湖南会同林区毛竹林地的土壤呼吸

采用CID-301PS光合分析仪(配带土壤呼吸室),对湖南会同林区毛竹林地土壤呼吸进行测定,结果表明,毛竹林地土壤总呼吸速率、异养呼吸速率、自养呼吸速率及凋落物呼吸速率的年平均值分别为2.13、1.44、0.69μmolCO2·m-2·s-1和0.31μmolCO2·m-2·s-1,并呈现明显的季节变化规律和日变化规律,季节变化曲线呈单峰型,表现为1～7月份随着气温、地温的升高呈上升的趋势,在8月达年呼吸速率的最大值,分别达4.95、3.01、1.94μmolCO2·m-2·s-1和0.80 μmolCO2·m-2·s-1,此后随温度的降低而呈逐渐递减的趋势,直到翌年的1月份或2月份,分别为0.76、0.70、 0.06μmolCO2·m-2·s-1 和 0.05μmolCO2·m-2·s-1.日变化曲线图表现为单峰形态,一般也是随着温度的升高而加大,随着温度的降低而减小.6:00～14:00,随着土壤温度的升高而增加,一般在16:00～18:00出现最高峰,此后,一直递减,直到次日4:00～8:00.由此计算出毛竹林地土壤年释放CO2量为33.94 t·hm-2·a-1,其中,林地异养呼吸、自养呼吸和凋落物呼吸分别占总呼吸的59.5%、28.3%和12.2%.     

塔克拉玛干沙漠腹地冬季土壤呼吸及其驱动因子

利用Li-8150系统测定了塔克拉玛干沙漠腹地冬季(1月)土壤呼吸,分析了环境驱动因子对极端干旱区荒漠生态系统土壤呼吸的影响。结果表明:(1)冬季土壤呼吸日变化呈现出显著的单峰曲线,土壤呼吸速率最大值出现在12:00,为0.0684μmol CO2m-2s-1,凌晨04:00附近出现最小值,为-0.0473μmol CO2m-2s-1;(2)土壤呼吸速率与各层气温,0cm地表温度均存在着极其显著或显著的线性关系,且都具有正相关性;(3)土壤呼吸速率与5cm土壤湿度存在着较为明显的线性关系,该层湿度能够解释土壤呼吸的69.5%;(4)0cm地表温度对土壤呼吸贡献最大,其次是5cm土壤湿度;(5)以0cm地表温度、5cm土壤湿度为变量,通过多元回归分析表明:土壤温度-湿度构成的多变量模型能够解释大于86.9%的土壤呼吸变化情况;(6)研究时段内土壤呼吸速率的平均值是-1.45mg CO2m-2h-1。     

云南香格里拉地区亚高山草甸不同放牧管理方式下的碳排放

青藏高原由于高寒低温限制了有机碳的分解,大量的碳积累在土壤碳库中,对全球升温的反应很敏感.放牧可能会对该区草甸的碳排放产生显著影响.采用闭合箱动态法测定了云南香格里拉地区不同放牧管理方式下的亚高山草甸生态系统呼吸与土壤呼吸.常年放牧草甸与季节性放牧草甸的生态系统呼吸和土壤呼吸均呈现相似且明显的单峰季节变化特征,7月份达最大值,生态系统呼吸分别为9.77、8.03 μmol CO2 m-2 s-1,土壤呼吸分别为8.05、7.74 μmol CO2 m-2 s-1;1月份达最低值,生态系统呼吸分别为0.21、0.48 μmol CO2 m-2 s-1,土壤呼吸分别为0.16、0.49 μmol CO2 m-2 s-1.受一天中气温和土温的影响,常年放牧草甸的生态系统呼吸与土壤呼吸的日变化在夏季与冬季均呈现明显的单峰曲线变化,最高值都出现在14:00左右,最低值出现在凌晨.在夏季6～10月份,常年放牧草甸的呼吸显著大于季节性放牧草甸,表明较高的放牧强度增加了亚高山草甸的碳排放.土壤温度的指数模型F = aebT比土壤水分能更好地解释呼吸的变异性(R2 = 0.50～0.78,P < 0.0001).二元回归模型F=aebTWc比单因子模型的效果更好(R2=0.56～0.89,P < 0.0001).土壤呼吸在整个亚高山草甸生态系统呼吸中占主导地位,在常年放牧草甸与季节性放牧草甸分别为63.0%～92.7%和47.5%～96.4%,地上植物呼吸随生长季的变化而变化,在生长旺季占有较大的比例.生态系统呼吸和土壤呼吸的长期Q10(1a)是短期Q10(1d)的2倍左右.季节性放牧草甸的长期Q10小于常年放牧草甸,表明在温度上升的背景下,放牧压力较小的草甸碳库较为稳定,具有较好的碳截存能力.     

黄河小浪底人工混交林冠层CO2储存通量变化特征

基于黄河小浪底人工混交林2008年的CO2浓度和碳通量数据,分析了不同天气条件下CO2浓度在时间和空间上的变化特征,对比了CO2浓度廓线法和涡度相关法估算的CO2储存通量,研究了CO2储存通量的日、季变化特征。结果表明:人工混交林冠层上方月平均CO2浓度具有明显的季节变化规律。月平均CO2浓度最大值出现在3月(370μmol/mol),最低值出现在8月(347μmol/mol)。涡度相关法估算的CO2储存通量比廓线法所得结果偏低9%。生长季,冠层CO2储存通量和净生态系统碳交换量(NEE)日平均值分别为-0.0004和-0.091 mg CO2m-2s-1,冠层CO2储存通量在NEE中仅占0.4%。2008年CO2储存通量和NEE分别为-46.1、-1133 g CO2m-2a-1。在年尺度上,CO2储存通量占NEE的4.1%。因此,在日和年尺度上计算黄河小浪底人工混交林NEE时,CO2储存通量可以忽略。     

原始兴安落叶松林生长季净生态系统CO2交换及其光响应特征

对2006-2008年寒温带原始兴安落叶松林生长季(6-10月份)生态系统CO2交换及其影响因素的分析表明：净生态系统CO2交换(NEE)呈单峰型曲线,最大值出现在9:00-10:00。兴安落叶松林的NEE在生长季前期(6-8月份)呈净碳吸收,生长季末期(9-10月份)呈碳排放。生长季6、7\,8月份的NEE平均值分别为-0.082、-0.082\,-0.061 mgCO2 ?m-2 ?s-1,生长季末期9\,10月份的NEE平均值分别为0.009\,0.014 mgCO2 ?m-2 ?s-1。6-10月份原始兴安落叶松林生长季每天的固碳时间从14h(5:00-19:00)逐渐缩短为9h(7:30-16:30)。从不同温度下NEE光响应特征可知,原始兴安落叶松林NEE最适气温是20-30 ℃,NEE最大值为-0.43 mgCO2 ?m-2 ?s-1。     

黄河三角洲芦苇湿地生态系统碳、水热通量特征

利用涡度相关法对黄河三角洲芦苇湿地生态系统进行了连续两年的通量观测,对2009—2010年生长季芦苇湿地的净生态系统碳交换量(NEE),感热通量(Hs)和潜热通量(LE)数据进行了分析。结果表明,在日尺度上,芦苇湿地NEE日变化特征表现为两个CO2吸收高峰,分别出现在11:00和16:00左右,其特点是在午间出现了碳交换通量的降低。CO2吸收的日最大值在两个生长季出现的时间有所不同,分别出现在2009年7月(-0.30 mg CO2m-2s-1)和2010年6月(-0.37 mg CO2m-2s-1)。CO2排放的日最大值两个生长季均出现在9月,分别为0.19和0.25 mg CO2m-2s-1。Hs和LE的日动态均为单峰型,极值都出现在中午前后,生长季生态系统的能量消耗主要以潜热为主,且在日尺度上,热通量和NEE有显著的负相关关系。在季节尺度上,芦苇湿地生长季具有明显的碳汇功能,2009年生长季生态系统白天固定354.63 g CO2/m2,同时期夜间释放159.24 g CO2/m2,净CO2吸收量为-195.39 g CO2/m2。2009年整个生长季生态系统总初级生产力(GPP)为-651.13 g CO2/m2,生态系统呼吸(Re)为455.74 g CO2/m2,系统表现为碳汇。路径分析表明:光合有效辐射(PAR)显著影响NEE的日动态(R2=0.46—0.84),而NEE的季节动态主要受土壤温度的影响,降水和PAR的影响次之。     

去除和添加凋落物对枫香（Liquidambar formosana）和樟树（Cinnamomum camphora）林土壤呼吸的影响

2007年1月至12月,在长沙天际岭国家森林公园,使用LI-COR-6400-09连接到LI-6400便携式CO2/H2O分析系统,测定亚热带枫香(Liquidambar formosana)和樟树(Cinnamomum camphora)林去除和添加凋落物(931.5 g · m-2a-1和1003.4 g · m-2a-1)的土壤呼吸速率以及5 cm土壤温、湿度,研究凋落物对2种森林生态系统中土壤呼吸速率的影响.结果表明:枫香和樟树林去除和添加凋落物的土壤呼吸速率季节变化显著,在季节动态上的趋势与5 cm土壤温度相似,均呈单峰曲线格局,全年去除凋落物土壤呼吸速率平均值分别为1.132 μmol CO2 · m-2s-1和1.933 μmol CO2 · m-2s-1,分别比对照处理1.397 μmol CO2 · m-2s-1和2.581 μmol CO2 · m-2s-1低18.62%和26.49%;添加凋落物土壤呼吸速率平均值分别为2.363 μmol CO2 · m-2s-1和3.267 μmol CO2 · m-2s-1,分别比对照处理高71.31%和39.18%.两种群落去除和添加凋落物土壤呼吸的季节变化均与5 cm土壤温度呈显著指数相关(P﹤0.001),与5 cm土壤湿度相关性不显著(P>0.05);土壤温度和湿度可以共同解释去除和添加凋落物后土壤呼吸变化的95.2%、93.7%和90.0%、92.8%.枫香和樟树群落去除和添加凋落物土壤呼吸温度敏感性Q10值分别为3.01、3.29和3.02、4.37,均比对照处理Q10值2.98和2.94高.这证明凋落物是影响森林CO2通量的一个重要因子.     

盐生荒漠净生态系统碳交换的涡度相关法和箱式法对比

将叶面积指数的季节动态,与箱式法同步观测得到的同化枝净光合（呼吸）速率和土壤呼吸速率相结合,对群落碳交换进行估算,并以此验证盐生荒漠涡度相关数据的可靠性。结果表明:盐生荒漠生态系统年叶片生物量为51.30±5.56 g·m^-2,其中90.45%以上来源于多枝柽柳的贡献;而整个生长季,群落叶面积指数（LAI）呈单峰形式变化,从5月30日—9月30日,LAI介于0.18⁰.30,并在第197天达到最大值。涡度相关法和箱式法对群落碳交换的测定结果表明,群落碳交换存在显著的季节变化,并于7月中旬达到碳同化峰值,与LAI有显著的相关性（P<0.001）。对比发现,两种测量方法对群落碳交换日过程的测定结果有很好的一致性,但对夜间生态系统呼吸的测定,涡度相关法较箱式法存在略微的低估,引起这种低估的原因可能是夜间湍流较弱。     

黄河口潮间盐沼湿地生长季净生态系统CO2交换特征及其影响因素

潮间盐沼湿地生物地球化学过程独特,生态系统CO2交换存在着极大的复杂性和不确定性。利用2012年黄河口潮间盐沼湿地生态系统生长季(4—10月)连续的涡度相关观测数据,分析了潮间盐沼湿地的净生态系统CO2交换(NEE)、总初级生产力(GPP)和生态系统呼吸(Reco)的变化特征及其影响因素。结果表明:生长季,生态系统NEE具有明显的日变化和季节变化。日尺度上,表现为白天CO2净吸收,夜间CO2净释放,NEE日平均值为-0.38 g CO2m-2d-1;月尺度上,平均气温最高的7月生态系统释放CO2最多(15.16 g C/m2),6月生态系统吸收CO2最多(25.07 g C/m2)。潮间盐沼湿地生态系统的CO2交换受到光合有效辐射(PAR)、土壤温度(Ts)、土壤含水量(SWC)和潮汐淹水的共同影响。白天NEE主要受控于PAR,且生态系统表观初始光能利用率(α)和最大光合速率(NEEsat)分别在6月和5月达到最大值,分别为(0.0086±0.0019)μmol CO2μmol-1光子和(4.79±1.52)μmol CO2m-2s-1。夜间NEE随Ts呈指数增加趋势,生态系统呼吸的温度敏感性(Q10)为1.33,且SWC越高,Q10值越大。研究典型晴天(6月19日—6月25日)表明,潮汐淹水增强了生态系统白天对CO2的吸收,同时也增强了夜间CO2释放,研究时段内,潮汐淹水使生态系统净CO2吸收增加了0.76 g CO2m-2d-1。整个生长季,黄河口潮间盐沼湿地生态系统表现为CO2的汇,NEE为-22.28 g C/m2(其中,吸收118.34 g C/m2,释放96.28 g C/m2)。研究结果利于对潮间盐沼湿地源汇功能和影响机制的进一步认识与研究。     

青藏高原高寒湿地生态系统CO2通量

依据涡度相关系统连续观测的2005年CO2通量数据,对青藏高原东北隅的高寒湿地生态系统源/汇功能及其部分环境影响因素进行了分析.结果表明,高寒湿地生态系统为明显的碳源,在植物生长季(5～9月份)吸收230.16 gCO2·m-2,非生长季(1～4月份及10～12月份)释放546.18 gCO2·m-2,其中净排放最高在5月份,为181.49 gCO2·m-2,净吸收最高在8月份,为189.69 gCO2·m-2,年释放量为316.02 gCO2·m-2.在平均日变化中,最大吸收值出现在7月份12:00,为(0.45±0.0012) mgCO2·m-2·s-1,最大排放速率出现在8月份0:00,为(0.22±0.0090) mgCO2·m-2·s-1.生长季中6～9月份表现为明显的单峰型日变化,非生长季的变化幅度较小.净生态系统交换量(NEE)和生态系统总初级生产力(GPP)与气温、空气水气饱和亏和地表反射率等环境因素呈现相似的相关性,与地上生物量和群落叶面积指数则为线性负相关,生态系统呼吸(Res)则与上述因子的相关性呈现相反的趋势.     

Modelling night‐time ecosystem respiration by a constrained source optimization method

One of the main challenges to quantifying ecosystem carbon budgets is properly quantifying the magnitude of night‐time ecosystem respiration. Inverse Lagrangian dispersion analysis provides a promising approach to addressing such a problem when measured mean CO₂ concentration profiles and nocturnal velocity statistics are available. An inverse method, termed ‘Constrained Source Optimization’ or CSO, which couples a localized near‐field theory (LNF) of turbulent dispersion to respiratory sources, is developed to estimate seasonal and annual components of ecosystem respiration. A key advantage to the proposed method is that the effects of variable leaf area density on flow statistics are explicitly resolved via higher‐order closure principles. In CSO, the source distribution was computed after optimizing key physiological parameters to recover the measured mean concentration profile in a least‐square fashion. The proposed method was field‐tested using 1 year of 30‐min mean CO₂ concentration and CO₂ flux measurements collected within a 17‐year‐old (in 1999) even‐aged loblolly pine (Pinus taeda L.) stand in central North Carolina. Eddy‐covariance flux measurements conditioned on large friction velocity, leaf‐level porometry and forest‐floor respiration chamber measurements were used to assess the performance of the CSO model. The CSO approach produced reasonable estimates of ecosystem respiration, which permits estimation of ecosystem gross primary production when combined with daytime net ecosystem exchange (NEE) measurements. We employed the CSO approach in modelling annual respiration of above‐ground plant components (c. 214 g C m^?2 year^?1) and forest floor (c. 989 g C m^?2 year^?1) for estimating gross primary production (c. 1800 g C m^?2 year^?1) with a NEE of c. 605 g C m^?2 year^?1 for this pine forest ecosystem. We conclude that the CSO approach can utilise routine CO₂ concentration profile measurements to corroborate forest carbon balance estimates from eddy‐covariance NEE and chamber‐based component flux measurements.     

Estimates of CO2 uptake and release among European forests based on eddy covariance data

The net ecosystem exchange (NEE) of forests represents the balance of gross primary productivity (GPP) and respiration (R). Methods to estimate these two components from eddy covariance flux measurements are usually based on a functional relationship between respiration and temperature that is calibrated for night‐time (respiration) fluxes and subsequently extrapolated using daytime temperature measurements. However, respiration fluxes originate from different parts of the ecosystem, each of which experiences its own course of temperature. Moreover, if the temperature–respiration function is fitted to combined data from different stages of biological development or seasons, a spurious temperature effect may be included that will lead to overestimation of the direct effect of temperature and therefore to overestimates of daytime respiration. We used the EUROFLUX eddy covariance data set for 15 European forests and pooled data per site, month and for conditions of low and sufficient soil moisture, respectively. We found that using air temperature (measured above the canopy) rather than soil temperature (measured 5 cm below the surface) yielded the most reliable and consistent exponential (Q₁₀) temperature–respiration relationship. A fundamental difference in air temperature‐based Q₁₀ values for different sites, times of year or soil moisture conditions could not be established; all were in the range 1.6–2.5. However, base respiration (R₀, i.e. respiration rate scaled to 0°C) did vary significantly among sites and over the course of the year, with increased base respiration rates during the growing season. We used the overall mean Q₁₀ of 2.0 to estimate annual GPP and R. Testing suggested that the uncertainty in total GPP and R associated with the method of separation was generally well within 15%. For the sites investigated, we found a positive relationship between GPP and R, indicating that there is a latitudinal trend in NEE because the absolute decrease in GPP towards the pole is greater than in R.     

Diurnal, seasonal and annual variation in net ecosystem CO2 exchange of an alpine shrubland on Qinghai-Tibetan plateau

Thus far, grassland ecosystem research has mainly been focused on low‐lying grassland areas, whereas research on high‐altitude grassland areas, especially on the carbon budget of remote areas like the Qinghai‐Tibetan plateau is insufficient. To address this issue, flux of CO₂ were measured over an alpine shrubland ecosystem (37°36′N, 101°18′E; 325 above sea level [a. s. l.]) on the Qinghai‐Tibetan Plateau, China, for 2 years (2003 and 2004) with the eddy covariance method. The vegetation is dominated by formation Potentilla fruticosa L. The soil is Mol–Cryic Cambisols. To interpret the biotic and abiotic factors that modulate CO₂ flux over the course of a year we decomposed net ecosystem CO₂ exchange (NEE) into its constituent components, and ecosystem respiration (R_eco). Results showed that seasonal trends of annual total biomass and NEE followed closely the change in leaf area index. Integrated NEE were ?58.5 and ?75.5 g C m^?2, respectively, for the 2003 and 2004 years. Carbon uptake was mainly attributed from June, July, August, and September of the growing season. In July, NEE reached seasonal peaks of similar magnitude (4–5 g C m^?2 day^?1) each of the 2 years. Also, the integrated night‐time NEE reached comparable peak values (1.5–2 g C m^?2 day^?1) in the 2 years of study. Despite the large difference in time between carbon uptake and release (carbon uptake time < release time), the alpine shrubland was carbon sink. This is probably because the ecosystem respiration at our site was confined significantly by low temperature and small biomass and large day/night temperature difference and usually soil moisture was not limiting factor for carbon uptake. In general, R_eco was an exponential function of soil temperature, but with season‐dependent values of Q₁₀. The temperature‐dependent respiration model failed immediately after rain events, when large pulses of R_eco were observed. Thus, for this alpine shrubland in Qinghai‐Tibetan plateau, the timing of rain events had more impact than the total amount of precipitation on ecosystem R_eco and NEE.     

Soil CO2 efflux in contrasting boreal deciduous and coniferous stands and its contribution to the ecosystem carbon balance

Similar nonsteady‐state automated chamber systems were used to measure and partition soil CO₂ efflux in contrasting deciduous (trembling aspen) and coniferous (black spruce and jack pine) stands located within 100 km of each other near the southern edge of the Boreal forest in Canada. The stands were exposed to similar climate forcing in 2003, including marked seasonal variations in soil water availability, which provided a unique opportunity to investigate the influence of climate and stand characteristics on soil CO₂ efflux and to quantify its contribution to the net ecosystem CO₂ exchange (NEE) as measured with the eddy‐covariance technique. Partitioning of soil CO₂ efflux between soil respiration (including forest‐floor vegetation) and forest‐floor photosynthesis showed that short‐ and long‐term temporal variations of soil CO₂ efflux were related to the influence of (1) soil temperature and water content on soil respiration and (2) below‐canopy light availability, plant water status and forest‐floor plant species composition on forest‐floor photosynthesis. Overall, the three stands were weak to moderate sinks for CO₂ in 2003 (NEE of ?103, ?80 and ?28 g C m^?2 yr^?1 for aspen, black spruce and jack pine, respectively). Forest‐floor respiration accounted for 86%, 73% and 75% of annual ecosystem respiration, in the three respective stands, while forest‐floor photosynthesis contributed to 11% and 14% of annual gross ecosystem photosynthesis in the black spruce and jack pine stands, respectively. The results emphasize the need to perform concomitant measurements of NEE and soil CO₂ efflux at longer time scales in different ecosystems in order to better understand the impacts of future interannual climate variability and vegetation dynamics associated with climate change on each component of the carbon balance.     

Annual cycles of water vapour and carbon dioxide fluxes in and above a boreal aspen forest

Water vapour and CO₂ fluxes were measured using the eddy correlation method above and below the overstorey of a 21-m tall aspen stand in the boreal forest of central Saskatchewan as part of the Boreal Ecosystem-Atmosphere Study (BOREAS). Measurements were made at the 39.5-m and 4-m heights using 3-dimensional sonic anemometers (Kaijo-Denki and Solent, respectively) and closed-path gas analysers (LI-COR 6262) with 6-m and 4.7-m long heated sampling tubing, respectively. Continuous measurements were made from early October to mid-November 1993 and from early February to late-September 1994. Soil CO₂ flux (respiration) was measured using a LI-COR 6000-09 soil chamber and soil evaporation was measured using Iysimetry. The leaf area index of the aspen and hazelnut understorey reached 1.8 and 3.3, respectively. The maximum daily evapotranspiration (E) rate was 5–6 mm d^?1. Following leaf-out the hazelnut and soil accounted for 22% of the forest E. The estimated total E was 403 mm for 1994. About 88% of the precipitation in 1994 was lost as evapotranspiration. During the growing season, the magnitude of half-hourly eddy fluxes of CO₂ from the atmosphere into the forest reached 1.2 mg CO₂ m^?2 s^?1 (33 μmol C m^?2 s^?1) during the daytime. Downward eddy fluxes at the 4-m height were observed when the hazelnut was growing rapidly in June and July. Under well-ventilated night-time conditions, the eddy fluxes of CO₂ above the aspen and hazelnut, corrected for canopy storage, increased exponentially with soil temperature at the 2-cm depth. Estimates of daytime respiration rates using these relationships agreed well with soil chamber measurements. During the 1994 growing season, the cumulative net ecosystem exchange (NEE) was -3.5 t C ha^?1 y^?1 (a net gain by the system). For 1994, cumulative NEE, ecosystem respiration (R) and gross ecosystem photosynthesis (GEP = R - NEE) were estimated to be -1.3, 8.9 and 10.2 t C ha^?1 y^?1 respectively. Gross photosynthesis of the hazelnut was 32% of GEP.     

On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm

This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (R_eco). In particular, we analyse the effect of the extrapolation of night‐time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long‐term data sets. For this analysis, we used 16 one‐year‐long data sets of carbon dioxide exchange measurements from European and US‐American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. We show that the temperature sensitivity of R_eco, derived from long‐term (annual) data sets, does not reflect the short‐term temperature sensitivity that is effective when extrapolating from night‐ to daytime. Specifically, in summer active ecosystems the long‐term temperature sensitivity exceeds the short‐term sensitivity. Thus, in those ecosystems, the application of a long‐term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half‐hourly to annual time‐scales, which can reach >25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long‐term temperature sensitivity is lower than the short‐term temperature sensitivity resulting in underestimation of annual sums of respiration. We introduce a new generic algorithm that derives a short‐term temperature sensitivity of R_eco from eddy covariance data that applies this to the extrapolation from night‐ to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and R_eco, we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data.     

Forest ecosystem respiration estimated from eddy covariance and chamber measurements under high turbulence and substantial tree mortality from bark beetles

Eddy covariance nighttime fluxes are uncertain due to potential measurement biases. Many studies report eddy covariance nighttime flux lower than flux from extrapolated chamber measurements, despite corrections for low turbulence. We compared eddy covariance and chamber estimates of ecosystem respiration at the GLEES Ameriflux site over seven growing seasons under high turbulence [summer night mean friction velocity (u*) = 0.7 m s^?1], during which bark beetles killed or infested 85% of the aboveground respiring biomass. Chamber‐based estimates of ecosystem respiration during the growth season, developed from foliage, wood, and soil CO₂ efflux measurements, declined 35% after 85% of the forest basal area had been killed or impaired by bark beetles (from 7.1 ± 0.22 μmol m^?2 s^?1 in 2005 to 4.6 ± 0.16 μmol m^?2 s^?1 in 2011). Soil efflux remained at ~3.3 μmol m^?2 s^?1 throughout the mortality, while the loss of live wood and foliage and their respiration drove the decline of the chamber estimate. Eddy covariance estimates of fluxes at night remained constant over the same period, ~3.0 μmol m^?2 s^?1 for both 2005 (intact forest) and 2011 (85% basal area killed or impaired). Eddy covariance fluxes were lower than chamber estimates of ecosystem respiration (60% lower in 2005, and 32% in 2011), but the mean night estimates from the two techniques were correlated within a year (r² from 0.18 to 0.60). The difference between the two techniques was not the result of inadequate turbulence, because the results were robust to a u* filter of >0.7 m s^?1. The decline in the average seasonal difference between the two techniques was strongly correlated with overstory leaf area (r² = 0.92). The discrepancy between methods of respiration estimation should be resolved to have confidence in ecosystem carbon flux estimates.     

Seasonal and interannual variations in carbon dioxide exchange over a cropland in the North China Plain

In China, croplands account for a relatively large form of vegetation cover. Quantifying carbon dioxide exchange and understanding the environmental controls on carbon fluxes over croplands are critical in understanding regional carbon budgets and ecosystem behaviors. In this study, the net ecosystem exchange (NEE) at a winter wheat/summer maize rotation cropping site, representative of the main cropping system in the North China Plain, was continuously measured using the eddy covariance technique from 2005 to 2009. In order to interpret the abiotic factors regulating NEE, NEE was partitioned into gross primary production (GPP) and ecosystem respiration (R_eco). Daytime R_eco was extrapolated from the relationship between nighttime NEE and soil temperature under high turbulent conditions. GPP was then estimated by subtracting daytime NEE from the daytime estimates of R_eco. Results show that the seasonal patterns of the temperature responses of R_eco and light‐response parameters are closely related to the crop phenology. Daily R_eco was highly dependent on both daily GPP and air temperature. Interannual variability showed that GPP and R_eco were mainly controlled by temperature. Water availability also exerted a limit on R_eco. The annual NEE was ?585 and ?533 g C m^?2 for two seasons of 2006–2007 and 2007–2008, respectively, and the wheat field absorbed more carbon than the maize field. Thus, we concluded that this cropland was a strong carbon sink. However, when the grain harvest was taken into account, the wheat field was diminished into a weak carbon sink, whereas the maize field was converted into a weak carbon source. The observations showed that severe drought occurring during winter did not reduce wheat yield (or integrated NEE) when sufficient irrigation was carried out during spring.     

Decrease in winter respiration explains 25% of the annual northern forest carbon sink enhancement over the last 30 years

Aim Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying microclimate, but the impacts of change in snow cover on the annual C budget at a large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Location Non‐permafrost region of the northern forest area. Methods Here, we used site‐based eddy covariance flux data to investigate the relationship between depth of snow cover and ecosystem respiration (R_eco) during winter. We then used the Biome‐BGC model to estimate the effect of reductions in winter snow cover on the C balance of northern forests in the non‐permafrost region. Results According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028 to 1.53 gC·m^?2·day^?1, accounting for 44 ± 123% of the annual C budget. Model simulation showed that over the past 30 years, snow‐driven change in winter C fluxes reduced non‐growing season CO₂ emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter R_eco significantly decreased by 0.33 gC·m^?2·day^?1·year^?1 in response to decreasing depth of snow cover, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Main conclusion Soil temperature is primarily controlled by snow cover rather than by air temperature as snow serves as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model‐simulated results show that both R_eco and NEE are significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming as less C is emitted to the atmosphere.     

Net ecosystem carbon dioxide exchange over grazed steppe in central Mongolia

This paper presents results of 1 year (from March 25, 2003 to March 24, 2004, 366 days) of continuous measurements of net ecosystem CO₂ exchange (NEE) above a steppe in Mongolia using the eddy covariance technique. The steppe, typical of central Mongolia, is dominated by C₃ plants adapted to the continental climate. The following two questions are addressed: (1) how do NEE and its components: gross ecosystem production (GEP) and total ecosystem respiration (R_eco) vary seasonally? (2) how do NEE, GEP, and R_eco respond to biotic and abiotic factors? The hourly minimal NEE and the hourly maximal R_eco were −3.6 and 1.2 μmol m⁻² s⁻¹, respectively (negative values denoting net carbon uptake by the canopy from the atmosphere). Peak daily sums of NEE, GEP, and R_eco were −2.3, 3.5, and 1.5 g C m⁻² day⁻¹, respectively. The annual sums of GEP, R_eco, and NEE were 179, 138, and −41 g C m⁻², respectively. The carbon removal by sheep was estimated to range between 10 and 82 g C m⁻² yr⁻¹ using four different approaches. Including these estimates in the overall carbon budget yielded net ecosystem productivity of −23 to +20 g C m⁻² yr⁻¹. Thus, within the remaining experimental uncertainty the carbon budget at this steppe site can be considered to be balanced. For the growing period (from April 23 to October 21, 2003), 26% and 53% of the variation in daily NEE and GEP, respectively, could be explained by the changes in leaf area index. Seasonality of GEP, R_eco, and NEE was closely associated with precipitation, especially in the peak growing season when GEP and R_eco were largest. Water stress was observed in late July to early August, which switched the steppe from a carbon sink to a carbon source. For the entire growing period, the light response curves of daytime NEE showed a rather low apparent quantum yield (α=−0.0047 μmol CO₂ μmol⁻¹ photons of photosynthetically active radiation). However, the α values varied with air temperature (T_a), vapor pressure deficit, and soil water content.