The CoCO2 project aims to develop prototype systems for monitoring and verifying European anthropogenic CO2 emissions using the CO2M model implemented within Copernicus. The Integrated Forecasting System global prototype uses optimized surface fluxes and uncertainties from observations like TROPOMI against prior information from a forward model. Updates to model components like using CB05-BASCOE for the CH4 chemical sink and CMEMS ocean fluxes improve fits to observations. However, errors in land auxiliary data like leaf area index remain a key source of uncertainty in the CO2 sink that the IFS inversion is working to correct.

1. The CoCO2 global nature run as an evaluation tool
of the integrated earth system model to support the
monitoring of greenhouse gas emissions
Anna Agusti-Panareda1*, Joe McNorton1, Gianpaolo Balsamo1, Cedric Bacour2, Vladislav Bastrikov3, Jean Bidlot1, Bertrand Bonan4, Nicolas Bousserez5, Souhail
Boussetta1, Gregoire Broquet2, Dominik Brunner6, Jean-Christophe Calvet4, Luca Cantarello1, Philippe Ciais2, Huilin Chen7, Frederic Chevallier2, Margarita Choulga1, Cyril
Crevoisier8, Hugo Denier van der Gon9, Michail Diamantakis1, Emanuel Dutra10, Richard Engelen1, Johannes Flemming1, Gabriele Arduini1, Cyril Germineaud11, Marc
Guevara12, Claire Granier13,14, Sander Houweling15, Greet Janssens-Maenhout16, Vincent Huijnen17, Martin Jung18, Thomas Kaminski19, Zak Kipling1, Rigel Kivi20, Ernest
Koffi5, Werner Kutsch21, Bavo Langerock22, Panagiotis Kountouris5, Maarten Krol23, Francisco Lopes24, Fabienne Maignan2, Julia Marshall25, Sebastien Massart1, Dario
Papale26, Mark Parrington1, Coralie Perruche11, Glen Peters27, A.M.Roxana Petrescu15, Wouter Peters23, Philippe Peylin2, Vincent-Henri Peuch5, Michel Ramonet2,
Patricia de Rosnay1, Marko Scholze28, Arjo Segers9, Alex Vermeulen29, Sophia Walther30, Thorsten Warneke31, Peter Weston1
Anna.Agusti-Panareda@ecmwf.int
The CoCO2 project has received funding from the European Union’s Horizon 2020 Research and Innovation programme.
1,5ECMWF 2LSCE, 3Science Partners, 4CNRM/Meteo-France, 6EMPA, 7University of Groningen, 8LMD, 9TNO, 10IPMA, 11Mercator Ocean International, 12BSC, 13Laboratoire
d’Aérologie,. 14Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder,15Vrije Universiteit Amsterdam, 16JRC, 17KNMI, 18,30 MPI-BGC,19Inversion
Lab, 20Finnish Meteorological Institute, 21ICOS ERIC Head Office,22Royal Belgian Institute for Space Aeronomy, Uccle, Belgium. 23Wageningen University, Wageningen,
Netherlands. 24University of Lisbon, 25DLR, 26Università degli Studi della Tuscia,27CICERO, 28Lund University, 29ICOS ERIC Carbon Portal,
31University of Bremen

2. What is The CoCO2 Project: Aims
Deliver prototype systems for
European anthropogenic CO2
emissions monitoring &
verification support (MVS)
capacity, implemented within
the Copernicus programme
using CO2M.

3. What is The CoCO2 Project: The IFS Global Prototype
Integrated
Forecasting
System
Optimised Fluxes and Uncertainties
Observations: TROPOMI
S5P, GOSAT, IASI, OCO-2,
CO2M etc.
Prior information: forward model with emissions,
natural fluxes and atmospheric chemistry and transport
Michael Buchwitz, IUP, Bremen
Posterior Emissions
Posterior - Prior Emissions
Consolidated Country/Regional
Emissions for End User

4. M
Ocean
fluxes
Fire
emissions
Anthropogenic
emissions
ATMOSPHERE
(Atmospheric transport and chemistry)
MODEL COMPONENTS
Biogenic
fluxes
Meteo and
tracer 1-day
simulation
Meteorological
Initial conditions
CHEMICAL
SOURCES/SINK
The IFS Global Prototype: IFS Forward Component
• CO2: Modelled NEE (GPP + Reco)
• CH4: Modelled wetlands and chemical sink
• Prescribed ocean fluxes, [lateral fluxes]
TRACER
TRANSPORT
• Anthropogenic emissions:
o EDGARv6 vs CAMS-GLOB-ANTv5.3 (based on EDGARv5)
o CO2 emissions from residential heating
o [CO2 emissions from Short C cycle: Human Respiration+Livestock, biofuel]
Total/partial
atmospheric column
Near-surface atmospheric
molar fraction
Vertical profiles of
atmospheric molar fraction
Surface fluxes
Model/pressure
levels
Satellite data
TCCON data
Aircore data
Aircraft data
In situ data
Eddy covariance
flux data
Observations
Nature run model output
Integrated Forecasting System (IFS)

5. CH4 emissions: A simple CH4 wetland model
Posterior Emissions
Wetland model is a simple parameterisation based on
• Temperature (Q10 function : 2.337 and soil Temperature : T)
• A proxy for substrate (PFT dependent soil respiration : Re0)
• Wetland fraction (fwet 0-1).
• Fluxes are globally scaled using a universal temperature
dependent methanogensis rate (S)
𝑓𝐶𝐻4
= 𝑆 ∙ 𝑓𝑤𝑒𝑡 ∙ Re0 ∙ 𝑞10
𝑇−25
10
Climatology Model TCCON OBS
[Deutscher et al., 2014]
[Sussmann et al., 2014]

6. CH4 chemical sink : A climatology versus IFS CB05-BASCOE model
Posterior Emissions
XCH4 evaluation with
TCCON observations
CH4 profile evaluation with
Aircore observations
Stratospheric CH4 column evaluation
with NDACC observations
Climatology
IFS
CB05-BASCOE

7. CO2 ocean fluxes: Jena Carbo-Scope vs CMEMS
Posterior Emissions
CMEMS CO2 flux product
JENA-Carbo Scope CO2 flux product
Many coastal stations continental stations are also affected by ocean fluxes (seasonal cycle
and some synoptic anomalies)
[Schuldt et al. (2020) GLOBALVIEWplus_v6.0_2020-09-11 (http://doi.org/10.25925/20200903)
Abbotsford (abt), British Columbia, Canada 49.0oN 122.3oW. (ECCC)
Churchill (chl), Manitoba Canada 58.7oN 93.8oW (ECCC)
South Pole (spo), Antarctica, United States -90.0oS 24.8oW. (NOAA GML)
Source: MERCATOR
Source: Rodenbeck et al. (2013)
[ppm]
[ppm]
[ppm]

8. Anthropogenic emissions: CAMS-GLO-ANTv5.2 vs EDGAR6.0
CAMS-GLO-ANTv5.3 EDGAR6.0 TCCON OBS
XCO2
XCH4
• Reduction in STDE (1ppb) and increase in BIAS (2ppb) for XCH4
• Small impact on XCO2

9. Anthropogenic emissions: odelling day-to-day variability
Posterior Emissions
- Current
- Literature Estimate
- New Model HDD
• Introduction of a residential emissions model (MEHDNI)
based on heating-degree-days and urban cover.
• Compares well with existing heating degree day models,
improving on the temporal resolution of the existing CAMS
product (top-left).
• Results agree well with independent gas consumption data
(top right).
• When simulated in IFS, MEHNDI provides improved total
column CO2 concentrations (validation with TCCON –
bottom-right).
Residential heating sector
Wennberg et al.
(2014)

10. Anthropogenic emissions
Posterior Emissions
o Short C cycle: biofuels and human+livestock respiration
with corresponding forest and crop sinks
o River efflux impact very small on XCO2 (very localized close
to surface)
[Schuldt et al. (2020) GLOBALVIEWplus_v6.0_2020-09-11 (http://doi.org/10.25925/20200903]
[Ciais et al. (doi:10.5194/gmd-2020-259), Zhu et al. (ESSD, 20210)] Latitude

11. Modelling GPP : New photosynthesis model and LAI climatology
Posterior Emissions
ECLand GPP climLAI_LU_new
ECLand GPP climLAI_LU_op
FLUXCOM GPP
FLUXSAT GPP
LAI is too high LAI is too low
TROPOSIF SIF
clim LAI_op
clim LAI_new
2019 LAI_new

12. Summary (1)
Posterior Emissions
• CH4 chemical sink from CB05-BASCOE significantly improves the fit to observations (profile and total column).
• CH4 wetland fluxes: overestimation in NH (requires revision of s factor), but some improvement in synoptic
variability.
• Ocean CO2 fluxes from CMEMS show positive impact on background sites over ocean as well some coastal and
continental sites.
• EDGARv6 anthropogenic emissions improve seasonal cycle of CH4 (compared to CAMS-GLOB-ANTv5.3 based on
EDGAR5).
• Short Carbon Cycle (Human+Livestock Respiration) enhances CO2 seasonal cycle.
• Lateral fluxes (riverine efflux) cannot be detected by current TCCON and in situ data network.
• Residential heating HDD model has a positive impact the variability of CO2 in urban areas.
• Large systematic errors in LAI climatology currently used operationally in the IFS remains the largest source of error
in atmospheric XCO2 and near-surface CO2.

13. Summary (2)
Posterior Emissions
NEE increments from IFS inversion correct for large-scale systematic error in LAI
Sink is reduced
Sink is increased
LAI is too low
LAI is too high
LAI is too high -> Reduction in sink
LAI is too low -> Increase in sink
See Nicolas Bousserez’s talk and Luca Cantarello’s poster for more info on the development and
testing of the IFS inversion in the CoCO2 project