What Can We Learn from Intensive Atmospheric Sampling Field Programs? John Lin 1, Christoph Gerbig 2, Steve Wofsy 1, Bruce Daube 1, Dan Matross 1, Mahadevan Pathmathevan 1, V.Y. Chow 1, Elaine Gottlieb 1, Arlyn Andrews 3, Bill Munger 1 1 Department of Earth & Planetary Sciences, Division of Engineering & Applied Sciences Harvard University 2 Max-Planck-Institute für Biogeochemie, Jena, Germany 3 Climate 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 CO 2 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 (CO 2 Budget & Rectification Airborne Study) Supported by: NASA, NSF, NOAA, and DoE COBRA 2000: COBRA 2003: COBRA 2004 (Maine): www-as.harvard.edu/chemistry/cobra /

“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: (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 CO 2 : implications for inverse analyses and space-borne sensors, Geophysical Research Letters, 31 (L23104), doi: /2004GL020957, 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: 1. Observed spatial variability from airborne platforms, J. Geophys. Res., 108(D24), 4756, doi: /2002JD003018, Determination of spatial variability of CO 2 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 CO 2 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 CO 2 Spatial Variability Longitude Latitude

Representation Error derived from Spatial simulation for CO 2, based on Variogram CO 2 [ppm] Representation error: Stdev(CO 2 ) within each subgrid of size  x  y error dependent on grid size (hor. resolution) and spatial variability of tracer

Representation error: continent vs ocean Pacific 0.15~3km Pacific 3~6km Pacific 6~9km COBRA-2000 PBL

Determination of spatial variability of CO 2 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: /2004JD004754), 2004.

direction of time, wind Mixed layer top DOWNSTREAM UPSTREAM CO 2 Objective: test method for providing tight atmospheric constraint on fluxes in targeted regions Lin et al., “Measuring fluxes of trace gases at regional scales by Lagrangian observations”, J. Geophys. Res. [2004]. Planning and Analysis of “Air-Following” Experiments using STILT

ME (daytime) Downstream CO 2 [ppmv] Upstream CO 2 (1) [ppmv] Upstream CO 2 (2) [ppmv] (2) (1) UT14 UT19 log 10 [ppm/(  mole/m 2 /s)]

ME (daytime) (a) (2) (1) UT14 UT19 log 10 [ppm/(  mole/m 2 /s)] Howland eddy covariance (c)(d ) CO 2 Flux [  mole/m 2 /s] Forest Cropland Fossil Fuel Observed Optimized Tot Modeled

Determination of spatial variability of CO 2 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: /2003JD003770, 2003.

0 Upstream CO 2 [ppmv]Downstream CO 2 [ppmv] Upstream CO [ppbv] Downstream CO [ppbv] UT22 UT18 Caution: Potential Effect of Errors in Wind Fields

Conclusions and Future Steps Intensive atmospheric observations provide unique information on the spatial variability of CO 2 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 long term network only Inversion using intensive data + long term network

Extra Material

“Grain size” of atmospheric CO 2 : Variogram 2  (h) [ppm 2 ] distance h [km] For pairs of locations x i, x j within a given “distance bin” and measured within 3 hours of each other 2  (h)=var(CO 2 (x i )-CO 2 (s j ))

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

COBRA-2000 Large-scale Observations CO 2 [ppm] Vegetation Condition Index (NOAA Environmental Satellite, Data, and Information Service)

Large-scale “biospheric” CO 2 signal CO 2 [ppm] Meas. Model zero conv. NORTH SOUTH

NORTH SOUTH Large-scale “biospheric” CO 2 signal CO 2 [ppm] Meas. Model excess. conv.

After adjustment to match tracer gradients Before adjustment UT22 UT18 UT22 UT18 CO 2 Flux  mole/m 2 /s  Distance Along Cross-section [km] CO 2 Flux  mole/m 2 /s  Caution: Potential Effect of Errors in Wind Fields