Assessing the spatial variability in peak season CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; exchange characteristics across the Arctic tundra using a light response curve parameterization

Mbufong, Herbert N.; Lund, Magnus; Aurela, Mika; Christensen, Torben R.; Eugster, Werner; Friborg, Thomas; Hansen, Birger Ulf; Humphreys, Elyn; Jackowicz-Korczyński, Marcin; Kutzbach, Lars; Lafleur, Peter M.; Oechel, W. C.; Parmentier, Frans‐Jan W.; Rasse, Daniel P.; Rocha, A. V.; Sachs, Torsten; Molen, M. K. van der; Tamstorf, Mikkel P.

doi:10.5194/bg-11-4897-2014

Cited by 25 publications

(27 citation statements)

References 75 publications

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“…It is also expected to lead to more efficient resorption of nutrients (Estiarte and Peñuelas 2014). Furthermore, earlier onset-of-senescence in an Arctic plant community changes the duration of peak season length, which corresponds to the period of maximum carbon uptake (Richardson et al 2009, Mbufong et al 2014. The length of peak season as well as the entire growing season is linked to various metrics of productivity in deciduous ecosystems (White et al 1999, Aurela et al 2004, Dragoni and Rahman 2012.…”

Section: Change In Timing and Rate-of-senescencementioning

confidence: 99%

Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence

et al. 2019

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The timing and duration of the plant growing season and its period of peak activity have shifted globally in response to climate change. These changes alter the period of maximum and potential total carbon uptake, especially in highly seasonal environments such as the Arctic. Earlier plant growth has been observed, and if plant senescence remains the same or is delayed, growing season extension will likely lead to greater carbon uptake and growth. We used phenology data from a multifactor climate change experiment to examine how altered seasonality influences the timing and rate‐of‐senescence and to compare direct observations of individual plant senescence with mathematical models of onset‐of‐senescence based on near‐surface remote sensing. Our three‐year experiment in an Arctic tundra ecosystem altered plant microclimates through factorial warming and earlier snowmelt treatments. We found that (1) early snowmelt and warmer temperatures led to earlier remotely sensed onset‐of‐senescence, but did not alter the rate‐of‐senescence, (2) the timing of color change for individual vascular plants did not change in response to the treatments, leading to a mismatch with remotely sensed phenology, and (3) cumulative, phenologically dependent microclimate metrics (e.g., soil cold degree‐days) best predicted the onset‐of‐senescence. Our study highlights the complexity of observing and understanding controls over phenological shifts that affect plant growth and consequently ecosystem functions. Experimental studies that include multiple approaches to observe and model phenological changes and microclimate are critical to develop phenological forecasting models.

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Section: Change In Timing and Rate-of-senescencementioning

confidence: 99%

Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence

et al. 2019

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“…Once the snow melts, the growing season (i.e. the part of the year when the weather conditions allow plant growth) has been reported as the most relevant period defining both spatial (Lund et al, 2010;Mbufong et al, 2014) and temporal (Aurela et al, 2004;Groendahl et al, 2007;Lund et al, 2012) CO 2 variability. The EC measurements were conducted at the EddyFen station (Fig.…”

Section: Measurementsmentioning

confidence: 99%

“…Different eco-types are linked to different C exchange rates (Bubier et al, 2003). Synthesis studies have found a significant spatial variability in NEE Mbufong et al, 2014) between different tundra sites Lund et al, 2010) and also large temporal variability within sites (Aurela et al, 2004Christensen et al, 2012;Grøndahl et al, 2008;Lafleur et al, 2012). Minor variations in the key process of photosynthesis (gross primary production, GPP) and ecosystem respiration (R eco ) may promote important changes in the sign and magnitude of the C balance (Arndal et al, 2009;Elberling et al, 2008;IPCC, 2007;Lund et al, 2010;Tagesson et al, 2012;Williams et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, GPP and R eco estimates can be calculated in different ways. Some algorithms fit an instantaneous temperature-respiration curve to night-time data to calculate R eco and estimate GPP Reichstein et al, 2005); others calculate R eco from a light-response curve (Gilmanov et al, 2003;Lindroth et al, 2007;Lund et al, 2012;Mbufong et al, 2014;Runkle et al, 2013). Unfortunately, different interpretations of the flux gap filling and partitioning lead to different estimates of NEE, GPP and R eco as well as undefined uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Exchange of CO<sub>2</sub> in Arctic tundra: impacts of meteorological variations and biological disturbance

López‐Blanco¹,

Lund

Williams

et al. 2017

Biogeosciences

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Abstract. An improvement in our process-based understanding of carbon (C) exchange in the Arctic and its climate sensitivity is critically needed for understanding the response of tundra ecosystems to a changing climate. In this context, we analysed the net ecosystem exchange (NEE) of CO 2 in West Greenland tundra (64 • N) across eight snow-free periods in 8 consecutive years, and characterized the key processes of net ecosystem exchange and its two main modulating components: gross primary production (GPP) and ecosystem respiration (R eco ). Overall, the ecosystem acted as a consistent sink of CO 2 , accumulating −30 g C m −2 on average (range of −17 to −41 g C m −2 ) during the years 2008-2015, except 2011 (source of 41 g C m −2 ), which was associated with a major pest outbreak. The results do not reveal a marked meteorological effect on the net CO 2 uptake despite the high interannual variability in the timing of snowmelt and the start and duration of the growing season. The ranges in annual GPP (−182 to −316 g C m −2 ) and R eco (144 to 279 g C m −2 ) were > 5 fold larger than the range in NEE. Gross fluxes were also more variable (coefficients of variation are 3.6 and 4.1 % respectively) than for NEE (0.7 %). GPP and R eco were sensitive to insolation and temperature, and there was a tendency towards larger GPP and R eco during warmer and wetter years. The relative lack of sensitivity of NEE to meteorology was a result of the correlated response of GPP and R eco . During the snow-free season of the anomalous year of 2011, a biological disturbance related to a larvae outbreak reduced GPP more strongly than R eco . With continued warming temperatures and longer growing seasons, tundra systems will increase rates of C cycling. However, shifts in sink strength will likely be triggered by factors such as biological disturbances, events that will challenge our forecasting of C states.

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“…Assumptions of flux homogeneity tend to rely on a qualitative and/or quantitative assessment of the spatial variability in some visible properties of the vegetation. However, this assumption ignores many difficult to measure factors known to vary spatially and influence C fluxes (Peukert et al, 2013;Glendell et al, 2014;Mbufong et al, 2014). It also ignores the evidence from both chamber and EC studies which have observed intra-ecosystem spatial variability of fluxes (Katul et al, 1999;Bubier et al, 2003;Oren et al, 2006;Riveros-Iregui & McGlynn, 2009;Peltola et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how?

Hill

Chocholek

Clement

2016

Global Change Biology

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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.

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Assessing the spatial variability in peak season CO<sub>2</sub> exchange characteristics across the Arctic tundra using a light response curve parameterization

Cited by 25 publications

References 75 publications

Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence

Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence

Exchange of CO<sub>2</sub> in Arctic tundra: impacts of meteorological variations and biological disturbance

The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how?

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Assessing the spatial variability in peak season CO&lt;sub&gt;2&lt;/sub&gt; exchange characteristics across the Arctic tundra using a light response curve parameterization

Cited by 25 publications

References 75 publications

Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence

Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence

Exchange of CO&lt;sub&gt;2&lt;/sub&gt; in Arctic tundra: impacts of meteorological variations and biological disturbance

The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how?

Contact Info

Product

Resources

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Assessing the spatial variability in peak season CO<sub>2</sub> exchange characteristics across the Arctic tundra using a light response curve parameterization

Exchange of CO<sub>2</sub> in Arctic tundra: impacts of meteorological variations and biological disturbance