We assessed the impact of forest floor micro-topography (furrows, ridges and the undisturbed flat areas between these), as created by ploughing during forest establishment on soil methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) fluxes. Monthly chamber flux measurements were carried out over 3 years in two areas of an upland Sitka spruce plantation forest in central Scotland. There were significantly higher CH4 fluxes from furrows than the ridge and flat zones and large differences in CH4 fluxes between the two areas of forest. Mean annual CH4 fluxes from furrows were an uptake of-93.8 mg C m-2 yr-1 in one area of the forest and a net source of 49.1 mg C m-2 yr-1 in the other compared with uptake of-264.7 and-198.1 mg C m-2 yr-1 from the ridge and flat zones, respectively (mean both areas). There were no significant differences in soil CO2 and N2O fluxes between micro-topography zones with mean annual fluxes of 576.2 g C m-2 yr-1 and 44.1 mg N m-2 yr-1 respectively, mean across the three topographies of two different locations. The soil moisture and temperature were not significantly different between the micro-topographies but their patterns in combination with those of wet deposition of ammonium (NH4+) and nitrate (NO3-) measurements explained 25%, 61% and 71% of the temporal variation of soil N2O, CH4 and CO2 fluxes respectively. The overall annual CH4 uptake was-187.5 mg C m-2 yr-1 when fluxes were upscaled to the total forest area based on the proportion of each micro-topography present, 16% higher than if this was not taken into account. Our results provide information on the magnitude of GHG fluxes from different forest floor micro-topographies and their overall effect on the fluxes to help inform GHG budgets for forest management and modelling. It also highlights the importance of adequately sampling the fine scale micro-topography for evaluating GHG fluxes.

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The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at the national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long‐term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short‐term, point observations to estimate large‐scale annual totals, and quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near‐continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterize the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.

The eddy covariance method is widely used to investigate fluxes of energy, water, and carbon dioxide at landscape scales, providing important information on how ecological systems function. Flux measurements quantify ecosystem responses to environmental perturbations and management strategies, including nature‐based climate‐change mitigation measures. However, due to the high cost of conventional instrumentation, most eddy covariance studies employ a single system, limiting spatial representation to the flux footprint. Insufficient replication may be limiting our understanding of ecosystem behavior. To address this limitation, we deployed eight lower‐cost eddy covariance systems in two clusters around two conventional eddy covariance systems in the Chihuahuan Desert of North America for a period of 2 years. These dryland settings characterized by large temperature variations and relatively low carbon dioxide fluxes represented a challenging setting for eddy covariance. We found very good closure of energy and water balance across all systems (within ±9% of unity). We found very good correspondence between the lower‐cost and conventional systems' fluxes of sensible heat (with concordance correlation coefficient (CCC) of ≥0.87), latent energy (evapotranspiration; CCC ≥ 0.89), and useful correspondence in the net ecosystem exchange ((NEE); with CCC ≥ 0.4) at the daily temporal resolution. Relative to the conventional systems, the low‐frequency systems were characterized by a higher level of random error, particularly in the NEE fluxes. Lower‐cost systems can enable wider deployment affording better replication and sampling of spatiotemporal variability at the expense of greater measurement noise that might be limiting for certain applications. Replicated eddy covariance observations may be useful when addressing gaps in the existing monitoring of critical and underrepresented ecosystems and for measuring areas larger than a single flux footprint.

Continuous time-series of CO2, water, and energy fluxes are useful for evaluating the impacts of climate-change and management on ecosystems. The eddy covariance (EC) technique can provide continuous, direct measurements of ecosystem fluxes, but to achieve this gaps in data must be filled. Research-standard methods of gap-filling fluxes have tended to focus on CO2 fluxes in temperate forests and relatively short gaps of less than two weeks. A gap-filling method applicable to other fluxes and capable of filling longer gaps is needed. To address this challenge, we propose a novel gap-filling approach, Random Forest Robust (RFR). RFR can accommodate a wide range of data gap sizes, multiple flux types (i.e. CO2, water and energy fluxes). We configured RFR using either three (RFR3) or ten (RFR10) driving variables. RFR was tested globally on fluxes of CO2, latent heat (LE), and sensible heat (H) from 94 suitable FLUXNET2015 sites by using artificial gaps (from 1 to 30 days in length) and benchmarked against the standard marginal distribution sampling (MDS) method. In general, RFR improved on MDS's R2 by 15% (RFR3) and by 30% (RFR10) and reduced uncertainty by 70%. RFR's improvements in R2 for H and LE were more than twice the improvement observed for CO2 fluxes. Unlike MDS, RFR performed well for longer gaps; for example, the R2 of RFR methods in filling 30-day gaps dropped less than 4% relative to 1-day gaps, while the R2 of MDS dropped by 21%. Our results indicate that the RFR method can provide improved gap-filling of CO2, H and LE flux timeseries. Such improved continuous flux measurements, with low bias, can enhance our understanding of the impacts of climate-change and management on ecosystems globally.

The Integrated Carbon Observation System Re-search Infrastructure aims to provide long-term, continuous observations of sources and sinks of greenhouse gases such as carbon dioxide, methane, nitrous oxide, and water vapour. At ICOS ecosystem stations, the principal technique for measurements of ecosystem-atmosphere exchange of GHGs is the eddy-covariance technique. The establishment and setup of an eddy-covariance tower have to be carefully reasoned to ensure high quality flux measurements being representative of the investigated ecosystem and comparable to measurements at other stations. To fulfill the requirements needed for flux determination with the eddy-covariance technique, variations in GHG concentrations have to be measured at high frequency, simultaneously with the wind velocity, in order to fully capture turbulent fluctuations. This requires the use of high-frequency gas analysers and ultrasonic anemometers. In addition, to analyse flux data with respect to environmental conditions but also to enable corrections in the post-processing procedures, it is necessary to measure additional abiotic variables in close vicinity to the flux measurements. Here we describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy while allowing for flexibility in order to observe specific ecosystem features.

Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

Eddy covariance has been the de facto method of analyzing scalar turbulent transport data. To refine the information available from these data, we derive a simplified version of the turbulent scalar-transport equation for the surface layer, which employs a more explicit form of signal decomposition and dispenses with Reynolds averaging in favour of an averaging operator based on the relevant scalar-flux driving variables. The resulting method, termed functional covariance, provides five areas of improvement in flux estimation: (i) Better representation of surface fluxes through closer correspondence of turbulent exchange with variations in the driving variables. (ii) An approximate 25% reduction in flux uncertainty resulting from improved independence of turbulent-flux samples. (iii) Improved data retention through less onerous quality control (stationarity) testing. (iv) Improved estimation of low-frequency flux contributions through reduced uncertainty and avoidance of driving-variable nonstationarity. (v) Potential elimination of flux-storage estimation when state driving-variables are used to define the functional-covariance flux averaging. We describe the important considerations required for application of functional covariance, apply both functional- and eddy-covariance methods to an example dataset, compare the resulting eddy- and functional-covariance estimates, and demonstrate the aforementioned benefits of functional covariance.

Eddy covariance (EC) continues to provide invaluable insights into the dynamics of Earth's surface processes. However, despite its many strengths, spatial replication of EC at the ecosystem scale is rare. High equipment costs are likely to be partially responsible. This contributes to the low sampling, and even lower replication, of ecoregions in Africa, Oceania (excluding Australia) and South America. The level of replication matters as it directly affects statistical power. While the ergodicity of turbulence and temporal replication allow an EC tower to provide statistically robust flux estimates for its footprint, these principles do not extend to larger ecosystem scales. Despite the challenge of spatially replicating EC, it is clearly of interest to be able to use EC to provide statistically robust flux estimates for larger areas. We ask: How much spatial replication of EC is required for statistical confidence in our flux estimates of an ecosystem? We provide the reader with tools to estimate the number of EC towers needed to achieve a given statistical power. We show that for a typical ecosystem, around four EC towers are needed to have 95% statistical confidence that the annual flux of an ecosystem is nonzero. Furthermore, if the true flux is small relative to instrument noise and spatial variability, the number of towers needed can rise dramatically. We discuss approaches for improving statistical power and describe one solution: an inexpensive EC system that could help by making spatial replication more affordable. However, we note that diverting limited resources from other key measurements in order to allow spatial replication may not be optimal, and a balance needs to be struck. While individual EC towers are well suited to providing fluxes from the flux footprint, we emphasize that spatial replication is essential for statistically robust fluxes if a wider ecosystem is being studied.

With the Arctic summer sea-ice extent in decline, questions are arising as to how changes in sea-ice dynamics might affect biogeochemical cycling and phenomena such as carbon dioxide (CO2) uptake and ocean acidification. Recent field research in these areas has concentrated on biogeochemical and CO2 measurements during spring, summer or autumn, but there are few data for the winter or winter–spring transition, particularly in the High Arctic. Here, we present carbon and nutrient data within and under sea ice measured during the Catlin Arctic Survey, over 40 days in March and April 2010, off Ellef Ringnes Island (78° 43.11′ N, 104° 47.44′ W) in the Canadian High Arctic. Results show relatively low surface water (1–10 m) nitrate (<1.3 µM) and total inorganic carbon concentrations (mean±SD=2015±5.83 µmol kg−1), total alkalinity (mean±SD=2134±11.09 µmol kg−1) and under-ice pCO2sw (mean±SD=286±17 µatm). These surprisingly low wintertime carbon and nutrient conditions suggest that the outer Canadian Arctic Archipelago region is nitrate-limited on account of sluggish mixing among the multi-year ice regions of the High Arctic, which could temper the potential of widespread under-ice and open-water phytoplankton blooms later in the season.

Quantifying landscape‐scale methane (CH4) fluxes from boreal and arctic regions, and determining how they are controlled, is critical for predicting the magnitude of any CH4 emission feedback to climate change. Furthermore, there remains uncertainty regarding the relative importance of small areas of strong methanogenic activity, vs. larger areas with net CH4 uptake, in controlling landscape‐level fluxes. We measured CH4 fluxes from multiple microtopographical subunits (sedge‐dominated lawns, interhummocks and hummocks) within an aapa mire in subarctic Finland, as well as in drier ecosystems present in the wider landscape, lichen heath and mountain birch forest. An intercomparison was carried out between fluxes measured using static chambers, up‐scaled using a high‐resolution landcover map derived from aerial photography and eddy covariance. Strong agreement was observed between the two methodologies, with emission rates greatest in lawns. CH4 fluxes from lawns were strongly related to seasonal fluctuations in temperature, but their floating nature meant that water‐table depth was not a key factor in controlling CH4 release. In contrast, chamber measurements identified net CH4 uptake in birch forest soils. An intercomparison between the aerial photography and satellite remote sensing demonstrated that quantifying the distribution of the key CH4 emitting and consuming plant communities was possible from satellite, allowing fluxes to be scaled up to a 100 km2 area. For the full growing season (May to October), ~ 1.1–1.4 g CH4 m−2 was released across the 100 km2 area. This was based on up‐scaled lawn emissions of 1.2–1.5 g CH4 m−2, vs. an up‐scaled uptake of 0.07–0.15 g CH4 m−2 by the wider landscape. Given the strong temperature sensitivity of the dominant lawn fluxes, and the fact that lawns are unlikely to dry out, climate warming may substantially increase CH4 emissions in northern Finland, and in aapa mire regions in general.

The present study investigated the utility of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived sPRI (scaled photochemical reflectance index) and its relationship to photosynthetic light-use efficiency (LUE) calculated from eddy covariance tower data. The analysis was performed over two consecutive years (2003-2004) in a Mediterranean Quercus cerris L. forest site in Italy. Temperature and rainfall conditions differed markedly over the study period, with 2003 being a notable drought year and 2004 a non-drought year. MODIS ocean bands 11 (centred at 531 nm) and 12 (centred at 551 nm) were used for calculating sPRI. LUE exhibited substantial variability within 2003 and 2004, and a moderate relationship between MODIS-sPRI and LUE was observed during the wet year, and for backscattering scenes. This demonstrated the capacity of sPRI to detect xanthophyll cycle activation by vegetation during high light conditions. However, our results show that sPRI should be used with care, particularly under severe water stress conditions, when an increased influence of confounding factors, such canopy structure, illumination, and viewing angles, is observed.

Scotland's 17% forested land cover sequesters 10% of Scotland's emissions of greenhouse gases. The expected rise of forested land cover to 25% by the middle of this century make forests an important component of the national carbon budget. This paper presents the carbon exchange measurement methodology, results, and modeling associated with a five-year experiment at a plantation forest in Scotland, UK, as part of a European initiative to develop and verify vegetation carbon-exchange models.We have developed a site specific correction for advective flux loses. While based on ustar corrections, it differs from current approaches in that it includes site topographic effects, corrects existing data instead of replacing it, and applies over the entire diel cycle instead of only during nocturnal periods.Models of soil respiration are compared and reveal the inadequacy of soil state parameterization. A non-rectangular hyperbola model of assimilation is selected to examine the maximum assimilation rate and quantum use efficiency parameters' responses to environmental temperature, vapour pressure, and sky condition. Intra-annual variability is found to be highest in early autumn, linked to a combination of seasonal changes in radiation and phenological changes in canopy quantum yield. Inter-annual variability was low, with less than %5 variation from year to year, and factors driving this variability were not clearly apparent.Over the 5 years of reported measurements, this forest sequestered about 6 tonnes of C per hectare per annum. Photosynthetically active radiation use efficiency of the forest is about 4.2%. Constancy of radiation use efficiency was observed at both diel and annual scales. This constancy depended on the ecosystem's thermal inertia and assimilation/radiation response curve characteristics at diel time scales but depended on thermal inertia and phenological changes in quantum yield at annual time scales.The net carbon uptake rates are consistent with two other flux sites in the British Isles and suggest that the relatively maritime climatology of these areas is highly suitable for the species of trees routinely planted here. (C) 2011 Elsevier B.V. All rights reserved.

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45°N). The sensitivity of NEE to mean annual temperature breaks down at ~ 16 °C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.

Using proton transfer reaction mass spectrometry equipped with a quadrupol mass analyser to quantify the biosphere-atmosphere exchange of volatile organic compounds (VOC), concentrations of different VOC are measured sequentially Depending on how many VOC species are targeted and their respective integration times, each VOC is measured at repeat rates on the order of a few seconds. This represents an order of magnitude longer sample interval compared to the standard eddy covariance (EC) method (5-20 Hz sampling rates). Here we simulate the effect of disjunct sampling on EC flux estimates by decreasing the time resolution of CO2 and H2O concentrations measured at 20 Hz above a temperate mountain grassland in the Austrian Alps. Fluxes for one month are calculated with the standard EC method and compared to fluxes calculated based on the disjunct data (1, 3 and 5 s sampling rates) using the following approaches: 1) imputation of missing concentrations based on the nearest neighbouring samples (iDEC(nn)), ii) imputation by linear interpolation (iDEC(li)), and iii) virtual disjunct EC (vDEC), i.e. flux calculation based solely on the disjunct concentrations. It is shown that the two imputation methods result in additional low-pass filtering, longer lag times (as determined with the maximum cross-correlation method) and a flux loss of 3-30% as compared to the standard EC method. A novel procedure, based on a transfer function approach, which specifically corrects for the effect of data treatment, was developed, resulting in improved correspondence (to within 2%). The vDEC method yields fluxes which approximate the true (20 Hz) fluxes to within 3-7% and it is this approach we recommend because it involves no additional empirical corrections. The only drawback of the vDEC method is the noisy nature of the cross-correlations, which poses problems with lag determination - practical approaches to overcome this limitation are discussed. (C) 2010 Elsevier Ltd. All rights reserved

Micrometeorological measurements of nighttime ecosystem respiration can be systematically biased when stable atmospheric conditions lead to drainage flows associated with decoupling of air flow above and within plant canopies. The associated horizontal and vertical advective fluxes cannot be measured using instrumentation on the single towers typically used at micrometeorological sites. A common approach to minimize bias is to use a threshold in friction velocity, u*, to exclude periods when advection is assumed to be important, but this is problematic in situations when in-canopy flows are decoupled from the flow above. Using data from 25 flux stations in a wide variety of forest ecosystems globally, we examine the generality of a novel approach to estimating nocturnal respiration developed by van Gorsel et al. (van Gorsel, E., Leuning, R., Cleugh, H.A., Keith, H., Suni, T., 2007. Nocturnal carbon efflux: reconciliation of eddy covariance and chamber measurements using an alternative to the u*-threshold filtering technique. Tellus 59B, 397–403, Tellus, 59B, 307-403). The approach is based on the assumption that advection is small relative to the vertical turbulent flux (FC) and change in storage (FS) of CO2 in the few hours after sundown. The sum of FC and FS reach a maximum during this period which is used to derive a temperature response function for ecosystem respiration. Measured hourly soil temperatures are then used with this function to estimate respiration RRmax. The new approach yielded excellent agreement with (1) independent measurements using respiration chambers, (2) with estimates using ecosystem light-response curves of Fc+Fs extrapolated to zero light, RLRC, and (3) with a detailed process-based forest ecosystem model, Rcast. At most sites respiration rates estimated using the u*-filter, Rust, were smaller than RRmax and RLRC. Agreement of our approach with independent measurements indicates that RRmax provides an excellent estimate of nighttime ecosystem respiration.