Abstract
Abstract. Data assimilation systems allow for estimating surface fluxes of greenhouse gases from atmospheric concentration measurements. Good knowledge about fluxes is essential to understand how climate change affects ecosystems and to characterize feedback mechanisms. Based on the assimilation of more than 1 year of atmospheric in situ concentration measurements, we compare the performance of two established data assimilation models, CarbonTracker and TM5-4DVar (Transport Model 5 – Four-Dimensional Variational model), for CO2 flux estimation. CarbonTracker uses an ensemble Kalman filter method to optimize fluxes on ecoregions. TM5-4DVar employs a 4-D variational method and optimizes fluxes on a 6° × 4° longitude–latitude grid. Harmonizing the input data allows for analyzing the strengths and weaknesses of the two approaches by direct comparison of the modeled concentrations and the estimated fluxes. We further assess the sensitivity of the two approaches to the density of observations and operational parameters such as the length of the assimilation time window. Our results show that both models provide optimized CO2 concentration fields of similar quality. In Antarctica CarbonTracker underestimates the wintertime CO2 concentrations, since its 5-week assimilation window does not allow for adjusting the distant surface fluxes in response to the detected concentration mismatch. Flux estimates by CarbonTracker and TM5-4DVar are consistent and robust for regions with good observation coverage, regions with low observation coverage reveal significant differences. In South America, the fluxes estimated by TM5-4DVar suffer from limited representativeness of the few observations. For the North American continent, mimicking the historical increase of the measurement network density shows improving agreement between CarbonTracker and TM5-4DVar flux estimates for increasing observation density.
Highlights
Sources and sinks of atmospheric carbon dioxide (CO2) largely control future climate change (Schimel, 2007)
Whereas the ensemble square root filter (EnSRF) in CarbonTracker reduces the dimension of the minimization problem of Eq (1) by solving sequentially for time-sliced state vectors, the 4DVar method in Transport Model 5 (TM5)-4DVar leaves the dimension of the state vector intact and approximates the solution using a limited set of search directions, corresponding to the dominant singular vectors of the inverse problem to approach the minimum of the cost function step by step
Our study evaluates the performance of the data assimilation models CarbonTracker and TM5-4DVar by comparing their a posteriori CO2 concentration fields to measurements and by comparing their a posteriori surface fluxes
Summary
Sources and sinks of atmospheric carbon dioxide (CO2) largely control future climate change (Schimel, 2007). They represent sources and sinks of carbon differently, for example by binning them by vegetation type or on a longitude–latitude grid They relate sources and sinks to observed atmospheric abundances using different air-mass transport models (Gurney et al, 2004, estimate their impact on fluxes). To analyze the impact from the representation of sources and sinks and from the inverse method, it is necessary to harmonize the observational constraints, the transport model and the prior concentration, and flux and flux covariance estimates between the approaches which are compared. Chatterjee and Michalak (2013) were the first to evaluate the performance of the two methods for the purpose of CO2 surface flux estimation They use a synthetic setup with simulated observations and a onedimensional transport model which has the advantage of knowing the true fluxes and for which a direct Bayesian inversion is computationally feasible.
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