Downscaling the NOAA CarbonTracker Inversion for North America Gabrielle Petron 1,2, Arlyn E. Andrews 1, Michael E. Trudeau 1,2,3, Janusz Eluszkiewicz 4, Thomas Nehrkorn 4, John Henderson 4, Roland Draxler 5, Ariel Stein 5, Colm Sweeney 1,2, Anna Karion 1,2, Andy Jacobson 1,2, Ken Masarie 1, Lori Bruhwiler 1, John B. Miller 1,2, Benjamin R. Miller 1,2, Wouter Peters 6, Sharon M. Gourdji 7, Kimberly L. Mueller 8, Anna M. Michalak 7, Pieter P. Tans 1 1 NOAA Global Monitoring Division, Boulder, CO 2 University of Colorado, CIRES, Boulder, CO 3 Colorado State University, CIRA, Fort Collins, CO 4 Atmospheric and Environmental Research, Lexington, MA 5 NOAA Air Resources Laboratory, Silver Spring, MA 6 Wageningen University, Wagenigen, USA 7 CIW, Department of Global Ecology, Stanford, CA 8 Congressional Science Fellow, USA A31B-0074 CarbonTracker 19 ecosystem types for two large regions, Boreal North America and Temperate North America (left), and the US corn belt (below). Weekly mean NEE is optimized for each regional ecosystem based on atmospheric observations collected over a 5 week period. Boreal North America Temperate North America Crops: one single ecoregion NEE for conterminous USA adjusted weekly 156 Weekly scaling factors are optimized -> 11 TRANSCOM land regions with at most 19 land cover types -> 30 ocean basins -> short-term biospheric flux variability based on: local temperature & radiation F(x, y, t) = λb Fbio(x, y, t) + λo Foce(x, y, t) + Fff(x, y, t) + Ffire(x, y, t) NOAA CarbonTracker (CT) is a global data assimilation system which adjusts weekly prior natural fluxes over large regions (NEE and net oceanic fluxes) to improve the match with observed atmospheric CO 2 mixing ratios. The North American Carbon Program MidContinent Intensive experiment around the state of Iowa ( ) revealed some strong spatial gradients in the cornbelt and dramatic summertime CO2 minimum. The goal of the study was to try to oversample a relatively homogenous region with a strong natural flux (driven by crops) and evaluate inversion results against reported detailed crop yield estimates. Assimilated observations include data from the NOAA long term collaborative sampling networks: surface weekly flasks, daily flasks at towers, and middle of the day or middle of the night averages calculated from in-situ tall tower records. Long- term observations from other labs (CSIRO, Australia; EC, Canada; IPEN, Brazil) involved in on-going intercomparison studies with the NOAA Global Monitoring Division are also used (Masarie et al., JGR, 2011). In the global CT, CO 2 vertical profiles from aircraft measurements over North America are not assimilated and are only used as an independent data set to evaluate the performance of the inversion (figure on left). Some of the largest biases in the free troposphere or at the surface occur in the summer time, when the surface sink may be too weak. Why downscaling global CT? High resolution inputs Surface vegetation Emissions models or inventories Quality controlled well calibrated CO2 dry air mixing ratios with objective uncertainty estimates Boundary conditions Based on global CT optimized CO2 mole fractions OR Based on the extrapolation of NOAA surface and aircraft measurements High resolution transport model WRF-ARW 40 km outer domain 10 km inner domain STILT/HYSPLIT Lagrangian model RKW RRL RMM RCE RGV WBI LEF Regional inversion components Differences between 3 estimates of the “background” contribution to CO2 at two NOAA tall towers (LEF, Wisconsin and WKT, Texas) CT: Boundary values sampled from CT2010 GV: First empirical boundary condition constructed using the GLOBALVIEW methodology applied to aircraft data and the marine boundary layer reference product. GV was used in Gourdji et al., BGD, NANEW: Second empirical boundary condition constructed using statistical fit to aircraft data and Pacific ocean only marine boundary layer reference product. The new empirical boundary condition NANEW and the values derived from global CT 2010 show larger differences at LEF and WKT in the summer and almost no differences in the fall and winter. Based on 10 day back-trajectories LEF WBI RRL RMM RCE RKW RGV Data from the NOAA ESRL Tall Tower Network and from Penn State's "Ring 2: High-precision, high-accuracy CO2 mixing ratio measurements in support of the North American Carbon Program Mid Continent Intensive" Experiment (Ken Davis, Tasha Miles, Scott Richardson) Two inversions were conducted using global CT2009. One inversion did not use the RING2 data (release run, blue) and the other one used afternoon averages of the RING2 CO 2 data (branched run, red). The detrended smoothed seasonal cycles derived from the observations (black line) and the two CT inversions are shown for various sites in the MidContinent. Global CT was not able to use the additional information provided by the RING2 data to reduce its summer positive bias. Boundary layer evaluation: CT vs empirical Lagrangian models intercomparison: WKT Fall 2010 Comparison of the fossil fuel (left) and biogenic (right) CO 2 total contributions to WKT midday mixing ratios based on 10 day back- trajectories and the convolution of surface influences with the same fluxes (CT 2010). Breakdown of the hourly contributions of fossil fuel (left) and biogenic (right) fluxes to CO 2 at WKT on 8/28/2010 for the entire 10 day period simulated with the Lagrangian models. Results from an on-going Lagrangian model intercomparison exercise. Contact Arlyn Andrews if you are interested in participating. Lauvaux et al., ACPD, 2011 Based on 56m resolution IRS satellite data