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%.     

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。     

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Biome-BGC模型和集合卡尔曼滤波方法的阔叶红松林生态系统水碳通量模拟

数据同化为模型与遥感观测结合提供了一条有效的途径,通过在模型运行过程中融入遥感观测数据,调整模型运行轨迹从而降低模型误差,提高模拟精度。本文利用集合卡尔曼滤波(En KF)算法同化生长季中分辨率成像光谱仪(MODIS)叶面积指数(LAI)与Biome-BGC模型模拟的LAI模拟长白山阔叶红松林的水碳通量。同时,通过改进模拟的雪面升华与土壤温度计算方法的参数,旨在降低冬季生态呼吸的模拟误差。结果表明,相对于原始模型,数据同化与模型改进后使得生态系统总初级生产力(GPP)的模拟值与观测值之间的相关系数提高0.06,中心化均方根误差(RMSE)降低0.48 g C·m~(-2)·d~(-1);生态系统呼吸(RE)的相关系数提高0.02,中心化均方根误差降低0.20 g C·m~(-2)·d~(-1);净生态系统碳交换量(NEE)相关系数提高0.35,中心化均方根误差降低0.50 g C·m~(-2)·d~(-1)。同时,数据同化对蒸散发(ET)的模拟精度没有显著影响,改进的模型提高了其相关系数。基于En KF算法的数据同化提高了长白山阔叶红松林碳通量模拟精度,对于精确估算区域碳通量有着重要的意义。     

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.     

祁连山不同植被类型土壤碳贮量和碳通量

采用野外调查测定、野外定位观测和室内分析相结合的方法,在植被类型变化较大林区,选择邻近相同海拔、坡向和土壤类型的天然林(青海云杉林、祁连圆柏林、高山灌丛林)、人工林(13年生华北落叶松林)、牧坡草地和农田等植被类型土壤为研究对象,研究了祁连山不同植被类型的土壤碳动态.结果表明:天然林、牧坡草地、农田和人工林的土壤有机碳含量分别为59.45～84.7、78.30、13.51和43.25 g·kg-1,平均土壤有机碳密度分别为15.96～19.95、17.74、10.63和15.97 kg·m-2,土壤有机碳平均周转时间分别为27～36、25、23和33 a;不同植被类型土壤CO2通量依次为青海云杉林584.03 g C·m-2·a-1,祁连圆柏林517.63 g C·m-1·a-1,高山灌丛林601.00 g C·m-2·a-1,牧坡草地796.89 g C·m-2·a-1,农田281.75 g C·m-1·a-1,人工林569.92 g C·m-2·a-1;同一植被类型中,土壤有机碳含量和土壤碳密度随土壤深度增加而降低,而土壤有机碳周转时间则随深度增加而增大.     

基于遥感和过程模型的亚洲东部陆地生态系统初级生产力分布特征

利用美国环境预测中心的再分析气象资料和由GIMMS NDVI资料生成的叶面积指数对BEPS生态模型进行驱动,模拟分析了2000-2005年亚洲东部地区总初级生产力(GPP)和总净初级生产力(NPP)的时空变化特征.在进行区域模拟计算前,使用15个站点不同生态系统的GPP观测数据及1300个样点的NPP观测数据对模型进行验证.结果表明:BEPS模型能较好地模拟不同生态系统的GPP和NPP变化,模拟的GPP与观测数据之间的R2为0.86 ～0.99,均方根误差(RMSE)为0.2～1.2 gC·m-2·d-1;BEPS模拟值能够解释78％的年NPP变化,其RMSE为118gC ·m-2 ·a-1.2000-2005年,亚洲东部地区GPP和NPP总量平均值分别为21.7和10.5 Pg C·a-1.NPP和GPP具有相似的时空变化特征.研究期间,NPP总量的变化范围为10.2～10.7 Pg C·a-1,变异系数为2.2％.NPP由东南向西北显著减少,高值区(＞1000 g C·m-2·a-1)出现在东南亚海岛国家,我国的西北干旱沙漠地区为低值区(＜30 g C·m-2·a-1),其空间格局主要由气候因子决定.不同国家的人均NPP差异很大,其中,蒙古最高,达70217 kgC·a-1,远高于中国的人均NPP(1921 kg C·a-1),印度的人均NPP最小,为757 kg C·a-1.     

数据处理方法不确定性对CO2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%.     

浙江天目山老龄森林生态系统CO_2通量特征

物种丰富的异龄老龄森林对陆地生态系统动态模型及全球碳收支具有十分重要的意义.目前,我国关于老龄森林碳通量的研究很少,亚热带地区的老龄林更鲜有报道.本研究利用涡度相关技术观测了我国中亚热带地区的浙江天目山一个老龄常绿落叶阔叶混交林生态系统的CO_2通量.以2013年7月到2014年6月的观测数据为依据,分析了此老龄林净生态系统碳交换量(NEE)、生态系统呼吸量(R_e)、生态系统总交换量(GEE)的变化.结果表明:研究期间老龄林常绿落叶阔叶混交林生态系统NEE月总量除12、2月为正值外(表现为碳源),其余月份均为负值(表现为碳汇).NEE月总量平均为-61.52 g C·m~(-2),各月碳吸收量以6月(-149.40 g C·m~(-2))最高,10月次之,呈双峰变化;最大碳源出现在2月(23.45g C·m~(-2)).各月NEE平均日变化差异明显,6月的平均通量峰值最大,达到-0.98 mg·m~(-2)·s~(-1),12月最小,为-0.35mg·m~(-2)·s~(-1);NEE符号改变的时间也呈明显的季节变化特征;全年NEE、R_e、GEE分别为-738.18、931.05、-1669.23g C·m~(-2).与相近纬度相近林型的其他森林生态系统相比,由于其复层结构和多种幼龄更新树木的存在,其测定的固碳量较大.表明我国中亚热带天目山地区的老龄森林生态系统不是处于碳收支稳定状态,而是具有相对较高的固碳能力.     

Influences of error distributions of net ecosystem exchange on parameter estimation of a process-based terrestrial model: A case of broad-leaved Korean pine mixed forest in Changbaishan, China

Model predictions can be improved by parameter estimation from measurements. It was assumed that measurement errors of net ecosystem exchange (NEE) of CO₂ follow a normal distribution. However, recent studies have shown that errors in eddy covariance measurements closely follow a double exponential distribution. In this paper, we compared effects of different distributions of measurement errors of NEE data on parameter estimation. NEE measurements in the Changbaishan forest were assimilated into a process-based terrestrial ecosystem model. We used the Markov chain Monte Carlo method to derive probability density functions of estimated parameters. Our results showed that modeled annual total gross primary production (GPP) and ecosystem respiration (Re) using the normal error distribution were higher than those using the double exponential distribution by 61–86 gC m^?2 a^?1 and 107–116 gC m^?2 a^?1, respectively. As a result, modeled annual sum of NEE using the normal error distribution was lower by 29–47 gC m^?2 a^?1 than that using the double exponential error distribution. Especially, modeled daily NEE based on the normal distribution underestimated the strong carbon sink in the Changbaishan forest in the growing season. We concluded that types of measurement error distributions and corresponding cost functions can substantially influence the estimation of parameters and carbon fluxes.     

北京八达岭林场人工林净碳交换及其环境影响因子

2011年11月-2012年10月,采用涡度相关法对北京市八达岭林场4年生针阔混交人工林的碳交换特征进行了连续观测.结果表明： 观测期间,该森林生态系统在7、8月为碳汇,其余月份均为碳源,净碳释放量与吸收量分别在4月和7月达到最大.生态系统净生产力为(-256±21) g C·m^-2·a^-1,其中生态系统呼吸为(950±36) g C·m^-2·a^-1,总初级生产力为(694±17) g C·m^-2·a^-1.生态系统呼吸与10 cm深度土壤温度呈较好的指数关系,其温度敏感性系数(Q₁₀)为2.2.在5-9月,白天生态系统净碳交换对光合有效辐射的响应符合直角双曲线方程,表观量子效率呈明显的季节变化(0.0219～0.0506 μmol CO₂·μmol^-1),生态系统最大光合速率和白天平均生态系统呼吸强度与光合有效辐射和温度的季节变化趋势相似.此外,7、8月饱和水汽压差与土壤含水量对白天生态系统净碳交换有显著的影响.
     

珠三角城市绿地CO2通量的季节特征

城市绿地是城市碳循环的重要组成部分,利用长期定位观测资料估算珠三角典型城市绿地的CO2通量,可以为应对气候变化、评价区域碳源汇提供参考。应用2009、2010年,东莞市植物园内的涡度相关法CO2通量定位观测资料,分析了净生态系统交换量(NEE)的年变化及其与气象要素的关系,结果表明:(1)年平均NEE总量为-104.2 gC.m-.2a-1,表明城市绿地生态系统具有固碳能力。(2)NEE随光温条件变化呈现明显的季节动态,12至3月表现为碳源,其他月份表现为碳汇。(3)根据白天NEE与光合有效辐射(PAR)逐月拟合Michaelis-Menten方程,得到年平均表观初始光能利用率(α)为(0.00134±0.00035)mgCO.2μmol-1光子,年平均光饱和生态系统生产力(Pmax)为(1.006±0.283)mgCO.2m-.2s-1。(4)利用夜间呼吸(Reco)与5 cm土壤温度(Ts)拟合指数方程,得到年平均Reco总量为1378.1 gC.m-.2a-1。(5)NEE与PAR、气温(Ta)和饱和水压差(VPD)的相关性分析显示,NEE与PAR偏相关系数的绝对值大于Ta和VPD,表明PAR对NEE的影响最大。     

我国亚热带毛竹林CO2通量的变异特征

2010年12月至2011年11月,利用涡度相关技术研究了我国亚热带(浙江)毛竹林生态系统的CO₂通量,分析了毛竹林净生态系统交换量(NEE)、生态系统呼吸量(RE)和生态系统总交换量(GEE)的变化.结果表明: 研究期间,毛竹林各月的NEE均为负值,7月最大,为-99.33 g C·m^-2,11月最小,仅-23.49 g C·m^-2,其变化曲线呈双峰型.各月CO₂通量平均日变化差异明显,9月最大,为-0.60 g CO₂·m^-2·s^-1,1月最小,为-0.30 g CO₂·m^-2·s^-1,且在NEE正负转换的时间点上呈明显的季节变化特征;全年RE呈单峰型变化,夏季最高、冬季最低,夜间RE与土壤温度呈极显著正相关.全年NEE、RE和GEE分别为-668.40、932.55和-1600.95 g C·m^-2·a^-1,NEE占GEE的41.8%.与其他生态系统相比,毛竹林的固碳能力极强.     

Estimating the uncertainty in annual net ecosystem carbon exchange: spatial variation in turbulent fluxes and sampling errors in eddy-covariance measurements

Above forest canopies, eddy covariance (EC) measurements of mass (CO₂, H₂O vapor) and energy exchange, assumed to represent ecosystem fluxes, are commonly made at one point in the roughness sublayer (RSL). A spatial variability experiment, in which EC measurements were made from six towers within the RSL in a uniform pine plantation, quantified large and dynamic spatial variation in fluxes. The spatial coefficient of variation (CV) of the scalar fluxes decreased with increasing integration time, stabilizing at a minimum that was independent of further lengthening the averaging period (hereafter a ‘stable minimum’). For all three fluxes, the stable minimum (CV=9–11%) was reached at averaging times (τ_p) of 6–7 h during daytime, but higher stable minima (CV=46–158%) were reached at longer τ_p (>12 h) during nighttime. To the extent that decreasing CV of EC fluxes reflects reduction in micrometeorological sampling errors, half of the observed variability at τ_p=30 min is attributed to sampling errors. The remaining half (indicated by the stable minimum CV) is attributed to underlying variability in ecosystem structural properties, as determined by leaf area index, and perhaps associated ecosystem activity attributes. We further assessed the spatial variability estimates in the context of uncertainty in annual net ecosystem exchange (NEE). First, we adjusted annual NEE values obtained at our long‐term observation tower to account for the difference between this tower and the mean of all towers from this experiment; this increased NEE by up to 55 g C m^?2 yr^?1. Second, we combined uncertainty from gap filling and instrument error with uncertainty because of spatial variability, producing an estimate of variability in annual NEE ranging from 79 to 127 g C m^?2 yr^?1. This analysis demonstrated that even in such a uniform pine plantation, in some years spatial variability can contribute ～50% of the uncertainty in annual NEE estimates.     

仪器的加热效应校正对生态系统碳水通量估算的影响

涡度相关技术的广泛应用为获取生态系统碳、水通量提供了可能,但在开路式涡度相关系统中,仪器的加热效应增大了观测数据的不确定性。为了衡量仪器的加热效应,以ChinaFLUX3个典型生态系统(长白山温带针阔混交林(CBS)、海北灌丛草甸(HBGC)、鼎湖山亚热带常绿阔叶林(DHS))为研究对象,就仪器的加热效应校正对碳、水通量估算的影响进行分析。结果表明:加热校正没有改变生态系统的能量闭合特征,也没有对水汽通量的估算产生影响,但显著减小了CBS和HBGC非生长季的净生态系统生产力(NEP),进而减少了NEP的年总量,对DHS没有显著影响。NEP减小幅度受到温度的强烈影响,CBS为7.7%～10.4%,远小于HBGC的76.6%～85.2%,HBGC的NEP大幅降低主要是由夜间NEP的改变导致生态系统呼吸(RE)的增大而引起。因而,在温带生态系统中,充分考虑加热校正对于准确估算生态系统的碳收支具有重要作用。     

Using ecosystem CO2 measurements to estimate the timing and magnitude of greenhouse gas mitigation potential of forest bioenergy

Forest bioenergy opportunities may be hindered by a long greenhouse gas (GHG) payback time. Estimating this payback time requires the quantification of forest‐atmosphere carbon exchanges, usually through process‐based simulation models. Such models are prone to large uncertainties, especially over long‐term carbon fluxes from dead organic matter pools. We propose the use of whole ecosystem field‐measured CO₂ exchanges obtained from eddy covariance flux towers to assess the GHG mitigation potential of forest biomass projects as a way to implicitly integrate all field‐level CO₂ fluxes and the inter‐annual variability in these fluxes. As an example, we perform the evaluation of a theoretical bioenergy project that uses tree stems as bioenergy feedstock and include multi‐year measurements of net ecosystem exchange (NEE) from forest harvest chronosequences in the boreal forest of Canada to estimate the time dynamics of ecosystem CO₂ exchanges following harvesting. Results from this approach are consistent with previous results using process‐based models and suggest a multi‐decadal payback time for our project. The time for atmospheric carbon debt repayment of bioenergy projects is highly dependent on ecosystem‐level CO₂ exchanges. The use of empirical NEE measurements may provide a direct evaluation of, or at least constraints on, the GHG mitigation potential of forest bioenergy projects.     

The Carbon Balance of Two Contrasting Mountain Forest Ecosystems in Switzerland: Similar Annual Trends,but Seasonal Differences

Net ecosystem exchange (NEE) of two contrasting mountain forest types in Switzerland was measured by eddy covariance (EC) measurements at a montane mixed forest, the Lägeren forest, over 5 years (2005–2009), and at a subalpine coniferous forest, the Seehornwald in Davos, over 12 years (1997–2009). NEE was validated against annual carbon (C) storage estimates, based on biometric and soil respiration measurements as well as soil C modeling. Three different approaches were used: (1) calculation of net ecosystem production by quantifying C pools and fluxes, (2) assessment of change in wood biomass and soil C storage (ΔC), and (3) application of biomass expansion factors. Although biometric estimates were sensitive to assumptions made for each method applied, they agreed well with measured NEE. Comparing 5 years of EC measurements available at both sites during 2005 and 2009 revealed that NEE, gross primary production (GPP), and total ecosystem respiration (TER) were larger at the Lägeren forest compared to the Davos forest, whereas soil respiration and soil C sequestration were of similar magnitudes. Both sites showed similar annual trends for NEE, GPP and TER, but different seasonal courses, due to different responses to environmental conditions (temperature, soil moisture, and radiation). Differences in the magnitude as well as in the seasonality of ecosystem CO₂ exchange could mainly be attributed to tree phenology, productivity, and carbon allocation patterns, which are combined effects of tree type (broad-leaved vs. coniferous trees) and site-specific climatic conditions. Flux differences between the two mountain sites highlight the importance of considering the role of altitude in ecological studies and modeling.     

The annual cycles of CO2 and H2O exchange over a northern mixed forest as observed from a very tall tower

We present the annual patterns of net ecosystem‐atmosphere exchange (NEE) of CO₂ and H2O observed from a 447 m tall tower sited within a mixed forest in northern Wisconsin, USA. The methodology for determining NEE from eddy‐covariance flux measurements at 30, 122 and 396 m above the ground, and from CO₂ mixing ratio measurements at 11, 30, 76, 122, 244 and 396 m is described. The annual cycle of CO₂ mixing ratio in the atmospheric boundary layer (ABL) is also discussed, and the influences of local NEE and large‐scale advection are estimated. During 1997 gross ecosystem productivity (947?18 g C m^?2 yr^?1), approximately balanced total ecosystem respiration (963±19 g C m^?2 yr^?1), and NEE of CO₂ was close to zero (16±19 g C m^?2 yr^?1 emitted into the atmosphere). The error bars represent the standard error of the cumulative daily NEE values. Systematic errors are also assessed. The identified systematic uncertainties in NEE of CO₂ are less than 60 g C m^?2 yr^?1. The seasonal pattern of NEE of CO₂ was highly correlated with leaf‐out and leaf‐fall, and soil thaw and freeze, and was similar to purely deciduous forest sites. The mean daily NEE of CO₂ during the growing season (June through August) was ?1.3 g C m^?2 day^?1, smaller than has been reported for other deciduous forest sites. NEE of water vapor largely followed the seasonal pattern of NEE of CO₂, with a lag in the spring when water vapor fluxes increased before CO₂ uptake. In general, the Bowen ratios were high during the dormant seasons and low during the growing season. Evapotranspiration normalized by potential evapotranspiration showed the opposite pattern. The seasonal course of the CO₂ mixing ratio in the ABL at the tower led the seasonal pattern of NEE of CO₂ in time: in spring, CO₂ mixing ratios began to decrease prior to the onset of daily net uptake of CO₂ by the forest, and in fall mixing ratios began to increase before the forest became a net source for CO₂ to the atmosphere. Transport as well as local NEE of CO₂ are shown to be important components of the ABL CO₂ budget at all times of the year.