ARTICLE IN PRESS
Journal of Quantitative Spectroscopy &
Radiative Transfer 93 (2005) 447–460
www.elsevier.com/locate/jqsrt
Resolution of the uncertainties in the radiative forcing of
HFC-134a
Piers M. de F. Forstera,b,, J.B. Burkholderb, C. Clerbauxc,d, P.F. Coheurd,
M. Duttae, L.K. Gohara, M.D. Hurleyf, G. Myhreg, R.W. Portmannb,
K.P. Shinea, T.J. Wallingtonf, D. Wuebblese
a
Department of Meteorology, The University of Reading, P.O. Box 243, Earley Gate, Reading, RG6 6BB, UK
b
NOAA Aeronomy Laboratory, 325 Broadway, Boulder, CO 80305-3328, USA
c
Service d’Aéronomie/CNRS, Institut Pierre-Simon Laplace, Paris, France
d
Service de Chimie Quantique et Photophysique, Université Libre de Bruxelles, Bruxelles, Belgium
e
University of Illinois, Urbana, IL, USA
f
Ford Motor Company, Dearborn, MI, USA
g
University of Oslo, Oslo, Norway
Received 30 January 2004; received in revised form 25 August 2004; accepted 25 August 2004
Abstract
HFC-134a (CF3 CH2 F) is the most rapidly growing hydrofluorocarbon in terms of atmospheric
abundance. It is currently used in a large number of household refrigerators and air-conditioning systems
and its concentration in the atmosphere is forecast to increase substantially over the next 50–100 years.
Previous estimates of its radiative forcing per unit concentration have differed significantly 25%. This
paper uses a two-step approach to resolve this discrepancy. In the first step six independent absorption
cross section datasets are analysed. We find that, for the integrated cross section in the spectral bands that
contribute most to the radiative forcing, the differences between the various datasets are typically smaller
than 5% and that the dependence on pressure and temperature is not significant. A ‘‘recommended’’ HFC134a infrared absorption spectrum was obtained based on the average band intensities of the strongest
bands. In the second step, the ‘‘recommended’’ HFC-134a spectrum was used in six different radiative
transfer models to calculate the HFC-134a radiative forcing efficiency. The clear-sky instantaneous
Corresponding author. Department of Meteorology, The University of Reading, P.O. Box 243, Earley Gate,
Reading, RG6 6BB, UK. Tel.: +44 118 378 6020; fax: +44 118 378 8905.
E-mail address: piers@met.rdg.ac.uk (P.M. de F. Forster).
0022-4073/$ - see front matter r 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jqsrt.2004.08.038
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radiative forcing, using a single global and annual mean profile, differed by 8%, between the 6 models, and
the latitudinally-resolved adjusted cloudy sky radiative forcing estimates differed by a similar amount. We
calculate that the radiative forcing efficiency of HFC-134a is 0:16 0:02 Wm2 ppbv1 :
r 2004 Elsevier Ltd. All rights reserved.
Keywords: HFC-134a; Radiative forcing; Uncertainties; Global warming potential
1. Introduction
As a result of the success of the implementation of the Montreal Protocol and its amendments,
many replacements to chlorofluorocarbons (CFCs) have been introduced or are being considered.
A popular replacement has proven to be the hydrofluorocarbons (HFCs). Unlike the CFCs and
the interim hydrochlorofluorocarbons (HCFCs), the HFCs do not destroy stratospheric ozone
[1,2]; however, many of them are potent greenhouse gases and they are included in the gases for
which emissions can be controlled under the Kyoto Protocol. HFC-134a is widely used in
refrigerators and air conditioning systems and it is currently emitted far more than any other
HFC. The potential potency of a greenhouse gas emissions is normally classified by its global
warming potential (GWP) which takes into account the radiative forcing that would arise from a
pulse in emission of 1 kg of the gas in the atmosphere and the lifetime of the gas [3]. Unfortunately
the two most recent studies that have attempted to quantify the radiative forcing of HFC-134a
differ by more than 20%: 0:200 Wm2 ppbv1 [4] and 0:159 Wm2 ppbv1 [5]. In light of the
widespread industrial use of HFC-134a it is important to reduce the uncertainty associated with
estimates of its contribution to radiative forcing of climate change. Recently a two-model
comparison [6] found agreement to within 7% for the HFC-134a radiative forcing; their values are
also reported here. Their reported values are 25% below that reported by Jain et al. [4] and
reasons for this remain unclear.
In this work we bring together 6 measurements of the absorption cross section of HFC-134a
and 5 radiative transfer modelling groups, including the three groups involved in the earlier
studies, to try to evaluate reasons for previous differences and provide an accurate estimate of the
radiative forcing of HFC-134a and its uncertainty. We employ a two-step approach. First, the
absorption cross-section data are reviewed and a recommended spectrum is derived. Second, the
recommended spectrum is given to the 5 modelling groups to calculate the radiative forcing.
2. Cross section analysis
The infrared absorption spectrum of HFC-134a has been the subject of numerous
investigations [7–14]. For this paper JBB performed another measurement of the absorption
cross section using a high resolution Fourier transform spectrometer at the NOAA Aeronomy
Laboratory (NOAA) and we critically evaluated it alongside the other 5 studies that had available
spectral data [9–14]. The source and experimental conditions of the HFC-134a spectra considered
in this evaluation are summarized in Table 1. The cross section data were downloaded from public
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449
Table 1
HFC-134a infrared absorption spectrum measurements considered in this evaluation
Spectra
T (K)
Pressure
(diluent gas)
(Torr)
Spectral
range
(cm1 )
Spectral
resolution
(cm1 )
URL
Clerbaux [9]
253–287
0
700–1500
0.03
Ford [10]
RAL [11]
NIST [12]
NOAA
Stony-Brook
[13]
296
203–296
295
296
190–296
700 (air)
0–750 (air)
0
630 ðN2 Þ
20–750
200–2000
600–1600
495–1596
620–1325
830–1430
0.50
0.03
0.20
0.004
0.01
http://cfa-www.harvard.edu/
HITRAN/
http://ara.lmd.polytechnique.fr/
mhurley3@ford.com
http://www.msf.rl.ac.uk/
http://www.nist.gov/kinetics/spectra/
burk@al.noaa.gov
http://cfa-www.harvard.edu/
HITRAN/
All spectra were recording using Fourier transform spectroscopy under the specified conditions. See text for comparison
and evaluation.
websites, the spectra were compared and the integrated band intensities were calculated using
common integration limits.
Spectra have been recorded over a range of temperature (190–296 K), diluent pressure
(0–750 Torr, air or N2 ), and resolution (0.004–0.50 cm1 ). The infrared spectrum of HFC-134a
consists of several strong bands in the region 800–1400 cm1 ; which are important for radiative
forcing calculations [15,16]: 843 cm1 (CF3 sym. stretch), 972 cm1 (CH2 rock), 1096 cm1 (CF3
stretch), 1189 cm1 (CF3 sym. stretch), 1296 cm1 (CH2 wag). The absorption bands are diffuse
with superimposed weak fine structure (e.g., see feature at 1203 cm1 in Fig. 1).
A summary of the band intensity results near room temperature from these studies is given in
Table 2. As shown in Table 2, the available spectra do not all cover the same spectral regions.
However, all spectra do include the three most intense spectral bands in the 1000–1400 cm1
region that account for approximately 80–85% of the total integrated band intensity. Absorption
spectra recorded in the various laboratories at resolutions between 0.03 and 0:50 cm1
demonstrate that the diffuse band shapes and integrated intensities are independent of resolution,
to within 5%. Only the weak fine structure showed any resolution dependence, however, this did
not influence the integrated band intensity. The laboratory spectra also demonstrate that the
integrated band intensities have no discernable dependence (o5%) on the presence of diluent gas
(0–740 Torr; air or N2 ). Finally, RAL [11] and Stony-Brook [13] have reported absorption spectra
recorded at reduced temperature, down to 190 K. At reduced temperatures, there is a slight
decrease in the absorption bandwidths; however, there is no discernable (o5%) change in the
integrated band intensities. Therefore, the infrared absorption spectrum of HFC-134a recorded at
room temperature, 296 K, and atmospheric pressure (N2 or air diluent) is appropriate for input in
radiative forcing calculations in the troposphere and stratosphere regions.
In the evaluation of the absolute band intensities, each spectrum was divided into eight bands.
The integration limits used in this analysis were 500–600, 620–700, 800–900, 940–1010, 1030–1130,
1140–1240, 1240–1325 and 1350–1490 cm1 : The integrated band intensities accounting for
missing bands are given in Table 2; it reveals that there is generally excellent agreement between
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2.5
2.5
Ford
NIST
Absorption Cross Section (10-18 cm2 molecule-1)
NOAA
2.0
2.0
RAL
Clerbaux
Stony Brook
1.5
1.5
1.0
1180
1185
1190
1195
1.0
0.5
0.0
1120
1140
1160
1180
1200
Wavenumber
1220
1240
1260
1280
(cm-1)
Fig. 1. IR absorption cross-section spectra of HFC-134a (CF3 CH2 F) compared in this study. The spectra are
designated as: Clerbaux [9], Ford [10], NIST [12], RAL [11], NOAA, and Stony-Brook [13]. The most important
absorption band for the radiative forcing is also shown (band 6 of Table 2), with a blow up of the 1180–1195 cm1
region.
the spectra. As seen from the bottom six rows of Table 2, for the three most intense bands (bands
5, 6, and 7) the integrated band intensities of each of the six spectra are generally within a few
percent of the average of all the spectra. For the less intense bands, the integrated cross sections of
the six spectra generally agree to within approximately 10%. The larger uncertainty of the weaker
bands reflects the influence of factors such as baseline uncertainty and the fact that measurement
conditions were typically optimized for the strongest bands, therefore leading to poorer signal to
noise in the weaker bands. Fig. 1 shows a comparison of the most intense band at
1140–1240 cm1 ; the agreement between the six spectra is excellent. The total integrated cross
sections given in the right hand column of Table 2 can also be compared with values of 1:33 1016 cm2 mol1 cm1 (440–1535 cm1 ) reported by Fisher et al. [7] and 1:32 1016 cm2 mol1 cm1 (610–1490 cm1 ) reported by Cappellani and Restelli [8]. Where the same
bands are included, the consistency of the total integrated cross sections is remarkably good. The
fact that the integrated cross sections of the three strongest bands, determined independently by
six laboratories, [9–13] span a range whose extremes are within 7% of the average indicates that
the infrared absorption spectrum of HFC-134a is well determined.
Spectrum
source
Band #1
Band #2
Band #3
Band #4
Band #5
Band #6
Band #7
Band #8
Total
500–600 cm1 620–700 cm1 800–900 cm1 940–1010 cm1 1030–1130 cm1 1140–1240 cm1 1240–1325 cm1 1350–1490 cm1 integrated
cross section
5.061018
4.901018
5.261018
5.901018
2.481018
2.381018
2.041018
2.611018
2.511018
8.401018
8.411018
7.911018
8.731018
8.061018
1.461017
1.481017
1.371017
1.541017
1.421017
1.571017
5.291017
5.441017
5.131017
5.701017
5.531017
5.591017
3.991017
4.151017
3.831017
4.291017
4.161017
4.191017
5.201018
5.701018
4.381018
Average (AVG)
Std Dev
Std Dev/Avg
1.331018
1.061019
0.080
5.281018
4.391019
0.083
2.401018
2.191019
0.091
8.301018
3.221019
0.039
1.471017
7.391019
0.050
5.451017
2.101018
0.039
4.101017
1.621018
0.039
4.941018
6.251019
0.126
Ford/AVG
NIST/AVG
NOAA/AVG
RAL/AVG
Clerbaux/AVG
St-Bk/AVG
1.057
0.943
0.958
0.928
1.117
0.996
1.032
0.990
1.086
0.849
1.044
1.012
1.013
1.051
0.953
0.971
0.990
1.006
1.046
0.930
0.964
1.065
0.971
0.999
1.047
0.942
1.015
1.027
0.973
1.012
1.045
0.935
1.015
1.020
1.053
1.154
0.887
4.481018
0.907
(units: cm2 mol1 cm1 ). The total integrated cross-sections from the last column include the group averages for missing bands.
1.301016
1.331016
1.241016
1.391016
1.321016
1.341016
1.321016
1.331016
1.271016
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Ford [10]
1.401018
NIST [12]
1.251018
RAL[11]
NOAA
Clerbaux [9]
Stony-Brook [13]
Fisher [7]
Cappellani [8]
P.M. de F. Forster et al. / Journal of Quantitative Spectroscopy & Radiative Transfer 93 (2005) 447–460
Table 2
Comparison of integrated band intensities calculated using cross section spectra downloaded from the URLs given in Table 1
451
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2.5
Absorption cross section (10-18 cm2 molecule-1)
2.0
1.5
1.0
0.5
0.0
400
600
800
1000
1200
1400
1600
1800
2000
Wavenumber (cm-1)
Fig. 2. Recommended HFC-134a infrared absorption spectrum used in the radiative forcing model evaluation. See text
the evaluation and determination of the recommended spectrum.
The HFC-134a spectrum used in the radiative forcing calculations presented in this paper is
shown in Fig. 2 and was obtained by normalizing the Ford [7] spectrum to the average of the six
spectra as given in Table 2 (scaling factor of 1.03). The normalization of a single spectrum, that
covers the entire wavenumber range of interest, was chosen to minimize any small systematic
errors that might be present in comparing individual bands. The absolute band intensity of the
recommended HFC-134a spectrum is estimated to be accurate to 5% at the 95% confidence level.
The radiative forcing from each band depends on the overlap of a band with Planck function at
atmospheric temperatures and the absorption bands of other gases. However, the most intense
HFC-134a band occurs in the atmospheric window and contributes most to the radiative forcing.
A digitized copy of HFC-134a infrared spectrum can be obtained from the journal archive or the
authors.
3. Radiative forcing calculations
Five groups, employing six different radiative transfer codes, were asked to use the
recommended IR cross section to calculate three radiative forcings for HFC-134a. Firstly, an
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instantaneous radiative forcing at the tropopause was calculated using the group’s own clear sky
global and annual mean profile. While differences may still arise through inter-group variations in
tropopause height and background profile (see Section 4), this forcing would hopefully compare the
radiative transfer models relatively directly. The second radiative forcing requested was a cloudysky instantaneous radiative forcing, using a single global mean profile. This was designed to
investigate the role of clouds. The third radiative forcing asked for their ‘‘best-estimate’’ of the
global and annual mean forcing, taking all factors into account. This gave groups considerable
latitude and differences in this ‘‘best-estimate’’ forcing would likely be representative of the total
uncertainty in the HFC-134a radiative forcing calculation. For all forcings we used a perturbation
of 0.1 ppbv from a background of zero, and scaled the answers by a factor of 10 to estimate the
forcing efficiency in Wm2 ppbv1 : This preserved the weak limit approximation for the gas. For
the instantaneous clear sky forcing HFC-134a was kept constant throughout the atmosphere and
for the ‘‘best-estimate’’ forcing its concentration varied with height as each group saw fit. In
practice only the ILLINOIS group applied this correction, the other groups simply reduced their
best-estimate radiative forcing by 5% to account for this effect to be consistent with the value
derived by ILLINOIS. Details of the radiative transfer schemes and methodologies of the five
participating groups are outlined next. The models used the HITRAN 2000 [17] line database,
except for OSLO and ILLINOIS, which used an earlier (1992) version of the HITRAN database.
The models all included atmospheric absorption by H2 O; CO2 ; CH4 ; N2 O; CFC-11 and CFC-12.
READING (RFM). The reference forward model (RFM) [18] version 4.21 was used. It is a
line-by-line radiative transfer model developed at Oxford University, and is based on the
GENLN2 model [19]. It has been compared with other line-by-line models and validated against
observations [5]. The water vapour continuum used is CKD 2.41 [20]. Only an instantaneous
clear-sky forcing was computed with this model.
READING (NBM). This is a 10 cm1 narrow band model (NBM) [21]. It uses the CKD 2.41
water vapour continuum [20]. The best-estimate forcing was calculated using 3 profiles
representing the tropics and extra tropics and included the effects of clouds and stratospheric
adjustment. For the best-estimate radiative forcing (READING in Table 4), the cloudy-adjusted
radiative forcing is scaled by the ratio of the instantaneous clear-sky radiative forcing between the
READING (RFM) and READING (NBM).
OSLO. The GENLN2 line-by-line model [22] has been used to calculate the optical depths and
includes the 1989 water vapour continuum of Clough et al. [23]. The DISORT model [24] is used
in the calculations of radiative fluxes [25]. Two background vertical profiles are adopted in the
radiative transfer calculations, one tropical and one extra tropical; clouds and stratospheric
temperature adjustment are included in the ‘‘best estimate’’ radiative forcing.
NOAA. This is a line-by-line model developed at NOAA [26] that uses the MT-CKD 1.0
continuum [27] . The ‘‘best-estimate’’ radiative forcing was calculated using randomly overlapping
clouds and stratospheric adjustment; it also employed a 10 zonally seasonal averaged climatology
of trace gas and clouds fields, using CTM 2-D fields for CH4 and N2 O:
ILLINOIS. This is a 10 cm1 NBM used in conjunction with a chemical transport model [4].
This radiative transfer model is also integral to the integrative assesment model used in climate
studies at Illinois. The latitudinal resolution employed for the ‘‘best estimate’’ radiative forcing
was 5 degrees and included the effects of clouds, stratospheric adjustment and vertical profile
changes of HFC-134a. The vertical profile was from the final year of a 10-year run of the 2-D
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chemical transport model, with a fixed 0.1 ppbv lower boundary. In contrast to the other
methodologies the model-derived trace gas and temperature climatology was used in the radiative
transfer scheme.
CNRS. The line-by-line radiative transfer model LBLRTM [28] was used. Radiance fluxes
calculations were compared with other model results and were validated using remote sensed
measurements [29]. The water vapour (CKD 2.4) and CO2 continua [20] were included. Only a
clear-sky instantaneous calculation was performed.
Table 3 presents the clear and cloudy-sky instantaneous radiative forcings calculated with a
single global and annually averaged profile calculated by the 6 models; Table 4 presents the best
estimate radiative forcing. For READING the line-by-line and NBM model employed the same
background profile. The tables also give some supplementary information to elucidate differences
in the radiative forcing calculations. We use the next section to discuss the results in the two
tables.
Table 3
Using a single background profile this table gives the global mean clear and cloudy sky instantaneous radiative forcing
for 10 0:1 ppbv of HFC-134a provided by the 5 modelling groups. Supplementary data and data from the NCEP
reanalysis project is also shown. For the READING, NOAA and CNRS model and the NCEP reanalysis the
tropopause height and temperature is calculated using the WMO lapse rate tropopause definition in the single global
mean temperature profile. For ILLINOIS and OSLO the tropopause height and temperature are global mean of
tropopause heights
Model
Clear-Rad. F. Cloudy-Rad. F. Rad. F. Ratio Clear OLR LW Cloud
SFC Temp Trop-T Trop-P
Cloudy/clear (Wm2 )
(K)
(hPa)
(Wm2 )
(Wm2 )
forcing (Wm2 ) (K)
READING (RFM)
READING (NBM)
OSLO
NOAA
ILLINOIS
CNRS
NCEP-reanal
0.205
0.200
0.200
0.197
0.211
0.212
0.154
0.150
0.143
0.158
259.5
254.0
253.2
256.5
269.4
258.1
267.8
0.77
0.75
0.72
0.75
287.2
19.7
18.9
28.6
31.7
31.6
287.5
287.2
288.2
287.1
287.6
210.0
As above
207.5
211.6
208.1
207.0
206.3
128.6
150.0
140.6
160.1
100.5
137.0
Table 4
Using a latitudinally resolved background profile this table gives the global mean best-estimate radiative forcing for
10 0:1 ppbv of HFC-134a provided by the 4 modelling groups
READING
OSLO
NOAA
ILLINOIS
NCEP-reanal
Rad. F.
(Wm2 )
Cloudy-sky
OLR (Wm2 )
Cloud forcing
(Wm2 )
SFC Temp
(K)
Trop-T
(K)
Trop-P
(hPa)
0.166
0.156
0.155
0.156
236.6
234.3
231.8
237.8
236.2
19.3
18.9
31.9
31.8
31.6
287.3
287.5
287.2
288.2
287.6
210.3
207.5
208.8
208.1
207.0
140.6
150.0
164.5
160.1
167.0
Supplementary data and data from the NCEP reanalysis project is also shown. The tropopause height and temperature
are global averages from the WMO lapse rate tropopause of the different profiles.
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4. Radiative forcing uncertainties
The calculation of radiative forcing as defined by IPCC [3] requires an estimate of the globally and
annually averaged net downward irradiance change at the tropopause after allowing for stratospheric
temperatures to adjust to radiative equilibrium. As well as the radiative transfer itself a number of
factors influence the radiative forcing calculation. These are discussed below, in rough order of
importance for HFC-134a. Table 5 summarizes the effects of the various factors on the clear sky
instantaneous radiative forcing and gives an overview of the uncertainties found from this section.
4.1. Tropopause height
Freckleton et al. [30] showed that for CFC-12 changing the tropopause from the top of
convection (identified by a change in static stability) to the cold point could increase the radiative
forcing by up to 10%. A smaller influence was identified if a single global and annually averaged
mean profile was used. A tropopause defined as the top of convection has been suggested as more
appropriate [31]. While the top of convection can easily be defined in a 1-D radiative convective
model it is not clear how to apply it in observed profiles. For this study all the groups choose a
tropopause using the WMO lapse rate definition. However, some groups (READING, NOAA,
CNRS) calculated their tropopause height for the global and annual mean profile from the global
and annual mean temperature profile and others (OSLO and ILLINOIS) used a global and
annual average of the tropopause heights themselves (Table 3). Mostly due to this there was a
50 mb (3 km) variation in their globally averaged value between the different climatologies
(Table 3). NOAA found an 8.0% increase in the radiative forcing in the instantaneous global
mean case when the tropopause is changed from the WMO definition to the temperature
minimum. In the best estimate case the change was 6.3%.
4.2. Clouds
They effectively raise the emitting surface, reducing the effective column of the gas. This surface
is also at a cooler temperature, reducing the upwelling radiation. These two mechanisms combine
Table 5
A summary of the effects of the various factors on the clear sky instantaneous radiative forcing and gives an overview of
the uncertainties found from this section
Process
Effect on clear-sky instantaneous
radiative forcing (%)
Contribution to total uncertainty in the
best-estimate of radiative-forcing (%)
Clouds
Stratospheric adjustment
Vertical profile of HFC-134a
Background profile
Tropopause height
Radiative transfer
Absorption cross section
25% reduction
10% increase
5% reduction
N/A
N/A
N/A
N/A
5%
4%
Not assessed
4%
10%
3%
5%
The uncertainty from the absorption cross section found in the previous section is also given.
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and, for HFC-134a, including clouds has been shown to reduce the radiative forcing by 25–30%
[4,10]. In this work the reduction due to clouds is similar (23–28%, Table 3). The extra uncertainty
in the radiative forcing introduced by clouds is approximately 5% (the uncertainty from
differences in the clear/cloudy radiative forcing ratio in Table 3). However, in our case, the range
of clear sky radiative forcing is no larger than the range in instantaneous cloudy-sky radiative
forcing.
4.3. Stratospheric adjustment
For most HFCs their strongest IR absorption bands occur in the window region. In the
stratosphere these bands absorb more upwelling radiation from a warmer troposphere than they
emit. Therefore addition of halocarbons generally leads to a warming of the lower stratosphere,
which acts to increase the radiative forcing by approximately 9% from its clear sky value. This is
in contrast to carbon dioxide increases, which cool the stratosphere, and lead to a reduction of the
radiative forcing [10]. For the models used here this increase is 8% for the NOAA model, 9% for
the ILLINOIS model, 12% for the READING model and 10% for the OSLO model [see also 6]
(these numbers are not shown in the tables). Therefore the uncertainty in this factor contributes
4% to uncertainty in the radiative forcing.
4.4. Background temperature and other trace-gas concentrations
In this work several different background profiles were used in the radiative calculations.
Sensitivity studies performed with the NOAA model found that a surface temperature uncertainty
of 0.2 K would give approximately a 1% error in radiative forcing. The NOAA model also found
that water vapour and pressure differences between two climatologies could also lead to radiative
forcing differences of 2%. Pinnock et al. [10] and Jain et al. [4] have previously assessed the
effects of absorption band overlap. In our study CNRS find that including present day
concentrations of CFC-11 and CFC-12 in the background profile reduced the HFC-134a forcing
by 1%. NOAA find a 15% increase in radiative forcing when excluding N2 O and CH4 from the
calculation, this differs from the 5% increase found for each gas separately in the previous studies
[4,10] and may partly explain why the NOAA model has the lowest radiative forcing.
4.5. Vertical profile of HFC-134a
Due to chemical destruction in the stratosphere and troposphere, compounds with a short
lifetime can exhibit a reduction of their mixing ratio with height. This leads to a reduction in
radiative forcing by as much as 30% for gases with a lifetime of less than 2 years [32]. The average
total lifetime of HFC-134a is quoted as 14.0 years [33]. Its decay comes primarily from reaction
with OH in the troposphere and thus its inferred stratospheric lifetime is much larger. This leads
to only a small drop off in the HFC-134a mixing ratio in the stratosphere and only a 4.5–5.5%
reduction in its radiative forcing [4,30,34]. The uncertainty in this effect cannot be assessed here as
all models use the ILLINOIS model as a basis for quantifying this effect. For simplicity we
reduced the constant vertical-profile forcing from the other models by 5%.
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4.6. Radiation scheme
The clear sky instantaneous forcing from the two READING models which use the same
background profile differ by 2% (Table 3). These two models employ the same water vapour
continuum and the same line strength databases. For HFC-134a changing these additional
features had little effect on the radiative forcing. For example, in the Reading NBM, a change
from the HITRAN 1992 to HITRAN 2000 line database reduced the clear-sky instantaneous
radiative forcing by less than 1%; the NOAA and CNRS model found a similarly small influence
with two quite different versions of the water vapour continuum. It should be noted that all
models explicitly integrate over zenith angle, use of a constant diffusivity parameter has
previously been shown to be inappropriate [10]. Line by line models are expected to be more
accurate than NBMs, as they can provide a better representation of overlap between gases.
4.7. Global averaging
As there can be significant nonlinearities in the radiative forcing calculation, a single calculation
with a global and annual mean profile will not necessarily give the same answer as averaging
radiative calculations over latitudinally resolved profiles. Variations of clouds, tropopause height,
temperature and the profile of the atmospheric gases can all contribute to this. Highwood and
Shine [32] find that there is significant absorption by HFC-134a below 800 cm1 ; where it overlaps
with water vapour. Therefore, it is important to perform radiative calculations using extratropical profiles of water vapour. The use of profiles, representative of the tropics and extra
tropics, is adequate for resolving these nonlinearities [30]. Accounting for the extra-tropical
profiles of water vapour, increases the globally averaged forcing by a few percent.
5. Discussion
5.1. Choosing a good model
The uncertainties can appear large in Table 5; however, by comparing aspects of model to the
available observational data the uncertainties can be minimized; in our case we have used the
NCEP reanalysis data [35] as an illustrative comparison. For example, making sure a model has
an outgoing longwave radiation (OLR) close to 236 Wm2 and a clear sky OLR close to
269 Wm2 to give a longwave cloud forcing of 30–35 Wm2 (the observed range quoted in IPCC
[3]) would be worthwhile goals. Although the LW cloud forcing is more uncertain than other
variables, the table shows that the READING (NBM) and OSLO models have a smaller cloud
LW forcing than NCEP data would suggest. However, there is no evidence that the differences in
cloud forcing have an impact on the derived forcing, and the effect may be small, as long as the
models are constrained to get the correct OLR. Tables 3 and 4 show that most models
underestimate of the clear-sky values of OLR, rather than overestimate the cloudy sky OLR
values. However, it is especially the clear sky OLR rather than the cloudy sky OLR that is
uncertain. Details such as the tropopause height and surface temperature used in the background
climatology can also readily be compared to the various observational datasets. For example, the
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surface temperature of 288 K in the ILLINOIS model’s climatology seems too high, when
compared to the NCEP reanalysis data.
In our comparison none of the four best-estimate radiative forcings stand out as being generally
worse or better than the others. For example, ILLINOIS do not use a more accurate line-by line
model for their radiative transfer calculation, yet they are the only group to calculate a vertical
profile for HFC-134a albeit with a 2-D model. In the future using a line-by line model with CTM
fields will give an optimum approach; further, rigorously constraining the model to as many
relevant observational truths is recommended. However, in the interim a multi-model average
may provide a more accurate representation of the true forcing than a single model result. Using
this methodology we obtain HFC-134a radiative forcing efficiency of 0:16 Wm2 ppbv1 :
5.2. Uncertainties
Despite the uncertainties discussed in Section 4 (see Table 5), the overall range of results from
the models is relatively small. There is an 8% range for the 6 instantaneous clear sky calculations
(Table 3); a 10% range for the 4 instantaneous cloudy sky calculations (Table 3); and only a 7%
range for the 4 best-estimate calculations (Table 4). This level of agreement is probably fortuitous,
as the best-estimate calculation would be expected to generally have the largest error. The results
of our work indicate that when comparing two model results one may expect: (i) up to a 7%
difference from using different cross section data and (ii) up to a 10% difference from the
different aspects of the radiative transfer calculation. It would be unlikely if all these differences
compounded. However, differences of around 14% (0:02 Wm2 ppbv1 ) would be likely (adding
RMS errors). Past differences of over 20% between the ILLINOIS and READING group appear
to come down to the cross-sections used, although the quoted cross-sections employed in these
past studies were not significantly different than that used in this study; other model differences
push the wrong way to explain the difference and are now much smaller (6%). Here READING
gives a larger forcing than the ILLINOIS group, whereas previously the ILLINOIS group gave a
25% larger forcing than READING.
6. Conclusions
We have succeeded in both understanding and reducing many of the uncertainties involved in
the calculation of the radiative forcing for HFC-134a. We explored uncertainties in cross section
data and radiative transfer modelling. Our four-model average gives a radiative forcing efficiency
of 0:16 0:02 Wm2 ppbv1 : This agrees well with the value of 0:15 Wm2 ppbv1 quoted in the
latest IPCC report [3]. Most of our uncertainty arises from the radiative forcing calculation,
rather than the absorption cross section. Our methodology illustrates the usefulness of comparing
supplementary material from models to help gauge an individual model’s performance and judge
the quality of its radiative forcing estimate. Multi-model comparisons are useful for understanding uncertainties; in the absence of particular Models with clearly defined advantages over
the others, and, in a future where economic and political decisions may be made employing
radiative efficiencies, it could be beneficial to obtain a multi-model consensus.
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Acknowledgements
CC and PFC are grateful to M. Iacono and T. Clough for providing the LBLRTM radiative
transfer code. PFC is Postdoctoral Researcher with the Fonds National de la Recherche
Scientifique (FNRS, Belgium). MDH and TJW thank Camilla Bacher and Ole John Nielsen
(Copenhagen University) for help obtaining IR data at low frequencies (200–600 cm1 ). The
University of Reading acknowledge support from NERC Grant NER/L/S/2001/0066, the
CRYOSTAT EC Project (EV2K-CT-2001-00116) and a NERC Advanced Research Fellowship
(PMF). This work was a Stratospheric Processes and their Role in Climate (SPARC) initiative.
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