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. We developed a carbon data assimilation system to estimate surfacecarbon fluxes using the local ensemble transform Kalman filter (LETKF) andatmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis ofthe meteorological field based on the Goddard Earth Observing System model,version 5 (GEOS-5). This assimilation system is inspired by the method ofKang et al. (2011, 2012), who estimated the surface carbon fluxes in anobserving system simulation experiment (OSSE) as evolving parameters in theassimilation of the atmospheric CO2 , using a short assimilation windowof 6 h. They included the assimilation of the standard meteorologicalvariables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variablelocalization”, and increased observation weights near the surface, theyobtained accurate surface carbon fluxes at grid-point resolution. Wedeveloped a new version of the local ensemble transform Kalmanfilter related to the “running-in-place”(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, thenew assimilation system uses the “no cost smoothing” algorithm for theLETKF (Kalnay et al., 2007b), which allows shifting the Kalmanfilter solution forward or backward within an assimilation window at no cost. In thenew scheme a long “observation window” (e.g., 7 d or longer) is used tocreate a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtainan accurate final analysis at 1 d. This new approach has the advantage ofbeing based on a short assimilation window, which makes it more accurate,and of having been exposed to the future 7 d observations, which improvesthe analysis and accelerates the spin-up. The assimilation and observationwindows are then shifted forward by 1 d, and the process is repeated.This reduces significantly the analysis error, suggesting that the newlydeveloped assimilation method can be used with other Earth system models,especially in order to make greater use of observations in conjunction withmodels.

geoscientific model development

Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1