Development of an Ensemble-Based Data-Assimilation System for CO2 Fluxes Using ICON-ART

Observation based quantification of surface CO2 fluxes relies on the consistent integration of atmospheric observations with numerical transport models. We present the development and demonstration of an ensemble-based data assimilation system that couples atmospheric CO2 observations to the ICON-ART modeling framework using a Local Ensemble Transform Kalman Filter (LETKF).

 

Starting with a flux estimate provided by CarbonTracker Europe High-Resolution we start with a dynamic model with hourly resolution with a focus on fluxes in Europe for 2021. We then create an ensemble of perturbed prior fluxes within assumed uncertainties using prescribed spatial and temporal correlation structures. We simulate the transport of these ensemble members in ICON-ART in limited area mode, while varying the meteorological conditions to represent meteorological uncertainties. Subsequently, we use the LETKF to update the state vector of concentrations and CO2 fluxes daily, resulting in an posterior estimate of surface CO2 fluxes over Europe. 

 

This work provides the foundation for an ICON-ART-based CO2 flux assimilation system and establishes a technical basis for future extensions toward longer assimilation periods, refined error modeling, and the assimilation of anthropogenic emission signals.