Net emissions of CH4 and CO2 in Alaska: implications for the region's greenhouse gas budget.

We used a biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to study the net methane (CH4) fluxes between Alaskan ecosystems and the atmosphere. We estimated that the current net emissions of CH4 (emissions minus consumption) from Alaskan soils are approximately 3 Tg CH4/yr. Wet tundra ecosystems are responsible for 75% of the region's net emissions, while dry tundra and upland boreal forests are responsible for 50% and 45% of total consumption over the region, respectively. In response to climate change over the 21st century, our simulations indicated that CH4 emissions from wet soils would be enhanced more than consumption by dry soils of tundra and boreal forests. As a consequence, we projected that net CH4 emissions will almost double by the end of the century in response to high-latitude warming and associated climate changes. When we placed these CH4 emissions in the context of the projected carbon budget (carbon dioxide [CO2] and CH4) for Alaska at the end of the 21st century, we estimated that Alaska will be a net source of greenhouse gases to the atmosphere of 69 Tg CO2 equivalents/yr, that is, a balance between net methane emissions of 131 Tg CO2 equivalents/yr and carbon sequestration of 17 Tg C/yr (62 Tg CO2 equivalents/yr).

Modelling carbon responses of tundra ecosystems to historical and projected climate: a comparison of a plot- and a global-scale ecosystem model to identify process-based uncertainties.

We are developing a process-based modelling approach to investigate how carbon (C) storage of tundra across the entire Arctic will respond to projected climate change. To implement the approach, the processes that are least understood, and thus have the most uncertainty, need to be identified and studied. In this paper, we identified a key uncertainty by comparing the responses of C storage in tussock tundra at one site between the simulations of two models - one a global-scale ecosystem model (Terrestrial Ecosystem Model, TEM) and one a plot-scale ecosystem model (General Ecosystem Model, GEM). The simulations spanned the historical period (1921-94) and the projected period (1995-2100). In the historical period, the model simulations of net primary production (NPP) differed in their sensitivity to variability in climate. However, the long-term changes in C storage were similar in both simulations, because the dynamics of heterotrophic respiration (RH ) were similar in both models. In contrast, the responses of C storage in the two model simulations diverged during the projected period. In the GEM simulation for this period, increases in RH tracked increases in NPP, whereas in the TEM simulation increases in RH lagged increases in NPP. We were able to make the long-term C dynamics of the two simulations agree by parameterizing TEM to the fast soil C pools of GEM. We concluded that the differences between the long-term C dynamics of the two simulations lay in modelling the role of the recalcitrant soil C. These differences, which reflect an incomplete understanding of soil processes, lead to quite different projections of the response of pan-Arctic C storage to global change. For example, the reference parameterization of TEM resulted in an estimate of cumulative C storage of 2032 g C m-2 for moist tundra north of 50°N, which was substantially higher than the 463 g C m-2 estimated for a parameterization of fast soil C dynamics. This uncertainty in the depiction of the role of recalcitrant soil C in long-term ecosystem C dynamics resulted from our incomplete understanding of controls over C and N transformations in Arctic soils. Mechanistic studies of these issues are needed to improve our ability to model the response of Arctic ecosystems to global change.

Modelling carbon responses of tundra ecosystems to historical and projected climate: sensitivity of pan-Arctic carbon storage to temporal and spatial variation in climate.

Historical and projected climate trends for high latitudes show substantial temporal and spatial variability. To identify uncertainties in simulating carbon (C) dynamics for pan-Arctic tundra, we compare the historical and projected responses of tundra C storage from 1921 to 2100 between simulations by the Terrestrial Ecosystem Model (TEM) for the pan-Arctic and the Kuparuk River Basin, which was the focus of an integrated study of C dynamics from 1994 to 1996. In the historical period from 1921 to 1994, the responses of net primary production (NPP) and heterotrophic respiration (RH ) simulated for the Kuparuk River Basin and the pan-Arctic are correlated with the same factors; NPP is positively correlated with net nitrogen mineralization (NMIN) and RH is negatively correlated with mean annual soil moisture. In comparison to the historical period, the spatially aggregated responses of NPP and RH for the Kuparuk River Basin and the pan-Arctic in our simulations for the projected period have different sensitivities to temperature, soil moisture and NMIN. In addition to being sensitive to soil moisture during the projected period, RH is also sensitive to temperature and there is a significant correlation between RH and NMIN. We interpret the increases in NPP during the projected period as being driven primarily by increases in NMIN, and that the correlation between NPP and temperature in the projected period is a result primarily of the causal linkage between temperature, RH , and NMIN. Although similar factors appear to be controlling simulated regional-and biome-scale C dynamics, simulated C dynamics at the two scales differ in magnitude with higher increases in C storage simulated for the Kuparuk River Basin than for the pan-Arctic at the end of the historical period and throughout the projected period. Also, the results of the simulations indicate that responses of C storage show different climate sensitivities at regional and pan-Arctic spatial scales and that these sensitivities change across the temporal scope of the simulations. The results of the TEM simulations indicate that the scaling of C dynamics to a region of arctic tundra may not represent C dynamics of pan-Arctic tundra because of the limited spatial variation in climate and vegetation within a region relative to the pan-Arctic. For reducing uncertainties, our analyses highlight the importance of incorporating the understanding gained from process-level studies of C dynamics in a region of arctic tundra into process-based models that simulate C dynamics in a spatially explicit fashion across the spatial domain of pan-Arctic tundra. Also, efforts to improve gridded datasets of historical climate for the pan-Arctic would advance the ability to assess the responses of C dynamics for pan-Arctic tundra in a more realistic fashion. A major challenge will be to incorporate topographic controls over soil moisture in assessing the response of C storage for pan-Arctic tundra.

Potential Net Primary Productivity in South America: Application of a Global Model.

We use a mechanistically based ecosystem simulation model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in South America. The Terrestrial Ecosystem Model (TEM) is designed to predict major carbon and nitrogen fluxes and pool sizes in terrestrial ecosystems at continental to global scales. Information from intensively studies field sites is used in combination with continental-scale information on climate, soils, and vegetation to estimate NPP in each of 5888 non-wetland, 0.5° latitude °0.5° longitude grid cells in South America, at monthly time steps. Preliminary analyses are presented for the scenario of natural vegetation throughout the continent, as a prelude to evaluating human impacts on terrestrial NPP. The potential annual NPP of South America is estimated to be 12.5 Pg/yr of carbon (26.3 Pg/yr of organic matter) in a non-wetland area of 17.0 ° 106 km2 . More than 50% of this production occurs in the tropical and subtropical evergreen forest region. Six independent model runs, each based on an independently derived set of model parameters, generated mean annual NPP estimates for the tropical evergreen forest region ranging from 900 to 1510 g°m-2 °yr-1 of carbon, with an overall mean of 1170 g°m-2 °yr-1 . Coefficients of variation in estimated annual NPP averaged 20% for any specific location in the evergreen forests, which is probably within the confidence limits of extant NPP measurements. Predicted rates of mean annual NPP in other types of vegetation ranged from 95 g°m-2 °yr-1 in arid shrublands to 930 g°m@ ?yr-1 in savannas, and were within the ranges measured in empirical studies. The spatial distribution of predicted NPP was directly compared with estimates made using the Miami mode of Lieth (1975). Overall, TEM predictions were °10% lower than those of the Miami model, but the two models agreed closely on the spatial patterns of NPP in south America. Unlike previous models, however, TEM estimates NPP monthly, allowing for the evaluation of seasonal phenomena. This is an important step toward integration of ecosystem models with remotely sensed information, global climate models, and atmospheric transport models, all of which are evaluated at comparable spatial and temporal scales. Seasonal patterns of NPP in South America are correlated with moisture availability in most vegetation types, but are strongly influenced by seasonal differences in cloudiness in the tropical evergreen forests. On an annual basis, moisture availability was the factor that was correlated most strongly with annual NPP in South America, but differences were again observed among vegetation types. These results allow for the investigation and analysis of climatic controls over NPP at continental scales, within and among vegetation types, and within years. Further model validation is needed. Nevertheless, the ability to investigate NPP-environment interactions with a high spatial and temporal resolution at continental scales should prove useful if not essential for rigorous analysis of the potential effects of global climate changes on terrestrial ecosystems.