Variability of large-scale atmospheric circulation indices ... - CiteSeerX

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Meteorologische Zeitschrift, Vol. 18, No. 4, 379-396 (August 2009) c by Gebrüder Borntraeger 2009

Variability of large-scale atmospheric circulation indices for the northern hemisphere during the past 100 years ¨ S TEFAN B R ONNIMANN *1 , A LEXANDER S TICKLER1 , T HOMAS G RIESSER2 , A NDREAS M. 2 F ISCHER , A NDREA G RANT2 , T RACY E WEN2 , T IANJUN Z HOU2 , M ARTIN S CHRANER2 , E UGENE ROZANOV1,3 and T HOMAS P ETER2 1 Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland 2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 3 PMOD/WRC, Davos, Switzerland

China

(Manuscript received December 16, 2008; in revised form April 1, 2009; accepted April 2, 2009)

Abstract We present an analysis of the large-scale atmospheric circulation variability since 1900 based on various circulation indices. They represent the main features of the zonal mean circulation in the northern hemisphere in boreal winter (such as the Hadley circulation, the subtropical jet, and the polar vortex in the lower stratosphere) as well as aspects of the regional and large-scale circulation (the Pacific Walker Circulation, the Indian monsoon, the North Atlantic Oscillation, NAO, and the Pacific North American pattern, PNA). For the past decades we calculate the indices from different reanalyses (NCEP/NCAR, ERA-40, JRA-25, ERAInterim). For the first half of the 20th century the indices are statistically reconstructed based on historical upper-air and surface data as well as calculated from the Twentieth Century Reanalysis. The indices from all these observation-based data sets are compared to indices calculated from a 9-member ensemble of “all forcings” simulations performed with the chemistry-climate model SOCOL. After discussing the agreement among different data products, we analyse the interannual-to-decadal variability of the indices in the context of possible driving factors, such as El Niño/Southern Oscillation (ENSO), volcanic eruptions, and solar activity. The interannual variability of the Hadley cell strength, the subtropical jet strength, or the PNA is well reproduced by the model ensemble mean, i.e., it is predictable in the context of the specified forcings. The source of this predictability is mainly related to ENSO (or more generally, tropical sea-surface temperatures). For other indices such as the strength of the stratospheric polar vortex, the NAO, or the poleward extent of the Hadley cell the correlations between observations and model ensemble mean are much lower, but so are the correlations within the model ensemble. Multidecadal variability and trends in the individual series are discussed in the context of the underlying anthropogenic and natural forcings. While consistent trends were found for some of the indices, results also indicate that care should be taken when analysing trends in reconstructions or reanalysis data. eschweizerbartxxx author

Zusammenfassung Diese Studie präsentiert Ergebnisse einer Analyse der Variabilität der großräumigen, atmosphärischen Zirkulation seit 1900, basierend auf verschiedenen Zirkulationsindizes. Die Indizes umfassen sowohl die Hauptcharakteristika der zonal gemittelten, nordhemisphärischen Zirkulation im Nordwinter (z.B. HadleyZirkulation, Subtropenjet und polarer Vortex in der unteren Stratosphäre) als auch Aspekte der regionalen und großräumigen Zirkulation (pazifische Walker-Zirkulation, indischer Monsun, nordatlantische Oszillation (NAO) und das “Pacific North American”-Muster (PNA)). Die Indizes wurden für die letzten Dekaden aus verschiedenen Reanalysen berechnet (NCEP/NCAR, ERA-40, JRA-25, ERA-Interim). Für die erste Hälfte des 20. Jahrhunderts wurden sie dagegen, basierend auf historischen Bodendaten und Messdaten aus der freien Troposphäre, statistisch rekonstruiert und zusätzlich aus der “Twentieth Century”-Reanalyse berechnet. All diese auf Messungen basierenden Indizeszeitreihen werden in der vorliegenden Studie mit entsprechenden, aus einem Ensemble von 9 Simulationen gewonnenen Indizeszeitreihen verglichen (alle externen “Forcings” aktiviert, Chemie-Klimamodell SOCOL). Nach der genaueren Betrachtung des Grades ¨ der Ubereinstimmung zwischen den verschiedenen Produkten wird die interannuelle und dekadale Variabilität der Indizes im Kontext möglicher Einflussfaktoren wie El Niño/Southern Oscillation (ENSO), Vulkanausbrüchen und Sonnenaktivität analysiert. Die interannuelle Variabilität der Hadleyzellstärke, des Subtropenjets oder des PNA-Musters wird vom Ensemblemittel des Modells erfolgreich reproduziert, d.h. sie ist vorhersagbar bei Kenntnis der spezifischen “Forcings”. Die Quelle der Vorhersagbarkeit liegt im Wesentlichen in der ENSO-Variabilität. Für andere Indizes wie die Stärke des stratosphärischen, polaren Vortex, die NAO oder die polwärtige Ausdehnung der Hadleyzelle sind die Korrelationen zwischen “Beobachtungen” und dem Ensemblemittel des Modells viel kleiner. Dasselbe gilt aber gleichermaßen für die Korrelationen innerhalb des Ensembles. Multidekadale Variabilität und Trends in den Zeitreihen werden im Zusammenhang mit zugrunde liegenden anthropogenen als auch natürlichen Antriebsfaktoren besprochen. Obwohl für einige Indizes konsistente Trends gefunden werden konnten, zeigen die Ergebnisse auch, dass beim Ableiten von Trends aus Reanalyse- oder Rekonstruktionsdaten Vorsicht geboten ist. Corresponding author: Stefan Brönnimann, Institute for Atmospheric and Climate Science, CHN M 11, Universitätstrasse 16, 8092 Zurich, Switzerland, e-mail: [email protected]

DOI 10.1127/0941-2948/2009/0389

0941-2948/2009/0389 $ 8.10 c Gebrüder Borntraeger, Berlin, Stuttgart 2009

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S. Brönnimann et al.: Variability of large-scale atmospheric circulation indices

1 Introduction In order to better understand – and eventually predict – interannual-to-decadal climate variability, it is important to address not only climate at the Earth’s surface and the possible drivers of its variability such as changing seasurface temperatures (SSTs), but also the large-scale atmospheric circulation that links the two. The focus on circulation variability provides an important perspective that allows process-oriented assessment of models and provides diagnostics for assessing changes in the climate system. Up to now, analysis of the global atmospheric circulation during the past 100 years was either limited to the Earth’s surface (which does not allow analyzing some important features of the large-scale circulation) or was restricted by the data availability to the past 50–60 years (a period that includes strong trends and may not represent the full range of variability). Here we analyse the variability of the large-scale circulation since 1900 based on various observation-based data sets as well as model simulations. Information on the large-scale circulation back into the first half of the 20th century is essential for understanding past extremes. For instance, the pronounced warming of the globe and particularly the Arctic between 1918 and 1945 is still not well understood (see F U et al., 1999; OVERLAND et al., 2004; G RANT et al., 2009). Other examples include the “Dust Bowl” droughts in the 1930s (S CHUBERT et al., 2004; S EAGER ¨ et al., 2008; B R ONNIMANN et al., 2009) or the strong El ¨ Niño in 1940–1942 (B R ONNIMANN et al., 2004). Forecast systems will only be able to predict such anomalies if the large-scale circulation response is correctly modelled. Validating the forced response of the large-scale circulation in a model by comparing it with observationbased information over the past 100 years is therefore important. In addition to extremes, trends are of particular interest. In fact, recent trends in the large-scale circulation systems such as the Hadley circulation or the Indian monsoon are uncertain (see T RENBERTH et al., 2007), as are predictions of future changes of these systems (see M EEHL et al., 2007). However, changes in the strength and position of these circulation systems have huge impacts on the amount, intensity, and seasonality of rainfall and the position of subtropical dry zones and thus affect a large fraction of the world population. A better understanding of such changes is therefore important and an analysis that reaches back into a period when the anthropogenic influence was arguably much smaller than today may help in this endeavour. In this study the large-scale circulation is analysed in the form of suitable indices. Many papers have described the variability of such indices (e.g., O ORT and Y IENGER, 1996; TANAKA et al., 2004; Q UAN et al., 2005; M ITAS and C LEMENT, 2005; Z HOU et al., 2009a). However, due to a lack of upper-air data, the work was restricted to the past decades. Some important eschweizerbartxxx author

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circulation indices such as the North Atlantic Oscillation (NAO), the Southern Oscillation (SOI) or the North Pacific Oscillation (NPI) are defined based on sea-level pressure (SLP) data and hence trends and variability in these indices can readily be studied back to the 19th century (e.g., T RENBERTH and H URRELL, 1994; H UR RELL, 2003; V ECCHI et al., 2006). Other important circulation systems such as the monsoons systems or the Hadley cell cannot directly be addressed in SLP data. Surface wind data may help to address these systems (E VANS and K APLAN, 2004), but ideally upper-level data should be used. Alternatively, proxy-based statistical reconstruction approaches have been used, which allow extending the time horizon back several hundred years (G ONG and L UTERBACHER, 2008). In this study we extend the analysis of large-scale circulation variability back to 1900 using reanalysis data as well as statistical reconstructions, which however are not based on proxies but on historical upper-air and surface observations. We describe the main features of the largescale atmospheric circulation variability back to 1900 and compare them to possible driving factors such as El Niño/Southern Oscillation (ENSO) or external forcing factors. Finally, we compare the indices with the output from a global chemistry-climate model. Note that the aim is to give an overview of large-scale circulation variability. A detailed discussion of all mechanisms is beyond the scope of the paper. The paper is organised as follows: In Section 2 we define the indices used, describe the reconstruction method and show the validation results. In the last part of this section details about the model simulations can be found. In Section 3 we present the index time series and discuss the results of the comparison of model and observations.

2 Data and methods a) Data The indices are based on monthly upper-level fields from various data sets: For the past decades this includes the reanalysis data sets NCEP/NCAR (NNR hereafter) from 1948 to 2009 (K ISTLER et al., 2001), ERA-40 from 1958 to 2002 (U PPALA et al., 2005), JRA-25 from 1979 to 2007 (O NOGI et al., 2007), and ERA-Interim from 1989 to 2007. All reanalyses are widely used. However, they may not be suitable for all purposes. Errors and inconsistencies in the assimilation system or in the data assimilated can lead to step changes in the data. For the case of NNR, there are several known shortcomings that may affect, in particular, the low-frequency variability (see e.g., S AN TER et al., 1999; R ANDEL et al., 2000; H ARNIK and C HANG, 2003, C HANG and F U, 2003; B ENGTSSON et al., 2004). The inclusion of satellite data after 1978 and a related processing error led to a step change in temperatures, mostly affecting the lower stratosphere over



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Table 1: Data used for the reconstruction.

Data HadSLP2 NASA-GISS surface air temperature Historical radiosonde, aircraft and kite data TD52 and TD53 upper level wind data

Period 1900-2002 1900-2002 1921-1947

African data set, upper-level wind

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the tropics. Also, during the earliest period (1948–1957) observation and analysis times were different and some of the assimilated radiosonde data had systematic errors (G RANT et al., in press). However, problems are also documented for ERA-40, for instance relating to the temperature structure in the Arctic (G RANT et al., 2008). T RENBERTH and S MITH (2006) show that trends in the tropics in ERA-40 are unreliable due to bias adjustment in assimilating satellite data. T RENBERTH et al. (2009) discuss the differences and remaining uncertainties in the ocean-to-land heat transport in these data. Although ERA-40 is normally considered more accurate than NNR (see S IMMONS et al., 2004; S ANTER et al., 2004; B ENGTSSON et al., 2004), we chose NNR as a reference for comparisons because it has the longest overlap with all other data sets. For the first half of the 20th century we used mainly two data sources. On the one hand, the indices were statistically reconstructed based on historical surface and upper-air data, termed REC hereafter (1900–1947, the approach is outlined in section 2c). On the other hand, they were calculated from the Twentieth Century Reanalysis (C OMPO et al., 20091 , see also W HITAKER et al., 2004; C OMPO et al., 2006, 2008). The data cover the period 1908–1958 and are termed 20CR in the following. Finally, indices based on geopotential height (GPH) were also calculated from reconstructed upperlevel fields (G RIESSER et al., 2008), 1900–1957. The reconstructions are based on monthly mean surface and upper-air information (see Fig. 1, Table 1). The surface information includes SLP fields from HadSLP2 (A LLAN and A NSELL, 2006) and surface air temperature station data from NASA-GISS (H ANSEN et al., 1999) reaching back to 1900. The historical upper-air data reach back to 1918 and comprise radiosonde, aircraft and kite data (providing temperature and some¨ times GPH) from various published sources (B R ONNI MANN , 2003; E WEN et al., 2008a; G RANT et al., in

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1 C OMPO ,

G.P., J.S. W HITAKER , P.D. S ARDESHMUKH , N. M ATSUI , R.J. A LLAN , X. Y IN , B.E. G LEASON , R.S. V OSE , G. RUTLEDGE , P. B ESSE ¨ MOULIN , S. B R ONNIMANN , M. B RUNET, R.I. C ROUTHAMEL , A.N. G RANT, P.Y. G ROISMAN , P.D. J ONES , M. K RUK , A.C. K RUGER , G.J. M ARSHALL , M. M AUGERI , H.Y. M OK , Ø. N ORDLI , T.F. ROSS , R.M. T RIGO , X. WANG , S.D. W OODRUFF , S.J. W ORLEY, 2009: The Twentieth Century Reanalysis Project, manuscript in preparation.

Reference/comments ALLAN and ANSELL, 2006 HANSEN et al., 1999 BRÖNNIMANN, 2003, EWEN et al., 2008a; GRANT et al., in press Obtained from NCAR, see http://dss.ucar.edu/docs/papersscanned/papers.html, documents RJ0167, RJ0168 Obtained from MétéoFrance

press) as well as several pilot balloon data sets (upperlevel wind only, see Table 1). Aircraft, kite, and most of the pilot balloon data cover the lower and middle troposphere, whereas the radiosonde data as well as some of the pilot balloon series reach up to the upper troposphere and lower stratosphere. The station locations are shown in Figure 1a. Figure 1b displays the number of available monthly means as a time series. It can be seen that prior to the 1930s the number of upper-air variables is very limited.

b) Definition of indices Important features of the global circulation can be addressed in terms of its zonal mean. Therefore, a first set of indices represents the zonal mean meridional (Hadley cell) and zonal (subtropical jet, polar vortex) circulation in the northern hemisphere. In addition we consider important regional circulation features, such as the Pacific Walker circulation, the Indian monsoon, the NAO, and the Pacific North American pattern (PNA). The focus is on the extended boreal winter period, defined as December to March. Exceptions are the Indian monsoon (Jun.Aug.) and the Pacific Walker circulation (Sep.-Jan.). In addition we also use the NINO3.4 index (SST average over the region [5◦ S–5◦ N, 170◦ W–120◦ W], Sep.–Feb.) calculated from HadISST data (R AYNER et al., 2003). Strength and poleward extent of the northern Hadley cell (HC and HCL): The strength of the Hadley circulation is often depicted by the maximum of the zonal mean meridional streamfunction Ψ (e.g., O ORT and Y IENGER, 1996; Q UAN et al., 2005; W EBSTER, 2005). Consequently, we define HC as the maximum of Ψ at 500 hPa between 0◦ N and 30◦ N. Note that absolute values are not necessarily comparable between different analyses (see Section 3). Nevertheless, this index is more reproducible between different analyses than alternative indices (e.g., the difference in zonal mean meridional wind between 200 hPa and 850 hPa at 10◦ N, see O ORT and Y IENGER, 1996; results are not shown). The poleward extent (HCL) of the Hadley circulation is defined as the latitude at which the 850 hPa zonal mean meridional wind becomes poleward when moving northward from the latitude of maximum Ψ. Strength of the northern subtropical jet (SJ): The strength of the subtropical jet (SJ) was defined as the

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Figure 1: a) Map showing the locations of the upper-air stations used in this study. Boxes indicate the sub-regions used for reconstruction of the PNA and DIMI indices. Lines at the margin indicate the approximate range of predictors used for the reconstruction of the individual indices. b) Number of variables per month in the reconstruction model (only station data are shown – SLP data are used in gridded form and are not included in the figure).

maximum in zonal mean zonal wind u at 200 hPa between the equator and 50◦ N (see F ISCHER et al., 2008b). It is an indicator of the subtropical circulation and is also relevant for teleconnections from the tropics to the extratropics (because the jet streams serve as a wave guide). A zonal mean definition of the subtropical jet is a simplification and does not account for the substantial differences between land and ocean areas (T RENBERTH and FASULLO, 2009, see also H ARNIK and C HANG, 2003; C HANG and F U, 2003). We therefore also analyse the subtropical jet over land areas (SJland ), defined as 120◦ – 80◦ W and 10◦ W–140◦ E (too little information is available over the oceans for skilful ocean-only reconstructions). Midlatitude circulation (Z300): The 300 hPa GPH averaged between 30◦ and 60◦ N is termed Z300. Because of the poleward sloping pressure surfaces, Z300 depends both on latitudinal changes of the pressure surfaces (reflecting changes in the midlatitude zonal mean circula-

tion, i.e., changes in the so-called “Ferrel cell”) and on vertical changes of the pressure surfaces (e.g., due to tropospheric temperature changes). Stratospheric polar vortex (Z100): The GPH differ¨ ence between 75◦ –90◦ N and 40◦ –55◦ N (see B R ONNI MANN et al., 2004) at 100 hPa (where we still have some historical upper-air data, though very limited in extent) is termed Z100. This index describes the lower stratospheric polar vortex, which is an important characteristic of the winter circulation. Note that high values of Z100 indicate a weak vortex and vice versa. The Pacific Walker Circulation (PWC): We define the Pacific Walker Circulation index (PWC) following O ORT and Y IENGER (1996) as the difference in the vertical velocity ω at 500 hPa between [10◦ S–10◦ N, 180– 100◦ W] and [10◦ S–10◦ N, 100–150◦ E] from September to January. Unfortunately we did not have ω available for JRA-25.



Dynamic Indian Monsoon Index (DIMI): This index (see WANG et al., 2001) is defined as the difference of the 850 hPa zonal winds between [5–15◦ N, 40–80◦ E] and [20–30◦ N, 70–90◦ E] for the summer season. It has been proven to be a useful index for depicting the vorticity of the Indian monsoon trough and associated southwesterly monsoon (WANG et al., 2001). The DIMI is significantly correlated with the all-Indian rainfall index. North Atlantic Oscillation (NAO): The NAO measures the strength of the quasi-stationary pressure centres that dominate the flow over the North Atlantic region, i.e., the Icelandic low and the Azores high. The index is defined as the difference in the standardised monthly SLP anomalies at Ponta Delgada (Azores) and Reykjavik (Iceland). In contrast to all other indices, it is defined based on SLP and hence there is no need for reconstruction (it is calculated from HadSLP2). Anomalies and standard deviations were calculated from 1961–1990 except for 20CR, JRA-25, and ERA-Interim, where all available data were used. Pacific North American pattern (PNA): This index captures changes in the quasi-stationary wave over the Pacific-North American sector at 500 hPa. Climate variability over the North American continent is closely correlated with this index. We follow the definition of WALLACE and G UTZLER (1981): PNA = 0.25*(Z[20◦ N,160◦ W] – Z[45◦ N,165◦ W] + Z[55◦ N,115◦ W] – Z[30◦ N,85◦ W]) where Z is standardised 500 hPa GPH. Anomalies and standard deviations were calculated from 1961–1990 except for 20CR, JRA-25, and ERA-Interim, where all available data were used. Note that SLP-based indices exist that also describe the circulation over the North Pacific (T RENBERTH and H URRELL, 1994).


c) Method of reconstruction Reconstructions of two of the indices were published in separate papers (PNA in E WEN et al., 2008b; DIMI in Z HOU et al., 2009a). In the following we focus on the other indices. In order to reconstruct monthly indices from historical surface and upper-air data (in the following termed predictor data), statistical models were calibrated in a period for which both the predictor data and the indices (termed predictand; we use the indices from NNR ¨ data) are available and have no gaps (see also B R ONNI MANN and L UTERBACHER , 2004). We used the 1958– 2000 period for this purpose, subsequently termed calibration period, although we are aware of quality differences within this period, e.g., before and after 1979. Surface station data have only few gaps in this period; they were filled with corresponding NNR data at 925 hPa after standardizing (see below). Upper-level station data, in contrast, have long gaps or are not even available for many locations in the 1958–2000 calibration period. Therefore, we decided to make systematic use of NNR data, interpolated to the station location and levels, in order to supplement all historical upper-level data after

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¨ 1948 (as in B R ONNIMANN and L UTERBACHER, 2004). Note that most of the data series, after 1948, were assimilated into NNR. Hence, where observations would be available, they are supposed to be well represented by NNR, and where they are not available, NNR is a good estimate for most regions. However, the interpolated reanalysis data might have less variability than the observational data as they do not represent local features as well as the errors expected in historical observations. Therefore, we perturbed the interpolated reanalysis data by a random Gaussian noise, whose magnitude was es¨ timated based on previous work (B R ONNIMANN , 2003; G RANT et al., in press). Random noise is a simplification, but the approach is consistent with the quality assessment of the upper-air data which used NNR-based (reconstructed) reference ¨ series data (see B R ONNIMANN , 2003; G RANT et al., in press). In this context, biases and correlations between NNR and the observational data were thoroughly analysed and the effects on reconstructions was addressed (see Table 1 in G RANT et al., in press). After constructing the gap-free calibration data set, all series were expressed as anomalies from their 1961– 1990 mean annual cycles and standardised. The reconstructions were then performed independently for each month in the reconstruction period (1900–1947), as each month has a different set of available variables. In other words, the following steps were repeated as many times as there are months in the historical period. To reconstruct the index for a given month, e.g., January 1922, we selected only those variables from the calibration data set for which data are available in January 1922 (not all predictors were used for all indices – Fig. 1a shows the subsets used for each index). We further restricted the calibration period with a three calendar-month moving window (e.g., to calibrate a model for January 1922 we used only Decembers, Januaries, and Februaries). This subset was further subdivided into SLP, surface air temperature, upper-level GPH/temperature, and upper-level wind. Within each subset, a principal component (PC) analysis was performed, retaining 90 % of the variance. The retained PC time series were standardised and weighted with their absolute correlation with the predictand. The four sets of PC time series were then combined with equal weight (i.e., each set was multiplied by the square root of the number of series in that set) and a second PC analysis was performed. In this second PC analysis 98 % of the variance was retained. The double PC procedure assures that both upper-air and surface data contribute information and at the same time minimises the number of variables retained. A multiple regression model was used to relate the set of PC time series (from the second PC analysis) to the predictand: Y = c0 + Σct P Ct + ε. Where Y is the predictand; the sum is over the first t = T PCs that sum up to at least the optimum fraction of retained variance. The coefficients c were estimated by least-squares fitting in the calibration data set.

S. Brönnimann et al.: Variability of large-scale atmospheric circulation indices a)

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The reconstruction then consists of the following steps: 1) Expansion of the time coefficients pertaining to the T first PC loading patterns to the historical time (e.g., January 1922) by projecting onto the historical data 2) Calculation of the index for January 1922 by the linear model Yˆ = c0 + Σct P Ct 3) Addition of the (initially subtracted) NNR climatology. The reconstructions were validated using two splitsample validations (termed VAL1 in the following), where the first and last third of the calibration period, respectively, were not used for calibrating the regression model but only for validation. Moreover, the reconstructions were also validated in the 1948–1957 period (VAL2). Results are shown in Fig. 2 in the form of seasonal mean values of the reduction of error (RE, see C OOK et al., 1994). Values of RE can lie between −∞ and 1, where RE > 0 is normally considered an indication that the model has skill (RE values for DIMI and PNA are from E WEN et al., 2008b and Z HOU et al., 2009a and are of the VAL1 type). VAL2 (shown only for Z300, SJ, and HC) yields lower and much more variable RE values than VAL1. This is expected for various reasons: (1) the first PC analysis was the same in all experiments and covered 1957– 2000, thus carrying information on the validation period in VAL1, but not VAL2 (this indicates that historical predictor data should be included in the first PC analysis if possible); (2) the calibration in VAL2 has more degrees of freedom than in VAL1, but the validation has less degrees of freedom, and (3) NNR data themselves are known to suffer from problems in 1948–1957; this error is here artificially attributed to the reconstruction error in VAL2. In the following (and in the figure) we discuss the error determined from the split-sample validation. Clearly, the skill of the reconstruction varies, but generally, for the chosen seasons, RE values are mostly above 0.5. The only exceptions are DIMI, which as a summer index seems to be more difficult to reconstruct than the winter indices, and prior to the 1920s SJland . In general, the skill increases when upper-air data is included (less so in VAL2). Therefore, comparisons were performed also for the period starting in 1930. Limitations of the reconstruction approach concern on the one hand the assumption of stationarity and on the other hand an underestimation of decadal changes and trends (see also C HRISTIANSEN et al., 2009). When applying the reconstruction models of the years 1900– 1947 to the period 1953–2000 for two indices with strong trends (Z300 and HC), we find ratios of reconstructed to observed trend of 0.83 and 0.54, respectively.


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Figure 2: Time series of seasonal averages of the Reduction of Error (RE) from the split-sample validation (a, b) and the 1948–1957 validation (c, not for all indices) for the reconstructed indices.

(S CHRANER et al., 2008). We used an ensemble of nine simulations performed in an “all forcings” setup starting in 1901 and ending in 1999. The model was constrained with monthly varying SSTs (HadISST; R AYNER et al., 2003), sea ice, land-surface conditions, stratospheric aerosols, solar variability, surface concentrations of greenhouse gases and ozone depleting substances, emissions of short lived species, and the Quasi-Biennial Oscillation (QBO) in the stratosphere (F ISCHER et al., 2008b). SOCOL is a combination of the middle atmosphere version of ECHAM4 (M ANZINI and M C FAR -



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Figure 3: Time series boreal winter averages (Dec.–Mar.) of HC, HCL, SJ, SJland , and Z300 since 1900. Thin and thick grey lines display individual ensemble members of the SOCOL simulation as well as the ensemble mean, different coloured lines represent observation-based data products. The blue shaded area indicates the estimated 95 % confidence interval of REC. Letters denote years with strong external perturbation of El Niño (E), La Niña (L), and volcanic eruptions (V). For display purposes, the series were shifted such that the mean values match in the corresponding overlapping periods with NNR. The shifts necessary are indicated at the right margin (REC fits with NNR, 1961–1990, by construction). LANE, 1998) coupled to the chemistry-transport model MEZON (E GOROVA et al., 2003). It is a spectral model with T30 horizontal truncation and 39 vertical levels with a model top at 0.01 hPa. The SOCOL model has been involved in the intercomparison within the framework of the CLIVAR “Climate of the 20th Century (C20CR)” Project (F OLLAND et al., 2002). It has shown reasonable performance for selected 20th century climate events (S CAIFE et al., 2008), as well as for monsoon variability (K UCHARSKI et al., 2008; Z HOU et al., 2008). All indices were calculated from monthly model output fields as defined above. In the comparison with observation-based indices we assess whether or not the variability in the observation-based series is reproduced in the ensemble mean, or whether it lies within the ensemble spread. For this purpose we consider not only the ensemble spread, but also the average of the correlations between one ensemble member and the average of all others (termed internal correlation) as well as the average of all mutual correlations between individual en-

semble members (mutual correlation). The comparison between the two gives some indications of the effect of the ensemble size. With respect to comparisons it is important to note that modelling and reconstructing are complementary and fully independent approaches. The reconstruction uses historical information on the state of the atmosphere (including surface air temperature over land, but not SSTs) to obtain a best estimate of the atmospheric circulation. The model uses historical information determining the boundary conditions for the atmospheric calculations (including external forcings as well as SSTs, but not surface air temperature over land) to obtain a set of different states of atmospheric dynamics and chemistry, all of which are physically consistent with the boundary conditions. Finally, the historical reanalysis uses both monthly SSTs (in common with the model simulations) as well as information on the state of the atmosphere, but in contrast to the reconstructions does not include upperair information and no surface air temperature over land.

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Figure 4: Time series of seasonally averaged values of Z100, PWC, DIMI, NAO, and PNA since 1900. All series are boreal winter (Dec.– Mar.) averages except PWC (Sep.–Jan.) and DIMI (Jun.–Aug.). Thin and thick grey lines display individual ensemble members of the SOCOL simulation as well as the ensemble mean, different coloured lines represent observation-based data products. The blue shaded area indicates the estimated 95 % confidence interval of REC. Letters denote years with strong external perturbation of El Niño (E), La Niña (L), and volcanic eruptions (V). For display purposes, the series were shifted such that the mean values match in the corresponding overlapping periods with NNR. The shifts necessary are indicated at the right margin (REC fits with NNR1961-1990, by construction except in the case of DIMI). The definitions of NAO and PNA require standardisation (see text) and hence they were not displaced.

3 Results and discussion Series of the seasonal averages of all indices are shown in Figs. 3 and 4, together with the corresponding model data (all ensemble members as well as the ensemble mean). Note that for display purposes, we have shifted all series such that their mean values match that of NNR during the corresponding overlapping periods (REC is consistent with 1961–1990 NNR by construction). The corresponding displacements are indicated at the right margin of the figure. Reconstructions are shown with an interval of ±1 seasonally averaged monthly root mean squared error. Assuming that most √ of the error is random (and thus scales with 1/ n) this corresponds to an approximate 95 % confidence interval. For better interpretation, El Niño and La Niña events are indicated by letters “E” and “L”, respectively, and strong tropi-

cal volcanic eruptions are denoted with a “V”. Correlations between different observation-based data sets, between observed and modelled indices (ensemble mean) as well as model internal and mutual correlations are given in Table 2. Correlations between the different indices (based on NNR) as well as between the indices and NINO3.4 are given in Table 3. For each index, we start by discussing the agreement among different observation-based data products and between models and observation. Then we analyse the variability in these series and discuss likely causes.

a) The Hadley circulation The correlation between HC indices from different observation-based data sets for the past decades are around 0.7. Similar coefficients are also found for the


387


Table 2: Pearson’s correlation coefficients between indices from different observation-based data sets (rows 1 to 6) and between observation-based data sets and model simulations (rows 7 to 13). The second last row gives the averaged correlation between an index from an ensemble member and the average of the index over all other ensemble members. The last row gives the average of all mutual correlations between the indices from all nine ensemble members. Significant correlation (α = 5 %) are marked in bold. Comparison REC/20CR REC/20CR NNR/ERA40 NNR/JRA25 NNR/ERA-INT JRA25/ERA-INT REC/SOCOL REC/SOCOL 20CR/SOCOL 20CR/SOCOL NNR/SOCOL ERA40/SOCOL JRA25/SOCOL SOCOL internal SOCOL mutual

Subperiod 1909-1947 1930-1947 1958-1999 1989-2007 1989-2007 1989-2007 1909-1947 1930-1947 1909-1947 1930-1947 1958-1999 1958-1999 1979-1999 1901-1999 1901-1999

n 39 18 42 19 19 19 39 18 39 18 42 42 21 99 99

HC 0.46 0.26 0.72 0.79 0.78 0.61 0.61 0.41 0.72 0.71 0.74 0.66 0.68 0.70 0.54

HCL 0.52 0.52 0.52 0.84 0.87 0.79 0.29 0.40 0.22 0.32 0.38 0.35 0.57 0.38 0.21

SJ 0.87 0.92 0.96 0.999 0.995 0.994 0.62 0.69 0.75 0.78 0.74 0.72 0.77 0.75 0.61

correlations between observation-based series and the model simulations as well as for the model internal correlation. Hence, the reproducibility of the strength of the Hadley cell on an interannual scale can be considered good. However, there are large offsets (in fact, in this case, NNR seems to be biased with respect to all other data sets). The correlation between 20CR and REC is somewhat lower. As the zonal mean Hadley circulation represents an ageostrophic, diabatic circulation, which is presumably more difficult to capture in statistical or numerical approaches than the geosptrophic flow, this is not surprising. The observed Hadley cell strength (HC) shows interannual variability that is related to ENSO (r = 0.56 with NINO3.4). Prominent El Niño events such as those in 1997/98, 1986/87, 1982/83, 1972/73, and 1940–1942 or the extended La Niña event 1916– 1918 stand out. The latter two also stand out in the HC reconstructions by E VANS and K APLAN (2004). The effect of ENSO on the strength of the Hadley cell is well known (e.g., T RENBERTH et al., 2002; T RENBERTH and S TEPANIAK, 2003; Q UAN et al., 2005). Previous studies have found that SOCOL has a good Southern Oscillation response (S CAIFE et al., 2008) and hence the relatively good agreement in the HC response is not surprising. Ocean basins other than the Pacific as well as the monsoon systems also affect HC, but are more difficult to isolate (see also WANG et al., 2009; Z HOU et al., 2009. Winters following strong tropical volcanic eruptions are expected to exhibit a low HC index because the cooling at the surface in the tropics increases static stability. No consistent response is seen for the four major eruptions since 1900, which however could be related to the fact that three of the four eruptions concurred with El Niño events (which would have an opposite effect). Decadal changes in HC could arise from the 11yr sunspot cycle. Based on simulations with an OAGCM (without considering ozone changes), M EEHL et al. (2005) suggest a strengthening HC with increased so-


SJland 0.55 0.65 0.89 0.993 0.98 0.99 0.44 0.50 0.42 0.58 0.56 0.64 0.64 0.49 0.32

Z100 0.42 0.47 0.995 0.999 0.997 0.999 -0.07 0.12 0.11 0.03 0.34 0.32 0.45 0.16 0.07

Z300 0.68 0.78 0.85 0.98 0.99 0.97 0.25 0.53 0.51 0.64 0.59 0.58 0.59 0.73 0.58

PWC DIMI 0.86 0.46 0.84 0.52 0.96 0.93 0.93 0.991 0.91 0.97 0.18 0.84 0.89 -0.20 0.11 0.93 0.93 -0.02 0.89 0.33 0.30 0.93 0.24 0.92 0.53 0.86 0.35

NAO 0.98 0.98 0.999 0.96 0.998 0.95 0.28 0.16 0.28 0.14 0.30 0.30 0.13 0.19 0.08

PNA 0.95 0.95 0.997 0.998 0.997 0.996 0.70 0.69 0.70 0.68 0.71 0.72 0.78 0.71 0.55

lar activity driven by increased evaporation in the subtropical cloud-free areas and a subsequent “moistening” of the Hadley circulation. Based on reanalysis data and on model simulations using off-line ozone changes, H AIGH (2003) suggests a weakening but broadening of the Hadley cell due to changes in stratospheric ozone and subsequent changes in the lower stratospheric temperature gradients, affecting tropospheric circulation. Observed and modelled HC indices show no correlations with the 11-yr component of total solar irradiance of L EAN (2004). However, even on a decadal scale, effects of ENSO are evident and hence more targeted analyses are needed to separate out the effects (see also G LEIS NER and T HEJLL, 2003). In addition, the observed series show multidecadal variability, with an increase in strength since the 1950s in all data sets. This positive trend in the strength of the Hadley circulation in winter and spring is well-known (e.g., M ITAS and C LEMENT, 2005; Q UAN et al., 2005), though one should keep in mind possible data quality issues in all data sets (see H ELD and S ODEN, 2006). SOCOL also shows a strengthening Hadley cell during the second half of the 20th century. The same is found in other atmospheric models (e.g., M ANTSIS, 2008). Between about 1920 and 1950, 20CR and SOCOL show a slight decrease in HC, while REC (and other reconstructions: E VANS and K APLAN, 2004) show no trend. Note, however, that REC captures only half of the HC trend in the calibration period. Trends in the Hadley circulation in the recent past are interesting in the context of anthropogenic influences and future climate scenarios. For a CO2 doubling or generally under global warming most studies find a weakening Hadley cell (e.g., OTTO -B LIESNER and C LEMENT, 2005; R IND and P ERLWITZ, 2005; L U et al., 2007; V ECCHI and S ODEN, 2007). This is partly explained by the notion that the global hydrological cycle accelerates more slowly than the Clausius-Clapeyron re-

388


lation, hence circulation must weaken (see also B ETTS, 1998). Interestingly, coupled ocean-atmosphere models (contrary to atmospheric models) show no trend or even a weakening Hadley cell over the past decades (M ITAS and C LEMENT, 2006). What then could have caused the recent strengthening in winter and spring? Q UAN et al. (2005) interpret the strengthening in winter and spring as being partly caused by a change in the frequency and strength of ENSO events (to which the HC reacts in a non-linear way). The change is consistent with an intensified hydrological cycle and dynamic changes in the extratropics in that season. The physical mechanisms governing the Hadley circulation are complex and depend on the organisation of convection, the Pacific ENSO system, the oceanic circulation, Atlantic and Indian Ocean influences, and the monsoon systems; changes in all of these facets need to be considered (see W EBSTER, 2005; H ELD and S ODEN, 2006). Since the satellite era the poleward extent of the Hadley cell (HCL) shows an excellent agreement across data sets (r ≈ 0.8), while correlations between NNR and ERA40 or between 20CR and REC are lower. HCL is not well captured in some late winter (Feb.-Mar.) months in 20CR, causing occasional positive outliers. HCL is less well predicted by the forcings than HC (though some ENSO effect is apparent, see Table 3), as is evidenced by the model simulations. Correlations between model and observations as well as model internal correlations are mostly below 0.4. No clear multidecadal variability is apparent, neither in the observation-based HCL indices nor in the model. This finding is interesting in the context of the “widening of the tropical belt” since 1979 that is found in other studies (e.g., H U and FU, 2007; S EIDEL et al., 2008). Some data sets (NNR, ERA-40, JRA-25) show a slight increase since 1979, but the 1997–2000 ENSO cycle dominates the variability. Note also that the widening is observed to be strongest in the summer season (S EIDEL et al., 2008). For the future, IPCC AR4 models suggest a broadening of the Hadley cell under global warming (L U et al,. 2007). The broadening in these simulations was suggested to be related to increased static stability in the subtropics and a concurrent poleward migration of the baroclinic instability zone that defines the outer boundary of the Hadley cell. eschweizerbartxxx author

b) The subtropical jet strength The subtropical jet strength is very consistent across data sets, with correlations around 0.9 or higher. The agreement is also excellent between REC and 20CR. This is understandable as the SJ represents the geostrophic part of the flow and shows a good qualitative agreement with the thermal wind balance. It is well reconstructed even when using only surface data (see Fig. 2). In the real atmosphere, however, the situation is more complex as both the meridional temperature gradient and extratropical eddies (only indirectly captured in a statistical approach) affect the SJ. The agreement among modelled


and observed SJ is similarly good as for HC. Hence, the strength of the subtropical jet is well modelled. SJ shows similar interannual variability as HC, a large fraction of which is again related to ENSO (note the exceptionally high values in 1998, 1940–1942 and the low values in 1988/89). In fact, SJ is not only strongly correlated with NINO3.4 (r = 0.72) but, based on NNR, is significantly correlated with all other indices, including Z100, NAO, and DIMI. This makes it an interesting integral index of the global atmospheric circulation. Volcanic eruptions are expected to weaken the jet as they reduce the meridional thermal gradient. No effect is seen in the data, which again might be due to the concurrence of volcanic winters and El Niño winters. The 1976/77 climate shift (T RENBERTH, 1990) that is related to tropical SSTs appears as a prominent feature in the SJ time series; an opposite shift appears in1942. In contrast to HC, no trend is apparent. The subtropical jet over land areas (SJland ) depends less on SST forcing but more on land mass effects (such as Eurasian snow cover). Different data sets again agree quite well though somewhat worse than for SJ, but the correlations between model and observations as well as the model internal correlations are lower. Variability in SJland also depends on ENSO, but less strongly than SJ (see Table 3). It is also correlated with the Indian monsoon and the poleward extent of the Hadley cell. The two climate shifts (1942, 1976) are not apparent in HCL.

c) The midlatitude circulation The Z300 index is well reproduced between different data sets, including between REC and 20CR. Offsets are large but can partly be explained by the horizontal and vertical interpolation. Disagreement between REC and 20CR is found in the early and mid 1940s and could be related to SST biases (T HOMPSON et al., 2008), possibly affecting 20CR but not REC. Further validations are necessary in this respect. Correlations between observation-based Z300 indices and the model simulations are somewhat lower than for HC or SJ. They are also lower than the SOCOL internal correlations. Z300 shows interannual variability, decadal variability, and trends. Only part of the interannual variability is related to ENSO (note that significant correlations are also found with HCL and NAO, see Table 3). Part of the decadal variability could be related to solar variability; in fact, the index captures the region where the strongest tropospheric signal of the 11-yr sunspot cycle is expected (H AIGH, 2003; C ROOKS and G RAY, 2005). ¨ In a previous paper B R ONNIMANN et al. (2006a) found a statistically significant 11-yr solar signal in this index back to 1922, which was interpreted as a poleward shift of the midlatitude Ferrel cell. All of the data presented here, both observation-based and modelled, confirm this finding, but correlations with the 11-yr component of solar irradiance (L EAN, 2004) are low (in the range of 0.2 to 0.3).


389


Table 3: Pearson’s correlation coefficients between different indices based on NNR. Significant correlation (α = 5 %) are marked in bold. DIMI is the index of the previous summer; NINO3.4 is from Sep.-Feb.

NINO3.4 0.56 -0.39 0.72 0.57 -0.06 0.17 -0.84 0.27 -0.07 0.65

HCL SJ SJland Z300 Z100 PWC DIMI NAO PNA 0.07 0.37 0.34 0.25 0.02 -0.27 0.12 0.23 0.58 -0.50 -0.43 0.39 -0.23 0.47 -0.05 0.42 -0.12 0.77 -0.34 0.29 -0.65 0.30 -0.35 0.75 -0.12 0.06 -0.62 0.31 -0.03 0.47 -0.29 0.23 -0.05 0.49 -0.14 -0.08 0.04 -0.51 0.17 -0.13 0.11 -0.48 0.17 0.34 -0.01

In addition to decadal variability, Z300 also shows multidecadal variability that reflects the global tropospheric temperature trend (although part of the multidecadal signal might also be of solar origin, see ¨ B R ONNIMANN et al., 2006a). In addition to the general warming caused by well-mixed greenhouse gases, the downward trend from the mid-1940s to the mid1980s and the upward trend since then might be affected by direct aerosols effects known as “global dimming” (O HMURA and L ANG, 1989) and “global brightening” (W ILD et al., 2005). Z300 represents the latitude band that is most strongly affected by anthropogenic aerosols. SOCOL has a stronger trend in the first half of the 20th century compared to both REC and 20CR. Note that SOCOL has no time-varying tropospheric aerosols and hence “sees” the direct aerosol effects only indirectly through SSTs.

a)

-200

-400

Z100 (gpm)

HC HCL SJ SJland Z300 Z100 PWC DIMI NAO PNA

-600

-800

-1000

-1200 360


380

400

420

440

460

Total ozone (DU) b)

d) The stratospheric polar vortex

-200

-400

Z100 (gpm)

The strength of the stratospheric polar vortex agrees exceptionally well between data sets in the recent decades (r > 0.99), but correlations are low between REC and 20CR. To investigate this further we compared the two versions of Z100 with a third one (based on G RIESSER et al., 2008) and with historical total ozone data from Tromsø, 69◦ N (H ANSEN and S VENØE, 2005), for March, 1936–1947 (Fig. 5a; for Dec-Feb no total ozone data are available). Z100 from 20CR shows a higher correlation with total ozone than Z100 from REC; the third version lies in the middle. This suggests that in this case, 20CR might better capture interannual variability than REC, although it shows a bias towards a too strong polar vortex (see Fig. 5b). The internal correlation in SOCOL is low and consequently low correlations are also found between model and observations (except for JRA-25, as is discussed below). Z100 shows much more “internal variability” (i.e., in the context of this paper, the variability that is not predictable by the forcings specified in the model). This may be surprising at first as many forcings are known to affect the polar vortex. Some of the peaks in Z100 (least negative values) such as the sequence of

REC Griesser REC this study 20CR

-600

-800

-1000

-1200 1936

1938

1940

1942

1944

1946

Year Figure 5: Monthly values of Z100 from REC (this study), from reconstructed fields (G RIESSER et al., 2008) and from 20CR for March, 1936–1947, plotted against total ozone at Tromsø (a) and as time series (b).

three weak-vortex winters from 1940-1942 correspond to ENSO events. The latter appear more pronounced ¨ in the reconstructions by B R ONNIMANN and L UTER BACHER (2004) and G RIESSER et al. (2008) than in REC (see also Fig. 5b). The effect of ENSO on the po-

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lar vortex is well known and also relatively well understood (see VAN L OON and L ABITZKE, 1987, M ANZINI ¨ et al., 2006; B R ONNIMANN , 2007), and it is modelled ¨ by SOCOL (B R ONNIMANN et al., 2006b; F ISCHER et al., 2008a). Tropical volcanic eruptions are often followed by a strong vortex, which also is well-known and at least partly understood (ROBOCK, 2000). Though qualitatively reproduced by SOCOL, the effect is too weak compared to observations. This could be due to a wrongly set single scattering albedo in one of the radiation channels (F ISCHER et al., 2008b) or the concurrence of volcanic eruptions and El Niño conditions. Other known influences on Z100 are exerted by the QBO (H OLTON and TAN, 1980) and the 11-yr solar cycle (see e.g., KODERA and K URODA, 2002; L ABITZKE et al., 2006); the latter effect is modulated by the former. While these features are qualitatively captured by SOCOL (including the QBO modulation of the solar effect), the modelled relations are not strong for the chosen seasonal average and level. Note also that the QBO data used to force the model are less accurate prior to the 1950s (when they were taken from a reconstruction) than later. Considering all these factors, it is not surprising that correlations between SOCOL and observationbased data are much higher in the period since 1979 (as evidenced in the comparison with JRA25), which includes two strong volcanic eruptions, several strong ENSO events, well characterised QBO, solar forcing, and strong anthropogenic forcings. On the other hand, the most prominent feature on the multiannual time scale, the remarkable sequence of strong-vortex winters in the early 1990s, is not reproduced in the model. Changes in the strength of the polar vortex system have the potential to affect climate at the ground (see BALDWIN and D UNKERTON, 2001). This provides a potential pathway by which forcings that primarily act upon the stratosphere can affect climate. Therefore, much attention has recently been devoted to better understanding changes in the polar stratosphere and its coupling with the troposphere. The radiative forcing by greenhouse gases leads to a cooling of the stratosphere which is non-uniform and strengthens the meridional temperature gradient. S HINDELL et al. (1999) found a strengthening polar vortex in their model due to CO2 doubling. Yet, later studies have shown that the result depends on model parameterisations and the vertical resolution (e.g., S IGMOND et al., 2008); it might also be modulated by solar activity (KODERA et al., 2008). In the Antarctic stratosphere chemical ozone destruction can exacerbate a meridional strengthening of the temperature gradient as well as the polar zonal winds (L ANGEMATZ et al., 2003; T HOMPSON and S OLOMON, 2002; G ILLETT and T HOMPSON, 2003). To a lesser extent this might also be the case for the Arctic, though much less is known (D ESER and P HILLIPS, 2009). Finally, a large portion of stratospheric perturbations are dominated by the interplay of the tropospheric and stratospheric flow through wave-mean flow interaction (H OLTON et al., 1995). In fact, some studies suggest eschweizerbartxxx author


that this mechanism tends to lead to a more disturbed vortex in a future climate, counteracting the radiative effects (see S CHNADT et al., 2002). One aspect of scientific concern are trends over the recent decades as well as changes in a future climate (L I et al., 2008). However, as detailed in F ISCHER et al. (2008b), trends in the vertical component of the Eliassen Palm (EPz) flux, a measure for wave-mean flow interaction are inconclusive in the reanalyses. Moreover large discrepancies with respect to trend magnitudes are reported among chemistry-climate models (see e.g., B UTCHART et al., 2006).

e) The Pacific Walker Circulation The PWC index compares very well across different data sets (including REC and 20CR) and also is well reproduced by the model. As the Walker circulation is strongly affected by SSTs, which are used in all data sets except REC, this agreement is not surprising. By its definition, the index gives more weight to those ENSO events that have their anomaly centre closer to the South American continent as compared to the “date line” El Niños. Past and future trends in the Walker circulation have been a matter of debate as this is the globally most important cause of precipitation variability. In SOCOL we find a slight weakening of the Pacific Walker circulation since about 1930, but our observation-based indices show no trend. V ECCHI et al. (2006), based on SLP fields, find a weakening Walker circulation during the 20th century that is reproduced by models forced with anthropogenic forcings. For simulations of future climate under global warming, model simulations suggest that both the zonally symmetric part of the tropical circulation (the Hadley cell, see above) but primarily the zonally asymmetric part, the Walker circulation, will weaken (V ECCHI and S ODEN, 2007).

f) The Indian monsoon The dynamic Indian monsoon index (DIMI) is consistent among different reanalyses (r > 0.9). For the first half of the 20th century, the agreement is worse (r is around 0.5). The correlation between observation-based and modelled indices is also worse, but still statistically significant in the case of NNR (also significant for REC for the period 1880–1957, see Z HOU et al., 2009a). Note that the reproducibility for DIMI is higher than for monsoon rainfall (see Z HOU et al., 2009a). As for Z300, the internal correlations in SOCOL are higher than the correlations between model and observations. The interannual variability in DIMI is partly related to ENSO (see Z HOU et al., 2009a). Significant correlations are found with PNA, SJ, and SJland (Table 3). The monsoon-ENSO relationship changes with time. Since the 1970s, the inverse relationship between the monsoon and the ENSO has weakened considerably (K UMAR et al., 1999). C HANG et al. (2001) suggests that the influence of a warm Eurasian surface opposes the effect of a



warm ENSO event, such that the effect of the resulting meridional temperature contrast was able to disrupt the influence of ENSO on the monsoon. Z HOU et al. (2009a) proposed that a warmer (colder) Asian continent tends to be accompanied by a weaker (stronger) monsoonENSO connection, suggesting the recent breakdown of the monsoon-ENSO relationship may not be unprecedented over the past history. However, the breakdown of the monsoon-ENSO connection in recent decades is not reproduced in SOCOL, partly due to the delayed response of the modelled Asian surface air temperature to the prescribed SST forcing (see Z HOU et al., 2009a). The Indian monsoon is not only related to ENSO, but also to SSTs in the Atlantic and Indian ocean (see also WANG et al., 2009; Z HOU et al., 2009). Concerning trends, a slight increasing trend between 1918 and 1940 is found in all three data sets for that period (REC, 20CR, SOCOL), but hardly any change is found in recent decades.

g) The North Atlantic Oscillation The NAO indices agree very well among different observation based data sets, with correlations higher than 0.95. The magnitudes are different, which mainly is due to the different reference time period for calculating the standard deviations. The correlation between observation-based and modelled NAO indices is between 0.25 and 0.3, which is not statistically significant, and the SOCOL internal correlations are even lower. NAO thus behaves similarly to the Z100 index, to which it is correlated with coefficients around 0.5. The NAO also correlates well with HCL, SJ (but not SJland ) and Z300. On the interannual scale, the NAO affects North Atlantic SSTs more than vice versa. In this sense, the SST forcing applied in SOCOL does not represent true predictability. Nevertheless, oceanic forcing from the North Atlantic sector might play some role in the form of a coupled mode in which oceanic changes feed back onto the atmosphere. Though there is a lot of evidence for this mechanism, the magnitude of the effect is believed to be small on the interannual time scale (see WAN NER et al., 2001; C ZAJA et al., 2003). A small influence from the tropical Pacific on NAO variability has also been detected in both observations and models (e.g., ¨ G REATBATCH and J UNG, 2007; B R ONNIMANN , 2007). Recently, aerosol optical depth over the subtropical Atlantic has been proposed as an additional trigger for phase changes in the NAO (L UO et al., 2009). Little is known about the effect of Arctic sea-ice changes on the NAO, but available model studies (M AGNUSDOTTIR et al., 2004; D ESER et al., 2004) suggest that Arctic sea ice acts as a negative feedback. Obviously, all of these relations are not sufficient to produce significant correlations between observations and model. Eurasian snow cover, another potential influence on NAO variability (S AITO and C OHEN, 2003), is also not prescribed in the model.


391

Downward propagation of stratospheric anomalies (see BALDWIN and D UNKERTON, 2001) might explain some of the interannual NAO variability. However, as the predictability of the stratosphere itself (based on the forcings considered here) is limited, this mechanism also does not add much to NAO predictability. The situation is somewhat different for the lowfrequency variability. It is generally believed that oceanic forcing does play a role for decadal NAO variability. Some models reproduce features of the low-frequency variability of the NAO. The suggested drivers are SSTs in the Indian Ocean, tropical Atlantic, and Western Pacific (e.g., H OERLING et al., 2001; BADER and L ATIF, 2003; K UCHARSKI et al., 2006). The focus of many model studies was on the observed increase of the NAO index between the 1960s and 1990s. In a recent GCM intercomparison experiment, none out of more than 100 simulations captured the magnitude of the trend (S CAIFE et al., 2008). SOCOL shows an increase from 1963 to 1995 of +0.15 per decade in the ensemble mean (max: +0.60, min –0.26), while NNR and ERA40 exhibit numbers of +0.8 to +1 per decade. G ILLETT (2005) also finds that models are unable to reproduce the magnitude of the recent trend, although its sign is consistent with an anthropogenic forcing. Note, however, that the observed NAO trend has reversed since the mid-1990s.

h) The Pacific North American pattern The PNA index agrees well among different data sets. Also, the correlation between REC and 20CR is very high (r = 0.95). Similar to the NAO, the magnitude of variability differs due to the different standardising periods used. The correlations between observation-based and modelled PNA indices is around 0.7; similar to the SOCOL internal correlation. The PNA shows mainly interannual, but also decadal variability. No long-term trend is discernible, but the 1976/77 and 1942/43 climate shifts appear. The interannual variability is dominated by ENSO and the zonal mean tropical and subtropical circulation (correlations with HC and SJ are strong). On the decadal scale a relation to the Pacific Decadal Oscillation (PDO) is found (see E WEN et al., 2008b). Note that in the combined (reconstructed and reanalysis-based) series, we also found a statistically significant, though weak, relation to the NAO (see E WEN et al., 2008b). Interestingly, the only period where the ensemble mean does not capture the observed PNA index well (1944-1951) is coinciding with the model’s lowest internal correlations. This period is not well reproduced for many of the series discussed above. ENSO indices show little variation in this period, hence the poor agreement might reflect the effects of a temporary muting of the dominant source of predictability. The low correlations might also reflect the effect of the 1940s SST bias (T HOMPSON et al., 2008).

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4 Conclusions In this paper we have attempted to give an overview of the variability of the large-scale circulation since 1900 based on indices that were derived from different data sets (representing different approaches) and model simulations. While a detailed discussion of all mechanisms could not be given and the study is necessarily incomplete, we nevertheless have identified a number of important points. With respect to the mutual agreement among different observation-based data products, we find (not surprisingly) that the correlations increase over time. For the most recent 20–30 years, the agreement is excellent with respect to interannual variability, although there are biases and differences in trend magnitudes. The agreement becomes worse backwards in time. However, correlations between REC and 20CR are mostly statistically significant and they are very good (0.8 to 0.95) for SJ, PWC, and PNA. Clearly, the two historical data products allow strong features in the large-scale upper-level circulation back to the beginning of the 20th century to be addressed. The comparison with simulations performed with the chemistry climate model SOCOL shows that the correlations between modelled and observed indices are generally similar to those between individual ensemble members and the mean of all other members. Based on these analyses, the strength of the subtropical jet, the strength of the Hadley cell, and the PNA are well reproduced by SST-forced simulations, which indicates that there might be some predictability. In contrast, the polar vortex and the poleward extent of the Hadley cell are dominated by unforced (internal) variability, although the observations fall within the ensemble spread. Note that these conclusions refer to this specific model; a more comprehensive overview over different models is given. e.g., in S CAIFE et al. (2008), K UCHARSKI et al. (2008), and Z HOU et al. (2008). Interestingly, some periods are badly reproduced in almost all of the data series discussed above. For instance, from 1944 to 1951 correlations drop considerably both between ensemble members and between model and observations. This could reflect a period in which ENSO varied little and hence was not a dominating factor in global circulation variability. The low correlations might also reflect the effect of the SST bias in the 1940s (note that SST data were used as a boundary condition in SOCOL and 20CR). Generally, correlations with SOCOL are slightly higher for 20CR than for REC. This could mean that 20CR is more accurate than REC, but it could also reflect the fact that both 20CR and SOCOL use observed SSTs as boundary condition whereas REC is independent of SSTs. With respect to the drivers of variability, the dominant influence on the interannual scale on many of the series is ENSO (or, more generally, tropical SSTs), both in the model and in the observations. Circulation indices that are strongly affected by ENSO show typically coreschweizerbartxxx author


relations between observations and model series of 0.7. Volcanic eruptions affect the polar vortex in the observations, but in general the volcanic effects are more difficult to address because most volcanic eruptions coincided with significant El Niño events (whose effects are expected to be opposite in many respects). Decadal influences can be detected and might be related to oceanic influences, solar variability, as well as, in the past 50 years, anthropogenic influences. Consistent trends were found in different data sets and in the model (e.g., in the case of the increasing strength of the Hadley circulation since 1950 or the strengthening monsoon between 1918 and 1940). However, care should be taken when analysing trends in these data; even in the recent reanalysis data. This study necessarily remains incomplete, and further studies must follow to address other important features of large-scale circulation variability such as the southern annular mode, the West African monsoon, or the wind shear over the tropical Atlantic. Yet, it shows that a 100-yr perspective of the large-scale circulation is possible.

Acknowledgments SB, TE, TG, AS, and AG were funded by the Swiss National Science Foundation, AF was funded through a TH grant of ETH Zurich, MS was funded by the ETH Polyproject “Variability of the Sun and Global Climate”, and TZ was funded through a Sino-Swiss Research Cooperation Fellowship. We wish to thank NOAA/NCDC, NCAR, and MétéoFrance for providing historical upper-level data. We thank the Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI) for providing JRA-25 data, ECMWF for providing ERA-40 and ERAinterim. The NCEP/NCAR Reanalysis was downloaded from the NOAA/ESRL website. 20th Century Reanalysis data were obtained from the NOAA/OAR/ESRL Physical Sciences Division Boulder, Colorado, USA, web site at http://www.esrl.noaa.gov/psd courtesy of G. C OMPO, J. W HITAKER, P. S ARDESHMUKH, and N. M ATSUI of the University of Colorado CIRES Climate Diagnostics Center and NOAA Earth System Research Laboratory Physical Sciences Division.

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