The importance of transport model uncertainties for the estimation of CO<sub>2</sub> sources and sinks using satellite measurements

Abstract

This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO₂ using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO₂ lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO₂. The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common CO₂ fluxes and initial concentrations. Simulated column averaged CO₂ (<i>x</i>CO₂) mixing ratios vary between the models by &sigma;=0.5 ppm over the continents and &sigma;=0.27 ppm over sea. A variable, but overall quite encouraging agreement is found in comparison with FTS measurements at Park Falls, Darwin, Spitsbergen, and Bremen. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 Pg C/yr per 10⁶ km² over land and 0.03 Pg C/yr per 10⁶ km² over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 Pg C/yr per 10⁶ km², and could also limit the overall performance of other CO₂ missions such as GOSAT. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO₂ not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Further development of these models should receive high priority

Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurements

“…In land retrievals, middle-to high-latitude areas have relatively larger biases and in ocean retrievals no latitude-dependent bias can be clearly seen. The target observations have a station-to-station variability around 0.35 ppm which approaches the systematic error required for regional CO 2 source-sink determination (Chevallier et al, 2005;Houweling et al, 2010;Chevallier et al, 2014b, a). The better comparison with TCCON for target mode retrievals compared to regular land retrievals could be attributed to the fact that under the target mode the OCO-2 satellite is directly looking at the place where TCCON sites are located and this provides a better collocation and therefore prevents apparent biases caused by local X CO 2 variations.…”

Large amplitude spatial and temporal gradients in atmospheric boundary layer CO2mole fractions detected with a tower‐based network in the U.S. upper Midwest

“…Previous studies of daily scale gradients have largely been limited to temporal analyses at single sites [ Hurwitz et al , 2004], modeling [ Wang et al , 2007] and aircraft studies [ Chan et al , 2004; Gerbig et al , 2003]. While there are numerous satellite remote sensing design studies [ Houweling et al , 2010], real data to inform these studies are scarce [ Lin et al , 2004].…”

Constraining fossil fuel CO&lt;sub&gt;2&lt;/sub&gt; emissions from urban area using OCO-2 observations of total column CO&lt;sub&gt;2&lt;/sub&gt;

“…10.6 km at nadir of OCO-2). Secondly, atmospheric transport modeling errors and the error correlations has been identified as a major source of uncertainty in the top-down method (Houweling et al, 2010;10 Lauvaux et al, 2012;Lauvaux and Davis, 2014;Lin and Gerbig, 2005;Miller et al, 2015). The improvement of model physics and/or data assimilation is required to reduce the systematic and random errors in the simulated meteorological conditions and to ensure policy-level requirements on annual emissions (less than 10% error) .…”