Greenhouse gas fluxes derived from regional
measurement networks and atmospheric
inversions: Results from the MCI and INFLUX experiments
Kenneth Davis
The Pennsylvania State University
and
The INFLUX and MCI research teams
Megacities workshop, Pasadena, CA, 8 May, 2012
Earth
Networks
Learn by doing
• What is needed to estimate regional CO2
fluxes using atmospheric data?
• The MCI and INFLUX are experiments
aimed at addressing this question
experimentally.
INFLUX • Indianapolis flux experiment goals:
– (see Paul’s list)
– Derive metro-scale GHG fluxes with
atmospheric inversions – whole city, and
spatially resolved (1 km2 ?), year-round,
segregating fossil and biogenic CO2 as well
as CH4 fluxes.
– Compare inverse estimates to the best
available regional inventory data.
– Evaluate experimental design needed to
operate long-term urban networks of known
and useful accuracy and precision.
Experimental design
• Dense, tower-based greenhouse gas
measurement network
• Relatively simple terrain and dense
meteorological data
These yield our best chances to derive robust
flux estimates using atmospheric inversions.
• Excellent “bottom-up” flux estimates from
inventory methods
This provides the test for the atmospheric
inversion methodology.
INFLUX ground-based measurements: (many to be deployed this summer)
• GHG sampling
• Two sites measuring CO2/CO/CH4 + weekly
flasks
• Three sites measuring CO2/CO + weekly flasks
• Three sites measuring CO2/CH4
• Four sites measuring CO2
• Regional weather data, including instrumented
commercial aircraft and surface stations
• Sonic anemometers, radiometers and LI-7500s for
surface energy balance, momentum flux and TKE at
N>1 sites.
• TCCON in east Indianapolis
• Doppler lidar to characterize ABL depth and
turbulent kinetic energy, and lower tropospheric
winds.
Tower locations
• 6 sites running.
• 3(4?) sites to be deployed shortly.
• 3(2?) agreements remain to be negotiated.
• Network has reached the max density possible for communications towers in the urban center.
1
2 1
Toolbox (used at Penn State)
Air Parcel Air Parcel
Air Parcel
Sources Sinks
wind wind
Sample
Sample
Network of tower-based GHG sensors:
(9 sites with CO2 for the MCI)
(~11 sites with CO2, CH4, CO and 14CO2 for INFLUX)
Atmospheric transport model:
(WRF, 10km for the MCI)
(WRF, 2km for INFLUX)
Prior flux estimate:
(SiB-Crop for MCI)
(Hestia for INFLUX)
Boundary conditions (CO2/met):
(Carbon Tracker and
NOAA aircraft profiles,
NCEP meteorology)
CO2 Concentration Network: 2008
Midcontinent
intensive, 2007-2009
INFLUX,
2010-201(3?)
Gulf coast
intensive,
2013-2014
Evolution of the NACP MCI • 1999 US Carbon Cycle Science Plan (Sarmiento and
Wofsy) proposed regional atmospheric inversions.
• 2002 white paper by Pieter Tans proposed the U.S.
midcontinent as a good experimental site – agricultural
fluxes are known because of harvest/inventory data.
• 2006 Midcontinent Intensive (MCI) science plan (Ogle et
al) spelled out the objectives of this contribution to the
North American Carbon Program (NACP).
• Field work, 2005-2009. Analyses are at hand!
• The primary objective of the NACP MCI is to test of our
ability to achieve convergence of “top-down” and “bottom-
up” estimates of the terrestrial carbon balance of a large,
sub-continental region.
• (2012. A new US Carbon Cycle Science Plan published.
Emphasizes need to study human dimensions.)
Δ SOC
Yield Total
Eroded C
Δ Live
Aboveground C
(0)
Δ Dead Aboveground
Litter C
Δ Dead Belowground
Litter C
Δ Live
Belowground C
(0)
MCI forest and ag inventories: Estimation based on stock change on cropland fields
and includes key lateral flows in harvested grain and
eroded C.
West et al., 2011; Ogle et al., 2010; Heath et al, 2004(?); NASS and FIA data
Inventory Uncertainty Assessment
Simulation
Model
Scaling
Uncertainty Results
Structural Uncertainty PDF
95%
Confidence
Interval
Input
Uncertainies*
Ogle et al., Global Change Biology, 2010
*For example:
-Crop yield data.
-Yield to carbon conversion coefficient.
publications
• Richardson et al., 2012. Performance of the Picarro
“model T” CRDS CO2 instruments in the MCI.
• Miles et al., 2012. Seasonal and synoptic CO2
distribution across the MCI region, 2007-2009.
• Lauvaux et al., 2012. MCI regional atmospheric
inversion, 2007 data.
• Lauvaux et al., accepted. MCI CO2 network design
study. 2007 data.
• Schuh et al, drafted. Comparison of two regional
inversions and agricultural inventory. 2007 data.
• Diaz et al, drafted. Evaluation of the impact of transport
differences on regional atmospheric CO2. 2007 data.
Lesson 1: 2007 - 2009 field
campaign,2012 - ? publications.
This stuff takes a while.
And a good data set is valuable for a
long time.
Corn-dominated sites
MCI Tower-Based CO2 Observational Network
Aircraft profile sites, flux towers omitted for clarity.
• Large differences in seasonal drawdown, despite nearness of stations.
• 2 groups: 33-39 ppm drawdown and 24 – 29 ppm drawdown. Tied to density of corn.
Mauna Loa
Miles et al, 2012, JGR-B
MCI 31 day running mean daily daytime average CO2
• Daily differences from site to site as large at 60 ppm.
Synoptic variability in boundary-layer CO2
mixing ratios: Daily daytime averages
Miles et al, 2012
• Large differences in seasonal drawdown, despite nearness of stations.
• 2 groups: 33-39 ppm drawdown and 24 – 29 ppm drawdown. Tied to density of corn.
Mauna Loa
Miles et al, 2012, JGR-B
MCI 31 day running mean daily daytime average CO2
Inversion results with SiBCrop prior
Prior flux – SiBCrop
-109 TgC
Posterior flux
-194 +/- 30 TgC
Units are TgC/degree2, Jun-Dec07 (Lauvaux et al., 2012, ACP)
Inversion results with CT prior
Prior flux – CT posterior
-198 TgC
Posterior flux
-215 +/- 30 TgC
Units are TgC/degree2, Jun-Dec07 (Lauvaux et al., 2012, ACP)
Regionally and time integrated C flux uncertainty
assessment:
Assumptions in the inversion method
Lauvaux et al, 2012, ACP
Average of many plausible posterior fluxes = -183 +/- 16 TgC.
(-183 +/- 16 gC m-2)
National-scale agricultural
inventory
26
Harvested biomass is
transported out of the MCI
region.
Agriculture is a strong sink
from the regional
atmospheric perspective
West et al, 2011
MCI domain
CO2 boundary correction
(Lauvaux et al., 2012, ACP) 0.55 ppm mean bias
Difference between CT inflow boundary and NOAA aircraft
Tests of sensitivity to boundary conditions
• 1 ppm constant bias to boundary inflow
yields a 45 TgC change in net flux.
• Assume 24 TgC (24 gC m-2) maximum
potential error (0.55 ppm inflow bias, but
correctable in this case with aircraft
profiles).
“Small” error only due to large net regional
flux – boundary error does not scale with
flux.
Lesson 5: The MCI network was more
than sufficient for determining the
regionally aggregated CO2 flux and
barely(?) sufficient for determining
spatial patterns in CO2 flux.
Regional NEE of CO2
for various subsets of
the MCI network,
2007.
Spatial pattern of NEE
is sensitive to the
network choice.
Lauvaux et al, accepted
Regionally aggregated NEE of CO2
Regional NEE is not very sensitive to the choice of network. Corn / non-
corn fluxes, however, change substantially.
Lauvaux et al, accepted
Error reduction across the MCI region
Site “influence areas” do not overlap very much. Lauvaux et al, accepted
Lesson 6: Atmospheric transport
matters a lot.
(Influence on inverse flux estimates has
yet to be properly evaluated.)
CO2 model-data residuals at towers
Carbon Tracker Observation – Carbon
Tracker
Carbon Tracker underestimates CO2 draw down across all sites,
but especially at “corn” sites.
Diaz et al, in prep
CO2 model-data residuals at towers WRF with CT fluxes and
boundary conditions WRF - observations
WRF CO2, with same boundary conditions and fluxes as CT, has
very different residual. It underestimates CO2 where CT prior
overestimates CO2 sink. Transport matters! And the inversion
responds by shifting the fluxes. Inversion structure matters!
Diaz et al, in prep
Different WRF surface and ABL
configurations CASES Land Surface Scheme PBL
Scheme
CASE1 Noah YSU
CASE2 Noah MYJ
CASE3 RUC MYJ
CASE4 RUC YSU
Lesson 1: Setting up a dense observational
network takes significant time (2010 – 2012) and
resources. Don’t do this lightly. Or drop the data
set quickly.
Lesson 2: Maintaining a consistent data stream
from those observational assets is challenging
(e.g. phone lines, computer hang-ups, pump
failures…)
Lesson 3: We can measure and model the
enhancement in ABL CO2 over Indianapolis.
(and direct measurement of GHG boundary
conditions helps a lot)
Backward footprint from LPDM simulation
for towers 1 and 2
Site to site differences simulated by WRF at 2km and
Observed, November 11, 2010
Tower-based measurements of atmospheric CO2 mixing ratios
around Indianapolis
Plumes “miss” the city.
Small del-CO2.
Site to site differences simulated by WRF at 2km and
Observed, November 11, 2010
Backward footprint from LPDM simulation
for towers 1 and 2
Tower-based measurements of atmospheric CO2 mixing ratios
around Indianapolis
Plumes “hit” the city.
Large del-CO2.
Particle
touchdown for
July 12, 2011
after a) 12 hours
and b) 72 hours.
Touchdown is
considered
within 50m of
surface. The
background
values are EPA
4km CO.
March 1 -15th.
Flux
perturbations
and the resultant
concentration
difference at the
individual
INFLUX tower
locations.
Aircraft measurements of atmospheric CH4 mixing ratios
around Indianapolis
Aircraft flight plan during March 1st 2011 with South East to South West wind conditions
CH4 mixing ratios observed during March 1st 2011
Aircraft measurements of atmospheric CH4 mixing ratios
around Indianapolis
Direct comparison between WRF at 1km
and the observed peak from the South Landfill
Backward footprint from LPDM simulation
Based on particles released at the location of the peak
Aircraft measurements of atmospheric CH4 mixing ratios
around Indianapolis
Direct comparison between WRF at 1km
(previous time steps included)
and the observed peak from the South Landfill
Wind direction at the surface observed at 2 stations
and simulated by WRF at 1km resolution
Aircraft measurements of atmospheric CH4 mixing ratios
around Indianapolis
Aircraft and tower measurements of atmospheric CO2/CH4 mixing ratios
around Indianapolis
Backward footprint from LPDM simulation
for both towers and aircraft (50sec before the peak)
Wind direction at the surface observed at 2 stations
and simulated by WRF at 1km resolution
Vertical Profiles for:
• 10/23/2010 @ 0100 UTC
• 06/09/2011 @ 2000 UTC
• 06/07/2011 @ 0300 UTC
• 06/07/2011 @ 1300 UTC
• 06/07/2011 @ 1900 UTC
Lessons we have yet to learn from INFLUX:
1. We need to sample (time of day) and (altitudes) and
(spatial density) to derive urban GHG emissions with
(x) and (y) accuracy and precision at (z) and (w)
spatial and temporal resolution.
1. We need (z) ancillary measurements to be able to
distinguish fossil from biogenic CO2.
1. (N) meteorological data and (M) data assimilation
methods are required with (P) transport schemes
and (Q) parameters to yield (x) and (y)
improvements in accuracy and precision.
2. (X) and (y) measurements and results are needed to
support policy/management goals.
Conclusions
• This (regional GHG inversion) problem isn’t easy
or quick.
• Don’t abandon established infrastructure or data
sets quickly.
• LA is scary…large and complex. We have
limited human and financial resources.
• LA is exciting…large and complex. And CARB
and LA care.
• Policy community needs to be integrated into the
scientific endeavor.