Modeling framework for estimation

1
Ninth Symposium on Integrated Observing and
Assimilation Systems for the Atmosphere, Oceans,
and Land Surface - 2005 AMS Annual Meeting 9-13
January, 2005, San Diego, California
Modeling framework for estimation of regional
CO2 fluxes using concentration measurements from
a ring of towers
Marek Uliasz and Scott Denning Department of
Atmospheric Science Colorado State University
2
Atmospheric CO2
South Pole Flask Data NOAA/CMDL (2001)

• Over the past 420,000 years atmospheric CO2 has
  varied between 180 and 280 parts per million,
  with concomitant swings of 10 C in the Earths
  climate.
• Since the Industrial Revolution, CO2 has risen
  dramatically, with an observed warming of 0.5 C
  in the past 100 years.

Law Dome ice core Etheridge et al (1999)
Vostok (400k yr) Ice Core data (Petit et al, 1999)
3
OUTLINE

• Atmospheric CO2 data
• Modeling framework for regional inversions
• The ring of towers campaign
• Example of CO2 flux estimation using pseudo-data
• Modeling approach to CO2 analysis
• Cold front passage
• Lake signature

4
Atmospheric CO2 Observations 2000
5
Atmospheric CO2 Observations 2007
6
Orbiting Carbon Observatory(Planned August 2007
launch)

• Estimated accuracy for single column 1.6 ppmv
• 1 x 1.5 km IFOV
• 10 pixel wide swath
• 105 minute polar orbit
• 26º spacing in longitude between swaths
• 16-day return time

7
Atmospheric CO2 Observations 2000
8
The Ring of Towers
40m Sylvania flux tower with high-quality
standard gases.
LI-820 sampling from 75m above ground
on communication towers.
447m WLEF tower. LI-820, CMDL in situ and
flask measurements.
data provided by Ken Davis, Scott J. Richardson
and Natasha Miles, The Pennsylvania State
University
9
modeling framework
global transport inflow fluxes
CSU RAMS
regional meteorology
SiB
LPD model
atmospheric transport
source-receptor matrix data analysis
influence functions
Bayesian inversion
estimation of regional CO2 fluxes
10
modeling framework
CSU RAMS
regional meteorology
SiB
LPD model
atmospheric transport
source-receptor matrix data analysis
influence functions
Bayesian inversion
estimation of regional CO2 fluxes
Ensemble Data Assimilation
Maximum Likelihood Ensemble Filter
11
modeling framework
Parameterized Chemical Transport Model (PCTM)
global transport inflow fluxes
CSU RAMS
regional meteorology
SiB
LPD model
atmospheric transport
source-receptor matrix data analysis
influence functions
Bayesian inversion
estimation of regional CO2 fluxes
Ensemble Data Assimilation
Maximum Likelihood Ensemble Filter
12
Climatology of influence functions for August 2000

• influence functions derived from RAMS/LPD model
  simulations
• passive tracer
• different configurations of concentration
  samples - time series from
• - a single level of WLEF tower
• - all levels of WLEF tower
• - WLEF tower six 76m towers

13
Example of estimation of NEE averaged for August
2000

• Bayesian inversion technique using influence
  function derived from CSU RAMS and
• Lagrangian particle model
• flux estimation for source areas in polar
  coordinates within 400 km from WLEF tower
• (better coverage by atmospheric transport)
• NEE decomposed into respiration and
  assimilation fluxes
• RR0, AA0 f(short wave radiation,
  vegetation class)
• inversion calculations for increasing number
  of concentration data (time series from towers)
• NEE uncertainty presented in terms of standard
  deviation derived from posteriori covariance
  matrix
• inflow CO2 flux is assumed to be known from a
  large scale transport model
• in further work, concentration data from
  additional tower will be used to improve the
  inflow flux
• given by a large scale model

Configuration of source areas with WLEF tower in
the center of polar coordinates
14
modeling approach to CO2 data analysis
Cold front passage across the ring
15
CO2 from 5 sites, April 29, 2004
1200 UTC
Ken Davis, Scott J. Richardson and Natasha Miles
The
Pennsylvania State University
16
(No Transcript)
17
sunset
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
(No Transcript)
26
(No Transcript)
27
(No Transcript)
28
sunrise
29
(No Transcript)
30
(No Transcript)
31
(No Transcript)
32
(No Transcript)
33
(No Transcript)
34
(No Transcript)
35
(No Transcript)
36
(No Transcript)
37
(No Transcript)
38
(No Transcript)
39
(No Transcript)
40
seasonal cycle
of CO2 flux at WLEF tower
41
seasonal cycle
diurnal cycle
of CO2 flux at WLEF tower
42
modeling approach to CO2 data analysis
Lake signature in CO2 data
43
influence function August 2003
entire domain
44
influence function August 2003
entire domain
land
45
influence function August 2003
entire domain
land
water
46
Relative contribution of different source areas
to tracer concentrations at 400m WLEF tower
May-November 2003 land
85.4 Lake Superior 9.5 Lake Michigan
1.8 other waters 3.1
47
Relative contribution of different source areas
to tracer concentrations at 400m WLEF tower
May-November 2003 land
85.4 Lake Superior 9.5 Lake Michigan
1.8 other waters 3.1
48
Relative contribution of different source areas
to tracer concentrations at 400m WLEF tower
May-November 2003 land
85.4 Lake Superior 9.5 Lake Michigan
1.8 other waters 3.1
49
Relative contribution of different source areas
to tracer concentrations at 400m WLEF tower
May-November 2003 land
85.4 Lake Superior 9.5 Lake Michigan
1.8 other waters 3.1
50
Relative contribution of different source areas
to tracer concentrations at 400m WLEF tower
May-November 2003 land
85.4 Lake Superior 9.5 Lake Michigan
1.8 other waters 3.1
51
Difference in observed CO2 at 400m WLEF
tower between transport from Lake Superior and
transport from land with 95 confidence intervals
2003
1996
52
Difference in observed CO2 at 400m WLEF
tower between transport from Lake Superior and
transport from land with 95 confidence intervals
2003
1996
data analysis in wind sectors without modeling
53
Travel time between Lake Superior and WLEF tower
two transport patterns in September
54
Further work

• Data analysis using influence functions
• Exploring vertical transport
• Influence functions integrated with CO2 fluxes
• SiB-RAMS simulation

• Estimations of Regional CO2 Fluxes
• PCTM RAMS LPDM
• pseudo-data inversions
• inversions using the data from the ring of
  towers