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June 2016Reading
UKThe contribution of greenhouse gases to the recent slow-down in
global-mean temperature trends: Supplementary Material
R. Checa-Garcia, K.P. Shine, M.I. HegglinDepartment of Meteorology,
University of ReadingEarley Gate, Reading RG6 6BB, UK
email: [email protected]
This supplementary material includes (1) a description of the main dataset built fromthe combination of greenhouse gases (GHGs) concentrations provided by the AGAGEnetwork, historical and IPCC information, (2) the description and results from alter-native dataset based on the observations of the GHGs concentrations from the NOAAnetwork, (3) the methods used to calculate the radiative forcings of GHGs, (4) specificinformation of the Energy balance model, (5) information about the CMIP5 modeltime series used and (6) Radiative Forcing datasets for the two datasets created.
S1 Reference dataset (based on AGAGE network) 2S1.1 Additional results . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
S2 Alternative dataset (based on NOAA-GMD network) 5S2.1 Results for the alternative dataset . . . . . . . . . . . . . . . . . . 7
S3 Estimation of Radiative Forcing from global concentrations 10S3.1 CO2, CH4 and N2O . . . . . . . . . . . . . . . . . . . . . . . . 10S3.2 Fluorinated gases and Ozone Depleting substances . . . . . . . . . 10
S4 Energy Balance Model 11
S5 CMIP5 multimodel RCP45 dataset 11
S6 Radiative forcing datasets 12S6.1 Python code to read the radiative forcing datasets . . . . . . . . . 14
S1 Reference dataset (based on AGAGE network)
Provides an exhaustivedataset of GHGs globalconcentrations
The main dataset used in the paper relies on a combination of observational values of GHGs concentra-tions coming from AGAGE (Advanced Global Atmospheric Gases Experiment) network (1), historicalvalues consistent with (2), and radiative forcing described in IPCC report (3). The AGAGE networkof instruments provides systematic global estimations of the observed concentrations for a long list ofsubstances. It comprises three types of instruments named Gas Chromatography - MultiDetector (GC-MD), a Gas Chromatography with Mass Spectrometry (ADS GC-MS) and MEDUSA Gas Chromatog-raphy with Mass Spectrometry (Medusa GC-MS). The instruments Medusa GC-MS has a broader setof substances systematically estimated since 2004. The instruments GC-MD includes fewer numberof substances but began in 1978. Given that a central part of our study involves Ozone DepletingSubstances (ODS), the most comprehensive dataset in terms of the number of substances is chosen.Consequently our main dataset relies on Medusa GC-MS and includes the main substances covered bythe Montreal Protocol. For those substances not included in AGAGE datasets we have used the IPCCradiative forcing values described in (3).
The Table S1 shows the different sources of information for this dataset. Figure S1 shows both thehistorical and AGAGE concentrations for a number of CFCs, HFCs and Halons substances. For bothnetworks of instruments Medusa GC-MS and the GC-MD, the global dataset of concentrations is givenwith monthly resolution, so for our study it was averaged to give mid-year values.
SUPPLEMENTAL MATERIAL, TABLE S1: Radiative forcing datasets are created from (3) and from global experimentalconcentrations of several gases. The global concentrations are translated to radiative forcing using the IPCC expressionsdetailed in section §S3 and the radiative efficiencies shown in Table 8.A.1 in (3). The datasets contain radiative forcinguntil 2014 and the values not present in the IPCC report are extended by assuming they are constant 2011 values.M-CONC indicated values obtained from the (3) tables of concentrations. IPCC-RF indicates (3) values of RadiativeForcing (from Table AII.1.2, pag. 1404). AG-MD indicates the Global dataset of AGAGE GC-MD concentrationswith monthly resolution averaged to give mid-year values. AG-MS means Global dataset of AGAGE GCMS-Medusaconcentrations with monthly resolution averaged to give mid-year values.
DATASET REFERENCESpecies Before 2003 After 2003 Formula Efficiency [Wm−2ppb−1]
CO2 IPCC-RF IPCC-RF CO2 -CH4 M-CONC AG-MD CH4 -N2O M-CONC AG-MD N2O -
Fluorinated GasesHFC-23 M-CONC AG-MS CHF3 0.18HFC-32 M-CONC AG-MS CH2F2 0.11
HFC-125 M-CONC AG-MS CHF2CF3 0.23HFC-134a M-CONC AG-MS CH2FCF3 0.16HFC-143a M-CONC AG-MS CH3CF3 0.16
HFC-227ea M-CONC AG-MS CF3CHFCF3 0.26HFC-245fa M-CONC AG-MS CHF2CH2CF3 0.24
Sulfur Hexafluoride M-CONC AG-MS SF6 0.57PFC-116 M-CONC AG-MS CF3CF2CF3 0.25
O3 depleting GasesCFC-11 M-CONC AG-MD CCl3F 0.26CFC-12 M-CONC AG-MD CCl2F2 0.32
CFC-113 M-CONC AG-MD CCl2FCClF2 0.30CFC-114 M-CONC AG-MS CClF2CClF2 0.31CFC-115 M-CONC AG-MS CClF2CF3 0.20HCFC-22 M-CONC AG-MS CHClF2 0.21
HCFC-141b M-CONC AG-MS CH3CCl2F 0.16HCFC-142b M-CONC AG-MS CH3CClF2 0.19Halon-1211 M-CONC AG-MS CBrClF2 0.29Halon-1301 M-CONC AG-MS CBrF3 0.30Halon-2402 M-CONC AG-MS C2Br2F4 0.31
Carbon Tetrachloride M-CONC AG-MS CCl4 0.17Methyl Bromide M-CONC AG-MS CH3Br 0.004Methyl Chloride M-CONC AG-MS CH3Cl 0.01
Methyl Chloroform M-CONC AG-MD CH3CCl3 0.08
Stratospheric H2O IPCC-RF IPCC-RF H2O -Tropospheric O3 IPCC-RF IPCC-RF O3 -Stratospheric O3 IPCC-RF IPCC-RF O3 -
SU
PP
LE
ME
NTA
LM
AT
ER
IAL,
FIG
UR
ES
1:
Dat
aset
base
don
AGAG
Ene
twor
kof
inst
rum
ents
:co
ncen
trat
ions
ofCF
Cs,H
CFC,
Hal
ogen
ated
and
Brom
ide
subs
tanc
es.
The
hist
oric
alva
lues
are
take
nfr
om(2
)up
toye
ar20
03th
enth
eex
peri
men
talc
once
ntra
tion
spr
ovid
edby
AGAG
Ene
twor
kar
eus
ed.
The
valu
eof
year
2004
isca
lcul
ated
toen
sure
cont
inui
tyon
the
conc
entr
atio
ns.
Anex
haus
tive
list
ofth
esu
bsta
nces
incl
uded
inou
rda
tase
tis
give
nin
Tabl
eS1
.
S1.1 Additional results
1940 1950 1960 1970 1980 1990 2000 20100.20
0.10
0.00
0.10
0.20
0.30
GM
ST
tren
d [K
per
dec
ade]
GHGs totalCO2
HadCRUT4.4
1940 1950 1960 1970 1980 1990 2000 20100.02
0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
GM
ST
tren
d [K
per
dec
ade]
CH4
Strat. H2O←CH4 oxid.N2OFluorinated gasesStratospheric O3
ODS
1940 1950 1960 1970 1980 1990 2000 2010Year
0.02
0.01
0.00
0.01
0.02
0.03
0.04
0.05
GM
ST
tren
d [K
per
dec
ade]
Tropospheric O3
CH4 and Strat. H2OODS and Strat. O3
N2O and Fluorinated GHGs
SUPPLEMENTAL MATERIAL, FIGURE S2: Overlapping GMST trends using a 5-year window. First panel: In-dividual greenhouse gas contributors to the GMST trend using the simple model. Second Panel: Contributionsgrouped into related forcings (methane and stratospheric water vapour, tropospheric ozone, ozone depleting sub-stances and stratospheric ozone, and other greenhouse gases (nitrous oxide and non-ODS fluorinated GHGs)).The red bar illustrates the size of the overlapping window. The smoothed GMST trend between 1985 and 2003 ishighlighted (purple-shaded area).
SUPPLEMENTAL MATERIAL, TABLE S2: Decadal Trend change from 1985 to 2003 [K per decade] for the differentGHGs groups analysed and for three values of the climate sensitivity parameter λ [K (W m−2)−1].
Trend Change [K/10yr]Component Group λ = 0.30 λ = 0.75 λ = 1.4CO2O 0.006 0.014 0.023N2O and Fluorinated Gases 0.001 0.003 0.004CH4 and Strat. H2O -0.012 -0.022 -0.027ODS and Strat. Ozone -0.018 -0.026 -0.028Tropospheric Ozone -0.009 -0.016 -0.020
All GHGs -0.031 -0.047 -0.049
S2 Alternative dataset (based on NOAA-GMD network)
Provides an alternativedataset of GHGs globalconcentrations to ensurerobustness of the mainconclusions.
To ascertain that our results do not depend significanlty on how the dataset is built, an alternativedataset is created based on NOAA network of instruments. NOAA-ERSL Global Monitoring Divisionnetwork is providing datasets covering the main species on each category: CO2, CH4, N2O, CFCs,HCFC, Halons (the last three groups are provided specifically by Halocarbons and other AtmosphericTrace Species (HATS) group, (4)) together with methyl chloroform and methyl bromide(5; 6). Even ifit is not as exhaustive in terms of the number of substances as AGAGE Medusa network, it still coversthe most important species since 1994. Therefore it is used as an alternative testing dataset to verifythe robustness of our conclusions against a different networks of instruments. Table S3 shows thesources of information for this dataset. Figure S2 shows both the historical and NOAA concentrationsfor a number of CFCs, HFCs and Halons substances.
SUPPLEMENTAL MATERIAL, TABLE S3: Radiative forcing datasets are created from IPCC report (3) and from globalexperimental concentrations of several gases. The global concentrations are translated to radiative forcing using theIPCC expressions detailed in section §S3 and the radiative efficiencies shown on the Table 8.A.1 in (3). M-CONCindicated values obtained from the (3) tables of concentrations. IPCC-RF indicates (3) values of Radiative Forcing.NOAA-A means NOAA-ESRL dataset with the global GHGs concentrations of CO2, CH4 and N2O averaged to givemid-year values. IPCC-RF means IPCC 2013 report dataset on Radiative Forcing (from Table AII.1.2, pag. 1404).NOAA-B indicates NOAA-ESRL dataset from S. Montzka (NOAA) with additional species with elements Br, F, and Cl.This dataset has monthly resolution and is averaged to give mid-year concentrations.
DATASET ALTERNATIVESpecies Before 1993 After 1994 Formula Efficiency [Wm−2ppb−1]
CO2 M-CONC NOAA-A CO2 -CH4 M-CONC NOAA-A CH4 -N2O M-CONC NOAA-A N2O -
Fluorinated GasesHFC-32 M-CONC NOAA-B CH2F2 0.11
HFC-125 M-CONC NOAA-B CHF2CF3 0.23HFC-134a M-CONC NOAA-B CH2FCF3 0.16HFC-143a M-CONC NOAA-B CH3CF3 0.16
HFC-227ea(*) M-CONC NOAA-B CF3CHFCF3 0.26
O3 depleting GasesCFC-11 M-CONC NOAA-B CCl3F 0.26CFC-12 M-CONC NOAA-B CCl2F2 0.32
CFC-113 M-CONC NOAA-B CCl2FCClF2 0.30HCFC-22 M-CONC NOAA-B CHClF2 0.21
HCFC-141a M-CONC NOAA-B CH3CCl2F 0.16HCFC-142a M-CONC NOAA-B CH3CClF2 0.19Halon-1211 M-CONC NOAA-B CBrClF2 0.29Halon-1301 M-CONC NOAA-B CBrF3 0.30Halon-2402 M-CONC NOAA-B C2Br2F4 0.31
Carbon Tetrachloride M-CONC NOAA-B CCl4 0.17Methyl Bromide M-CONC NOAA-B CH3Br 0.004
Methyl Chloroform M-CONC NOAA-B CH3CCl3 0.08
Stratospheric H2O IPCC-RF IPCC-RF H2O -Tropospheric O3 IPCC-RF IPCC-RF O3 -Stratospheric O3 IPCC-RF IPCC-RF O3 -
(*) HFC-227ea global concentrations are provided by NOAA since 2007, therefore the historical values of (2) areconsidered until 2006 for this GHG.
SU
PP
LE
ME
NTA
LM
AT
ER
IAL,
FIG
UR
ES
3:
Dat
aset
base
don
NO
AAne
twor
kof
inst
rum
ents
:co
ncen
trat
ions
ofCF
Cs,
HCF
C,H
alog
enat
edan
dBr
omid
esu
bsta
nces
.Th
ehi
stor
ical
valu
esar
eta
ken
from
(2)
upto
year
year
1993
then
the
expe
rim
enta
lcon
cent
rati
ons
prov
ided
byN
OAA
netw
ork
are
used
.Th
eva
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calc
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edto
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uity
onth
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ions
.An
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usti
velis
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the
subs
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esin
clud
edin
our
data
set
isgi
ven
inTa
ble
S3.
Not
eth
atH
FC-2
27ea
glob
alco
ncen
trat
ions
are
prov
ided
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isG
HG
.
S2.1 Results for the alternative dataset
1940 1950 1960 1970 1980 1990 2000 20100.20
0.10
0.00
0.10
0.20
0.30
GM
ST
tren
d [K
per
dec
ade]
GHGs totalCO2
HadCRUT4.4
1940 1950 1960 1970 1980 1990 2000 20100.01
0.00
0.01
0.02
0.03
0.04
0.05
GM
ST
tren
d [K
per
dec
ade]
CH4
Strat. H2O←CH4 oxid.N2OFluorinated gasesStratospheric O3
ODS
1940 1950 1960 1970 1980 1990 2000 2010Year
0.01
0.00
0.01
0.02
0.03
0.04
0.05
GM
ST
tren
d [K
per
dec
ade]
Tropospheric O3
CH4 and Strat. H2OODS and Strat. O3
N2O and Fluorinated GHGs
SUPPLEMENTAL MATERIAL, FIGURE S4: Alternative dataset based result for overlapping GMST trends usinga 15-year window for comparison with Figure 2 of main text. (a) Observed trends using HadCRUT4.4 (red)(7) and simple model results for CO2-only (light blue), and for all GHGs (black). (b) Individual greenhouse gascontributors to the GMST trend using the simple model. (c) Contributions grouped into related forcings (methaneand stratospheric water vapour, tropospheric ozone, ozone depleting substances and stratospheric ozone, andother greenhouse gases (nitrous oxide and non-ODS fluorinated GHGs)). The red bar illustrates the size of theoverlapping window. The smoothed GMST trend between 1985 and 2003 is highlighted (purple-shaded area).
0.4
0.6
0.8
1.0
1.2
1.4
Sen
sitiv
ity P
aram
eter
[K
(Wm
2 )1 ]
0.045
0.060
0.075
0.090
0.105
0.120
Panel a: CO2
0.013
0.017
Panel b: N2O and Fluorinated GHGs
0.4
0.6
0.8
1.0
1.2
1.4
Sen
sitiv
ity P
aram
eter
[K
(Wm
2 )1 ]
0.140
0.130
0.120
0.110
0.1000.090
0.080
Panel c: CH4 and Stratospheric H2O
0.140
0.130
0.120
0.1100.100
Panel d: ODS and Stratospheric O3
10 12 14 16 18Overlapping Window Size [yr]
0.4
0.6
0.8
1.0
1.2
1.4
Sen
sitiv
ity P
aram
eter
[K
(Wm
2 )1 ]
0.100
0.090
0.080
0.070
0.0600.050
Panel e: Tropospheric O3
10 12 14 16 18Overlapping Window Size [yr]
0.255
0.240
0.225
0.2100.1950.180
Panel f: GHGs total
0.000
0.015
0.030
0.045
0.060
0.075
0.090
0.105
0.120
0.150
0.255
0.225
0.195
0.140
0.120
0.100
0.080
0.060
0.010
SUPPLEMENTAL MATERIAL, FIGURE S5: Alternative dataset based result for the smoothed trend in the simple-model mean GMST for 2003 minus 1985, divided by the mean value of the smoothed observed trend for thesame period for comparison with Figure 3 of the main text. The dependence of this metric on the overlappingsize window from 5 to 20 years, and the climate sensitivity parameter λ from 0.3 to 1.4 K (W m-2)-1 is shown:(a) carbon dioxide, (b) nitrous oxide and non-ozone-depleting fluorinated gases, (c) methane and stratosphericwater vapour changes due to methane oxidation, (d) ozone depleting substances and stratospheric ozone, (e)tropospheric ozone and (f) all components.
1940 1950 1960 1970 1980 1990 2000 20100.20
0.10
0.00
0.10
0.20
0.30
GM
ST
tren
d [K
per
dec
ade]
GHGs totalCO2
HadCRUT4.4
1940 1950 1960 1970 1980 1990 2000 20100.02
0.01
0.00
0.01
0.02
0.03
0.04
0.05
0.06
GM
ST
tren
d [K
per
dec
ade]
CH4
Strat. H2O←CH4 oxid.N2OFluorinated gasesStratospheric O3
ODS
1940 1950 1960 1970 1980 1990 2000 2010Year
0.02
0.01
0.00
0.01
0.02
0.03
0.04
0.05
GM
ST
tren
d [K
per
dec
ade]
Tropospheric O3
CH4 and Strat. H2OODS and Strat. O3
N2O and Fluorinated GHGs
SUPPLEMENTAL MATERIAL, FIGURE S6: Alternative dataset based result for overlapping GMST trends using a5-year window. First panel: Individual greenhouse gas contributors to the GMST trend using the simple model.Second Panel: Contributions grouped into related forcings (methane and stratospheric water vapour, troposphericozone, ozone depleting substances and stratospheric ozone, and other greenhouse gases (nitrous oxide and non-ODS fluorinated GHGs)). The red bar illustrates the size of the overlapping window. The smoothed GMST trendbetween 1985 and 2003 is highlighted (purple-shaded area).
S3 Estimation of Radiative Forcing from global concentrations
The methodology used to estimate the radiative forcing is consistent with the reference (3)
S3.1 CO2, CH4 and N2O
For the case of CO2 it is used the expression:
RFCO2 = 5.35ln(XCO2[ppm]/278.0) (1)
For the case of CH4 and N2O it is introduced a function to estimate the interference between bothspecies
F (XA, XB , XC) = 0.47ln(1 + 2.01 · 10−5(XAXB)0.75 + 5.31 · 10−15XA(XAXB)1.52)+ 0.47ln(1 + 2.01 · 10−5(XCXB)0.75 + 5.31 · 10−15XC(XCXB)1.52)
and we estimated the radiative forcing as:
RFCH4 = 0.036√XCH4 −
√X0
CH4 + F (XCH4, X0N2O, X
0CH4)) (2)
For the case of NO2
RFN2O = 0.12√XN2O −
√X0
N2O + F (X0CH4, XN2O, X
0N2O) (3)
and X0N2O = 270ppb and X0
CH4 = 722ppb
S3.2 Fluorinated gases and Ozone Depleting substances
For the Fluorinated gases and Ozone Depleting substance the estimation of the radiative forcing isbased on the efficiency provided by Table 8.A.1 in (3)
RFi = εi(Xi −X0i ) (4)
the values of the the efficiency εi [Wm−2ppb−1] are given in the Tables 1 and 2. The concentrationsin the past are taken as zero in consistency with IPCC report (3)
S4 Energy Balance Model
The energy balance model applied can be written as system of ordinary differential equations (ODEs)defining a simple two-box model:
dTm
dt= 1
Cm
[δF − Tm
λ− κρcp(Tm − Td)α
](5)
dTd
dt= 1
Cd[κρcp(Tm − Td)α] (6)
Where Tm and Td are the mixed layer and deep ocean temperatures change respectively, and t is time.The different values and units are detailed in the Table S4
Variable Symbol Unit Typical Value
Mixed Layer depth dm [m] 100Deep Layer depth dd [m] 900Diffusivity κ [m2 s−1] 0.0001Water density ρd [kgm−3] 1000.Specific heat water cp [J kg−1 K−1] 4128.Heat capacity Deep layer Cd [J K−1 m−2] ρdcpdd
Heat capacity Mixed layer Cm [J K−1 m−2] ρmcmdm
Climate Sensitivity λ [K (W m−2)−1] ∈ [0.3, 1.4]Radiative Forcing δF [W m−2] ∈ [−8, 8]
SUPPLEMENTAL MATERIAL, TABLE S4: Main variables and parameters of the Two Layer Energy Balance Model.
S5 CMIP5 multimodel RCP45 dataset
The Figure 1 of the main paper includes a time series of GMST estimated from climate models. Thistime series relies on a multi-model mean over 42 simulations corresponding to the same ensemblemember r1i1p1.
Detailed information on the multi-model dataset can be found in (8), while specific information aboutthe scenario RCP45 in contained in the Atlas Index Supplementary Material 4.5 (AISM4.5). In partic-ular the Table AI.SM4.5.1 details all the models used for the multi-model mean.
S6 Radiative forcing datasets
The two radiative forcing datasets for GHGs build for this study are available as plain ASCII files (inboth cases the data is organized in columns with the first column an standard date-time string.) andXLS/CSV. The plain ASCII files has a header with the main information of the sources.
For the reference dataset it is
# ------------------------------------------------------------------------------# Radiative Forcing dataset created from:## 1 AGAGE global concentrations:## http://agage.eas.gatech.edu/data_archive/global_mean/global_mean_md.txt# http://agage.eas.gatech.edu/data_archive/global_mean/global_mean_ms.txt## 2 Historical data of concentrations analogous to CMIP5 project from## Meinshausen, M., S. J. Smith, K.V. Calvin, J.S. Daniel, M.L.T. Kainuma,# J.F. Lamarque, K. Matsumoto, S. A. Montzka, S. C. B. Raper, K. Riahi,# A. M. Thomson, G. J. M. Velders and D. van Vuuren 2011.# "The RCP Greenhouse Gas Concentrations and their Extension from# 1765 to 2300." Climatic Change Special Issue,# DOI: 10.1007/s10584-011-0156-z## 3 IPCC expressions for Radative Forcing:## Table 6.2 in "Radiative Forcing of Climate Change" pag. 358# Co-ordinating Lead Author: V. Ramaswamy# http://www.grida.no/climate/ipcc_tar/wg1/pdf/tar-06.pdd## 4 IPCC Radiative Efficiencies as given in 2013 report Table 8.A.I## Myhre, G., D. Shindell, F.-M. Breon, W. Collins, J. Fuglestvedt, J. Huang,# D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock,# G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural# Radiative Forcing. In: Climate Change 2013: The Physical Science Basis.# Contribution of Working Group I to the Fifth Assessment Report of the# Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin,# G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,# V. Bex and P.M. Midgley eds.]. Cambridge University Press, Cambridge,# United Kingdom and New York, NY, USA.## 5 IPCC Radative Forcings for CO2, Stratospheric H2O, Stratospheric O3 and# Tropospheric O3 given on the 2013 IPCC Report Table AII.1.2## IPCC, 2013: Climate Change 2013: The Physical Science Basis.# Contribution of Working Group I to the Fifth Assessment Report of the# Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin,# G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,# V. Bex and P.M. Midgley eds.]. Cambridge University Press, Cambridge,# United Kingdom and New York, NY, USA, 1535 pp.### --- Dataset RF created in: 2016-06-10 13:52:24## -----------------------------------------------------------# Contact INFO: [email protected]# University of Reading, Deparment of Meteorology.# ------------------------------------------------------------------------------### FIELDS organized by columns as:#
while for the alternative dataset it is
# ------------------------------------------------------------------------------# Radiative Forcing dataset created from:## 1 NOAA global concentrations:## http://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/#global_data# http://www.esrl.noaa.gov/gmd/ccgg/trends/# ftp://ftp.cmdl.noaa.gov/hats/n2o/insituGCs/CATS/global/insitu_global_N2O.txt# ftp://ftp.cmdl.noaa.gov/hats/Total_Cl_Br/## 2 Historical data of concentrations analogous to CMIP5 project from## Meinshausen, M., S. J. Smith, K.V. Calvin, J.S. Daniel, M.L.T. Kainuma,# J.F. Lamarque, K. Matsumoto, S. A. Montzka, S. C. B. Raper, K. Riahi,# A. M. Thomson, G. J. M. Velders and D. van Vuuren 2011.# "The RCP Greenhouse Gas Concentrations and their Extension from# 1765 to 2300." Climatic Change Special Issue,# DOI: 10.1007/s10584-011-0156-z## 3 IPCC expressions for Radative Forcing:## Table 6.2 in "Radiative Forcing of Climate Change" pag. 358# Co-ordinating Lead Author: V. Ramaswamy# http://www.grida.no/climate/ipcc_tar/wg1/pdf/tar-06.pdd## 4 IPCC Radiative Efficiencies as given in 2013 report Table 8.A.I## Myhre, G., D. Shindell, F.-M. Breon, W. Collins, J. Fuglestvedt, J. Huang,# D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock,# G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural# Radiative Forcing. In: Climate Change 2013: The Physical Science Basis.# Contribution of Working Group I to the Fifth Assessment Report of the# Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin,# G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,# V. Bex and P.M. Midgley eds.]. Cambridge University Press, Cambridge,# United Kingdom and New York, NY, USA.## 5 IPCC Radative Forcings for Stratospheric H2O, Stratospheric O3 and# Tropospheric O3 given on the 2013 IPCC Report Table AII.1.2## IPCC, 2013: Climate Change 2013: The Physical Science Basis.# Contribution of Working Group I to the Fifth Assessment Report of the# Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin,# G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia,# V. Bex and P.M. Midgley eds.]. Cambridge University Press, Cambridge,# United Kingdom and New York, NY, USA, 1535 pp.### --- Dataset RF created in: 2016-06-10 13:52:23## -----------------------------------------------------------# Contact INFO: [email protected]# University of Reading, Deparment of Meteorology.# ------------------------------------------------------------------------------## FIELDS organized by columns as:#
S6.1 Python code to read the radiative forcing datasets
1 """2 Code to read the Radiative Forcing datasets as numpy3 structured arrays. It is programmed with python 34 syntax but changing the print statement it can work5 with python 2.7.67 Usage: (f_name is the name or path to provided .txt files)89 data = read.RF_dataset(f_name, dataset=’AGAGE’) # or dataset=’NOAA’
1011 year = data[’date’] # gives information on datetime array12 o3_trop_RF = data[’o3trop_RF’] # gives values of O3 tropospheric RF time series.1314 Note: with datetime module it is possible to convert the string date15 values to datetime objects:1617 year = [datetime.datetime.strptime(a, ’%Y-%m-%d %H:%M:%S’) for a in data[’date’]]1819 """2021 __author__ = "Ramiro Checa-Garcia"22 __copyright__ = "SMURPHS project, University of Reading"23 __credits__ = ["Ramiro Checa-Garcia", "Keith P. Shine", "M. I. Hegglin"]24 __license__ = "GPL"25 __maintainer__ = "Ramiro Checa-Garcia"26 __email__ = "[email protected]"27 __status__ = "Production"28 __version__ = "1.0"2930 import numpy as np3132 dic_dtype_NOAA = {’names’: (’date’,33 ’ccl4_RF’,’cfc-113_RF’,’cfc-11_RF’,’cfc-12_RF’,34 ’ch3br_RF’,’ch3ccl3_RF’,’ch4_RF’,’co2_RF’,35 ’fhalon_RF’,’h-1211_RF’,’h-1301_RF’,’h-2402_RF’,36 ’h2ostrat_RF’,’hcfc-141b_RF’,’hcfc-142b_RF’,37 ’hcfc-22_RF’,’hfc-125_RF’,’hfc-134a_RF’,38 ’hfc-143a_RF’,’hfc-227ea_RF’,’hfc-32_RF’,39 ’n2o_RF’,’o3dep_RF’,’o3strat_RF’,’o3trop_RF’),40 ’formats’: (’a20’,41 np.float, np.float, np.float, np.float,42 np.float, np.float, np.float, np.float,43 np.float, np.float, np.float, np.float,44 np.float, np.float, np.float, np.float,45 np.float, np.float, np.float, np.float,46 np.float, np.float, np.float, np.float,47 np.float)}4849 dic_dtype_AGAGE = {’names’: (’date’,50 ’c2f6_RF’,’ccl4_RF’,’cfc-113_RF’,’cfc-114_RF’,51 ’cfc-115_RF’,’cfc-11_RF’,’cfc-12_RF’,’ch3br_RF’,52 ’ch3ccl3_RF’,’ch3cl_RF’,’ch4_RF’,’co2_RF’,53 ’fhalon_RF’,’h-1211_RF’,’h-1301_RF’,’h-2402_RF’,54 ’h2ostrat_RF’,’hcfc-141b_RF’,’hcfc-142b_RF’,55 ’hcfc-22_RF’,’hfc-125_RF’,56 ’hfc-134a_RF’,’hfc-143a_RF’,’hfc-227ea_RF’,57 ’hfc-23_RF’,’hfc-245fa_RF’,’hfc-32_RF’,’n2o_RF’,58 ’o3dep_RF’,’o3strat_RF’,’o3trop_RF’,’sf6_RF’),59 ’formats’: (’a20’,60 np.float, np.float, np.float, np.float,61 np.float, np.float, np.float, np.float,62 np.float, np.float, np.float, np.float,63 np.float, np.float, np.float, np.float,64 np.float, np.float, np.float, np.float,65 np.float, np.float, np.float, np.float,66 np.float, np.float, np.float, np.float,67 np.float, np.float, np.float, np.float)}68697071 def read_RF_dataset(f_name, dataset=’AGAGE’):7273 if dataset == ’NOAA’:74 dataset = np.loadtxt(f_name, dtype=dic_dtype_NOAA)75 elif dataset==’AGAGE’:76 dataset = np.loadtxt(f_name, dtype=dic_dtype_AGAGE)77 else:78 print(’Dataset name should be NOAA or AGAGE’)7980 return dataset
Acknowledgements
The authors acknowledge AGAGE for the concentrations global datasets. Also the authors acknowl-edge NOAA-ERSL-Global Monitoring Division for their GHGs concentrations global estimations, andKNMI for their climate explorer platform. The AGAGE network is supported principally by NASA(USA) grants to MIT and SIO, and also by: DECC (UK) and NOAA (USA) grants to Bristol University;CSIRO and BoM (Australia): FOEN grants to Empa (Switzerland); NILU (Norway); SNU (Korea); CMA(China); NIES (Japan); and Urbino University (Italy).
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