CAN ‘ORDINARY CONTRAILS’ CHANGE THE WEATHER ?
A COLLECTION OF INTERESTING SCIENTIFIC PAPERS AND REFERENCES ALL IN PUBLIC DOMAIN BE YOUR OWN JUDGE!
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NATURAL AEROSOLS AND AIRCRAFT AEROSOL WX ENGINEERING
A possible change in cloud radiative forcing due to aircraft exhaust
Geophysical Research Letters, Volume 25, Issue 10, p. 1673-1676 (GeoRL Homepage)
Atmospheric Composition and Structure: Cloud physics and chemistry, Atmospheric Composition and Structure: Pollution-urban and regional, Meteorology and Atmospheric Dynamics: Radiative processes
(c) 1998: American Geophysical
Aircraft exhaust may reduce the crystal size in natural cirrus. This work investigates the change in cloud radiative forcing from such a size reduction by assuming a constant ice water content. A 1-dim model with radiative properties that depend on the mean crystal size is used to compute the radiative transfer for an atmospheric column. The results show that the negative shortwave cloud forcing is enhanced with smaller crystals as they mainly increase the reflectivity of clouds. The change in the longwave cloud forcing is always positive although its magnitude depends strongly on the ice water path. The weighted sum of SW and LW cloud forcings depends on the mean crystal size, surface albedo and ice water content. It appears that there is a range of diameters between 15 and 25 μm where the response to a reduction in crystal size is fairly insensitive. Below and above this range the change is negative or positive, respectively. In regions of dense airtraffic the magnitude of the change in cloud forcing could be on the order of 0.3 Wm-2 under the assumption of a 20% decrease of the mean crystal size from about 30 μm to 24 μm. Aircraft exhaust thus has the potential to affect the climate but the results should be taken with caution as they are based on parameterized optical properties for cirrus clouds.
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AMERICAN METEOROLOGICAL SOCIETY
| CONTROLLING THE
—Atmospheric and Environmental
Ross N. Hoffman, Atmospheric and
In final form
©2002 American Meteorological Society
It had not been easy to persuade the surviving superpowers to relinquish their orbital for-
tresses and to hand them over to the Global Weather Authority, in what was—if the meta-
phor could be stretched that far—the last and most dramatic example of beating swords into
plowshares. Now the lasers that had once threatened mankind directed their beams into care-
fully selected portions of the atmosphere, or onto heat-absorbing target areas in remote re-
gions of the earth. The energy they contained was trifling compared with that of the smallest
storm; but so is the energy of the falling stone that triggers an avalanche, or the single neu-
tron that starts a chain reaction.—A
Technological advances over the next 30–50 years may make it possible to control the
weather. If we can, should we? Are “weather wars” inevitable?
The earth’s atmosphere has been hypothesized to be chaotic. Chaos implies that there is a finite predictability time limit no matter how well the atmosphere is observed and modelled. It is generally accepted that this limit is typically 2 weeks for large-scale weather systems (Lorenz 1982), although some situations may be more or less predictable, and smaller scales are certainly less predictable. Chaos also implies sensitivity to small perturbations. The most realistic numerical weather prediction (NWP) models are very sensitive to initial conditions. It is therefore very likely that the atmosphere is also extremely sensitive to small perturbations.
A series of such perturbations to the atmosphere might be devised to effectively control the evolution of the atmosphere, if the atmosphere is observed and modelled sufficiently well. We present system architecture to control the global weather that might be implemented within a few decades.
It is a dream of mankind to control the weather—not to make every day the same, but to protect lives and property. We believe that this dream is in fact a possibility. Just imagine: no droughts, no tornadoes, no snowstorms during rush hour, etc.
We probably cannot eliminate hurricanes, but we might be able to control the paths of hurricanes, and essentially prevent hurricanes from striking population centres. Our goal is not to change the climate, but to control the precise timing and paths of weather systems. For ex-ample, eliminating hurricanes and the associated mix-
ing of the upper layers of the ocean would presumably change the climate in many indirect ways. Because of the intensive coupling of the weather over different regions of the globe, nothing short of control of the global weather should be considered.
The nation that controls its own weather will necessarily control the weather of other nations. If there are several nations, each attempting to control the
weather over its territory, then each may operate at odds with the others and “weather wars” are conceivable.
An international weather control treaty may be prudent now. In the future, an international agency may be required so that weather control is used “for the good of all.” Perhaps for the good of all is unattainable. Any change to weather will have both positive and negative effects. How can the interests of both the “winners and losers” be accommodated?
OF course, weather always has both positive and negative effects, and there are winners and losers now. In what follows we present the underlying concepts for our approach and then outline the system architecture of a controller for the global atmosphere, describing the components of such a controller. Legal and ethical questions are only touched on, and the issues of feasibility and cost–benefit trade-offs are only briefly considered. Our proposed controller is similar in general to feedback control systems common in many industrial processes; however, it is greatly complicated by the number of degrees of freedom required to represent the atmosphere adequately, the nonlinear nature of the governing equations, the paucity of observations of the atmosphere, the difficulty of effecting control, and the requirements that control be effected at significant time lags. However, the existence of the technology to implement the weather controller is plausible at the time range of 30–50 yr.
CHAOS, THE LIMITS OF PREDICTABILITY,
AND IMPLICATIONS FOR CONTROL.
Theoretical and model studies have established that the dynamics governing the atmosphere can be extremely sensitive to small changes in initial conditions (e.g., Rabier et al. 1996). Current operational practice at NWP centres illustrate this daily. Examples summarized in what follows include data assimilation, generation of ensembles, and targeted observations.
The key factor enabling control of the weather is that
the atmosphere is sensitive to small perturbations.
That is, it is the very instability of the atmosphere’s dy-
namics that makes global weather control a possibility.
Chaos causes extreme sensitivity to initial condi-
tions. Although the atmosphere, and indeed realistic
models of the atmosphere, have not been proven to
be chaotic, the theory of dynamical systems and chaos
provide a useful background for this discussion. In a
realistic NWP model, since small differences in ini-
tial conditions can grow exponentially, small but cor-
rectly chosen perturbations induce large changes in
the evolution of the simulated weather. Therefore we
hypothesize that as we observe and predict the atmo-
sphere with more and more accuracy, we will become
able to effect control of the atmosphere with se-
quences of smaller and smaller perturbations. Note
the basic difference between predictability and con-
trol theory: Predictability theory states that small dif-
ferences grow; control theory states that a sequence
of small perturbations can be used to track a desired
solution. By tracking (i.e., following) a desired solu-
tion, our control method may overcome differences
between model and reality. We will expand and ex-
plain these basic ideas in the following paragraphs.
The phase space description of dynamical systems. The
evolution of dynamical systems is conveniently dis-
cussed using the phase space description of Poincaré
(Lorenz 1963). The state of the system is specified by
n variables. For continuous systems, such as the at-
mosphere, we may first approximate the continuous
system by discretization and thereby obtain a large
number of coupled nonlinear ordinary differential
equations. For a physically realizable system, the col-
lection of feasible points in the n-dimensional phase
space will be bounded.
For a single time, the state of the system is repre-
sented by a single point. As the system evolves, the
point representing the system will in general describe
a curved line. This is termed the trajectory. If the sys-
tem is in a stable state, the trajectory is just the single
point. Small perturbations about the point decay in
time toward the stable point. A stable point is an
attractor. A stable point is also a fixed point of the
system. There can be unstable fixed points. Some tra-
jectories form closed curves—these represent peri-
For a realistic model of the atmosphere with fixed
boundary conditions, periodic solutions probably ex-
ist but are unstable. There are many unstable periodic
solutions close to chaotic attractors. Chaotic systems
are aperiodic, but given enough time, return arbi-
trarily close to points in the attractor. For the atmo-
sphere, the lack of success for analog forecast tech-
niques suggests that this return time is very long.
Chaotic systems.The strict definition of chaos describes
it as a behavior of purely deterministic systems with
as few as three components for a continuous phase
space flow (e.g., Lorenz 1963), or as few as a single
component for an iterated mapping (e.g., Lorenz
1964). Chaotic systems can appear to be random when
sampled at timescales that are large compared to the
dynamical timescale. The key characteristics associ-
ated with chaos are that the system be bounded and
AMERICAN METEOROLOGICAL SOCIETY
possess at least one positive Lyapunov exponent
(Lorenz 1965). A positive Lyapunov exponent implies
average growth in the associated direction that is ex-
ponential. Typically in the phase space of such sys-
tems, a small initial sphere of radiusε will over a short
time deform into an ellipsoid. The axes of the ellip-
soid might be called the finite time or local Lyapunov
directions, and the ratio of these axes to ε might be
called the finite time or local Lyapunov factors. As the
ellipsoid evolves it tends to flatten parallel to the
attractor of the system. Chaotic attractors are also
called strange attractors. A characteristic of these
attractors is that perturbations perpendicular to the
attractor collapse exponentially, while perturbations
parallel to the attractor grow exponentially.
It is for these reasons that we say the small pertur-
bations can grow exponentially. A randomly chosen
perturbation may be decomposed into contributions
from the finite time Lyapunov directions. Some, per-
haps most, will decay, but the others will grow. The
perturbation may therefore first decrease in size, be-
fore growing explosively. A perturbation may also be
constructed which projects only onto a particular
growing mode. Such a perturbation will initially grow
The limits to predictability. Since small differences grow
rapidly in chaotic systems, chaotic systems are diffi-
cult to predict. Inevitably small errors will exist in our
specification of the initial conditions. Further, errors
in model formulation induce errors in the model state
at every model time step. Although the magnitude of
the error may initially decay with time, eventually
small errors will begin to grow exponentially and
continue to do so until they become large. It is gen-
erally accepted that useful forecasts of the instanta-
neous weather beyond 2–3 weeks are impossible
(Lorenz 1982; Simmons et al. 1995).
For the atmosphere, motions occur over a huge
spectrum of scales. Smaller spatial scales have shorter
timescales. Errors in the smallest scales will com-
pletely contaminate those scales on the characteristic
timescale associated with that spatial scale. These er-
rors will then induce errors in the next larger scale
and so on (Lorenz 1969). In fluids, advection implies
that tiny errors on the large scales will in turn cause
large errors on the shortest scales. These interactions
lead to a finite predictability time limit.
Control of chaotic systems. Since chaos may appear to
be random, control of chaos might seem impossible.
But sensitivity to initial conditions also implies sen-
sitivity to small perturbations. As we have mentioned,
small perturbations in some directions decay quickly,
but properly chosen perturbations grow quickly.
Therefore a sequence of very small amplitude but pre-
cisely chosen perturbations will steer the chaotic sys-
tem within its attractor. There have been many stud-
ies reported in the literature that support this view
(Kapitaniak 1996). We note two examples of the con-
trol of chaotic systems.
The first is the phenomena of resonance (Pecora and
Carroll 1990). Suppose that there are two copies of
an evolving dynamical system. Initially the two sys-
tem states are arbitrarily different. One system evolves
freely but is observed. In particular, one variable of
that system is accurately observed. The correspond-
ing variable in the second system is constantly reset
to the value observed in the first system. Over time
all variables of the second system approach the values
of the corresponding variables in the first system. We
say that the second system has become entrained by
the first system.
Second, within the attractor of a chaotic system,
there are a multitude of unstable periodic orbits.
Techniques to compute these orbits are available.
Once the system is close to one of these orbits, it is
possible to continually follow the orbit by regularly
applying small perturbations (Ott et al. 1990).
Control of realistic atmospheric models. To control the
weather we must effect changes on timescales shorter
than those of the examples of the previous section, and
to a system of huge complexity. The numerical meth-
ods used must be computationally feasible. The NWP
community has already taken the first steps to con-
trol large dynamical systems. One current NWP data
assimilation practice, called 4DVAR, finds the small-
est perturbation at the start of each data assimilation
period, which grows to best fit all the available data,
thereby demonstrating the practical control of large-
scale realistic systems. Current 4DVAR practice finds
the smallest global perturbation, as measured by the
a priori or background error covariances, but it should
be possible to modify 4DVAR to find the smallest lo-
cal perturbation or the smallest perturbation of a par-
ticular type. This method is described further in the
section about data assimilation systems. Further, some
other current NWP technology may be adapted to de-
termine the optimal perturbations to effect control.
These techniques are described in what follows.
.Singular vectors are the fastest grow-
ing perturbations about a given model forecast over
a finite time interval, say 24 or 72 h, with respect to a
particular measure of difference. (For example, the
size of the perturbation might be taken to be its en-
ergy.) Singular vectors are currently calculated opera-
tionally at the European Centre for Medium-Range
Weather Forecasts (ECMWF) for the purpose of en-
semble forecasting (Molteni et al. 1996). In principle,
ensemble forecasting introduces equally likely small
perturbations in the initial conditions of each en-
semble member. In practice, because each of the fore-
casts within the ensemble is computationally expen-
sive, only perturbations that are rapidly growing are
included. The growth rates of these perturbations are
explosive—24-h amplification factors of 10–20 are
reported for large-scale calculations with limited
physics, and much larger amplification factors are
expected when smaller scales and moist physics are
included. A basic version of control can be effected
by calculating the leading singular vectors, determin-
ing if a positive or negative perturbation along one of
these modes would produce a desired result, and then
introducing that perturbation, if it was feasible.
. During the last decade there
has been considerable research on targeted observa-
tions (Lorenz and Emanuel 1998; Bergot et al. 1999;
Bishop and Toth 1999). Given a current forecast of
some storm of interest, we can backtrack from the
forecast to find that region of the initial state that, if
better observed, would improve the forecast of that
storm. The theory and methodology of this approach
have advanced sufficiently so that actual trials were
undertaken for several field experiments including the
Fronts and Atlantic Storm-Track Experiment
(FASTEX; Joly et al. 1997), the North Pacific Experi-
ment (NORPEX; Langland et al. 1999), and the 1999
Winter Storms Reconnaissance Program (WSRP 99;
Bergot et al. 1999).
This technology can be adapted to calculate the
optimal perturbation. Determining where to target
observations is related to the problem of determin-
ing where to introduce perturbations to effect a cer-
tain change in the forecast. In both cases we are opti-
mizing a figure of merit or objective function that is
calculated in terms of the forecast with respect to
some change in the initial conditions. Note that the
figure of merit can include both costs and benefits.
THE GLOBAL WEATHER CONTROL SYS-
TEM. The global weather control (GWC) system we
envision is a feedback control system, made complicated
by a number of factors. These include the following:
• The number of degrees of freedom required to
represent the atmosphere adequately.
• The nonlinear nature of the governing equations.
The atmosphere is nonlinear and sometimes dis-
continuous. For example, clouds have sharp edges.
• The paucity and inaccuracy of observations of the
atmosphere. Satellites provide a huge volume of
information. However this information is not al-
ways in the right place, accurate enough, or of the
• The control must be effected at significant time lags
to minimize the size of the perturbations, yet the
system is inherently unpredictable at long lead times.
• The difficulty of effecting control. The control
mechanisms do not yet exist. The ideal perturba-
tions, while small in amplitude, may be large in
• The ambiguous nature of the figure of merit. For
cane threat to that city may take precedence over
all else. But in general attempting to satisfy mul-
tiple objectives may result in conflicts.
The GWC system is sketched in Fig. 1. The “con-
troller” and “random effects” perturb the system state.
The controller must therefore compete with random
effects. However the controller perturbations are de-
signed to grow, while the random effects perturba-
tions tend to decay. The “governing equations” ad-
vance the system from time t
to time t
. If we
eliminate the “observations” and controller elements
in this figure we have a sketch showing how a NWP
model approximates the atmosphere. On the other
hand, if we remove only the random effects element,
we have a sketch of a system that must be simulated
within the controller element in order to estimate the
system state and then the optimal perturbations. Note
the various noise sources: The observations are inex-
act, the perturbations are effected with some inaccura-
cies, the model introduces further errors. The statistics
of these errors are also inexact and must be estimated
empirically (from the time history of the differences
between short-term forecasts and observations).
Cost–benefit trade-offs. Controlling small-scale phe-
nomena will not be cost effective. Certainly we want
to control destructive tornadoes, but the time- and
space scales are so fine that this may be impossible on
an individual basis. It may be more effective to elimi-
nate the large-scale conditions leading to the forma-
tion of tornadoes. In general, theoretical predictabil-
ity studies (Lorenz 1969) suggest that doubling the
resolution of the observations will only increase pre-
dictability by an amount similar in magnitude to the
timescale of the motions of the smallest resolved phe-
AMERICAN METEOROLOGICAL SOCIETY
nomena. For example, since the timescale for the evo-
lution of a thunderstorm is smaller than 1 h, observ-
ing details of individual thunderstorms will improve
predictability by no more than 1 h. Effecting control
at very large scales may not be cost effective either.
The largest spatial scales have the largest “inertia.”
These scales have the longest associated timescales and
the greatest part of the energy (Nastrom et al. 1984).
The GWC system will be subject to optimization
itself. Our control of the weather will increase as we
increase the skill of the NWP models, the accuracy of
the observations, and the size of the controlling per-
turbations. All three facets of the problem require re-
sources. A cost–benefit analysis will balance resources
devoted to remote sensing, computer power, and per-
turbations. As advances in the supporting disciplines
accumulate, the optimal point will shift, become fea-
sible, and eventually become economically sensible.
Enabling technology.Implementation of the overall sys-
tem architecture will require major advances in many
disciplines. Here we discuss the required discoveries
and refinements. Although it is difficult to predict the
pace of technological advance, the control of the
weather is a plausible outcome of advances in various
fields over the time span of a few decades.
UMERICAL WEATHER PREDICTION
. NWP is now a mature
science (Kalnay et al. 1998). Advances in computer
power will enable the refinement of NWP. Current
high-resolution mesoscale models point the way for
advances in global models. In early NWP models,
many physical processes were either removed by
filtering approximations or modeled by parameteri-
zations. As NWP models evolve, more and more of
the physics of the atmosphere are resolved explicitly.
A recent report (ECMWF 1999) makes estimates
of the spatial resolving power of NWP models over
the next decade. In summary, this report predicts
horizontal resolution increasing from the current 60
to 15 km by 2008. Extrapolating for another 30 yr sug-
gests global resolution of approximately 250 m.
(Currently vertical resolution is much finer than hori-
zontal resolution, but at the much higher future hori-
zontal resolution, the same scale will be appropriate
for both horizontal and vertical resolution in the tro-
posphere. This would allow even higher resolution
than our simple extrapolation would suggest.)
ATA ASSIMILATION SYSTEMS
. Data assimilation systems
estimate the state of the atmosphere given limited
observations and an imperfect model of the evolution
of the atmosphere. This problem is complicated by the
paucity of observations, the huge number of degrees
of freedom needed to specify the atmosphere, and
the extreme nonlinearity of the governing equa-
tions. The data assimilation system is a key part of
the controller of Fig. 1—the data assimilation pro-
vides estimates of the current state of the atmo-
The current state of the art is 4DVAR or four-
dimensional variational data assimilation. Opera-
tional 4DVAR assumes a perfect model over short
time periods (6 or 24 h) and finds the initial condi-
tion at the start of the period that best fits all avail-
able observations during the period (e.g., Thépaut
et al. 1993). [This optimization is made efficient by
the adjoint technique. In practical implementations,
the adjoint model performs a backward in time in-
tegration of the sensitivity of the objective function
to the model state (Courtier 1997).] Because the
NWP model is used to extrapolate the initial condi-
tions, the 4DVAR solution is necessarily dynamically
consistent. The end point of the solution from the
previous period is called the background and is used
as a special type of observation. The error structure
of the background is necessarily complex but has a
greatly simplified representation in current versions
of 4DVAR. In the near future we expect to see
higher resolution used in 4DVAR in line with in-
creases in resolution in NWP models, better esti-
mates of the background error statistics, and a con-
vergence to the Kalman filter methodology (Todling
and Cohn 1994; Houtekamer and Mitchell 1998).
ATELLITE REMOTE SENSING
. Satellites observe the at-
mosphere and the earth’s surface with global cover-
age, rapid refresh, and high horizontal resolution in vis-
.1. Schematic global weather controller flow chart.
ible, infrared, and microwave spectral domains.
Sensors currently being prepared for launch have
very high spectral resolution, which in turn will pro-
duce higher vertical resolution for the retrieved tem-
perature and moisture profiles. Advances are ex-
pected in terms of higher resolution, greater numbers
of satellites, and higher accuracy in the future. Ac-
tive sensors, such as the Tropical Rainfall Measuring
Mission (TRMM) precipitation radar, may be used
more in the future.
The ability to collect observations from space cur-
rently outstrips our ability to use these data in global
NWP. Typically, the observations are thinned to re-
duce resolution and quantity. This will be more of a
problem with higher spectral resolution sensors.
However, advances in computing power and data as-
similation techniques will improve this situation.
. Everything mankind does that can be
controlled may be considered a source of perturba-
tions. Here we mention a few possibilities:
• Aircraft produce contrails. Contrails are essentially
cirrus clouds and influence both the solar and ther-
mal radiation (Poellot et al. 1999). Slight variations
in the timing, levels, and routes of aircraft would
produce perturbations (Murcray 1970).
• Solar reflectors, in low earth orbit, capable of vary-
ing orientation, would produce bright spots on the
night side, and shadows on the day side, thereby
changing the heating of the atmosphere. First
steps have already been taken. However, the lat-
est Russian experiment, named Znamya 2.5, failed
to unfurl a 25-m diameter thin sheet mirror in
space in February 1999 (Beatty 1999). In the fu-
ture inflatable structures may be used (Dornheim
• Solar-powered generators in geostationary orbit
have been suggested as a low-cost energy source.
A concern is that losses from the microwave down-
would be a heat source (
spatial area and timing of the downlink were con-
trolled this would be a source of perturbations. In
addition, tuning of the microwave downlink fre-
quency would control the height in the atmosphere
of the energy deposition.
• An enormous grid of fans that doubled as wind
turbines might transfer atmospheric momentum
in the form of electric energy.
To be effective the individual actions must be co-
ordinated, so that the total perturbation is one that
produces a desired effect. This may be difficult.
. Computer processing capabil-
ity has been increasing exponentially. The require-
ments of GWC are truly staggering, but global NWP
models at the subkilometer scale seem attainable in the
30–50 yr time frame, if the pace of advances in com-
puter technology can be maintained. (If computer
power doubles every year, then after 30 yr it will have
increased 1 billion times.) However, current estimates
growth of chip functionality as well as the exponen-
tial growth of the cost of chip fabrication facilities will
encounter physical obstacles around 2012 (Birnbaum
and Williams 2000). Potential breakthroughs in nano-
technology, quantum devices, or in other areas will be
.The GWC system is a megasystem.
Development of tools and methodologies for
megasystems engineering is driven by recent defense
and aerospace projects, such as the space shuttle, the
strategic defense initiative (SDI), etc. In some ways
the GWC system is analogous to SDI. Both require
huge real-time data gathering, prediction, and com-
mand capabilities. For GWC the problem is more
complex, but the timescale is more relaxed and there
is no active opposing intelligence.
Concluding remarks: The next step.The next step should
involve demonstration tests in simulation. We suggest
a focus on the hurricane problem. This problem is
both important and feasible. Controlling the path of
hurricanes will be a first-order priority of GWC. A hur-
ricane track is largely determined by winds of the
large-scale environment. Reasonable forecasts of hur-
ricane tracks can be made without modeling the in-
ternal dynamics of the hurricane. Recent studies have
examined the sensitivity of such a model to changes
in initial conditions (Aberson and Franklin 1999;
Cheung and Chan 1999).
For the demonstration tests, we would be con-
cerned only with the forecasting and control of the
hurricane tracks. For this purpose our “NWP” model
could be a simple quasigeostropic model. The hurri-
cane could be modeled as a vertical tracer. A plausible
control mechanism would be localized height pertur-
bations. The goal would be to protect the Gulf and
East Coast populations centers. This setup is feasible
and capable of exploring some of the issues related to
the practicality of global weather control and to quan-
tify, albeit in a limited context, the required resources
to effect GWC. Of course application to real hurricanes
will require a model that faithfully predicts hurricane
AMERICAN METEOROLOGICAL SOCIETY
On a personal note, I first put the main ideas ex-
pressed here on paper in the fall of 1977 as part of a
potential thesis proposal. My advisor, E. N. Lorenz,
commented that this was an interesting idea but too
risky for a thesis topic. Control of the global atmo-
sphere is still a risky research topic, but there have
been substantial technological advances in many of
the supporting disciplines—computers, models, re-
mote sensing, etc. We believe there is a good reason
to pursue this research now.
The concept of global weather control raises a host
of sociological, ecological, and political issues. These
issues will only receive proper attention when global
weather control seems plausible. The questions raised
in these arenas will not be easy to resolve, and progress
is likely to be slow compared to the advance of tech-
nology. Therefore, it seems important to demonstrate
this plausibility now, long before technology advances
to the point of potential implementation, in order to
motivate a thorough discussion of whether or not, and
if so, to what extent and under what circumstances
we actually do wish to control the weather.
ACKNOWLEDGMENTS. I have benefitted from com-
ments on this paper by R. Rosen, K. Emanuel, J. Hansen,
R. Anthes, and an anonymous reviewer. This work was sup-
ported in part by the NASA Institute for Advanced Con-
cepts (NIAC) through a grant from the Universities Space
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Photographs of a 120 degrees parhelion and a 22 degrees parhelion within persistent contrails are presented. These phenomena result from hexagonal plate-shaped ice crystals oriented horizontally with diameters between 300 mum and 2 mm. From our observations and reinvestigation of previous reports, we conclude that a subset of the population in persistent contrails can consist of highly regular, oriented, hexagonal plates or columns comparable to the most regular crystals in natural cirrus clouds. This is explained by measured ambient humidities below the formation conditions of natural cirrus. The resulting strong azimuthal variability of the scattering phase function impacts the radiative transfer through persistent contrails.
PMID: 18253447 [PubMed - in process]
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Authors: Fichter, Christine; Marquart, Susanne; Sausen,
Source: Meteorologische Zeitschrift, Volume 14, Number 4, August 2005 , pp. 563-572(10)
Publisher: E. Schweizerbart'sche Verlagsbuchhandlung
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Within the framework of the European Fifth Framework Project TRADEOFF, the impact of changing cruise altitudes on contrail coverage and corresponding radiative forcing was investigated. On the basis of the reference year 1992, a series of aircraft emissions inventories with changed flight altitudes was prepared. These emission scenarios provide flown distances, fuel consumption and NOx emissions on a three-dimensional grid. The vertical resolution of these inventories was significantly increased over that used in former inventories. With a downshift of cruise altitude by 2000 ft(Throughout this paper we denote flight levels in ft. 2000 ft convert to approximately 610 m.), 4000 ft, and 6000 ft global annual mean contrail coverage is reduced in an approximately linear manner, reaching a maximum decrease of almost 45 % for a 6000 ft lower cruise altitude. Contrary to this, a slight increase by 6 % of global annual mean contrail coverage resulted for a 2000 ft higher maximum flight altitude. Relative changes of corresponding radiative forcing were shown to be very similar to those of contrail coverage. For changes in contrail coverage and radiative forcing associated with changes in flight altitudes, a strong seasonal and regional variability was found. This study only considers contrail radiative forcing. Trade-offs from other aviation related radiative impacts, e.g., from CO2 or O3, have not been studied.
Document Type: Research article
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