Volcanic forcing improves Atmosphere-Ocean Coupled General Circulation Model scaling performance

0.39 MB·English

by  Dmitry Vyushin

Volcanic forcing improves Atmosphere-Ocean Coupled General Circulation Model
scaling performance
Dmitry Vyushin,1,∗ Igor Zhidkov,1,† Shlomo Havlin,1,‡ Armin Bunde,2,§ and Stephen Brenner3,¶
1Minerva Center and Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
2Institut fu¨r Theoretische Physik III, Justus-Liebig-Universit¨at Giessen,
Heinrich-Buff-Ring 16, 35392 Giessen, Germany
3Department of Geography, Bar-Ilan University, Ramat-Gan 52900, Israel
4 (Dated: February 2, 2008)
0
Recent Atmosphere-Ocean Coupled General Circulation Model (AOGCM) simulations of the
0
twentieth century climate, which account for anthropogenic and natural forcings, make it possi-
2
ble to study the origin of long-term temperature correlations found in the observed records. We

n study ensemble experiments performed with the NCAR PCM for 10 different historical scenarios,
a including no forcings, greenhouse gas, sulfate aerosol, ozone, solar, volcanic forcing and various
J combinations, such as natural, anthropogenic and all forcings. We compare the scaling exponents

8 characterizingthelong-termcorrelations oftheobservedandsimulatedmodeldatafor16represen-
2 tative land stations and 16 sites in the Atlantic Ocean for these scenarios. We find that inclusion
of volcanic forcing in the AOGCM considerably improves the PCM scaling behavior. The scenar-

] ios containing volcanic forcing are able to reproduce quite well the observed scaling exponents for
h
the land with exponents around 0.65 independent of the station distance from the ocean. For the
p
AtlanticOcean, scenarios with thevolcanic forcing slightly underestimate the observed persistence
-
o exhibitingan average exponent 0.74 instead of 0.85 for reconstructed data.
a
. PACSnumbers: 92.60.Wc, 92.70.Gt,02.70.Hm,92.60.Bh
s
c
i
s
While many modelers and climatologists focus their tal sites do show long-term correlation and the α values
y
h studies on trends caused by natural and anthropogenic donotdependonthedistanceofthesitefromtheocean.
p forcings during the twentieth century [1, 2, 3, 4, 5, 6] In this Letter we show that the recent PCM model
[ we here focus on another important aspect of temper- simulations properly reproduce the observed long-term

1 ature anomalies - long-term correlations. One of the correlationsforSATonlandonlyforthosescenariosthat
v firststudiesonthequestionhowgeneralcirculationmod- include volcanic forcing. These scenarios also show bet-
3 elsreproduceobservedclimatevariabilitywasperformed ter scaling performance over the ocean than the other
4 by [7]. Using power spectrum analysis, which is affected scenarios.
1 by nonstationarities in time series, they argue that the
Inordertopresenttheland-surfacetemperatureprofile
1 GFDL AOGCM reproduced the natural climate vari-
0 for the last century, 16 observed daily maximum tem-
ability on decadal and centennial scales correctly for a
4 perature time series are considered. They have been
1000 year control run integration. Several recent stud-
0 collected from different representative weather stations
ies of [8, 9, 10, 11, 12, 13, 14] clearly demonstrate that
/ around the globe for the following sites: Vancouver,
s surface air temperature (SAT) anomalies are long-term
c Tucson, Cheyenne, Luling, Brookings, Albany, Oxford,
i correlated with a fluctuation exponent α close to 0.65. Prague,Kasan,Tashkent,Surgut,Chita,Seoul,Jakutsk,
s
On the other hand, results of [15, 16, 17, 18, 19] indi-
y Melbourne and Sydney. We also analyze the gridded
catethatAOGCMsunderestimatesurfaceairtemperture
h monthly mean sea surface temperature (SST) for 16
p (SAT) persistence for control run, greenhouse gas forc-
sites in the Atlantic ocean with a spatial resolution of
: ing only and greenhouse gas forcing plus sulfate aerosols
v 2.5ox2.5o for the period of 1900−2002 from the Kaplan
scenarios. In contrast, recent studies of reconstructed
i ExtendedSSTA data set (see also [23]). For 1900−1981
X data [20, 21] claim that inner continental regions do not
this is the analysis of [24] which uses optimal estimation
r showlong-termcorrelations,andthusAOGCMssuccess-
a inthespaceof80empiricalorthogonalfunctions (EOFs)
fullyreproducethenaturalpersistenceforthecontrolrun
inorderto interpolate shipobservationsofthe U.K.Me-
andgreenhousegasforcingonlyscenarios. Recently,this
teorological Office database [25]. The data after 1981
claim was tested on observed records by [22] with the
consists of gridded data from the National Center for
finding that the SAT fluctuationexponentsfor continen-
EnvironmentalPredictionoptimalinterpolationanalysis,
which combines ship observations with remotely sensed
data [26]. This analysis is performed on the same set of
80 EOFs as used in [24] in order to provide enhanced
∗Electronicaddress: [email protected]
data quality.
†Electronicaddress: [email protected]
‡Electronicaddress: [email protected] The model considered in our study is the ParallelCli-
§Electronicaddress: [email protected] mate Model (PCM), which was developed at the Na-
¶Electronicaddress: [email protected] tional Center for Atmospheric Research (NCAR). It is a
2
fully coupled global ocean-atmosphere-sea ice-land sur- 1010 (a) OBSERVED (b) B06.61 PCM scenario 1010
face model that produces a stable climate without flux (daily) Prague (daily) Prague
adjustment. Thehorizontalresolutionoftheatmosphere 108 Seoul Seoul 108
icsale.qRuievsaolleunttiotnoo2f.8thoxe2o.8coe,anwiisthro1u8ghlleyve2l/s3ion, itnhcerevaesrintig- Vancouver Vancouver
to 1/2o at the equator, with 32 levels. The detailed de- 106 Cheyenne Cheyenne 106
svcarriipotuisonfoorfcitnhgesmaonddetlhaenidr croesmubltisnfartoiomnsexmpaeyrimbeenfotsuunsdining F(s)104 BKraosoaknings BKraosoaknings 104
[3, 27, 28] and [4]. Luling Luling
Here we study 10 forcing combinations: no forcings, 102 Chita Chita 102
greenhouse gas, sulfate aerosol, ozone, solar, volcanic, Oxford Oxford
solar + volcanic, ozone + solar + volcanic, greenhouse Sydney Sydney
gas + sulfate aerosol + ozone, all forcings. Greenhouse 100 0.65 0.65 100
0.5 0.5
gas forcing is based on historical observations of CO ,
2
N2O, CH4, CFC-11, CFC-12, and ozone [28]. Evolution 10-2 10-2
of direct forcing from tropospheric sulfate aerosol is re- 101 102 103 104 101 102 103 104
s [days] s [days]
ported by [29]. Historical changes of solar irradiance
were reconstructed by [30] and volcanic forcing by [4]. FIG. 1: DFA2 fluctuation functions F(s) for the daily sur-
The period of all experiments is 1890-1999. faceairmaximumtemperatureanomaliesat10landsites: (a)
For the no forcings and solar+volcanic scenarios we observeddataand(b)NCARPCMB06.61(allforcings)sim-
analyze the available 3-member ensembles, whereas for ulateddata. ThescaleofF(s)isarbitrary. Thestraightlines
other scenarios 4-member ensembles are available. For crossing each curve represent the best asymptotic fit. The
two lines shown at thebottom haveslopes 0.65 and 0.5.
eachscenario,weselectedthetemperaturerecordsofthe
4 grid points closest to each site, and bilinearly interpo-
lated the data to the location of the observed site.
For each record, we analyse daily (or monthly) tem- All curves are shown in a double logarithmic presenta-
perature anomalies ∆T . The ∆T are called long-term tion. We plot 10 typical DFA curves chosen from our 16
i i
correlated if their autocorrelation function C(s) decays sites over land. The sites chosen include coastal, near
with time lag s by a power law coastal, and inland locations.
The approximate period of the observed records is
C(s)∼s−γ, 0<γ <1. (1)
1880-1990, with the maximum length for Prague (1775-
1992) and the minimum for Seoul (1908-1993). The pe-
To overcome possible nonstationarities in the data, we
riodoftheB06.61runis1890-1999. TheslopesinFigure
do not calculate C(s) directly. Instead we construct
1a correspond to fluctuation exponents of the observed
the “profile” Y = Pn ∆T and study the fluctua-
n i=1 i SAT anomalies, and vary from 0.62 to 0.68, with an av-
tion function F(s) of the profile in segments of length s
erage close to 0.65. Figure 1a demonstrates that SAT
by using the second orderdetrended fluctuation analysis
anomalies for all sites studied obey long-term power-law
(DFA2) [31, 32]. In DFA2 we determine in eachsegment
correlations independent of the distance from the near-
the best second-order polynomial fit of the profile. The
est ocean. The slopes in Figure 1b range from 0.62 to
standard deviation of the profile from these polynomials
0.69. Comparing Figures 1a and 1b shows that the scal-
representsthesquareofthefluctuationsineachsegment.
ingofthe NCARPCMoutputagreesquite wellwiththe
The fluctuation function F(s) is the root mean square
scaling of the observed data over land.
of the fluctuations in all segments. For the relevant case
Figure2showsDFA2 curvesforthe 10sites inthe At-
of long-term power-law correlations given by Eq. (1),
lantic ocean for the Kaplan reconstructed monthly SST
with 0 < γ < 1, the fluctuation function F(s) increases
anomalies (Figure 2a) and for the NCAR PCM monthly
according to a power law,
averagedSST anomaliesfromtheallforcingsB06.61run
γ (Figure 2b). The slopes for the reconstructed SSTA
F(s)∼sα, α=1− . (2)
2 vary from 0.71 in the equatorial part of the Atlantic to
1.0 in the Northern Atlantic, with an average of 0.85.
For uncorrelated data (as well as for short-range corre- TheSSTAexponentscharacterizingthememoryeffecton
lations represented by γ ≥ 1 or exponentially decaying decadalandcentennialscalesseemtodependoncomplex
correlation functions), we have α = 1. For long-term ocean circulation dynamics. The variation of the scaling
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correlations we have α> 1. exponents over the Atlantic Ocean is significantly larger
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FirstweplottheresultsofDFA2(DFAcurvesofhigher thanonland,whichisprobablyduetodifferentoceancir-
ordershowthesameperformance)fortheobserveddaily culation patterns in equatorial, mid-latitude, and high-
maximumtemperature(Figure1a)andNCARPCMsim- latitude regions. In a double logarithmic presentation,
ulations from the B06.61 run (Figure 1b). Run B06.61 the slopes of the DFA2 curves for the simulated ocean
representsoneofthe runsfromthe allforcingsensemble. recordshaveanaverageof0.72whichisnoticeablylower
3
106 (a) RECONSTRUCTED (b) B06.61 PCM scenario 106
F(s)111000024 (monthly) ((2(2((532(2523.(.2(25.2(52.2(.525W.24W25.W5.W2.5W5,W,5. W,W5,W4 ,5 , W222 ,7,1 ,71..7 1,25.152. 3.575N.75N57..N5N2.5S)5)N.5)S))S5S)))S)) 0.8 (monthly)((2(23((((235522(2((.42225.222.55.2..W.2.555W55W..5WWW5WW,W ,,W5 ,,,,,42 7 ,121,2117 . 52725.377.55.2..N.7.555N55N..5NNS)5SS))S)S)))))) 0.8111000024
0.5 0.5
10-2 10-2
101 102 101 102
s [months] s [months]
FIG.2: DFA2fluctuationfunctionsF(s)formonthlyseasur-
face temperature anomalies at 10 sites in the Atlantic ocean
: (a) the Kaplan reconstructed data and (b) NCAR PCM
B06.61 (all forcings) run. Thescale of F(s)is arbitrary. The FIG. 3: Histograms of the fluctuation exponents α for the
straight lines crossing each curve represent the best asymp- observedrecords(greycolumn)andthesimulatedrecords,for
totic fit. The two lines shown at the bottom have slopes 0.8 land stations. The considered 10 scenarios are: no forcings,
and 0.5. greenhouse gas (G), sulfate (Su), ozone (Oz), solar (S), vol-
canic (V), solar + volcanic (S+V), ozone + solar + volcanic
(Oz+S+V), greenhouse gas + sulfate + ozone (G+Su+Oz),
than the observed averageof 0.85. and all forcings. Four bins in each panel correspond to α in
Figure3presentsthefluctuationexponentdistribution theintervals [0.5,0.56), [0.56,0.62),[0.62,0.68), and [0.68,0.74]
respectively. The grey column in each panel corresponds to
fortheobserveddataandforthe10NCARPCMscenar-
thefluctuationexponentdistributionfortheobservedrecords,
ios considered for the 16 land locations. In each panel
theblack columns are for simulated records.
the“grey”columnintherange[0.62,0.68)representsthe
distributionofthefluctuationexponentsfortheobserved
data. As seen from the figure, scenarios containing vol-
land. However the NCAR PCM still underestimates the
canic forcing best reproduce the observations since they
observed persistence of the oceans.
haveapeakatthesamerange[0.62-0.68)astheobserved
data. Theiraveragefluctuationexponentiscloseto0.65. The main conclusion from our research is that the
In contrast, the other six scenarios that do not contain NCAR PCMis able to reproduce the scalingbehaviorof
volcanic forcing, have an average fluctuation exponent the observed land SAT records for the last century after
for land less than 0.6 (see also [17]). takingintoaccountallhistoricallybasednaturalandan-
thropogenicforcings. However,eventhebestscenariofor
Similar behavior is found over the Atlantic Ocean.
the landslightly underestimatesnaturalSST persistence
Only those scenarios containing the volcanic forcing ex-
in the ocean, possibly due to errors in the simulations
hibit an average fluctuation exponent greater than 0.7
of deep ocean circulation,the atmosphere-oceaninterac-
with the largestvalue equalto 0.76for the volcanicforc-
tion, and/or an insufficiently long spin up period of the
ing only scenario. Figure 4 shows the fluctuation expo-
ocean component of the AOGCM.
nent distribution for the Atlantic Ocean for the Kaplan
data (in grey) and for 10 studied scenarios (in black). The results presented in this letter may also help to
ThusthePCMunderestimatesthefluctuationexponents clarify the controversy about the values of SAT fluctu-
obtainedforreconstructeddatafortheAtlanticOceanby ation exponents for inner continental regions (see [20]).
10-15%. Our study indicates that not only the observed records
Therefore, we can conclude that for the NCAR PCM for inner continental regions are long-term correlated in
addition of volcanic forcing to any other forcing combi- agreement with [22], but also the recent PCM simu-
nation immediately improves its scaling behavior both lations for these regions show similar fluctuation expo-
for the land and the ocean. This fact suggests that (be- nents, characteristic of long-term persistence.
sides the atmosphere-ocean coupling) the volcanic forc- Finally,this letter alsosupportsthe suggestionof [17]
ing is mostly responsible for the presence of the long- thattheinabilityofthesevenleadingAOGCMs,fortheir
termcorrelationsintheNCARPCMoverlandonannual controlruns,greenhousegasforcingonly,andgreenhouse
and decadal scales. For the ocean, the addition of vol- gas plus aerosols scenarios, to mimic the observed SAT
canicforcingleadstostrongermemoryandconsequently persistenceiscausedbytheabsenceofnaturalforcings,in
higher fluctuation exponents comparing to those for the particular volcanic forcing. The results of our detrended
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fluctuation analysis for the 16 land stations around the
globe and the 16 sites in the Atlantic ocean suggestthat
volcanic forcing has by far the largest impact on the
AOGCM long-memory persistence.
Acknowledgments
This work has been supported by the Deutsche
FIG. 4: Histograms for the fluctuation exponents for the re- Forschungsgemeinschaft and the Israel Science Founda-
constructed(ingrey)andsimulated(inblack)recordsforthe tion. We aregratefulto W.M. Washington,M.Wehner,
Atlantic Ocean. The five bins correspond to α values in the and G. Strand for providing access to the NCAR PCM
intervals [0.5,0.6),[0.6,0.7),[0.7,0.8),[0.8,0.9), and [0.9,1.0] re- simulationsdata. The authorsthank J.Eichner,J.Kan-
spectively. telhardt and E. Koscielny-Bunde for fruitful discussions.
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