An ensemble data assimilation system to estimate CO₂ surface fluxes from atmospheric trace gas observations

Abstract

[1] We present a data assimilation system to estimate surface fluxes of CO 2 and other trace gases from observations of their atmospheric abundances. The system is based on ensemble data assimilation methods under development for Numerical Weather Prediction (NWP) and is the first of its kind to be used for CO 2 flux estimation. The system was developed to overcome computational limitations encountered when a large number of observations are used to estimate a large number of unknown surface fluxes. The ensemble data assimilation approach is attractive because it returns an approximation of the covariance, does not need an adjoint model or other linearization of the observation operator, and offers the possibility to optimize fluxes of chemically active trace gases (e.g., CH 4 , CO) in the same framework. We assess the performance of this new system in a pseudodata experiment that resembles the real problem we will apply this system to. The sensitivity of the method to the choice of several parameters such as the assimilation window size and the number of ensemble members is investigated. We conclude that the system is able to provide satisfactory flux estimates for the relatively large scales resolved by our current observing network and that the loss of information in the approximated covariances is an acceptable price to pay for the efficient computation of a large number of surface fluxes. The full potential of this data assimilation system will be used for near-real time operational estimates of North American CO 2 fluxes. This will take advantage of the large amounts of atmospheric data that will be collected by NOAA-CMDL in conjunction with the implementation of the North American Carbon Program (NACP).Citation: Peters, W., J. B. Miller, J. Whitaker, A. S. Denning, A. Hirsch, M. C. Krol, D. Zupanski, L. Bruhwiler, and P. P. , An ensemble data assimilation system to estimate CO 2 surface fluxes from atmospheric trace gas observations, J. Geophys.

Applications of information theory in ensemble data assimilation

“…As a result of this progress, many different variants of the EnKF have evolved (e.g. Houtekamer and Mitchell, 1998;Pham et al, 1998;Lermusiaux and Robinson, 1999;Hamill and Snyder, 2000;Keppenne, 2000;Mitchell and Houtekamer, 2000;Anderson, 2001;Bishop et al, 2001;Pham, 2001;van Leeuwen, 2001;Reichle et al, 2002a,b;Whitaker and Hamill, 2002;Hoteit et al, 2002Hoteit et al, , 2003Tippett et al, 2003;Zhang et al, 2004;Ott et al, 2005;Peters et al, 2005;Szunyogh et al, 2005;Zupanski, 2005;, just to mention some). While there are respectable differences between different variants of the EnKF, they are all closely related in data assimilation problems involving Gaussian Probability Density Functions (PDFs) and linear dynamical forecast models.…”

A fundamental theorem of Earth’s surface modelling

“…NOAA's Earth System Research Laboratory (ESRL) built a new data assimilation system to integrate the diversity of data into a consistent estimate of surface CO 2 exchange (Peters et al 2005). This CarbonTracker system can retroactively analyze the recent flux history of CO 2 , using a state-of-the-art atmospheric transport model coupled to an ensemble Kalman filter.…”

Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

“…CO 2 tt and CO 2 ff mole fraction measurements are used to invert for CO 2 ff and CO 2 bio fluxes in scenario 3. decaying function of the distance between two grid points. The Spatial Correlation Length (SCL) at which the correlation between two separated grid points is less than 0.5 is defined to characterize the spatial correlation in the prior flux error structures corresponding to off-diagonal elements in B matrix (Houweling et al, 2004;Peters et al, 2005;Saide et al, 2011;Wu et al, 2011). Neither the random error magnitude nor the spatially correlated error structures are well known.…”