Accounting for the effect of transport errors on tracer inversions

John C. Lin, Christoph Gerbig

Geophysical Research Letters · 2005 · 162 citations · 24 references

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TL;DR

Tracer flux estimates from concentration observations are biased by atmospheric transport errors, yet inverse methods typically ignore or only roughly assess these errors. The study proposes a method to quantitatively account for transport errors by incorporating wind uncertainties into stochastic parcel motions. Wind error statistics derived from assimilated‑radiosonde comparisons are propagated through stochastic parcel motions in the STILT Lagrangian model, and the method is illustrated with a simulated CO₂ inverse analysis over the continent to assess impacts on regional carbon flux estimates. The inverse analysis shows that transport errors produce significant bias in flux estimates, but the proposed method successfully corrects for these errors.

Abstract

Errors in atmospheric transport of tracers lead to errors in estimates of tracer fluxes based upon concentration observations. Typically, such “inverse” methods either neglect transport errors or only assess their effects roughly. We describe a method to quantitatively account for transport errors by incorporating uncertainties in winds into stochastic motions of air parcels. The magnitude of errors in wind fields, as well as their spatiotemporal covariances, are determined by direct comparison of assimilated winds to radiosonde observations. These statistics of transport errors are propagated through stochastic motions of air parcels in a Lagrangian model (STILT). We illustrate this method by conducting an inverse analysis using simulated CO 2 observations over the continent and examine the effect of transport errors on estimates of regional terrestrial carbon fluxes. The inverse analysis demonstrates that transport errors can cause significantly biased estimates. We show that the proposed method properly accounts for these errors.

References

24