Ninth International Conference on Permafrost ... - IARC Research

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Non-Summer CO 2Measurements Indicate Tundra Ecosystem Annual Net Source ofCarbon Double Net Summer SinkCheryl LaskowskiSan Diego State University, San Diego, CA, USAGeorge BurbaLiCor, Inc., Lincoln, NE, USAWalter OechelSan Diego State University, San Diego, CA, USAIntroductionThe Arctic tundra ecosystems are dominated by winterconditions, but data during the non-summer months is oftenlacking in Arctic annual carbon exchange estimates. Theextreme conditions (temperatures regularly below freezing,low light levels, and snow-covered frozen ground) are oftenless than ideal for biological processes, and it has been largelyassumed that little activity occurs under these conditions(McKane et al. 1997). While winter arctic conditionsmay not be ideal for high levels of photosynthesis andrespiration, biological activity still occurs under snow andin subzero temperatures (Romanovsky & Osterkamp 2000,Sturm et al. 2005). Although there is substantial evidenceshowing the importance of non-summer periods to Arcticcarbon metabolism, the difficulty of making ecosystemmeasurements under Arctic winter conditions means thatestimates of carbon flux rates across the tundra are typicallymade based solely on summer (June–August) carbon fluxdata (Kwon et al. 2006, Vourlitis & Oechel 1999).Annual estimates that ignore the non-summer periodare likely to be largely in error with respect to the annualcarbon balance (Oechel et al. 1997). Given that snowmeltis occurring earlier (Stone et al. 2002) and the growingseason has lengthened (Chapman & Walsh 1993, Keyseret al. 2000), the traditional 10- to 12-week field campaignno longer encompasses even the full snow-free period.Prior attempts to predict annual carbon budgets based onwinter field measurements have often lacked continuousmonitoring throughout the year, relying on a few data pointsto model seasonal carbon estimates (Zimov et al. 1996,Oechel et al. 1997). Attempts at continuous monitoring(i.e., through the eddy covariance method) have had limitedsuccess while highlighting some of the major limitations.The limitations that have posed the greatest hindrance tocontinuous monitoring in the past are now diminishing, andinclude instrument icing (minimized by heaters), instrumentoperating limits (that have been recently modified to operateat very low temperatures), and mismatch between CO 2fluxestimates and ecological/biological theory (for which thereis a new correction provided by Burba et al. 2006).MethodsDirect and continuous measures of mass (water vaporand CO 2), momentum, and energy exchange were measurednear the village of Atqasuk, Alaska, located 100 km south ofBarrow, Alaska, during 2006. Net ecosystem exchange wasmeasured using the open-path eddy covariance method andincluded an open-path infrared gas analyzer (IRGA, Li-7500,LiCor, Inc., Nebraska, USA) and ultrasonic anemometer(R3, Gill Instruments, Lymington, England).Eddy covariance data were calculated in half-hour dataseries using the EdiRe program (University of Edinburgh,Edinburgh, England), and applying standard corrections forsimultaneous latent and sensible heat measurements (Webbet al. 1980) and quality control techniques (AmeriFlux 2006).Data gaps were filled using techniques outlined in Falge etal. (2001) to represent seasonal/annual carbon exchangeestimates. In addition, the data are corrected for an apparentuptakesignal due to heating of the air mass in the absorptionpath of the IRGA. The correction is modified from Burba etal. 2006, to account for non-vertical sensor mounting angle.ResultsWeather conditions during 2006 were typical of historicclimate averages for Atqasuk, Alaska, with July as thewarmest month of the year and March, the coldest month(air temperatures of 8.2°C and -29.8°C, respectively). Eddycovariance data availability varied greatly, from a low of14% in February to a high of 80% in June. Average summer(June–August) data recovery was 71%, and data wereprimarily rejected due to precipitation events. Average nonsummer(September–May) data recovery was 36%, and dataloss was mainly due to icing events and conditions outsideof the instruments’ operating specifications.Summer carbon flux data showed a distinct diurnal patterncharacterized by a midday CO 2drawdown and slight midnightrelease, with the pattern being strongly temperatureandlight-dependent. Non-summer diurnal patterns werenot evident, due to the lack of intense solar radiation duringmuch of the season.Cumulative carbon exchange during the summer monthsresulted in a net uptake of 21.5 g C m -2 , whereas thecumulative non-summer carbon exchange showed a net lossof 53.5 g C m -2 . This resulted in an annual carbon release of31.8 g C m -2 to the atmosphere (Fig. 1).DiscussionThe results show that non-summer season carbon exchangeis not only significant, it can be of greater cumulativemagnitude than summer carbon exchange—in this case,165
Ni n t h In t e r n at i o n a l Co n f e r e n c e o n Pe r m a f r o s tg C m-240.0030.0020.0010.000.00-10.00-20.000 50 100 150 200 250 300 350Day of YearFigure 1. Cumulative carbon exchange for Atqasuk, Alaska, 2006,in g C m -2 . Line from day of year 0–365 represents the cumulativeannual carbon exchange, while the line from day of year 152–243(June–August) represents the summer-only cumulatie carbonexchange.nearly 2.5 times the summer estimate. This also emphasizesthe importance of year-round estimates in determining sourceversus sink activity. Capturing only summer-season carbonexchange would result in a net uptake of carbon rather thana net release. These results indicate that not only is wintercarbon emission for tundra ecosystems important in annualcarbon estimates, it dominates the annual signal, shifting thenet carbon exchange from a sink to a source.Disproportionately more data were rejected or lost in nonsummermonths, primarily due to instrument icing. Thisresults in higher uncertainty of non-summer data comparedto summer data. Reducing data loss by using heaters orincreased site visits may lessen data loss. In addition, thecorrection factor (Burba et al. 2006) used in this study mustbe further tested to verify the accuracy of the correctionparameters. These features are currently being implementedat this and other sites. However, these data are, to ourknowledge, the first near-continuous carbon exchange datafor Alaskan Arctic tundra, and provide evidence that notonly is the non-summer season important, it may in fact bedominant over summer season net carbon exchange.Falge, E. et al. 2001. Gap filling strategies for long termenergy flux data sets. Agricultural and ForestMeteorology 107(1): 71-77.Kappen, L. 1993. Plant activity under snow and ice, withparticular reference to lichens. Arctic 46(4): 297-302.Kwon, H.J. et al. 2006. Effects of climate variability oncarbon sequestration among adjacent wet sedgetundra and moist tussock tundra ecosystems. Journalof Geophysical Research-Biogeosciences 111(G3).McKane, R.B. et al. 1997. Climatic effects on tundra carbonstorage inferred from experimental data and a model.Ecology 78(4): 1170-1187.Oechel, W.C. et al. 1997. Cold season CO 2emission fromarctic soils. Global Biogeochemical Cycles 11(2):163-172.Romanovsky, V.E. & Osterkamp, T.E. 2000. Effects ofunfrozen water on heat and mass transport processesin the active layer and permafrost. Permafrost andPeriglacial Processes 11(3): 219-239.Stone, R.S. et al. 2002. Earlier spring snowmelt in northernAlaska as an indicator of climate change. Journal ofGeophysical Research-Atmospheres 107(D10).Sturm, M. et al. 2005. Winter biological processes couldhelp convert arctic tundra to shrubland. Bioscience55(1): 17-26.Vourlitis, G.L. & Oechel, W.C. 1999. Eddy covariancemeasurements of CO 2and energy fluxes of an Alaskantussock tundra ecosystem. Ecology 80(2): 686-701.Webb, E.K. et al. 1980. Correction of flux measurements fordensity effects due to heat and water-vapor transfer.Quarterly Journal of the Royal MeteorologicalSociety 106(447): 85-100.Zimov, S.A. et al. 1996. Siberian CO 2efflux in winter as aCO 2source and cause of seasonality in atmosphericCO 2. Climatic Change 33(1): 111-120.AcknowledgmentsWe would like thank D. Whiteman, R. Bryan, S. Delapena,N. Panzarini, A. Sharma, BASC, VPR, NSB, and NSF’sStudy of the Northern Alaskan Coastal System’s researchgrant number 046177.ReferencesBurba, G.G., Anderson, D.J., Xu, L. & McDermitt, D.K.2006. Additional term in the Webb-Pearman-Leuningcorrection due to surface heating from an open-pathgas analyzer. Eos Trans. AGU 87(52), Fall Meet.Suppl., C12A-03.Chapman, W.L. & Walsh, J.E. 1993. Recent variations of seaice and air-temperature in high-latitudes. Bulletin ofthe American Meteorological Society 74(1): 33-47.166
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ContentsPreface ...................
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Mapping and Modeling the Distributi
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Satellite Observations of Frozen Gr
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xiiIce Wedge Thermal Regime in Nort
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xivPermafrost Response to Dynamics
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NICOP SponsorsUniversitiesUniversit
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Deep Permafrost Studies at the Lupi
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Effect of Fire on Pond Dynamics in
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Cryological Status of Russian Soils
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Acoustical Surveys of Methane Plume
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Permafrost Delineation Near Fairban
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Preparatory Work for a Permanent Ge
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A Provisional Soil Map of the Trans
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Martian Permafrost Depths from Orbi
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Time Series Analyses of Active Micr
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Impact of Permafrost Degradation on
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DC Resistivity Soundings Across a P
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Modeling Thermal and Moisture Regim
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A Provisional Permafrost Map of the
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Alpine Permafrost Distribution at M
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Cryogenic Formations of the Caucasu
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Modeling Potential Climatic Change
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A Hypothesis: A Condition of Growth
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Modeled Continual Surface Water Sto
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Freeze/Thaw Properties of Tundra So
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Discontinuous Permafrost Distributi
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Thermal Regime Within an Arctic Was
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Seasonal and Interannual Variabilit
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Twelve-Year Thaw Progression Data f
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Continued Permafrost Warming in Nor
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Landsliding Following Forest Fire o
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A Permafrost Model Incorporating Dy
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Seasonal Sources of Soil Respiratio
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Greenland Permafrost Temperature Si
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The Importance of Snow Cover Evolut
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The Account of Long-Term Air Temper
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The Combined Isotopic Analysis of L
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Adaptating and Managing Nunavik’s
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Human Experience of Cryospheric Cha
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HiRISE Observations of Fractured Mo
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A Soil Freeze-Thaw Model Through th
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Mapping and Modeling the Distributi
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First Results of Ground Surface Tem
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Historical Changes in the Seasonall
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Rock Glaciers in the Kåfjord Area,
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Snowpack Evolution on Permafrost, N
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Climate Change in Permafrost Region
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Maximizing Construction Season in a
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Pleistocene Sand-Wedge, Composite-W
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