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A Cook's Tour of the DoE ARM Program Work on
Clouds and Climate (including some from my
team)Warren Wiscombe
NASA Goddard
16 Jun 03 ESSIC Lunch Talk 2
Why are Clouds So Important?
Del Genio: “IPCC global climate sensitivity range (to 2xCO2) of 1.5–4.5°C is unchanged in > 20 years. The sign of cloud feedback is still contentious; don’t all those Tb of satellite data tell us anything?”
Cess: “Eighteen atmospheric GCMs, using prescribed SSTs, have been compared to top-of-atmosphere radiation fluxes from ERBE. A small subset of the GCMs does a reasonable job of replicating the ERBE data, but typically the models tend to overestimate cloud cooling. This is because their clouds are either too bright, or at too low an altitude, or a combination of both.”
16 Jun 03 ESSIC Lunch Talk 3
Clouds Bedevil GCM Predictions
Report of Ad Hoc Study Group on Carbon Dioxide and Climate, Woods Hole, 1979:“We believe that the equilibrium surface global warming due to doubled CO2 will be in the range 1.5 to 4.5C”.
IPCC 2001: Essentially the same as above.
16 Jun 03 ESSIC Lunch Talk 4
Net Cloud Radiative Forcing, 19 GCMs
16 Jun 03 ESSIC Lunch Talk 5
From a public policy view...
(and perhaps too simply stated...) the enterprise of predicting global warming remains locked in a Sisyphean battle with clouds, in which no clear breakthrough has been made in spite of millions of dollars invested.
ARM stepped into this battle and promised that, instead of merely making excuses about clouds, they would actually do something about them...
but it has been a tough battle because the cloud problem is so hard...
16 Jun 03 ESSIC Lunch Talk 6
A Cook’s Tour of the Strangeness and Beauty of Clouds
Kelvin-Helmholtz waves
16 Jun 03 ESSIC Lunch Talk 7
A “River” of Cloud, and a Supercell
16 Jun 03 ESSIC Lunch Talk 8
Cloud “Halo
s”
16 Jun 03 ESSIC Lunch Talk 9
Open-Cell Clouds off
Florida (MODIS)
(cold air being drawn south over warm Caribbean water by low-pressure system off Massachusetts — action at a distance...)
16 Jun 03 ESSIC Lunch Talk 10
S. Georgia Island, S. Atlantic
16 Jun 03 ESSIC Lunch Talk 11
Amazon Thunderstorms
16 Jun 03 ESSIC Lunch Talk 12
Ship Tracks Off
California
16 Jun 03 ESSIC Lunch Talk 13
Airplane Tracks Over S. France
16 Jun 03 ESSIC Lunch Talk 14
and the ultimate anthropogenic cloud...
16 Jun 03 ESSIC Lunch Talk 15
Why is the cloud problem so hard?
• Clouds are harder than turbulence!– (they are a turbulent colloid with double phase
change)
• Clouds are harder than vegetation interactions– veget’n: limited range of scales, nearly 2-D, slowly
varying
• We do better with problems that– vary over a limited range of scales– are smooth below a certain scale (then gridding makes
sense)– vary slowly (not much change in an hour)– satisfy simple macroscopic laws
• Some of the simplest cloud questions are hard!– why does it warm-rain so fast?– what makes Sc last so long? disappear so quickly?
drizzle?– why are simple theories of the drop dist’n so wrong?
16 Jun 03 ESSIC Lunch Talk 16
Example of trying
to understan
d the complex nature of
clouds
but, if you look closely, much of this is just hand-waving, and talk about mere sign rather than magnitude
16 Jun 03 ESSIC Lunch Talk 17
Compare clouds to the human body
Both have complicated 3-D structure which is hard to follow in a spatially-detailed, time-resolved way...
and, with no way to see what was going on inside, medicine remained relatively primitive. Thus, as also happened with clouds, it gravitated to a broad-category approach (out with all their tonsils! or leave them all in...).
3-D imaging technology is revolutionizing medicine and customizing it to individual cases. But the scale is so much more manageable than clouds!
Cloud tomography is also possible...
16 Jun 03 ESSIC Lunch Talk 18
We lack a good cloud database to study
• (unlike temperature, sunspots, volcanic eruptions, earthquakes, or even El Nino)
• Historical record? Only cloud fraction:– a very limited quantity for global change work– hard to define precisely enough to make it useful
• Paleo-record? Clouds are “the ghosts of paleoclimate”
• The existing database is very limited in number of variables, and controversial:– ISCCP (mean cloud optical depth = 3.5 ??)– Hahn/Warren surface climatology– Terra (1999 launch) making first major effort to go
beyond these limited efforts (but retrievals hard to validate!)
16 Jun 03 ESSIC Lunch Talk 19
An example of using our best NASA technology to learn more about
cloudsAnimating these views over the 3 min needed to get them shows that higher clouds move more (parallax, not true motion)...
which is leading to an algorithm to get cloud height.
16 Jun 03 ESSIC Lunch Talk 20
ARM has promised to develop a coordinated dataset of cloud and
radiation variables that will undoubtedly lead to
breakthroughs in understanding
but this remains a work in progress because ARM inherited mediocre in-situ radiation measurement technology and has had to
develop the cloud measurement technology almost from scratch
16 Jun 03 ESSIC Lunch Talk 21
Thumbnail History of ARM• 1980s: huge disagreements in radiation model
intercompar’n
• 1991: SPECTRE field program created to begin the process of precise comparison of observations with models
• ARM arises from SPECTRE; ~ $40M/yr
• ARM quickly focused on getting clouds and radiation correctly into climate models
• through 1997: getting 3 major sites operational, including development of many new instruments (incl. cloud radar)
• slowdown from discovery that COTS instruments like radiosondes, pyranometers inadequate for the cloud or radiation problems; and new instruments had bugs
• blindsided by “enhanced cloud absorption” issue– but this forced revolutionary improvements in instruments
and observing strategies
16 Jun 03 ESSIC Lunch Talk 22
ARM Oklahoma: A “Field of Beams”
16 Jun 03 ESSIC Lunch Talk 23
ARM: Major Cloud Characterization Instruments
WholeSkyImager(DoD)
Microwave radiometer(for liquid water path)
16 Jun 03 ESSIC Lunch Talk 24
ARM Major Cloud Instruments (2)
the cloud radar (35 GHz);built by NOAA ETL, Boulder(30 GHz = 1 cm wavelength)
sample of radar reflectivity data; you get time-height slice but not whole 4-D cloud field
16 Jun 03 ESSIC Lunch Talk 25
History of ARM (cont.)
• ARM responds to Science Working Groups made up of PI’s from Science Team
• Radiation Working Group commanded much early attention; clear-sky rad’n took longer than expected to nail down, and many were reluctant to tackle the cloud problem
• Focus shifted to Single-Column Model and Cloud working groups in mid to late 1990s
• Single-Column strategy foundered on poor knowledge of boundary cond’ns; replaced by Cloud-Resolving Model strategy– the latest idea: “super-parameterizations” (Randall)
• Struggle continues to characterize clouds observationally in enough 4-D detail to permit convincing modeling of cloud radiation; hope now centers on cloud radar
16 Jun 03 ESSIC Lunch Talk 26
ARM let theoreticians do things like...help lead field programs (“IOPs”)
suggest new instruments
and take observations!
16 Jun 03 ESSIC Lunch Talk 27
My team focused on...
• modeling the 3-D spatial variation of cloud liquid water (various multifractal approaches)
• modeling the photon field produced by realistic 3–D cloud fields (Monte Carlo and SHDOM)
• simulating aircraft field program sampling for ARESE, and in general
• pioneering multiple-scattering lidar
• developing a “cloud mode” for the AERONET instruments, to retrieve cloud optical depth
16 Jun 03 ESSIC Lunch Talk 28
Why Fractals?
One reason was that natural landscapes were being modeled with fractals in ways that were strikingly realistic.
16 Jun 03 ESSIC Lunch Talk 29
Clouds as Fractals?
• Everything began with Lovejoy’s 1982 Science paper showing the perimeter-area relation for clouds was not Euclidean but fractal
• It took until the late 1980s to extend fractal models:– beyond the simple monofractals in Mandelbrot’s book– beyond cloud geometry, to cloud liquid water
• Fractal models took time to catch hold; people were still modeling clouds as Euclidean shapes into the early 1990s
• Two attractive features of multi-fractal models: – simpler than any Euclidean model (fewer parameters) – better connected to the underlying scaling physics best
exemplified in Kolmogorov approach to turbulence
16 Jun 03 ESSIC Lunch Talk 30
Data Analysis Looking for Scaling Behavior
V = Variability
V(scale r) ~ r
thus...
V(r) = V(r)
scaling behavior always indicates an underlying fractal
16 Jun 03 ESSIC Lunch Talk 31
We analyzed lots of aircraft cloud liquid water data, looking for scaling
behavior
16 Jun 03 ESSIC Lunch Talk 32
Cloud Liquid Water Power
Spectra from 3 Field
Programs
so we found scaling behavior over a range of scales from 10 m to ~50 km!
16 Jun 03 ESSIC Lunch Talk 33
Simple scheme for
characterizing liquid
water data
16 Jun 03 ESSIC Lunch Talk 34
Simple fractal
model for Sc clouds
akin to cascade models for eddy kinetic energy in turbulence theory
16 Jun 03 ESSIC Lunch Talk 35
My team’s goal in cloud
radiation
16 Jun 03 ESSIC Lunch Talk 36
Scaling analysis
for Landsat
radiances over
cloudsnote scale break at ~0.5 km
16 Jun 03 ESSIC Lunch Talk 37
Deep analysis of
the Landsat
scale break led us to the basic
ideas underlying
multiple scattering
lidar
16 Jun 03 ESSIC Lunch Talk 38
Nature’s Multiple Scattering Lidar
16 Jun 03 ESSIC Lunch Talk 39
The Small-Volume Barrier
In-situ cloud probes sample cm3.
Remote sensing instruments sample much bigger volumes:• > m3 for radars• approaching km3 for satellites
Other problems:• aircraft fly horizontally; ARM cloud radar points vertically• clouds evolve while aircraft fly through them
To match aircraft scale with radar and/or satellite scale (both time and space!), aircraft needs to perform “long-range scans”!
16 Jun 03 ESSIC Lunch Talk 40
Current aircraft cloud sampling probes
16 Jun 03 ESSIC Lunch Talk 41
Breaking the Small-Volume Barrier:In Situ Lidar (a NASA SBIR project)
laserpulsesout side here...
and photons are measured as a function of time here
16 Jun 03 ESSIC Lunch Talk 42
First, a Simulated Cloud Extinction Field...
X–Z cross-section
X–Y cross-section...using our well-testedmultifractal cloud liquid water models
16 Jun 03 ESSIC Lunch Talk 43
Then, Simulate Photon Arrivals at Detector
Fits based on simplediffusion theory
16 Jun 03 ESSIC Lunch Talk 44
Then, Scatterplot True vs. Retrieved Extinction
16 Jun 03 ESSIC Lunch Talk 45
Then, Test Concept in the Field
Laser shoots upward through roof Compare retrieved extinction with more traditional cloud probes:• FSSP (top)• new SPEC extinctometer (bottom)
Detector looks horizontally
16 Jun 03 ESSIC Lunch Talk 46
Note how so many tools have to play together to make this kind of
advance• Multifractal models of cloud liquid water
spatial distribution
• Monte Carlo models capable of billions of photons
• Pulsed lasers and fast-responding detectors
• A serious investment of money ($0.6M)
16 Jun 03 ESSIC Lunch Talk 47
Super-Parameterization of Clouds: Quo Vadis?
• Computationally, it is/will be possible
• It is the only credible proposal to make progress on the “2–5° 2xCO2 dilemma”– because you can tweak real cloud physics and see if
the model gets “better” or not
• Is it a good long-term solution? Probably not...
• Modeling clouds from aerosol to macro-scales is, in the end, a tours de force, and brute force
• Someday, I believe we must return to find the “Laws of Clouds” — the equivalent of thermodynamics for clouds
16 Jun 03 ESSIC Lunch Talk 48
Epilogue
Just as we see some light at the end of the tunnel, exhaustion with the cloud problem is setting in. Other subjects like carbon cycle are demanding attention even though the cloud problem is not even close to being solved. This is natural!
Maybe a fallow period will be beneficial.
Maybe we could start thinking about the Laws of Clouds.
Maybe we can meditate on our defeats and come up with a much better cloud observation system.
Maybe we should re-think the value of a “cloud field program”.
But we need to keep building up the cloud database!
And clouds are the biggest uncertainty in PAR which in turn determines Net Primary Productivity.
16 Jun 03 ESSIC Lunch Talk 49
Some of the folks who made ARM
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