Intercomparison among chamber, tower, and aircraft net CO2 and energy fluxes measured during the Arctic System Science Land‐Atmosphere‐Ice Interactions (ARCSS‐LAII) Flux Study

Oechel, Walter C.; Vourlitis, George L.; Brooks, Steven B.; Crawford, Timothy L.; Dumas, Edward J.

doi:10.1029/1998jd200015

Cited by 77 publications

(78 citation statements)

References 40 publications

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“…This high sensitivity of the CO 2 balance to asymmetric biases of CO 2 uptake and CO 2 release is of major importance as closed chamber CO 2 flux measurements based on linear regression are used for local, regional and global carbon budgets and for the evaluation of the carbon source or sink characteristics of ecosystems or even vegetation zones (e.g. Oechel et al, 1993Oechel et al, , 1998Oechel et al, , 2000.…”

Section: Discussionmentioning

confidence: 99%

“…Jensen et al, 1996;Xu and Qi, 2001;Pumpanen et al, 2003Pumpanen et al, , 2004Reth et al, 2005;Wang et al, 2006). Also, it is often applied to quantify the net CO 2 exchange between the atmosphere and low-stature canopies typical for tundra (Vourlites et al, 1993;Christensen et al, 1998;Oechel et al, , 1998Oechel et al, , 2000Zamolodchikov and Karelin, 2001), peatlands (Alm et al, , 2007Tuittila et al, 1999;Bubier et al, 2002;Nykänen et al, 2003;Burrows et al, 2004;Drösler, 2005;Laine et al, 2006), forest understorey vegetation (Goulden and Crill, 1997;Heijmans et al, 2004) and agricultural crop stands (Dugas et al, 1997;Wagner et al, 1997;Maljanen et al, 2001;Steduto et al, 2002). Advantageously, the closed-chamber method is relatively low in cost and power consumption, simple to operate and can therefore be used in remote, logistically difficult areas.…”

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confidence: 99%

“…Vourlites et al, 1993;Oechel et al, 1993Oechel et al, , 1998Oechel et al, , 2000Jensen et al, 1996;Alm et al, 1997Alm et al, , 2007Goulden and Crill, 1997;Christensen et al, 1998;Tuittila et al, 1999;Maljanen et al, 2001;Xu and Qi, 2001;Bubier et al, 2002;Nykänen et al, 2003;Pumpanen et al, 2003;Burrows et al, 2004;Heijmans et al, 2004;Drösler, 2005;Reth et al, 2005;Laine et al, 2006;Wang et al, 2006). Usually, the authors justify the use of linear regression by keeping the closure time short and assuming the concentration change over time to be still in the linear range.…”

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confidence: 99%

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CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

et al. 2007

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Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO 2 ) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO 2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO 2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO 2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO 2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO 2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO 2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO 2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evoCorrespondence to: L. Kutzbach (kutzbach@uni-greifswald.de) lution in the chamber headspace and estimation of the initial CO 2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO 2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO 2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO 2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO 2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO 2 balances than in the individual fluxes. To avoid serious bias of CO 2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

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Section: Discussionmentioning

confidence: 99%

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CO2 flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

et al. 2007

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“…So far, there exist very few data sets of its temporal evolution, many of which focus on the CO 2 exchange at the ice-atmosphere interface using either eddy correlation techniques (Baldocchi et al, 1988;Oechel et al, 1998) or chamber techniques (Livingston and Hutchinson (1995) and references therein; see also Schrier-Uijl et al (2010) and Loose et al (2011)). This lack of data is due, to some degree, to the fact that sea ice has for a long time been regarded as an impermeable lid on the ocean (Tison et al, 2002;Bates and Mathis, 2009), despite early works that clearly demonstrated significant gas exchange through sea ice (Gosink et al, 1976).…”

Section: Observationsmentioning

confidence: 99%

The multiphase physics of sea ice: a review for model developers

et al. 2011

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Abstract. Rather than being solid throughout, sea ice contains liquid brine inclusions, solid salts, microalgae, trace elements, gases, and other impurities which all exist in the interstices of a porous, solid ice matrix. This multiphase structure of sea ice arises from the fact that the salt that exists in seawater cannot be incorporated into lattice sites in the pure ice component of sea ice, but remains in liquid solution. Depending on the ice permeability (determined by temperature, salinity and gas content), this brine can drain from the ice, taking other sea ice constituents with it. Thus, sea ice salinity and microstructure are tightly interconnected and play a significant role in polar ecosystems and climate. As large-scale climate modeling efforts move toward "earth system" simulations that include biological and chemical cycles, renewed interest in the multiphase physics of sea ice has strengthened research initiatives to observe, understand and model this complex system. This review article provides an overview of these efforts, highlighting known difficulties and requisite observations for further progress in the field. We focus on mushy layer theory, which describes general multiphase materials, and on numerical approaches now being explored to model the multiphase evolution of sea ice and its interaction with chemical, biological and climate systems.

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“…The vegetation variability is driven by environmental factors that can also have important direct controls on CH 4 emissions (e.g., hydrology [18]), which can further enhance the link between vegetation type and CH 4 flux. Therefore, the spatial heterogeneity of tundra vegetation communities can potentially explain the spatial variability in CH 4 fluxes [24], but can also make it difficult to fully quantify and understand localised differences in CH 4 emissions [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

et al. 2017

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Abstract:Arctic tundra ecosystems are a major source of methane (CH 4 ), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH 4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100-1000 m, whereas model estimates are typically made at thẽ 100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH 4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH 4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH 4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales.

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Intercomparison among chamber, tower, and aircraft net CO₂ and energy fluxes measured during the Arctic System Science Land‐Atmosphere‐Ice Interactions (ARCSS‐LAII) Flux Study

Cited by 77 publications

References 40 publications

CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

The multiphase physics of sea ice: a review for model developers

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

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Intercomparison among chamber, tower, and aircraft net CO2 and energy fluxes measured during the Arctic System Science Land‐Atmosphere‐Ice Interactions (ARCSS‐LAII) Flux Study

Cited by 77 publications

References 40 publications

CO&lt;sub&gt;2&lt;/sub&gt; flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

CO&lt;sub&gt;2&lt;/sub&gt; flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

The multiphase physics of sea ice: a review for model developers

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

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Product

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Intercomparison among chamber, tower, and aircraft net CO₂ and energy fluxes measured during the Arctic System Science Land‐Atmosphere‐Ice Interactions (ARCSS‐LAII) Flux Study

CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression