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

Anna Agustí-Panareda,Anton Beljaars,Emanuel Dutra,Souhail Boussetta,Gianpaolo Balsamo,Sébastien Massart,Frédéric Chevallier

doi:10.5194/acp-16-10399-2016

Abstract

Abstract. Forecasting atmospheric CO2 daily at the global scale with a good accuracy like it is done for the weather is a challenging task. However, it is also one of the key areas of development to bridge the gaps between weather, air quality and climate models. The challenge stems from the fact that atmospheric CO2 is largely controlled by the CO2 fluxes at the surface, which are difficult to constrain with observations. In particular, the biogenic fluxes simulated by land surface models show skill in detecting synoptic and regional-scale disturbances up to sub-seasonal time-scales, but they are subject to large seasonal and annual budget errors at global scale, usually requiring a posteriori adjustment. This paper presents a scheme to diagnose and mitigate model errors associated with biogenic fluxes within an atmospheric CO2 forecasting system. The scheme is an adaptive scaling procedure referred to as a biogenic flux adjustment scheme (BFAS), and it can be applied automatically in real time throughout the forecast. The BFAS method generally improves the continental budget of CO2 fluxes in the model by combining information from three sources: (1) retrospective fluxes estimated by a global flux inversion system, (2) land-use information, (3) simulated fluxes from the model. The method is shown to produce enhanced skill in the daily CO2 10-day forecasts without requiring continuous manual intervention. Therefore, it is particularly suitable for near-real-time CO2 analysis and forecasting systems.

Highlights

Earth-observing strategies focusing on carbon cycle systematic monitoring from satellites, flask and in situ networks (Ciais et al, 2014; Denning et al, 2005) are leading to an increasing number of near-real-time observations available to systems such as those developed in the framework of the European Union Copernicus Atmosphere Monitoring Service (CAMS)
This paper addresses the challenge of designing an online bias correction for an atmospheric CO2 analysis/forecasting system
The overarching aim is to deliver an atmospheric CO2 analysis and forecast that can be useful to the scientific community, e.g. working on data assimilation of atmospheric CO2 observations, the development of the CO2 observing system and providing boundary conditions for CO2 regional modelling

Summary

Introduction

Earth-observing strategies focusing on carbon cycle systematic monitoring from satellites, flask and in situ networks (Ciais et al, 2014; Denning et al, 2005) are leading to an increasing number of near-real-time observations available to systems such as those developed in the framework of the European Union Copernicus Atmosphere Monitoring Service (CAMS). The purpose of the real-time CO2 analysis/forecasting system is to provide timely products that can be used by the scientific community among other users Those working on new instruments, field experiments, satellite retrieval products, regional models requiring boundary conditions, or planning flight campaigns. Agustí-Panareda et al.: Biogenic flux adjustment scheme for CO2 analysis and forecasting system http://icos-atc.lsce.ipsl.fr); Environment Canada (www.ec. gc.ca/mges-ghgm) – which are assimilated by global tracer transport models to infer changes in atmospheric CO2 (e.g. Massart et al, 2015) or by flux inversion systems (e.g. Peylin et al, 2013) to estimate the large-scale surface fluxes of CO2

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