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Spatial and seasonal variations of CO2 flux, and photosynthetic and respiratory
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parameters of larch forests in East Asia
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Kentaro TAKAGI1 ([email protected]), Ryuichi HIRATA2
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([email protected]), Reiko IDE2 ([email protected]), Masahito UEYAMA3
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([email protected]), Kazuhito ICHII4 ([email protected]), Nobuko
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SAIGUSA2 ([email protected]), Takashi HIRANO5 ([email protected]),
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Jun ASANUMA6 ([email protected]), Sheng-Gong LI7 ([email protected]),
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Takashi MACHIMURA8 ([email protected]), Yuichiro NAKAI9
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([email protected]), Takeshi OHTA10 ([email protected]), Yoshiyuki
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TAKAHASHI2 ([email protected])
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University, Horonobe 098-2943, Japan
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Teshio Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido
Center for Global Environmental Research, National Institute for Environmental Studies,
Tsukuba 305-8506, Japan
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Graduate School of Life and Environmental Sciences, Osaka Prefecture University,
Sakai 599-8531, Japan
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Department of Environmental Geochemical Cycle Research, Japan Agency for
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Marine-Earth Science and Technology, Yokohama 236-0001, Japan
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Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan
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Terrestrial Environment Research Center, University of Tsukuba, Tsukuba 305-8577,
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Japan
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Key Laboratory of Ecosystem Network Observation and Modeling, Institute of
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Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
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Beijing 100101, China
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Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka
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565-0871, Japan
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Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687,
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Japan
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Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601,
Japan
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Corresponding author: Kentaro Takagi, Teshio Experimental Forest, Field Science
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Center for Northern Biosphere, Hokkaido University, Toikanbetsu, Horonobe 098-2943,
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Japan
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Phone: +81-1632-6-5211
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Fax: +81-1632-6-5003
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E-mail: [email protected]
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Type of contribution: Full-length paper
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Running title: Ecophysiological parameters of larch forests
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Abstract
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Larch forests are predominantly distributed across high latitudes of Eurasia. They
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potentially have a strong influence on the terrestrial carbon and energy cycles, because of
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their vast area and large carbon stocks in their peat soils in the permafrost. In this study,
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we elucidated intersite variation of ecosystem photosynthetic and respiratory parameters
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of eight larch forests in East Asia using the CarboEastAsia carbon flux and
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micrometeorology dataset. These parameters were determined by using the empirical
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relationship between the carbon fluxes (photosynthesis and respiration) and
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micrometeorological variables (light and temperature). In addition, we examined leaf
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area index (LAI) determined by Moderate Resolution Imaging Spectroradiometer
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(MODIS) remote sensing data to explain the intersite variation. Linear or exponential
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relationships with annual mean temperature or seasonal maximum LAI at the study sites
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were found for the annual carbon fluxes (gross primary production [GPP] and total
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ecosystem respiration [RE]) as well as for four of the five seasonal maximum values of
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determined photosynthetic and respiratory parameters (maximum GPP at light saturation,
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initial slope of the light-response curve, daytime respiration, and RE at the reference
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temperature of 10ºC). Phenological indices, such as start day of the growing season,
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growing season length, and growing season degree days explained much of the intersite
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variation of GPP and RE of the studied larch forests, however the relationship between
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MODIS LAI and photosynthetic or respiratory parameters implies that the intersite
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variation in GPP and RE was caused not only by the temperature variation (abiotic factor),
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but also by the variation in the photosynthetic and respiration activity by vegetation
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(biotic factor) through the change in leaf (or whole vegetation) biomass. Our analysis
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shows that MODIS LAI serves as a good index to explain the variation of the ecosystem
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photosynthetic and respiratory characteristics of East Asian larch forests.
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Key words: Larix, leaf area index, MODIS, photosynthesis, respiration
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1. Introduction
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Larch forests are characterized by their deciduous habit, a trait that allows them to endure
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the extremely cold and dry winters across high latitudes of Eurasia, including the Siberian
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taiga (Gower and Richards 1990). These forests are considered to have a strong influence
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on the terrestrial carbon and energy cycles, because of their vast area and potentially large
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carbon stocks in their peat soils in the permafrost (Schulze et al. 1999; Dolman et al.
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2004; Ueyama et al. 2010). Siberian forests constitute 20% of the world’s forested area
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(Dolman et al. 2004), and larch forests cover 37% (Abaimov et al. 1998), 70% (Gunin et
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al. 1999), and 13.6% (Jiang and Zhou 2002) of forested areas in Russia, Mongolia, and
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China, respectively. Larch forests on the permafrost region are considered to be a
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vulnerable ecosystem, because of the rapid warming during the past few decades due to
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climate change (Dolman et al. 2008) and irreversible degradation by permafrost melting
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(Demek 1994). Larix species also have been planted intensively throughout northern
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Japan and China, because of their high cold tolerance and timber productivity (Hirata et al.
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2007). For these reasons, it is important to gain a comprehensive understanding of the
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carbon and energy exchange characteristics of larch forests (Li et al. 2005; Machimura et
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al. 2005; Wang et al. 2005b; Hirata et al. 2007; Nakai et al. 2008; Ohta et al. 2008).
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In this synthesis study, we aimed to elucidate the spatial variation of the
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ecosystem photosynthetic and respiratory characteristics of larch forests in East Asia
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using the CarboEastAsia carbon flux dataset (Saigusa et al. 2013) and additional data
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obtained at two other larch forest sites (Neleger, Russia, and Fuji Hokuroku Flux
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Observation Site, Japan). Several previous studies were able to explain the spatial
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variation of carbon fluxes such as gross primary production (GPP), total ecosystem
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respiration (RE), and net ecosystem CO2 exchange (NEE) in East Asian Forests by using
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temperature and precipitation as the explanatory variables (Hirata et al. 2008; Chen et al.
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2013; Saigusa et al. 2013). However, because GPP, RE, and NEE are the products of
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abiotic (environment) and biotic (vegetation) factors, in this study we evaluated the biotic
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characteristics by inversely estimating photosynthetic and respiratory parameters based
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on carbon flux and micrometeorology datasets, as well as explain their intersite variation
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of GPP, RE, and NEE. Several studies have succeeded in relating the spatial variation in
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photosynthetic and respiratory parameters to environmental and plant functional indices
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and applying that relationship to develop semi-empirical models to estimate terrestrial
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carbon cycles at large scale from micrometeorology data (Saito et al. 2009; Groenendijk
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et al. 2011a, 2011b) or the vegetation indices (Ide et al. 2010). Our study uses similar
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approaches and is the first step to develop a semi-empirical model to estimate carbon
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cycles in larch forests in East Asia. We used Moderate Resolution Imaging
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Spectroradiometer (MODIS) LAI product (MOD15A2 Collection 5; Myneni et al. 2002)
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for the vegetation index to explain the intersite variations of carbon fluxes and their
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photosynthetic and respiratory parameters, because it can be a universal index reflecting
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the vegetation structure and the magnitude of the stand biomass, and can be obtained at
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every larch forest with short interval period.
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2. Materials and Methods
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2.1 Site description
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CO2 flux and micrometeorology data obtained at eight larch forest sites distributed over
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East Asia were used for the analysis (Fig. 1; Table 1). Three Russian sites (TUR, NLG,
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YLF) and SKT in Mongolia are naturally regenerated forests, whereas three Japanese
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sites (TSE, TMK, FHK) and LSH in northeastern China are artificial plantations. These
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sites belong to the AsiaFlux network (Mizoguchi et al. 2009) and cover a broad range of
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climatic values, with total annual precipitation ranging from 249 mm (NLG) to 1800 mm
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(FHK) and annual mean air temperatures from –9.4 ºC (NLG) to 9.2ºC (FHK). Sites TUR,
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NLG, and YLF are characterized by a severe continental boreal climate with cold winters
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and short, warm, dry summers, and the permafrost soil has an active layer depth of about
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1–1.2 m. Site SKT is characterized by a sub-boreal continental climate with cold winters
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and hot and dry summers. A temperate monsoon climate dominates the LSH site, which
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has much more precipitation during the summer than that of SKT. Sites TSE, TMK, and
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FHK are characterized by a cool-temperate climate with a warm summer and snow cover
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in winter, although TSE has much more snow accumulation and colder winters than the
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other two sites.
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TUR is a uniformly aged (105 yr) Larix gmelinii forest located in Tura in the
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Evenkia Autonomous District in central Siberia (Nakai et al. 2008). The stand density of
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living L. gmelinii is 5500 trees ha–1, and the mean height is 3.4 m. The ground surface is
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covered densely with lichens and mosses, and the understory is comprised of woody
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shrubs of Betula nana, Ledum palustre, Vaccinium uliginosum, and Vaccinium
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vitis-idaea with height <1.5 m. The soil type is Cryosol, and the permafrost table is within
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1 m depth.
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YLF is a Larix cajanderi forest located approximately 20 km north of Yakutsk in
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eastern Siberia (Ohta et al. 2001, 2008), where there is continuous permafrost and the
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active layer is approximately 1.2 m deep. The larch trees have a stand density of 840 trees
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ha–1 and a mean stand height of 18 m. Betula platyphylla dominates the understory, and
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the forest floor is fully covered by V. vitis-idaea.
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NLG is a L. cajanderi forest located about 25 km northwest of Yakutsk
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(Machimura et al. 2005, 2008). The forest is on continuous permafrost, with an active
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layer depth of approximately 1.0 m. The average and maximum tree heights are 8.6 and
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21 m, respectively, and the density is 2100 trees ha−1. The forest floor is covered with
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mosses and shrubs including V. vitis-idaea, V. uliginosum, and Pyrola incarnata. Soil
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texture is loamy, and soil organic carbon content in the top 1-m layer was 13.2 kg C m−2
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(Sawamoto et al. 2003).
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SKT is a Larix sibirica forest, mixed with scattered or patchy B. platyphylla trees
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in some places. It is on a southwest-facing gently sloping hill of the Khentii Mountains
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located about 25 km northeast of the Mongonmorit village in the Tov province of
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Mongolia (Li et al. 2005). The mean stand height and density are 20 m and 1120 trees ha−1,
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respectively. The dense understory forms a distinct layer of grasses and scattered shrubs.
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The grasses are dominated by Carex spp., Koeleria spp., and Chamaenerion
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angustifolium, and the dominant shrub is Potentilla fruticosa. The forest experienced a
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large-scale fire in 1996–1997, and approximately 37.5% of the trees have fire scars on the
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trunks. In addition, there were tree stumps of 570 ha−1 left after timber harvesting during
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the last three decades. As a result of these disturbances, the age structure of the forest
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spanned from 70 to >150 yr old, with the oldest trees >300 yr. The soil is a seasonal
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Cryosol with a coarse texture that is highly leached and gray in color.
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LSH is a L. gmelinii plantation in northeastern China (Wang et al. 2005a, 2005b).
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The forest was established in 1969 on a complex terrain, and some broadleaf species (e.g.,
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B. platyphylla and Fraxinus mandshurica) are sparsely distributed throughout the canopy.
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The mean canopy height is about 17 m. Shrub species such as Ulmus propinqua, Corylus
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heterophylla, and Lonicera ruprechtiana grow in the forest. The soil is classified as a
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typical dark brown forest soil.
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TSE is a young hybrid larch (L. gmelinii × Larix kaempferi) plantation located in
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northernmost Hokkaido, in northern Japan (Takagi et al. 2009). After clear-cutting of
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trees covering an area of 13.7 ha and strip-cutting the former dense undergrowth of Sasa
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senanensis and Sasa kurilensis into alternating 4-m-wide cut and uncut rows in the
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clear-cut area in 2003, 2-yr-old hybrid larch was planted in the strip-cut rows at a density
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of 2500 saplings ha–1. In 2011, the mean heights of larch and Sasa were 3.5 and 1.5 m,
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respectively, and the Leaf are index (LAI) values were 1.7 and 6.7 m2 m−2 at the seasonal
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maximum, showing the minor contribution of larch to the whole forest leaf area. The soil
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is a Gleyic Cambisol, and its surface organic horizon is about 10 cm thick.
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TMK is a Japanese larch (L. kaempferi) plantation located 15 km northwest of
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Tomakomai, Hokkaido, Japan (Hirano et al. 2003; Hirata et al. 2007). Trees were about
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45 yr old at the time of the measurement. The forest includes scattered deciduous
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broadleaf trees (Betula ermanii, B. platyphylla, and Ulmus japonica) and spruce (Picea
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jezoensis), with dense understory of ferns (Dryopteris crassirhizoma and Dryopteris
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austriaca). In 1999, the canopy height was about 15 m and the stand densities were 673,
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459, 18, and 1150 trees ha−1, respectively, for larch, broadleaf trees, spruce, and all
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species. The soil is a volcanogenic Regosol with 1- to 2-cm thick fresh litter and a 5- to
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10-cm thick decomposed organic layer.
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FHK is another Japanese larch (L. kaempferi) plantation located at the northern
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foot of Mt. Fuji in central Japan (Ueyama et al. 2012). The stand age was 45–50 yr old at
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the time of the measurement. The forest includes scattered coniferous trees (Pinus
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densiflora and Abies homolepis) with an understory of shrubs (Prunus incisa). The
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canopy height was about 22 m, and the stand density was 433 trees ha−1.
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2.2 Calculation of net ecosystem CO2 exchange
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We used 15 sites × years data of 30-min NEE for this study (Table 2). NEE was calculated
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as the sum of the eddy CO2 flux (Fc) and the CO2 storage change in the air column below
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the flux measurement height (Fs), although Fc was measured directly as NEE at YLF and
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TSE because of the limitation of the instruments and short vegetation after clear-cutting,
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respectively. Fc was measured using the eddy covariance technique with 3D-sonic
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anemometer-thermometers and open-path (TUR, NLG, YLF, SKT) or closed-path (LSH,
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TSE, TMK, FHK) infrared gas analyzers. Measurement systems and calculation
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protocols were based on EUROFLUX methodology (Aubinet et al. 2000). The CO2
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storage change was evaluated using the vertical profile of the atmospheric CO2
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concentration (NLG, TMK, FHK) or a single height measurement of CO2 concentration
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at the Fc measurement height (TUR, SKT, LSH). The instruments and features for the
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calculation are listed in Table 2. Because of the severe winter climate, the flux was
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measured only from day of the year (DOY) 156 to 255 at TUR; from DOY 97 to 283 at
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YLF; and from DOY 104 to 282 (in 2003), DOY 122 to 274 (in 2004), and DOY 168 to
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270 (in 2005) at NLG.
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2.3 Quality control, gap filling of net ecosystem CO2 exchange, and flux partitioning
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Quality control procedures were applied by each principal investigator of the site; these
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included the removal of spikes, performing tests on 10-Hz data (Vickers and Mahrt 1997)
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and instationarity ratio and integral turbulence characteristics tests (Foken and Wichura
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1996), as well as footprint analysis (Kormann and Meixner 2001). Features of the quality
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control procedures are shown in Table 2.
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Friction velocity (u*) filtering was applied to the quality-assured nighttime NEE
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data. The u* threshold was determined for each site and year according to the procedures
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proposed by Papale et al. (2006), but the highest values among years (Table 2) were used
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throughout the entire study period for sites with multiyear data.
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Gaps in the remaining NEE data after u* filtration were filled, and the GPP and RE
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were determined by using an online server
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(http://www.bgc-jena.mpg.de/bgc-mdi/html/eddyproc/) for gap-filling and
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flux-partitioning calculation (Reichstein et al. 2005; Papale et al. 2006; Takagi et al.
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2007), which is the basic procedure of FLUXNET synthesis activity. The FLUXNET
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gap-filling strategy is a combination of several filling methods proposed by Falge et al.
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(2001), which primarily adopts lookup tables, where bin-averaged NEE for similar air
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temperature, vapor pressure deficit, and solar radiation ranges is calculated within a
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time-window of ± 7 days (or 14 days, if no similar meteorological conditions are present
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within the 7 days window). If none of these meteorological data are present, missing NEE
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value is replaced by the average value at the same time of the day. Refer Appendix A in
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Reichstein et al. (2005) for more detail. Nighttime NEE can be assumed to be equivalent
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to RE, thus for the determination of RE, 30-min nighttime NEE (NEEnight) within a
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15-day moving window (10-day overlap) was related to the air temperature using the
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Lloyd and Taylor (1994) equation as,
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E
NEE night  R10 exp  a
R
 1
1 



 Tr  T0 Ta  T0 
(1)
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where R10 is RE (μmol m−2 s−1) at the reference temperature (Tr: 283.15 K), Ea is the
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apparent temperature sensitivity (J mol−1), R is the universal gas constant (8.314 J mol−1
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K−1), Ta is the air temperature (K), and T0 is 227.13 K. The coefficient R10 was determined
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every 5 days as the time variable, whereas Ea was determined as a constant for each year,
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and daytime RE was estimated every 30 min using this equation and air temperature at
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that time. GPP was calculated as RE − NEE. GPP and RE were calculated every 30 min,
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and the daily or annual sum was used for the following analyses. Owing to the limited
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measurement period under severe winter climate at the three Russian sites, gap-filled
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NEE, GPP, and RE were determined for DOY 97 to 257 at TUR; DOY 45 to 333 at YLF;
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and DOY 45 to 341 (in 2003), DOY 63 to 333 (in 2004), and DOY 173 to 329 (in 2005) at
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NLG. These were then used as annual values. This treatment would decrease the annual
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sum of RE, thus increase the annual NEE, although the magnitude would not be
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significant because of the small respiration rate during the severe winter.
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2.4 Determination of ecosystem photosynthetic and respiratory parameters
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Photosynthetic parameters, the maximum GPP at light saturation (Amax: μmol m−2 s−1),
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initial slope of the light-response curve (: mol mol−1), and daytime respiration (Rd: μmol
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m−2 s−1) as the y-intercept of the light-response curve (equation 2), were determined every
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day by the least-squares method using daytime (Q  10 µmol m–2 s–1) 30-min NEE
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(quality controlled but not gap filled) and Q data within a 15-day moving window:
NEE daytime 
  Q  Amax 
 Q  Amax 2  4  Q  Amax
2
 Rd
(2)
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where Q is the photosynthetic photon flux density (μmol m−2 s−1) and θ is the convexity of
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the light-response curve. We used a fixed value (0.9) for θ to apply the least-squares
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method.
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We used the same equation (1) for the determination of respiratory parameters, the
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apparent temperature sensitivity (Ea) and RE at the reference temperature of 10ºC (R10),
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but were determined every day by the least-squares method using nighttime (Q < 10 µmol
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m–2 s–1) 30-min NEE (quality controlled with u* filtration but not gap filled) and Ta data
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within a 29-day moving window.
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2.5 LAI data and phenological parameters
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Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MOD15A2
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Collection 5; Myneni et al. 2002) was used as LAI in each site to explain the intersite
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variations of carbon fluxes and their photosynthetic and respiratory parameters, because
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MODIS LAI includes the effect of understory leaves when it is estimated based on the
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radiative transfer theory (Myneni et al. 2002) and the methods of on-site LAI observation
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were site specific; some sites used plant area index including the shades by the stems,
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branches, and culms in addition to the leaves of the canopy. We used MODIS 1-km
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resolution subset data sets (collection 5; http://daac.ornl.gov/MODIS/), each of which
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consisted of 7-by-7 km regions centered on the flux towers. At each time step, we
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averaged the MODIS observations by only using high-quality pixels (with the mandatory
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quality assurance – QA – flag being good in the QA data) based on the method of Yang et
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al. (2007) and Ichii et al. (2010), and missing data were replaced by a long-term average
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calculated using high-quality pixels. The original eight days composite products were
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converted to monthly averages during study period for each site, although LAI for TSE is
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that only in 2010.
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We also determined the following phenological indices for each site; start (SG) and
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end (EG) day of the growing season, growing season length (GSL), growing season
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degree days (GSD), and degree days until SG (PGD). SG and EG was determined as the
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first and last day when GPP was continuously more than 1 g C m–2 d–1. GSL was
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determined as the day length between SG and EG. GSD and PGD were determined as the
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cumulative daily average air temperature above 5 ºC during GSL and until SG,
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respectively.
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3. Results and Discussion
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3.1 Seasonal and intersite variation of carbon fluxes and MODIS LAI
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Seasonal variation of GPP, RE, and NEE for eight larch forests are shown in Fig. 2. Here,
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we showed the max-min ranges of the interannual variation as shades to show the
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possible uncertainty caused by the different year’s observation. Photosynthesis began to
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increase in late May at the three Siberian sites (TUR, NLG, YLF) and the Mongolian site
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(SKT), whereas the increase began in late April at the other sites (Fig. 2). GPP reached its
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seasonal maximum during early to mid July at TUR, NLG, YLF, and SKT, whereas this
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occurred during early to mid June at LSH, TMK, and FHK. Thus, the start and peak of
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GPP at the Chinese and Japanese (except TSE) sites were about a month earlier than at the
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Siberian and Mongolian sites. TSE had a unique seasonal variation in GPP, which
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showed a gradual increase from May to July and a peak from July to August. This site was
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covered with dense Sasa dwarf bamboo and the contribution of larch to ecosystem leaf
300
area was minor. Thus, the GPP seems to be strongly affected by the seasonal variation of
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Sasa, which developed new leaves from July to August and the LAI increased 1–3
302
months after that of the trees (Fukuzawa et al. 2007). On the other hand, RE was
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maximized from late July to early August at all study sites owing to the seasonal
304
maximum temperature during that period. As a result, a high sequestration rate (minimum
305
NEE) was observed in June at most of the study sites (Fig. 2).
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MODIS LAI reached its seasonal maximum at similar period with that of GPP
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except LSH, TSE and FHK (Fig. 3). This discrepancy for the three sites may partly be
308
owing to the different spatial representativeness between MODIS (up to 7 km) and flux
309
observation (up to several hundred meters to kilometers).
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Seasonal maximum daily GPP and RE ranged from 4.3 g C m−2 d−1 (TUR) to 20.0
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g C m−2 d−1 (TMK) and from 3.1 g C m−2 d−1 (TUR) to 14.3 g C m−2 d−1 (LSH),
312
respectively (Table 3). Seasonal minimum daily NEE (maximum CO2 absorption) ranged
313
from −11.3 g C m−2 d−1 (TMK) to −2.1 g C m−2 d−1 (TUR). Annual GPP, RE, and NEE
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ranged from 234 g C m−2 yr−1 (TUR) to 1827 g C m−2 yr−1 (TMK), 145 g C m−2 yr−1
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(TUR) to 1564 g C m−2 yr−1 (LSH), and −342 g C m−2 yr−1 (FHK) to 95 g C m−2 yr−1
316
(LSH), respectively.
317
GPP and RE increased exponentially with the increase in annual mean air
318
temperature (r2=0.87, p<0.001 for GPP and r2=0.84, p<0.01 for RE) and linearly with the
319
increase in seasonal maximum MODIS LAI of the site (r2=0.72, p<0.01 for GPP and
320
r2=0.77, p<0.01 for RE) (Fig. 4). Except at the two larch plantations (LSH and TSE), net
321
CO2 absorption rate (−NEE) tended to increase with the rise in annual mean air
322
temperature or seasonal maximum LAI. Linear relationships were also observed between
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annual carbon fluxes (g C m-2 yr-1) and annual precipitation (Pr; mm yr-1) among study
324
sites (GPP=0.90×Pr +310, RE=0.86×Pr +197, NEE=−0.11×Pr −141 excluding the two
325
plantations (LSH and TSE)), however the coefficients of the determination (r2=0.67,
326
p<0.05 for GPP, r2=0.61, p<0.05 for RE, r2=0.69, p<0.05 for NEE) were smaller than
327
those for annual mean air temperature. There was no clear relationship (r2<0.12) between
19
328
annual carbon fluxes and cumulative photosynthetic photon flux density (mol m-2) during
329
the growing season (DOY153−257). Because we also found strong linear relationships of
330
annual GPP and RE to SG (r2=0.94, p<0.001 for GPP and r2=0.99, p<0.001 for RE), GSL
331
(r2=0.89, p<0.001 for GPP and r2=0.92, p<0.001 for RE), and GSD (r2=0.87, p<0.001 for
332
GPP and r2=0.93, p<0.001 for RE) (Fig. 5), the GPP and RE were highly restricted by the
333
length and temperature of the growing season, and the apparent relationships to mean
334
annual temperature was likely observed. PGD, cumulative daily average air temperature
335
above 5 ºC until SG, was not constant through the study sites and tended to increase with
336
the delay in SG (Fig. 6). This implies that the larch forest requires more heat
337
accumulation to start photosynthesis at colder region, and this may be owing to the
338
permafrost.
339
Ueyama et al. (2013) also reported that the MODIS LAI is the best vegetation
340
index to explain the spatial variation of GPP in Arctic and boreal ecosystems in Alaska,
341
and they suggested that the inclusion of the understory leaves in the MODIS LAI
342
estimation was the probable reason for the better performance to explain the intersite
343
variation of photosynthetic capacity in Alaska, where the understory vegetation plays an
344
important role in the total ecosystem photosynthesis, than other vegetation indices such as
345
the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) ,
20
346
which are corrected for the canopy background signal. Transfer functions from NDVI and
347
EVI to LAI were different in different vegetation types (Street et al. 2007) and this would
348
be another possible reason. However, we need to test the applicability for other
349
ecosystems to confirm the universal applicability.
350
Hirata et al. (2008) and Chen et al. (2013) reported linear increase in GPP and
351
exponential increases in RE, with the increase in the annual mean air temperature of the
352
studied forests across East Asia. However, we obtained better correlation coefficients by
353
exponential regression of both GPP and RE to the annual mean air temperature, similar to
354
those reported by Saigusa et al. (2013), within the limited forest type and temperature
355
range (from −9.4ºC (NLG) to 9.2ºC (FHK)).
356
Compared with the GPP and RE, it was difficult to find an apparent relationship
357
between annual NEE and temperature or LAI (Figs 4 and 5), because of the two outliers
358
(LSH and TSE). The TSE site was a 7-yr-old larch plantation after clear-cutting in 2003,
359
when data were acquired, and former Sasa undergrowth still strongly affected the carbon
360
fluxes. The LSH site is also a relatively young plantation (35 yr old) compared with other
361
study sites (>45 yr old at TMK), and the carbon fluxes have possible uncertainty caused
362
by the complex terrain. Although the annual GPP and RE increased with the stand age at
363
the first stage of the stand development (< 50 years old), these tended to decrease with the
21
364
stand age (Fig. 7), therefore, the disturbance history may partly affect the relationship of
365
NEE to temperature or LAI, as suggested by process-based ecosystem model studies
366
(Ueyama et al. 2010; Ichii et al. 2013). However, the effects of other environmental
367
factors are included in this apparent relationship and it is difficult to distinguish these
368
effects in our bulk analyses, thus the age or management effect must be clarified by the
369
comparison among different-aged forest stands under similar environments in future
370
studies.
371
372
3.2 Ecosystem photosynthetic and respiratory parameters
373
Seasonal variation of daily Amax, , Rd, R10, and Ea for eight larch forests are shown in Fig.
374
8. Again, we showed the max-min range of the interannual variation to show the possible
375
uncertainty caused by the different year’s observation. The two photosynthetic
376
parameters (Amax and ) showed peaks from late June to early July and gradually
377
decreased thereafter at all the sites except the young plantation dominated by Sasa (TSE).
378
Seasonal maximum values of Amax and  ranged from 5.9 µmol m−2 s−1 (TUR) to 45.2
379
µmol m−2 s−1 (TMK) and 0.025 mol mol−1 (TUR, NLG, and SKT) to 0.065 mol mol−1
380
(TMK), respectively (Table 4). The reference respiration rate (R10) showed seasonal
381
variation similar to that of Amax and , although an apparent decrease was observed from
22
382
July to August at some sites (LSH and FHK). Compared with R10, a delayed increase was
383
observed in the seasonal variation of the daytime respiration rate (Rd). Because Rd is a
384
temperature-dependent parameter, it was likely enhanced by the temperature increase
385
during July to August, although the respiratory parameter normalized by temperature
386
(R10) tended to be suppressed by temperature increases at some sites (LSH and FHK). Ea
387
did not show clear seasonal and interannual variation, but decreased during midsummer
388
at some sites (NLG, YLF, SKT, and TMK). The decrease in the ecosystem respiratory
389
parameters can be caused by the hot and dry environment (Janssens and Pilegaard 2003)
390
or the decreased plant respiratory activity (Liang et al. 2010), as observed in soil
391
respiration studies. Seasonal maximum values of Rd, R10, and Ea ranged from 1.7 µmol
392
m−2 s−1 (TUR) to 9.4 µmol m−2 s−1 (TSE), 1.1 µmol m−2 s−1 (TUR) to 6.6 µmol m−2 s−1
393
(TMK), and 1.9 kJ mol−1 (TUR) to 5.5 kJ mol−1 (FHK), respectively (Table 4).
394
We observed a linear or exponential increasing trend for seasonal maximum Amax,
395
, Rd, and R10 with the increase in annual mean air temperature (r2=0.77, p<0.01 for Amax,
396
r2=0.83, p<0.01 for Rd and r2=0.74, p<0.01 for R10) and seasonal maximum LAI (r2=0.73,
397
p<0.01 for Amax, r2=0.66, p<0.05 for , r2=0.78, p<0.01 for Rd and r2=0.81, p<0.01 for
398
R10) across the eight sites (Fig. 9). Because an increasing trend with temperature or LAI
399
was observed not only for the carbon fluxes (GPP and RE), but also for the ecosystem
23
400
photosynthetic and respiratory parameters, it is clear that the increase in GPP and RE
401
along the latitudinal gradient was caused by the enhancement of vegetation activity
402
(photosynthesis and respiration; biotic factor) through the increase in leaf (or whole
403
vegetation) biomass as well as the increased temperature (abiotic factor), although the
404
biomass itself also depends on the temperature. On the other hand, the temperature
405
sensitivity of RE (Ea) was likely independent of the temperature or LAI gradients of the
406
study sites. Because four (Amax, , Rd, and R10) of the five parameters were proportional to
407
the magnitude of seasonal maximum LAI, we divided the values of the parameters in Fig.
408
9 by the seasonal maximum of monthly MODIS LAI (parameters were normalized by the
409
LAI) in order to cancel the biomass effect on these parameters (Fig. 10). Linear increase
410
with the temperature was still observed for Amax and Rd, however such relationship
411
disappeared for  and R10. It means that the abiotic factor (temperature) effect is dominant
412
in the inter-site variation of Amax, while such effect is not important in the variation of 
413
and temperature normalized respiratory parameter (R10).
414
415
4. Conclusions
416
Temperature and MODIS LAI explained much of the spatial variation of GPP and RE of
417
larch forests distributed over East Asia. Together, our findings indicate that the intersite
24
418
variation of ecosystem photosynthetic and respiratory parameters of larch forests can be
419
explained well by MODIS LAI, suggesting that the intersite variation in GPP and RE was
420
caused not only by the temperature variation (abiotic factor), but also by the variation in
421
the photosynthetic and respiration activity by vegetation (biotic factor) through the
422
change in leaf (or whole vegetation) biomass, and the applicability of this index to
423
ecosystem carbon cycle models.
424
425
Acknowledgements
426
This research was financially supported by the A3 Foresight Program (CarboEastAsia) of
427
the Japan Society for the Promotion of Science, and in part by Grants-in-Aid for
428
Scientific Research (no. 23255009 and 25241002) from the Ministry of Education,
429
Culture, Sports, Science and Technology, Japan.
430
25
431
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34
260††
296
724
–9.4
–9.1††
–1.5
4.8
6.0
200
220
1630
370
70
140
1100
62°19'N, 129°31'E
62°15'N, 129°14'E
48°21'N, 108°39'E
45°20'N, 127°34'E
45°03'N, 142°06'E
42°44'N, 141°31'E
35°26'N, 138°45'E
NLG
YLF
SKT
LSH
TSE
TMK
FHK
Neleger, Russia
Yakutsuk, Russia
Southern Khentei Taiga, Mongolia
Laoshan, China
CC-LaG experiment site, Japan
Tomakomai Flux Research site, Japan
Fuji Hokuroku Flux Observation site, Japan
Plant Area Index including trunks and blanches
Data in 2010
††††
†††
Data gaps in the winter were extrapolated using the data obtained at the nearby weather station
Average value with the range in parentheses are shown for sites with multiple years of observation
††
†
20
††
††
50
20–25
1800
9.2
2.9
5.6
4.9 (4.5–5.3)
45
15
1040
6.3
3.3
T akagi et al. (2009)
L..gmelinii×
L.. kaempferi
1.7††††
3.9†††
7
3.5
1278
L. kaempferi
Ueyama et al. (2012)
Hirata et al. (2007)
Wang et al. (2005a, b)
L.. gmelinii
2.5
4
35
17
L. kaempferi
Li et al. (2005)
Ohta et al. (2001,
2008)
Machimura et al.
(2005, 2008)
L.. sibirica
L. cajanderi
L. cajanderi
Nakai et al. (2008)
2.7
1.9
L. gmelinii
Reference
1.9 (1.7–2.1)
2.6 (2.4–2.8)
< 0.3
Species
110
180
>200
1.6
Ground observation
1.6
18
8.6
105
MODIS†
Seasonal max of canopy LAI (m2 m–2 )
2.8
249
360††
–8.9††
250
64°12'N, 100°27'E
TUR
Tura, Russia
3.4
Annual
precipitation (mm)
Annual mean air
temperature (ºC)
Elevation
(m)
Location
595
Site
Site code
Stand height Stand age
(yr)
(m)
Table 1. Site characteristics. Seasonal maximum Leaf area index (LAI) values obatained by ground observation and evaluated from monthly average MODIS LAI product (MOD15A2) are shown.
594
Table 1
35
LI-7500c
LI-7500c
c
c
LI-7000c
LI-6262c
LI-6262c
b
R-3a
SAT-550
b
R-3a
SAT-550
DA600-3TVb
DA600-3TVb
DA600-3TVb
NLG
YLF
SKT
LSH
TSE
TMK
FHK
35
27
5.7
Planar fit e
Planar fit e
Planar fit e
rotation d
Double
rotation d
Double
rotation d
Double
rotation
d
Double
rotation
d
Double
Coordinate
rotation
p
transfer function
o
lag/High frequency losses /Empirical
l
Wind direction
q
Integral turbulence characteristics /
q
Absolute value p / Precipitation/ Raw data
spikes and outliers p / Instationarity q /
Flow distortion n /Cross wind
Wind direction q
transfer function o
effect k /Humidity correction g /Time
Integral turbulence characteristics /
q
lag/High frequency losses /Empirical
l
Absolute value p / Precipitation/ Raw data
spikes and outliers p / Instationarity q /
Flow distortion n /Cross wind
/Footprint
r
effect k /Humidity correction g /Time
correction
m
spikes and outliers p / Instationarity q /
Integral turbulence characteristics q
Absolute value p / Precipitation/ Raw data
Absolute value / Raw data spikes
p
Absolute value / Precipitation
p
correction /WPL correction /Time
j
i
Absolute value p / Precipitation
lag/High frequency losses l /Band pass
g
Cross wind effect k /Humidity
Time lag/WPL correction
j
WPL correction /Cospectral correction
h
WPL correction h
Footprint
r
Absolute value p / Instationarity q /
flowq
correction h
Linear trend removing/WPL correction h
spikes and outliers p /Inclination of wind
Absolute value p / Precipitation /Raw data
QC/QA
removing/Humidity correction g /WPL
Angle of attack errors f/Linear trend
Corrections for flux calculation
Gill Instruments Ltd., Lymington, UK/ b KAIJOSONIC Corporation, Tokyo, Japan/ cLI-COR Inc., Lincoln, USA/ d McMillen (1988)/ eWilczak et al. (2001)
LI-7000
29
30
32
21
20
Flux measurement
height (m)
0.1
CO2
concentration at
flux measurement
height
CO2
concentration at
10 heights
CO2
concentration at 7
heights
o
Aubinet et al. (2000)/ p Vickers and Mahrt (1997)/ q Foken and Wichura (1996)/ rKormann and Meixner (2001)
0.1
0.13
(0.1–0.13)
0.13
(0.1–0.13)
0.1
CO2
concentration at
flux measurement
height
Not considered
0.23
0.28
(0.21–0.28)
0.1
(m s -1 )
u * threshold
Not considered
CO2
concentration at 6
heights
CO2
concentration at
flux measurement
height
CO2 storage
change
Nakai et al. (2006)/ g Hignett (1992)/ h Webb et al. (1980)/ i Eugster and Senn (1995)/ j Leuning and King (1992)/ k Kaimal and Gaynor (1991)/ l Moore (1986)/ mKowalski et al. (2003)/ n Shimizu et al. (1999)/
a
f
LI-7500c
R-3a
TUR
LI-7500
IRGA model
Sonic anemometer
model
2006
2001–2003
2010–2012
2004
2004–2005
2004
2003–2005
2004
Observation
year
597
Site code
Table 2. Flux measurement, calculation, and quality control and assurance (QC/QA) procedures for each site. Method used to determine CO 2 storage change flux is also listed.
See text for the determination of threshold value for friction velocity (u *) filtering.
596
Table 2
36
598
599
Table 3
Table 3. Carbon fluxes of each study site. Average value and range are shown for sites with multiple years of observation.
Seasonal max. daily Seasonal max. daily Seasonal min. daily
†
Annual GPP
Annual RE
Annual NEE
Site code
GPP (gC m–2 d –1 )
RE (gC m–2 d –1 )
NEE (gC m–2 d –1 )
(gC m–2 yr–1 )
(gC m–2 yr–1 )
(gC m–2 yr–1 )
TUR
4.3
3.1
–2.1
234†
145†
–89†
NLG
9.3
(5.9 to 13.1)
6.6
(4.0 to 10.9)
–4.5
(–4.9 to –3.7)
463†
(415 to 539)
286†
(211 to 379)
–177†
(–224 to –148)
YLF
5.7
3.1
–4.3
470†
267†
–203†
SKT
8.8
(7.5 to 10.1)
5.8
(4.8 to 6.9)
–4.4
(–4.9 to –3.9)
586
(562 to 610)
371
(327 to 416)
–215
(–236 to –194)
LSH
15.4
14.3
–7.5
1469
1564
95
TSE
11.4
(10.2 to 12.8)
11.9
(11.0 to 12.6)
–3.9
(–4.3 to –3.6)
1092
(988 to 1190)
1120
(1032 to 1193)
28
(3.8 to 44)
TMK
20.0
(18.9 to 21.0)
12.4
(11.7 to 13.0)
–11.3
(–11.9 to –10.3)
1827
(1761 to 1933)
1551
(1494 to 1616)
–277
(–317 to –219)
FHK
16.3
10.7
–9.9
1758
1416
–342
Limitted period for the determination (see text)
37
600
Table 4
Table 4. Ecosystem photosynthetic and respiratory parameters. Seasonal maximum values were
obtained by averaging the top 10 daily values. Average value and range are shown for
sites with multiple years of observation.
Seasonal max. A max
–2
–1
Seasonal max. 
–1
Seasonal max. R d
–2
–1
Seasonal max. R 10
–2
–1
Seasonal max. E a
Site code
(μmol m s )
(mol mol )
(μmol m s )
(μmol m s )
(kJ mol–1 )
TUR
5.9
0.025
1.7
1.1
1.9
NLG
12.3
(9.6 to 14.1)
0.025
(0.023 to 0.028)
3.4
(2.5 to 3.9)
2.8
(2.2 to 3.7)
4.4
(4.2 to 4.6)
YLF
9.9
0.054
3.0
2.2
3.7
SKT
13.8
(13.2 to 14.4)
0.025
(0.021 to 0.028)
3.1
(2.3 to 3.8)
3.4
(3.0 to 3.8)
3.7
(3.1 to 4.4)
LSH
27.3
0.040
6.7
4.5
4.2
TSE
22.3
(19.8 to 24.8)
0.054
(0.050 to 0.063)
9.4
(8.8 to 10.6)
4.9
(4.7 to 5.3)
4.2
(4.0 to 4.3)
TMK
45.2
(42.6 to 49.9)
0.065
(0.062 to 0.070)
8.7
(8.6 to 8.9)
6.6
(6.3 to 6.9)
4.2
(3.9 to 4.7)
FHK
36.3
0.053
7.9
4.5
5.5
601
38
602
Figure legends
603
Figure 1 Location of studied larch forests. Refer to Table 1 for each site code.
604
605
Figure 2 Seasonal variation of daily gross primary production (GPP), total ecosystem
606
respiration (RE), and net ecosystem CO2 exchange (NEE) for eight larch forests. The
607
max-min range of interannual variation is shown for sites with multiple years of
608
observation. Refer to Table 1 for each site code.
609
610
Figure 3 Seasonal variation of monthly MODIS LAI (MOD15A2) for eight larch forests.
611
The max-min range of interannual variation is shown for sites with multiple years of
612
observation, although LAI for TSE is that only in 2010. Refer to Table 1 for each site
613
code.
614
615
Figure 4 Relationship of annual gross primary production (GPP), total ecosystem
616
respiration (RE), and net ecosystem CO2 exchange (NEE) with annual mean air
617
temperature (Ta) and seasonal maximum of monthly MODIS LAI (MOD15A2). The
618
average and the max-min range of interannual variation of carbon fluxes are shown as
619
symbols and bars, respectively for sites with multiple years of observation. Regression
39
620
equation between NEE and Ta or LAI was obtained while excluding two larch plantations
621
(LSH and TSE). Refer to Table 1 for each site code.
622
623
Figure 5 Relationship of annual gross primary production (GPP), total ecosystem
624
respiration (RE), and net ecosystem CO2 exchange (NEE) with start day of the growing
625
season (SG; left), growing season length (GSL; middle), growing season degree days
626
(GSD; right). The average and the max-min range of interannual variation of carbon
627
fluxes and phenological indices are shown as symbols and bars, respectively for sites with
628
multiple years of observation.
629
630
Figure 6 Relationship between start day of the growing season (SG) and degree days
631
until SG (PGD) for each site. The average and the max-min range of interannual variation
632
of phenological indices are shown as symbols and bars, respectively for sites with
633
multiple years of observation. Refer to Table 1 for each site code.
634
635
Figure 7 Relationship of annual gross primary production (GPP), total ecosystem
636
respiration (RE), and net ecosystem CO2 exchange (NEE) with the stand age. The average
637
and the max-min range of interannual variation of carbon fluxes are shown as symbols
40
638
and bars, respectively for sites with multiple years of observation. Refer to Table 1 for
639
each site code.
640
641
Figure 8 Seasonal variation of daily Amax (maximum gross primary production at light
642
saturation),  (initial slope of the light-response curve), Rd (daytime respiration), R10
643
(total ecosystem respiration at the reference temperature of 10ºC), and Ea (apparent
644
temperature sensitivity of RE) for eight larch forests. The max-min range of interannual
645
variation of the daily values is shown for sites with multiple years of observation. Refer to
646
Table 1 for each site code.
647
648
Figure 9 Relationship of seasonal maximum daily Amax (maximum gross primary
649
production at light saturation),  (initial slope of the light-response curve), Rd (daytime
650
respiration), R10 (total ecosystem respiration at the reference temperature of 10ºC), and Ea
651
(apparent temperature sensitivity) with annual mean air temperature and seasonal
652
maximum of monthly MODIS LAI (MOD15A2). Seasonal maximum values for each
653
photosynthetic and respiratory parameter were determined by averaging the top 10 daily
654
values. The average and the max-min range of interannual variation are shown as
655
symbols and bars, respectively, for sites with multiple years of observation. Refer to Table
41
656
1 for each site code.
657
658
Figure 10 Same as Figure 9, but all parameters were divided by the seasonal maximum of
659
monthly MODIS LAI (normalized by LAI).
660
42