Abstract
Abstract. Imperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosonde-derived mixing heights and mixing heights derived from ECMWF meteorological data during May–June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixing layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that flux inversions employing transport models based on current generation meteorological products have misrepresented an important part of the model error structure likely leading to biases in the estimated mean and uncertainties. We strongly recommend including the solution presented in this work: better, higher resolution atmospheric models, a proper description of correlated random errors, and a modification of the overall sampling strategy.
Highlights
Exchange of CO2 and other greenhouse gases between the surface and the atmosphere leaves atmospheric signatures behind that can be used to retrieve information about the surface fluxes
For CO2, biosphere-atmosphere fluxes can be assessed by a range of methods – each covering specific spatial scales, including eddy-covariance measurements (Baldocchi et al, 2001) with flux-footprints extending over ∼1 km2, remote sensing driven diagnostic light use efficiency (LUE) models (Lin et al, 2006; Running et al, 2004) with resolutions of several hundred meters, more process based biosphere models covering multiple scales (Moorcroft et al, 2001; Running and Hunt Jr., 1993), and atmospheric inversions of measured trace gas mixing ratios
Offline transport simulations use profiles of temperature, humidity, and horizontal winds from forecasted or analyzed meteorological fields to determine the profile of turbulent mixing within the boundary layer, or to determine the mixing height, which can be regarded as the altitude up to which surface fluxes are mixed on short timescales
Summary
Exchange of CO2 and other greenhouse gases between the surface and the atmosphere leaves atmospheric signatures behind that can be used to retrieve information about the surface fluxes. For CO2, biosphere-atmosphere fluxes can be assessed by a range of methods – each covering specific spatial scales, including eddy-covariance measurements (Baldocchi et al, 2001) with flux-footprints extending over ∼1 km, remote sensing driven diagnostic light use efficiency (LUE) models (Lin et al, 2006; Running et al, 2004) with resolutions of several hundred meters, more process based biosphere models covering multiple scales (Moorcroft et al, 2001; Running and Hunt Jr., 1993), and atmospheric inversions of measured trace gas mixing ratios (the so called top-down method). When combined with prior flux information for example from eddy flux measurements and remote sensing, regional scale inversions start to become feasible (Matross et al, 2006)
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