What Can We Learn from Intensive Atmospheric Sampling

What Can We Learn from Intensive Atmospheric Sampling

What Can We Learn from Intensive Atmospheric Sampling Field Programs? John Lin1, Christoph Gerbig2, Steve Wofsy1, Bruce Daube1, Dan Matross1, Mahadevan Pathmathevan1, V.Y. Chow1, Elaine Gottlieb1, Arlyn Andrews3, Bill Munger1 Department of Earth & Planetary Sciences, Division of Engineering & Applied Sciences Harvard University 2 Max-Planck-Institute fr Biogeochemie, Jena, Germany 3 Climate Monitoring and Diagnostics Laboratory, National Oceanographic and Atmospheric Administration 1 Current affiliation: Colorado State University Unique Value of Intensive Aircraft Sampling The ability to probe tracers both in the vertical and the horizontal at multiple scales, enabling: Determination of spatial variability of CO2 and other tracers Direct constraint of regional-scale fluxes from airmassfollowing experiments Model testing: diagnosis of errors in atmospheric modelling (e.g., PBL ht, wind vectors, convection) Validation of space-borne sensors COBRA (CO2 Budget & Rectification Airborne Study) COBRA 2000: www-as.harvard.edu/chemistry/cobra/ COBRA 2003: www.fas.harvard.edu/~cobra/ COBRA 2004 (Maine): www.deas.harvard.edu/cobra/ Supported by: NASA, NSF, NOAA, and DoE Coalition of the Willing COBRA Participants: Steven C. Wofsy, Paul Moorcroft, Bruce Daube, Dan Matross, Bill Munger, V.Y. Chow, Elaine Gottlieb, Christoph Gerbig, John Lin: (Harvard University) Tony Grainger, Jeffrey Stith: (University of North Dakota) Ralph Keeling, Heather Graven: (Scripps Institution of Oceanography) Britton Stephens: (National Center for Atmospheric Research ) Pieter Tans, Peter Bakwin, Arlyn Andrews, John Miller, Jim Elkins, Dale Hurst: (Climate Monitoring & Diagnostic Laboratory) Dave Hollinger: (University of New Hampshire) Ken Davis: (Pennsylvania State University) Scott Denning, Marek Uliasz: Larry Oolman, Glenn Gordon: (Colorado State University) (University of Wyoming) Determination of spatial variability of CO2 and other tracers Direct constraint of regional-scale fluxes from airmass-following experiments Model testing: diagnosis of errors in atmospheric modelling (e.g., PBL ht, wind vectors, convection) Lin, J.C., C. Gerbig, B.C. Daube, et al., An empirical analysis of the spatial variability of atmospheric CO2: implications for inverse analyses and space-borne sensors, Geophysical Research Letters, 31 (L23104), doi:10.1029/2004GL020957, 2004. Gerbig, C., J.C. Lin, S.C. Wofsy, B.C. Daube, et al.. Toward constraining regional-scale fluxes of CO2 with atmospheric observations over a continent: 1. Observed spatial

variability from airborne platforms, J. Geophys. Res., 108(D24), 4756, doi:10.1029/2002JD003018, 2003. Models of the Atmosphere Divide it Up into Many Individual Boxes (gridcells) For spatially heterogeneous field of CO2 concentration over land, there is can be large differences between an observation at a point location and the gridcell-averaged value. (representation error) 50 40 30 20 10 Latitude 60 70 Aircraft Observations used in analysis of CO2 Spatial Variability 120 160 -160 -120 -80 Longitude COBRA-2000 COBRA-2003 PEMWESTA PEMWESTB PEMTROPICSA PEMTROPICSB TRACEP Representation error: Stdev(CO2) within each subgrid of size x y 0 200 400 600 x [km] 800 1000 CO2 [ppm] 365 0 200 350 355 360 y [km] 400 600 error dependent

on grid size (hor. resolution) and spatial variability of tracer 800 1000 Representation Error derived from Spatial simulation for CO2, based on Variogram Representativeness Error [ppm] 0.5 1.0 1.5 2.0 2.5 3.0 Representation error: continent vs ocean COBRA-2000 PBL Pacific 0.15~3km Pacific 3~6km Pacific 6~9km 0 200 400 600 Size of Gridcell [km] 800 1000 Determination of spatial variability of CO2 and other tracers Direct constraint of regional-scale fluxes from airmass-following experiments Model testing: diagnosis of errors in atmospheric modelling (e.g., PBL ht, wind vectors, convection) Lin, J.C., C. Gerbig, S.C. Wofsy, et al., Measuring fluxes of trace gases at regional scales by Lagrangian observations: Application to the CO 2 Budget and Rectification Airborne (COBRA) study, J. Geophys. Res., 109 (D15304, doi:10.1029/2004JD004754), 2004. Planning and Analysis of Air-Following Experiments using STILT Objective: test method for providing tight atmospheric constraint on fluxes in targeted regions UPSTREAM direction of time, wind DOWNSTREAM Mixed layer top CO2 Lin et al., Measuring fluxes of trace gases at regional scales by Lagrangian observations, J. Geophys. Res. [2004]. ME (daytime) UT14 (1) UT19 (2) -5 -4 -3 -2 -1

log10[ppm/(mole/m /s)] 2 upxsecnum=241 upxsecnum=242 364 Reg.251: CO CO2 [ppmv] Downstream 2 4000 4000 Reg.241: CO2(1) [ppmv] Upstream CO 2 362 358 364 360 360 360 360 360 362 360 360 362 360 358 358 356 354 356 362 360 360 360 360 358 356 354 354 354 1000 360 Altitude [m ASL] 2000 3000 364

364 1000 Altitude [m ASL] 2000 3000 364 356 354 4000 Reg.242: -50 0 CO2 50 Distance Along Cross-section [km] 365 Upstream CO2 (2) [ppmv] Altitude [m ASL] 2000 3000 355 -50 0 50 Distance Along Cross-section [km] 355 360 362 1000 360 358 356 -60 355 -40 -20 0 20 Distance Along Cross-section [km] 365 40 365 UT14 ME (daytime) (1) (2) UT19 -5 -4 -3 -2 -1 log10[ppm/(mole/m /s)] 2 (a)

upxsecnum=241 upxsecnum=242 reg.251co2fluxp Forest Cropland (c) Fossil Fuel -50 0 50 Distance Along Cross-section [km] CO2 Flux [mole/m2/s] -25 -15 -5 0 CO2 Flux [mole/m2/s] -25 -15 -5 0 reg.251co2fluxp Howland eddy covariance Observed Tot Modeled Optimized (d) -50 0 50 Distance Along Cross-section [km] Determination of spatial variability of CO2 and other tracers Direct constraint of regional-scale fluxes from airmass-following experiments Model testing: diagnosis of errors in atmospheric modelling (e.g., PBL ht, wind vectors, convection) Gerbig, C., J.C. Lin, S.C. Wofsy, B.C. Daube, et al., Toward constraining regional-scale fluxes of CO 2 with atmospheric observations over a continent: 2. Analysis of COBRA data using a receptor-oriented framework, J. Geophys. Res., 108(D24), 4757, doi:10.1029/2003JD003770, 2003. Caution: Potential Effect of Errors in Wind Fields Reg. 339 : output of XsecPart.ssc timesteps plotted [hr]: -4 UT18 4000 364 364 362 362 365 362 360 354 354 362 362 360

360 354 -10 0 10 20 Distance Along Cross-section [km] 30 -20 355 Reg.322: CO Upstream CO [ppbv] 4000 355 358 362 -10 356 358 0 10 20 30 40 Distance Along Cross-section [km] 365 4000 0 -20 356 364 362 1000 1000 362 354 Reg.331: CO2 Downstream CO2 [ppmv] upxsecnum=322 Altitude [m ASL] 2000 3000 Altitude [m ASL] 2000 3000 4000 Reg.322: CO2 Upstream CO2 [ppmv] UT22 Reg.331: CO Downstream CO [ppbv] 100

Altitude [m ASL] 1000 2000 3000 100 90 90 90 100 1000 Altitude [m ASL] 2000 3000 100 90 100 110 90 100 120 90 100 120 120 -30 90 110 110 -20 -10 0 10 20 Distance Along Cross-section [km] 110 100 -20 30 90 110 0 20 40 Distance Along Cross-section [km] Conclusions and Future Steps Intensive atmospheric observations provide unique information on the spatial variability of CO2 Airmass-following experiments yield constraints on regional fluxes The high-resolution atmospheric observations are valuable for improving models => Intensive atmospheric sampling is an important complement to the long-term observational network! (within context of coordinated research efforts such as the North American Carbon Program) Intensive aircraft campaigns during targeted periods that yield enhanced observations to evaluate the representativeness of long-term network observations and to develop

modeling and analysis tools North American Carbon Program Intensive Atmospheric Sampling as Complement to Long-term Observational Network Inversion using intensive data + long term network ? = Inversion using long term network only Extra Material Grain size of atmospheric CO2: Variogram For pairs of locations xi, xj within a given distance bin h | xi - x j | and measured within 3 hours of each other 30 20 10 0 2(h) [ppm2] 40 2(h)=var(CO2(xi)-CO2(sj)) 100 200 300 400 distance h [km] 500 Improved Forecasting for Planning Lagrangian Experiments: using wind fields from multiple mesoscale models Model Compare: HOWLAND 061120 52 Model Compare: CYCH 060913 b) 50 50 55 a) 48 Chicoutimi-Jonquiere Quebec 46 45 Trois-Rivieres Moncton Nepean Richmond Montreal St. Catharines Sherbrooke Gloucester Saint John Halifax AVN ETA40 MM545

44 40 Kingston resid air in morn w/ winderr AVN ETA40 -90 -85 -80 -75 -70 forecast hr: avn=060712; eta40=060712; mm545=060712 MM545 w/ winderr ETA12 Syracuse -65 hrs = 0, -16 Albany -76 -74 -72 -70 -68 -66 -64 forecast hr: avn=061006; eta12=061006; eta40=061006; mm545=061006 hrs = 0, -8 Lin et al, Designing Lagrangian Experiments to Measure Regional-scale Trace Gas Fluxes, Manuscript. 10000 Maine => NoDak (northern legs) 8/18&19/2000 3 3 3 6 6 4 3 6 6 3 7 8 0 4 8 2 3 altitude/[m] 6000 3 6 6 6

6 4 3 3 COBRA-2000 Large-scale Observations 6 6 6 6 6 6 3 3 6 6 4 4 3 3 3 3 3 6 6 6 6 2 4 6 4 2000 3 3 3 5 6 6 2 0 8 3 6 0

3 4 6 4 6 3 3 6 5 4 6 3 5 0 3 5 2 3 3 4 8 3 5 0 3 5 2 3 3 5 5 6 3 6 2 3 5 8 3 5 6 3 5 4

3 5 6 3 5 8 3 4 6 5 8 3 6 5 4 2 3 3 6 4 0 3 3 6 6 6 3 0 3 6 2 7 7 4 0 3 7 8 0 3 -95 10000 350 -90 -85

-80 longitude/[deg] -75 -70 CO2 [ppm] Idaho => Maine (southern legs) 8/6-11/2000 370 3 7 8 0 4 8 2 2000 altitude/[m] 6000 3 3 6 6 6 6 3 6 3 6 0 3 6 2 Vegetation Condition Index 6 3 3 3 7 7 6 (NOAA Environmental Satellite, Data, and Information Service) 2 0 0 3 3 7 0

3 3 7 -110 350 7 8 3 8 370 7 3 7 3 4 7 -90 longitude/[deg] CO2 [ppm] 7 3 6 6 4 3 6 5 3 2 6 8 6 0 8 8 3 -100 6 4 2 0 3 5 4 3 3

3 7 0 3 -80 8 2 -70 CO2 - background & fossil CO2 (northern legs) -2 0 2 6 1 10000 10000 Large-scale biospheric CO2 signal CO2 - background & fossil CO2 (southern legs) 0 0 6 1 2 0 4 0 6 1 2 0 4 0 4 0 2 6 1 0 4 -2 -4 0 altitude/[m] 6000 Meas. altitude/[m] 6000 -2 -2 -4 -2 -4 -2 0

-2 -2 -8 -6 -2 -4 0 -2 -2 -1 -1 2000 2000 -2 2 -2 4 -2 -8 -2 -1 8 -1 -1 -1 2 -1 0 -6 -4 4 -4 -6 -1 -4 0 -6 6 -4 -2 -8 -1 -95 -20 -90 WEST

0 -8 0 0 -2 0 -8 -85 -80 -75 -70 longitude/[deg] EAST 0 10 -20 2 6 1 0 6 -2 -4 -6 -4 -6 -2 0 -110 CO2 [ppm] -100 WEST -90 -80 longitude/[deg] EAST -70 0 10 CO2 [ppm] vegetation CO2 signal (northern legs, opt.) 10000 10000 0 0 0 modeled vegetation CO2 (no conv., south) 0 0 0 0

0 0 -2 -2 0 -4 -2 altitude/[m] 6000 altitude/[m] 6000 -2 0 -2 0 -6 -4 2000 2000 -2 -2 0 0 -6 -4 -2 -1 4 -1 2 -1 0 -8 -6 -6 -8 -1 0 -6 -8 -1 0 0 0

Model zero conv. 0 -95 -20 0 10 CO2 [ppm] -90 WEST -85 -80 -75 -70 longitude/[deg] EAST NORTH 0 -110 -20 0 10 CO2 [ppm] -100 WEST -2 -4 -90 -80 longitude/[deg] EAST SOUTH -70 CO2 - background & fossil CO2 (northern legs) -2 0 2 6 1 10000 10000 Large-scale biospheric CO2 signal CO2 - background & fossil CO2 (southern legs) 0 0 2 6 1 0 4 0 6 1 2 0 4 0 4

0 2 6 1 0 4 -2 -4 0 altitude/[m] 6000 Meas. altitude/[m] 6000 -2 -2 -4 -2 -4 -2 0 -2 -2 -8 -6 -2 -4 0 -2 -2 -1 -1 2000 2000 -2 2 -2 4 -2 -8 -2 -1 8 -1 -1 -1 2

-1 0 -6 4 -4 -4 -6 -1 -4 0 -6 6 -4 -2 -8 -1 -95 -20 -90 WEST 0 0 -8 0 -2 0 -8 -85 -80 -75 -70 longitude/[deg] EAST 0 10 -20 2 6 1 0 6 -2 -4 -6 -4 -6 -2 0 -110

CO2 [ppm] -100 WEST -90 -80 longitude/[deg] EAST -70 0 10 CO2 [ppm] vegetation CO2 signal (northern legs, opt.) 10000 10000 0 0 -2 -2 -2 modeled vegetation CO2 (conv., south) 0 0 -2 altitude/[m] 6000 altitude/[m] 6000 -6 -2 -2 -6 -6 -6 -4 -4 2000 2000 -8 -6 -2 -4 -6 -8 -8 -8 -1 0 -6 -6

-8 -1 0 -1 -6 0 -6 2 -6 0 0 Model excess. conv. -2 -95 -20 0 10 CO2 [ppm] -90 WEST -85 -80 -75 -70 longitude/[deg] EAST NORTH 0 -2 -4 -6 -110 -20 2 0 10 CO2 [ppm] -100 WEST -90 -80 longitude/[deg] EAST SOUTH -70 Caution: Potential Effect of Errors in Wind Fields Reg. 339 : output of XsecPart.ssc timesteps plotted [hr]: -4 Reg. 331 : output of XsecPart.ssc timesteps plotted [hr]: -4 After adjustment to match

tracer gradients Before adjustment UT18 UT18 UT22 UT22 upxsecnum=322 10 0 -10 -20 mole/m2 /s CO 2Flux -30 -40 -40 -30 10 0 -10 -20 mole/m2 /s CO 2Flux upxsecnum=322 -20 -10 0 10 20 30 Distance Along Cross-section [km] 40 -20 -10 0 10 20 30 Distance Along Cross-section [km] 40

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