Article
Meteorologische Zeitschrift, Vol. 18, No. 6, 585-596 (December 2009)
c by Gebrüder Borntraeger 2009
Intercomparison of radiative forcing calculations of
stratospheric water vapour and contrails
G UNNAR M YHRE1,2 ∗ , M ARIA K VALEV ÅG1 , G ABY R ÄDEL3 , J OLENE C OOK3 , K EITH P. S HINE3 ,
H ANNAH C LARK4,5 , F ERNAND K ARCHER4 , K RZYSZTOF M ARKOWICZ6 , A LEKSANDRA K ARDAS6 ,
PAULINA WOLKENBERG6 , Y VES BALKANSKI7 , M ICHAEL P ONATER8 , P IERS F ORSTER9 ,
A LEXANDRU R AP9 and RUBEN RODRIGUEZ D E L EON10
1 Department
of Geosciences, University of Oslo, Norway
Research-Oslo (CICERO), Oslo, Norway
Reading, UK
4 CNRM/GAME Météo France, Toulouse, France
5 current affiliation: Laboratoire d’Aérologie, Université de Toulouse, Toulouse, France
6 Institute of Geophysics, University of Warsaw, Poland
7 LSCE/IPSL, Laboratoire CEA-CNRS-UVSQ, Cedex, France
8 Deutsches Zentrum für Luft und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen,
Germany
9 School of Earth and Environment, University of Leeds, Leeds, UK
10 Manchester Metropolitan University, UK
2 Center for International Climate and Environmental
3 Department of Meteorology, University of Reading,
(Manuscript received March 17, 2009; in revised form June 8, 2009; accepted July 27, 2009)
Abstract
Seven groups have participated in an intercomparison study of calculations of radiative forcing (RF) due
to stratospheric water vapour (SWV) and contrails. A combination of detailed radiative transfer schemes
and codes for global-scale calculations have been used, as well as a combination of idealized simulations
and more realistic global-scale changes in stratospheric water vapour and contrails. Detailed line-by-line
codes agree within about 15 % for longwave (LW) and shortwave (SW) RF, except in one case where the
difference is 30 %. Since the LW and SW RF due to contrails and SWV changes are of opposite sign, the
differences between the models seen in the individual LW and SW components can be either compensated or
strengthened in the net RF, and thus in relative terms uncertainties are much larger for the net RF. Some of
the models used for global-scale simulations of changes in SWV and contrails differ substantially in RF from
the more detailed radiative transfer schemes. For the global-scale calculations we use a method of weighting
the results to calculate a best estimate based on their performance compared to the more detailed radiative
transfer schemes in the idealized simulations.
Zusammenfassung
Sieben Forschungsgruppen haben an einem Ergebnisvergleich hinsichtlich des Strahlungsantriebes von
Kondensstreifen und stratosphärischem Wasserdampf teilgenommen. Dies umfasst einen Vergleich sowohl
von aufwändigen Strahlungsübertragungsmodellen mit Parametrisierungsschemata aus globalen Klimamodellen als auch einen Vergleich von idealisierten und realistischen Mustern von Kondensstreifen und
stratosphärischem Wasserdampf. Aufwändige Linienmodelle weichen für den langwelligen (LW) und
kurzwelligen (SW) Strahlungsantrieb um etwa 15 % voneinander ab, in einem Fall beträgt die Abweichung
30 %. Da die LW und SW Komponenten sowohl für Kondensstreifen als auch für stratosphärischen Wasserdampf ein unterschiedliches Vorzeichen haben, können sich die Differenzen in den Komponenten bzgl. des
Netto-Strahlungsantriebes kompensieren und verstärken. Daher sind relativ gesehen die Modellunsicherheiten beim Netto-Strahlungsantrieb am größten. Einige der Modelle, die in globalen Klimasimulationen
zur Anwendung kommen, weichen für die Strahlungsantriebe von Kondensstreifen und Änderungen des
stratosphärischen Wasserdampfes deutlich von den Ergebnissen aufwändigerer Strahlungsmodelle ab. Um
eine beste Abschätzung für die Berechnungen des globalen Strahlungsantriebes geben zu können, wichten
wir die Ergebnisse der einzelnen Modelle unter Berücksichtigung der Abweichungen, die in den idealisierten
Vergleichsrechnungen im Vergleich zu den Ergebnisse der detailliertesten Modelle festgestellt wurden.
1 Introduction
Human-induced climate change is caused by many radiative forcing mechanisms, due to either their influ∗ Corresponding
author: Gunnar Myhre, Center for International Climate and
Environmental Research-Oslo (CICERO), P.O. Box. 1129, Blindern, N-0318
Oslo, Norway, e-mail: gunnar.myhre@cicero.uio.no
DOI 10.1127/0941-2948/2009/0411
ence on the thermal infrared radiation, on solar radiation, or on a combination of the two (F ORSTER et al.,
2007). Radiative transfer codes are generally adopted
for estimating the radiative forcing, which introduces
an uncertainty. Many intercomparison exercises have
been performed to identify and quantify the uncertainty
in such calculations (B OUCHER et al., 1998; C OLLINS
0941-2948/2009/0411 $ 5.40
c Gebrüder Borntraeger, Berlin, Stuttgart 2009
586
G. Myhre et al.: Stratospheric water vapour and contrails
et al., 2006; E LLINGSON et al., 1991; F ORSTER et
al., 2005; F ORSTER et al., 2001; G OHAR et al., 2004;
M EERK ÖTTER et al., 1999; S HINE et al., 1995). For
changes in the atmospheric constituents that affect both
thermal infrared radiation and solar radiation, their contributions may have the same sign (e.g. changes in ozone
in the troposphere) but it is more challenging if their
contribution gives rise to radiative forcings of opposite sign. Here we will investigate two radiative forcing
mechanisms that have positive longwave (LW) radiative
forcing (RF) and negative shortwave (SW) RF for an
increase in their abundance in the atmosphere, namely
water vapour in the stratosphere and contrails. Since the
net radiative forcing is the residual of LW and SW radiative forcings, the uncertainties in the net forcing may
be particularly large, especially if the LW and SW components are of a similar magnitude. Changes in stratospheric ozone and mineral dust are examples of other radiative forcing mechanisms that have opposite signs for
the LW and SW forcings. The problem is also relevant to
the direct aerosol component that has an opposing forcings for the absorbing and scattering SW components
(S CHULZ et al., 2006).
Water vapour emitted by aircraft perturbs background
concentrations of water vapour in the atmosphere, and
can lead to the formation of contrails (S AUSEN et al.,
2005; S CHUMANN, 2005). It is in the stratosphere where
increase in water vapour from aircraft traffic may be
significant because the background concentration is low
and removal is slow (G AUSS et al., 2003). It is likely that
stratospheric water vapour (SWV) has increased over recent decades, but the magnitude of the trend and causes
for the trend are uncertain (S CHERER et al., 2008). Oxidation of methane in the stratosphere and a climate
change feedback are the most probable cause for the observed increase in SWV (H ANSEN et al., 2005). Model
simulations indicate that the SWV abundance responds
to changes in the upper tropospheric and lower stratospheric temperature, such as can be caused by aerosols
from volcanic eruptions and by changes in stratospheric
ozone (J OSHI and S HINE, 2003; S TUBER et al., 2001).
The current contribution from aircraft to the increase in
SWV is likely to be small, but could be significantly
larger in the future, especially should there be fleets
of high-flying supersonic aircraft (S ØVDE et al., 2007).
There is much uncertainty in the radiative forcing of
contrails and SWV from aircraft traffic (S AUSEN et al.,
2005). This is due to many factors such as uncertainties in the amount and spatial distribution of the water
vapour emitted and the amount, spatial distribution and
properties of contrails formed, and to uncertainties in the
radiative transfer calculations themselves.
In this study we concentrate on quantifying the uncertainties introduced by the radiative transfer simulations.
We perform idealized calculations for one vertical profile to identify differences just due to the radiation codes
themselves. Thereafter, we perform global-scale calcu-
Meteorol. Z., 18, 2009
lations to explore the impact of factors such as temperature, clouds, and surface albedo in addition to the radiation codes on the RF due to stratospheric water vapour
and contrails. The purpose of the intercomparison is to
assess the uncertainty in the RF calculations, in particular for the broad-band codes used in the climate model
calculations. It is not the purpose of the paper to explain
the differences, as this would be a formidable challenge,
although in some cases we are able to identify which
models are likely to be more reliable.
2 Models
A short description of each model together with appropriate references is given in Table 1. The radiative
transfer codes are divided into three groups: line-by-line
(LBL) models, intermediate complexity models, and
broad-band models used for global calculations either
for stand-alone RF calculations or as a part of a general
circulation model (GCM). Two LBL codes (with different versions of one of the LBL codes for stratospheric
water vapour) are included in this study.
There are several possible reasons why radiation codes
may disagree. These include the number of spectral
bands, the method of calculating multiple scattering, the
origin of the data used to model gaseous absorption and
the way that absorption data is used in the model. We
note in particular that there have been many updates to
the near-infrared spectral line data for water vapour in
HITRAN in recent years (see e.g. C HAGAS et al., 2001;
G IVER et al., 2000; ROTHMAN et al., 2005) which have
led to a systematic increase in the calculated absorption.
These factors mean that models with higher spectral resolution are not necessarily more reliable, and this especially applies to the differences between intermediate
and broadband models, where the transmittance calculation has to be parameterized in some way.
3 Intercomparison setup
Calculations with the mid-latitude summer single profile were performed for various solar zenith angles and
surface albedos. These calculations acted as a reference.
The mid-latitude summer profile with a solar zenith angle of 30 degrees and a surface albedo of 0.2 was selected for the rest of the single column calculations and
perturbations to stratospheric water vapour, and contrails were then introduced. The concentration of stratospheric water vapour was fixed at 3.0 ppmv and then
at 3.7 ppmv and the RF due to this change was calculated. For contrails, the optical depth was varied between 0.1, 0.3, and 0.52 with no wavelength dependence. We span this range because of uncertainties in
the actual contrail optical depth. To ensure that differences in single-scattering properties of contrail particles
did not influence the intercomparison, the asymmetry
Meteorol. Z., 18, 2009
Table 1: Model descriptions.
G. Myhre et al.: Stratospheric water vapour and contrails
587
588
G. Myhre et al.: Stratospheric water vapour and contrails
Meteorol. Z., 18, 2009
Table 2: LW, SW, and net RF for global SWV changes (in Wm−2 ). LW and net RF shown for both instantaneous and stratospheric
temperature adjusted RF.
SWV 3.0 – 3.7 ppmv
LW inst
LW adj
SW
Net inst
Net adj
UiO_BBM
0.41
0.29
-0.058
0.35
0.23
UoR_NBM
0.40
0.27
-0.020
0.38
0.25
UoR_E-S
0.52
0.29
-0.030
0.49
0.26
ECHAM4/ATT
0.41
0.25
-0.051
0.35
0.20
ECHAM4/SLT
0.25
0.19
0.16
UW_FU
0.37
CNRM_ARPEGE
0.31
UoL_E-S
0.49
0.40
-0.034
0.22
-0.052
0.32
-0.055
0.26
-0.021
0.49
0.38
Aircraft 2050
LW inst
LW adj
SW
Net inst
Net adj
UiO_BBM
0.058
0.062
-0.011
0.047
0.050
UoR_NBM
0.081
0.052
-0.005
0.076
0.047
ECHAM4/ATT
0.056
0.043
-0.008
0.047
0.035
ECHAM4/SLT
0.044
0.035
-0.007
0.037
0.028
UW_FU
0.059
-0.009
0.049
CNRM_ARPEGE
0.044
-0.009
0.035
factor was specified to be wavelength independent (0.8)
and the single scattering albedo was 1.0 in the solar
spectrum and 0.6 in the terrestrial spectrum. These are
typical values for contrail optical properties (S TRAUSS
et al., 1997). Simulations were performed for a 100 %
contrail cover, with the contrails between 10 and 11 km
height. Instantaneous LW, SW, and net RF are reported
at the tropopause (179 hPa).
Global calculations for the change in stratospheric
water vapour from 3.0 to 3.7 ppmv and for change
due to emissions by sub and supersonic aircrafts were
performed. In these simulations SWV is fixed to these
abundances and no feedback from the temperature
change from SWV is taken into account. The aviation water vapour increase has been derived from water vapour emissions for 2050 generated by the SCENIC
project (S ØVDE et al., 2007). Radiative forcing (instantaneous as well as with stratospheric adjustment) at the
tropopause is reported. For the intercomparison of the
effect of contrails on the global scale, a 1 % homogeneous cover with a contrail top at 11 km was used. The
contrails had an optical depth of 0.3, and the other optical properties were similar to the single profile exercise. Radiative forcing at the top of the atmosphere is
reported. Each modelling group used their own data for
temperature, clouds, water vapour in the troposphere,
surface albedo, and tropopause altitude for the globalscale calculations.
4 Results
4.1 Single profile cases
4.1.1 Stratospheric water vapour
The results for the change in SWV from 3.0 to 3.7 ppmv
are presented in Figure 1. For the net RF, the difference
among the models is more than a factor of two, but the
separate LW and SW RF also differ by this same factor. Differences between the net RF for the LBL models
are small, but that is due to a compensation of larger
differences for the LW and SW RF, particularly the latter. The method of integration to irradiances has little influence on the results for the two versions of the
UoR LBL codes. The stronger short-wave forcing in the
UoR LBL is likely to be due to the use of more recent spectral line data (HITRAN-2001) compared to the
UiO LBL calculations (HITRAN-1992) (see section 2).
To investigate the implementation of radiative transfer
schemes, three types of codes are used at two different groups. The Streamer codes give identical results,
and the two groups using the Fu-Liou code have identical LW results and with SW results being only slightly
different. The E-S code has been implemented at UoR
and UoL with identical LW RF, but with SW RF that
differs by more than 50 %. This is due to differences
in the absorption data in a different version of the E-S
code. Three of the intermediate complex models give
a weak SW RF (at least a factor of two weaker than
the LBL codes) and thus a high net RF. The differences
G. Myhre et al.: Stratospheric water vapour and contrails
Meteorol. Z., 18, 2009
LONG WAVE
for the differences. In general the deviations are larger
for the broad-band models compared to the intermediate complexity models (in particular for LW) indicating
a need for further investigation of the parameterizations
for shortwave absorption and their dependence on the
source of absorption data.
UiO_LBL
UoR_RFM*
UoR_RFM
UW_MOD
UW_STR
UoR_NBM
IPSL_STR
UiO_CCM
UiO_BBM
UoR_FU
UoR_E−S
UoL_E−S
UW_FU
CNRM_ARPEGE
ECHAM4
0
0.1
0.2
0.3
589
0.4
0.5
0.6
0.7
4.1.2 Contrails
Wm−2
SHORT WAVE
UiO_LBL
UoR_RFM*
UoR_RFM
UW_MOD
UW_STR
UoR_NBM
IPSL_STR
UiO_CCM
UiO_BBM
UoR_FU
UoR_E−S
UoL_E−S
UW_FU
CNRM_ARPEGE
ECHAM4
−0.2
−0.18
−0.16
−0.14
−0.12
−0.1
−0.08
−0.06
−0.04
−0.02
0
Wm−2
NET
UiO_LBL
UoR_RFM*
UoR_RFM
UW_MOD
UW_STR
UoR_NBM
IPSL_STR
UiO_CCM
UiO_BBM
UoR_FU
UoR_E−S
UoL_E−S
UW_FU
CNRM_ARPEGE
ECHAM4
0
0.1
0.2
0.3
0.4
0.5
0.6
Wm−2
Figure 1: Instantaneous long-wave (light-grey), shortwave (grey),
and net (black) RF at the tropopause (179 hPa) for a change in
stratospheric water vapour from 3 to 3.7 ppmv for single column
conditions (see text).
between the eight codes used for global-scale simulations are particularly large. The UoR-E-S, UoL-E-S, and
CNRM ARPEGE deviate particularly from the rest. The
UoR-E-S, UoL-E-S models show the highest RF in the
LW which is not offset in the net, because of the small
negative SW RF. These models thus have the largest
net RF values. CNRM ARPEGE and ECHAM4 use a
LW scheme with few broad bands and have been optimized for computationally-fast calculations in GCMs,
which may lead to absorption band combinations which
are unfavourable for representing SVW RF (F ORSTER
et al., 2001). Both models produce a weak LW RF, but
as the negative SW RF from ECHAM4 is also weak,
the CNRM APEGE model comes out with the lowest
value of all for the net RF. Except for the UoR E-S and
UoL E-S models, the codes used for global calculations
underestimate the net RF compared with the LBL models.
The differences between the radiative transfer codes
for SWV are generally larger than for other components.
This indicates that absorption data for water vapour, particularly in the stratosphere, is probably the main cause
The second part of the intercomparison concerned the
addition of contrails to the single-profile cases. The
properties of the contrails were described in section 3.
Figure 2 shows the RF due to contrails for the different models and at three different solar zenith angles (30,
60, and 75 degrees) upon which the RF is known to depend substantially (M EERK ÖTTER et al., 1999; M YHRE
and S TORDAL, 2001; S TUBER et al., 2006). All results
shown are for a 100 % contrail cover and for a contrail
optical depth of 0.3.
The differences in the LW and SW components between the models are much smaller for the contrail RF
than they were for the RF due to SWV. The differences
are a maximum of 35 % for both LW and SW RF if
the CNRM ARPEGE code is excluded. The magnitude
of the SW RF for contrails is, for some solar zenith angles, more comparable to the LW RF than is the case
for SWV. Therefore, the relative difference in the net RF
between models can be large, despite rather small differences in SW and LW RF. Differences for the LBL
codes are about 15 % for LW (smaller for SW) and the
difference for the net RF is enhanced, since the deviations for LW and SW RF go in the same direction for
the two codes. The sign for the net RF is still the same
for the two LBL codes for the three solar zenith angles. The two Streamer codes differ somewhat for LW
and SW RF, but for the net RF the deviation is small.
The net RF changes from positive for a solar zenith angle of 30 degrees to negative for 75 degrees, except for
CNRM ARPEGE. By contrast, the sign of the net RF
at a solar zenith angle of 60 degrees varies amongst the
models. CNRM ARPEGE has a high LW RF, and the
SW code caused a less distinct strengthening in the RF
with an increasing solar zenith angle compared to the
other models, leaving the net RF significantly positive
also in the 75 degree case.
4.2 Global calculations
4.2.1 Stratospheric water vapour
Figure 3 shows global and annual mean LW, SW, and net
RF resulting from a change in SWV from 3.0 ppmv to
3.7 ppmv, and Figure 4 shows global and annual mean
LW, SW, and net RF of estimated change in SWV from
sub and supersonic aircraft in 2050 (S ØVDE et al., 2007).
The results shown in the figures include stratospheric
temperature adjustment. Table 2 summarizes RF for instantaneous and adjusted RF. The agreement in Figure 3
590
G. Myhre et al.: Stratospheric water vapour and contrails
Meteorol. Z., 18, 2009
LONG WAVE
UiO_LBL
UoR_RFM
UW_MOD
UW_STR
IPSL_STR
UiO_BBM
UoR_FU
UW_FU
UoL_E−S
CNRM_ARPEGE
0
5
10
15
20
25
30
35
40
45
50
−2
Wm
SHORT WAVE
sza = 30°
NET
sza = 30°
UiO_LBL
UiO_LBL
UoR_RFM
UoR_RFM
UW_MOD
UW_MOD
UW_STR
UW_STR
IPSL_STR
IPSL_STR
UiO_BBM
UiO_BBM
UoR_FU
UoR_FU
UW_FU
UW_FU
UoL_E−S
UoL_E−S
CNRM_ARPEGE
CNRM_ARPEGE
−20
−18
−16
−14
−12
−10
−8
−6
−4
−2
0
−10
−5
0
5
−2
Wm
10
15
20
25
30
5
10
15
20
5
10
15
20
Wm−2
sza = 60°
sza = 60°
UiO_LBL
UiO_LBL
UoR_RFM
UoR_RFM
UW_MOD
UW_MOD
UW_STR
UW_STR
IPSL_STR
IPSL_STR
UiO_BBM
UiO_BBM
UoR_FU
UoR_FU
UW_FU
UW_FU
UoL_E−S
UoL_E−S
CNRM_ARPEGE
CNRM_ARPEGE
−40
−35
−30
−25
−20
−15
−10
−5
0
−20
−15
−10
−5
−2
sza = 75°
sza = 75°
UiO_LBL
UiO_LBL
UoR_RFM
UoR_RFM
UW_MOD
UW_MOD
UW_STR
UW_STR
IPSL_STR
IPSL_STR
UiO_BBM
UiO_BBM
UoR_FU
UoR_FU
UW_FU
UW_FU
UoL_E−S
UoL_E−S
CNRM_ARPEGE
−50
0
Wm−2
Wm
CNRM_ARPEGE
−45
−40
−35
−30
−25
Wm−2
−20
−15
−10
−5
0
−20
−15
−10
−5
0
Wm−2
Figure 2: Longwave (light-grey, left), shortwave (grey, lower left), and net (black, lower right) RF at the top of the atmosphere due to
contrails with optical depth 0.3. Shortwave and net RF are shown for solar zenith angles of 30 degrees, 60 degrees, and 75 degrees (see text
for further details).
591
G. Myhre et al.: Stratospheric water vapour and contrails
Meteorol. Z., 18, 2009
LONG WAVE
LONG WAVE
UiO_BBM
UiO_BBM
UoR_NBM
UoR_NBM
UoR_E−S
ECHAM4/ATT
ECHAM4/ATT
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
Wm−2
UoL_E−S
0
0.1
0.2
0.3
0.4
0.5
SHORT WAVE
0.6
−2
Wm
UiO_BBM
SHORT WAVE
UoR_NBM
UiO_BBM
UoR_NBM
ECHAM4/ATT
UoR_E−S
−0.02
−0.015
−0.01
−0.005
0
−2
Wm
ECHAM4/ATT
NET
UoL_E−S
−0.07
UiO_BBM
−0.06
−0.05
−0.04
−0.03
−0.02
−0.01
0
−2
Wm
UoR_NBM
NET
ECHAM4/ATT
UiO_BBM
−0.1
−0.06
−0.04
−0.02
0
0.02
0.04
0.06
0.08
0.1
−2
Figure 4: Global and annual mean longwave (light-grey), shortwave
(grey), and net (black) RF for a global change in stratospheric water
vapour from sub and super sonic aircrafts estimated for 2050 in the
SCENIC project (S ØVDE et al., 2007). Stratospheric temperature
adjustment is included in the simulations.
UoR_E−S
ECHAM4/ATT
UoL_E−S
−0.1
−0.08
Wm
UoR_NBM
0
0.1
0.2
0.3
0.4
0.5
0.6
Wm−2
Figure 3: Global and annual mean longwave (light-grey), shortwave
(grey), and net (black) RF for a global change in stratospheric water
vapour from 3.0 to 3.7 ppmv. Stratospheric temperature adjustment
is included in the simulations.
is better than in Figure 1. The difference is more than
50 % in the net RF, but this is less than in Figure 1, even
though additional factors, such as spatial variability in
meteorological fields of temperature, humidity, clouds
as well as stratospheric temperature adjustment, have
been introduced.
In Table 2, the strong reduction in the RF (LW and
net) as a result of stratospheric temperature adjustment
for the homogeneous SWV change from 3.0 to 3.7 ppmv
can be seen in all six models that calculate both instantaneous and adjusted RF. The strongest reduction occurs
in the UoR E-S and the weakest in the ECHAM/SLT
and UoL E-S. The strong effect of stratospheric temperature adjustment in the UoR E-S explains the much better agreement in Figure 3 than in Figure 1 for the E-S
model. The UoL E-S has a weaker effect of the stratospheric temperature adjustment than the UoR E-S. The
former model has a tropopause level at 100 hPa in the
tropics and 250 hPa at mid and high latitudes. However,
the stratospheric temperature adjustment is included at
altitudes above 250 hPa at all latitudes and this could be
a source for the difference in the effect of stratospheric
temperature adjustment. It can also be seen in Table 2
that UW FU and CNRM have a weaker instantaneous
RF than most of the other models.
ECHAM4/SLT and ECHAM4/ATT both indicate
derivatives of the well-established ECHAM4 climate
model, with increased vertical resolution in the upper troposphere/lower stratosphere region (L AND et
al., 2002). In ECHAM4/ATT the operational semiLagrangian transport (SLT) scheme for water vapour
and cloud water has been replaced by the Lagrangian
advection scheme ATTILA (R EITHMEIER and S AUSEN,
2002). Using Lagrangian transport significantly reduces
a distinct wet and cold bias in the extratropical lowermost stratosphere (S TENKE et al., 2008) with a number
of beneficial side effects such as a lowering of the extratropical tropopause (which is located at a too high altitude in ECHAM/SLT). The impact of the difference
between ECHAM4/SLT and ECHAM4/ATT is shown
592
G. Myhre et al.: Stratospheric water vapour and contrails
in Table 2 to be significant and almost as large as the
range between the other models, even though the radiation scheme is identical in both model versions. In the
rest of the analysis we use the ECHAM/ATT model.
The deviations in net RF for SWV change estimated
for 2050 in Figure 4 are significant, but still smaller
than shown for the single profile cases. This is partly
due to a compensation of the LW and SW RF, as well
as the different effects of the stratospheric temperature
adjustment. For a homogeneous SWV increase, cooling will occur in the entire stratosphere; however, for
water vapour changes that reach their maximum at altitudes higher than the tropopause, heating may occur just
above the tropopause (M YHRE et al., 2007). Whereas
UoR NBM and ECHAM4/ATT have a strong reduction
in the RF due to the effect of the stratospheric temperature adjustment, the UiO BBM has a weak increase. The
much weaker SW RF for UoR NBM is also consistent
with the results shown in Figure 1 and 3.
The SW RF results for SWV show larger differences
than the LW RF. This is partly because stratospheric
temperature adjustment reduces the difference in the LW
calculations but also that some of the models with the
weakest LW RF have not calculated the full RF. However, it seems that factors such as background humidity,
temperature and clouds do not introduce an additional
large uncertainty in the calculation of RF due to SWV.
4.2.2 Contrails
Global and annual mean RF for a 1 % homogeneous
contrail cover is shown in Figure 5 for clear and all-sky
conditions. For UiO BBM, UoR FU, and UW FU the
difference between the clear and all-sky RF is small, as
shown in earlier studies (M YHRE and S TORDAL, 2001;
R ÄDEL and S HINE, 2008; S TUBER and F ORSTER,
2007). The agreement between the five models is
slightly better for the all-sky conditions than for the
clear-sky conditions, because UoL E-S has a large impact of clouds on the LW RF and that UoL E-S and
CNRM ARPEGE have a weak impact of clouds on the
SW RF. Differences between some of the models are of
similar magnitude for the clear sky condition as in Figure 2, but slightly amplified for the global simulations
(e.g. UW FU versus UiO BBM). CNRM ARPEGE and
UoL E-S have substantially stronger LW RF for the
clear-sky conditions, and for the former model this is
consistent with the findings in Figure 2. The relatively
large clear sky LW RF from UoL E-S compared to results in Figure 2 could be due to several factors among
them temperatures and water vapour.
Figure 6 shows the geographical distribution of the
annual mean all-sky RF for a 1 % homogeneous contrail
cover (global mean shown in lower panel of Figure 5).
Despite differences in the magnitude, the spatial pattern
from the five models has many similarities with maximum values in sub-tropical regions and particularly over
the Sahara. The latter is due to a maximum LW RF and
Meteorol. Z., 18, 2009
weak SW RF because of the high reflectivity over the
Saharan region. It should be mentioned here that contrail formation in some of the regions with maximum
RF is not so likely at 10–11 km due to the dry conditions. Weak RF is simulated near the equator due to large
cloud cover, in particular for UiO BBM with even negative values in a narrow region. There is also a consistent pattern between the models with rather low values
at high latitudes (even negative in CNRM ARPEGE).
To convert the idealized experiment with a homogeneous contrail cover over to a more realistic case,
the contrail cover as estimated in R ÄDEL and S HINE
(2008) is adopted. The UiO BBM, UoR FU, UW FU,
and CNRM ARPEGE net RFs for a realistic contrail
cover (and contrail optical depth of 0.3) are 9.3, 10,
and 12, and 15 mWm−2 , respectively. This is a slightly
smaller difference than shown in Figure 5 because the
estimated contrail cover is small where the differences
in Figure 5 are the largest (i.e. in the tropics).
5 Interpretation of results
The single profile calculation can be used in a ranking
of the results and for an evaluation of a best estimate for
the global calculations. For the single profile cases we
have the benchmark LBL calculations available; despite
the fact that there remain uncertainties in the LBL codes,
they still must be regarded as the most reliable. The LBL
model formulation and their complexity are much closer
to first principles than schemes used for global calculations. We treat therefore LBL as more accurate, but acknowledge that there is an uncertainty associated with
the LBL as well. However, because of their heavy computational requirements, it is impractical to use them
for global calculations. To generate a “best estimate”
for the global calculations from the models contributing
to the intercomparisons, a number of options are available. These include (a) simply choosing the global results from the intermediate or broad-band model that are
closest to the LBL code for the single profile cases, (b)
taking a simple arithmetic average of all the model results or (c) finding an appropriate weighting to combine
the model results. We regard option (a) as too restrictive,
as just because a model agrees with the LBL results for
one profile should not be taken to imply it does so for all
cases. However option (b) neglects all information from
the single profile comparisons, and the degree to which
the model agrees with the reference results. Therefore
we adopt option (c), but also compare it with the results
from option (b)
For option (c) we use a weighting adopted by
(M URPHY et al., 2004) and the following formula:
2
Vi = e(−0.5∗∆RFi /σ
2)
where ∆RFi is the absolute difference in RF between
model i and the reference (mean of LBL results), and σ
LONG WAVE
LONG WAVE
UiO_BBM
UiO_BBM
UoR_FU
UoR_FU
UW_FU
UW_FU
UoL_E−S
UoL_E−S
CNRM_ARPEGE
CNRM_ARPEGE
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0
0.05
0.1
0.15
−2
UiO_BBM
UoR_FU
UoR_FU
UW_FU
UW_FU
UoL_E−S
UoL_E−S
CNRM_ARPEGE
CNRM_ARPEGE
−0.08 −0.06 −0.04 −0.02
0
−0.2
−0.18 −0.16 −0.14 −0.12
−2
0.35
0.4
−0.1
−0.08 −0.06 −0.04 −0.02
0
−2
Wm
Wm
NET
NET
UiO_BBM
UiO_BBM
UoR_FU
UoR_FU
UW_FU
UW_FU
UoL_E−S
UoL_E−S
CNRM_ARPEGE
−0.1
0.3
SHORT WAVE
UiO_BBM
−0.1
0.25
Wm
SHORT WAVE
−0.18 −0.16 −0.14 −0.12
0.2
−2
Wm
−0.2
593
G. Myhre et al.: Stratospheric water vapour and contrails
Meteorol. Z., 18, 2009
CNRM_ARPEGE
−0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
−2
Wm
−0.1
−0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
−2
Wm
Figure 5: Global and annual mean net radiative forcing due to 1 % homogeneous contrails for clear sky (left) and all sky (right). Separate
results for longwave (light-grey), shortwave (grey), and net (black) RF are shown.
is the standard deviation of the results of the single profile calculations. Results from identical models have not
been double counted in the calculation of the standard
deviation.
The weighting Vi for each model is calculated separately for SW and LW and thereafter applied for calculation of a best estimate for SW and LW by simply multiplying the percentage weighting of each model
with the RF. This method has been applied to the global
SWV changes with weighting factors 36.7 %, 41.9 %,
5.4 %, 10.5 %, and 5.4 % for LW and 41.5 %, 4.4 %,
11.6 %, 3.6 %, and 38.9 % for SW for UiO BBM,
UoR NBM, UoR E-S, ECHAM4, and UoL E-S, respectively. The resulting best estimate is 0.284, –0.039,
and 0.245 Wm−2 for LW, SW, and net RF of SWV
change from 3.0 to 3.7 ppmv. A simple average of the
results (option b above) gives 0.300, -0.038, and 0.262
Wm−2 for LW, SW, and net RF. Thus the weighting has
a small impact on the SW RF, but gives a weaker LW
RF since the models with very high LW RF have a large
deviation from the LBL models.
The SW single profile calculations of contrails revealed strong solar zenith angle dependence, as did the
deviations from the LBL results. To calculate the SW
weighting we have averaged the results for the three
solar zenith angle results. The resulting weighting factors for LW are 12.6 %, 20.8 %, 20.8 %, 28.4 %, and
17.4 %, respectively, for UiO BBM, UoR FU, UW FU,
CNRM ARPEGE, and UoL E S. The SW weighting
factors for the same models are 24.9 %, 26.1 %, 24.7 %,
2.7 %, and 21.6 %. Including these factors in the calculations of 1 % contrail cover results in 0.260, –0.097, and
0.163 Wm−2 , respectively for LW, SW, and net RF. The
corresponding numbers for a simple average is 0.250,
–0.107, and 0.143 Wm−2 . A similar deviation for LW
and SW RF for the two methods adds up to a twice as
594
G. Myhre et al.: Stratospheric water vapour and contrails
a)
b)
c)
d)
Meteorol. Z., 18, 2009
e)
Figure 6: Geographical distribution of the annual mean all sky net radiative forcing at the top of the atmosphere for a homogeneous 1 %
contrail cover, UIO BBM a), UoR FU b), UW FU c), CNRM d), UoL E-S e).
large deviation for the net RF of 0.020 Wm−2 .
The uncertainties in terms of standard deviations
among the models considered in the above analysis
show similar ranges in global calculation as in the single
profile cases for SWV changes, indicating that the uncertainties associated with input parameters do not exceed the uncertainties in the use of the radiation codes.
For contrails, the standard deviations among the models
are slightly higher for global calculations than the single
profile calculations, but smaller than for SWV changes.
• Changes in SWV lead to larger differences in RF
than contrails.
• Results from several of the models for global calculations of changes in SWV differ substantially from
detailed radiative transfer schemes both for LW and
SW radiation.
6 Summary
• Simulations of global changes in SWV differ by
about a factor of 2 between the models, but cancelations of differences may indicate that uncertainties
are larger.
With varying complexity in radiative transfer schemes,
we have explored the uncertainties in the RF of changes
in stratospheric water vapour and contrails. Seven groups
have been involved in this intercomparison study as part
of the EU project QUANTIFY. Our main findings are:
• Although the SW RF has a magnitude much more
comparable to the LW RF for contrails than it does
for SWV changes, the sign of the net RF due to
contrails for various solar zenith angles is similar
among most of the models
Meteorol. Z., 18, 2009
G. Myhre et al.: Stratospheric water vapour and contrails
• The net RF due to global changes in contrails agree
within a factor of two for a homogeneous contrail
cover; the agreement is even better for a realistic
contrail cover.
• The pattern of geographical distribution of the RF for
a homogeneous contrail cover is consistent between
five models, but the magnitude is significantly different.
• Differences in the global distributions of input fields
of temperature, humidity, clouds, and surface albedo
do not introduce a substantial uncertainty compared
to the uncertainty in the radiative transfer schemes in
the RF for contrails and change in stratospheric water
vapour. In the case of stratospheric water vapour,
the choice of external parameters (like tropopause
height) may require more attention, if they have a
direct influence on the estimated perturbation itself.
• Weighting the results based on their performance
against detailed radiative transfer schemes does not
lead to an average that differs substantially from a
simple average, but is to be preferred since the best
estimate is less influenced by outliers compared to
detailed schemes.
• The results, and in particular the deviations between
the benchmark line-by-line models and the simpler
models, indicate aspects of the simpler models that
appear to be in need of attention, if their radiative forcing calculations are to be improved. This
is particularly the case for absorption data for water vapour. In the solar spectrum the absorption data
have been significantly updated in recent years and
the absorption data in the models should be accordingly updated. In the LW spectrum the parameterization of absorption by water vapour in the stratosphere
should be investigated further.
Acknowledgments
This study is funded by the EU FP6 project QUANTIFY.
UoR would like to thank Piero C AU for valuable contributions in the early stages of the project. We thank two
referees for their many useful comments.
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