Contrails in a comprehensive global climate model: Parameterization and radiative forcing results

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D13, 4164, /2001JD000429, 2002 Contrails in a comprehensive global climate model: Parameterization and radiative forcing results Michael Ponater, Susanne Marquart, and Robert Sausen Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany Received 23 January 2001; revised 9 July 2001; accepted 16 July 2001; published 3 July [1] A parameterization of contrails for use in comprehensive global climate models is introduced. It is based on the thermodynamic theory of contrail formation, which has been applied in a consistent way with the cloud parameterization scheme of the version 4 European Center/Hamburg General Circulation Model. Both the coverage and the optical properties of contrails are calculated as functions of instantaneous values of atmospheric variables as they are provided by the climate model. The resulting spatial distributions of contrail parameters prove to be useful for explaining observed differences between contrails in different geographical regions. The time mean properties of the simulated contrails are in fair agreement with observations, though the values of ice water path and optical depth tend to be somewhat lower than those reported from in situ measurements. The radiative forcing of contrails resulting from the climate model experiments is substantially lower than estimated in a previous study, where mean parameters for contrails and the ambient atmosphere were prescribed in a radiative transfer model. One contribution to this disagreement arises from the smaller mean ice water content in the climate model simulations. However, the largest part must be related to a different treatment of the interference of contrails with natural high clouds. The sensitivity of the contrail radiative forcing to systematic errors in simulated ambient atmospheric variables (like temperature, humidity, and natural clouds) as well as to the parameterization of cloud and contrail overlap needs to be investigated further. INDEX TERMS: 3300 Meteorology and Atmospheric Dynamics; 3359 Meteorology and Atmospheric Dynamics: Radiative processes; 1620 Global Change: Climate dynamics (3309); 0320 Atmospheric Composition and Structure: Cloud physics and chemistry; KEYWORDS: contrails, radiative forcing, Cirrus cloud parameterization Copyright 2002 by the American Geophysical Union /02/2001JD Introduction 1.1. Contrails and Their Climate Impact [2] Condensation trails (contrails) are line-shaped ice clouds forming in the wake of jet aircraft. Like natural high clouds (cirrus), they influence the climate system. The Intergovernmental Panel on Climate Change (IPCC) special report Aviation and the Global Atmosphere [IPCC, 1999] estimated the global radiative forcing (RF) of contrails to be of the order of 0.02 W m 2 for 1992 worldwide air traffic. This value might be regarded as small in absolute numbers but makes up for 40% of the total RF caused by all aircraft effects, exceeding the contribution from CO 2 production by kerosene burning [Brasseur et al., 1998; IPCC, 1999]. Furthermore, IPCC [1999] estimates that the relative importance of contrails will even increase to a fraction of 52% of the total aviation effect by 2050 if air traffic grows according to scenario Fa1 (which is based on the same economic assumptions as the IPCC reference scenario IS92a). [3] The contrail climate impact assessment mentioned above has been derived by combining the current theoretical knowledge about contrail formation and development with observational evidence and model studies. Contrail coverage can be determined directly from multiyear satellite observations, but respective studies are available for few geographic regions only [Bakan et al., 1994; Mannstein et al., 1999; Minnis et al., 2001; Meyer et al., 2002]. The extent of ice-supersaturated areas in the upper troposphere and lower stratosphere has been estimated by Gierens et al. [1997, 1999a] from regionally available in situ temperature and humidity measurements. These areas can be taken as an upper limit of contrail coverage (potential coverage). As the thermodynamic theory [Schmidt, 1941; Appleman, 1953; Schumann, 1996] describes contrail formation as a function of combustion parameters and ambient atmospheric conditions, the global contrail coverage could be determined indirectly from a global multiyear atmospheric data set [Sausen et al., 1998]. Direct and indirect estimations agree qualitatively, but an uncertainty factor of 2 is still apparent. Current best estimates of contrail coverage in the 1990s range around 0.1% for the actual global coverage and around 15% for potential global coverage. In regions with high air traffic density (like central Europe or North America), time mean actual coverages of more than 2% have been found. ACL 2-1

2 ACL 2-2 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL [4] Besides coverage, the optical properties of contrails must also be known to estimate the contrail climate impact. Local observations suggest a large variety of particle shapes and of possible values for ice water content, particle size, and optical depth, even if only persistent contrails are considered. A strong dependence on the age of the contrail, the ambient temperature and humidity, and the atmospheric aerosol loading (soot and sulfur) has been noticed [e.g., Gayet et al., 1996; Sassen, 1997; Jäger et al., 1998; Betancor Gothe et al., 1999; Schröder et al., 2000]. Thus defining a typical value for the optical depth of persistent contrails is just as complicated as it is for natural cirrus clouds [Liou, 1986]. Values between 0.05 and 0.5 can all be regarded as consistent with individual local measurements. [5] If the climate impact of contrails is estimated in terms of RF or atmospheric temperature change, the large uncertainty of the basic quantities requires some caution in interpreting the results. This holds for local one-dimensional (1-D) calculations but even more for global 3-D studies. Fortuin et al. [1995] and Meerkötter et al. [1999] pointed out the dependency of the RF of contrails on ice water content and particle size. Contrails usually produce a positive RF because of the dominance of the cloud greenhouse over the cloud albedo effect. The net RF is strongest during nighttime, whereas it is reduced during daytime by the negative contribution from backscattering of solar radiation. The compensation is strongest for large solar zenith angles, but according to Meerkötter et al. [1999] the net forcing remains positive, except for a few specific parameter combinations (high ice water content, small particles, low surface albedo). Minnis et al. [1999] (hereinafter referred to as M99) provided the first estimate of the global annual mean RF of contrails, combining the global distribution of contrail coverage provided by Sausen et al. [1998] with optical depths of 0.1, 0.3, and 0.5 (which they regarded as representing typical values for contrails, based on measurements over the United States). Their best estimate for 1992 conditions was 0.02 Wm 2, with an uncertainty factor of about 4. The high sensitivity of the best estimate value to new discoveries within this developing research area was recognized by both M99 and IPCC [Fahey and Schumann, 1999] Why Parameterizing Contrails? [6] All previous attempts to estimate the global RF of contrails have one common conceptual disadvantage: They must specify typical mean ambient conditions and typical mean contrail properties to yield a spatial distribution of contrail RF and, subsequently, a global mean RF value. Though the validity of the results can be (and has been) checked with respect to sensitive parameters like surface albedo, solar zenith angle, or contrail optical depth, this approach remains unsatisfactory because it does not account for the multitude of parameter combinations that can occur at various locations or in various seasons. Averaging over a perturbation that is highly inhomogeneous in space and time can have substantial influence on the global mean RF, as was demonstrated by Feichter et al. [1997] in their calculations of the indirect climate impact of sulfate aerosols. One can avoid some of these shortcomings by using a parameterization scheme for contrails in a comprehensive global climate model. Such a scheme relates contrail coverage and optical properties to the instantaneous state of the atmosphere, thus allowing contrail properties to change with geographical location, synoptic situation, season, or daytime. At the same time, nonlinear averaging effects on the RF are accounted for. A further advantage is that eventually, feedbacks of the contrails on the environment can be included to simulate the net climate effect. On the other hand, climate models have their own systematic errors, which must be taken into account when interpreting the results. Likewise, the crude representation of cloud processes in current general circulation models (GCMs) puts some limits to the formulation of a comprehensive physically based contrail parameterization. [7] There are only a few studies that have attempted to determine the climate impact of contrails from GCM simulations [Ponater et al., 1996a; Rind et al., 1996, 2000]. None of them employed a contrail parameterization; rather, they estimated the climate impact of a cirrus cloud increase introduced by more or less artificial means. Yet these studies have shown that contrails have the potential to force a detectable temperature signal. [8] We have developed an online parameterization for contrails, which is introduced in the present paper. The scheme is based on the thermodynamic theory of contrails (Schmidt-Appleman theory), which has been adapted to the cloud parameterization scheme of the version 4 European Center/Hamburg General Circulation Model (ECHAM4) along the line sketched by Ponater et al. [1996b]. Section 2 gives a detailed description of the method. Section 3 presents the simulated contrail properties and the resulting RF distributions, which are discussed in connection with specific GCM properties in Section 4. The paper is closed with a critical appraisal of the method and of the results, leading to an outlook on potential improvements. 2. A Parameterization of Contrails for ECHAM4 [9] In this section, ECHAM4 is briefly described, with some emphasis on the parameterization for cirrus clouds. The extension of the cloud scheme to include contrails is explained in detail ECHAM4 [10] As the host model for our contrail parameterization, we have chosen ECHAM4 [Roeckner et al., 1996], which has been applied for numerous climate sensitivity and climate change experiments [e.g., Feichter et al., 1997; Roeckner et al., 1999; Bengtsson et al., 1999] and which can be regarded to represent the state-of-the-art in climate modeling. A special version with 39 vertical layers (denoted as ECHAM4.L39 (DLR)) by Land et al. [1999]) has been used that offers a vertical resolution of about 700 m in the upper troposphere and lower stratosphere where contrails mainly occur. This is less than half the layer depth in the standard ECHAM4 model with 19 layers. The horizontal resolution has been chosen for the present study as spectral T30 (about 670 km isotropically) with a time step of 30 min.

3 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL ACL 2-3 The physical parameterization schemes are only slightly modified in comparison to the standard model. Likewise, there are only few deviations with respect to the basic climatology, as pointed out by Land et al. [1999] Cloud Parameterization [11] The ECHAM4 cloud parameterization scheme was described in detail by Roeckner [1995] and is briefly recalled here as it sets the framework for the treatment of contrails. The scheme includes two prognostic equations for water vapor mixing ratio q and cloud water mixing ratio m, ¼ Rq ð Þ bc cl ð1 bþc 0 þ ð1 bþe 0 ¼ Rm ð ÞþbC cl þ ð1 bþc 0 bp cl : ð2þ Following the concept originating from Sundqvist [1978], a fractional cloud coverage b is calculated diagnostically from the grid box-averaged relative humidity r. Hence phase change rates can be parameterized individually for the cloud-covered part of the grid box (indicated by index cl) and for the cloud-free part of the grid box (indicated by index 0). Condensation and evaporation of cloud water (C cl, C 0 ), autoconversion of cloud droplets to rain or sedimentation of ice crystals (P cl ), and evaporation of rain drops (E 0 ) are calculated as specified by Roeckner et al. [1996] and Land et al. [1999]. The terms R(x) describe transport processes (advection, convection, etc.). The cloud water mixing ratio m comprises the liquid and the ice phase, which are distinguished diagnostically as a function of grid point mean temperature. [12] Following Sundqvist et al. [1989], the fractional cloud cover is calculated as 8 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 r r crit >< ; r r crit b ¼ r sat r crit >: 0; r < r crit Henceforth we restrict our considerations to those levels of the atmosphere associated with contrail formation, where clouds almost exclusively consist of ice particles. Thus r sat indicates the (relative) saturation humidity with respect to ice and the relative humidity r is also calculated over ice. Here r crit is a critical threshold value that indicates a minimum relative humidity necessary to allow cloud formation within a grid box. As proposed by Xu and Krueger [1991], r crit is calculated as a function of altitude. It approaches a value of r crit = 0.7 in the upper troposphere. [13] For each grid box containing clouds the water vapor q is separated in two fractions, one related to the cloudcovered part, where saturation is assumed, the other one related to the cloud-free part: ð3þ q ¼ bq sat þ ð1 bþq 0 : ð4þ A positive moisture convergence (R(q) > 0) from transport for a given time step will result, simultaneously, in increasing the humidity in the cloud-free part and in causing supersaturation in the cloud-covered part. Supersaturation may also occur if a radiative or dynamic cooling causes a decrease of the saturation mixing ration q sat. The net saturation excess inside the cloud will be converted into cloud water: C cl ¼ Rq ð sat =@t: In contrast, if a negative moisture convergence produces a saturation deficit in the cloud-covered part (C cl < 0), saturated conditions will be restored by evaporating preexisting cloud water. Only in cases where not enough cloud water is available will the cloud disappear Contrail Parameterization [14] Contrail formation occurs in ambient air that is cold and moist enough to allow supersaturated conditions (with respect to liquid water) during the mixing between the aircraft exhaust plume and its environment. The thermodynamic theory predicts threshold values for temperature (T contr ) and humidity (r contr (T )). No contrails are possible if T > T contr and, for given T, if r < r contr (T ). A good approximation for T contr (in C) has been given by Schumann [1996] as T contr ¼ 46:46 þ 9:43lnðG 0:053Þþ0:72½lnðG 0:053ÞŠ 2 ; ð6þ where G has the unit of Pa K 1 and is defined as ð5þ G ¼ EI H2O c p p ½eQð1 h ÞŠ; ð7þ representing the slope of the mixing line in a temperaturehumidity diagram. Apart from constants (specific heat at constant pressure c p, ratio of the molecular masses of water and air e) and an atmospheric parameter (pressure p), G contains some parameters related to the combustion process: the emission index of water vapor EI H2 O, the propulsion efficiency of the jet engine h, and the specific combustion heat Q. These indicate the dependency of contrail formation on specific fuel and aircraft characteristics, as pointed out by Schumann [1996, 2000]. For the present study we chose a value of h = 0.3, which we regard as a reasonable average propulsion efficiency for aircraft in use during the first half of the 1990s. The critical relative humidity r contr can range from 0 to 1 and is dependent on G, T contr, and the ambient temperature T as h i. r contr ðtþ ¼ G ðt T contr Þþe ðlþ ðt contr Þ sat e ðlþ sat ðtþ; where e L sat is the saturation pressure of water vapor with respect to the liquid phase. [15] Equations (6) (8) do not make any prediction for contrail persistence. However, it is reasonable to assume that only long-lived contrails cause a relevant climate impact [Ponater et al., 1996a]. This requires an environment that is supersaturated with respect to ice, where contrails may exist for a longer time, up to some hours, before they gradually transform into cirrus clouds or eventually disappear [e.g., Gierens and Jensen, 1998; Minnis et al., 1998]. ð8þ

4 ACL 2-4 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL that can be covered by contrails under the given ambient conditions. [17] Finally, the depencency of the actual contrail coverage on the density of air traffic must be considered. Following Sausen et al. [1998], the local amount of aircraft fuel consumption F is used as a linear weighting factor to calculate the actual contrail coverage from the potential pot contrail coverage b contr as b contr ¼ gfb pot contr: ð12þ Figure 1. Parametrization of the fractional coverage of clouds (dashed), potential contrails (dotted), and the sum of clouds and potential contrails (solid) in case of r contr =0.5 and r crit = 0.7 (see text). Of course, F varies in space and time according to the aircraft emission inventory that is applied. g is a nonphysical adjustment factor that is required to calibrate the temporal and spatial average of contrail coverage to observed conditions. In the absence of global or hemispheric observations this calibration procedure must currently be restricted to some reference area where sufficiently reliable observations are available. Respective details will be explained in section 3. [18] The criterion for contrail persistence is met by the second basic idea inherent in our scheme: the occurrence of supersaturation in the contrail-covered part of the grid box is required, i.e., in analogy to (5): C contr ¼ Rq ð sat =@t > 0: ð13þ [16] It is obvious that the theory of local contrail formation cannot be applied in the GCM framework without adapting it to the model s cloud scheme. For example, if one allowed persistent contrails to be present only in grid boxes where supersaturation prevails, this grid box would be covered completely with cirrus clouds according to (3). The first idea of our parameterization is to introduce a modified threshold value r crit * for contrail formation by combining the theoretical threshold r contr from the Schmidt-Appleman theory with the threshold r crit of the cloud scheme: r crit * ¼ r crit r contr ð9þ Because r crit * < r crit, contrails may form under less restrictive conditions than cirrus clouds, which is consistent with observational evidence. Applying the same formula as in (3), a potential coverage of all high clouds (both contrails and natural cirrus) is calculated on the basis of r crit * as 8 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 r r crit * >< ; r r crit * b ¼ r sat r crit * >: 0; r < r crit * ð10þ This, in turn, allows the defining of a potential fractional coverage for contrails as the difference between b total and b: b pot contr ¼ b total b: ð11þ pot Figure 1 displays b, b total, and b contr as a function of the grid box mean relative humidity (with respect to ice) in case of r contr = 0.5 and r crit = 0.7. The potential contrail coverage pot (dotted line in Figure 1) describes the maximum area b contr As C contr is the only source of contrail ice water, the contrail coverage b contr is reset to zero for C contr < 0. In this case it is assumed that contrails will evaporate within a time range much shorter than one model time step (i.e., 30 min). Sedimentation of ice crystals out of the contrails within one time step is not included in the scheme. [19] We remark that the contrail coverage b contr, like the cirrus coverage b, is defined for each model layer: These values enter the radiative transfer calculations. However, a total contrail coverage is also calculated for validation and calibration purposes by summing up b contr over the layers using the random overlap assumption [Manabe and Strickler, 1964; Sausen et al., 1998]. [20] Our contrail parameterization can be regarded as a diagnostic extension of the prognostic cloud scheme described in section 2.2. In principle, the scheme allows the transfer of the contrail ice water calculated via (13) to the cirrus ice water in the subsequent time step, thus making a most simple parameterization for the contrail-to-cirrus transition. However, in the present paper this possibility will not be exploited as we only calculate contrail properties and radiative forcing values in a purely diagnostic way, not yet considering interactive feedbacks of the contrails to the climate system Optical Properties of Cirrus Clouds and Contrails [21] The radiative effect of clouds in the GCM is described by the single-scattering albedo, the asymmetry factor, and the optical depth in the solar part of the spectrum and by the emissivity in the terrestrial part. These quantities are parameterized in terms of the cloud water content m* and the particle size (effective radius). Cloud water content is assumed to be concentrated in the cloud-covered part of the grid box (m* = m/b). Concerning ice clouds, the calculation of the radiative key parameters for the shortwave

5 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL ACL 2-5 Table 1. Globally and 10 Year Averaged Coverage of Visible Contrails and of All Contrails As Simulated in the Reference Experiment Coverage, % Visible All January April July October Year radiative transfer is based on Mie theory, assuming spherical ice particles [Rockel et al., 1991]. The ice crystal effective radius is itself parameterized dependent on ice water content, being derived from m* according to Heymsfield [1977] and McFarlane et al. [1992]. The relations of Rockel et al. [1991] for single-scattering albedo, asymmetry factor, and optical depth have been fitted to the spectral resolution of the radiation scheme and have been documented by Boucher and Lohmann [1995] in the form used in ECHAM4. In order to account for the nonsphericity of ice particles the respective asymmetry factor has been empirically reduced by a factor of 0.91 [Roeckner, 1995]. Emissivity in the terrestrial part of the spectrum is approximated by an exponential function of ice water content m* and effective crystal radius as proposed by Stephens et al. [1990]. The respective relations are given by Roeckner [1995]. [22] The optical properties of contrails can be calculated in exactly the same way as those of natural cirrus clouds. This straightforward procedure has been applied for our reference simulation (see section 3.1). However, a distinguished handling of contrails is also possible as the parameterization provides their coverage and ice water content individually, independent of the respective values for cirrus that may or may not be present in the same grid box. Hence known differences between the optical properties of cirrus and contrails can be accounted for by making specific assumptions for optical depth or particle size of contrails. [23] Radiative transfer and heating rates in the GCM are calculated using the radiation parameterization of Fouquart and Bonnel [1980] and Morcrette [1991] for the solar and the terrestrial spectrum, respectively. The radiative forcing of contrails is calculated online. A consistent determination of the stratosphere-adjusted radiative forcing has been provided by Stuber et al. [2001] and was used to yield the respective results in section 3.2. [24] An evaluation of clouds and their radiative effect in ECHAM4 has been reported by, for example, Lohmann and Roeckner [1996] and Chen and Roeckner [1996, 1997]. Wild et al. [1998] showed that the shortwave radiative budget simulated by ECHAM4 is closer to observations than those for most other GCMs, though to some extent this favorable result may be incidental rather than due to a superior physical formulation. 3. Results 3.1. Reference Experiment: Contrail Properties [25] The main simulation that we performed for this study follows the lines explained in section 2 and will be referred to as the reference experiment throughout this paper. It was extended over 10 annual cycles, thus providing statistically reliable mean distributions of the relevant contrail parameters. We found, however, that the interannual variability is not critical when comparing different experiments with respect to the quantities considered in this paper. Sea surface temperature and sea ice extent in the reference experiment were prescribed by a mean annual cycle derived for the Atmospheric Model Intercomparison Project (AMIP) period [Gates, 1992]. The surface albedo values are also prescribed and range from 0.07 over sea to between 0.1 and 0.4 over land and up to 0.8 over ice. [26] The version 2 Deutsches Zentrum für Luft- und Raumfahrt (DLR) aircraft emission data set used to calculate the actual contrail coverage from the potential coverage reflects the air traffic density distribution at the beginning of the 1990s [Schmitt and Brunner, 1997]. This inventory is provided with a vertical resolution of 1 km, which is adequately matched by the vertical resolution of our GCM. The regional contrail coverage over the east Atlantic and western Europe was calibrated to an annual area mean value of 0.5% as determined by Bakan et al. [1994] from visual inspection of National Oceanic and Atmospheric Administration (NOAA) satellite images. As discussed by Gierens et al. [1999b], this choice has a direct effect on our estimation of global mean contrail coverage (and its climate impact). Later studies using an objective detection algorithm [Mannstein et al., 1999; Meyer et al., 2002] report substantially smaller contrail coverages in the Atlantic/ European region. However, we did not adopt them here as they consider only strictly line-shaped contrails. [27] From reasons to be discussed later we distinguish between the coverage by all contrails and the coverage by visible contrails: In order to be classified as visible, contrails must exceed a minimum optical depth, and they must not be disguised by natural clouds. Hence contrails in a certain layer are defined as invisible if their optical depth in the visible part of the spectrum is lower than 0.02 or if the natural cirrus coverage in the layers above or immediately below is larger than 80%. Calibration in the reference area has been done for the visible contrails. Note that the calibration procedure retains the possibility of evaluating seasonal and geographic variations in comparison to observations. [28] The global mean total coverage for visible contrails and for all contrails is listed in Table 1 for the 10 year annual average as well as for the months January, April, July, and October. Our annual average for visible contrail coverage amounts to 0.07%, which is clearly smaller than the 0.09% estimated by Sausen et al. [1998]. This difference vanishes, however, if the diagnostic method of Sausen et al. is applied to ECHAM4 model data instead of to observational data [Sausen, 2001]. [29] The geographical distribution of the total coverage of visible contrails is presented in Figure 2 for January and July. With distinct maxima of contrail coverage over the North Atlantic, the North Pacific, and, particularly, the eastern United States and over western and central Europe, the field structure is largely determined by the main flight routes. Typical time mean values within the latter areas range from 0.5% up to about 5%, but it should be added that at individual time steps, contrails coverages of about 20%

6 ACL 2-6 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL Figure 2. Long-term average of total contrail coverage (%) by visible contrails for January and July. See color version of this figure at back of this issue. may occur, i.e., 1 order of magnitude larger than in the mean. [30] A closer inspection of Figure 2 reveals that besides air traffic density, the spatial and seasonal variations of the atmospheric conditions are also important. Contrail coverage displays substantial seasonal variations, which cannot be explained by the weak annual cycle inherent in the aircraft inventory of Schmitt and Brunner [1997]. The maximum of contrail coverage over North America extends over a larger area and shows higher peak values in winter than in summer. This is in fair agreement with the seasonal changes found in observational studies [Minnis et al., 1997; Sausen et al., 1998] and can be related to the annual temperature cycle: In summer the upper troposphere over North America is often too warm to carry contrails, whereas they can form quite frequently in winter. Over western Europe the model simulates a higher peak value in summer than in winter, which is in agreement with Bakan et al. [1994] but in contradiction to Meyer et al. [2002], who find a clear contrail minimum over Europe in summer. Sausen et al. [1998] also find a summer minimum though they applied a method similar to ours except for using observed atmospheric data. Note, however, that they could calculate a contrail coverage but had no optical properties to account for the contrail visibility problem in the way we do. Table 1 shows that almost half of the contrails in our simulation are invisible according to our definition. Invisible and marginally visible contrails are quite frequent over Europe in winter (see below), thus their inclusion or exclusion clearly influences the seasonal cycle. [31] Contrail coverage distributions at two individual model layers (at 200 and 250 hpa, respectively) are displayed in Figure 3, again for January and July. Contrail ice water path (IWP) and optical depth for the same layers and months are shown in Figure 4 and Figure 5, respectively. The optical depth refers to the spectral interval between 250 and 680 nm. All fields presented are climatological means averaged over the 10 respective months contained in the reference experiment. In order to produce a mean value for IWP and optical depth, only situations with visible contrails were counted, and these were not weighted with the actual coverage. Hence no values are displayed at grid points where no contrail occurred during the whole simulation. [32] It can be noticed that contrail frequency in the extratropics is higher at 250 hpa than at 200 hpa, especially in winter (Figure 3). The latter level is often located in the stratosphere, where it is too dry to allow contrail persistence. In the tropics, particularly over southeast Asia, contrail frequency is higher at 200 hpa because the temperature tends to be too high to allow contrail formation at levels below 230 hpa. However, the mean contrail coverage in the tropics is rather small as air traffic density is low. Optical depth and IWP generally decrease with height as less water vapor is available for condensation at higher and colder levels. The impact of temperature variability is also apparent in the geographical and seasonal variations of contrail IWP (Figure 4). It is smaller in the extratropics (mean values between 0.2 and 2 g m 2 at 250 hpa) than in the tropics (up to 5 g m 2 at 250 hpa). In the extratropics the IWP at the same level is higher in summer than in winter. With an average contrail depth of 700 m (which is given by the model s vertical resolution but is certainly an overestimation), estimated values of ice water content in the extratropics range between about 0.3 and 3 mg m 3. This is still comparable with, but rather on the lower end of, measurements in contrails [Gayet et al., 1996; Schröder et al., 2000; Schumann, 2002]. [33] The parameterization of IWP, effective particle radius, and optical depth described in section 2.4 exhibits a rather weak dependency of the effective radius on ice water content for small IWP values as found for the simulated contrails. Contrail effective radii are found in a range from about 15 down to 12 mm, the latter value being the minimum that may occur in this parametrization if the IWP approaches zero. Hence the spatial and temporal variability of the optical depth (Figure 5) is closely following the IWP variability. The

7 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL ACL 2-7 Figure 3. Long-term average of contrail coverage (%) by visible contrails at 200 (layer 15) and 250 hpa (layer 18) for January and July. See color version of this figure at back of this issue. time-averaged contrail optical depth in the extratropics is considerably smaller in winter (mostly below 0.1) than in summer (where values up to 0.4 occur over North America). The model simulates thicker contrails over North America than over Europe, which is consistent with apparent differences in observed contrail optical depth: M99 estimate a characteristic value of 0.3 over the United States, whereas Meyer et al. [2002] yield substantially lower values Figure 4. Long-term average of ice water path (g m 2 ) for visible contrails at 200 (layer 15) and 250 hpa (layer 18) for January and July. A conditional mean is shown that includes only dates on which contrails existed. See color version of this figure at back of this issue.

8 ACL 2-8 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL Figure 5. As in Figure 4 but for the optical depth for visible contrails. See color version of this figure at back of this issue. (about 0.11) over Europe. As is evident from Figure 5, the mean contrail optical depth over Europe often ranges below 0.05, though we excluded invisible contrails from the ensemble before averaging. It is hard to decide whether the frequent occurrence of very thin contrails in the extratropics (in particular, over Europe) is a realistic feature or rather a model artifact. Contrails in the tropics are typically thicker than in the extratropics, which appears to be a reasonable result considering the larger amount of ambient water vapor available for condensation. [34] In Figure 6 we present several time series of contrail optical depth at 250 hpa for all 120 monthly means simulated in the reference experiment. The maximum values found within each month are also shown. Area mean values over the United States range between 0.04 and 0.25, depending on season. The annual variability is larger than the interannual deviation between different realizations of the same calendar month. Maximum values within a month can be larger by an order of magnitude compared to the monthly mean. On the global scale, mean contrail optical depth fluctuates around a value of 0.15 with little annual or interannual variability. Not surprisingly, the global maximum value found within a given month may exceed the global mean by up to a factor of 30. [35] Overall, we conclude that the simulated results fit within the wide range of local observations, both with respect to the mean effective particle size and the optical depth. However, the low variability range of the effective radius in the model may be an underestimation of the variability actually occurring in persistent contrails. The mean contrail optical depth tends to be lower in the simulation than in the available local observations, similar to the IWP values already considered. If one assumes that the measurements are appropriate to derive the characteristic mean microphysical properties of contrails, there are two possibilities why the simulation may produce a systematic underestimation: either the parameterization is erroneous at least for certain ambient conditions (e.g., weather situations) or the frequency distribution of various ambient conditions associated with contrails is not correctly captured by the climate model Reference Experiment: Radiative Forcing [36] In this section we discuss the radiative forcing (RF) of the contrails in the reference run. All contrails (not only the visible ones) enter the radiation scheme to calculate the contrail RF, but the radiative influence of those with extremely low optical depth may be expected to be negligible. Although the stratosphere-adjusted RF at the tropopause is regarded as the better quantitative predictor of climate change [IPCC, 1995], we will frequently refer to the instantaneous RF at the top of the atmosphere (TOA) in the present paper, as previous results have been given mostly in terms of the instantaneous TOA forcing.

9 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL ACL 2-9 Figure 6. a month). Time series of contrail optical depth at the 250 hpa level (monthly means and maxima within [37] Figure 7 shows the geographical distribution of the stratosphere-adjusted RF at the tropopause caused by contrails. The fields are long-term averages over the reference simulation. The net forcing (Figure 7 (bottom)) is the sum of the longwave and shortwave components, which are of different sign but of similar magnitude. The structure of the contrail coverage distribution is clearly apparent in all panels. The influence of the optical depth is evident in some regions where RF values of similar magnitude may occur, though the contrail coverage is markedly different (e.g., compare the North Atlantic flight corridor with the route along the east Asian coast in January). Most striking, however, are the small RF values apparent in Figure 7, particularly for the net RF: They are almost 2 orders of magnitude smaller than those presented by M99. Over western Europe and the United States, where contrail coverage is largest, the longwave and shortwave RF both yield amounts of about 30 mw m 2 with small-scale maxima exceeding 100 mw m 2. The compensation between the positive longwave forcing and the negative shortwave forcing is so effective that the net RF hardly exceeds 30 mw m 2 anywhere. North of 55 N in July even a negative contrail net RF is simulated. The respective region is characterized by long days and large solar zenith angles, thus favoring strong compensation of the longwave and shortwave forcing. However, previous studies [Fortuin et al., 1995; Meerkötter et al., 1999] have suggested that negative net RF may occur only if contrails contain much more ice water than it is usually the case in our reference simulation. [38] Figure 8 shows the global and annual mean vertical profiles of the stratosphere-adjusted radiative flux change caused by the contrails and of the corresponding radiative heating rates. Except for the small absolute values the profiles are fully consistent with previous estimations for thin cirrus and contrails [e.g., Liou, 1986; Francis et al., 1994; Meerkötter et al., 1999]. There is a warming in and below the contrails but a longwave and net cooling above the contrail top. Toward the ground, the net forcing is dominated by the shortwave flux reduction, resulting in a net cooling impact on the Earth s surface. Table 2 displays global mean values of the stratosphere-adjusted RF at the tropopause. The influence of the seasonal variation in contrail coverage (Table 1) on the RF is evident. The shortwave RF compensates about 80% of the longwave forcing in the annual average, leaving a global mean net forcing as small as 0.4 mw m 2. The instantaneous net RF at the TOA is even smaller, yielding 0.2 mw m 2, thus 2 orders of magnitude smaller than M99 s estimate of 20 mw m 2. This large deviation cannot be explained by the moderate difference in contrail coverage, and it is also evident in regions where the simulated contrail optical depth is not far below the standard values assumed by M99.

10 ACL 2-10 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL Figure 7. Stratosphere-adjusted radiative forcing (mw m 2 ) at the tropopause due to contrails for January and July: (top) longwave and (middle) shortwave components and (bottom) net forcing. Negative values are emphasized by dashed contour lines. See color version of this figure at back of this issue. Figure 8. Vertical profiles of (left) the stratosphere-adjusted radiative forcing and (right) the respective radiative heating rates due to contrails. The values for the TOA have been placed at 5 hpa for display reasons.

11 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL ACL 2-11 Table 2. Globally and 10 Year Averaged Stratosphere-Adjusted Radiative Forcing at the Tropopause Due to Contrails for the Longwave (LW) and Shortwave (SW) Component As Well As for the Net Effect Adjusted RF, mw m 2 LW SW Net January April July October Annual mean Hence we performed a number of sensitivity experiments to check the plausibility of our RF results Sensitivity Experiments [39] There are several basic differences that may explain the discrepancy between the RF results of M99 and those we yield in our reference experiment: (1) They used cloud and contrail parameters fixed on a monthly mean basis, while the parameterization retains the high variability in time. (2) They assumed a higher ice water path in contrails than is generally simulated in our reference run. (3) They had all contrails located at 200 hpa altitude, while we allow contrail formation at varying altitudes. Contrail particle size is not substantially different in both studies: M99 assume a constant effective radius of 10.9 mm in fair agreement to the values simulated by our model. [40] We now discuss a number of additional model runs that have been performed to study the sensitivity of the RF with respect to some individual effects. To this end we have abandoned certain features of the parameterization in favor of fixing contrail properties in a way more similar to M99 s configuration. The sensitivity experiments cover 2 model years only. [41] As the horizontal structure of the RF distribution in the sensitivity experiments is always similar while the overall magnitude undergoes large changes, only the global mean RF values are considered. Table 3 shows the instantaneous RF (longwave, shortwave, and net) at the TOA for all sensitivity experiments. The first run (experiment 1) listed in Table 3 is the reference run, characterized by variable contrail height and optical depth. As discussed in section 3.2, it yields a net RF as small as 0.2 mw m 2, with a high level of compensation between the longwave and the shortwave components. [42] Experiment 2 treats contrail coverage and altitude as in the reference run, but an optical depth of 0.3 is appointed to each contrail, independent of the value actually calculated by the parameterization. As done by M99, we inversely define a fictitious IWP for the radiation scheme from the prescribed optical depth and a prescribed effective particle radius (12 mm). It should be noted that in this experiment (as in other ones with fixed optical depth to be discussed below) the distinction between visible contrails and total contrails becomes obsolete. The global mean contrail coverage in this experiments has the same value (0.07%) as in the reference run (this holds for all sensitivity runs considered here). Changing the optical depth of the contrails in the GCM to M99 s default value doubles the global mean net RF. Both longwave and shortwave forcing increase by about 150%, so their compensation remains very effective, and the net RF of 0.4 mw m 2 is still far away from the result reported by M99. [43] In experiment 3 the optical depth of contrails is again fixed at 0.3, but additionally, all contrails are misplaced to the 200 hpa altitude (model layer 15), not accounting for the layer in which they are actually simulated. Of all our experiments this one is most similar to M99 s configuration. The change in contrail altitude has quite a different effect on the RF components. As the shortwave RF is independent from temperature, it changes only little with respect to the previous experiment. In contrast, the longwave RF strongly depends on the temperature (and therefore on the height) of the contrails, and it rises to 11.1 mw m 2. The absolute value of the shortwave RF is only 45% of its longwave counterpart in this experiment. The longwave (net) RF is larger by a factor 5.5 (30) in comparison to the reference run. Yet the net RF of 6 mw m 2 is still only one third in comparison to M99. [44] It appears reasonable also to check the sensitivity of contrail RF to the presence of natural clouds in the simulation. While this reduces the similarity to the experiment design of M99, who adapted a background cloud distribution from ISCCP data, it allows a comparison with other previous estimations [Fortuin et al., 1995; Meerkötter et al., 1999] that have been given for a cloud-free atmosphere. Experiment 4 is equivalent to the reference run but gives the clearsky radiative effect of the contrails, while experiment 5 again employs a constant contrail optical depth of 0.3. In these experiments the contrail shortwave (longwave) RF increases by about 50% (100%) compared to the respective cloudysky results. The shortwave response is probably due to the absence of the low-level natural clouds, decreasing the Table 3. Globally and Annually Averaged Radiative Forcing of Contrails in the Reference Experiment (1) and in the Five Sensitivity Experiments (2 6; See Text) a Experimental Conditions for Contrails and Clouds Instantaneous Radiative Forcing at TOA, mw m 2 Number Optical Depth Height Clouds Longwave Shortwave Net 1 variable variable yes variable yes hpa yes variable variable no variable no hpa no a Note that in contrast to Table 2, the instantaneous forcing at the TOA is given.

12 ACL 2-12 PONATER ET AL.: CONTRAILS IN A GLOBAL CLIMATE MODEL planetary albedo and making the contrail backscattering a more important effect. The longwave response can be related to the absence of clouds in the middle and upper troposphere, making the contrail greenhouse forcing more effective over the warm surface. As the latter impact appears to be quantitatively more important than the former, the clearsky net RF is almost 1 order of magnitude larger than for the equivalent cloudy-sky experiments. Meerkötter et al. [1999] presented several case studies (representing several different temperature, solar zenith angle, and surface albedo conditions), which indicate strongly varying shortwave/longwave RF compensation. They also note a considerable dependency of this compensation on the particle shape (either spherical or hexagonal) in contrails. On average, the compensation of shortwave and longwave RF in our clear-sky experiments is not far off their results. Closer insight, however, requires a separation of classes representing various characteristic ambient conditions (parameter combinations typical for contrail occurrence) in our simulations. [45] The last of our sensitivity experiments (experiment 6) is equivalent to experiment 3 in using constant contrail optical depth (0.3) and altitude (200 hpa) but, again, determines the contrail RF for a clear-sky atmosphere. Experiment 6 yields a global mean net RF for contrails of 10.6 mw m 2, the largest value in all experiments we performed. Nevertheless, the net RF remains smaller than that found by M Discussion [46] Generally, we regard our results for the basic contrail properties to be in reasonable agreement with available observations and microphysical simulations. The largest part of the fundamental quantitative disagreement between the RF estimation from our reference experiment and that given by M99 could be explained by conceptual differences in the respective model simulations. However, even for the sensitivity experiment we designed to be in closest agreement with M99 s model world, a nonnegligible difference in the RF results has remained. Hence the question arises how reliable our GCM is for an adequate calculation of the contrail effect. Relevant systematic errors that may question the simulated contrail climate impact are contained in the background distributions of temperature, humidity, and natural clouds in the GCM, the quality of its radiation scheme with respect to thin cirrus, and the treatment of contrail-cirrus overlap in the radiation scheme. [47] Like in most other current GCMs [Gates et al., 1999], the climatological temperature distribution in ECHAM4 suffers from the so-called cold bias in the extratropical lowermost stratosphere, which peaks at the 200 hpa level with temperature being too low by about 10 K there [Roeckner et al., 1996]. Although swiftly decreasing below 250 hpa, the error affects the altitude range where contrails generally form. It was found in dedicated tests (F. Mager, personal communication, 2001) that the influence of the cold bias on contrail frequency is not as severe as the magnitude of the error would suggest because at 200 hpa the actual temperature is usually lower than the critical temperature for contrail formation anyway. However, the fact that the calibration to observed mean contrail coverage is done in a region affected by the cold bias leads to an artificial reduction of contrail coverage in tropical regions. As shown by Sausen [2001], the result is a reduced global annual mean contrail coverage of 0.07%, in comparison to the 0.09% value given by Sausen et al. [1998]. It may be further assumed that the cold bias in the temperature is accompanied by a dry bias in absolute humidity, thus reducing the amount of water vapor available for cloud water formation, particularly at 200 hpa. This could be a reason why the ice water content of the contrails is simulated on the low side of in situ measurements at this level. However, other possible reasons exist, like a potentially higher ice water content in contrails compared to natural cirrus at the same temperature [Meerkötter et al., 1999]. A better quantification of model systematic errors with respect to humidity in the upper troposphere and lowermost stratosphere requires further evaluation of available water vapor measurements at these altitudes [e.g., Gierens et al., 1999a; Ovarlez et al., 2000]. [48] Previous validation studies with respect to cloudiness in ECHAM4 [Chen and Roeckner, 1996, 1997] clearly indicate an underestimation of low clouds (and planetary albedo) in the extratropics. In contrast, high clouds at winter polar latitudes are overestimated in comparison to ISCCP data. Both features could favor the excessive compensation between shortwave and longwave contrail RF apparent in our results as the shortwave effect could be overestimated because of reduced low level albedo while the greenhouse effect could be underestimated because of too many high clouds in the immediate vicinity of the contrails. [49] The insufficient capture of cloud inhomogeneity effects [Chen and Roeckner, 1996] in ECHAM4 and a possibly inadequate treatment of cloud overlap [Räisänen, 1998; Morcrette and Jakob, 2000] could have a substantial influence on the contrail RF we calculate. This has to be investigated further as the cloudy-sky RF values appear to be particularly low in our experiments. In view of the large longwave-shortwave compensation the uncertainties with respect to the specification of the ice crystal shape (asymmetry factor) for contrails may also be of considerable relevance for the net RF. [50] Summarizing, it is a complex and difficult task to assess quantitatively the potential influence of GCM systematic errors and parameterization uncertainties on the results reported in section 3. Important features of the water vapor, cloud coverage, and cloud water vertical profiles in the upper troposphere have not been validated sufficiently because a respective observational data basis is just developing and is still rather sparse. However, we think that useful information could be gained by employing microphysical models and radiative transfer models to evaluate the contrail optical properties and RF for specific situations in the GCM rather than for climatological mean conditions. Hence a classification based on various characteristic ambient conditions for contrail occurrence throughout a multiyear simulation is certainly desirable. [51] There are two additional points that should be mentioned. First, the RF of contrails that are variable in space and time may be different from that yielded if constant mean coverage and mean radiative properties are prescribed for each situation. It is conceivable that contrail occurrence and cirrus occurrence are positively correlated. As the contrail net effect reaches its maximum in the