Data assimilation of atmospheric co 2 at ecmwf in the context of the gems project
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Data assimilation of atmospheric CO 2 at ECMWF in the context of the GEMS project. Richard Engelen ECMWF. Thanks to Soumia Serrar and Fr é d é ric Chevallier. Global and regional Earth-System (atmosphere) Monitoring using Satellite and in-situ data (GEMS).

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Data assimilation of atmospheric co 2 at ecmwf in the context of the gems project

Data assimilation of atmospheric CO2 at ECMWF in the context of the GEMS project

Richard Engelen

ECMWF

Thanks to Soumia Serrar and Frédéric Chevallier


Global and regional earth system atmosphere monitoring using satellite and in situ data gems

Global and regional Earth-System (atmosphere) Monitoring using Satellite and in-situ data (GEMS)

T. Hollingsworth1, O. Boucher2, H. Eskes3,

C. Granier4, V.H. Peuch5, P. Rayner6,

M. Schultz7, A. Simmons11 ECMWF; 2 UK Met Office; 3 KNMI, NL;

4 Service d'Aéronomie CNRS, 5 Meteo-France;

6 Laboratoire des Sciences du Climat et de l‘Environnement;

7 Forschungszentrum Jülich, Germany


Prime objectives
Prime Objectives

  • Creation of an pre-operational global monitoring system for greenhouse gases, reactive gases, and aerosols in the troposphere and in the stratosphere by early 2009.

  • This system will be capable of producing near-real-time (depending on data availability) and retrospective global analyses for monitoring atmospheric composition, and short-range forecasts to drive regional air-quality models.

  • It will provideinformation relevant to the Kyoto and Montreal protocols, and to the UN Convention on Long-Range Trans-boundary Air Pollution.


Gems themes
GEMS themes

GHG

AER

GRG

RAQ

Greenhouse Gases (GHG)

Global Reactive Gases (GRG)

Aerosols (AER)

Regional Air Quality (RAQ)

Production (PRO) & Validation (VAL)


Greenhouse gases ghg
Greenhouse Gases (GHG)

GHG

  • Theme coordinator: Peter Rayner, LSCE, France

  • Development of an operational system to use satellite and in-situ data to monitor the concentrations of greenhouse gases, and their associated surface sources and sinks, and to attribute these sources and sinks to controlling processes.

[g-C/m2/day]

Carbon fluxes from the terrestrial biosphere on August 10, 2003 (heat wave in Europe and drought in the USA) as simulated by the model ORCHIDEE


2 step process
2-step process

GOSAT

IASI

AIRS

OCO

4D-Var atmospheric data assimilation

(12 hour assimilation window)

Flasks

Prior fluxes

Tall towers

4D-Var flux inversion

(1 year inversion window)

Updated fluxes


Terminology

  • Free running CO2 model: CO2 is transported using constrained meteorology from operations every 12 hours.

  • Reanalysis/assimilation: CO2 is constrained by AIRS, while meteorology is constrained by all other available operational observations at the same time.

Fluxes used in the model

  • CASA diurnal natural biosphere

  • Takahashi ocean

  • Andres anthropogenic

  • GFED2 fire emissions


Co 2 reanalysis with airs
CO2 reanalysis with AIRS

Monthly mean CO2 column-mean mixing ratios, after 8 months of assimilating AIRS radiances, show small but significant changes to a simulation with free-running CO2.

After validation these differences will be used to estimate modifications in the prior surface flux fields.

Analysed CO2


The AIRS radiances are also able to adjust the shape and amplitude of a CO2 plume resulting from extensive biomass burning over Africa.

The GEMS monitoring system will have the capability to follow these plumes in near-real time in terms of CO2, CO, and aerosol concentrations, constrained by various satellite observations.


Early validation
Early Validation amplitude of a CO

Comparisons with flight data over Hawaii (courtesy of Pieter Tans, NOAA/ESRL, shows a clear improvent of the analysis over the free-running model.

The top plot shows a best-case individual comparison (11 May 2003).

The bottom plot shows an average over all available flight profiles between 1 January and 30 October 2003.


Molokai island hawaii
Molokai, Island, Hawaii amplitude of a CO

Blue: free-running model

Red: reanalysis

Black: observations


Molokai island hawaii1
Molokai, Island, Hawaii amplitude of a CO

Blue: free-running model

Red: reanalysis


Surgut siberia courtesy of dr machida nies
Surgut, Siberia amplitude of a COcourtesy of Dr Machida, NIES


Surgut siberia
Surgut, Siberia amplitude of a CO


Molokai island hawaii2
Molokai, Island, Hawaii amplitude of a CO


Individual profiles
Individual Profiles amplitude of a CO

11 May 2003

10 ppmv

9 October 2003


  • Issues: amplitude of a CO

  • Missing sink is not modelled and therefore cannot take out excess CO2 from the atmosphere

  • The background constraint is too strong

  • The AIRS bias correction might not work properly

  • AIRS might not provide a strong enough constraint to adjust the concentrations

There is a summer trend in atmospheric CO2 that is not corrected by the AIRS observations at the moment.

  • Possible solutions:

  • Increase background errors to allow bigger adjustments from the AIRS observations

  • Apply a model bias correction to keep the problem as un-biased as possible. This has to be accounted for in subsequent flux inversions.

  • Use optimized fluxes instead of CASA


Conclusions
Conclusions amplitude of a CO

  • GEMS is well on its way to build an atmospheric composition monitoring system

  • First 1-year CO2 reanalysis has been run using AIRS observations

  • Potential problems are being identified and where possible corrected before a new run will be started

  • Results will be provided to inverse flux model at LSCE


Molokai island hawaii3
Molokai, Island, Hawaii amplitude of a CO

Blue: without fire emissions

Red: with fire emissions


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