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Forecasting atmospheric CO2 daily at the global scale with a goodaccuracy like it is done for the weather is a challenging task. However, itis also one of the key areas of development to bridge the gaps betweenweather, air quality and climate models. The challenge stems from the factthat atmospheric CO2 is largely controlled by the CO2 fluxes at thesurface, which are difficult to constrain with observations. In particular,the biogenic fluxes simulated by land surface models show skill in detectingsynoptic and regional-scale disturbances up to sub-seasonal time-scales, butthey are subject to large seasonal and annual budget errors at global scale,usually requiring a posteriori adjustment. This paper presents a scheme todiagnose and mitigate model errors associated with biogenic fluxes within anatmospheric CO2 forecasting system. The scheme is an adaptive scalingprocedure referred to as a biogenic flux adjustment scheme (BFAS), and it can beapplied automatically in real time throughout the forecast. The BFAS methodgenerally improves the continental budget of CO2 fluxes in the model bycombining information from three sources: (1) retrospective fluxes estimatedby a global flux inversion system, (2) land-use information, (3) simulatedfluxes from the model. The method is shown to produce enhanced skill in thedaily CO2 10-day forecasts without requiring continuous manualintervention. Therefore, it is particularly suitable for near-real-timeCO2 analysis and forecasting systems

atmospheric chemistry and physics

A biogenic CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; analyses and forecasts