Carbon Flux Data Assimilation - wmo.int€¦ · Saroja Polavarapu Environment Canada DAOS Working...
Transcript of Carbon Flux Data Assimilation - wmo.int€¦ · Saroja Polavarapu Environment Canada DAOS Working...
Saroja PolavarapuEnvironment Canada
DAOS Working group Meeting, 15-16 Aug. 2014
Carbon Flux Data Assimilation
Thanks: D. Jones (U Toronto), D. Chan (EC), A. Jacobson (NOAA)
• The natural carbon cycle involves CO2 exchange between the terrestrial biosphere, oceans/lakes and the atmosphere.
• Fossil fuel combustion and anthropogenic land use are additional sources of CO2 to the atmosphere.
Pg C/yrhttp://www.scidacreview.org/0703/html/biopilot.html
The Global Carbon Cycle
Earth’s crust 100,000
1 Pg = 1 Gt = 1015 g
Net surface to atmosphere flux for biosphere or ocean is a small difference between two very large numbers
Net perturbations to global carbon budget
LeQuere et al. (2014, ESSD)
• Based on 2002-2012• 50% of emissions remain
in atmosphere• 25% is taken up by
terrestrial biosphere• 25% is taken up by
oceans
Interannual variability: 1870-2013LeQueré et al. (ESSD, 2014)
Atmospheric accumulation has strong variability due to land uptake. This is due to climate variability.
Ocean uptake is not as variable.
What are the key science questions?• The atmospheric burden of the global carbon cycle can be
determined by the difference between the anthropogenic emissions and natural fluxes (biosphere, ocean and land use change). The interannual variability and largest uncertainties are with the terrestrial biospheric uptake.
• What is the observing network needed to be able to reduce uncertainty on biospheric fluxes in order to identify anthropogenic fluxes on policy relevant scales?
– What is the spatial and temporal density of observations we need and what type?
– How do we deal with issues in the model? Inversion methodology needs perfect transport.
– Can we use multiple tracers to better constrain the carbon budget? (e.g. C13, C14, CO, etc.)
• What will be the long term evolution of sources and sinks? Are the nonlinear feedback processes sufficiently well represented in climate-carbon cycle models? (parameter estimation for ecosystem models)
Atmospheric observations give feedback on model forecasts
• If forecast does not match observation, difference could be due to errors in CO2 initial conditions, meteorological analyses, prescribed fluxes, model formulation, representativeness, or observation errors.
CO2 forecast Fluxes
caaf ScxMc ),(Meteorologyanalysis
CO2analysis
Forecast model Model error
Conventional inverse problem setup
World Data Centre for Greenhouse Gases (WDCGG)
http://gaw.kishou.go.jp/cgi-bin/wdcgg/map_search.cgi http://transcom.project.asu.edu
22 TransCom regions
Weekly avg obs
One or more yearsJ F M A M J J A S O N D J F M A M J J
Monthly mean flux
Inversions using surface network
• Inversion methods differ in:
– Methodology– Observations
▪ Sfc: 100 flask + continuous
– A priori fluxes– Transport models
• Interannual variability is similar and due to land
Peylin et al. (2013)
1 5-62 7-83 94 10
11
Mean XCO2 Aug. 2009 GOSAT Greenhouse Gas Observing Satellite
The changing observing system
World Data Centre for Greenhouse Gases
http://gaw.kishou.go.jp/cgi-bin/wdcgg/map_search.cgi
• ~100 highly accurate surface stations with weekly or hourly data
• Regular aircraft obs over Pacific
• Satellites: GOSAT (2009), OCO-2 (2014) +…
v2.0 averaged at 0.9°x0.9°
GOSAT figure courtesy of Ray Nassar, EC
How is data assimilation relevant to Carbon Flux estimation problems?
• In flux inversions, if one solves for fluxes only, the transport model is needed to relate the flux to the observation: model is a strong constraint
• Exact mass conservation in transport model overs years of simulation is needed to attribute model-data mismatch to fluxes.
• Techniques used to solve inverse problem: 4D-Var, EnsKF, Bayesian Inversion, Markov Chain Monte Carlo (MCMC)
• With a CTM for transport, analyses or reanalyses are needed: IFS, ERA-I, MERRA, JRA55, etc.
• Parameter estimation for ecosystem models: Carbon Cycle DAS• Ultimately coupled atmosphere-ocean-land data assimilation could
be envisioned
TfobsTfobsTbTb ScHcScHcSSSSSJ 11 RB21
21)(
flux conc obs
Spatial interpolation Forecast model
Prior flux
The future vision: Comprehensive Carbon Data Assimilation System
Comprehensive carbon assimilation systems are being built by NASA, NOAA, agency-consortiums in Europe, Japan and EC.
GEO Carbon Strategy Report (2010)
How is Carbon Flux estimation relevant to NWP data assimilation?• Need mass conservation over long time scales (years). Global
mass of CO2 is very sensitive to model errors. Can help improve NWP, air quality and climate models.
– Key model processes affecting CO2 forecast distribution:▪ Advection (NWP model)▪ Boundary layer mixing (NWP model)▪ Convective transport of tracers (Air quality model)▪ Ecosystem modeling of biospheric respiration and
photosynthesis (Climate model)• Radiance assimilation (RTTOV) assumes constant CO2 value.
Impact of using 3D CO2 fields is reduction in range of bias corrections, and slight improvement in tropical forecasts near 200 hPa. (Engelen and Bauer 2011)
Future directions Near real time CO2 forecasts• Coupled CO2 and meteorological assimilation
– ECMWF: Real time operational 5-day CO2 forecasts since 2013. No assimilation of CO2 obs. Updated initial conditions from flux inversions every Jan. 1. Plans: Near-real time assimilation of surface obs of CO2 with coupled meteorological/tracer assimilation
– GEOS5: Coupled CO and CO2 assimilation to meteorological assimilation. Weakly couple ocean and land data assimilation systems to atmospheric assimilation system
– Justification▪ Provide boundary conditions for regional modelling and flux inversions.▪ Improve modelling of radiative transfer, evapotranspiration▪ Feedback on modeling of boundary layer, convection, advection▪ Provide a prioris for satellite retrievals of CO2 and CH4
• Coupled CO2, flux and meteorological estimation:– Kalnay group (U Maryland)– EC-CAS (Polavarapu)
Summary
• Although similar DA tools and techniques are being used to estimate carbon cycle dynamics, the problem is different because the system is not chaotic: tracer transport is linear.
• The challenge is to estimate highly variable surface fluxes with too few data, as opposed to the NWP problem of finding an accurate analysis (and uncertainty) with which to make forecasts.
EXTRA SLIDES
Variations in Atmospheric CO2
• Diurnal variations, linked to surface sources and sinks, are strongly attenuated in the free troposphere
• Diurnal variations in column CO2are less than 1 ppm
• Large changes in the column reflect the accumulated influence of the surface sources and sinks on timescales of several days
Olsen and Randerson (2004, JGR)
Surface CO2
Column CO2
Diurnally varying surface fluxes
Modeled CO2 at Park Falls
5-day running mean surface fluxes
But what is the spatial distribution of the fluxes and how is it changing?
Figure courtesy of Elton Chan, CCMR
With the increased coverage from new satellite data, can we get flux estimates at higher spatial resolution?
Mean XCO2 Aug. 2009 GOSAT Greenhouse Gas Observing Satellite
v2.0 averaged at 0.9°x0.9°
Figure courtesy of Ray Nassar, CCMR
OCO-2 launch July 2014
http://oco.jpl.nasa.gov/
Atmospheric model is not perfect
Even with the same sources/sinks, different models give different CO2.
Largest discrepancy between model transport is N.Hemisphere biospheric exchange.
Model errror is not random but systematic and will lead to bias in flux estimates.
Gurney et al. (2003, Tellus)
Zonal mean annual mean CO2
Meteorological winds are not perfectLiu et al. (2011, GRL)
Using same sources/sinks, same model, same initial condition, CO2forecasts are still different due to errors in wind fields.
Forecast spread due to uncertainty in winds creates CO2 spread where gradients are large (near sources/sinks), not where wind uncertainty is large.
Imperfect model transport impacts source/sink estimates
Modelled vertical gradients don’t match aircraft obs
Too strong gradient too little vertical mixing larger emissions are needed to match obs in NH
Verticalgradienttoo weak
Pos
t inv
ersi
on fl
ux (P
gCyr
-1)
Larger gradient produces larger NH uptake
Stephens (2007, Science)
gradientis too strong
Spatial information
• Group 1 all solve for fluxes on grid scale and use obs at sampled time instead of monthly means
Peylin et al. (2013)
If we want to know the spatial distribution of fluxes, then methodology matters!
25N-25S
ECMWF
• Real time operational 5-day CO2 forecasts since 2013– No assimilation of CO2 obs– Updated initial conditions from flux inversions every Jan. 1– Online parameterized ecosystem model (CTESSEL)– Fluxes: GFAS v1.0 (Fire, Kaiser et al., 2012), ocean (Takahashi et
al. 2009), anthropogenic (EDGAR version 4.2)
• Justification– Provide boundary conditions for regional modelling and flux
inversions.– Improve modelling of radiative transfer, evapotranspiration– Feedback on modeling of boundary layer, convection, advection– Provide a prioris for satellite retrievals of CO2 and CH4
• Next: Near-real time assimilation of surface obs of CO2– Coupled meteorological/tracer assimilation– Needs near-real time obs provision
European Centre for Medium Range Weather Forecasting
USA
• GEOS-5– Couple CO and CO2 assimilation to meteorological assimilation– Weakly couple ocean and land data assimilation systems to
atmospheric assimilation system– Dynamic vegetation
Key Future GHG missions with surface sensitivity Slide from Ray Nassar (EC)
• Lots more observations are expected soon, from satellite-based platforms• Can the new space-based measurements help fill in data-gaps in ground-
based network? Can we then get regional flux estimates over Canada?
Key Future International GHG Satellites
OCO launch photo by Matt Rogers,Colorado State University
• Orbiting Carbon Observatory 2is scheduled to launch in July 2014• CO2 mission to replace a 2009 mission lost to a launch mishap
• TanSat is China’s CO2 mission scheduled to launch in mid-2015• Has a very similar design to OCO-2
Chinese Academy of Sciences (CAS), Ministry of Science and Technology (MOST), Chinese Meteorological Agency (CMA)
• CarbonSat (candidate for launch in 2019)• Design optimized for monitoring CO2 and CH4 emissions including point sources like power plants, oil sands
• GOSAT-2 potential launch ~2017• Will measure CO2 and CH4 with more coverage and smaller pixel size than GOSAT
25
Slide from Ray Nassar (EC)
Orbiting Carbon Observatory 2
• OCO-2 launched on 2014-07-02 at 2:56 PDT by a Delta-II• OCO-2 will measure reflected sunlight in the 0.76, 1.61 and
2.06 m bands• Footprints (nadir): GOSAT 10.5 km (d), OCO-2 ≤1.29x2.25 km2
• GOSAT: 4 sec / obs, OCO-2: 8 obs x 3 times per second• OCO-2 will have ~200 times as many measurements as
GOSAT • Glint range from sub-solar latitude: GOSAT ±20°, OCO-2 ±80°
OCO-2
OCO-2 GOSATWashington DC
OCO-2
Sun glint over water
Slide from Ray Nassar (EC)
OCO-2 and beyondNature (June 26, 2014) 510,451–452. doi:10.1038/510451a