The uptake of water by contrails in ice-supersaturated air and the release of
water after ice particle advection and sedimentation dehydrates the
atmosphere at flight levels and redistributes humidity mainly to lower
levels. The dehydration is investigated by coupling a plume-scale
contrail model with a global aerosol–climate model. The contrail model
simulates all the individual contrails forming from global air traffic
for meteorological conditions as defined by the climate model. The
computed contrail cirrus properties compare reasonably with
theoretical concepts and observations. The mass of water in aged
contrails may exceed 10
Contrail ice particles grow by the uptake of humidity from ambient ice-supersaturated air masses and release their water content after sedimentation or advection with the wind into regions with lower relative humidity. Knollenberg (1972) derived the ice mass inventory in a contrail for a single aircraft from measurements and found that the water present as ice in the contrail exceeds that in the original aircraft exhaust by at least 4 orders of magnitude. Hence, contrails dry or dehydrate the atmosphere at places where they form, and redistribute humidity to places in the atmosphere where they sublimate (Fahey and Schumann, 1999). Small relative changes of humidity in the troposphere and small absolute changes in the tropopause region have large effects on radiative forcing (Riese et al., 2012). Ice is far more efficient in radiative forcing than water vapor (Meerkötter et al., 1999; Chen et al., 2000; Fusina et al., 2007; Wilcox et al., 2012). The redistribution of humidity may make contrails thinner. In regions with heavy air traffic, contrail cirrus persistence can modify or even suppress natural cirrus formation (Unterstrasser, 2014), with consequences for radiative forcing (Burkhardt and Kärcher, 2011). Falling ice particles may enhance precipitation from mixed-phase or warm clouds at lower altitudes by increasing humidity and thus the liquid water content or by the Wegener–Findeisen–Bergeron process, both of which are thought to increase the likelihood of precipitation (Murcray, 1970; Korolev and Mazin, 2003; Yun and Penner, 2012). Dehydration from contrails may follow similar processes as dehydration by thin cirrus at the tropical tropopause (Jensen et al., 1996; Fueglistaler et al., 2009).
Contrails have been investigated in many observational and numerical studies
(Schumann, 2002; Mannstein and Schumann, 2005; Burkhardt et al., 2010;
Heymsfield et al., 2010; Yang et al., 2010; Unterstrasser and Gierens, 2010b;
Minnis et al., 2013; Lewellen, 2014; Voigt et al., 2015). Nevertheless, the
dehydration effects from contrails are not well known. Previous assessments
of the climate impact of aviation (Schumann, 1994; Brasseur et al., 1998,
2015; Penner et al., 1999; Sausen et al., 2005; Lee et al., 2009, 2010;
Boucher et al., 2013) discussed the dehydration effects from contrails
qualitatively. Burkhardt and Kärcher (2011) were the first to quantify
the dehydration effects within a global climate model. Contrail formation was
treated as a sub-grid-scale (SGS) process which included a separate cloud
class for young contrails. They found that contrail cirrus causes
a significant decrease in natural cloudiness, which partly offsets their
warming effect. They estimated the cooling from reduced cirrus at about
7
Observations show ice particles precipitating from contrails in
ice-supersaturated air (Heymsfield et al., 1998) and
A contrail prediction model, CoCiP (Contrail Cirrus Prediction model), has been developed to simulate the formation and decay of all individual contrail segments for given air traffic and ambient meteorology (Schumann, 2012) including contrail-induced radiative forcing (Schumann et al., 2012b). CoCiP uses a simplified model designed to approximate the essential contrail physics for efficient simulation of contrails from global traffic over long periods. The contrail model bridges the gap between the different scales of the aircraft wake and the global atmosphere. Various of the model results compare reasonably well with observations (Voigt et al., 2010; Schumann, 2012; Jeßberger et al., 2013; Schumann and Graf, 2013; Schumann et al., 2013a, b). In the past, the model has been run in an offline mode for given meteorological fields, without exchange of humidity between contrails and background air.
In this study, the contrail model is coupled with the global climate model
CAM3
For small climate disturbances, to which aviation effects belong, the analysis of climate impact from free-running climate simulations is hampered by the noise inherent in such climate models because of the chaotic nature of atmosphere dynamics. For a climate model study with a diagnostic linear contrail model, Ponater et al. (2005) used a fuel consumption larger by a factor of 20 and Rap et al. (2010a) used contrail optical depth enhanced 100 times to obtain statistically significant results from 30- to 50-year climate simulations. This is a valid approach as long as the climate response to the disturbances is about linear. Gettelman and Chen (2013) and Chen and Gettelman (2013) were able to reduce the climate noise using a 20-year climate model (CAM5) simulation nudged to the pressure, winds and atmospheric and sea surface temperatures from a previous 1-year simulation. In order to quantify the effects of this nudging, one would need comparisons with and without nudging. Here, we try to overcome climate noise by using enhanced emissions and estimate the linearity of the responses.
The method is a new combination of CoCiP with CAM3
CoCiP is a Lagrangian model which traces individual contrail segments forming
along flight routes for many flights. The model is documented and discussed
in Schumann (2012). In the following, the major features are explained with
a few modifications. CoCiP simulates the lifecycles of contrails from their
formation behind individual aircraft until final dissipation. Contrails are
assumed to form when the Schmidt–Appleman criterion is satisfied for a given
ambient temperature and humidity, a given fuel (
CoCiP simulates contrail segments for each flight from departure until
arrival for a maximum lifetime, set to 36
The CoCiP results depend on various critical model parameters; see
Table 2 in Schumann (2012). In particular, plume diffusivities are modeled as
in Schumann and Graf (2013), with vertical plume diffusivities computed for
CAM calls CoCiP as a subroutine each time step, providing the most recent meteorological fields as input. The fields include three-dimensional (3-D) fields of wind, temperature, humidity, ice water content, and cloud cover as a function of pressure. In addition, two-dimensional fields are provided for surface pressure, outgoing longwave radiation, reflected shortwave radiation, and incoming solar direct radiation. CoCiP interpolates in these fields linearly in space and time to obtain the values at any position.
In the offline mode, each contrail segment is simulated for the given ambient meteorological fields without changing background meteorology. This simplification is unavoidable when CoCiP is driven by the output of numerical weather prediction models, as has been done in the past. The offline mode allows for the efficient simulation of the contrails from millions of flights. For the coupled model, CoCiP is run either offline or online.
In the online mode, CoCiP returns effective emissions (besides
To avoid negative vapor concentrations in regions with many contrails forming
during a time step, CoCiP accounts for local
We note that the coupling between CoCiP and CAM transfers grid cell mean
values from CAM to CoCiP and the sum of all contrail sources or sinks within
a grid cell back from CoCiP to CAM. As a consequence, the mass of
Three runs were performed with CAM3
Run specification.
Annual and global mean contrail properties from run 0 and 1 with
standard deviations
This section describes the contrail results in some detail to explain the physics simulated and to compare them with observations. Some annual and global mean contrail properties for run 0 and 1 are given in Table 2. Unless otherwise stated, quantitative results are from run 1. The interannual variability in the 30-year mean values of CoCiP results as listed is small, and the run 1–0 differences in Table 2 are significant.
The emissions included in CAM are derived from 182.2
CoCiP computes the contrail properties for each given aircraft
type. The average fuel consumption, mass, speed, and overall
propulsion efficiency of contrail-forming aircraft are
4.60
Probability density function (pdf) of relative humidity over ice in the freshly forming contrail segments without (black: reference case, run 0) and with (red: coupled, run 1) humidity exchange.
CoCiP computes that there are about 3100 contrail segments of
36
The aircraft emit on average 5.3
Pdf of contrail properties from CoCiP–CAM for run 0 (white symbols:
reference) and 1 (red symbols: coupled). Ice water content (IWC; blue:
computed from temperature; Schumann, 2002), ice particle concentration
(
On average, the IWC in contrails is found to be equivalent to an amount of water vapor at relative humidity over ice of about 15 %. This value is consistent with the mean RHi in the ambient air. A growing contrail may contain less ice water and a shrinking contrail more ice water than this mean value. Hence, as shown in Fig. 1, long-lived contrails also exist in subsaturated air (as observed by Kübbeler et al., 2011; Iwabuchi et al., 2012; and Kaufmann et al., 2014).
Contrail properties per length unit in run 1.
The total mean and median values of contrail properties per unit
length vary over several orders of magnitude; see Table 3. The values
are averages over all contrail segments without accounting for
contrail overlap. The median values are smaller than the mean values,
which are controlled by a few very thick, old contrails. The ice mass
per flight distance values (6–50
Because the number of ice particles is nearly constant per flight distance but variable in the plume cross section, the volume concentration
The mean volume radius varies over a large range, from about half
a micrometer to half a millimeter (see Fig. 2). The lower bound results
from the water mass and the number of soot particles nucleating ice in
fresh contrails. The upper size limit is determined by
sedimentation. The fall speed reaches values on the order of
0.5
These particle sizes appear far larger than usually assumed for linear
contrails. Bedka et al. (2013) found an average particle effective
radius of 9
The optical depth
Pdf of local radiative forcing by contrails in the shortwave (red) and longwave (blue) ranges (top) and net RF (bottom).
The RF induced by contrail segments varies strongly (see Fig. 3). In
rough agreement with observations (Vázquez-Navarro et al., 2015),
individual contrail segments may cause local RF values in areas covered by contrails exceeding 60
The age of the simulated contrails varies between a few minutes and
36
The lifetimes depend among other things on vertical motions in the
ambient air. In the model, the contrails experience larger mean uplift
(100
Pdf of contrail ages. Symbols for CoCiP runs 0 and 1 (significant
below ages of about 8
An important metric for contrail radiative properties as a whole,
independent of the definition of contrail width
Ice particle cross-section area
In addition to the comparisons mentioned, we compare the computed
contrail properties with satellite observations. Iwabuchi
et al. (2012) used satellite pictures (MODIS) to identify linear
contrails and derived their altitude and thickness from collocated
space lidar (CALIPSO – Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) observations. The method was applied for the
domain 15–85
Contrail occurrence computed with CoCiP–CAM (red upward triangles;
run 1) and analyzed from MODIS–CALIPSO observations (black downward
triangles; data from Iwabuchi et al., 2012), for
180
Figure 8 shows that the pdf of optical depth from CoCiP is close to
that derived from MODIS and CALIPSO. The differences between the model
results for run 1 and 0 are significant but comparable to the
differences between the measurements in the 2 years (with slightly
different lidar properties; Iwabuchi et al., 2012). Figure 9 compares
the computed and observed width and vertical geometrical depth of
contrails. We note the large scatter of the data. Perhaps CoCiP
slightly overestimates the total depth. The effective depth appears to
fit the observations better. The contrail width pdf (not shown) is
a maximum at zero width and decreases exponentially, with a 5
Contrail occurrence vs. latitude as in Fig. 6. Red symbols: CoCiP-CAM; black: MODIS–CALIPSO data from Iwabuchi et al. (2012). The colored lines are linear fits to the respective data.
Pdf of solar optical depth of contrails in CoCiP–CAM simulations. Top: run 0; bottom: run 1. The curves in both panels are the same and are gamma functions approximating MODIS–CALIPSO observations in 2007 and 2009 (full and dashed, respectively), as reported by Iwabuchi et al. (2012).
Figure 10 compares the difference in the diurnal cycle of cirrus cover
and outgoing longwave radiation (OLR) between the North Atlantic
region (NAR; 45–55
Also, the interannual variability in the MSG results is comparable in magnitude to the variability in the CAM–CoCiP results. This suggests that CoCiP simulates most of the processes controlling this contrail cirrus signal. The ratio of regional LW RF to global LW RF (see Table 2) is 6.12 and 6.13 in runs 0 and 1, respectively. The ratio was 5.71 in the previous study with ECMWF meteorology. This ratio was used to extrapolate the regional LW RF to the global RF. Hence, the coupling does not change the main conclusions from earlier CoCiP studies.
Contrail Gaussian plume depth
We looked for a local response of cirrus cover and OLR to dehydration
following the diurnal traffic cycle. The results from CAM do not
reflect such a diurnal cycle. Different timescales of contrail cirrus
and dehydration effects would be important when discussing mitigation
options. Also, Chen and Gettelman (2013) computed a far smaller
amplitude of a double-wave diurnal cycle in global model results of LW
RF for this region than observed. Hence, the dehydration effects of
the contrails within CAM are either slow or not large enough to excite
a semidiurnal cycle. Note that most contrails are thinner than
1
Diurnal cycle of anomalies of differences between a North Atlantic region and a South Atlantic region for air traffic density (top panel), cirrus cover (middle), and outgoing longwave radiation (bottom) vs. universal time of day. The error bars denote the standard deviations of annual means. In the two lower panels, black symbols denote CAM results, red symbols the sum of CAM and CoCiP contributions, and blue symbols results derived from 8 years of satellite (Meteosat second generation, MSG) infrared observations (Graf et al., 2012; Schumann and Graf, 2013).
Panel
Figure 11 shows the annual mean global cirrus and contrail cover.
The mean cirrus cover computed in CAM is 40 %. The value depends
critically on the method used and is specified here as a function of the
assumed probability density function of supersaturation within each grid
(Wang and Penner, 2010). The result is roughly consistent with a range of
satellite observations of thin and opaque high-level clouds
(Stubenrauch et al., 2013). The mean contrail cover with optical depth
The global contrail cover estimated in early assessments was below
0.1 % (Sausen et al., 1998; Penner et al., 1999). The computed
contrail cover is about 5 times larger than that derived from linear
contrails in satellite data (Palikonda et al., 2005; Meyer et al.,
2007). More recent observation results provide higher values (Minnis et al.,
2013). Burkhardt and Kärcher (2009) and Frömming et al. (2011)
show that the computed contrail cover depends strongly on the assumed
threshold value of optical depth used to discriminate contrails from
clear sky. Rap et al. (2010b) estimated the global mean annual linear
contrail coverage for air traffic of the year 2002 to be approximately
0.11 %. Burkhardt and Kärcher (2011) reported a contrail
cirrus cover for the year 2002 of about 0.23 %. Schumann and
Graf (2013), for the year 2006, computed a global mean cover of 0.23 %. The
differences of the present study from previous results using CoCiP
come mainly from the larger soot number emission index
(10
As described above, we compute contrail RF by the difference
in net incoming radiative fluxes at top of the atmosphere with and
without contrails. The longwave (LW) part of this RF is always
positive and warming, the shortwave (SW) part is negative and cooling,
and the net effect (sum of LW and SW RF) is often small compared to the LW
forcing and may be positive or negative locally. The global RF
distribution is shown in Fig. 12. The net RF reaches maximum values of
more than 1
The computed RF values are far larger than those estimated previously for linear contrails (Minnis et al., 1999; Rap et al., 2010b; Frömming et al., 2011; Yi et al., 2012; Chen and Gettelman, 2013; Spangenberg et al., 2013), 5 times larger than the value estimated for contrail cirrus for the same traffic by Chen and Gettelman (2013), and nearly double the value estimated with a global contrail cirrus model for traffic in the year 2002 by Burkhardt and Kärcher (2011).
As indicated, some of the comparisons point to possible overestimates
of contrail cover and optical thickness by CoCiP. This would imply
overestimates of SW and LW RF. As in previous CoCiP studies, the
magnitude of the computed SW
Global map of annual mean radiative forcing by contrails –
Zonal and annual mean water emission rates (in units of mass mixing
ratio per time) vs. latitude and pressure
Figure 13 depicts the annual and zonal mean emissions of water from aircraft
engines into the atmosphere, either directly (EA) or into contrails (EC). The
figure also depicts the water released from contrails (CA). As explained
above, the contrails take water from engine emissions and from
background
humidity in ice-supersaturated air masses (negative CA) and release water
when sublimating in subsaturated air (positive CA). Since the amount of
The
Contrail formation reduces ambient humidity locally (Fig. 1) with the consequence of getting fewer or thinner contrails (Fig. 2), which are slightly longer living (Fig. 4). Contrail ice particle sedimentation brings humidity to lower levels. Even without sedimentation, contrails in subsiding air sublimate at lower levels. Contrails in rising air masses occur often because relative humidity increases from adiabatic cooling. Hence, some hydration occurs at higher levels but does not show up in the longitudinal mean values.
The effect of humidity exchange on contrails and the background atmosphere
can be quantified by comparing the mean results of runs 0 and 1 (see
Table 2). The contrails in the coupled model run 1 have 5 % more ice
particles but 29 % less ice water content and a 23 % smaller
effective radius than in run 0. The total
The redistribution of water by contrails in the atmosphere should have the
strongest effects on humidity in the background atmosphere at northern
midlatitudes, where most contrails form. For normal traffic, the CAM results
show only small changes. The run 1–0 differences are small compared to the
interannual variability in the atmosphere (see Fig. 14). In order to
understand this, we estimate the order of magnitude of the source rate
required to cause an appreciable change in background humidity. A background
humidity mass concentration on the order of 100
Vertical profiles of changes in normalized absolute humidity
(
Radiative forcing should respond strongly to humidity and cloud changes in
the troposphere and the lower stratosphere (Chen et al., 2000; Riese et al.,
2012). Figure 15 shows the RF computed from the difference between
run 1
Annual and global mean shortwave (SW), longwave (LW) and net
(SW
The annual mean RF values vary from year to year and show significant correlations with other annual and global mean diagnostics from CAM. Figure 16 shows strong correlations of RF with liquid water path and with low-level cloud cover. For SW RF, the correlation with low cloud cover is stronger than with high-level cloud cover. Hence, the interannual variability in RF appears to be linked mainly to the variability in low-level cloudiness.
SW (left panels) and LW (right) RF correlations with liquid and ice water path (LWP, IWP), water vapor path (WVP), and high- and low-level cloud cover in annual mean values of the differences of CAM results in run 1 and run 0.
Change in contrail properties for fuel consumption 100 times larger.
In order to increase the signal-to-noise ratio in the CAM simulations, we consider run 2 with traffic emissions 100 times enhanced. The increased traffic emissions are implemented in CoCiP using the same number of flights but fuel consumption 100 times larger, implying water mass and soot number emissions 100 times larger. This causes large changes in the contrail properties (see Table 4). We see a number of ice particles per unit length that is 94 times larger and an ice particle number volume concentration that 6 times larger but 60 % less specific ice water content. Hence, as expected, e.g., from Unterstrasser and Gierens (2010a), the increased soot emission causes far more contrail ice particles, while the enhanced water emissions are less important. Moreover, CoCiP computes a doubled mean contrail lifetime, an optical depth 4 times larger, and 8 times more contrail cover, and about a net contrail RF 14 times larger.
CAM does not see the soot but sees changes in water emissions CA (with a small contribution from EA). CoCiP computes a contrail ice water mass inventory that is about 10 times larger and about the same sedimentation depth. Figure 13 (lower panels) shows the distributions of the effective emissions CA for runs 1 and 2. We find similar distributions with CA values about 10 times larger in run 2. The ratios of the maximum, minimum, and global mean rms values of CA in runs 2 and 1 are 12.4, 9.8 and 12.9, respectively. Hence, the water inventory, the exchange between contrails and the background atmosphere in run 2 are about 10 times larger than in run 1.
Figure 14 shows that the mean humidity profile responds to the larger water exchange significantly. The contrails cause global dehydration mainly of the tropopause region (including the lower stratosphere) and a local increase in humidity in the mid-troposphere below the main flight levels at northern midlatitudes. The global mean humidity decreases. Hence, the redistribution of humidity by contrails changes the entire hydrological cycle.
Annual and global mean CAM results for normal (run 1) and 100
Figure 17 plots the RF of dehydration derived by CAM from run 1–0
differences as a function of the contrail ice water inventory, which is used
as a measure of the change in water exchange CA. The mean values are compared
in Table 5. For run 2, the RF values are computed from the 1-year mean of run
2 and 30 annual mean values of run 0. The standard deviation from 30 years of
run 2 might be a factor of
The mean SW and LW RF results are significant at the 95 % confidence level for enhanced fuel consumption. SW RF is positive in this case, suggesting that dehydration reduces cloud cover, both in the upper and lower troposphere, causing lower Earth albedo and, hence, warming the atmosphere. LW RF is negative (cooling), which would be consistent with reduced cloud cover and reduced water vapor in the cold tropopause region. The net RF values are small and have different signs in runs 1 and 2.
Table 5 shows that dehydration by contrails causes significant changes in CAM mean values for enhanced emissions. We find reduced cloud cover and a reduced water path in all phases. All of these changes are consistent with a causal impact of humidity redistribution by contrails on the hydrological cycle. The results suggest that ice particles sedimenting from contrails transport humidity downwards causing low-level cloud changes. The added humidity at lower levels may enhance liquid water content and cloud droplet sizes and, hence, precipitation. The available diagnostics do not allow us to quantify how much the Wegener–Findeisen–Bergeron process contributes to ice particle growth from evaporating cloud droplets, thereby enhancing precipitation.
Low-level cloud changes by aviation aerosol have been found before (Righi
et al., 2013), but such effects from dehydration have not been reported
before. The SW plus LW clear-sky RF (see Table 5), mainly from the reduced
water vapor path, is of opposite sign and far larger in magnitude than the RF
from aviation water emissions without contrail formation (about
0.001
Interpolating linearly in the ice mass inventories (Fig. 17) suggests that
the magnitudes of the SW and LW RF components of the dehydration effects for
nominal traffic are about 0.04
SW and LW RF from humidity redistribution by contrails in CAM for
nominal (run 1
This paper studied the effects of contrails from aviation on the
redistribution of humidity in the atmosphere. For this purpose, we coupled
the contrail model CoCiP with the climate model CAM3
The major findings are as follows:
The mean contrail ensemble properties are as expected from
the present understanding and are consistent with available observations. The computed optical depth values are close to those observed by
lidar and satellites from space. In agreement with previous studies, the optical bulk properties of
the contrails are strongly linked to ice particle sedimentation in ice-supersaturated air. In the coupled model, contrail water content may be 10 Contrail growth causes dehydration at flight levels; the large
ice particles sediment, on average by 700 The drying at flight levels changes mean contrail properties by
The model simulates a diurnal cycle of cirrus properties in the
North Atlantic, which reflects the diurnal cycle of air traffic in that
region and which is close to the cycle observed by
satellites. Dehydration-driven diurnal-cycle cirrus changes in the
global model were not detectable. The total dehydration RF is too small to be computed for nominal
emissions because of climate noise in the freely running atmosphere
climate model (interannual RF standard deviations about
0.2 Increasing the fuel consumption by 100 shows significant
changes. The contrails respond strongly to the increases in soot
emissions, causing a larger ice mass inventory in contrails and
stronger water exchange between contrails and the background
atmosphere. The larger contrail water exchange drives significant mean
dehydration effects in the global atmosphere. Based on these simulations, the redistribution of water by
contrails causes negative LW RF because of reduced humidity near the
tropopause (opposite sign and far larger than RF from aviation water
emissions without contrails) and positive SW from reduced cloud cover,
with magnitudes for normal traffic likely less than
In the global model, dehydration impacts the entire hydrological
system, including high- and low-level clouds. Both liquid and ice water
paths and cloud cover of low- and high-level clouds are reduced.
The quantitative results are sensitive to model details. For example, the
sedimentation is only crudely simulated with CoCiP because the details depend
on the particle size spectrum, which is not resolved in CoCiP. Possibly, the
simulated contrails are slightly thicker than expected from the observations.
Thinner contrails would appear, e.g., for a smaller effective soot emission
index. As a whole, the comparisons with observations show that the coupled
model provides results in reasonable agreement with observations. This is
a positive indicator not only for the quality of CoCiP but also the quality
of the input fields provided by CAM, in particular with respect to ice
supersaturation, which is crucial to the prediction of long-lived contrails.
This paper discussed the effects of water exchange between contrails and ambient air. Aircraft aerosols from aircraft engines emissions, possibly changed in contrails, may also impact the entire hydrological cycle and could be studied with an extension of this model in the future.
J. E. Penner and U. Schumann designed the research and wrote the paper. U. Schumann, Y. Chen, C. Zhou, and K. Graf coded the programs and data analysis and discussed the results.
This research was supported by the Federal Aviation Administration (FAA) within the ACCRI project, and by DLR within the DLR projects CATS and WeCare. J. E. Penner, Y. Chen and C. Zhou also acknowledge funding from the National Science Foundation (NSF). Computing resources (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory, sponsored by the NSF and other agencies. The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: B. Vogel