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

1Department of Earth & Planetary Sciences, Division of Engineering & Applied Sciences Harvard University2Max-Planck-Institute fr Biogeochemie, Jena, Germany3Climate Monitoring and Diagnostics Laboratory, National Oceanographic and Atmospheric Administration

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 airmass-following 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)Supported by: NASA, NSF, NOAA, and DoECOBRA 2000: COBRA 2003:COBRA 2004 (Maine):www-as.harvard.edu/chemistry/cobra/www.fas.harvard.edu/~cobra/www.deas.harvard.edu/cobra/

Coalition of the WillingCOBRA 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: (Colorado State University)Larry Oolman, Glenn Gordon: (University of Wyoming)

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.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)

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)

Aircraft Observations used in analysis of CO2 Spatial Variability

LongitudeLatitude

Representation Error derived from Spatial simulation for CO2, based on Variogram

CO2 [ppm]Representation error: Stdev(CO2) within each subgrid of size x y error dependent on grid size (hor. resolution) and spatial variability of tracer

Representation error: continent vs oceanPacific 0.15~3kmPacific 3~6kmPacific 6~9kmCOBRA-2000 PBL

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 CO2 Budget and Rectification Airborne (COBRA) study, J. Geophys. Res., 109 (D15304, doi:10.1029/2004JD004754), 2004.

Planning and Analysis of Air-Following Experimentsusing STILT

direction of time, windMixed layer topDOWNSTREAMUPSTREAMCO2Objective: test method for providing tight atmospheric constraint on fluxes in targeted regionsLin et al., Measuring fluxes of trace gases at regional scales by Lagrangian observations, J. Geophys. Res. [2004].

ME (daytime)Downstream CO2 [ppmv]Upstream CO2 (1) [ppmv]Upstream CO2 (2) [ppmv](2)(1)UT14UT19log10[ppm/(mmole/m2/s)]

ME (daytime)(a)(2)(1)UT14UT19log10[ppm/(mmole/m2/s)]Howland eddy covariance(c)(d)CO2 Flux [mmole/m2/s]CO2 Flux [mmole/m2/s]ObservedOptimizedTot Modeled

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 CO2 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

Upstream CO2 [ppmv]Downstream CO2 [ppmv]Upstream CO [ppbv]Downstream CO [ppbv]UT22UT18

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 toolsNorth American Carbon Program

Intensive Atmospheric Sampling as Complement to Long-term Observational Network?= Inversion using long term network only Inversion using intensive data + long term network

Extra Material

Grain size of atmospheric CO2: Variogram

For pairs of locations xi, xj within a given distance bin and measured within 3 hours of each other2g(h)=var(CO2(xi)-CO2(sj))

a)b)Improved Forecasting for Planning Lagrangian Experiments: using wind fields from multiple mesoscale modelsLin et al, Designing Lagrangian Experiments to Measure Regional-scale Trace Gas Fluxes, Manuscript.

COBRA-2000 Large-scale ObservationsCO2 [ppm]CO2 [ppm]Vegetation Condition Index (NOAA Environmental Satellite, Data, and Information Service)

Large-scale biospheric CO2 signalCO2 [ppm]CO2 [ppm]CO2 [ppm]CO2 [ppm]Meas.Modelzeroconv.NORTHSOUTH

Large-scale biospheric CO2 signalNORTHSOUTHCO2 [ppm]CO2 [ppm]CO2 [ppm]CO2 [ppm]Meas.Modelexcess.conv.

Caution: Potential Effect of Errors in Wind Fields

After adjustment to match tracer gradientsBefore adjustmentUT22UT18UT22UT18CO2 Flux [mmole/m2/s]Distance Along Cross-section [km]Distance Along Cross-section [km]CO2 Flux [mmole/m2/s]

Especially using AIRCRAFTCOBRA-2003did racetrack TWICECOBRA-2004 figuredoesnt show ALL the flights; ADVERTISE DAN MATROSSs posterNeed to know the discrepancy in order to properly use the observationsInverse analysis, error covariance matrixNASA GTE observations: by Stephanie Vay and Bruce AndersonMention: aircraft can also assess representativeness of long-term observation sites

Improve upon previous methods=>mention 1-D PBL budgets: FIRST TIME this was attempted in carbon cycle scienceREASON why wasnt planned before: no flight planning tool that told you where and how large the upstream cross-section has to be; needed to run BACKWARD in time! STILT ingested products outputted by SUPERCOMPUTERS at weather prediction centersMeasure CROSS-SECTIONSParticles are transported backward from the receptor in the downstream cross-section.They pick up an initial tracer value from the measured upstream cross-section.Along the way, they are affected by surface emissions when they dip into the mixed-layer.

Matched upstream with downstream using atmospheric modelMerge top-down and bottom-up constraints!Very few other ways to get at regional scale fluxImportance of getting atmospheric transport correct to analyze highly variable CO2 concentrations over the continent!Intensive observations would provide all of the following: 1) spatial variability => representativity 2) airmass-following 3) model testing

Once we have the models which can deal with the spatial variability of CO2, the dataset collected by vertical profiling will allow to assess the representativity of the network configuration. A possible way to do this, would be to look at how inversion results change when using the combined dataset compared to using the long term network only. Such differences would correspond to biases caused by under-sampling of the atmosphere, and might lead to information about where additional sites are needed.

Flight paths, interpolation between profilesNeed a model like STILT to interpret these signals! Otherwise could have large representation errorsNorth sampled air travelling across Canada, where vegetation was well-watered and happySouth sampled air travelling across the Southern U.S., where vegetation was not so happyNOAA Environmental Satellite, Data, and Information ServiceVegetation condition index: monitors greenness of the ground (NDVI)Now could begin to interpret these signals: test analysis frameworkWe DO NOT have convective mass fluxes from EDAS!Problem of Convection.We know the truth lies somewhere in between the two extremes of convection. . .Stress that FIRST TIME that we have been able to interpret CO2 distributions over the continent

We developed a simple parameterization of convective cloud transport to give an upper estimate of the effect of sub-gridscale vertical redistribution and provide a lower limit for exch. This estimate would then be compared with estimates with no convective cloud transport in order to bound the effects due to sub-gridscale vertical redistribution. The convective cloud transport scheme randomly assigns a vertical position between the surface and the limit of convection altitude (zLOC) to each particle with a vertical position below zLOC. The random redistribution is weighted by density and takes place whenever a new update of meteorological data is read (every 3 hours for EDAS) and whenever zLOC is higher than zi (mixed layer height). zLOC is taken to be the uppermost level at which the cloud air parcel is buoyant, i.e. the highest altitude where the parcels virtual temperature exceeds that of the surrounding air. The virtual temperature profile in the cloud was calculated by lifting a cloud parcel starting at the lowest model layer, lifteddry adiabatically to the lifting condensation level (LCL), and then moist adiabatically to the profile top. The presence of liquid water was neglected. We assumed that the virtual temperature from the assimilated field could be used as proxy for virtual temperature in the cloud-free environment at the levels at which cloud and non-cloud virtual temperatures are equal (i.e. zLOC).