算的重要因子;异常值剔除、u*c确定及数据插补模型选择导致NEE年总量估算偏差分别为0.62～21.31 g C.m-2.a-1(0.84%～65.31%)、4.06～30.28 g C.m-2.a-1(3.76%～21.58%)和0.69～27.73 g C.m-2.a-1(0.23%～55.62%),草地生态系统NEE估算对数据处理方法参数设置更敏感;数据处理方法不确定性引起的总生态系统碳交换量和生态系统呼吸年总量估算相对偏差分别为3.88%～11.41%和6.45%～24.91%.     

Modeling the effects of snowpack on heterotrophic respiration across northern temperate and high latitude regions: Comparison with measurements of atmospheric carbon dioxide in high latitudes

Simulations by global terrestrial biogeochemical models (TBMs) consistently underestimate the concentration of atmospheric carbon dioxide (CO₂ at high latitude monitoring stations during the non-growing season. We hypothesized that heterotrophic respiration is underestimated during the nongrowing season primarily because TBMs do not generally consider the insulative effects of snowpack on soil temperature. To evaluate this hypothesis, we compared the performance of baseline and modified versions of three TBMs in simulating the seasonal cycle of atmospheric CO₂ at high latitude CO₂ monitoring stations; the modified version maintained soil temperature at 0 °C when modeled snowpack was present. The three TBMs include the Carnegie-Ames-Stanford Approach (CASA), Century, and the Terrestrial Ecosystem Model (TEM). In comparison with the baseline simulation of each model, the snowpack simulations caused higher releases of CO₂ between November and March and greater uptake of CO₂ between June and August for latitudes north of 30° N. We coupled the monthly estimates of CO₂ exchange, the seasonal carbon dioxide flux fields generated by the HAMOCC3 seasonal ocean carbon cycle model, and fossil fuel source fields derived from standard sources to the three-dimensional atmospheric transport model TM2 forced by observed winds to simulate the seasonal cycle of atmospheric CO₂ at each of seven high latitude monitoring stations. In comparison to the CO₂ concentrations simulated with the baseline fluxes of each TBM, concentrations simulated using the snowpack fluxes are generally in better agreement with observed concentrations between August and March at each of the monitoring stations. Thus, representation of the insulative effects of snowpack in TBMs generally improves simulation of atmospheric CO₂ concentrations in high latitudes during both the late growing season and nongrowing season. These simulations highlight the global importance of biogeochemical processes during the nongrowing season in estimating carbon balance of ecosystems in northern high and temperate latitudes.     

FLUXNET and modelling the global carbon cycle

Measurements of the net CO₂ flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO₂ source–sink patterns, CO₂ flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO₂ (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model‐data comparison. Flux measurements made over four vegetation types were used to calibrate the land‐surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land‐surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO₂ uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). This model is forced by satellite‐measured absorbed radiation and suggests that global net primary production (NPP) increased 6.2% over 1982–1999. Good agreement is found between global trends in NPP estimated by the PEM and a dynamic global vegetation model (DGVM), and between the DGVM and estimates of global NEE derived from a global inversion of atmospheric CO₂ measurements. Combining the PEM, DGVM, and inversion results suggests that CO₂ fertilization is playing a major role in current increases in NPP, with lesser impacts from increasing N deposition and growing season length. Both the PEM and the inversion identify the Amazon basin as a key region for the current net terrestrial CO₂ uptake (i.e. 33% of global NEE), as well as its interannual variability. The inversion's global NEE estimate of −1.2 Pg [C] yr⁻¹ for 1982–1995 is compatible with the PEM‐ and DGVM‐predicted trends in NPP. There is, thus, a convergence in understanding derived from process‐based models, remote‐sensing‐based observations, and inversion of atmospheric data. Future advances in field measurement techniques, including eddy covariance (particularly concerning the problem of night‐time fluxes in dense canopies and of advection or flow distortion over complex terrain), will result in improved constraints on land‐atmosphere CO₂ fluxes and the rigorous attribution of mechanisms to the current terrestrial net CO₂ uptake and its spatial and temporal heterogeneity. Global ecosystem models play a fundamental role in linking information derived from FLUXNET measurements to atmospheric CO₂ variability. A number of recommendations concerning FLUXNET data are made, including a request for more comprehensive site data (particularly historical information), more measurements in undisturbed ecosystems, and the systematic provision of error estimates. The greatest value of current FLUXNET data for global carbon cycle modelling is in evaluating process representations, rather than in providing an unbiased estimate of net CO₂ exchange.     

A growing season climatic index to simulate gross primary productivity and carbon budget in a Tibetan alpine meadow

Accurate estimation of gross primary production (GPP) of ecosystem is needed to evaluate terrestrial carbon cycle at various spatial and temporal scales. Eddy covariance (EC) technique provides continuous measurements of net ecosystem CO₂ exchange (NEE) and can be used to separate GPP from NEE in real time series. However, seasonal and inter-annual variation and consequently ecosystem carbon budget is still very difficult to simulate from climatic and environment. To address this limitation, we develop a growing season indicator (GSI) based on low temperature and soil water stress to model and predict intra and inter-annual dynamic of gross primary productivity (GPP). Validation of this new index was conducted using continuous six-year consective EC measurement from 2004 to 2009 at a Tibetan alpine meadow. Simulated GPP agreed well with the observed GPP in terms of seasonal and inter-annual variation. The six-year correlation coefficients on seasonal scale between GSI and scalar GPP derived from EC reached more than 0.85 no matter in dry years or wet years. In addition, the temporal GPP estimation derived from GSI model was quite similar to those from observed values by EC measurement. Moreover, accumulated GSI values can predict annual variability of net ecosystem production (NEP). Higher yearly accumulated GSI corresponded to more annual NEP. When cumulative GSI arrived up to 92, the target ecosystem was a carbon sink. This is probably a threshold which Tibetan alpine meadow changes from carbon source to carbon sink. It is indicated that the GSI model is a simple, alternative approach to estimating GPP and has the potential to simulate spatial GPP in a larger scale. However, the performance of GSI model in other vegetation types or regions still needs a further verification.     

Annual balances and extended seasonal modelling of carbon fluxes from a temperate fen cropped to festulolium and tall fescue under two‐cut and three‐cut harvesting regimes

This study reports the annual carbon balance of a drained riparian fen under two‐cut or three‐cut managements of festulolium and tall fescue. CO₂ fluxes measured with closed chambers were partitioned into gross primary production (GPP) and ecosystem respiration (ER) for modelling according to environmental factors (light and temperature) and canopy reflectance (ratio vegetation index, RVI). Methodological assessments were made of (i) GPP models with or without temperature functions (Ft) to adjust GPP constraints imposed by low temperature (<10 °C) and (ii) ER models with RVI or GPP parameters as biomass proxies. The sensitivity of the models was also tested on partial datasets including only alternate measurement campaigns and on datasets only from the crop growing period. Use of Ft in GPP models effectively corrected GPP overestimation in cold periods, and this approach was used throughout. Annual fluxes obtained with ER models including RVI or GPP parameters were similar, and also annual GPP and ER fluxes obtained with full and partial datasets were similar. Annual CO₂ fluxes and biomass yield were not significantly different in the crop/management combinations although the individual collars (n = 12) showed some variations in GPP (?1818 to ?2409 g CO₂‐C m^?2), ER (1071 to 1738 g CO₂‐C m^?2), net ecosystem exchange (NEE, ?669 to ?949 g CO₂‐C m^?2) and biomass yield (556 to 1044 g CO₂‐C m^?2). Net ecosystem carbon balance (NECB), as the sum of NEE and biomass carbon export, was only slightly negative to positive in all crop/management combinations. NECBs, interpreted as emission factors, tended to favour the least biomass producing systems as the best management options in relation to climate saving carbon balances. Yet, considering the down‐stream advantages of biomass for fossil fuel replacement, yield‐scaled carbon fluxes are suggested to be given additional considerations for comparison of management options in terms of atmospheric impact.     

Constraining future terrestrial carbon cycle projections using observation‐based water and carbon flux estimates

The terrestrial biosphere is currently acting as a sink for about a third of the total anthropogenic CO₂ emissions. However, the future fate of this sink in the coming decades is very uncertain, as current earth system models (ESMs) simulate diverging responses of the terrestrial carbon cycle to upcoming climate change. Here, we use observation‐based constraints of water and carbon fluxes to reduce uncertainties in the projected terrestrial carbon cycle response derived from simulations of ESMs conducted as part of the 5th phase of the Coupled Model Intercomparison Project (CMIP5). We find in the ESMs a clear linear relationship between present‐day evapotranspiration (ET) and gross primary productivity (GPP), as well as between these present‐day fluxes and projected changes in GPP, thus providing an emergent constraint on projected GPP. Constraining the ESMs based on their ability to simulate present‐day ET and GPP leads to a substantial decrease in the projected GPP and to a ca. 50% reduction in the associated model spread in GPP by the end of the century. Given the strong correlation between projected changes in GPP and in NBP in the ESMs, applying the constraints on net biome productivity (NBP) reduces the model spread in the projected land sink by more than 30% by 2100. Moreover, the projected decline in the land sink is at least doubled in the constrained ensembles and the probability that the terrestrial biosphere is turned into a net carbon source by the end of the century is strongly increased. This indicates that the decline in the future land carbon uptake might be stronger than previously thought, which would have important implications for the rate of increase in the atmospheric CO₂ concentration and for future climate change.     

Effects of Elevated CO2 and Nitrogen Deposition on Ecosystem Carbon Fluxes on the Sanjiang Plain Wetland in Northeast China

Background

Increasing atmospheric CO₂ and nitrogen (N) deposition across the globe may affect ecosystem CO₂ exchanges and ecosystem carbon cycles. Additionally, it remains unknown how increased N deposition and N addition will alter the effects of elevated CO₂ on wetland ecosystem carbon fluxes.

Methodology/Principal Findings

Beginning in 2010, a paired, nested manipulative experimental design was used in a temperate wetland of northeastern China. The primary factor was elevated CO₂, accomplished using Open Top Chambers, and N supplied as NH₄NO₃ was the secondary factor. Gross primary productivity (GPP) was higher than ecosystem respiration (ER), leading to net carbon uptake (measured by net ecosystem CO₂ exchange, or NEE) in all four treatments over the growing season. However, their magnitude had interannual variations, which coincided with air temperature in the early growing season, with the soil temperature and with the vegetation cover. Elevated CO₂ significantly enhanced GPP and ER but overall reduced NEE because the stimulation caused by the elevated CO₂ had a greater impact on ER than on GPP. The addition of N stimulated ecosystem C fluxes in both years and ameliorated the negative impact of elevated CO₂ on NEE.

Conclusion/Significance

In this ecosystem, future elevated CO₂ may favor carbon sequestration when coupled with increasing nitrogen deposition.     

The use of CO2 flux measurements in models of the global terrestrial carbon budget

CO₂ flux measurements give access to two critical terms of the carbon budget of terrestrial ecosystems, the gross primary productivity (GPP) and the net ecosystem productivity (NEP). CO₂ fluxes measured by micrometeorological methods have spatial and temporal characteristics that make them potentially useful in modelling the global terrestrial carbon budget. The first use is in parameterizing ecosystem physiological processes. We present an example, based on parameterizing the mean light response of GPP. This parameterization can be used in diagnostic, satellite-based GPP models. A global application leads to realistic estimates of global GPP. The second use is in testing the seasonality of fluxes predicted by global models. Our example of this use tests two global GPP models. One is a diagnostic, satellite-based model, and one is a prognostic, process-based model. Despite the limitations of the models, both agree reasonably well with the measurements. The agreements and disagreements are useful in addressing the problems of available input datasets and representation of processes, in global models. Long-term CO₂ flux measurements give access to key variables of terrestrial vegetation models and therefore offer exciting perspectives.     

Abrupt permafrost thaw drives spatially heterogeneous soil moisture and carbon dioxide fluxes in upland tundra

Permafrost thaw causes the seasonally thawed active layer to deepen, causing the Arctic to shift toward carbon release as soil organic matter becomes susceptible to decomposition. Ground subsidence initiated by ice loss can cause these soils to collapse abruptly, rapidly shifting soil moisture as microtopography changes and also accelerating carbon and nutrient mobilization. The uncertainty of soil moisture trajectories during thaw makes it difficult to predict the role of abrupt thaw in suppressing or exacerbating carbon losses. In this study, we investigated the role of shifting soil moisture conditions on carbon dioxide fluxes during a 13-year permafrost warming experiment that exhibited abrupt thaw. Warming deepened the active layer differentially across treatments, leading to variable rates of subsidence and formation of thermokarst depressions. In turn, differential subsidence caused a gradient of moisture conditions, with some plots becoming consistently inundated with water within thermokarst depressions and others exhibiting generally dry, but more variable soil moisture conditions outside of thermokarst depressions. Experimentally induced permafrost thaw initially drove increasing rates of growing season gross primary productivity (GPP), ecosystem respiration (R_eco), and net ecosystem exchange (NEE) (higher carbon uptake), but the formation of thermokarst depressions began to reverse this trend with a high level of spatial heterogeneity. Plots that subsided at the slowest rate stayed relatively dry and supported higher CO₂ fluxes throughout the 13-year experiment, while plots that subsided very rapidly into the center of a thermokarst feature became consistently wet and experienced a rapid decline in growing season GPP, R_eco, and NEE (lower carbon uptake or carbon release). These findings indicate that Earth system models, which do not simulate subsidence and often predict drier active layer conditions, likely overestimate net growing season carbon uptake in abruptly thawing landscapes.     

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO₂ between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO₂ release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO₂ fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NN_C‐part). NN_C‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R² > .94) and RECO (R² > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NN_C‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO₂ fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO₂ fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN_C‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.