Julia Marshall

An intercomparison of inverse models for estimating sources and sinks of CO2 using GOSAT measurements

This study presents the outcome of an inverse modeling intercomparison experiment on the use of total column CO2 retrievals from Greenhouse Gas Observing Satellite (GOSAT) for quantifying global sources and sinks of CO2. Eight research groups submitted inverse modeling results for the first year of GOSAT measurements. Inversions were carried out using only GOSAT data, a combination of GOSAT and surface measurements, and using only surface measurements. As expected, the most robust flux estimates are obtained at large scales (e.g., within 20% of the annual flux at the global scale), and they quickly diverge toward the scale of the subcontinental TRANSCOM regions and beyond (to >100% of the annual flux). We focus our analysis on a shift in the CO2 uptake over land from the Tropics toward the Northern Hemisphere Extra tropics of ∼1 PgC/yr when GOSAT data are used in the inversions. This shift is largely driven by TRANSCOM regions Europe and Northern Africa, showing, respectively, an increased uptake and release of 0.7 and 0.9 PgC/yr. Inversions using GOSAT data show a reduced gradient between midlatitudes of the Northern Hemisphere and the Tropics, consistent with the latitudinal shift in carbon uptake. However, the reduced gradients degrade the agreement with background aircraft and surface measurements. To narrow the range of inversion-derived flux, estimates will require further efforts to understand the differences not only between the retrieval schemes but also between inverse models, as their contributions to the overall uncertainty are estimated to be of similar magnitude.

How Much CO2 Is Taken Up by the European Terrestrial Biosphere

The conventional and established estimates of the amount of carbon taken up by the European terrestrial biosphere from the Atlantic to the Urals rely on two conceptually different types of ground-based measurements. On the one hand, in situ measurements of atmospheric CO2 concentrations are globally obtained at about 100 sites on a regular basis. On the other hand, conventional bottom-up estimates of surface carbon fluxes are obtained from field measurements. Additional in situ measurement sites are needed to better constrain the surface fluxes of the northeastern part of Europe with inverse models, where the strongest uptake is expected. Field campaigns in this region, including flux and biomass measurements, can contribute to bottom-up estimates and serve as an additional anchor point for ABC satellite measurements. Regularly updated inventories and land cover classification are also essential for reliable bottom-up estimates. Likewise, reliable estimates of the flux uncertainties from bottom-up methods that should include all kinds of upscaling uncertainties and propagated measurement errors are essential. In addition to the continuation of existing satellite missions, new satellite missions are needed to provide denser and more accurate and precise measurements of the atmospheric CO2 concentration.

Error budget of the MEthane Remote LIdar missioN (MERLIN) and its impact on the uncertainties of the global methane budget.

MEthane Remote LIdar missioN (MERLIN) is a German-French space mission, scheduled for launch in 2024 and built around an innovative light detecting and ranging instrument that will retrieve methane atmospheric weighted columns. MERLIN products will be assimilated into chemistry transport models to infer methane emissions and sinks. Here the expected performance of MERLIN to reduce uncertainties on methane emissions is estimated. A first complete error budget of the mission is proposed based on an analysis of the plausible causes of random and systematic errors. Systematic errors are spatially and temporally distributed on geophysical variables and then aggregated into an ensemble of 32 scenarios. Observing System Simulation Experiments are conducted, originally carrying both random and systematic errors. Although relatively small (±2.9 ppb), systematic errors are found to have a larger influence on MERLIN performances than random errors. The expected global mean uncertainty reduction on methane emissions compared to the prior knowledge is found to be 32%, limited by the impact of systematic errors. The uncertainty reduction over land reaches 60% when the largest desert regions are removed. At the latitudinal scale, the largest uncertainty reductions are achieved for temperate regions (84%) and then tropics (56%) and high latitudes (53%). Similar Observing System Simulation Experiments based on error scenarios for Greenhouse Gases Observing SATellite reveal that MERLIN should perform better than Greenhouse Gases Observing SATellite for most continental regions. The integration of error scenarios for MERLIN in another inversion system suggests similar results, albeit more optimistic in terms of uncertainty reduction.