A view of climate engineering from the perspective of weather modification

William R. CottonDept. of Atmospheric Science

Colorado State University

Outline

• 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 Pacificdecadal 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), multi-decadal 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 in-plume aerosol coagulation: parameterization and global implications”.

• A major assumption used in recent cloud- and climate-modeling 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

Open cells

Closed cells

“Pockets of Open Cells” on October 28th 2008 (VOCALS; GOES-10)

Closed to open cell transition

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.

Homogeneous Freezing Nucleation:T < - 40°C, RHi > 145%.Higher nucleation rates, smaller crystals,lower fall speeds, greater IWP & coverageColdest = strongest greenhouse effectHeterogeneous nucl. higher Vi, lesscloud coverage, more OLR T = -40°C

Frozen haze droplet

HeterogeneousNucleation Processes: T > - 40°C, 100% < RHi < 145%Lower nucleation rates,larger ice crystals, higher fallspeeds, lower IWP & coverage

Frozen droplet

• 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.