© Copyright by Cheryl Laskowski 2010 All rights reserved
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This dissertation is dedicated to my mother and father, Barbara and Leonard Laskowski, and to my sister, Janet Laskowski
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Acknowledgements I could write another entire chapter on how each of the following people helped, guided, encouraged, taught, and inspired me. To my field assistants: I learned so much from you all! Through you, I learned to be a better manager, teacher, researcher, and person. Aditya Sharma, Andrew Steyers, Samantha Delapena, Irene Kopetz, Indra Kartawijawa, Rena Bryan, Natalio Panzarini. To Douglas Deutschman: I sincerely enjoyed the time we worked together to find the story hiding among so many data points. Your encouragement and guidance in all things research, teaching, and dog-related have been invaluable. An extra thanks to your family, for welcoming me into your home. To Susan Ustin: Thank you for your support, guidance, and extra effort to make this achievement happen. To Steve Hastings and Joe Verfaillie: Your knowledge and patience are limitless! You always went the extra step for me, and I don’t know what GCRG is without you two. To George Burba: Thank you for believing in me and the data. To the employees at Li-Cor and Campbell Scientific: Companies are the sum of their people. Your people are truly dedicated to research, and their commitment is why I could conduct the research I did. To my friends: You were always there when I needed you. A special thanks to Kirstin Skadberg, who is more than a friend, but also an amazing researcher, co-worker, sounding board, mentor, and source of constant encouragement. iv
To my family: Your constant love and support meant so much to me during this process. I love you all so much! Finally, to my advisor, Walt Oechel: Thank you for the opportunity to fulfill my dream.
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Table of Contents
Title Page ....................................................................................................................................... i Dedication .................................................................................................................................... iii Acknowledgements ..................................................................................................................... iv Abstract ....................................................................................................................................... vii Introduction ...................................................................................................................................1 Chapter 1: Quantifying seasonal contributions to the total annual carbon budget of an Alaskan Arctic tundra ecosystem ..................................................................................................................9 Chapter 2: Seasonal, annual, and interannual carbon dynamics of a remote tussock tundra ecosystem in Ivotuk, Alaska .........................................................................................................52 Chapter 3: Latitudinal and temporal patterns in carbon dioxide exchange in the Alaskan Arctic tundra ecosystem ...........................................................................................................................96 Conclusions ................................................................................................................................128
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Abstract
This research focuses on the spatial and temporal patterns of, and controls on, CO2 in the Alaskan Arctic tundra ecosystem. The sites investigated—wet sedge, moist acidic, and low tussock tundra—represent the dominant land cover types in the Arctic tundra ecosystem, yet none have previously been investigated continuously throughout the year. In the first part of the research presented here, new definitions of season are presented, which will allow better comparisons across sites, seasons, and years in the Arctic tundra, where season length varies among years and locations. The results of this, the first, continuous, yearlong Arctic tundra study in a moist acidic tundra region, show that while summer uptake was detected (–11 g C m–2 yr–1), the annual carbon signal was overwhelmed by the non-summer seasons, resulting in a net annual carbon release of nearly 38 g C m–2 yr–1. Winter showed low metabolic rates over a long season resulting in a net source of carbon to the atmosphere. The transitional seasons of spring and fall demonstrated active rates over short durations and were also sources of carbon to the atmosphere. In addition to the variable pattern of carbon exchange, the controls on carbon varied by season as well. For example, the effect of increasing soil temperature was negatively related to net ecosystem exchange (NEE) during winter and summer, but positively related to NEE during spring and fall. These results indicate that continuous monitoring of carbon, and related environmental variables, is important in accurate estimation of the current total annual and seasonal carbon budgets. This information, in turn, is critical to our ability to predict, with confidence, future carbon budgets. In the second part of the research, three years of continuous carbon measurements are presented for a low tussock tundra region. This southern site is especially vulnerable to climate change effects because it is at the southern extent of the tundra ecosystem near the graminoidshrub boundary and increased rates of decomposition, and the region is likely to undergo vii
community compositional changes in the near future. This region is likely to experience deeper active layers in the future, potentially exposing large stocks of carbon. This southern system was a net source of carbon over the three-year period of study, with only two of the three summer seasons acting as net carbon sinks. In one year, drought was so severe that even during the summer season, respiration overwhelmed photosynthesis, leading to a large (87 g C m–2 yr–1) annual efflux compared to the other years of the study (which had 0.04 and 49 g C m–2 yr–1 annual carbon release). In the last part of this research, NEE was measured at three sites located along a latitudinal gradient that spanned the North Slope of Alaska. Only in the northernmost site at Barrow was net annual carbon uptake detected, leading to an average uptake rate of 80 g C m–2 yr–1. Increased temperatures and decreased rainfall led to greater uptake in this, the coldest and least well drained of the sites. The two inland sites were both net sources of carbon to the atmosphere over the three-year period, resulting in an average of 30 and 45 g C m–2 yr–1 at each of the sites. Site differences were the primary controls on carbon variation among the sites, but inter and intra-annual variation were also significant. These data represent the first continuous measurements in the Arctic tundra ecosystem, and highlight the high degree of heterogeneity in the tundra ecosystem. These data may be used to validate and further develop climate and ecosystem models and to more accurately depict the variability, both spatially and temporally, in the Arctic tundra ecosystem.
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1 Introduction The Arctic is highly vulnerable to climate change and is critical to understanding and predicting future global climate change. However, much remains unknown about itthe current patterns and controls on ecosystem function including the seasonal controls, non-summer processes, and feedbacks to processes already undergoing change. The need to better understand the Arctic response to, and impact on, climate change is urgent given that change is already occurring and that the Arctic is disproportionately affected by climate change (Jones et al. 1999, Serreze et al., 2000; ACIA, 2005). The Arctic displays feedbacks that affect the Arctic region and global systems (Oechel et al. 1993; Callaghan et al,. 2004; Euskirchen et al., 2007). The existing evidence in the Arctic emphasizes the speed and severity of some of these changes to date including: greater-than-average temperature increases (Lachenbruch and Marshall, 1986; IPCC, 2007), loss of sea ice thickness and extent (Chapman and Walsh, 1993; Johannessen, 1995; Comiso, 2006), declining snow cover (Serreze et al., 2000), retreating glaciers (Arendt et al., 2002) and permafrost warming and degradation (Osterkamp and Romanovsky, 1999; Osterkamp, 2005). One of the primary gaps in the functioning of Arctic ecosystem metabolism concerns non-summer processes and contributions to annual budgets, including energy and carbon budgets. The paucity of information from non-summer periods may be surprising due to the dominance of snow-covered conditions (i.e., for up to nine months of the year). The extreme conditions (e.g., low temperatures low solar angles, low winter light levels, and snow-covered frozen ground) are often as less than ideal for biological processes, and in the past it was largely assumed that little activity occurred under these conditions (McKane et al., 1997). While winter Arctic conditions may not be ideal for high levels of photosynthesis and respiration, biological activity still occurs under snow at sub-zero temperatures. Cold-adapted plants have been shown to photosynthesize beneath the snow due to light penetration through the snow and warmer, snow-insulated layer
2 under the snow (Sturm et al., 2005). Lichens have been shown to photosynthesize at temperatures below −10°C (Kappen, 1993) and vascular plants to below −4°C (Semikhatova et al., 1992) when adequate light is present. Microbial respiration may occur at even lower temperatures and persist through the winter season. Within frozen soils, liquid water persists on surfaces and soil particles (Romanovsky and Osterkamp, 2000), and may represent up to 10% of the volume of frozen soils (Sturm et al., 2005). Microbial activity and respiration has been measured to below −17 °C in the field and −39 °C in the laboratory (Panikov et al., 2006) and net soil CO2 emissions have been reported to −7.5 °C (Oechel et al., 1997). Adaptation to cold, robust physiology and the presence of water in frozen soils allows respiration and photosynthesis to occur throughout much of the year. Although there is substantial evidence showing the importance of non-summer periods to Arctic carbon metabolism, the difficulty of making ecosystem measurements under Arctic winter conditions means that estimates of carbon flux rates across the tundra are typically made based solely on summer (June-August) information and carbon flux data (Kwon et al., 2006; Vourlitis and Oechel, 1999). Annual estimates that ignore the non-summer period are likely to be largely in error with respect to the annual carbon balance (Oechel et al., 1997). Given that snowmelt is occurring earlier (Stone et al., 2002) and the growing season has lengthened (Chapman and Walsh, 1993; Keyser et al., 2000), the traditional 10- to 12-week field campaign no longer encompasses even the full snow-free period. Prior attempts to predict annual carbon budgets based on winter field measurements have often lacked continuous monitoring throughout the year, relying on a few data points to model seasonal carbon estimates (Zimov et al., 1996; Oechel et al., 1997). In addition to the lack of non-summer Arctic tundra measurements, the environmental controls on Arctic carbon flux are also inadequately understood both on a temporal and spatial scale. Spatially, vegetation type can vary significantly, from sedge- and moss-dominated tundra
3 in the north, to tussock tundra and prostrate shrubs in the south. Environmental factors differ latitudinally in the Arctic. The length of the growing season can be up to a month shorter at the coastal tundra sites compared to southern, inland sites. Even given similar weather events, net ecosystem exchange (NEE) can display high variability among locations (Kwon et al., 2006). Temporally, conditions in the Arctic are rapidly changing and extreme in their seasonal differences, such as in available light, soil moisture content, and plant development stage. Kwon et al. (2006) and others have acknowledged the temporally changing controls within the growing season, and the lack of non-summer measurements has limited the analysis on non-summer periods, suggesting that the controls are carbon are complex, and that insufficient data exists to describe the patterns of and controls on carbon in the Arctic tundra ecosystem. The research presented contributes to the understanding of the carbon cycle in the Arctic tundra ecosystem both spatially and temporally. Eddy covariance was measured continuously over a period of three years at three sites located along a latitudinal gradient in the North Slope of Alaska in order to (1) continuously monitor non-summer carbon patterns; (2) monitor carbon over a wide range of environmental conditions; and (3) determine the controlling factors on carbon exchange in the Alaskan Arctic tundra ecosystem. In the first paper, the first continuous, yearlong carbon dataset from the Arctic tundra is presented, and the relative importance of the non-summer seasons is quantified. In addition, a functional definition of Arctic seasons is presented to allow consistent seasonal carbon comparisons among sites and years, especially when full year data are not available. In the second paper, a three-year continuous carbon dataset is presented for a tussock tundra region at the southern extent of the Arctic ecosystem. Carbon fluctuations in this region are especially vulnerable to changes in temperature, community composition, and feedback effects, but limited data exist on the controls of carbon in this area. The data presented may also
4 be useful as a baseline for understanding the changes to the carbon cycle as the region transitions to a shrub-dominated system (Sturm et al., 2005). Finally, the third paper presents continuous data from three years (2005-2007) for three Arctic sites, located 2, 100, and 300 km from the Arctic Ocean, or approximately the entire latitudinal extent of the Alaskan tundra ecosystem. The data are unique in both their spatial and temporal coverage, and provide much needed information on the variability of carbon exchange over a wide range of environmental conditions. The controls on carbon exchange are investigated in order to identify whether the Arctic tundra is heterogeneous in space (100-km scale) and time (inter- and intra-annually). The cumulative carbon estimates are the most comprehensive for the Arctic to date.
5 References
ACIA (2005) Arctic Climate Impact Assessment, Cambridge; New York, Cambridge University Press. Arendt A.A., Echelmeyer K.A., Harrison W.D., Lingle C.S., Valentine V.B. (2002) Rapid wastage of Alaska glaciers and their contribution to rising sea level. Science, 297, 382-386. Callaghan T.V., Bjorn L.O., Chernov Y. et al. (2004) Effects of changes in climate on landscape and regional processes, and feedbacks to the climate system. Ambio, 33, 459-468. Chapman W.L., Walsh J.E. (1993) Recent variation of sea ice and air-temperature in high-latitudes. Bulletin of the American Meteorological Society, 74, 33-47. Comiso J.C. (2006) Abrupt decline in the Arctic winter sea ice cover. Geophysical Research Letters, 33. Cornelissen J.H.C., Van Bodegom P.M., Aerts R. et al. (2007) Global negative vegetation feedback to climate warming responses of leaf litter decomposition rates in cold biomes. Ecology Letters, 10, 619-627. Dormann C.F., Woodin S.J. (2002) Climate change in the Arctic: using plant functional types in a meta-analysis of field experiments. Functional Ecology, 16, 4-17. Euskirchen E.S., Mcguire A.D., Chapin F.S. (2007) Energy feedbacks of northern highlatitude ecosystems to the climate system due to reduced snow cover during 20th century warming. Global Change Biology, 13, 2425-2438. IPCC (2007) Climate Change 2007 – The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel
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on Climate Change. Cambridge, Cambridge University Press. Johannessen O.M., Miles M., Bjorgo E. (1995) The Arctic’s shrinking sea-ice. Nature, 376, 126-127. Jones M.H., Fahnestock J.T., Welker J.M. (1999) Early and late winter CO2 efflux from arctic tundra in the Kuparuk River watershed, Alaska, USA. Arctic Antarctic and Alpine Research, 31, 187-190. Kappen L. (1993) Plant activity under snow and ice, with particular reference to lichens. Arctic, 46, 297-302. Keyser A.R., Kimball J.S., Nemani R.R., Running S.W. (2000) Simulating the effects of climate change on the carbon balance of North American high-latitude forests. Global Change Biology, 6, 185-195. Kwon H.J., Oechel W.C., Zulueta R.C., Hastings S.J. (2006) Effects of climate variability on carbon sequestration among adjacent wet sedge tundra and moist tussock tundra ecosystems. Journal of Geophysical Research-Biogeosciences, 111. Lachenbruch A.H., Marshall B.V. (1986) Changing climate - Geothermal evidence from permafrost in the Alaskan Arctic. Science, 234, 689-696. Mckane R.B., Rastetter E.B., Shaver G.R., Nadelhoffer K.J., Giblin A.E., Laundre J.A., Chapin F.S. (1997) Climatic effects on tundra carbon storage inferred from experimental data and a model. Ecology, 78, 1170-1187. Miller J.R., Chen Y.H., Russell G.L., Francis J.A. (2007) Future regime shift in feedbacks during Arctic winter. Geophysical Research Letters, 34. Myneni R.B., Keeling C.D., Tucker C.J., Asrar G., Nemani R.R. (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698-702.
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Oechel W.C., Hastings S.J., Vourlitis G., Jenkins M., Riechers G., Grulke N. (1993) Recent Change of Arctic Tundra Ecosystems from A Net Carbon-Dioxide Sink to A Source. Nature, 361, 520-523. Oechel W.C., Vourlitis G., Hastings S.J. (1997) Cold season CO2 emission from arctic soils. Global Biogeochemical Cycles, 11, 163-172. Olsson P.Q., Sturm M., Racine C.H., Romanovsky V., Liston G.E. (2003) Five stages of the Alaskan Arctic cold season with ecosystem implications. Arctic Antarctic and Alpine Research, 35, 74-81. Osterkamp T.E. (2005) The recent warming of permafrost in Alaska. Global and Planetary Change, 49, 187-202. Osterkamp T.E., Romanovsky V.E. (1999) Evidence for warming and thawing of discontinuous permafrost in Alaska. Permafrost and Periglacial Processes, 10, 17-37. Panikov N.S., Flanagan P.W., Oechel W.C., Mastepanov M.A., Christensen T.R. (2006) Microbial activity in soils frozen to below −39 degrees C. Soil Biology & Biochemistry, 38, 785-794. Romanovsky V.E., Osterkamp T.E. (2000) Effects of unfrozen water on heat and mass transport processes in the active layer and permafrost. Permafrost and Periglacial Processes, 11, 219-239. Semikhatova O.A. (1992) Relations between choloplasts and mitochondria in the dark. Soviet Plant Physiology, 39, 391-395. Serreze M.C., Walsh J.E., Chapin F.S. et al. (2000) Observational evidence of recent change in the northern high-latitude environment. Climatic Change, 46, 159-207.
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Stone R.S., Dutton E.G., Harris J.M., Longenecker D. (2002) Earlier spring snowmelt in northern Alaska as an indicator of climate change. Journal of Geophysical Research-Atmospheres, 107. Sturm M., Douglas T., Racine C., Liston G.E. (2005) Changing snow and shrub conditions affect albedo with global implications. Journal of Geophysical Research-Biogeosciences, 110. Vourlitis G.L., Oechel W.C. (1999) Eddy covariance measurements of CO2 and energy fluxes of an Alaskan tussock tundra ecosystem. Ecology, 80, 686-701. Zimov S.A., Davidov S.P., Voropaev Y.V., Prosiannikov S.F., Semiletov I.P., Chapin M.C., Chapin F.S. (1996) Siberian CO2 efflux in winter as a CO2 source and cause of seasonality in atmospheric CO2. Climatic Change, 33, 111-120.
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Chapter 1 Quantifying seasonal contributions to the total annual carbon budget of an Alaskan Arctic tundra ecosystem Submitted to Global Change Biology
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Quantifying seasonal contributions to the total annual carbon budget of an Alaskan Arctic tundra ecosystem
Cheryl A. Laskowski1*, George Burba2, Douglas Deutschman1, Walter Oechel1
1
San Diego State University, San Diego, CA 92116
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LI-COR Biosceinces, Lincoln, NE 68504
*
Corresponding Author: phone: 01-619-594-6379; fax: 01-619-594-7831; e-mail:
[email protected]
Keywords: Climate change; CO2; eddy covariance; heating correction; seasons; winter; WPL correction, moist acidic tundra; Atqasuk; NEE
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Abstract The Arctic is critical to understanding impacts and feedbacks on the global climate and carbon budget. While carbon exchange during the summer seasons in the Arctic is relatively well studied, uncertainty on the rates and patterns of non-summer seasons remains. Here, a yearlong study of carbon dioxide fluxes in an Alaskan Arctic tundra ecosystem is presented. Environmental controls on CO2 fluxes and the impacts of season timing and duration on the CO2 budget of the Arctic are examined. A new protocol to quantitatively define seasons in the Arctic based on available energy is presented, which is believed to be more useful and flexible under a changing environment and when comparing among regions. Eddy covariance fluxes were corrected using the standard density (WPL) term, adjusted for influences of surface heating in the path of an openpath sensor. The result was a significant reduction in the magnitude of the sink terms when compared to the WPL alone. The non-summer period was a source of carbon to the atmosphere that overwhelmed the net CO2 uptake of the summer period. Summer was a net sink of 11.4 g C m–2 yr–1, while the non-summer seasons released more than four times the observed summer uptake, resulting in a net annual biosphere release of 37.6 g C m–2 yr–1. Regression analyses to determine the most significant controls on CO2 flux improved significantly when seasons were included as variables in the analyses (P < 0.01). Significant relationships between net ecosystem CO2 exchange (NEE) and certain environmental factors, such as that of soil temperature on carbon exchange, only became apparent when seasonality was included, because the relationship changed by season. These results demonstrate that continuous measurements are needed to accurately
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calculate NEE in the Arctic and that carbon patterns and controls cannot be assumed to remain constant among seasons.
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Introduction The carbon budget of the Arctic is a critical feedback on climate change for a number of reasons. The Arctic contains over 1,000 Gt carbon as organic matter in the upper 3 m of soil and permafrost, which represents over 43% of the global carbon content at this depth (Tarnocai et al., 2009). This carbon has been sequestered since at least the Holocene (Zimov et al., 2009); however, the ability of soils to accumulate carbon does not appear to be infinite (Belyea and Clymo, 2001). Soils may have already switched to a source of CO2 to the atmosphere beginning around the mid-1970’s in some areas (Gorham, 1991; Oechel et al., 1993) and are likely to increase in source activity due to climate change including warming, soil drying, deepening of the active layer, and loss of permafrost (Hinzman et al., 2005). The net carbon budget of the Arctic is highly impacted by the very long “winter” season outside the short Arctic growing season (Groendahl et al., 2007). This long, nonsummer, period of low or no vascular plant growth is increasingly recognized as a period of significant biological activity and large trace gas fluxes (Zimov et al., 1993; Oechel et al, 1997). This non-summer period may be the dominant period of carbon flux in the Arctic. Our knowledge of the patterns and controls on non-summer fluxes is still very limited. There are a number of reasons for this that include a slow realization of the large contribution of the non-summer period to the annual trace gas budgets (McKane et al., 1997; Oechel et al., 1997; Fahnestock et al., 1999; Welker et al., 2000) and the historic view that the bulk of the activity occurs in the summer and that with the onset of winter, the Arctic becomes inactive (Oechel et al., 1995). There has been a general lack of
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appreciation of the impact of freeze-thaw events (Grogan et al., 2004), liquid water in soils (Sturm et al., 2005), and cold-adapted plants (Bate and Smith, 1983; Kappen, 1993) on microbial and plant carbon fluxes at sub-zero temperatures. In addition, the assumption that the production of CO2 during the non-summer period is driven almost entirely by temperature and can be modeled by Q10 relationships further reduced the impetus for direct CO2 measurements in the winter (Fang and Moncrieff, 2001; Mikan et al., 2002).
In addition to a non-linear relationship of temperature on ecosystem
respiration and CO2 efflux, other factors are important in simulating non-summer CO2 efflux including latitude, day of year, and snow depth (Fahnestock et al., 1998; Zamolodchikov and Karelin, 2001; Elberling, 2007). The drivers of CO2 exchange likely change more than just during the “summer” and “non-summer” season as well, particularly during the transitional seasons when snow is melting or accumulating (Kwon et al., 2006). For example, vascular plants may photosynthesize to below −3°C under the snow (Bate and Smith, 1983), lichens and mosses have been shown to photosynthesize down to below −10°C (Walton and Doake, 1987; Kappen, 1993) under the snow, and microbial respiration has been observed at −40°C (Zimov et al., 1996; Michaelson and Ping, 2003; Panikov et al., 2006). Subnivian CO2, released during snowmelt, may lead to net carbon source activity although radiation and photosynthesis are increasing, in apparent contradiction to the general assumption of increased radiation leading to increasing NEE, as generally observed during the summer season (Semikhatova et al., 1992). Interacting processes and patterns lead to interesting and complex carbon exchange patterns throughout the year, and suggest that a functional definition of
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seasonality could help identify and model the changing patterns of and controls on carbon exchange throughout the year. The lack of appropriate technology and the difficulties in making quality trace gas flux measurements in the harsh non-summer seasons (Oechel et al., 1995), have contributed to the current dearth of data and understanding (Sullivan et al., 2008) of CO2 exchange during the non-summer period. This lack of data coupled with inadequate understanding has crippled our ability to effectively estimate and model current annual CO2 fluxes, let alone predict with any certainty the annual carbon balance for the Arctic under expected future environmental conditions (Elberling and Brandt, 2003). The work undertaken here had three principal objectives: 1) Determine the seasonal CO2 fluxes in a moist acidic tundra ecosystem near Atqasuk, Alaska, 2) Analyze the changing controls on CO2 flux as seasons progress, 3) Define seasons in the Arctic in a way that will better allow carbon balance to be analyzed, understood, and predicted under changing climate.
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Materials and Methods Site description and instrumentation The study was conducted ~100 km inland (south) of the Arctic Ocean (70°28'10.6"N: 157°24'32.2"W, 24m elevation) on Alaska’s North Slope near the village of Atqasuk, during 2006. The land cover is moist acidic tundra, dominated by tussocks (Eriophorum vaginatum) and other vascular species (Carex bigelowii, Vaccinium vitis-idaea, Ledum palustre), with scattered prostrate shrubs (Komarkova and Webber, 1980). The landscape within the study area is primarily flat, and vegetation height generally does not exceed 0.2 meters. A detailed description of the site can be found in Kwon et al. (2006). The eddy covariance method was used to assess net ecosystem carbon exchange (Baldocchi et al., 1988). The CO2 energy (latent and sensible heat) fluxes were measured at a height of 2.5 meters above the plant canopy. Carbon dioxide and water vapor measurements were made with a LI-7500 infrared open-path gas analyzer, IRGA (LICOR Biosciences, Lincoln, NE) with a sampling frequency of 10 Hz, along with threedimensional wind speed, sonic temperature, and direction using an ultrasonic anemometer (R3, Gill Instruments, Hampshire, UK). Other micrometeorological inputs were recorded every 15 seconds and averaged over half-hour periods using a Campbell Scientific 23X datalogger (Campbell Scientific, Logan, UT). Data included temperature and relative humidity (HMP45, Vaisala, Helsinki, Finland), net radiation (Q7 REBS, Seattle, WA), photosynthetically active radiation (PAR) (LI-190SB, LI-COR Biosciences, Lincoln, NE), soil temperature (Type-T thermocouples, Omega, Stamford, CT), ground heat flux (HFT-1, REBS, Seattle, WA), wind speed (03002 Wind Sentry Set,
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R. M. Young, Traverse City, MI) and precipitation (TE 525, Texas Electronics, Dallas, TX). Snowfall was not collected as part of this research. ---------------------------------------------- Table 1.1 ------------------------------------------------Net ecosystem exchange data were collected continuously throughout the year; however, some data were rejected due to sensor or system malfunction, conditions occurring outside of the instruments’ operating ranges, or data spikes. Nearly 4000 hours of raw CO2 data were accepted, representing 44% of possible half-hour periods (Table 1.1). During spring, summer, and fall season, the instrument monitoring occurred at least once per week to ensure proper functioning, leading to approximately 70% data retention during these seasons. Winter had much lower data retention due to the extreme conditions of Arctic winter and reduced on-site maintenance in early 2006. Data capture for the winter improved significantly (from below 15% to nearly 40%) in late winter when instruments were checked and cleaned (of ice, snow, or debris) at least weekly.
Data analysis Average half-hour fluxes of carbon dioxide and water vapor were calculated from raw data using EdiRe software (University of Edinburgh, Edinburgh, Scotland). Twodimensional wind rotation, despiking routines, and quality control checks of the calculated fluxes followed FluxNet guidelines (Lee et al. 2004). Gaps in the flux data were filled following methodology adapted from Falge et al. (2001), using the online gap-filling tool provided by the Forest Ecology Laboratory at the University of Tuscia (http://gaia.agraria.unitus.it/database/eddyproc).
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Two corrections for air density fluctuations were applied according to Webb, Pearman, and Leuning (“WPL”, Webb et al., 1980) and Burba et al. (2008). The former is a wellknown term that accounts for changes in ambient air density due to sensible and latent heat fluxes, and uses temperature data from the sonic anemometer. The latter is a recently developed correction compensating for the additional heat flux produced by elements surrounding the open sampling path. The open path gas analyzer is bound by source and detector windows and by support spars, which may have temperatures different from those of ambient air due to internal electronics, and radiative heating and cooling of the surfaces. Such phenomenon could lead to an additional temperature variation in the sampling path, especially important at low ambient temperatures, and could cause a departure between the air temperatures measured at 10 Hz by the sonic anemometer and the actual air temperatures within the optical path of the open-path analyzer (Grelle and Burba, 2007). The size of the heating correction is quite small ranging from zero to about 0.6 µmol of CO2 m–2 s–1 for most cases (Burba et al., 2008), which is 10-50 times smaller than standard eddy covariance flux corrections, such as the open-path WPL or closedpath frequency response corrections, and similar in magnitude to open-path frequency response corrections. However, if left uncorrected, the very small hourly bias leads to overestimation of net ecosystem uptake when integrated over longer periods in cold environments (Grelle and Burba, 2007; Clement et al., 2009; Burba et al., 2008, Jarvi et al., 2009), and results in an apparent net uptake under low-temperature conditions, when efflux is expected (this study and as reported by others; Amiro et al., 2006; Hirata et al., 2007; Ono et al., 2008).
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The surface heating correction was applied to all CO2 flux data, after it was adjusted to reflect specific site conditions different from those in which the correction was tested (Grelle and Burba, 2007; Burba et al., 2008, Jarvi et al., 2009), notably an inclined IRGA and lower ambient temperatures. The correction was calibrated by identifying a period when CO2 flux is assumed negligible, calculating the correction factor accordingly, and then applying it to the rest of the measurements. The “negligibleflux” period used in this study was during the two weeks with the lowest soil temperatures for the year, because minimal rates of carbon exchange are expected under these conditions (Zimov et al., 1993; Elberling, 2007). The derivation of “daytime” and “nighttime” instrument surface temperatures in Burba et al. (2008) were applied to high and low light conditions here (> 75 W m−2, and ≤ 75 W m−2, respectively, after Kwon et al., 2006). It is important to note that this method of applying the correction results is a conservative estimate of actual CO2 efflux, as it is likely that diffusion through the snowpack results in a small net source of CO2 (Panikov et al., 2006) under conditions we assumed to have zero CO2 efflux. Therefore, the actual CO2 efflux values are likely to be somewhat underestimated. Input parameters that were missing for some half-hour periods were either averaged seasonally (CO2 and H2O densities), or filled with data collected near the study location (i.e., wind speed from the Department of Energy’s Atmospheric Radiation Measurement Program, R2 = 93%) (http://www.arm.gov/). Season extents used in this analysis are characterized in the following way. Spring season begins when daily average net radiation (Rnet) > 0 Wm−2 for 3 or more consecutive days. Summer begins when 3 or more consecutive days are measured at Rnet >100 Wm–2, and
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ends at the last period of 3 or more consecutive days of Rnet > 100 Wm–2. Fall is the period after summer, and ends when 3 or more consecutive days are below 0 Wm–2. The period between fall and spring is winter, occurring at the beginning and end of the calendar year. Simple and multiple regression analyses were used to determine which variables control carbon exchange. Regression analyses using the software program SYSTAT were performed on daily cumulative carbon exchange being predicted from five main-effects variables including air temperature, soil temperature, net radiation, PPFD (photosynthetic photon flux density), and wind speed (Table 1.2, “Main-effects Model”). Next, to determine whether season was a significant factor in predicting carbon exchange, a categorical explanatory variable representing each season was added to the model as a sixth factor (“Additive Model”). The significance of the additional term was tested using a Likelihood Ratio Test (LRT). ----------------------------------------- Table 1.2 --------------------------------------------------The Additive Model does not allow the functional relationships of each explanatory variable to vary with season. For example, if increased soil temperature in the summer results in greater carbon uptake (e.g., due to more favorable conditions for photosynthesis), but a decrease in carbon uptake (or carbon release) during fall (e.g., plants have senesced but soil microbes continue to respire), the Additive Model would be insufficient. To assess whether the functional relationships also varied with season, an interaction term was added for each of the five Main-effects variables (“Interaction Model”). Again, a Likelihood Ratio Test was performed to test whether the Interaction model performed significantly better than the Additive Model.
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Results Average annual air temperature at the study site was −10.33 °C (Table 1.3); minimum daily temperature was −39.69 °C on 1 February, and maximum daily temperature was 17.02 °C on 25 July. Soil temperatures at 5 cm depth were more moderate, with an annual average of −5.42 °C, minimum of −21.09 °C, and maximum of 15.42 °C. February was unusually warm, with average daily air temperatures reaching nearly 0 °C mid-month (Figure 1.1). Annual rainfall totaled 83.7 mm, falling predominantly during July – September. Daily carbon exchange rates for the entire study year are shown in Figure 1.2. The annual carbon exchange for this site resulted in a net source of 37.59 ± 1.62 g C m−2 yr−1 (Table 1.4). Daily cumulative carbon exchange was highly variable throughout the year, ranging from −1.33 g C m−2 day−1 during peak growing season (2 July) to 1.23 g C m−2 day−1 (21 May) during the rapid snowmelt, with negative numbers indicating net ecosystem carbon uptake. Variability arises largely from the extreme changes in meteorological and physical conditions in the Arctic. ----------------------------------------- Table 1.3 --------------------------------------------------Winter was the longest season in 2006 and lasted 205 days, from 1 January – 23 April and 1 October – 31 December (Table 1.1). Soil temperatures throughout winter were an average of nearly 9 °C warmer than air temperature (see Table 1.3 for average seasonal environmental conditions). There was no distinct carbon-exchange diurnal pattern and little net daily activity, due to fairly constant and low solar radiation and temperatures (Figure 1.3). Daily winter cumulative carbon exchange in this study exhibited a maximum release of 0.80 g C m−2 day−1. Due to its long duration, however, the winter season contributed the greatest proportion to the annual carbon budget,
22
although the daily carbon exchange rate was the lowest of any season. Total cumulative winter exchange was an efflux of 28.15 ± 0.73 g C m–2, due in part to the fact that respiration was the dominant process during this season (Table 1.4). Spring was the shortest season (30 days for 2006, Table 1.1) but significant due to the sharp increase in daylight and energy availability during this period. The greatest daily carbon release was measured at the end of spring (release of 1.23 g C m–2 d–1), when snowmelt was occurring. During spring, there was little diurnal pattern (Figure 1.3), only becoming discernable late in the season, when snowmelt was beginning and creates patches of snow-free ground. Although the cumulative carbon impact was the smallest of any season, the average daily exchange rate was higher than both winter and summer. Little uptake was detected during this period, with a maximum daily uptake value of 0.16 g C m−2 day−1. The cumulative carbon exchange for this period was a net efflux of 5.35 ± 0.32 g C m−2 season−1 (Table 1.4), which was an average daily release of 0.18 g C m−2 day−1 over the season. ----------------------------------------- Table 1.4 ----------------------------------------------------Summer was the season that coincided most closely with the “growing season” period commonly measured, lasting from 24 May to 14 August (83 days Table 1.1). Diurnal patterns of carbon exchange during summer showed strong midday uptake and minimal nighttime release (Figure 1.3). Peak carbon exchange occurred an hour earlier in the summer (1200 AST) than in the spring. The greatest daily uptake occurred during this season (−1.33 g C m−2 day−1, on 2 July). Average daily temperatures were 6.4 °C and the majority of rain fell during summer (67.3 mm). Summer was the only season to show a net uptake of carbon (−11.43 ± 1.23 g C m−2 season−1, Table 1.4).
23
Fall contributed a net efflux of 15.52 g C m–2 (± 0.70 g C m−2 season−1 released, Table 1.4), and although some carbon uptake occurred, fall had the greatest daily carbon exchange rate (0.33 g C m−2 day−1). Average air temperature fell by more than three degrees from summer, averaging 3.0 °C, while nearly 15 mm rainfall occurred over the 47-day period. Soils continued to thaw, but at a slower rate than during summer. A distinct diurnal pattern in CO2 exchange was discernable during fall, but of smaller magnitude than in summer (Figure 1.3).
Model Comparison Increased net radiation and decreased wind speed and air temperature led to increased carbon exchange under the Main-effects Model (R2 = 26.0%; P 0.85), only one of the correlated terms was entered into the equation. Radiation terms (Rnet and PAR) were highly correlated throughout summer and fall (r > 0.998) and therefore a single “radiation” parameter was used in all summer and fall regression models. Tair and Tsoil were generally correlated only in the beginning of summer, and therefore both were entered into the model once the correlation dropped below 0.85. When only radiation or temperature term is used, the interpretation of results is indistinguishable between the correlated variables. Table 2.5 indicates when both temperature variables were used in model fitting. Linear regressions by year (Table 2.5) showed that the drivers of NEE change over the summer season, in both magnitude and sign. Early summer showed efflux in all years and driven by radiation; however in early summer 2007 there was little diurnal pattern in carbon exchange and the model was the most variable. Throughout summer and fall 2005, the system is a net source of carbon, and radiation is shown consistently to be a driver of efflux; higher radiation led to increased carbon efflux. Mid-summer 2005 showed the least efflux, and the regression indicated that an increase in rain led to decreased efflux. This may indicate greater photosynthesis under wetter conditions that depressed the net respiration signal during mid season. Efflux continued into fall, until the rate of carbon exchange declined in middle to late fall. After the early-summer efflux in 2006, the ecosystem became a net sink of carbon during the summer (Figure 2.5), which was driven by radiation and air temperatures. Increased respiration was associated
70
with increased soil temperatures, which would indicate microbial respiration. The carbon signal was strong in early fall, but weakened as the season progressed, and the regressions were unable to explain much of the variability (Table 2.5). Similar patterns were detected in 2007, when mid-summer to early fall show distinct diurnal patterns of uptake, and less variation in the later parts of fall. To examine at the controls of carbon in summer and fall in more detail, the univariate regression coefficients were plotted by year and season block (Figure 2.6). The bars indicate the influence of the parameter (radiation or soil temperature) by season and year. Figure 6a shows the importance of radiation throughout the summer and fall seasons, both as a driver of efflux in early summer and late fall, and as a driver of uptake or efflux during the middle of the period. Soil temperature is generally a driver of carbon efflux (Figure 2.6b), although its importance generally declines as the season progresses.
71
4.1 Discussion This study examined the variability in carbon exchange over three years (2005-2007) of a low Arctic tussock tundra ecosystem in Northern Alaska using an autonomous eddy covariance tower and support system. The results provide the most complete and comprehensive study of an Arctic ecosystem near the tundra-shrub transition, and therefore highly susceptible to major ecosystem-level changes due to climate change (Harding et al., 2002; Tape et al., 2006). The autonomous instrumentation system was the first of its kind to be developed and deployed in extremely harsh environmental conditions, and it performed remarkably well. Equipment down time was low, as was data rejection. The percent data retention is high even when compared to systems that are regularly monitored and in much less harsh conditions (Wilson et al., 2002), suggesting the system was robust and reliable and could be used as a prototype for eddy covariance systems in other remote locations. Controls on winter carbon exchange were among the most complex in the system and showed periods of significant activity—source and sink—under the snow during 2006 and 2007. The periods of uptake occurred only during periods when radiation was present aboveground (Figure 2.7). Other studies have shown that light may penetrate snow to depths of 20 cm to 2 m (Richardson and Salisbury, 1977; Starr and Oberbauer, 2003), depending on the density of snow and light conditions at the surface, and although light attenuates exponentially below snow, 25% of light may be available at depths greater than 20 cm. Photosynthesis has been shown in mosses at subzero temperatures with light levels down to 100 or even 50 µmol m–2s–1 (Atanasiu, 1971; Pannewitz et al., 2005). Even with light attenuation of 75%, there would be substantial radiation for moss
72
photosynthesis during periods of uptake observed in this study (Figure 2.8). A typical day of winter uptake is shown in Figure 8, both with surface PAR availability and PAR with 75% light attenuation, showing over 100 µmol m–2 s–1 during the periods of uptake. In contrast, 2005 was relatively inactive during the winter and had 20 cm greater snow depth, reaching over 50cm, at which depth light has likely fully attenuated. The deeper snow may have led to light attenuation preventing biological activity, or acted as a barrier to diffusion of carbon dioxide through the snow where it could be detected by the eddy covariance instrumentation. During spring, radiation and air temperatures increased, and while the ground was still snow covered, some layers of snow began to melt (Figure 2.3). The increasing air temperature allowed built up winter carbon dioxide to be released, and all three years showed significant carbon release during this period, which was similar among all years, despite differences in timing and duration of the season (Table 2.1). The snow-free periods (summer and fall) of 2006 and 2007 showed similar net carbon exchange rates, particularly in summer (Table 2.3), when the system took up carbon at a rate of −29 g C m–2 summer–1 each year. In contrast, 2005 remained a net source of carbon throughout the summer and fall seasons. Ivotuk is a well-drained site, and the marked differences in timing and magnitude of rainfall may account for the lack of net uptake in 2005. In 2007, rainfall was abundant and consistent (Figure 2.7) and showed continuous uptake during the growing season. While 2005 and 2006 had much less rainfall, the timing of the rain was much different. In 2007, rain fell more consistently and a greater proportion fell during summer, rather than fall. During 2005, precipitation events in summer are associated with a substantial drawdown in the system;
73
however as a period of drought set in, this trend was reversed and significant uptake followed. The next large rain event occurred late in the season when plants were already senescing. Rainfall later in the snow-free season (fall) appears to have much less effect on uptake, as seen in all three years. Undisturbed ecosystems are generally thought to be annual net sinks of carbon (Falge et al., 2002); however, arctic tundra ecosystems have been shown to be either carbon sources or sinks depending on the year and location. This site was a net source of carbon over the study period, averaging 45 g C m–2 year–1 carbon release. The current loss of carbon from this ecosystem is unsustainable and consists of a positive feedback on climate change in this low Arctic region. One year, 2006, produced a net balance of carbon and displayed patterns generally thought to be “typical” of Arctic tundra—long unproductive winters, productive summers, and short transitional periods in between— however, this was atypical of the three-year trend. In a short three year record, it is impossible to know the long term pattern at this site, or the current trajectory for change. However, the pattern of net loss of carbon from the region may reflect the step warming of the North Slope that occurred in the mid 1970s (Oechel et al., 2000). These data show a range of environmental conditions and subsequent carbon response, some of which are predicted under future climate scenarios (IPCC, 2007), and may provide some insight into future carbon scenarios for this region. For example, the deeper snow depth of 2005 led to later and longer spring efflux. The environmental conditions in spring and early summer are highly favorable to carbon uptake. The clear skies, high incoming radiation and moderate temperatures promote high photosynthetic rates while soil respiration is depressed by the still frozen soils. A later snowmelt may dampen the total carbon uptake
74
for the season by impairing vegetation full access to these conditions. In addition, the precipitation regime for the Arctic is expected to increase, although models are not consistent (ACIA, 2005). This study provided both wet and dry conditions, and as precipitation forecasts become more predictive, the direction of carbon exchange may be better modeled. Long-term, continuous measurement of NEE in climate sensitive regions, such as the southern arctic tundra is needed to understand the current controls on ecosystem metabolism, the changes that are already occurring, and to develop the understanding needed to predict, with confidence, a future carbon budget under the anticipated climate change. The timing of the onset of snow melt appears critical to the following seasons’ assimilation, which has opposing implications for scenarios of lengthened growing season. Increased snow depth may cause delayed snowmelt and shorten the summer period of carbon uptake. Summer precipitation scenarios are among the most uncertain but are critical for accurate prediction of non-winter NEE. Since summer is the only period of net uptake in this system, a net loss of carbon during summer, as seen in 2005, will likely lead to unrecoverable, unsustainable feedbacks. Finally, winter carbon dynamics are still poorly understood. The lack of continuous CO2 measurements weakens the understanding of controls during this period, which is predicted to undergo the most marked climatic changes. As seen in this study, there are periods of significant uptake and loss in the system that need to be understood and may be missed without ongoing measurements. It is clear that the Arctic carbon budget is highly sensitive to varying environmental conditions and that not only are continuous measurements possible under
75
even extreme conditions, they are essential to understanding the current and predicting the future arctic carbon balance.
76
References ACIA (2005) Arctic Climate Impact Assessment, Cambridge; New York, Cambridge University Press. Atanasiu L. (1971) Photosynthesis and Respiration of Three Mosses at Winter Low Temperatures. The Bryologist, 74, 23-27. Baldocchi D., Falge E., Gu L.H. et al. (2001) FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82, 2415-2434. Baldocchi D.D., Hicks B.B., Meyers T.P. (1988) Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods. Ecology, 69, 1331-1340. Burba G.G., Mcdermitt D.K., Grelle A., Anderson D.J., Xu L.K. (2008) Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers. Global Change Biology, 14, 1854-1876. Chapin F.S., Sturm M., Serreze M.C. et al. (2005) Role of land-surface changes in Arctic summer warming. Science, 310, 657-660. Corradi C., Kolle O., Walter K., Zimov S.A., Schulze E.D. (2005) Carbon dioxide and methane exchange of a north-east Siberian tussock tundra. Global Change Biology, 11, 1910-1925. Fahnestock J.T., Jones M.H., Welker J.M. (1999) Wintertime CO2 efflux from arctic soils: Implications for annual carbon budgets. Global Biogeochemical Cycles, 13, 775-779.
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Falge E., Baldocchi D., Olson R. et al. (2001) Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 107, 4369. Falge E., Baldocchi D., Tenhunen J. et al. (2002) Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agricultural and Forest Meteorology, 113, 53-74. Gilmanov T.G., Svejcar T.J., Johnson D.A., Angell R.E., Saliendra N.Z., Wylie B.K. (2006) Long-term dynamics of production, respiration, and net CO2 exchange in two sagebrush-steppe ecosystems. Rangeland Ecology & Management, 59, 585599. Grogan P., Chapin F.S. (1999) Arctic soil respiration: Effects of climate and vegetation depend on season. Ecosystems, 2, 451-459. Harazono Y., Mano M., Miyata A., Zulueta R.C., Oechel W.C. (2003) Inter-annual carbon dioxide uptake of a wet sedge tundra ecosystem in the Arctic. Tellus Series B-Chemical and Physical Meteorology, 55, 215-231. Harding R., Kuhry P., Christensen T.R., Sykes M.T., Dankers R., Van Der Linden S. (2002) Climate feedbacks at the tundra-taiga interface. Ambio, 47-55. IPCC (2007) Climate Change 2007 – The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, Cambridge University Press. Kutzbach L., Wille C., Pfeiffer E.M. (2007) The exchange of carbon dioxide between wet arctic tundra and the atmosphere at the Lena River Delta, Northern Siberia. Biogeosciences, 4, 869-890.
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Kwon H.J., Oechel W.C., Zulueta R.C., Hastings S.J. (2006) Effects of climate variability on carbon sequestration among adjacent wet sedge tundra and moist tussock tundra ecosystems. Journal of Geophysical Research-Biogeosciences, 111. Lafleur P.M., Humphreys E.R. (2008) Spring warming and carbon dioxide exchange over low Arctic tundra in central Canada. Global Change Biology, 14, 740-756. Mcguire A.D., Chapin F.S., Walsh J.E., Wirth C. (2006) Integrated regional changes in arctic climate feedbacks: Implications for the global climate system. Annual Review of Environment and Resources, 31, 61-91. Mcguire A.D., Sturm M., Chapin F.S. (2003) Arctic transitions in the land-atmosphere system (ATLAS): Background, objectives, results, and future directions. Journal of Geophysical Research-Atmospheres, 108. Miller P.C., Kendall R., Oechel W.C. (1983) Simulating carbon accumulation in northern ecosystems. Simulation, 40, 119-131. Oechel W.C., Vourlitis G.L., Hastings S.J., Zulueta R.C., Hinzman L., Kane D. (2000) Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature, 406, 978-981. Pannewitz S., Green T.G.A., Maysek K. et al. (2005) Photosynthetic responses of three common mosses from continental Antarctica. Antarctic Science, 17, 341-352. Rabinowitch E. (1951) PHOTOSYNTHESIS. Annual Review of Physical Chemistry, 2, 361-382. Richardson S.G., Salisbury F.B. (1977) Plant responses to the light penetrating snow. Ecology (Washington D C), 58, 1152-1158. Saito M., Kato T., Tang Y. (2009) Temperature controls ecosystem CO2 exchange of an
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alpine meadow on the northeastern Tibetan Plateau. Global Change Biology, 15, 221-228. Starr G., Oberbauer S.F. (2003) Photosynthesis of arctic evergreens under snow: Implications for tundra ecosystem carbon balance. Ecology, 84, 1415-1420. Starr G., Oberbauer S.F., Ahlquist L.E. (2008) The photosynthetic response of Alaskan tundra plants to increased season length and soil warming. Arctic Antarctic and Alpine Research, 40, 181-191. Sturm M., Schimel J., Michaelson G. et al. (2005) Winter biological processes could help convert arctic tundra to shrubland. Bioscience, 55, 17-26. Tape K., Sturm M., Racine C. (2006) The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Global Change Biology, 12, 686-702. Tarnocai C., Canadell J.G., Schuur E.A.G., Kuhry P., Mazhitova G., Zimov S. (2009) Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles, 23. Thornley J.H.M. (1976) Mathematical models in plant physiology, London, Academic Press. Timlin M.S., Walsh J.E. (2007) Historical and projected distributions of daily temperature and pressure in the Arctic. Arctic, 60, 389-400. Webb E.K., Pearman G.I., Leuning R. (1980) Correction of Flux Measurements for Density Effects Due to Heat and Water-Vapor Transfer. Quarterly Journal of the Royal Meteorological Society, 106, 85-100. Wilson K., Goldstein A., Falge E. et al. (2002) Energy balance closure at FLUXNET sites. Agricultural and Forest Meteorology, 113, 223-243.
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Table 2.1. Dates of each season by study year, with seasons as described by in Chapter 1. Spring
Summer
Fall
Winter
2005
24-Mar
6-Jun
2-Aug
1-Oct
2006
20-Mar
27-May
9-Aug
8-Oct
2007
3-Apr
29-May
28-Jul
3-Oct
81
Table 2.2. Seasonal and annual averages for environmental conditions at Ivotuk, Alaska, 2005-2007. Air and soil temperatures are in °C, Rnet is net radiation and is in W m−2, PAR is photosynthetically active radiation and is in µmol m−2 s−1. Standard deviations are in parentheses. Interannual mean (࢞ഥ) and standard error (SE) by season and annually.
Interannual 2005
2006
2007
ഥ ࢞
SE
o
Air Temperature ( C) Winter −20.97 (9.70) Spring −10.11 (11.20) Summer 9.50 (4.19) Fall 5.55 (6.24) Annual
−18.61 (10.26) −14.58 (10.92) 8.42 (4.40) 3.31 (4.67)
−20.32 (10.37) −9.11 (7.17) 11.70 (3.83) 6.59 (6.02)
−19.91 −11.29 9.86 5.17
0.69 1.68 0.97 0.97
−9.65 (15.36)
−8.77 (14.49)
−8.39 (15.79)
−8.91
0.40
−4.18 (3.07) −5.49 (3.02) 2.42 (1.74) 2.77 (2.00) −2.27 (4.33)
−5.00 (3.58) −8.18 (2.77) 2.64 (2.21) 2.15 (1.52) −2.86 (5.12)
−6.59 (4.72) −7.21 (2.58) 3.42 (2.30) 4.01 (2.46) −3.09 (6.15)
−5.24 −6.96 2.83 2.98 −2.74
0.71 0.79 0.31 0.55 0.24
−9.4 (17.9) 23.4 (26.8) 124.0 (42.3) 43.5 (29.9) 26.8 (53.6)
−13.0 (21.8) 18.3 (15.4) 104.7 (32.5) 38.4 (28.6) 25.2 (50.8)
−11.6 (20.8) 23.6 (22.0) 122.6 (34.0) 47.5 (39.2) 26.7 (55.5)
−11.33 21.74 117.10 43.14 26.21
1.06 1.73 6.20 2.62 0.53
38.7 (55.5) 454.3 (129.5) 457.8 (166.8) 199.5 (119.2)
32.6 (46.2) 414.1 (130.2) 373.4 (132.5) 175.8 (98.3)
47.9 (72.8) 464.1 (116.0) 450.3 (150.8) 216.9 (116.4)
39.69 444.17 427.14 197.63
4.52 15.30 26.98 12.08
214.9 (217.2)
196.7 (191.0)
208.9 (208.7)
206.71
5.51
o
Soil Temperature ( C) Winter Spring Summer Fall Annual -2
Net Radiation (Wm ) Winter Spring Summer Fall Annual –2 –1
PAR (µmol m s ) Winter Spring Summer Fall Annual
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Table 2.3. Seasonal and yearly carbon budgets for Ivotuk, Alaska, 2005-2007, in g C m−2. Values in brackets are 95% confidence intervals, determined by bootstrapping. Interannual mean (࢞ഥ) and standard error (SE) by season and annually. Interannual ഥ ࢞ SE
2005
2006
2007
Winter
21.12 {14.52, 27.45}
1.36 {−7.95, 9.87}
40.12 {22.61, 56.59}
20.89 11.21
Spring
11.71 {7.11, 15.81}
11.59 {8.77, 14.21}
20.65 {15.90, 24.63}
14.65
Summer
34.36 {24.35, 47.28}
−29.45 {−38.48, −19.84}
−29.35 {−42.52, −16.48}
Fall
19.35 {15.59, 23.62}
16.54 {11.57, 21.02}
17.69 {8.12, 25.81}
Annual
86.54 {71.35, 103.13}
0.04 {−16.87,16.79}
49.11 {20.19, 75.87}
Cumulative
86.54 {71.35, 103.13}
86.58 {80.12, 93.46}
135.69 {129.83, 143.41}
3.00
−8.15 21.25 17.86
0.82
45.25 25.05
83
Table 2.4. Regression analysis for winter and spring by year. Tair is air temperature, Tsoil is soil temperature at 5 cm, Rnet is net radiation, and PAR is photosynthetically active radiation. Values indicate regression coefficients, and only significant (P 0.85). The term “Radiation” is PAR (x 103 µmol m–2 s–1), but was always tightly correlated with Rnet (r > 0.998).
85
2005 β SU1
SU2
0.0849
(0.0041)
Tsoil
0.0135
(0.0051)
F2
2
95.0%
r
0.0556
(0.0036)
–0.0375
(0.0121)
2
85.8%
β
r
0.0138
(0.0041)
0.0155
(0.0048)
--
--
--
Rain
n.s.
n.s.
n.s.
Radiation
0.0547
(0.0054)
73.3%
–0.0616
(0.0032) (0.0017)
89.3%
0.0315
(0.0132)
n.s.
0.0099
0.0603
(0.0048)
Tair
--
n.s.
–0.0147
(0.0018)
Rain
–0.1105
(0.0319)
n.s.
n.s.
0.1367
(0.0061)
Radiation
91.6%
n.s.
95.9%
n.s.
0.0829
(0.0038)
0.0830
(0.0058)
--
–0.0265
(0.0008)
–0.0151
(0.0011)
Rain
n.s.
n.s.
–0.1337
(0.0466)
Radiation
0.2178
(0.0116)
Tsoil
0.0380
Tair
–0.0103
Rain
n.s.
97.1%
76.2%
0.0626
(0.0218)
(0.0036)
0.0584
(0.0116)
0.0340
(0.0037)
(0.0011)
–0.0177
(0.0030)
–0.0094
(0.0007)
n.s. 75.9%
(0.0178)
Tsoil
0.0599
(0.0113)
n.s.
n.s.
Tair
–0.0066
(0.0019)
n.s.
n.s.
n.s.
n.s.
(0.0049)
40.3%
–0.0296
Tsoil
n.s.
n.s.
Tair Rain
n.s. n.s.
n.s. 0.2130
89.7%
n.s.
0.1177
0.0273
46.4%
81.2%
n.s.
Radiation
Radiation
0.0318
2
87.5%
n.s.
Tair
Rain F3
β
2007
Tair
Tsoil
F1
r
Radiation
Tsoil
SU3
2006
(0.0074)
(0.0103)
28.8%
26.7%
–0.0669
0.0521
(0.0098)
50.1%
(0.0177)
19.2%
n.s. (0.0551)
–0.0026 n.s.
(0.0013)
86
Figure 2.1. Map of Ivotuk, Alaska.
87
3
Soil Temperature Anomalies 2 1 oC
0 -1 -2 -3
a
2005
2006
2007
-4 1.5
Rain Anomalies
1
mm
0.5 0
-0.5
b -1 80
2005
2006
2007
PAR Anomalies µmol m-2 s-1
40
0
-40
c 2005
-80 1
2006
2007
Carbon Anomalies g C m-2 d-1
0.5
0
-0.5
d -1
2005
2006
2007
88
Figure 2.2. Monthly differences in (a) soil temperature; (b) rain; (c) PAR; and (d) carbon from the three year monthly average. Soil temperature is in °C; rain is in mm; PAR is in µmol m–2 s–1; and carbon is in g C m–2 d–1. Positive values indicate greater than average conditions for the month compared to the three-year average for (a) (b) and (c), and positive values in (d) indicate greater carbon release than average conditions for the month compared to the three-year average. Shaded areas distinguish years.
89
Figure 2.3. Snow depth by year in meters. Blue squares represent 2005, red dots represent 2006, and green diamonds represent 2007. Inset shows a 21-day period during which snowmelt occurred in the three years.
90
2005
2007
2006 2005 2006 2007
Figure 2.4. 14-day running mean of NEE by year, in g C m–2 day–1. Blue dash-dot represents 2005, red dots represent 2006 and solid green represents 2007. Negative values indicate net biospheric uptake. Year symbols are the same as in Figure 3. Bars at the bottom of the graph indicate seasons for 2005, 2006, and 2007. Grey indicates winter, yellow indicates spring, green indicates summer, and pink indicates fall.
91
0.2
0.1
0.0
a -0.1 0.05
0.00
-0.05
b -0.10
0.1
0.0
-0.1
c -0.2 0
6
12
18
24
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Figure 2.5a. Early, middle, and late summer season diurnal NEE pattern for (a) 2005, (b) 2006, and (c) 2007. NEE is in mg CO2 m–2 s–1.
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Figure 2.5b. Early, middle, and late fall season diurnal NEE pattern for (a) 2005, (b) 2006, and (c) 2007. NEE is in mg CO2 m–2 s–1.
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Figure 2.8. Diurnal pattern of CO2 exchange (blue diamonds with a 2-hour running mean, blue line) and aboveground PAR (2-hour running mean, red line) on March 13, 2007. Long-dash black curve represents PAR with 75% attenuation.
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Chapter 3 Latitudinal and temporal patterns in carbon dioxide exchange in the Alaskan Arctic tundra ecosystem: A three-year investigation at three sites To be submitted to Ecology
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Latitudinal and temporal patterns in carbon dioxide exchange in the Alaskan Arctic tundra ecosystem: A three-year investigation at three sites
Cheryl A. Laskowski*, Douglas H. Deutschman, Walter C. Oechel San Diego State University, San Diego, CA 92182 *
Corresponding Author: phone: 01-619-594-6379; fax: 01-619-594-7831; e-mail:
[email protected]
Keywords: Annual; Atqasuk; Barrow; climate change; CO2; eddy covariance; Ivotuk; seasonal; trace gas flux; winter
Abstract Global climate change has the potential to release substantial amounts of permafrost-stored carbon in the northern high latitudes. Here we present three years of continuous eddy covariance data for three sites located along a latitudinal gradient in the Alaskan Arctic tundra. The results show that the Arctic landscape is highly heterogeneous over space and time, with northern, coastal regions being the only net sink of carbon annually. Further inland (Atqasuk) and to the southern boundary of the tundra ecosystem (Ivotuk), net annual source activity was measured, although interannual variability at all sites was large. Annual net carbon exchange rates varied from −108 g C m–2 yr–1 (Barrow) to +87 g C m–2 yr–1 (Ivotuk). Environmental controls on carbon exchange varied by site. Warm, dry summers resulted in greater uptake at the northern sites (Barrow and Atqasuk), while summer drought led to greater carbon efflux further inland (Ivotuk). During the transitional seasons between winter and summer, Barrow continued to show
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some net uptake; however, Atqasuk and Ivotuk were net sources of carbon. Winter was the only season that showed consistent carbon activity across sites and years, measuring net efflux, although Ivotuk showed more carbon activity than the northern sites were less so. These data suggest that continual, long-term data are needed to adequately characterize the Arctic tundra carbon budget, and that net carbon sink activity may only be in the most northern regions.
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Introduction Arctic ecosystems play a critical potential role in the global climate system because the carbon stored in the top 3 meters of soil in the northern permafrost region is 1.5 times that currently in the atmosphere in a cold-dominated system that is currently undergoing rapid warming (IPCC, 2007; Tarnocai et al., 2009). The historic role of the Arctic as a net sink of carbon has recently changed, at least in some regions, but the actual net carbon balance for the region is poorly understood. There is evidence of recent net carbon loss from tundra to the atmosphere, and this constitutes a positive feedback effect that will enhance warming scenario (Oechel et al., 1993; Welker et al., 1999; Joiner et al., 1999). Evidence from other Arctic regions indicates that the net efflux could have been transitory, and that tundra ecosystems are again net sinks of carbon dioxide (Corradi et al., 2005; Kutzbach et al., 2007). Non-summer periods have generally not been included in past studies of Arctic carbon exchange and these periods may account for a substantial amount of the annual carbon budget, perhaps overwhelming any sink activity during the growing season (Oechel et al., 1997; Nordstroem et al., 2001; Nobrega and Grogan, 2007). In addition, there is substantial evidence that the Arctic tundra does not act in unison spatially— vegetation composition and environmental factors vary significantly spatially, and may respond differently to predicted changes in temperature and moisture (McGuire et al., 2002; Kwon et al., 2006). Therefore, the Arctic tundra represents a complex landscape that is not easily characterized, yet is important regionally and globally for understanding carbon cycle dynamics and climate models. Here, we examine the patterns and controls on seasonal net ecosystem exchange (NEE) across the North Slope of Alaska, from Barrow to Ivotuk, over a continuous three-year period. The sites represent the major vegetation types in the Alaskan Arctic ecosystem, including the wet coastal plain in the North at Barrow, tussock tundra at the edge of
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the tundra-shrub ecosystem border to the South at Ivotuk, and sandy soil based moist tundra in between at Atqasuk.
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Materials and methods Study Sites This study was conducted at three unmanaged Arctic tundra sites across Alaska’s North Slope 2005-2007. The sites included Barrow (BRW), a wet sedge dominated landscape, characterized by polygons and located 2 km south of the Arctic Ocean; Atqasuk (ATQ), a moist tussock dominated site located 100 km inland from the Arctic Ocean; and Ivotuk (IVO), a typical tussock site at the foothills of the Brooks Mountain Range (Figure 3.1). All sites are underlain by permafrost. BRW is located 2 km south of the Arctic Ocean in Barrow, Alaska (71° 19’ 21.1” N, 156° 36’ 33.2” W, 7.2 m elevation). It is a coastal tundra site, dominated by moss species (Sphagnum spp.) and graminoids (Carex aquatilis, Eriophorum angustifolium) (Walker et al., 2003). Soils are fine-textured, acidic, contain high organic content, and are poorly drained (Kwon et al., 2006). ATQ is located 100 km south of BRW in a moist tussock area in Atqasuk, Alaska (70° 28’ 10.6” N, 157° 24’ 32.2” W, 24 m elevation). Vegetation cover is dominated by graminoids, including small, underdeveloped tussocks (E. vaginatum) and other vascular species (C. bigelowii, Vaccinium vitis-idaea, Ledum palustre), with scattered prostrate shrubs and mosses (Komarkova and Webber 1980). Soils are acidic and consist mainly of sand (Walker et al., 2003; Kwon et al., 2006). IVO is located approximately 300 km south of BRW in the foothills of the Brooks Mountain Range, in Ivotuk, Alaska (68° 29’ 13.2” N, 155° 44’ 52.8” W, 543.3 m elevation). This site is also dominated by E. vaginatum, but the tussocks are much larger at this site (30 cm, compared to 100 W m−2 day−1, and the subsequent seasons (summer and fall) are snow-free. Although summer is snow free, the onset of the season is distinguished from the rest of the season by a period of efflux, which is generally followed by net uptake as vegetation greening occurs. All sites showed this pattern except for ATQ and IVO
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in 2005. Both sites showed substantial net efflux throughout the summer season with only brief periods of net uptake. During 2006 and 2007, both sites showed net summer carbon uptake, and although the rate of carbon exchange varies considerably during the active summer season, the average rate of uptake at each site was similar across years. ATQ accumulated an average of 0.33 g C m−2 d−1 in 2006 and 0.34 g C m−2 d−1 in 2007. IVO accumulated carbon at a rate of three times that in ATQ, accumulating an average of 0.40 and 0.49 g C m−2 d−1 in 2006 and 2007, respectively. BRW had net carbon uptake for all observed summer seasons, although the rate varied threefold over the measurement period (0.38, 0.72, and 1.2 g C m−2 d−1 average uptake for 2005, 2006, and 2007). Fall (when net radiation < 100 W m−2 day−1) also showed site and year variability. At BRW, carbon continued to accumulate during this period, with 2005 showing considerable uptake (1.1 g C m−2 d−1 versus 0.14 and 0.68 g C m−2 d−1 in 2006 and 2007), but ATQ and IVO returned to net carbon sources, at average daily rates nearly double those measured in the previous spring.
Seasonal and annual CO2 exchange Winter at all sites for all years showed either near zero net carbon exchange or net efflux, and net efflux reached 40 g C m−2 season−1at IVO during 2007, although generally rates were much lower. During spring, ATQ and IVO consistently showed net efflux, with ATQ efflux rates much lower (3-5 g C m−2 season−1) than those seen in IVO (12−21 g C m−2 season−1). BRW was carbon neutral (2005) or showed low uptake (−5 and −4 g C m−2 season−1 in 2006 and 2007, respectively). Summer fluxes varied considerably both among and within sites. ATQ and IVO unexpectedly showed net efflux during summer 2005, and returned to net seasonal sinks the following two years. Net efflux rates (27 and 34 g C m−2 season−1) for ATQ and IVO were
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higher than the subsequent rates of uptake (−11 at ATQ and −29 g C m−2 season−1 at IVO for both summers). Carbon exchange rates at BRW were variable as well, but showed net uptake all three years (−25, −46, and −75 g C m−2 season−1 for 2005, 2006, and 2007, respectively). During fall, ATQ and IVO returned to net carbon sources, ranging from 5 to 18 g C m−2 season−1 at ATQ and 17 to 19 g C m−2 season−1 at IVO. BRW continued to show net uptake, and accumulated carbon at high rates during 2005 and 2007 (−57 and −36 g C m−2 season−1, respectively). While 2006 also showed net uptake, the rate was much lower than the other years (−7 g C m−2 season−1). CO2 exchange varied significantly spatially (ranging over 150 g Cm−2 yr−1 in one year among sites) and interannually (ranging nearly 90 g Cm−2 yr−1 at a single site over three years) (Figure 3.5), with annual carbon uptake occurring at the northern BRW site, and annual release or balance occurring at the more southern sites, ATQ and IVO. BRW showed cumulative carbon uptake of 241 g C m−2 for 2005-2007, an average of 80 g C m−2 yr−1. ATQ was a net carbon source of 91 g C m−2 over the study period, which is an annual release of 30 g C m−2 yr−1 to the atmosphere. IVO was the greatest source of carbon to the atmosphere among the sites, releasing 136 g C m−2 for the three years of study (2005-2007), averaging 45 g C m−2 yr−1.
Latent and sensible heat fluxes Patterns of latent (LE) and sensible heat (H) flux for all sites are shown in Figure 3.3. During winter, there is little net daily energy exchange, with LE near zero and H between −10 and 0 W m−2 at all sites. Turbulent exchange increases in spring and diurnal variations were seen in all sites for LE and H. The least variation was seen in IVO, where average diurnal fluxes in spring never exceeded ± 5 Wm−2 for LE or H. ATQ and BRW showed more pronounced variation in
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diurnal turbulent energy. Nighttime values of H and LE were approximately −5 and 0 W m−2, respectively, for both sites. Peak fluxes of H and LE were observed midday and varied from 15 to 30 W m−2 at ATQ and 5 to 20 W m−2 at BRW for H, while peak LE ranged from 5 to 15 at both sites across years. Summer showed the greatest turbulent fluxes at all sites, and H was generally greater than or equal to LE. In 2005, BRW received nearly 60 mm of rainfall and turbulent fluxes were equally divided between LE and H, both reaching midday peaks around 100 W m−2. The proportion of energy in H and LE changed in the following two years, and energy partitioned into H was over double that of LE in 2006 (peak flux 120 versus 54 W m−2) and nearly triple in 2007 (170 versus 60 W m−2). Rainfall was also lower in these 2006 and 2007, 45 mm and 1.2 mm, respectively. Summer turbulent energy partitioning did not follow rainfall patterns in ATQ as they did in BRW for summer. During 2005 and 2006, ATQ received over 60 mm of rainfall, while only 12 mm fell in 2007. However, 2005 and 2007 showed similar patterns of turbulent energy exchange, with midday H double LE. In 2006, fluxes were more equally divided. At IVO, LE and H followed similar patterns and averaged a midday peak of 100 W m−2 in 2005 and 2007, while in 2006 midday H was double LE and only reached 80 W m−2. Rainfall in 2006 was double that of 2005 and 2007. Finally, fall turbulent fluxes were followed a more predictable pattern, with H and LE being nearly equal in all years at all sites, reaching average midday peaks of 60 W m−2.
Controls on carbon exchange Winter and spring controls on NEE were examined by day for each site and year through linear regression models (Table 3.4). From 4 to 84% of the variability in NEE could be accounted for
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by radiation, temperature, or VPD, and generally regressions were more successful for spring NEE than winter, and for IVO than the BRW or ATQ. IVO also showed greater fluctuation of NEE during winter and spring. A surprising and unexplained result was the often significant negative relationship between air temperature and NEE during winter. The controls on carbon were more apparent during the snow-free seasons (summer and fall). Each of these seasons was divided into an “early” “middle” and “late” season to examine whether controls on NEE changed (i.e., from negative to positive) or shifted (i.e., in magnitude) over the course of the season. In addition, only radiation (PAR) and temperature (soil at 5 cm depth) were examined in the regressions. Radiation, temperature, and moisture are often the dominating controls of tundra systems, but in this study, rainfall was rarely a significant predictor and soil moisture content was not available for all sites. By limiting the regressions to two variables, further analysis on the evolution of these controls could be examined in more detail. Table 3.5 shows the regression coefficients for the summer and fall sub-season analyses. The two-variable regression model performed well across sites, especially during the middle of the snow-free seasons. The individual regressions produce a magnitude and direction (slope) for the parameters (radiation and soil temperature) by season, year, and site. The slopes can then be compared across all of the regressions to understand how the parameters change spatially and temporally. That is, if radiation influences NEE similarly in all sites and years, the radiation slope will be the same from all regressions. To evaluate how radiation and soil temperature affect NEE among sites, years, and seasons, the regression slopes (coefficients) were used in a regression analysis with year, season, and site as main-effects predictors.
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For radiation, all main-effects and interaction terms were significant except for Season x Site (Table 6, r2 = 76.1%). The main-effects predictors accounted for 51.6% of the variability. Site was the single best predictor, indicating site differences accounted for most of the variability of radiation on NEE (Figure 3.4a). Radiation had a strongly negative relationship effect on NEE at BRW, weaker negative effect on NEE at ATQ, and no significant effect on NEE at IVO when assessed over all seasons and years. Timing of the snow-free season was also an important predictor of how influential radiation was to NEE (Figure 3.4b). During the middle of the snowfree seasons, increased radiation led to greater carbon uptake across sites and years. The effect of PAR was not only variable among sites and within a year, but interannually as well. In 2005, increased radiation had much lower effect on NEE than in 2006 and 2007, indicating that radiation is not a constant predictor of NEE in either space or time. Interaction terms Site x Year and Season x Year were also significant, indicating that interannual variability differed by both location and time of season. Similarly, slopes of soil temperature from each regression were evaluated to determine the predictability of soil temperature on NEE. Unlike radiation, in which an increase could lead to greater carbon uptake or release, an increase of soil temperature correlated with an increase of respiration, or carbon release (Table 3.5). In addition, the effect of soil temperature was less spatially variable, and site differences in soil temperature effect on NEE were not significant. However, interannual differences were pronounced (Figure 3.4d), especially during 2007, when temperatures were higher than in the previous years. Significant differences were also observed at a finer temporal scale (Figure 3.4e), in which higher soil temperatures in middle and late summer led to increasing higher respiration than the other snow-free times.
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These results indicate that radiation and soil temperature significantly influence NEE during snow-free periods, but these relationships are not static over space or time.
Discussion Total annual carbon exchange varied widely in this study among sites, and had a general trend toward greater uptake in northern regions and annual efflux further south. The coastal site, BRW, showed consistent net annual uptake each year, with the greatest uptake occurring under conditions of higher temperature and slightly early snowmelt. The short growing season was highly productive, suggesting rapid response to favorable environmental conditions. BRW had potential for a secondary period of significant uptake late in the season. The effect of the environmental factors on NEE change in magnitude through the growing seasons and also direction. Except for IVO 2005, an increase of PAR generally correlated with an increase in CO2 uptake during the middle of the season. However, in early summer and late fall, PAR was often positively correlated with CO2 release at ATQ and IVO. This pattern did not occur in BRW, where despite a shorter snow-free season, the number of days of net CO2 uptake was greater. The relationship between soil temperature and NEE was much less variable in direction—an increase in soil temperature was usually associated with greater CO2 efflux. However, the magnitude of effect on NEE with a change in soil temperature did vary within years, among years, and among sites, suggesting rapidly changing response to environmental conditions (over weeks). Finally, the overall explanatory power of the two-variable linear model performed very well, especially in the summer and early fall seasons. Middle and late fall seasons are likely to have other factors becoming more important. Variables are less tightly coupled as the season progresses, and whereas “soil temperature” in the model during the early season could generally
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be replaced with “air temperature” with no difference to the model output, later in the season, the influence of air versus soil temperatures may become more important. The elongated snow-free period of ATQ and IVO did not result in a longer period of net carbon uptake, suggesting that under climate change, NEE may not be assumed to increase. Based on the environmental variables investigated, ATQ was the most predictable site in terms of carbon exchange, with annual cumulative exchange varying less than 30 g C m−2 year−1 over the three years. The site’s response to environmental parameters was also the most predictable over the course of the study. This may be due to lower diversity of vegetation at this site, allowing for more uniform response to conditions. IVO showed net uptake during 2005 that was likely due to extreme drought, but even in years of more abundant moisture, the site did not act as a net carbon sink. This may suggest that, although shrubs may take over the region at some time, current vegetation cannot respond to climate changes. This could result in a positive feedback effect, including increased thaw depth, but longer-term data are needed for this assessment. Others have noted the bias in total season or annual estimations based on noncontinuous measurement (Bjorkman et al., 2010). This study confirms that even in winter, environmental conditions and carbon exchange can vary widely, and therefore extrapolation from sporadic observations may lead to significant errors. The spatial variability in annual and seasonal NEE reported here are not captured in the current generation of global models used to simulate Arctic carbon flux. We show significant variation in not only NEE, but also in ecosystem response to key environmental variables that control NEE. Since the response of NEE to key environmental variables is nonlinear, averaging environmental conditions and ecosystem response to environmental factors is unlikely to produce reliable response surfaces. Global scale models will benefit from analysis and
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incorporation of processes and conditions at scales of
