Surface Carbon Fluxes Research Articles

Wind-driven changes in the ocean carbon sink

Abstract. We estimate changes in the historical ocean carbon sink and their uncertainty using an ocean biogeochemical model driven with wind forcing from six different reanalyses and using two different eddy parameterization schemes. First, we quantify wind-induced changes over the extended period from 1871 to 2010 using the 20th Century Reanalysis winds. Consistent with previous shorter-term studies, we find that the wind changes act to reduce the ocean carbon sink, but the wind-induced trends are subject to large uncertainties. One major source of uncertainty is the parameterization of mesoscale eddies in our coarse resolution simulations. Trends in the Southern Ocean residual meridional overturning circulation and the globally integrated surface carbon flux over 1950 to 2010 are about 2.5 times smaller when using a variable eddy transfer coefficient than when using a constant coefficient in this parameterization. A second major source of uncertainty arises from disagreement on historical wind trends. By comparing six reanalyses over 1980 to 2010, we show that there are statistically significant differences in estimated historical wind trends, which vary in both sign and magnitude amongst the products. Through simulations forced with these reanalysis winds, we show that the influence of historical wind changes on ocean carbon uptake is highly uncertain, and the resulting trends depend on the choice of surface wind product.

Global carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models

AbstractIn this paper, a global carbon assimilation system (GCAS) is developed for optimizing the global land surface carbon flux at 1° resolution using multiple ecosystem models. In GCAS, three ecosystem models, Boreal Ecosystem Productivity Simulator, Carnegie‐Ames‐Stanford Approach, and Community Atmosphere Biosphere Land Exchange, produce the prior fluxes, and an atmospheric transport model, Model for OZone And Related chemical Tracers, is used to calculate atmospheric CO2 concentrations resulting from these prior fluxes. A local ensemble Kalman filter is developed to assimilate atmospheric CO2 data observed at 92 stations to optimize the carbon flux for six land regions, and the Bayesian model averaging method is implemented in GCAS to calculate the weighted average of the optimized fluxes based on individual ecosystem models. The weights for the models are found according to the closeness of their forecasted CO2 concentration to observation. Results of this study show that the model weights vary in time and space, allowing for an optimum utilization of different strengths of different ecosystem models. It is also demonstrated that spatial localization is an effective technique to avoid spurious optimization results for regions that are not well constrained by the atmospheric data. Based on the multimodel optimized flux from GCAS, we found that the average global terrestrial carbon sink over the 2002–2008 period is 2.97 ± 1.1 PgC yr−1, and the sinks are 0.88 ± 0.52, 0.27 ± 0.33, 0.67 ± 0.39, 0.90 ± 0.68, 0.21 ± 0.31, and 0.04 ± 0.08 PgC yr−1 for the North America, South America, Africa, Eurasia, Tropical Asia, and Australia, respectively. This multimodel GCAS can be used to improve global carbon cycle estimation.

Effects of vegetation structure on biomass accumulation in a Balanced Optimality Structure Vegetation Model (BOSVM v1.0)

Abstract. A myriad of interactions exist between vegetation and local climate for arid and semi-arid regions. Vegetation function, structure and individual behavior have large impacts on carbon–water–energy balances, which consequently influence local climate variability that, in turn, feeds back to the vegetation. In this study, a conceptual vegetation structure scheme is formulated and tested in the new Balanced Optimality Structure Vegetation Model (BOSVM) to explore the importance of vegetation structure and vegetation adaptation to water stress on equilibrium biomass states. Surface energy, water and carbon fluxes are simulated for a range of vegetation structures across a precipitation gradient in West Africa and optimal vegetation structures that maximize biomass for each precipitation regime are determined. Two different strategies of vegetation adaptation to water stress are included. Under dry conditions vegetation tries to maximize the water use efficiency and leaf area index as it tries to maximize carbon gain. However, a negative feedback mechanism in the vegetation–soil water system is found as the vegetation also tries to minimize its cover to optimize the surrounding bare ground area from which water can be extracted, thereby forming patches of vertical vegetation. Under larger precipitation, a positive feedback mechanism is found in which vegetation tries to maximize its cover as it then can reduce water loss from bare soil while having maximum carbon gain due to a large leaf area index. The competition between vegetation and bare soil determines a transition between a "survival" state to a "growing" state.

Do we (need to) care about canopy radiation schemes in DGVMs? Caveats and potential impacts

Abstract. Dynamic global vegetation models (DGVMs) are an essential part of current state-of-the-art Earth system models. In recent years, the complexity of DGVMs has increased by incorporating new important processes like, e.g., nutrient cycling and land cover dynamics, while biogeophysical processes like surface radiation have not been developed much further. Canopy radiation models are however very important for the estimation of absorption and reflected fluxes and are essential for a proper estimation of surface carbon, energy and water fluxes. The present study provides an overview of current implementations of canopy radiation schemes in a couple of state-of-the-art DGVMs and assesses their accuracy in simulating canopy absorption and reflection for a variety of different surface conditions. Systematic deviations in surface albedo and fractions of absorbed photosynthetic active radiation (faPAR) are identified and potential impacts are assessed. The results show clear deviations for both, absorbed and reflected, surface solar radiation fluxes. FaPAR is typically underestimated, which results in an underestimation of gross primary productivity (GPP) for the investigated cases. The deviation can be as large as 25% in extreme cases. Deviations in surface albedo range between −0.15 ≤ Δα ≤ 0.36, with a slight positive bias on the order of Δα ≈ 0.04. Potential radiative forcing caused by albedo deviations is estimated at −1.25 ≤ RF ≤ −0.8 (W m−2), caused by neglect of the diurnal cycle of surface albedo. The present study is the first one that provides an assessment of canopy RT schemes in different currently used DGVMs together with an assessment of the potential impact of the identified deviations. The paper illustrates that there is a general need to improve the canopy radiation schemes in DGVMs and provides different perspectives for their improvement.

The effect of optimization and the nesting domain on carbon flux analyses in Asia using a carbon tracking system based on the ensemble Kalman filter

To estimate the surface carbon flux in Asia and investigate the effect of the nesting domain on carbon flux analyses in Asia, two experiments with different nesting domains were conducted using the CarbonTracker developed by the National Oceanic and Atmospheric Administration. CarbonTracker is an inverse modeling system that uses an ensemble Kalman filter (EnKF) to estimate surface carbon fluxes from surface CO2 observations. One experiment was conducted with a nesting domain centered in Asia and the other with a nesting domain centered in North America. Both experiments analyzed the surface carbon fluxes in Asia from 2001 to 2006. The results showed that prior surface carbon fluxes were underestimated in Asia compared with the optimized fluxes. The optimized biosphere fluxes of the two experiments exhibited roughly similar spatial patterns but different magnitudes. Weekly cumulative optimized fluxes showed more diverse patterns than the prior fluxes, indicating that more detailed flux analyses were conducted during the optimization. The nesting domain in Asia produced a detailed estimate of the surface carbon fluxes in Asia and exhibited better agreement with the CO2 observations. Finally, the simulated background atmospheric CO2 concentrations in the experiment with the nesting domain in Asia were more consistent with the observed CO2 concentrations than those in the experiment with the nesting domain in North America. The results of this study suggest that surface carbon fluxes in Asia can be estimated more accurately using an EnKF when the nesting domain is centered in Asian regions.

Constraining surface carbon fluxes using in situ measurements of carbonyl sulfide and carbon dioxide

AbstractUnderstanding the processes that control the terrestrial exchange of carbon is critical for assessing atmospheric CO2 budgets. Carbonyl sulfide (COS) is taken up by vegetation during photosynthesis following a pathway that mirrors CO2 but has a small or nonexistent emission component, providing a possible tracer for gross primary production. Field measurements of COS and CO2 mixing ratios were made in forest, senescent grassland, and riparian ecosystems using a laser absorption spectrometer installed in a mobile trailer. Measurements of leaf fluxes with a branch‐bag gas‐exchange system were made across species from 10 genera of trees, and soil fluxes were measured with a flow‐through chamber. These data show (1) the existence of a narrow normalized daytime uptake ratio of COS to CO2 across vascular plant species of 1.7, providing critical information for the application of COS to estimate photosynthetic CO2 fluxes and (2) a temperature‐dependent normalized uptake ratio of COS to CO2 from soils. Significant nighttime uptake of COS was observed in broad‐leafed species and revealed active stomatal opening prior to sunrise. Continuous high‐resolution joint measurements of COS and CO2 concentrations in the boundary layer are used here alongside the flux measurements to partition the influence that leaf and soil fluxes and entrainment of air from above have on the surface carbon budget. The results provide a number of critical constraints on the processes that control surface COS exchange, which can be used to diagnose the robustness of global models that are beginning to use COS to constrain terrestrial carbon exchange.

Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.

Assessing scalar concentration footprint climatology and land surface impacts on tall-tower CO2 concentration measurements in the boreal forest of central Saskatchewan, Canada

Reducing the large uncertainties in current estimates of CO2 sources and sinks at regional scales (10(2)-10(5) km(2)) is fundamental to improving our understanding of the terrestrial carbon cycle. Continuous high-precision CO2 concentration measurements on a tower within the planetary boundary layer contain information on regional carbon fluxes; however, its spatial representativeness is generally unknown. In this study, we developed a footprint model (Simple Analytical Footprint model based on Eulerian coordinates for scalar Concentration [SAFE-C]) and applied it to two CO2 concentration towers in central Canada: the East Trout Lake 106-m-tall tower (54A degrees 21'N, 104A degrees 59'W) and the Candle Lake 28-m-high tower (53A degrees 59'N, 105A degrees 07'W). Results show that the ETL tower's annual concentration footprints were around 10(3)-10(5) km(2). The monthly footprint climatologies in summer were 1.5-2 times larger than in winter. The impacts of land surface carbon flux associated with heterogeneous distribution of vegetation types on the CO2 concentration measurements were different for the different heights, varied with a range of +/- 5 % to +/- 10 % among four heights. This study indicates that concentration footprint climatology analysis is important in interpreting the seasonal, annual and inter-annual variations of tower measured CO2 concentration data and is essential for comparing and scaling regional carbon flux estimates using top-down or bottom-up approaches.

Global atmospheric carbon budget: results from an ensemble of atmospheric CO<sub>2</sub> inversions

Abstract. Atmospheric CO2 inversions estimate surface carbon fluxes from an optimal fit to atmospheric CO2 measurements, usually including prior constraints on the flux estimates. Eleven sets of carbon flux estimates are compared, generated by different inversions systems that vary in their inversions methods, choice of atmospheric data, transport model and prior information. The inversions were run for at least 5 yr in the period between 1990 and 2010. Mean fluxes for 2001–2004, seasonal cycles, interannual variability and trends are compared for the tropics and northern and southern extra-tropics, and separately for land and ocean. Some continental/basin-scale subdivisions are also considered where the atmospheric network is denser. Four-year mean fluxes are reasonably consistent across inversions at global/latitudinal scale, with a large total (land plus ocean) carbon uptake in the north (−3.4 Pg C yr−1 (±0.5 Pg C yr−1 standard deviation), with slightly more uptake over land than over ocean), a significant although more variable source over the tropics (1.6 ± 0.9 Pg C yr−1) and a compensatory sink of similar magnitude in the south (−1.4 ± 0.5 Pg C yr−1) corresponding mainly to an ocean sink. Largest differences across inversions occur in the balance between tropical land sources and southern land sinks. Interannual variability (IAV) in carbon fluxes is larger for land than ocean regions (standard deviation around 1.06 versus 0.33 Pg C yr−1 for the 1996–2007 period), with much higher consistency among the inversions for the land. While the tropical land explains most of the IAV (standard deviation ~ 0.65 Pg C yr−1), the northern and southern land also contribute (standard deviation ~ 0.39 Pg C yr−1). Most inversions tend to indicate an increase of the northern land carbon uptake from late 1990s to 2008 (around 0.1 Pg C yr−1, predominantly in North Asia. The mean seasonal cycle appears to be well constrained by the atmospheric data over the northern land (at the continental scale), but still highly dependent on the prior flux seasonality over the ocean. Finally we provide recommendations to interpret the regional fluxes, along with the uncertainty estimates.

Global air-sea surface carbon dioxide transfer velocity and flux estimated using 17 a altimeter data and a new algorithm

The global distributions of the air-seaCO(2) transfer velocity and flux are retrieved from TOPEX/Poseidon and Jason altimeter data from October 1992 to December 2009 using a combined algorithm. The 17 a average global, area-weighted, Schmidt number-corrected mean gas transfer velocity is 21.26 cm/h, and the full exploration of the uncertainty of this estimate awaits further data. The average total CO2 flux (calculated by carbon) from atmosphere to ocean during the 17 a was 2.58 Pg/a. The highest transfer velocity is in the circumpolar current area, because of constant high wind speeds and currents there. This results in strongCO(2) fluxes. CO2 fluxes are strong but opposite direction in the equatorial east Pacific Ocean, because the air-sea CO2 partial pressure difference is the largest in the global oceans. The results differ from the previous studies calculated using the wind speed. It is demonstrated that the air-sea transfer velocity is very important for estimating air-sea CO2 flux. It is critical to have an accurate estimation for improving calculation of CO2 flux within climate change studies.

앙상블 칼만 필터 기반 탄소추적시스템의 아시아 지역 탄소 순환 진단에의 적용

CO2 is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of CO2 on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric CO2 measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer CO2 observations. The root mean square error of the model CO2 calculated by the posterior flux is less than the model CO2 calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

Estimation of surface carbon fluxes with an advanced data assimilation methodology

We perform every 6 h a simultaneous data assimilation of surface CO2 fluxes and atmospheric CO2 concentrations along with meteorological variables using the Local Ensemble Transform Kalman Filter (LETKF) within an Observing System Simulation Experiments framework. In this paper, we focus on the impact of advanced variance inflation methods and vertical localization of column CO2 data on the analysis of CO2. With both additive inflation and adaptive multiplicative inflation, we are able to obtain encouraging multiseasonal analyses of surface CO2 fluxes in addition to atmospheric CO2 and meteorological analyses. Furthermore, we examine strategies for vertical localization in the assimilation of simulated CO2 from GOSAT that has nearly uniform sensitivity from the surface to the upper troposphere. Since atmospheric CO2is forced by surface fluxes, its short‐term variability should be largest near the surface. We take advantage of this by updating observed changes only into the lower tropospheric CO2 rather than into the full column. This results in a more accurate analysis of CO2 in terms of both RMS error and spatial patterns. Assimilating synthetic CO2ground‐based observations and CO2 retrievals from GOSAT and AIRS with the enhanced LETKF, we obtain an accurate estimation of the evolving surface fluxes even in the absence of any a priori information. We also test the system with a longer assimilation window and find that a short window with an efficient treatment for wind uncertainty is beneficial to flux inversion. Since this study assumes a perfect forecast model, future research will explore the impact of model errors.

Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data

Abstract. Satellite remote sensing provides continuous temporal and spatial information of terrestrial ecosystems. Using these remote sensing data and eddy flux measurements and biogeochemical models, such as the Terrestrial Ecosystem Model (TEM), should provide a more adequate quantification of carbon dynamics of terrestrial ecosystems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI) and carbon flux data of AmeriFlux to conduct such a study. We first modify the gross primary production (GPP) modeling in TEM by incorporating EVI and LSWI to account for the effects of the changes of canopy photosynthetic capacity, phenology and water stress. Second, we parameterize and verify the new version of TEM with eddy flux data. We then apply the model to the conterminous United States over the period 2000–2005 at a 0.05° × 0.05° spatial resolution. We find that the new version of TEM made improvement over the previous version and generally captured the expected temporal and spatial patterns of regional carbon dynamics. We estimate that regional GPP is between 7.02 and 7.78 Pg C yr−1 and net primary production (NPP) ranges from 3.81 to 4.38 Pg C yr−1 and net ecosystem production (NEP) varies within 0.08–0.73 Pg C yr−1 over the period 2000–2005 for the conterminous United States. The uncertainty due to parameterization is 0.34, 0.65 and 0.18 Pg C yr−1 for the regional estimates of GPP, NPP and NEP, respectively. The effects of extreme climate and disturbances such as severe drought in 2002 and destructive Hurricane Katrina in 2005 were captured by the model. Our study provides a new independent and more adequate measure of carbon fluxes for the conterminous United States, which will benefit studies of carbon-climate feedback and facilitate policy-making of carbon management and climate.

The application of a coupled hydrological and biogeochemical model (CHANGE) for modeling of energy, water, and CO2exchanges over a larch forest in eastern Siberia

[1] A coupled hydrological and biogeochemical model (CHANGE) that evaluates heat, water, and CO2 exchange between the biosphere and atmosphere across a spectrum of various time and space scales is described in this paper. The CHANGE model, which merges important components and functions in Arctic terrestrial ecosystems, is a process model with a self-constrained nature that is based on a complex and nonlinear interplay among hydrological, physiological, biochemical, ecological, and edaphic factors and meteorological conditions. This model was applied to a larch forest in eastern Siberia for the period of 1998–2006. A key objective was to assess the seasonal and interannual variability of the surface water, energy, and carbon fluxes over the larch forest in order to understand the responses of this ecosystem to climate change and to provide their controlling factors. Two types of simulations were performed with half-hourly and daily forcing data, and temporal correlation and other statistical measures supported the agreement between the simulations and observations. The simulated annual evapotranspiration (ET) ranged from 125 to 196 mm with a mean of 164 mm, 67% of which was contributed by transpiration. The simulated annual mean net ecosystem exchange (NEE) was −138.6 g C m−2 y−1 with a range of −79 to −195 g C m−2 y−1. The NEE variation was closely correlated with the net primary production (NPP). The simulation showed 23–43% interannual variability in heterotrophic respiration (Rh) for a mean of 273.1 g C m−2 y−1. Soil water was found to be a determinant that influences ET and CO2 fluxes in the larch forest. NEE was largely correlated to precipitation (PG). Thicker snow depth in the previous winter season contributed to higher NEE. The contribution of snow depth to NEE was significant in the dry years. The combination of summer PG and snow water resulted in higher NEE, which was found since 2004. In dry years, the access of roots to soil-thawed water alleviated soil water deficit and contributed to ecosystem net C uptake. The model sensitivity addressed the potential importance of the dynamic soil organic carbon on soil temperature.

Importance of fossil fuel emission uncertainties over Europe for CO<sub>2</sub> modeling: model intercomparison

Abstract. Inverse modeling techniques used to quantify surface carbon fluxes commonly assume that the uncertainty of fossil fuel CO2 (FFCO2) emissions is negligible and that intra-annual variations can be neglected. To investigate these assumptions, we analyzed the differences between four fossil fuel emission inventories with spatial and temporal differences over Europe and their impact on the model simulated CO2 concentration. Large temporal flux variations characterize the hourly fields (~40 % and ~80 % for the seasonal and diurnal cycles, peak-to-peak) and annual country totals differ by 10 % on average and up to 40 % for some countries (i.e., the Netherlands). These emissions have been prescribed to seven different transport models, resulting in 28 different FFCO2 concentrations fields. The modeled FFCO2 concentration time series at surface sites using time-varying emissions show larger seasonal cycles (+2 ppm at the Hungarian tall tower (HUN)) and smaller diurnal cycles in summer (−1 ppm at HUN) than when using constant emissions. The concentration range spanned by all simulations varies between stations, and is generally larger in winter (up to ~10 ppm peak-to-peak at HUN) than in summer (~5 ppm). The contribution of transport model differences to the simulated concentration std-dev is 2–3 times larger than the contribution of emission differences only, at typical European sites used in global inversions. These contributions to the hourly (monthly) std-dev's amount to ~1.2 (0.8) ppm and ~0.4 (0.3) ppm for transport and emissions, respectively. First comparisons of the modeled concentrations with 14C-based fossil fuel CO2 observations show that the large transport differences still hamper a quantitative evaluation/validation of the emission inventories. Changes in the estimated monthly biosphere flux (Fbio) over Europe, using two inverse modeling approaches, are relatively small (less that 5 %) while changes in annual Fbio (up to ~0.15 % GtC yr−1) are only slightly smaller than the differences in annual emission totals and around 30 % of the mean European ecosystem carbon sink. These results point to an urgent need to improve not only the transport models but also the assumed spatial and temporal distribution of fossil fuel emission inventories.

“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation

[1] In ensemble Kalman filter, space localization is used to reduce the impact of long-distance sampling errors in the ensemble estimation of the forecast error covariance. When two variables are not physically correlated, their error covariance is still estimated by the ensemble and, therefore, it is dominated by sampling errors. We introduce a “variable localization” method, zeroing out such covariances between unrelated variables to the problem of assimilating carbon dioxide concentrations into a dynamical model using the local ensemble transform Kalman filter (LETKF) in an observing system simulation experiments (OSSE) framework. A system where meteorological and carbon variables are simultaneously assimilated is used to estimate surface carbon fluxes that are not directly observed. A range of covariance structures are explored for the LETKF, with emphasis on configurations allowing nonzero error covariance between carbon variables and the wind field, which affects transport of atmospheric CO2, but not between CO2 and the other meteorological variables. Such variable localization scheme zeroes out the background error covariance among prognostic variables that are not physically related, thus reducing sampling errors. Results from the identical twin experiments show that the performance in the estimation of surface carbon fluxes obtained using variable localization is much better than that using a standard full covariance approach. The relative improvement increases when the surface fluxes change with time and model error becomes significant.

Multiple constraint analysis of regional land–surface carbon flux

We applied and compared bottom-up (process model-based) and top-down (atmospheric inversion-based) scaling approaches to evaluate the spatial and temporal patterns of net ecosystem production (NEP) over a 2.5 × 10 5 km 2 area (the state of Oregon) in the western United States. Both approaches indicated a carbon sink over this heterogeneous region in 2003 (a relatively warm, dry year in western Oregon) and 2007 (near normal), with carbon uptake primarily in forested and agricultural areas. The statewide mean NEP for 2007 using the bottom-up approach was 80 gC m −2 yr −1 , which compares with 145 gC m −2 yr −1 for the top-down approach. Seasonality of daily NEP at the ecoregion scale showed similar patterns across the two approaches, but with less sensitivity to seasonal drought in the top-down model. In 2003, simulated annual NEP was lower than in 2007 for both scaling approaches, but the reduction was stronger with the bottom-up approach. Estimates of mean NEP on forested lands from a forest inventory approach, and from the CarbonTracker inversion scheme, bracketed that of our bottom-up approach (ratios to bottom-up estimates were 1.3 and 0.3, respectively). These results support the need for a multiple constraint approach to evaluation of regional trace gas budgets.

Carbon dioxide atmospheric vertical profiles retrieved from space observation using ACE-FTS solar occultation instrument

Abstract. Major limitations of our present knowledge of the global distribution of CO2 in the atmosphere are the uncertainty in atmospheric transport and the sparseness of in situ concentration measurements. Limb viewing spaceborne sounders such as the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) offer a vertical resolution of a few kilometres for profiles, which is much better than currently flying or planned nadir sounding instruments can achieve. After having demonstrated the feasibility of obtaining CO2 vertical profiles in the 5–25 km altitude range with an accuracy of about 2 ppm in a previous study, we present here the results of five years of ACE-FTS observations in terms of monthly mean profiles of CO2 averaged over 10° latitude bands for northern mid-latitudes. These results are compared with in-situ aircraft measurements and with simulations from two different air transport models. Key features of the measured altitude distribution of CO2 are shown to be accurately reproduced by the ACE-FTS retrievals: variation in altitude of the seasonal cycle amplitude and extrema, seasonal change of the vertical gradient, and mean growth rate. We show that small but significant differences from model simulations could result from an over estimation of the model circulation strength during the northern hemisphere spring. Coupled with column measurements from a nadir viewing instrument, it is expected that occultation measurements will bring useful constraints to the surface carbon flux determination.

REDD Mitigation

Tropical deforestation is a major driver of climate change accounting for ∼12% of global anthropogenic CO2 emissions. A mitigation strategy named Reduction Emission from Deforestation and forest Degradation (REDD) has been developed to tackle emissions due to forest loss in developing countries. REDD will be the core instrument in any post-2012 climate agreement according to the final document of the 15th UN Conference of the Parties. Nonetheless, REDD's implementation presents several political and scientific challenges. A review of current and future deforestation estimates in terms of forest surface change, carbon densities, and carbon fluxes is under preparation to aid the scientific community. REDD mitigation potential estimates and a case study have also been examined. Preliminary results of this review are presented.

Surface fluxes and water balance of spatially varying vegetation within a small mountainous headwater catchment

Abstract. Precipitation variability and complex topography often create a mosaic of vegetation communities in mountainous headwater catchments, creating a challenge for measuring and interpreting energy and mass fluxes. Understanding the role of these communities in modulating energy, water and carbon fluxes is critical to quantifying the variability in energy, carbon, and water balances across landscapes. The focus of this paper was: (1) to demonstrate the utility of eddy covariance (EC) systems in estimating the evapotranspiration component of the water balance of complex headwater mountain catchments; and (2) to compare and contrast the seasonal surface energy and carbon fluxes across a headwater catchment characterized by large variability in precipitation and vegetation cover. Eddy covariance systems were used to measure surface fluxes over sagebrush (Artemesia arbuscula and Artemesia tridentada vaseyana), aspen (Populus tremuloides) and the understory of grasses and forbs beneath the aspen canopy. Peak leaf area index of the sagebrush, aspen, and aspen understory was 0.77, 1.35, and 1.20, respectively. The sagebrush and aspen canopies were subject to similar meteorological forces, while the understory of the aspen was sheltered from the wind. Missing periods of measured data were common and made it necessary to extrapolate measured fluxes to the missing periods using a combination of measured and simulated data. Estimated cumulative evapotranspiratation from the sagebrush, aspen trees, and aspen understory were 384 mm, 314 mm and 185 mm. A water balance of the catchment indicated that of the 699 mm of areal average precipitation, 421 mm was lost to evapotranspiration, and 254 mm of streamflow was measured from the catchment; water balance closure for the catchment was within 22 mm. Fluxes of latent heat and carbon for all sites were minimal through the winter. Growing season fluxes of latent heat and carbon were consistently higher above the aspen canopy than from the other sites. While growing season carbon fluxes were very similar for the sagebrush and aspen understory, latent heat fluxes for the sagebrush were consistently higher, likely because it is more exposed to the wind. Sensible heat flux from the aspen tended to be slightly less than the sagebrush site during the growing season when the leaves were actively transpiring, but exceeded that from the sagebrush in May, September and October when the net radiation was not offset by evaporative cooling in the aspen. Results from this study demonstrate the utility of EC systems in closing the water balance of headwater mountain catchments and illustrate the influence of vegetation on the spatial variability of surface fluxes across mountainous rangeland landscapes.