Atmospheric inverse modeling to constrain regional-scale CO2budgets at high spatial and temporal resolution (original) (raw)
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Journal of Geophysical Research, 2010
1] We present an atmospheric inverse modeling framework to constrain terrestrial biosphere CO 2 exchange processes at subregional scales. The model is operated at very high spatial and temporal resolution, using the state of Oregon in the northwestern United States as the model domain. The modeling framework includes mesoscale atmospheric simulations coupled to Lagrangian transport, a biosphere flux model that considers, e.g., the effects of drought stress and disturbance on photosynthesis and respiration CO 2 fluxes, and a Bayesian optimization approach. This study focuses on the impact of uncertainties in advected background mixing ratios and fossil fuel emissions on simulated flux fields, both taken from external data sets. We found the simulations to be highly sensitive to systematic changes in advected background CO 2 , while shifts in fossil fuel emissions played a minor role. Correcting for offsets in the background mixing ratios shifted annual CO 2 budgets by about 47% and improved the correspondence with the output produced by bottom-up modeling frameworks. Inversion results were robust against shifts in fossil fuel emissions, which is likely a consequence of relatively low emission rates in Oregon. Citation: Göckede, M., D. P. Turner, A. M. Michalak, D. Vickers, and B. E. Law (2010), Sensitivity of a subregional scale atmospheric inverse CO 2 modeling framework to boundary conditions,
Terrestrial biospheric models (TBMs) are used to extrapolate local observations and process-level understanding of land-atmosphere carbon exchange to larger regions, and serve as predictive tools for examining carbon-climate interactions. Understanding the performance of TBMs is thus crucial to the carbon cycle and climate science communities. In this study, we present and assess an approach to evaluating the spatiotemporal patterns, rather than aggregated magnitudes, of net ecosystem exchange (NEE) simulated by TBMs using atmospheric CO 2 measurements. The approach is based on statistical model selection implemented within a high-resolution atmospheric inverse model. Using synthetic data experiments, we find that current atmospheric observations are sensitive to the underlying spatiotemporal flux variability at sub-biome scales for a large portion of North America, and that atmospheric observations can therefore be used to evaluate simulated spatiotemporal flux patterns as well as to differentiate between multiple competing TBMs. Experiments using real atmospheric observations and four prototypical TBMs further confirm the applicability of the method, and demonstrate that the performance of TBMs in simulating the spatiotemporal patterns of NEE varies substantially across seasons, with best performance during the growing season and more limited skill during transition seasons. This result is consistent with previous work showing that the ability of TBMs to model flux magnitudes is also seasonally-dependent. Overall, the proposed approach provides a new avenue for evaluating TBM performance based on sub-biome-scale flux patterns, presenting an opportunity for assessing and informing model development using atmospheric observations.
Regional net carbon fluxes of terrestrial ecosystems could be estimated with either biogeochemistry models by assimilating surface carbon flux measurements or atmospheric CO 2 inversions by assimilating observations of atmospheric CO 2 concentrations. Here we combine the ecosystem biogeochemistry modeling and atmospheric 5 CO 2 inverse modeling to investigate the magnitude and spatial distribution of the terrestrial ecosystem CO 2 sources and sinks. First, we constrain a terrestrial ecosystem model (TEM) at site level by assimilating the observed net ecosystem production (NEP) for various plant functional types. We find that the uncertainties of model parameters are reduced up to 90 % and model predictability is greatly improved for all 10 the plant functional types (coefficients of determination are enhanced up to 0.73). We then extrapolate the model to a global scale at a 0.5 • × 0.5 • resolution to estimate the large-scale terrestrial ecosystem CO 2 fluxes, which serve as prior for atmospheric CO 2 inversion. Second, we constrain the large-scale terrestrial CO 2 fluxes by assimilating the GLOBALVIEW-CO2 and mid-tropospheric CO 2 retrievals from the Atmospheric In-15 frared Sounder (AIRS) into an atmospheric transport model (GEOS-Chem). The transport inversion estimates that: (1) the annual terrestrial ecosystem carbon sink in 2003 is −2.47 Pg C yr −1 , which agrees reasonably well with the most recent inter-comparison studies of CO 2 inversions (−2.82 Pg C yr −1 ); (2) North America temperate, Europe and Eurasia temperate regions act as major terrestrial carbon sinks; and The posterior 20 transport model is able to reasonably reproduce the atmospheric CO 2 concentrations, which are validated against Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) CO 2 concentration data. This study indicates that biogeochemistry modeling or atmospheric transport and inverse modeling alone might not be able to well quantify regional terrestrial carbon fluxes. However, combining the two modeling 25 approaches and assimilating data of surface carbon flux as well as atmospheric CO 2 mixing ratios might significantly improve the quantification of terrestrial carbon fluxes.
2002
It is important to improve estimates of large-scale carbon fluxes over the boreal forest because the responses of this biome to global change may influence the dynamics of atmospheric carbon dioxide in ways that may influence the magnitude of climate change. Two methods currently being used to estimate these fluxes are process-based modeling by terrestrial biosphere models (TBMs), and atmospheric inversions in which fluxes are derived from a set of observations on atmospheric CO 2 concentrations via an atmospheric transport model. Inversions do not reveal information about processes and therefore do not allow for predictions of future fluxes, while the process-based flux estimates are not necessarily consistent with atmospheric observations of CO 2 . In this study we combine the two methods by using the fluxes from four TBMs as a priori fluxes for an atmospheric Bayesian Synthesis Inversion. By doing so we learn about both approaches. The results from the inversion indicate where the results of the TBMs disagree with the atmospheric observations of CO 2 , and where the results of the inversion are poorly constrained by atmospheric data, the process-based estimates determine the flux results. The analysis indicates that the TBMs are modeling the spring uptake of CO 2 too early, and that the inversion shows large uncertainty and more dependence on the initial conditions over Europe and Boreal Asia than Boreal North America. This uncertainty is related to the scarcity of data over the continents, and as this problem is not likely to be solved in the near future, TBMs will need to be developed and improved, as they are likely the best option for understanding the impact of climate variability in these regions. Now at NCAR,
Sources and sinks of atmospheric carbon dioxide largely control future climate. They moderate the fraction of emitted carbon which remains in the atmosphere, the main anthropogenic driver of global warming. However the sources and sinks are hard to measure directly. Therefore they are estimated using inverse models which combine a prior estimate, inferred from characteristics of biosphere and oceans, with atmospheric measurements and adjust the sources and sinks to fit the atmospheric measurements. Deriving a robust estimate of the global distribution of sources and sinks requires estimating systematic errors. Previous studies investigated, among other parameters, the uncertainty due to atmospheric transport which connects surface fluxes to atmospheric measurements. This study is the first to investigate the uncertainty of surface fluxes due to the choice of the inverse method and the representation of fluxes using real measurements and two well-established inverse models. The inverse models are run with harmonized input, prior fluxes, measurements, atmospheric transport and flux covariance. Comparing the mismatch between the calculated atmospheric concentration and measurements which are not used to adjust the fluxes, gives an estimate of the quality of the inversion. The difference of this mismatch between the models is smaller than the uncertainty of the mismatch. Therefore differences in the fluxes estimated by the different models provide an estimate for the contribution of inter-model errors to the uncertainty of estimated sources and sinks. For the sink in North America, where the density of measurement sites is highest, this study finds a lower limit for the uncertainty of 0.1 Pg carbon per year, about 10% of the estimated biospheric sink. For other continents the uncertainty is on the order of 0.25 Pg carbon per year. Varying the number of observation sites used in the models showed that this uncertainty is controlled by the density of measurements. Integrating additional measurements reduces the uncertainty due to differences between the models. To investigate the effect of complementary observations, measurements of the aggregated vertical column of CO₂ from ground-based spectrometers were implemented in one of the models. Evaluating the calculated fluxes when using these additional measurements showed that total column measurements correct mismatches introduced by using temporally sparse aircraft measurements. The strength of the Eurasian biospheric sink was derived as 3.5 ± 1 Pg carbon per year, and it was shown that a robust estimate of the European sink requires at least one additional measurement site in boreal Asia. This study completes the assessment of different contributions to the uncertainty of inverse source/sink estimates of CO₂. It shows that adding measurements decreases the uncertainty of the estimated fluxes due to differences between the models and that total column measurements complement in-situ measurements. To this end it implements usage of ground-based total column measurements for inverse modelling which lays the foundation for adding more measurement sources, from ground as well as from satellite.
Journal of Geophysical Research: Atmospheres, 2003
We analyze observations of CO 2 and CO over North America acquired during the CO 2 Budget and Rectification Airborne (COBRA) study in 2000. The COBRA dataset is unique in its dense spatial coverage and extensive profiling in the lower atmosphere. Statistical analyses indicate that CO 2 mixed layer averages can be determined from vertical profiles with an accuracy limited by atmospheric variance to ±0.2 ppm. The data show that models require horizontal resolution of smaller than 30 km to fully resolve spatial variations of atmospheric CO 2 near the earth's surface and to avoid inaccuracies due to representativeness error. Strong signatures of land surface fluxes were observed in the active and relic mixed layers of the atmosphere. We present a "receptor-oriented" analysis framework to quantitatively link concentrations at measurement locations (receptors) to surface fluxes in upwind regions. The analysis incorporates two main components: 1) the Stochastic Time-Inverted Lagrangian Transport (STILT) model, driven with assimilated winds and running backward in time to map out the source-receptor relationship (footprint) at high temporal and spatial resolution, and 2) a parameterization for biosphere-atmosphere fluxes, derived from the AmeriFlux network of eddy covariance measurements, that serves as a "first-guess" for fluxes. Combining these components with an observationbased lateral boundary condition for CO 2 allows quantitative comparison between the top-down constraint on fluxes from airborne observations of CO 2 , with the bottom-up constraint of eddy flux measurements. Discrepancies between simulated and observed CO 2 distributions are assessed to indicate where improvements are needed, including representation of biosphere-atmosphere fluxes and convective processes in atmospheric transport.
Atmospheric Chemistry and Physics, 2011
We have implemented six different inverse carbon flux estimation methods in a regional carbon dioxide (CO 2) flux modeling system for The Netherlands. The system consists of the Regional Atmospheric Mesoscale Modeling System (RAMS) coupled to a simple carbon flux scheme which is run in a coupled fashion on relatively high resolution (10 km). Using an Ensemble Kalman filter approach we try to estimate spatiotemporal carbon exchange patterns from atmospheric CO 2 mole fractions over The Netherlands for a two week period in spring 2008. The focus of this work is the different strategies that can be employed to turn first-guess fluxes into optimal ones, which is known as a fundamental design choice that can affect the outcome of an inversion significantly. Different state-of-the-art approaches with respect to the estimation of net ecosystem exchange (NEE) are compared quantitatively: (1) where NEE is scaled by one linear multiplication factor per land-use type, (2) where the same is done for photosynthesis (GPP) and respiration (R) separately with varying assumptions for the correlation structure, (3) where we solve for those same multiplication factors but now for each grid box, and (4) where we optimize physical parameters of the underlying biosphere model for each land-use type. The pattern to be retrieved in this pseudo-data experiment is different in nearly all aspects from the first-guess fluxes, including the structure of the underlying flux model, reflecting the difference between the modeled fluxes and the fluxes in the real world. This makes our study a stringent test of the performance of these methods, which are currently widely used in carbon cycle inverse studies. Our results show that all methods struggle to retrieve the spatiotemporal NEE distribution, and none of them succeeds in finding accurate domain averaged NEE with correct spatial and temporal behavior. The main cause is the difference between the structures of the first-guess and true CO 2 flux models used. Most methods display overconfidence in their estimate as a result. A commonly used daytime-only sampling scheme in the transport model leads to compensating biases in separate GPP and R scaling factors that are readily visible in the nighttime mixing ratio predictions of these 3356
Optimized biogenic carbon fluxes for Europe were estimated from high resolution regional scale inversions, utilizing atmospheric CO 2 measurements at 16 stations for the year 2007. Additional sensitivity tests with different data-driven error structures were performed. As the atmospheric network is rather sparse and consequently contains large spatial gaps, we use a priori biospheric fluxes to further constrain the inversions. The biospheric fluxes were simulated by the Vegetation Photosynthesis and Respiration Model (VPRM) at a resolution of 0.1º and optimized against Eddy covariance data. Overall we estimate an a priori uncertainty of 0.54 GtC y-1 related to the poor spatial representation between the biospheric model and the ecosystem sites. The sink estimated from the atmospheric inversions for the area of Europe (as represented in the model domain) ranges between 0.23 and 0.38 GtC y-1 (0.30 and 0.49 GtC y-1 up-scaled to geographical Europe). This is within the range of posterior flux uncertainty estimates of previous studies using ground based observations.
Investigating spatial differentiation of model parameters in a carbon cycle data assimilation system
Global Biogeochemical Cycles, 2011
Better estimates of the net exchange of CO 2 between the atmosphere and the terrestrial biosphere are urgently needed to improve predictions of future CO 2 levels in the atmosphere. The carbon cycle data assimilation system (CCDAS) offers the capability of inversion, while it is at the same time based on a process model that can be used independent of observational data. CCDAS allows the assimilation of atmospheric CO 2 concentrations into the terrestrial biosphere model BETHY, constraining its process parameters via an adjoint approach. Here, we investigate the effect of spatial differentiation of a universal carbon balance parameter of BETHY on posterior net CO 2 fluxes and their uncertainties. The parameter, b, determines the characteristics of the slowly decomposing soil carbon pool and represents processes that are difficult to model explicitly. Two cases are studied with an assimilation period of 1979 to 2003. In the base case, there is a separate b for each plant functional type (PFT). In the regionalization case, b is differentiated not only by PFT, but also according to each of 11 large continental regions as used by the TransCom project. We find that the choice of spatial differentiation has a profound impact not only on the posterior (optimized) fluxes and their uncertainties, but even more so on the spatial covariance of the uncertainties. Differences are most pronounced in tropical regions, where observations are sparse. While regionalization leads to an improved fit to the observations by about 20% compared to the base case, we notice large spatial variations in the posterior net CO 2 flux on a grid cell level. The results illustrate the need for universal process formulations in global-scale atmospheric CO 2 inversion studies, at least as long as the observational network is too sparse to resolve spatial fluctuations at the regional scale.