Transcript Dykema_2011Nov08_FarIRworkshop

Far-Infrared Satellite Measurement
Applications for Energy, Security, and
Economics
John A. Dykema, Yi Huang, Stephen Leroy, James G. Anderson
[email protected]
Motivation
Applications
Technology
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Gulf Coast
Conclusions
Motivation
Applications
Technology
Data Analysis
Gulf Coast
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
C
5
Motivation
Applications
Technology
Data Analysis
Recent emissions
Conclusions
0
1850
2000 Combustion
2050
2100
Trajectory of1900
Global1950
Fossil Fuel
CO2 Emissions (GtC y-1)
10
9
8
7
Actual emissions: CDIAC
Actual emissions: EIA
450ppm stabilisation
650ppm stabilisation
A1FI
A1B
A1T
A2
B1
B2
2008
2006
2010
(proj)
2005
Observed
2000-2006
3.3%
6
5
1990
1995
2000
Raupach et al. 2007, PNAS
2005
2010
Motivation
Applications
Technology
Data Analysis
Conclusions
What drives the demand for global energy?
(Population)
x
Units: joules
(Per Capita
Income)
x
(Energy
demand per
dollar of
output)
2005
0.4 zettajoules of energy/yr
=
Global
Energy
Demand
2050
1.0 zettajoules of
Motivation
Applications
Technology
What is this .6 zettajoule of increased
energy demand per year by 2050
equivalent to?
•
The construction of 1000 large coal
burning power plants per year for
the next forty years.
or
•
Commissioning of 250 nuclear
power plants per year for the next
forty years.
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
Motivation
Applications
Technology
Climate change in Sub-Saharan Africa
Data Analysis
Conclusions
Increased Civil Conflict in Africa
Marshall B. Burke, Edward Miguel, Shanker Satyanath, John A. Dykema, and
David B. Lobell, PNAS December 21, 2010 vol. 107 no. 51 E185
Also see Hsiang et al. Nature 476 (25 August 2011)
Motivation
Applications
Technology
Data Analysis
Conclusions
Climate Change Impacts on US Agriculture
Marshall Burke, John Dykema, David Lobell, Edward Miguel, Shanker
Satyanath, NBER Working Paper No. 17092, Issued in May 2011
Applications Technology Data Analysis
Climate
Projection Uncertainty
Motivation
Conclusions
Uncertainty in Long-term Climate Projections
Motivation
Applications
Technology
Test for human influence
Test climate models
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
Climate feedbacks and their uncertainty in climate models
•
Bony et al. 2006
Wetherald and Manabe [1988]
Ts  R  (  
R Xi 1
)
Xi Ts
Planck Damping
Surface T
Feedbacks
Change
TOA Radiation
Imbalance
Ts: surface
temperature
R: radiation flux
Xi: meteorological
variable (e.g.
atmospheric
temperature, water
vapor concentration,
or cloud properties.)
Sensitivity
Notations:
Water vapor (WV), clouds (C),
lapse rate (LR), albedo (A)
Motivation
Applications
Technology
A healthy, secure, prosperous
and sustainable society for
all people on Earth
“The United States does not have, nor
are there clear plans to develop, a
long-term global benchmark record of
critical climate variables that are
accurate over very long time periods,
can be tested for systematic errors by
future generations, are unaffected by
interruption, and are pinned to
international standards.“
NRC
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
From Huang et al. J.Clim. 23(22) 6027 (2010)
Motivation
Applications
Technology
Data Analysis
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
On-Orbit Blackbody:
•Finite Aperture
•Temperature Gradient
Dykema and Anderson,
Metrologia 43 287 (2006)
Motivation
Applications
Technology
Data Analysis
Complete Hemispherical Laser-based Reflectometer (CHILR)
Cavity
Detector
Integrating sphere
Sphere
rotation
& X-Y stage
1.32 µm 10.6 µm
0.00015
0.00010
0.00005
0.00000
20
20
10
Y (mm)
10
0
0
-10
X (mm)
-10
-20
-20
-5.0e-05 0.0e+00 5.0e-05 1.0e-04 1.5e-04
Apr22_HVBB_10_6_XY_txt_30
Conclusions
Motivation
Applications
Technology
Data Analysis
From Theocharous,
Applied Optics, 2008
Conclusions
Motivation
Applications
Technology
Data Analysis
From Sinha and Harries, J. Climate, 1997
Conclusions
Motivation
Applications
Technology
Data Analysis
CO2
Surf. Temp.
Atmos. Temp
Water vapor
Linear Regression
Cloud
Conclusions
Motivation
Applications
clear-sky
In all-sky, spectral fingerprints
resemble the clear-sky fingerprints.
Technology
Data Analysis
all-sky
Conclusions
Motivation
Applications
Technology
Data Analysis
Conclusions
• Major contribution to decision support is
through climate models
• Far-infrared essential to testing climate
models
• Testing climate models is a longer-term
endeavor
• What more immediate contributions could
far infrared make?
Thanks for your attention!