Using satellite measurements of carbon dioxide to diagnose carbon balance in Central Siberia

A significant part of anthropogenic emissions of the main greenhouse gas – CO₂ – is absorbed by terrestrial ecosystems, which determines the key role of studying ecosystem carbon fluxes (ECF) in the context of climate change research. ECF estimates are also in demand for applied purposes, such as, for example, developing national low-carbon strategy policies. Hence, obtaining accurate estimate of these fluxes is an urgent task for both Russia and many other countries.

To study and evaluate the ECF, inverse modeling methods that involve searching for agreement between measurements of the atmospheric CO₂ mixing ratio and corresponding model simulations have been widely used. Most frequently, this approach involves global chemistry transport models (CTM). However, due to their low spatial resolution, such models can hardly ensure sufficient accuracy for regional carbon balance estimates that are becoming increasingly in demand in recent years. To obtain reliable estimates of the ECF in the regions of Russia, the regional inverse modeling system RIGGO (Regional inversion of greenhouse gases observations) is being developed at the IAP RAS. The RIGGO system includes the ecosystem exchange model VPRM (Vegetation Photosynthesis and Respiration Model), the CHIMERE CTM, the WRF meteorological model, and an original software for processing and analyzing model and measurement data [1]. ECF are estimated by optimizing the VPRM parameters.

In this work, RIGGO was used to study the ECF in Central Siberia. It was found that optimization of the VPRM parameters based on OCO-2 satellite CO₂ measurements resulted in a more than twofold narrowing of the uncertainty interval of the a priori integral estimate of net ecosystem productivity (NEP) for the warm period of the year. The posterior NEP estimate was compared with an estimate based on the GFED inventory estimate and both estimates were found to agree within the confidence intervals. The resulting gross primary productivity (GPP) estimates showed good agreement with the corresponding estimates from the well-known satellite data product MOD17A2HGF, with a difference of less than 15%. To validate the results of the ECF estimation, net ecosystem exchange (NEE) measurements taken at two towers of the ZOTTO observation station were used. Optimization of the VPRM parameters improved the agreement between the modeled and measured NEE values by reducing the standard deviation and increasing the correlation coefficient for the corresponding hourly NEE values.