A view of climate engineering from the
perspective of weather modification
William R. Cotton
Dept. of Atmospheric Science
Colorado State University
Outline
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My background
Lessons learned from Cloud seeding
Engineering clouds and climate
Social/political reactions to cloud seeding
and likely response to climate engineering
• Why research climate engineering?
My Background
• My areas of expertise are in cloud physics, cloud
dynamics, and mesoscale meteorology, with an
emphasis on aerosol impacts on clouds, precipitation,
severe weather and climate
• My PhD graduate studies at Penn State and first post
PhD employment in NOAA under the direction of Dr.
Joanne Simpson were related to cloud seeding research
on cumulus clouds.
• Since then as a professor at Colorado State University I
have had little funded research in weather modification
but have kept up with the literature and been a member
of the Weather Modification Association for many years.
• I have done recent research examining the
hypothesis that seeding tropical cyclones
with pollution-sized aerosol will lead to
weakening of those storms; a scientifically
fascinating area of research with little
chance of ever being used operationally!
• I am viewed as a skeptic of greenhouse warming
impacts on climate by most of the climate community
• From my perspective, I am a scientist with no political
agenda. I believe in continued analysis and evaluation of
not only greenhouse gas impacts, but aerosol pollution
and dust impacts on weather and climate, land-use
impacts on climate, as well as weather modification
research and operations.
• I believe that if we were a completely water-free planet,
enhanced greenhouse gas concentrations would lead to
predictable warming.
The Hydrological Cycle
• But, this is not planet Earth(fortunately!).
• The presence of clouds, precipitation, and oceans greatly
complicates Earth’s response to greenhouse gases.
• At least on time scales of a decade or less, I argue that climate is
not predictable.
• In fact, Lovejoy and Schertzer(2013) argue that from the stochastic
point of view - and notwithstanding the vastly different time scales that predicting natural climate change is very much like predicting
the weather. This is because the climate at any time or place is the
consequence of climate changes that are (qualitatively and
quantitatively) unexpected in very much the same way that the
weather is unexpected.
• I have performed regional NWP weather
forecasts for decades and continue to
produce weekly weather forecasts for my
gliding club for even longer
• Weather Prediction is a very humbling
experience!
• I suspect that we will find that predicting
climate is likewise humbling!
• I begin by discussing what we have
learned from weather modification that is
relevant to climate engineering.
Books and chapters related to
climate engineering
• Cotton, W.R., and R.A. Pielke, 2007: Human Impacts on
Weather and Climate, 2nd Edition. Cambridge Univ.
2007.
• Cotton, W. R. 2009. Weather and climate engineering.
In: Clouds in the Perturbed Climate System: Their
Relationship to Energy Balance, Atmospheric Dynamics,
and Precipitation, ed. J. Heintzenberg and R. J.
Charlson.Strugmann Forum Report, vol. 2. Cambridge,
MA: The MIT Press.
Lessons learned from Wx Mod
• The scientific community has established a set of criteria
for determining that there is “proof” that seeding has
enhanced precipitation.
• For firm “proof” [see NRC, 2003; Garstang et al., 2005]
that seeding affects precipitation, both strong physical
evidence of appropriate modifications to cloud structures
and highly significant statistical evidence is required.
• Likewise, for firm “proof” that climate engineering is
affecting climate, or even that that CO2 is modifying
climate, both strong physical evidence of appropriate
modifications to climate and significant statistical
evidence is required.
• Another lesson from evaluating cloud seeding
experiments is that “natural variability” of clouds and
precipitation can be quite large and thus can inhibit
conclusive evaluation of even the best designed
statistical experiments.
• The same can be said for evaluating the effects of
climate engineering or that human-produced CO2 is
altering climate. If the signal is not strong, then to
evaluate if human activity has produced some observed
effect (cause and effect), one requires much longer time
records than is available for most if not all data sets.
• We do not have an adequate measure of the “natural
variability” of climate(the record is too short and use of
proxi data is not sufficiently quantitative).
• The most challenging obstacle to
evaluating cloud seeding experiments to
enhance precipitation, is the inherent
“natural variability” of precipitation in space
and time, and the inability to increase
precipitation amounts to better than ~10%.
Climate Variability
An example of regional climate variability
Imprint of the Atlantic multi-decadal oscillation and Pacific
decadal oscillation on southwestern US climate: past,
present,and future
• By Petr Chylek, Manvendra K. Dubey,Glen Lesins,
Jiangnan Li, and Nicolas Hengartner
• The surface air temperature increase in the
southwestern United States was much larger during the
last few decades than the increase in the global mean.
While the global temperature increased by about 0.5 C
from 1975 to 2000, the southwestern US temperature
increased by about 2 C.
• What are the major drivers of southwestern climate
change?
• They performed multiple-linear regression of the past
100 years of the SW US temperature and precipitation.
Major findings:
• The early twentieth century warming was dominated by a
positive phase of the Atlantic multi-decadal oscillation
(AMO) with minor contributions from increasing solar
irradiance and concentration of greenhouse gases.
• The late twentieth century warming was about equally
influenced by increasing concentration of atmospheric
greenhouse gases (GHGs) and a positive phase of the
AMO.
• The current southwestern US drought is associated with
a near maximum AMO index occurring nearly
simultaneously with a minimum in the Pacific decadal
oscillation (PDO) index.
• Current climate models have not been able to predict the
behavior of the AMO and PDO indices.
• The regression model does support the climate models
(CMIP3 and CMIP5 AOGCMs) projections of a much
warmer and drier southwestern US only if the AMO
changes its 1,000 years cyclic behavior and instead
continues to rise close to its 1975–2000 rate.
• If the AMO continues its quasi-cyclic behavior the US
SW temperature should remain stable and the
precipitation should significantly increase during the next
few decades.
The long term Streamflow
record for the CRB
• The southwest US experiences huge
swings in precipitation largely due to
natural causes associated with the El Nino
Southern Oscillation (ENSO), multidecadal variability of the North Atlantic and
Pacific Oceans, as reflected by the Pacific
Decadal Oscillation (PDO) and the Atlantic
Multidecadal Oscillation (AMO).
This is an example of natural variability of
climate on the regional scale
• Venturing into climate engineering
recognizing that potentially large “natural
variability” may exist is hazardous indeed!!
Engineering climate by cloud
modification
Role of Clouds in Earths Radiation Budget
Albedo enhancement of boundary layer
clouds
Ship tracks
• Latham [1990; 2002] proposed generating sea
water drops around 1 μm in size near the ocean
surface to enhance droplet concentrations.
• A spray of sea water drops would be produced
either by high volume atomizers or blowing air
through porous pipes that would produce air
bubbles that would rise to the sea surface and
burst much like natural wave action produces
the bubbles.
• Latham claims the power requirements for their
operation could be supplied by solar or wave
action, or even wind power.
Artists concept
• Advantage is that it is non-polluting and
costs of producing aerosol minimal
• It will take a lot of specially engineered
ships, however, to have an appreciable
global impact(read $$$$$).
• A disadvantage is that increasing CCN
can alter cloud dynamics such that in
some cases decreases in optical thickness
can result thus decreasing or having no
affect on albedo(see below).
Critique of the method
• Stuart et al 2013 examined the “Reduced efficacy of
marine cloud brightening by geoengineering due to inplume aerosol coagulation: parameterization and global
implications”.
• A major assumption used in recent cloud- and climatemodeling studies is that all sea spray was emitted
uniformly into some oceanic grid boxes, and thus these
studies did not account for sub-grid aerosol coagulation
within the sea-spray plumes.
• They find that the final number of particles depends on
meteorological conditions, including wind speed and
boundary-layer stability, as well as the emission rate and
size distribution of aerosol emitted.
• They made global estimates accounting
for sub-grid scale coagulation. It reduces
cloud droplet number concentrations by
46% over emission regions, and reduces
the global mean radiative flux perturbation
from −1.5 W m-2 to −0.8 W m-2.
• Thus a far greater number of ships is
required($$$) than in original estimates.
Buffering of cloud responses to enhanced
CCN
• Jiang et al(2002) performed LES of a drizzling marine
boundary layer. In the case simulated, drizzle settled
part way through the boundary layer(BL) before it totally
evaporated.
• This resulted in cooler air overlying the warmer lower BL,
which destabilized the BL as a whole.
• They found that enhanced CCN concentrations
suppressed drizzle formation which produces a more
stable BL and less vigorous stratocumulus layer.
• As a result cloud LWCs were reduced and cloud albedo
either reduced or remained the same.
• CCN seeding of those clouds would have had no effect!!
• Ackerman et al(2004) performed single-column model
simulations of a stratocumulus cloud layer
• They found that enhanced CCN concentrations led to
greater cloud top entrainment, whereupon if the layer
above the clouds was relatively dry, cloud LWCs were
diminished and cloud albedo was reduced.
• Only if the layer above cloud top was quite moist would
enhanced CCN concentrations lead to enhanced albedo
• Again CCN seeding in a dry-air-above cloud top regime
would not work!
Cloud organization
Closed
cells
Open cells
Closed to open cell transition
“Pockets of Open Cells”
on October 28th 2008
(VOCALS; GOES-10)
Cloud organization
• Cloud organization is also strongly influenced by
precipitation.
• Drizzle causes sub-cloud cooling, low-level divergence
of air beneath the clouds, and convergence at their
periphery.
• Factors influencing these mesoscale circulations are
aerosol(CCN concentrations), winds, height of BL.
• Model estimates so far indicate that massive aerosol
perturbations to an open cellular system increases the
cloud cover/albedo but does not change the cellular
structure to a closed state (the system is thought to be
robust)
Seeding of Open Cells
• The robustness of these circulations
implies that seeding them will not result in
transformation of open cells to closed
cells.
• If such a transformation were possible,
changes in cloud albedo would be the
order of 20-30% compared to 4-5% for just
microphysical changes.
Representing BL Clouds in
GCMs
• GCMs have been shown to be highly sensitive to the
representation or parameterization of boundary layer
clouds such as marine stratocumulus clouds and
tradewind cumuli.
• Large scale controls on boundary layer clouds include
subsidence, wind shear, inversion strength, boundary
layer humidity and aerosols. GCM’s only crudely
represent inversion strength owing to their limited vertical
resolution.
• GCMs still do not represent a drizzling boundary layer
well, particularly cloud dynamic feedbacks to drizzle. A
very high percentage of marine boundary layer clouds
are drizzling!
• GCMs do not represent differences in
cloud organization, yet albedo differences
between open and closed cellular
convection is of the order of 20-30% while
that due to aerosols is maybe 4-5%!
• In my opinion, GCMs over-estimate global
cloud cooling, especially due to aerosols,
and as a result if that were fixed in the
models they would grossly over-estimate
the amount of greenhouse warming!
Mid-level stratus seeding
• Non-freezing stratus clouds:
• Systematic seeding of these clouds by day with
pollution aerosols (small hygroscopic particles)
to increase their albedo and by night seed with
giant CCN to cause them to rain out
• Supercooled stratus:
• Seed with pollution aerosols during the daytime
to increase their albedo but at night seed with
glaciogenic seeding materials such as AgI.
How to seed?
• Use commuter aircraft with their jet fuels
doped with aerosol generators. Also the
use of UAVs or blimps for aerosol
dispersal could be considered.
• Some industries with tall stacks could
have their affluent doped with the
appropriate aerosol.
Potential adverse consequences
• Impacts on precipitation
• Local cold temperature extremes (which
would also impact fossil fuel demands)
• Impacts on the hydrological cycle.
• Costs would be high to achieve sufficient
global coverage
Conclusion
• The payoff on seeding mid-level clouds is
minimal and requires expensive airborne
operations with differing methodologies
depending on temperatures and water
contents. This requires a lot of realtime
decision making, and reliable forecasts!.
• There is too much opportunity to make
errors!
Seeding to reduce cirrus cloud
coverage
• As long as cirrus clouds are not optically thick
wherein their albedo exceeds absorption of
upwelling LW radiation, reducing cirrus coverage
should contribute to cooling.
• An approach that might be feasible is to attempt
to make cirrus clouds precipitate more readily to
reduce their ice water path and optical thickness
to make them more transparent to LW radiation.
Frozen haze
droplet
Frozen droplet
Homogeneous Freezing Nucleation:
T < - 40°C, RHi > 145%.
Higher nucleation rates, smaller crystals,
lower fall speeds, greater IWP & coverage
Coldest = strongest greenhouse effect
Heterogeneous nucl.
higher Vi, less
cloud coverage, more OLR
T = -40°C
Heterogeneous
Nucleation Processes:
T > - 40°C, 100% < RHi < 145%
Lower nucleation rates,
larger ice crystals, higher fall
speeds, lower IWP & coverage
• DeMott et al(1994) showed that if concentrations of IN
were increased at lower levels in cirrus clouds, the
growth of ice crystals at warmer temperatures would
deplete supersaturations with respect to ice and thereby
lead to reduced homogeneous nucleation of ice crystals.
• Thus not only would a cirrus cloud seeded with IN
produce faster falling ice crystals but reduce the total
concentration of ice crystals.
Dave Mitchel Conclusions:
1. Preliminary CAM5 simulations estimating the maximum impact
of heterogeneous nucleation from observations suggest that
the cooling effect of efficient ice nuclei for T < -40°C may be 1-2
W m-2 in the mid-latitudes.
- This GCM experiment is not valid for the tropics,
although anvil cirrus observations of De vs. T were similar.
But how to seed anvil cirrus?
2. The same GCM experiment was executed on ECHAM5 for
an exploratory one year run. Preliminary results show net
cooling in mid-latitudes between about -2 and -5 W m-2 with a
global mean near -2 W m-2. This compares with a global mean
warming of 3.7 W m-2 for a CO2 doubling.
3. A great amount of research is needed to understand the likely
impact of cirrus cloud seeding on climate. Improvements in the
treatment of cirrus clouds in GCMs are greatly needed.
Cirrus seeding with carbon
black
• Cotton(2009) proposed seeding with black
carbon aerosol
• The absorbed solar heating would reduce
incoming radiation to the surface(cooling) and
would have a semi-direct effect of dissipating
cirrus.
• That is, the warmed cirrus layer would lower the
RH wrt ice and contribute to evaporation of
cirrus.
• Moreover, one could “engineer” carbon black so that not
only is it strongly absorbing of solar radiation but also
acts as an heterogeneos IN. It is known that some forms
of black carbon can serve as an IN.
• Thus carbon black seeding could potentially not only
reduce reduce incoming solar radiation, but warm the
cirrus layer and reduce cirrus cloud coverage, and if
functioning as an IN further deplete cirrus clouds.
• This approach would not work for optically thick cirrus
and anvils as they have high albedo, thus dissipating
them would have a warming affect as well as requiring
huge amounts of aerosol to have any effect.
• Can we predict in advance which cloud will be present?
Dissadvantages
• Costs$$$ due to need for high-altitude
delivery systems
• Greater cooling would increase heating
costs and result in more CO2 production
• Possible impacts on upper tropospheric
circulations
• If the cirrus clouds that are seeded are too
optically thick(have higher albedo cooling
than LW warming) then seeding them may
contribute to additional warming.
Social/political responses to
climate modification
• In the case of cloud seeding, any adverse
weather associated with cloud seeding
projects is always attributed to cloud
seeding.
• Examples are the 1972 Rapid City, SD
flood(over 15” of rain in 6 hours, resulting
in 238 deaths, 3,057 injuries1,335 homes
and 5,000 automobiles destroyed, and
US$160 million in 1972 dollars. ).
• Seeding operations were conducted in the
area just prior to the storm. Litigation
followed the event for years afterwards.
The School of Mines had funding issues
afterwards.
• I Googled the event because I couldn’t
remember the year and the first thing that
came up was: the occurrence of such a
devastating flood was caused by cloud
seeding. This is in Wikepedia!
• Another such event occurred in the UK in
1952 near Lynmouth. Seeding was done
just prior to a major flash flood storm.
Even to this day, you can find reports on
the web that cloud seeding caused that
flood even though it was a widespread
storm. Litagation followed and
subsequently the UK outlawed cloud
seeding!
Project Cirrus
• This was the foundation research program
for the modern era of cloud seeding. It was
operated by GE Corp.
• On October 10, 1947 Irving Langmuir and
Vincent Schaefer seeded a hurricane with
the hope of altering its strength and
direction. Following seeding the storm
made an abrupt change of direction and
made landfall causing considerable
damage.
• The change in direction was probably a
result of the storm’s interaction with the
large-scale flow. Nonetheless, GE was
sued for damages and shortly thereafter
got out of doing cloud seeding research.
• Project Stormfury, a NOAA project, whose
goal was to diminish the strength of
hurricanes always operated under
constraints to not experiment anywhere
near landfall.
Project FACE
• The goal of FACE was to increase
precipitation in convective clouds by
invigorating there dynamics
• During experimental operations in south
Florida in the early 1970’s a small
hailstorm occurred which broke the
windshield of someone’s car. NOAA was
sued for damages and settled out of court
by replacing the windshield.
Wintertime Orographic Cloud
seeding
• All Ops have suspension criteria if a major
snow storm is forecast
• There is concern about avalanches,
highway accidents following a cloud
seeding operation
Consider now Climate Social/Political
consequences of Engineering Operations
• Note no predictability on decadal or less
timescales!
• Remember my earlier climate variability
figure
Climate Variability
• Suppose that owing to natural variability of
climate, temperatures cooled appreciably
during the period of climate engineering.
Assuming that it was successful in its
goals, the climate would cool even more!
• This could lead to crop losses, frost,
greater winter-season heating costs, etc.
• One could image a huge reaction with
lawsuits, and great political unrest
Hurricane natural variability
• There are huge swings in the yearly variability of
hurricane activity(especially land-falling hurricanes)
• As evidenced by this season, seasonal hurricane
predictability is low!
• Consider a scenario of a higher than normal incidence of
land-falling hurricanes during a year that climate
engineering is performed
• Can you imagine the social unrest that would follow?
Climate Engineering is already
being blamed!
• “Stop Spraying us and blocking our sun. I
found nano particles all over my plants
and car. Aluminum. We had no summer in
PA thanks to this evil Geo-Engineering
(Chemtrails and HAARP, Weather
warfare). My cucumbers would not turn
green. We want our blue skies back. I am
contacting many state reps to get this
stopped in PA. Thanks Dorene”
Should there be a national program of
Climate Engineering Research?
• If for no other reason, we know from cloud seeding that if there is a
drought or major weather disaster, politicians call for cloud seeding
to do “something” without due regard for the consequences—”a
political placebo”
• I expect if we find ourselves in a real climate disaster politicians will
likewise call for implementation of climate engineering strategies
• It is important that it be done with the most advanced scientific
knowledge and with full understanding of the consequences of our
actions; hence I support the idea of a major initiative in climate
engineering research!
• As far as actual implementation of climate engineering operations, I
see little chance of international agreement to support such
initiatives, and given our current political climate, even less likely
that the US congress would agree to support such operations.
Recommendations
• Implement major initiatives in climate
engineering research using the most advanced
models throughout the world.
• Before implementation of climate engineering
can be done, fundamental research is needed to
advance our quantitative understanding of the
climate system, of climate variability, the
scientific possibilities of climate engineering,
technical requirements, social impacts, and
political structures is needed for its
implementation.
How to proceed
• Perform detailed exploratory simulations of the
proposed modification
• What is needed first of all is a demonstrated
climate model forecast skill that is large enough
to be able to extricate the climate modification
signal from the “natural variability” or “noise” of
the climate system.
• Once this predictive skill is achieved then there
is the opportunity to apply advanced statistical
methods that use model-output statistics and
observed response variables that can confirm
the hypothesis.