The mission of the Agro-Meteorology Group is to clarify predictions of the impacts and atmospheric changes in agricultural ecosystems caused by climate change and elevated atmospheric CO₂ levels.  The Agro-Meteorology Group consists of 3 units: 1) Climate Resources Unit, 2) Atmospheric Impacts Unit, and 3) Air Quality Conservation Unit.

The research aim of the Climate Resources Unit is to develop monitoring techniques for evaluating climatic resources, to construct a reliable database for evaluating the effects of climate change and elevated CO₂ on agricultural water resources, and to develop methods for predicting these changes.  The research objective of the Atmospheric Impacts Unit is to develop models for predicting the effects of elevated atmospheric CO₂ on agricultural ecosystems by analysis of the results of free air CO₂ enrichment (FACE) experiments.  The research of the Air Quality Conservation Unit is focused on clarifying the processes of emission, diffusion, and deposition of air components such as trace gases, pollens, and dust in agricultural ecosystems.

In FY 2002, the following research was conducted by the 3 units: 1) understanding and predicting of spatio-temporal change in agricultural water resources, 2) assessment of the impact of climate change on agricultural production, 3) study of a model for estimating regional paddy water and soil temperatures by using routine weather data, 4) impact assessment of climate change from the viewpoint of agricultural production management, 5) prediction of the impacts of atmospheric CO₂ increase on crop production and water use, 6) process-based modeling of agricultural ecosystems under rising temperatures and atmospheric CO₂ concentration, 7) impacts of increasing atmospheric CO₂ on heat stress in crop plants, 8) modeling and simulation of canopy microclimate and fluid dynamics for developing open-air warming systems, 9) modeling and estimation of emission and diffusion processes controlling air quality in agro-ecosystems, 10) examination of the relationship between ground surface conditions and eolian dust outbreaks, 11) assessment of temporal and spatial variations in the bio-meteorological environment on alpine grassland ecosystems, and 12) assessment of the climate mitigation function of agricultural land.

The 19th Meteorology Workshop, entitled "Carbon and nitrogen cycling in terrestrial ecosystems under global environmental change: overview and perspective of monitoring, experimenting with, and modeling of ecosystem processes" was held at NIAES on 25 February 2003.  Thirteen original papers and 1 review were published in international and domestic journals in 2002.  From 24 March 2002, Dr. S. Yonemura visited the Max -Planck Institute for Chemistry in Germany for 18 months to study the exchange of trace gases between the biosphere and the atmosphere.

Topic A: A dynamic water model for evaluating agricultural water resources on a continental scale

The time has come for us to evaluate how much of our water resources we are using for agriculture.  To assess our agricultural water resources we must examine not only the available precipitation but also the availability of water from other sources for irrigation.  To determine the latter, we must develop an understanding of water dynamics on a continental scale, of which river discharges are among the most important components.  In this context, we attempted to construct a simple water dynamics model to calculate the amount of water discharged from river basins and to evaluate water requirements for irrigating croplands throughout East Eurasia.

To minimize geographical inconsistencies between actual drainage basins and those defined by coarse grids and to represent natural water flow controlled by the topography, the model based on small drainage basins were used.  The model consists of a runoff sub-model and a river flow sub-model.  The former estimates the amount of runoff from basins, and the latter represents the lateral movement of water through the network of river channels.  In the runoff sub-model, independent calculations were made for each of 6 land-cover categories.  Limiting parameters of soil water condition and seasonal changes in vegetation cover, which are expressed by seasonal variation of Normalized Difference Vegetation Index, were introduced in the runoff sub-model.  Evapotranspiration was expressed as the sum of evaporation and transpiration, both of which were calculated by the FAO-56 method.  By this method, crop water requirement was estimated as the deficit of the actual transpiration against the transpiration without water stress.  In the river flow sub-model, the amount of runoff from each stream was calculated by accumulating the values from the actual area of the present basin and the flow from the upper streams.

The model simulation was performed using CRU-TS2.0 dataset (Mitchell et al, 2003).  This is a global historical monthly climatic data, which were constructed at a 0.5-degree resolution from 1901 to 2000, and then the annual discharge of each stream was estimated by the model (Fig. 1).  We used GRDC and IHP/UNESCO discharge data from observations at 118 selected gauging stations on several main rivers for validation of the simulated discharges.  In many cases, we found that the model tended to underestimate the discharge in comparison with the observations.  This tendency seemed more obvious in areas with sparse vegetation, thus indicating that the model tended to overestimate evaporation from bare soil.  (Y. Ishigooka)

Topic B: Estimating the first day of Japanese cedar pollen release under global climate change

The pollen of Japanese cedar (Cryptomeria japonica) causes hay fever and is a major problem in Japan.  Global warming is likely to worsen the problem, but there have been few predictions of the extent of this worsening.  We examined the effects of snowfall on the day of initiation of Japanese cedar pollen release, and predicted changes in this day under global climate change.  We used a statistical method to investigate how much sooner pollen release would start, using data on predicted meteorological conditions under global warming in the city of Yamagata.

We performed a single regression analysis using the day of initiation of pollen release in Yamagata as the dependent variable and the mean January-February temperature as the independent variable, and obtained the following regression equation with a contribution ratio of 0.63:  

Y = -7.40X₁ + 66.63              (1),

where Y is the first day of the year (DOY) on which pollen is released and X₁ is the January-February mean temperature (°C).  We then performed a multiple regression analysis using temperature and monthly snowfall depth as independent variables.  Adding snowfall depth improved both the degree-of-freedom-adjusted contribution ratio and the estimation error over estimates that used only January-February mean temperature as the independent variable.  When only February data were used for snowfall depth, the degree-of-freedom-adjusted contribution ratio rose to 0.79, the best result.  For this reason we chose the following multiple regression equation using January-February mean temperature and February snowfall depth as independent variables to estimate the day of initiation of pollen release:

Y = -5.65X₁ + 0.13X₂ + 55.46              (2),
where X₂ is the February snowfall depth (cm).

We verified the equation by plugging the meteorological data for Yamagata from 1983 to 1998 into Equations (1) and (2) (Fig. 2).  The close fit of the equation predictions to the experimental data shows that adding snowfall depth yields more accurate estimates of the first day of pollen release than using air temperature alone, thereby making possible estimates with an error of within 5 days.  Under the predicted global climate change, we expect the first day of pollen release to become earlier in Yamagata (Fig. 3).  The date would be 8 February in approximately 100 years' time, compared with 20 February (at the earliest) now.  Eighty years from now the date could be earlier than any date recorded so far.  (S. Inoue and S. Kawashima)

The Ecosystems Group consists of 5 units.  The Material Ecocycling Unit is studying the nitrogen and nutrient flow in agro-ecosystems to evaluate the relationship between anthropogenic activities and material cycles in Japan and East Asia (see Topic 2).  The Ecological Management Unit is studying historical changes in the spatial structure of rural ecosystems, and the conservation and management of the wildlife that inhabit rural environments of the Kanto District.  The Remote Sensing Unit has been determining environmental characteristics that can be observed at a regional scale through satellite imaging such as multi-band and multi-polarization SAR, NOAA/AVHRR, and TERRA/MODIS.  The Agro-Ecological Sensing Unit is developing remote sensing and modeling methods for monitoring plant and environmental dynamics in agricultural and natural ecosystems based on optical and electromagnetic measurements ranging from leaf scale to regional scale.  The Statistics Unit is developing novel statistical methodologies for sampling, classifying, and analyzing agro-environmental data (see Topic 1).  In FY 2002, we carried out 8 research projects funded by the Ministry of the Environment, MEXT, and MAFF.  Our researchers attended 8 meetings abroad related to anthropology, ecology, remote sensing, and statistics, and 2 researchers participated in  OECD expert meetings on biodiversity and remote sensing.  Further, researchers made 6 overseas visits - to the United States, France, China, Korea, Laos, and Thailand - for field work and cooperative projects.  Domestically, we made a total of 26 presentations at academic meetings on statistics, remote sensing, geography, environmental sciences, and anthropology.

Topic 1: Estimating larger phylogenetic trees for biodiversity studies

Analysis of biological diversity gives fundamental knowledge for various branches of biology and agronomy.  A taxonomic or species-based database would be a useful tool for cataloguing the world's biota.  However, the construction of such a taxonomic database or inventory is not the only task we need to undertake if we are to understand, maintain, and recreate biodiversity.  Taxonomic classification has intuitive appeal to us as humans, because we like to catalog living things into groups within groups according to the degrees of similarity of various characters.  However, such taxonomic groups or 'taxa' - species, genus, family, and so forth - do not have explicit historical or evolutionary implications.  All organisms on Earth are the products of biological evolution.  Current states of biodiversity have their evolutionary origin in the past, and if we are to maintain and control biodiversity, we need to know its evolutionary history.  If our research were based on evolutionary biology, we would be able to obtain more definite knowledge of the origin of biodiversity.

Estimation of the history of organisms ('phylogeny') is one of the most rapidly growing fields of research in evolutionary biology.  It is also a flourishing area of interaction between biology, statistics, mathematics, and computer science.  In the past the science of phylogeny ('phylogenetics') was denigrated as mere speculation driven predominantly by the action of bold imagination on scant empirical data.  However, because of the growing accumulation of molecular biological data (DNA or amino acid sequence data) and methodological advances in phylogenetic biology over the past 40 years, we now have  techniques for estimating more reliable phylogenetic trees by using high-speed computers.  Recent advances in genome informatics have given rise to the new interdisciplinary science of bioinformatics, and comparable advances in phylogenetic reconstruction will open up the promising field of phyloinformatics in the near future.

Phylogenetic reconstruction, whose purpose is to find an optimal graph called phylogenetic tree from character data (e.g., DNA sequences, morphology), has been faced with a serious problem in computer science: "NP-completeness".  A computational problem of class P (polynomial) requires us polynomial computing time proportionate to  its  size.  On the other hand the category of NP-complete problems are known to possess the highest level of computational difficulty in terms of discrete optimization because they require NP (nondeterministic polynomial) computing time.  NP-completeness implies exponential time for resolving problems.  The problem of finding optimal phylogenetic trees was proven to belong to the NP-complete category in 1982.  Its computational difficulty can be understood intuitively from the fact that phylogeneticists must make comparisons among alternative trees whose number N is calculated by the formula: N = 1 × 3 × 5 × ... × (2n-3), where n is the number of organisms under study.  The tree space for searching will rapidly magnify as n increases (e.g., N is more than 34 million for n = 10).  For this reason, a more efficient heuristic algorithm is required to calculate larger phylogenetic trees in a shorter time.

BOGEN is a new, more efficient program for calculating the most parsimonious trees from molecular sequence data.  The principal characteristic of our program is that it incorporates a new heuristic search strategy for building optimal initial trees using simultaneous subtree-connections.  The upper limit of the size of the data matrix is 10 000 organisms / 50 000 base pairs for the current version of BOGEN.  From our benchmark test, the computing time on a Pentium 4 (2.26 GHz) Windows PC is as follows: for 500 organisms / 1000 bp, 50 s (initial tree) and 37 min (branch-swapping); for 1000 organisms / 1000 bp, 220 s (initial tree); for 5000 organisms / 1000 bp, 1 h (initial tree).  Calculating a 10 000-organisms tree requires, on the average, at least 30 h for optimal initial tree reconstruction.  In our comparisons so far of optimal trees from BOGEN with those from PAUP* 4.0 (currently the most popular software for phylogenetic reconstruction), BOGEN trees have been consistently shorter lengths than PAUP* trees, even without branch-swapping operations.  We are now developing a branch-swapping program for larger trees.  Several examples are presented in the following references for comparison of BOGEN with other parsimony programs.

References
Minaka N., Suemura T., Asano T., Yamamoto H. and Machii K. (2003) BOGEN: A faster parsimony program for computing larger phylogenetic trees.  Paper presented at the 22nd Annual Meeting of the Willi Hennig Society held at the New York Botanical Garden, New York, 20--25 July, 2003. 

[Abstract]
Minaka N., Yamamoto H., Asano T., Suemura T. and Machii K. (2003) A more efficient heuristic algorithm for the large phylogenetic Steiner problem.  Cladistics 19: 157.

Topic 2: Estimation of nitrogen flow in the food production-supply system of East Asia and its environmental effects

In Asia east of Pakistan, about 55% of the world's population lives on 18% of the world's land area, cultivating 30% of the world's farmland.  Food production has been increased to supply these people with adequate food.  As a result, the amount of nitrogen fertilizer consumed in the region has increased more than 20 times from the 1960s to 2000.  Currently, yearly consumption of nitrogen is about 43.6 × 10⁶ t, or 53% of world consumption, and nitrogen pollution of surface and groundwaters is a serious concern.

We created a numerical model that predicts the nitrogen load in East Asian countries, mainly from FAO statistics (Fig. 4).  Nitrogen is added to farmland in fertilizer and by biological fixation from crop cultivation, and is taken out of farmland by harvesting and consumed as feed and food.  We assumed that all crop residues, livestock manure and meat and fish not used for food and feed are returned to farmlands, and the balance of the nitrogen is discharged from the farmland to the environment (NL_f).  All nitrogen used by humans is discharged to the environment directly or through the sewerage system (NL_w).  We also accounted for NOx emission due to energy production (NL_e) and emission from natural ecosystems (grasslands and forests) (NL_n).  The nitrogen loads from these sources were distributed over a 0.5 × 0.5 degree grid according to the land use and population in each grid cell.

Estimated total nitrogen load in 1999 for the study region (from 60 ° E to 150 ° E and 12 ° S to 60 ° N) was 77.9 × 106 t (2.4 t km^-2).  NL_f contributed the most (54%) to the total nitrogen load, followed by NL_w (21%), NL_n (18%), and NL_e (7%).  Fig. 5 shows the spatial distribution of NL_f , which ranged from 0.002 to 43.5 t km^-2y^-1.  The maximum nitrogen load occurred around the east coast of central China.  Discharged nitrogen was assumed to infiltrate through the soil, where a fraction was removed by denitrification and organic matter accumulation, expressed as a first-order reaction whose rate was a function of temperature and residence time.  The surplus nitrogen flows to the river and then to the sea.  The estimated nitrogen concentration in river water was extremely high in the area near the east coast of the North China Plain because of the high nitrogen load and low precipitation; the maximum concentration was 90.2 ppm.  The model was validated by measurements of river and groundwater in this area, where nitrogen concentrations were over 100 ppm in some locations.

Considerable attention has been paid to the likelihood of significant changes in world climate due to increased atmospheric concentrations of greenhouse gases (GHGs) in recent years. GHGs, such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), can absorb thermal radiation from the surface of the earth and thus contribute to the warming of the atmosphere. The Intergovernmental Panel on Climate Change (IPCC) has reported that concentrations of atmospheric GHGs and their radiative forcing have continued to increase as a result of various human activities.

Agriculture contributes to over 20% of global anthropogenic GHG emissions. In particular, 55% to 60% and 65% to 80% of total emissions of CH₄ and N₂O, respectively, are derived from agricultural sources. These GHGs are emitted to the atmosphere as a result of accelerated turnover of carbon and nitrogen in agricultural soils and the surrounding environment by increased input of fertilizer and other agro-materials. This increased input also results in increased emission of nitric oxide (NO) and ammonia (NH₃), which are the precursors of acid rain, and in pollution of rivers and groundwater by leaching of nitrogen and carbon components. The Greenhouse Gas Emission Team studies emission and absorption of these environmentally important gases in association with different land uses and agricultural management. The activities of the team are based on field measurements of GHG exchange, laboratory experiments, data interpretation, and modeling. The goals of the team are: 1) to quantify and model the processes of GHG emission and absorption, 2) to estimate the rates of GHG emission and absorption, and 3) to develop promising and feasible technologies that reduce GHG emissions (Fig. 6). The studies have been developed to address scientifically and socially important questions related to the environmental impacts of agriculture.

Topic: Methane and nitrous oxide emissions from agricultural and natural ecosystems in China: studies in collaboration with Chinese institutes

Because of China's huge land area and rapid economical development, it is very important to assess the effects of land-use and its change in China on the biogeochemical cycles of GHGs. In addition, there is a research need to develop options for the mitigation of GHG emissions from agro-ecosystems.

In this light, the Greenhouse Gas Emission Team has been studying CH₄ and N₂O emissions from agricultural and natural ecosystems in China, in collaboration with 2 research institutes of the Chinese Academy of Sciences: the Institute of Soil Science (ISS) in Nanjing and the Institute of Applied Ecology (IAE) in Shenyang (Fig. 7).

The collaborative study with ISS focused on the development of technologies to mitigate agricultural CH₄ and N₂O emissions in the Changjiang (Yangtze) valley. A field experiment in Chongqing, Sichuan Province, focused on the effect of winter drainage of paddy fields on CH₄ emissions. Because of constant precipitation over the year and the need to preserve water resources, it is common to flood paddy fields all year round in the hilly areas of southwest China. However, this practice maintains anaerobic conditions in the soil and is very likely to enhance CH₄ emissions. The results of our experiment clearly showed that CH₄ emissions during the fallow period from rice harvesting to transplanting in the following year contributed to nearly half of the annual emission from the field (Fig. 8). No-tillage treatment reduced the annual emission by 33%. A dramatic effect was achieved by winter drainage for cropping wheat: there were negligible CH₄ emissions in the wheat period in addition to the reduction in emissions in the rice period. Winter drainage reduced annual CH₄ emissions by 67%. Another field experiment in Yintang, Jiangxi Province, focused on N₂O emissions from regional cropping systems of upland rice, oil rapeseed, and beans. Emission rates of N₂O were relatively low because of low soil pH. Emission factors against applied nitrogen accounted for 0.13%, 0.22%, and 0.40% for upland rice, an oil rapeseed-peanut rotation, and a broadbean-soybean rotation, respectively, suggesting that leguminous crops contribute markedly to N₂O emissions.

Our collaborative research with IAE focused on in situ measurements of N₂O and CH₄ fluxes from temperate grasslands in northwestern China. Two study sites - in the southwest of the Songnen Plain, Liaoning Province, and in the west of the Kerqin Steppe of east Inner Mongolia - were monitored for N₂O and CH₄ fluxes from 2000 to 2002. The results showed that the 2 temperate grasslands were sources of N₂O. N₂O fluxes averaged over the 3 years at the Songnen and Kerqin sites were 27.1 and 5.4 mg N m^-2 h^-1, respectively. The two grasslands were also identified to be sources of CH₄, but the flux varied greatly among years. A trade-off between N₂O and CH₄ emissions was observed with variations in the water regimes of the fields.

In 2003, a new project that focuses on developing technologies to mitigate CH₄ and N₂O emissions from agricultural ecosystems was initiated as part of our continued collaboration with these 2 Chinese institutes. Two field experiments - in Yixing, Jiangsu Province, and Shenyang, Liaoning Province (Fig. 7) - are studying the effects of fertilizer and crop residue management on CH₄ and N₂O emissions from cropped fields. (K. Yagi, S. Sudo, H. Akiyama, and S. Nishimura)

The mission of the Food Production Prediction Team is to assess 1) the impact of global environmental change on food production and 2) the efficacy of technologies designed to mitigate adverse environmental changes. Major research domains are assessment of the impact of global warming on agriculture, monitoring and modeling of environmental changes in agricultural ecosystems, development of regional climate change scenarios, and assessment of variability of climate systems in Asian Monsoon countries. This year, all 14 researchers on the team went abroad for field surveys or presentations at international conferences. The team hosted 7 guest scientists from overseas and accepted 2 students from domestic universities.

The following 3 activities were initiated in FY 2002: 1) prediction of changes in agricultural productivity in light of responses to global warming, 2) development of risk assessment techniques for agro-ecosystems, considering the variability and regionality of its influence, and 3) construction of a model and development of regional estimation techniques for space-time changes in soil carbon flux.

The following 5 activities were completed this fiscal year: 1) detection and evaluation of environmental disasters by using microwave sensor data and global information systems, 2) studies of the feedback effects on the atmosphere of terrestrial ecosystem change under global warming, 3) global mapping of carbon-derived products in terrestrial ecosystems, 4) modeling of the carbon circulation process in terrestrial ecosystems, and 5) assessment of the carbon balance in a paddy field ecosystem in terms of dissolved carbon fluxes and their stable carbon isotope ratios.

Ongoing activities are: 1) development of advanced techniques for projecting future climate change by using ocean-atmosphere-coupled global climate modeling (GCM) and statistical methods, 2) prediction of the impacts of climate change on food supplies, 3) analysis of the hydrological cycle and its variability in Asia, 4) evaluation of the impact of global climate change on soil environments in East Asia, 5) development of an index for evaluating the environmental resource changes occurring with desertification in China, and 6) study of the distribution and patterns of fluctuation of agricultural water resources.

Topic : Evaluation of vulnerability of cereal crop production in China under climate change and climate variability

Agricultural production is primarily regulated by the availability of natural environmental resources such as temperature, radiation, surface water, and soil fertility. The spatial distribution of available resources determines patterns of cultivation areas and croplands. Climate change and the associated climate variability are very likely to have major impacts on the hydrological cycle and consequently on available water resources, flood and drought potential, and agricultural productivity. Asian countries, which rely traditionally on highly water-consuming cultivation systems such as paddy rice crops, are very vulnerable to variations in water resources (Thomas 2000). In China, which has the world's largest cropland area and population, water is the most critical resource.

We explored the changing trends in agricultural water demands and variability in soil moisture associated with both drought and increased surface runoff in Chinese croplands during the last half-century (1946-95) and their impacts on agricultural production. We plotted temporal and spatial changes in agricultural water demands, soil moisture, soil-moisture variability, soil-moisture deficit, yield index, and surface runoff on a grid of 0.5-degree resolution using a simple water budget model. The same analyses were also done under future climate change, using interpolated climate change scenarios (2031-65) based on the HadCM2 GCM.

We found a trend toward increased agricultural water demand and increased soil drying, as well as significant changes in soil-moisture variability, on the North China Plain and the Northeast China Plain (Fig. 9). There was a significant decrease in agricultural water demand and a significant increase in soil-moisture levels in southwest China, and generally insignificant increasing or decreasing trends in agricultural water demand and soil-moisture levels in southeast China (Fig. 10). These changes in agricultural water demand and soil-moisture levels had corresponding impacts on soil-moisture deficit, and consequently on agricultural production. Increased surface runoff was found in the mountainous areas of the southwest and northeast, and in some areas along the south coast.

Under the projected climate change, it was predicted that the suitability of early rice cultivation would decrease owing to surface water shortages, especially in southern China. The suitability of winter wheat cultivation was, however, predicted to increase in the Northeast China Plain. For maize cultivation, suitability would slightly decrease in the same region. Changes in cropland suitability would bring about changes in cropping systems and would consequently result in land-use change.

As a next step, such vulnerability evaluation studies should focus on specific areas within the region where higher vulnerability has been evaluated. For example, we will examine some small river basin areas on the North China Plain and the Northeast China Plain and will conduct an integrated evaluation using process-based crop growth models. (M. Yokozawa and F. Tao)

To investigate seasonal and inter-annual variations in carbon and energy exchange between agricultural ecosystems and the atmosphere, the Ecosystem Gas Exchange Team conducts long-term observations of gas and energy fluxes at 3 sites: a single-cropping rice paddy field in central Japan, a natural wetland in eastern Hokkaido, Japan, and a wet sedge tundra at Barrow, Alaska. This study is closely related to AsiaFlux, which utilizes tower-based observation sites of carbon, water vapor, and energy exchange between terrestrial ecosystems and the atmosphere in Eastern and Southeastern Asia as part of worldwide network, FLUXNET. At each of our 3 observation sites, we are measuring fluxes of carbon dioxide (CO₂), methane (CH₄), water vapor, and sensible heat by using the eddy covariance method, along with standard measurements of meteorological and ecological variables. Intensive experiments are conducted during growing periods, but some key measurements are continued without a break throughout the year because gas and energy exchange between soil and the atmosphere continues during post-growing periods as well.

A unique feature of our observations in AsiaFlux and FLUXNET is that we are measuring CO₂ and CH₄ fluxes simultaneously. Both fluxes are important components of the carbon and greenhouse gas (GHG) budgets of wetland ecosystems. To realize long-term continuous measurements of CH₄ flux in fields, we developed a micrometeorological method, in which the CH₄ concentration gradient measured with an FID (flame ionization detector) gas analyzer is combined with the eddy diffusivity determined with a sonic anemometer. This method is being used successfully at the 3 sites.

In addition to the flux measurements, we began studies of the processes of CH₄ and CO₂ exchange in paddy fields by utilizing stable isotopes from the 2002 growing season. The results of these isotopic measurements will be combined with those of gas flux measurements and will provide us with invaluable information on CH₄ and CO₂ budgets, such as oxidation rates of CH₄ in the soil or the partitioning of CO₂ flux into photosynthesis and respiration rates. The data accumulated in the observation are integrated into the Ecosystem Database, which was jointly developed by NIAES and Japan Science and Technology Corporation, and will be accessible to the public at http://ecomdb.niaes.affrc.go.jp .

Topic: Annual exchange of CO₂ and CH₄ between wet sedge tundra in the Alaskan Arctic and the atmosphere

As a result of increasing concentrations of GHGs in the atmosphere, temperature rises are predicted to appear first in the Arctic. These temperature rises, in turn, affect GHG exchange between terrestrial ecosystems in the Arctic (tundra) and the atmosphere through the increased activities of plants and microbes. To assess the influence of climatic variations on GHG exchange between tundra and the atmosphere, and to predict future changes in the GHG budget in the ecosystem, we conducted a joint Japan - United States study in the Alaskan Arctic between 1999 and 2002, with the financial support of the then Science and Technology Agency of Japan and the Ministry of Education, Culture, Sports, Science and Technology of Japan. In collaboration with San Diego State University and the International Arctic Research Center at Fairbanks, Alaska, we performed long-term tower-based measurements of CO₂ and CH₄ fluxes on the arctic tundra at Barrow (71°19 ' N, 156 ° 37 ' W, 1 m above sea level). The observation site (Photo 1) is categorized as wet sedge tundra, and the vegetation consists of wet sedges, grasses, mosses, and lichens. The growing season starts immediately after snowmelt in mid-June and lasts until the end of August or late September, depending on the weather in any given year. Thaw depth (depth of the active layer, which lies above the permafrost and melts in summer) reaches about 30 to 35 cm late in the growing season. The observation site is flooded after snowmelt and partly inundated, even in mid-summer. The inundated tundra, like the Barrow site, occupies about 25% of the total tundra area in the world.

The tundra ecosystem assimilates CO₂ through photosynthesis in summer (gross primary production; GPP), whereas it releases CO₂ through plant respiration and decomposition of organic matter in the soil (ecosystem respiration; RE). CO₂ fluxes in summer, as measured by the eddy covariance method, were partitioned into GPP and RE by determining RE as an empirical function of temperature. By accumulating half-hourly fluxes, we calculated the annual budgets of CO₂ and CH₄ (Fig. 11). In the annual budget of CO₂, the sum of RE occupied 20% to 30% of the GPP, and the remaining part of the GPP was accumulated into the tundra ecosystem as net ecosystem production (NEP). NEP at the site was larger than previously reported NEPs for wetlands in high latitudes, presumably because water overlying the soil regulated CO₂ diffusion from the ground and consequently reduced RE. NEP in 1999 was twice as much as in 2000 and 2001. These year-to-year variations of CO₂ budget are primarily caused by differences in GPP, because GPP is dependent on the meteorological conditions in each year. With regard to the CH₄ budget, most emissions occur in summer, but a small amount of CH₄ is released in early winter, when the surface soil temperature is around 0 ° C. The largest CH₄ emission was observed in 1999, together with the largest GPP, implying a close relationship between CO₂ assimilation and CH₄ emission.

From the budgets of CO₂ and CH₄ and the meteorological data, we estimated the annual exchange of GHGs at the site in a year when air temperature and precipitation were close to their long-term averages (Fig. 12). The annual NEP (= GPP - RE) was 80 g C m^-2 y^-1, whereas the amount of CH₄ emitted was 4% of the NEP and could be neglected in the carbon budget at the site. However, the CH₄ emitted was equivalent to 34% of NEP when we considered the global warming potential with a time horizon of 100 years. The present wet sedge tundra thus works as a sink of GHGs, but one-third of the net CO₂ assimilation is compensated by CH₄ emission. Further studies, including integration of observation data and modeling, are needed to predict future changes in the GHG budget of the ecosystem under global warming. (A. Miyata)