Presentation on theme: "The EC-Carbon Assimilation System Saroja Polavarapu, Ray Nassar, Doug Chan (CCMR/CRD) Dylan Jones, Mike Neish, Shuzhan Ren, Feng Deng (U Toronto) John."— Presentation transcript:
The EC-Carbon Assimilation System Saroja Polavarapu, Ray Nassar, Doug Chan (CCMR/CRD) Dylan Jones, Mike Neish, Shuzhan Ren, Feng Deng (U Toronto) John Lin, Myung Kim (U Waterloo) Meeting on Air Quality Data Assimilation and Fusion R&D, Jan , 2011
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. 8.6 Pg C/yr The Global Carbon Cycle
Only 50-60% of anthropogenic CO 2 emissions remain in the atmosphere The uncertainty and interannual variability in the global CO 2 uptake is mainly attributed to the terrestrial biosphere Reducing uncertainties in CO 2 sources and sinks is an active area of research and has important implications for climate mitigation policies. [IPCC, 2007]
Numerous research groups over the past ~20 years have combined the highly-accurate but sparse atmospheric CO 2 measurements from the ground-based network with models, to give estimates of CO 2 sources and sinks on coarse spatial scales. With the coming wealth of satellite observations, more sophisticated methods of data assimilation that can handle the large data volume needed to provide estimates on finer scales. Greenhouse gas measurement network NOAA-ESRL (US), Environment Canada, CSIRO (Australia), JMA (Japan)... WMO - World Data Centre for Greenhouse Gases (WDCGG)
Variations in Atmospheric CO 2 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 [Olsen and Randerson, JGR, 2004] Surface CO 2 Column CO 2 Diurnally varying surface fluxes Modeled CO 2 at Park Falls 5-day running mean surface fluxes
Satellite observations Surface observations are highly accurate but sparse Satellite observations can complement spatial coverage though vertical resolution of nadir obs is typically poor Current missions (nadir): HIRS (1978-), AIRS (2002+), SCIAMACHY (2003+), TES (2006+), IASI (2007+), GOSAT (2009+) Current missions (limb): ACE-FTS (2004+) Future missions: IASI (2012,2016), OCO-2 (2013/4), TanSat (2015), GOSAT-2 (2016), CarbonSat (2018), PCW/PHEMOS-FTS (2018), ASCENDS (2020)
Strategies for Temporal and Spatial Coverage Sun-synchronous Low Earth Orbit (LEO) missions only measure at a single point in the diurnal cycle Increasing temporal coverage requires a constellation of LEO satellites or different orbits CarbonSat constellation of LEO satellites has been proposed Geostationary missions like NASA’s GEO-CAPE could have CO 2 capability added for continuous coverage from ~50°S-50°N Canada’s Polar Communications and Weather (PCW) Polar Highly Elliptical Molniya Orbit Science (PHEMOS) Weather, Climate and Air quality (WCA) proposal (currently in phase-A) would use a Highly Elliptical Orbit (HEO) for high latitude quasi-continuous coverage Slide from Ray Nassar, CCMR
Slide courtesy of H. Bovensmann, U. Bremen
Estimating CO 2 fluxes from surface Can use all observations over a time window to estimate CO 2 fluxes at all times Standard method is Bayesian Synthesis Inversion EC CO 2 flux inversion capability: –Bayesian Synthesis Inversion –Transport model: NIES (Japan), TM5 (Europe), GEOS-Chem (US) –Ground-based observations Can standard methods of flux estimation handle coming wealth of observations? photo credit: Matt Rogers, Colorado State University CO 2 / t = transport + flux
The flux estimation problem All source region amplitudes at all times ~22x12=264 All observations at all times ~100x12=1200 previous estimate Connection between obs and fluxes at all previous times for all regions. 264 model integrations of 1 year Estimate monthly averaged fluxes from ground-based obs for 1 year As number of source regions increases too many model integrations!
Bayesian Synthesis Inversions Advantages: Uses all obs at all times (months) to determine all monthly fluxes over 1-3 years If assumptions are correct, this is the best, most general solution Problem 1: Want to use more obs (e.g. continuous, aircraft, satellite) so we can capture finer time and space scales in fluxes Solution 1: Use 4D-Var (e.g. GEOS-Chem, Chevallier et al. (2007,9)) –~100 forward+ADJ runs –Need to develop and maintain TLM and ADJ models Solution 2: Use Kalman smoother (e.g CarbonTracker) –Sequential estimation means using obs only over a short time period, then marching forward. Smoother means improving estimate based on future obs –Does not use all obs to estimate all sources
The flux estimation problem Estimate monthly averaged fluxes from ground-based obs for 1 year All error sources convolved into 1 error estimate ( R ). In practice only obs and rep errors are accounted for. Often, no correlations are assumed. Random errors due to: Initial conditions in CO 2 Driving wind analyses Model formulation representativeness Instrument error 1200x1200 Random errors in source amplitudes 264x264
The flux estimation problem Estimate monthly averaged fluxes from ground-based obs for 1 year Random errors due to: Initial conditions in CO 2 Driving wind analyses Model formulation representativeness Instrument error 1200x1200 Random errors in source amplitudes 264x264 If B and R are incorrect, then uncertainty estimates are wrong Estimation error
Relaxing the assumptions Problem 2: Assumptions made in practice are not correct, e.g. no errors for analysed wind fields, initial CO 2 field, model formulation, source region definitions. Often no error correlations. Because assumptions are not valid, we cannot believe uncertainties Solution : Use data assimilation to estimate concentrations, simultaneously inferring fluxes as a “model parameter or forcing” Use ensemble of forecasts to explicitly account for initial state, meteorology, model, representativeness, obs, source region errors. Fully evolve covariances in time, producing full spatial correlations Ensemble Kalman Smoother used by Japanese (Miyazaki 2011, JGR) for CO 2 fluxes.
EC-CAS: Carbon Assimilation System – New EC-CAS (Carbon Assimilation System) proposed for monitoring carbon and policy/verification purposes – Project started in April EC/UT/UW collaboration. – Can be used to answer questions on observing system needs (space-based, and EC’s ground-based obs) – Will be run routinely but behind real time since it takes time for flux to reach measurement locations. – EC-CAS is based on EnsKF with GEM-MACH but will be a Kalman smoother for estimating surface fluxes – Parameters for EnsKF not clear yet: update frequency, and data window (6h normally)
The future vision: Comprehensive Carbon Data Assimilation System EC-CAS will form the basis of a comprehensive carbon assimilation system, comparable to those of NASA, NOAA and agency-consortiums in Europe and Japan.
Starting point with GEM D.Chan/M.Ishizawa had CO 2 version with GEM v3.2.0 to see if GEM can capture synoptic scale variability. It does seem to do this Time series of CO 2 at Fraserdale The minimum CO 2 concentration during these two months was subtracted so the time series start from a zero value. Complete time series (top) Daily variability was removed by plotting afternoon mean values only (bottom) Figure from D. Chan, CCMR
Early issues with model choice Our development uses MAESTRO which is used to run the EnsKF (CMC uses this for operational EPS) Choice of GEM version for EC-CAS: –EnsKF uses GEM v4.2.0 and is not backward compatible so Doug Chan’s GEM v3.2.0 with CO 2 tracer v3.2.0 not feasible. –Decided to choose GEM-MACH because it already handles emissions, tracers, vertical diffusion and they will move to v Also this permits future interaction and collaboration with AQRD. –Model testing with GEM-MACH (v3.3.3) but EnsKF development needs v4.4.0 which is under beta testing.
GEM-MACH-GHG version GEM-MACH was developed for CO 2 simulation by –Started from global version (based on v3.3.3) used for stratospheric ozone and developed by Jean deGrandpre (ARQI) –Reduced resolution to 400x200 (roughly 1 degree), 80 levels –Adding 6 CO 2 tracers, one for each emission source plus a total CO 2 and a background CO 2 (with no emissions) –Coupled tracers to emissions fields –Obtained monthly emissions from Doug Chan, and regridded these to Z grid, 400x200 (preserving total mass) –Uses GEM-MACH emissions preprocessor with global fields
Model validation run How well can GEM-MACH simulate Carbon? Key concern: mass conservation over multiyear runs. Diagnostics: Seasonal cycle, hemispheric gradients, mass conservation. Comparison against obs and other models (CarbonTracker, GEOS-Chem) –Simulation for January 1, 2009 – Jan. 2012? –Dates related to GOSAT launch (Jan. 2009) and GEM-strato analyses availability (Operational implementation on June 22, 2009) –Initial condition from CarbonTracker for Jan. 1, 2009 –Meteorology: surface fields (archived surface analyses), 3D winds (prelim cycle, parallel run, operations) –Emissions: ▪Every 3 hours (area type) though GEM-MACH set up for monthly fields with diurnal variation ▪biosphere (CarbonTracker a posteriori) ▪ocean (CarbonTracker a posteriori) ▪Fossil Fuel (CDIAC) ▪Biomass burning (GFED v3)
EC-CAS development priorities Model –GEM-MACH based on v4.4.0 beta-9 runs in CO 2 mode w/o emissions. Need to add emissions. Reconnect vertical diffusion. –Repeat model validation run with GEM-MACH-GHG v4.4.0 Assimilation (EnsKF) –Allow EnsKF and MAESTRO to use GEM-MACH instead of GEM –Change control vector change from meteorology to tracers/species + fluxes –Develop observation operators for all new obs to be assimilated or monitored –Complete EnsKF and test with surface obs –Extend EnsKF to a Kalman Smoother (use future obs to estimate current flux) Observations –convert surface obs to BURP for ingestion by data assimilation codes. –examine GOSAT data, determine biases, quality control procedures, bias correction procedures. Emissions –Incorporate diurnal/weekly scaling factors developed by Ray Nassar
GHG and AQ assimilation synergies GEM-MACH development can be coordinated, e.g. vertical diffusion, mass conservation EnsKF development by EC-CAS will be usable (but not tested) with reactive chemistry Primary/Initial fociiGHG flux assimilationAir Quality assimilation Assimilation needsInverse problem (source estimation) Smoother Forecasting problem Filter Model needsTransport Emissions Mass conservation Transport Emissions Reactive chemistry Time scalesMonths to yearsDays Space scalesGlobal, regionalRegional
Observations from a Three-Apogee Orbit 8 (60x60) arrays wide 6 (60x60) arrays tall 10 x 10 km 2 footprint 2 satellites, each with 16 h orbit apogee = km perigee = 8100 km Images 16 h / 48 h per region NIR-TIR FTS similar to GOSAT TANSO-FTS (ABB Group) could measure CO 2 and CH 4 over ice-free land surfaces Nassar et al. (in prep.) Various pointing scenarios for PCW-PHEMOS are currently under consideration Slide from Ray Nassar, CCMR
Canadian Greenhouse Gas Measurement Program Figure from Elton Chan
Global Greenhouse Gas Measurement Network World Data Centre for Greenhouse Gases NOAA-ESRL (US), Environment Canada, CSIRO (Australia), JMA (Japan)... WMO - World Data Centre for Greenhouse Gases (WDCGG)
Present satellite instruments InstrumentData avail Latitudinal coverage Vertical sensitivity HIRS S-20NUpper trop ~10 km AIRS S-80NUpper trop SCIAMACHY S-80N landTotal column ACE-FTS S-82N sparse km, 3 km TES S-40NMid trop ~5 km IASI S-20NUpper trop, ~12 km TANSO-FTS (GOSAT) S-80N land 25S-25N ocean Total column, Upper trop All are nadir except ACE which is occultation (limb)
CO 2 Flux Inversion with Regional Focus on North America Deng et al. (2007) 30 small regions in North America, 20 large regions for the rest of the globe, and 88 CO 2 stations (GlobalView-2005)
Annual Result for 2003 North American biosphere is a sink of −0.97 ± 0.21 Pg C, Canada’s sink is −0.34 ± 0.14 Pg C. Deng et al. (2007)