Transcript Lecture2(b) - San Jose State University

METR112 Global Climate Change -- Lecture 2
Energy Balance (2)
Prof. Menglin Jin
San Jose State University
Arctic sea ice coverage, 1979 and 2003
NASA http://www.learner.org/channel/courses/envsci/unit/text.php?unit=12&secNum=7
The Earth is not warming uniformly.
Notably, climate change is expected to affect the polar
regions more severely:
•The Arctic is warming nearly twice as rapidly as the rest of the world;
•winter temperatures in Alaska and western Canada have risen by up to 3–4°C
in the past 50 years, and
•Arctic precipitation has increased by about 8 percent over the past century
(mostly as rain)
Less snow
Due to, partly:
Positive albedo feedback
Smaller albedo
More insolation in surface
Higher surface temperature
Albedo of Earth
•albedo is used to represent reflectivity of Earth's surface
•The term albedo (Latin for white) is commonly used to or
applied to the overall average reflection of an object.
For example,
the albedo of the Earth is 0.39 (Kaufmann 1991 ) and
this affects the equilibrium temperature of the Earth.
•The greenhouse effect, by trapping infrared radiation,
can lower the albedo of the earth and cause global warming.
This is why albedo is important
Albedo
The ratio of the outgoing solar radiation reflected by
an object to the incoming solar radiation incident upon it.
Earth Observatory Glossary
By NASA,
Responsible NASA official: Dr. Michael D. King,
http://earthobservatory.nasa.gov/Library/glossary.php3?mode=all
α=
IIN
I IN
IOUT
IOUT
Dimensionless
Range: 0 (dark) – 1 (bright)
The word is derived from Latin albedo "whiteness", in turn from albus "white".
Albedo depends on wavelength and is determined by the structural and optical properties
of the surface, such as shadow-casting, mutiple scattering, mutual shadowing,
transmission, reflection, absorption and emission by surface elements,
facet orientation distribution and facet density.
Surface Albedo properties
1. Form 0-1.0
2. Varied with solar zenith Angle
3. Vary with surface properties (what color soil, if vegetation covered, etc)
4. Vary with surface roughness
5. Vary with soil moister
Why Is Surface Albedo Critical?
Surface Energy Budget:
(1-α)Sd +LWd-εσTskin4 +SH+LE + G= 0
Answer: albedo plays the key role in surface energy balance as it decides
how much surface insolation is kept in Earth surface system
This is a black spruce forest in the BOREAS experimental region in Canada.
Left: backscattering (sun behind observer), note the bright region (hotspot)
where all shadows are hidden. Right: forwardscattering (sun opposite observer),
note the shadowed centers of trees and transmission of light through
the edges of the canopies. Photograph by Don Deering.
http://www-modis.bu.edu/brdf/brdfexpl.html
A soybean field. Left: backscattering (sun behind observer). Right: forwardscattering
(sun opposite observer), note the specular reflection of the leaves.
Photograph by Don Deering. http://www-modis.bu.edu/brdf/brdfexpl.html
A barren field with rough surface Left: backscattering (sun behind observer),
note the bright region (hotspot) where all shadows are hidden.
Right: forwardscattering (sun opposite observer), note the specular reflection.
Photograph by Don Deering. http://www-modis.bu.edu/brdf/brdfexpl.html
Albedo is function of wavelength.
Boradband albedo
Boradband albedo is the integrated value of spectral emissivity at all wavelength
Spectral albedo – the reflectivity for specific wavelength
0.0 ..................................................................0.25
Shortwave White-Sky Albedo from Aqua and Terra:
Level 3, 16-day, one kilometer MODIS BRDF/Albedo Products
An albedo value of 0.0 indicates that the surface absorbs
all solar radiation, and a 1.0 albedo value represents total reflectivity.
http://theothermy.blogspot.com/2007/12/albedo-and-cool-roofs.html
CMG Broadband White-Sky Albedo (0.3-5.0mm)
14 - 29 September, 2001
No Data
0.0
0.2
0.4+
CMG Broadband White-Sky Albedo (0.3-5.0mm)
1 - 16 January, 2002
No Data
0.0
0.2
0.4+
CMG Broadband White-Sky Albedo (0.3-5.0mm)
18 February - 5 March, 2002
No Data
0.0
0.2
0.4+
CMG Broadband White-Sky Albedo (0.3-5.0mm)
7 - 22 April, 2002
No Data
0.0
0.2
0.4+
White Sky Spectral Albedo
7 - 22 April, 2002
No Data
NIR (0.1-0.4) Red (0.0-0.16) Blue (0.0-0.18)
Nadir BRDF-Adjusted Reflectance (NBAR)
7 - 22 April, 2002
No Data
NIR (0.1-0.4) Red (0.0-0.16) Green (0.0-0.18)
Surface Reflectance at NearInfrared Wavelengths
• Surface reflectance is
high at 2.2 µm,
moderate at 0.66 µm,
and low at 0.49 µm
• The aerosol effect on
reflected solar
radiation is small at
2.2 µm and large at
0.49 µm
• MODIS operational
algorithm over land
assumes
Kaufman et al. (1997)
Ag(0.47 µm) = 0.5Ag(0.66 µm)
= 0.25Ag(2.1 µm)
Desert Albedo
True-color White-sky Spectral Albedo from MODIS
Desert albedos are clearly nonuniform, with implications for
global climate modeling
(Tsvetsinskaya et al., GRL, 2002)
Snow versus Non-snow Albedos 40–50°N Nov 00–Jan 01
Jin et al., How does Snow Impact the Albedo of Vegetated Land Surfaces as Analyzed with MODIS
Data?, in press, Geophys. Res. Let., 2002
References
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Friedl, M.A., D. Muchoney, D.K. McIver, A.H. Strahler, and J.C.F. Hodges 2000:
Characterization of North American land cover from AVHRR Data, Geophysical
Research Letters, vol. 27, no. 7, pp. 977-980.
Friedl, M.A., C. Woodcock, S. Gopal, D. Muchoney, A.H.Strahler, and C. BarkerSchaaf 2000. A note on procedures used for accuracy assessment in land cover
maps derived from AVHRR data, International Journal of Remote Sensing, vol. 21,
pp.1073-1077.
Friedl, M.A., Brodley, C.E. and A.H. Strahler 1999: Maximizing land cover
classification accuracies at continental to global scales, IEEE Transactions on
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Friedl, M.A. and C.E. Brodley 1997: Decision tree classification of land cover from
remotely sensed data, Remote Sensing of Environment, vol. 61, pp. 399-409.
Mciver, D.K. and M.A. Friedl 2002. Using prior probabilities in decision-tree
classification of remotely sensed data, Remote Sensing of Environment, Vol. 81, pp.
253-261.
McIver, D.K. and M.A. Friedl 2001. Estimating pixel-scale land cover classification
confidence using non-parametric machine learning methods, IEEE Transactions on
Geoscience and Remote Sensing. Vol 39(9), pp. 1959-1968.
Muchoney, D., Borak, J, Chi, H., Friedl, M.A., Hodges, J. Morrow, N. and A.H.
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Remote Sensing, Vol 21, no 6 & 7, pp. 1115-1138.
Muchoney, D. M., and Strahler, A. H., 2001, Pixel and site-based calibration and
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