A Cook's Tour of the DoE ARM Program Work on Clouds and Climate (including some from my team) Warren Wiscombe NASA Goddard

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.”


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.


Net Cloud Radiative Forcing, 19 GCMs


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...


A Cook’s Tour of the Strangeness and Beauty of Clouds

Kelvin-Helmholtz waves


A “River” of Cloud, and a Supercell


Cloud “Halo

s”


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...)


S. Georgia Island, S. Atlantic


Amazon Thunderstorms


Ship Tracks Off

California


Airplane Tracks Over S. France


and the ultimate anthropogenic cloud...


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?


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


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...


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!)


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.


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


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


ARM Oklahoma: A “Field of Beams”


ARM: Major Cloud Characterization Instruments

WholeSkyImager(DoD)

Microwave radiometer(for liquid water path)


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


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


ARM let theoreticians do things like...help lead field programs (“IOPs”)

suggest new instruments

and take observations!


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


Why Fractals?

One reason was that natural landscapes were being modeled with fractals in ways that were strikingly realistic.


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


Data Analysis Looking for Scaling Behavior

V = Variability

V(scale r) ~ r

thus...

V(r) = V(r)

scaling behavior always indicates an underlying fractal


We analyzed lots of aircraft cloud liquid water data, looking for scaling

behavior


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!


Simple scheme for

characterizing liquid

water data


Simple fractal

model for Sc clouds

akin to cascade models for eddy kinetic energy in turbulence theory


My team’s goal in cloud

radiation


Scaling analysis

for Landsat

radiances over

cloudsnote scale break at ~0.5 km


Deep analysis of

the Landsat

scale break led us to the basic

ideas underlying

multiple scattering

lidar


Nature’s Multiple Scattering Lidar


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”!


Current aircraft cloud sampling probes


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


First, a Simulated Cloud Extinction Field...

X–Z cross-section

X–Y cross-section...using our well-testedmultifractal cloud liquid water models


Then, Simulate Photon Arrivals at Detector

Fits based on simplediffusion theory


Then, Scatterplot True vs. Retrieved Extinction


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


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)


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


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.


Some of the folks who made ARM

Documents

A Cook's Tour of the DoE ARM Program Work on Clouds and Climate (including some from my team) Warren Wiscombe NASA Goddard