Carbon Flux Data Assimilation

Transcription

1 Carbon Flux Data Assimilation Saroja Polavarapu Environment Canada Thanks: D. Jones (U Toronto), D. Chan (EC), A. Jacobson (NOAA) DAOS Working group Meeting, Aug. 2014

2 The Global Carbon Cycle Pg C/yr 1 Pg = 1 Gt = g Earth s crust 100,000 Net surface to atmosphere flux for biosphere or ocean is a small difference between two very large numbers The natural carbon cycle involves CO 2 exchange between the terrestrial biosphere, oceans/lakes and the atmosphere. Fossil fuel combustion and anthropogenic land use are additional sources of CO 2 to the atmosphere.

3 Net perturbations to global carbon budget LeQuere et al. (2014, ESSD) Based on % of emissions remain in atmosphere 25% is taken up by terrestrial biosphere 25% is taken up by oceans

4 Interannual variability: LeQueré 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.

5 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)

6 Atmospheric observations give feedback on model forecasts c CO 2 forecast f Forecast model a a M ( x, c ) Fluxes S Model error c Meteorology analysis CO 2 analysis If forecast does not match observation, difference could be due to errors in CO 2 initial conditions, meteorological analyses, prescribed fluxes, model formulation, representativeness, or observation errors.

7 Conventional inverse problem setup 22 TransCom regions World Data Centre for Greenhouse Gases (WDCGG) Weekly avg obs Monthly mean flux J F M A M J J A S O N D J One or more years F M A M J J

8 Inversions using surface network Inversion methods differ in: Methodology Observations Sfc: 100 flask + continuous A priori fluxes Peylin et al. (2013) Transport models Interannual variability is similar and due to land

9 The changing observing system World Data Centre for Greenhouse Gases Mean XCO2 Aug GOSAT Greenhouse Gas Observing Satellite v2.0 averaged at 0.9 x0.9 ~100 highly accurate surface stations with weekly or hourly data Regular aircraft obs over Pacific Satellites: GOSAT (2009), OCO-2 (2014) + GOSAT figure courtesy of Ray Nassar, EC

10 How is data assimilation relevant to Carbon Flux estimation problems? flux J ( S) 1 2 Prior flux b T 1 b T 1 obs f S S B S S c H c S T obs f R c H c S 1 2 conc obs Spatial interpolation Forecast model T 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

11 The future vision: Comprehensive Carbon Data Assimilation System GEO Carbon Strategy Report (2010) Comprehensive carbon assimilation systems are being built by NASA, NOAA, agency-consortiums in Europe, Japan and EC.

12 How is Carbon Flux estimation relevant to NWP data assimilation? Need mass conservation over long time scales (years). Global mass of CO 2 is very sensitive to model errors. Can help improve NWP, air quality and climate models. Key model processes affecting CO 2 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 CO 2 value. Impact of using 3D CO 2 fields is reduction in range of bias corrections, and slight improvement in tropical forecasts near 200 hpa. (Engelen and Bauer 2011)

13 Future directions Near real time CO 2 forecasts Coupled CO 2 and meteorological assimilation ECMWF: Real time operational 5-day CO 2 forecasts since No assimilation of CO 2 obs. Updated initial conditions from flux inversions every Jan. 1. Plans: Near-real time assimilation of surface obs of CO 2 with coupled meteorological/tracer assimilation GEOS5: Coupled CO and CO 2 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 CO 2 and CH 4 Coupled CO 2, flux and meteorological estimation: Kalnay group (U Maryland) EC-CAS (Polavarapu)

14 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.

15 EXTRA SLIDES

16 Variations in Atmospheric CO 2 Modeled CO 2 at Park Falls Column CO 2 Olsen and Randerson (2004, JGR) Diurnal variations, linked to surface sources and sinks, are strongly attenuated in the free troposphere Diurnal variations in column CO 2 are less than 1 ppm Large changes in the column reflect the accumulated influence of the surface sources and sinks on timescales of several days Surface CO 2 Diurnally varying surface fluxes 5-day running mean surface fluxes

17 But what is the spatial distribution of the fluxes and how is it changing? Mean XCO 2 Aug GOSAT Greenhouse Gas Observing Satellite v2.0 averaged at 0.9 x0.9 Figure courtesy of Ray Nassar, CCMR Figure courtesy of Elton Chan, CCMR With the increased coverage from new satellite data, can we get flux estimates at higher spatial resolution? OCO-2 launch July

18 Atmospheric model is not perfect Even with the same sources/sinks, different models give different CO 2. 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. Zonal mean annual mean CO 2 Gurney et al. (2003, Tellus)

19 Meteorological winds are not perfect Liu et al. (2011, GRL) Using same sources/sinks, same model, same initial condition, CO 2 forecasts are still different due to errors in wind fields. Forecast spread due to uncertainty in winds creates CO 2 spread where gradients are large (near sources/sinks), not where wind uncertainty is large.

20 Imperfect model transport impacts source/sink estimates Stephens (2007, Science) Modelled vertical gradients don t match aircraft obs Vertical gradient too weak gradient is too strong Post inversion flux (PgCyr -1 ) Too strong gradient too little vertical mixing larger emissions are needed to match obs in NH Larger gradient produces larger NH uptake

21 Spatial information Peylin et al. (2013) Group 1 all solve for fluxes on grid scale and use obs at sampled time instead of monthly means If we want to know the spatial distribution of fluxes, then methodology matters! 25N-25S

22 ECMWF European Centre for Medium Range Weather Forecasting Real time operational 5-day CO 2 forecasts since 2013 No assimilation of CO 2 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 CO 2 and CH 4 Next: Near-real time assimilation of surface obs of CO 2 Coupled meteorological/tracer assimilation Needs near-real time obs provision

23 USA GEOS-5 Couple CO and CO 2 assimilation to meteorological assimilation Weakly couple ocean and land data assimilation systems to atmospheric assimilation system Dynamic vegetation

24 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 groundbased network? Can we then get regional flux estimates over Canada?

25 Key Future International GHG Satellites Orbiting Carbon Observatory 2 is scheduled to launch in July 2014 CO 2 mission to replace a 2009 mission lost to a launch mishap Slide from Ray Nassar (EC) TanSat is China s CO 2 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) GOSAT-2 potential launch ~2017 Will measure CO 2 and CH 4 with more coverage and smaller pixel size OCO launch photo by Matt Rogers, Colorado State University than GOSAT CarbonSat (candidate for launch in 2019) Design optimized for monitoring CO 2 and CH 4 emissions including point sources like power plants, oil sands 25

26 Orbiting Carbon Observatory 2 OCO-2 Slide from Ray Nassar (EC) OCO-2 GOSAT Washington DC OCO-2 launched on 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 x2.25 km 2 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 Sun glint over water

27 OCO-2 and beyond Nature (June 26, 2014) 510, doi: /510451a