What Can We Learn from Intensive Atmospheric Sampling Field Programs? John Lin 1, Christoph Gerbig...
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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
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
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 fr
Biogeochemie, Jena, Germany 3 Climate Monitoring and Diagnostics
Laboratory, National Oceanographic and Atmospheric Administration
Current affiliation: Colorado State University
Slide 2
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
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)
Slide 5
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:10.1029/2004GL020957, 2004. 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:10.1029/2002JD003018, 2003.
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)
Slide 6
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)
Slide 7
Aircraft Observations used in analysis of CO 2 Spatial
Variability Longitude Latitude
Slide 8
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
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:10.1029/2004JD004754), 2004.
Slide 11
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
Slide 12
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)]
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:10.1029/2003JD003770, 2003.
Slide 15
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
Slide 16
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)
Slide 17
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
Slide 18
Intensive Atmospheric Sampling as Complement to Long-term
Observational Network ? = Inversion using long term network only
Inversion using intensive data + long term network
Slide 19
Extra Material
Slide 20
Grain size of atmospheric CO 2 : Variogram 2 (h) [ppm 2 ]
distance h [km] 100200300400500 0 10 20 30 40 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 ))
Slide 21
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.
Slide 22
COBRA-2000 Large-scale Observations CO 2 [ppm] Vegetation
Condition Index (NOAA Environmental Satellite, Data, and
Information Service)
Slide 23
Large-scale biospheric CO 2 signal CO 2 [ppm] Meas. Model zero
conv. NORTH SOUTH
Slide 24
NORTH SOUTH Large-scale biospheric CO 2 signal CO 2 [ppm] Meas.
Model excess. conv.
Slide 25
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