Components of precipitation and temperature anomalies and change associated with modes of the Southern Hemisphere

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 29: (2009) Published online 4 December 2008 in Wiley InterScience ( Components of precipitation and temperature anomalies and change associated with modes of the Southern Hemisphere I. G. Watterson* CSIRO Marine and Atmospheric Research, Aspendale, Australia ABSTRACT: A long simulation of climate by the CSIRO Mark 3 coupled atmosphere ocean model is analysed to assess the role of three modes of the Southern Hemisphere (SH) circulation on the inter-annual variability of precipitation and surface temperature and the contribution of the modes to patterns of change under global warming. Indices of El Niñosouthern oscillation (ENSO) and the high- and low-latitude modes (HLM, LLM) of zonal mean zonal wind are extracted from monthly means during the period The model El Niño brings mostly drier and warmer conditions to Australia New Zealand (NZ), South America and southern Africa in both annual and seasonal means. The HLM and LLM modes impact rainfall and temperature in banded patterns. Positive HLM [which is closely related to the southern annular mode (SAM) in pressure] moistens and cools around S particularly near the east coast of each continent. Farther south it mostly dries and warms, especially in the austral winter. The LLM has some impacts, including increased summer rain in western Australia, and temperature anomalies in eastern South America in autumn. Mostly similar patterns are found in the ERA40 observational data. Changes in the indices in the 22nd century, following warming forced by the A1B greenhouse gas (GHG) scenario, are positive, in most cases, and similar in magnitude to their inter-annual variability. The drying and warming of Australia associated with an El Niño-like change are largely countered by the HLM-like change, and similar effects are seen in parts of the other continents. The far south of NZ and South America are dried and warmed by the HLM change, but these relative changes are countered by non-modal effects in the Southern Ocean. Overall the impact of the simulated modes is largest on Australia, with the HLM being as important as ENSO in the mid-latitudes. Copyright 2008 Royal Meteorological Society KEY WORDS climate change; modes of variability; precipitation; weather patterns Received 20 December 2007; Revised 31 July 2008; Accepted 17 September Introduction Large-scale patterns of variability of the atmosphere have long been recognized as having an important influence on both surface temperature and precipitation over many inhabited regions. A prime example is the tropical, coupled atmosphere ocean mode El Niño-southern oscillation (ENSO) associated with the sea surface temperature (SST)anomalies ofel Niño, whose southern oscillation surface pressure component is linked to seasonal variability in many parts of the world, including regions of Australia, South America and southern Africa (e.g. Figure 3 of) Meinke et al., 2005). More recently, annular patterns of surface pressure extending from the polar to middle latitudes in each hemisphere have been identified using Principal Component (PC) analysis (Thompson and Wallace, 2000). Gillett et al. (2006) detected influences of the southern annular mode (SAM) on both temperature and precipitation in each of the continents of the Southern Hemisphere (SH), * Correspondence to: I. G. Watterson, CSIRO Marine and Atmospheric Research, Aspendale, Australia. Ian.Watterson@csiro.au and these have been confirmed in regional studies (see later sections). The SAM is closely associated with meridional shifts in the mid-latitude jet, sometimes referred to as zonal wind vacillation (e.g. Watterson, 2000). Analysis of the zonal winds (e.g. Kidson, 1988) produces as the first Empirical Orthogonal Function (EOF) a mid-latitude dipole pattern with peaks around 60 S and 40 S, which has often been called the high-latitude mode (HLM). Indices of the HLM and SAM are highly correlated. A second wind EOF of similar barotropic form, with peaks around 50 S and 30 S, is termed the low-latitude mode (LLM) (e.g. Cai and Watterson, 2002). Both HLM and LLM can be expected to relate to the climate of the SH mid-latitudes, indeed perhaps more closely than SAM, which has its largest pressure anomalies in the Antarctic. Lorenz and Hartmann (2001) showed that variability in both SH EOFs is primarily driven by anomalous horizontal momentum fluxes associated with weather systems, although a positive feedback of the wind anomaly on the fluxes is largely lacking in the LLM. As discussed by Watterson (2007) there is little support for the term annular mode in the case of LLM with regard to the Copyright 2008 Royal Meteorological Society

2 810 I. G. WATTERSON relationship between winds from different longitudes or sectors. Even SAM/HLM has only qualified support in this mechanistic sense. The term is used here largely in the customary sense of a pattern that can be defined through a hemispheric-scale component analysis. In any case, the LLM dipole pattern should also relate to variability confined to a sector, as for example the Australian blocking index of Pook et al. (2006). Watterson (2007) found that the structures of the HLM and LLM in simulations of the 20th century by the CSIRO Mark 3.0 (or Mk3) coupled atmosphere ocean general circulation model (GCM) appeared quite realistic compared with those in the European Centre for Medium- Range Weather Forecasts (ECMWF) ERA40 reanalysis data set (Kallberg et al., 2004). The simulation of ENSO is also relatively good (Cai et al., 2003), although in common with some other models the associated SST variability in the equatorial Pacific extends farther west than the observed and well beyond 180 W. The period of the variability is rather short, with the spectrum having too much power around 2 years. One aim of this paper is to analyse the impacts of the HLM and LLM on the simulated rainfall and temperature and compare it to those of the model ENSO. Some comparisons are made with corresponding results from ERA40 data. Much of the recent interest in large-scale modes has been due to the recognition that they may represent a substantial component of externally forced changes in climate (e.g. Vera et al., 2004; Meehl et al., 2007). Yamaguchi and Noda (2006) showed that most models in the CMIP3 (Coupled Model Intercomparison Project, see www-pcmdi.llnl.gov/ipcc/about ipcc.php) ensemble of simulations of climate change forced by rising greenhouse gas (GHG) concentrations, including Mk3, simulated an El Niño-like change in the Pacific Ocean. Miller et al. (2006) and Cai and Cowan (2007) showed that this ensemble also projected a positive SAM-like change, forced initially by Antarctic stratospheric ozone depletion (in models that included this), and then by GHG. Watterson (2007) showed that the change in the SH zonal winds in the 22nd century from Mk3, after stabilization of GHG, projected on the HLM, and less so on the LLM. A further aim of the present paper is to examine this change of climate regionally, and assess the role of the three hemispheric modes. In the following section the model and the simulation analysed are briefly described. Indices for all three modes are extracted. The HLM and LLM from Mk3 are briefly compared with those from the observational data. The relationships between the inter-annual variability of the modes and that of SH local rainfall and temperatures are presented in Section 3. Results for the case of annual means are shown for the hemisphere, including the ocean. Mechanisms are considered. Seasonal results are then presented, focusing on three continents in turn. A similar sequence is followed for the change of climate, in Section 4. A discussion, including consideration of the potential role of long-term changes in the ocean, follows. The conclusions of the study are given in Section Simulation of modes by Mk The model and 20th century simulation The CSIRO Mk3 coupled atmosphere ocean model (version Mk3.0) includes a now-standard set of physical and dynamical processes in the atmosphere, ocean and land surface. Dynamical variables of the atmosphere are represented on 18 levels, with a horizontal resolution of T63 (defined spectrally). Surface temperature and rainfall are represented on the corresponding grid of around 1.8 of longitude and latitude. SSTs are taken from the first of 31 layers of the oceanic component, with depth 10 m. Surface air temperature is evaluated at height 2 m. The model is fully described by Gordon et al. (2002), and also on the CMIP3 website. (Note that a modified model version Mk3.5 has recently been added to CMIP3.) In the CMIP3 simulations, the radiation in Mk3 is perturbed through variations in the specified ozone distribution and GHG concentration. Some effects of anthropogenic aerosol are included in the model through a surface albedo adjustment. The simulation that is analysed here represents climate over the years , with observationally based forcing for the years to 2000 (as described by Watterson and Dix, 2005). Forcing in the 21st century follows the SRES A1B scenario, which by 2100 has an effective GHG concentration approximately three times the 1871 value. The specified Antarctic ozone (Dai et al., 2001) is almost returned to the constant pre-1971 values in Mk3, after a steady recovery. All concentrations are stabilized from 2100 onwards. As in Watterson (2007), the focus is on the first and last 100-year periods, with the years , loosely denoted 20th century or 20C, being analysed in this section. Comparison is made with results from the 44 full years, , of the ERA40 reanalysis data set. The reduced statistical certainty from this shorter set limits this assessment (likely more so than the shortness of the overlap of years). In general, the Mk3 climatology for these years compares well with observational data, as documented by Collier et al. (2004), Watterson and Dix (2005) and others Indices of Modes With seasonal variability being the focus, the analysis is confined here to the monthly mean (rather than daily) data available for both Mk3 and ERA40. As described more fully by Watterson (2007), indices of the three modes are defined using data from all 1200 months of 20C. All data are first detrended, separately for each of the 12 months of the year, and expressed as anomalies about the mean annual cycle. The index representing the variability of ENSO is defined using SST anomalies averaged over the central equatorial Pacific Ocean. The standard NINO3.4 region (5 S 5 N, W) is used for the observational data. The Mk3 ENSO is defined, as before, from SSTs farther west (150 E to 190 E). The standard deviation (SD) of the monthly values from Mk3 is 0.74 K, which compares well with the observational

3 COMPONENTS OF PRECIPITATION AND TEMPERATURE ANOMALIES 811 result over of 0.80 K. This Mk3 ENSO index is well correlated with the Mk3 NINO3.4, with coefficient r = However, the western region relates somewhat more strongly with both equatorial rainfall and subtropical zonal winds in Mk3. Following Watterson (2007), the basic variable for the annular mode analysis is the vertical integral of zonal wind (u) with respect to pressure, from the surface pressure to zero pressure. To retain the dimensions of wind, the result is divided by 1000 hpa, and denoted U. Away from the equator, the zonal mean of this quantity is typical of mid-tropospheric winds, and as shown by Watterson (2007) the mean model winds in the SH closely match those from ERA40. For both data sets monthly anomalies were formed as above. The influence of ENSO was then removed using linear regression with the index, again for each month separately. This removed some 6% of the SH variance of zonal mean U in Mk3, and 7% in ERA40. PC analysis was then applied to each set of SH U anomalies. Weighting by area was included, and the results normalized so that the PCs or index time series have zero mean and an SD of 1. The latitudinal structures are the EOFs, giving the U perturbation for an index of 1. In ERA40 the first EOF has 46% of the (remaining) variance and the classical HLM structure shown in Figure 1, with the sign of the EOF chosen consistent with a positive SAM anomaly. In Mk3 the EOF1 has a slightly greater fraction (53%) but the structure is very similar. The second modes (20% for ERA40, 22% for Mk3) are also very similar to each other. This midto low-latitude dipole structure is the LLM analysed by Watterson (2007). SDs of the annual and seasonal averages of the model indices are given in Table I. The ENSO value is relatively large for the annual case, because of its greater persistence. (The autocorrelation of the full monthly series is 0.96 for ENSO, 0.42 for HLM and 0.29 for LLM.) The HLM and LLM SDs vary more with season. After partitioning the indices by month of the year, Watterson (2007) showed that this is due to annual cycles Figure 1. Structure of the high (HLM) and low-latitude modes (LLM) of the vertically integrated zonal wind (U) from all months of the Mk3 and ERA40 data. Table I. Inter-annual standard deviations of indices for five averaging periods. Period ENSO HLM LLM Annual DJF MAM JJA SON The ENSO index has the unit K, the others are non-dimensional. The seasons are December January February, DJF; March April May, MAM; June July August, JJA; and September October November, SON. in both the magnitude of the modes and their month-tomonth persistence. These relate to seasonal movement of the mean mid-latitude jet itself, and the resulting variation in wave-mean flow feedback processes. The three 1200-month index series are uncorrelated. However, another consequence of the jet movement is that the seasonal and annual means of the indices can be slightly correlated. The ENSO index remains almost uncorrelated with the others, except for a coefficient r = 0.15 between ENSO and HLM in the annual case. The largest correlation between the HLM and LLM indices is 0.29 for June July August (JJA). It is worth noting that the PCs formed from U without the removal of the ENSO component in Mk3 were very similar to HLM and LLM, but PC2 contained an additional low-frequency component (see Watterson, 2007). 3. Relationships in the 20C climate The analysis of Mk3 data has produced time series representing indices of three distinct modes of largescale variability, HLM, LLM and ENSO. In this respect, they can be related to different components of variability of temperature (T ) and precipitation (P ). Impacts for the annual and seasonal cases can be determined using regression of the 100 yearly values from detrended data averaged over the relevant months. Regression analyses between the HLM and LLM indices and zonal means of U produce large correlations at the peak latitudes of the modes. Multiplying the correlation coefficient by the inter-annual SD of the field produces a dimensional anomaly for a positive 1-SD index value. In the case of the Mk month series, this reproduces the mode EOFs (Figure 1), even though the data still include the ENSO component. Naturally, the signs of such anomalies are reversed for negative index values. Watterson (2007) presented maps of correlations with monthly grid point U and sea-level pressure (SLP). These showed that the Mk3 modes relate to anomalies at all longitudes, but the largest values, particularly for the LLM,tendtooccurintheAustraliansector.

4 812 I. G. WATTERSON Both correlations and dimensional anomalies are important in assessing impacts. In this section, the presentation will be of the correlation fields, with the inferred anomalies in mind. Comparison with similar fields from the ERA40 data will be made, although these can differ from station observations, for temperature and rainfall especially. Except where noted T is surface air temperature. Related dimensional results from Mk3 will be featured in Section Annual Correlation maps for the SH are shown here for annual means of three quantities: SLP, T and P. To highlight contrasts between land and sea values, colouring of grid squares is used. The pressure pattern associated with ENSO, in Figure 2, features the familiar tropical east west dipole, although its node (zero) is farther west than in ERA40 (Figure 2(d)). The HLM (Figure 2) relates to much of the pressure variation in the Antarctic and around 45 S, consistent with the SAM pattern (see also Watterson, 2007). A positive HLM is associated with a southward shift of the mid-latitude westerlies. The greater importance of both HLM and LLM (Figure 2(c)) from about 60 E eastwards to 120 W is again evident. Similar patterns occur in ERA40 (Figure 2(e) and (f)), and the observational results for SAM shown by Gillett et al. (2006). It should be noted that a correlation of magnitude 0.2 or more is statistically significant at the 5% level, for a sample of 100 pairs of values (assumed independent), but only the 20% level for 44 pairs (as for ERA40). The ±0.2 contours are shown in the figures. Of course, local r values might need to be considerably larger than this to be of practical significance. The 90% confidence interval of r = 0.5, typical of the larger values plotted, is for 100 pairs, and for 44 pairs. By definition, the Mk3 ENSO index is highly correlated with SST anomalies in the western Pacific, as seen in Figure 3. SSTs farther east are also well correlated, whereas those in the Indonesian region are negatively correlated, consistent with an ENSO-like pattern. However, there is a clear westward bias relative to ERA40 (Figure 3(d)) near the equator. The pattern extends to the South Pacific and Southern Ocean. Watterson (2007) showed that both HLM and LLM drive significant anomalies in the zonal mean SST in Mk3. These tend to lag the wind anomalies, but for longer averaging periods the correlations for zero lag are also large (Watterson 2001b). The correlations are also significant (at a modest level) at many ocean points in Figure 3 and (c). For HLM, the higher-latitude cool band is disrupted by positive anomalies (for positive HLM) in the Weddell Sea, very similarly to the results of Sen Gupta and England (2006). The LLM (Figure 3(c)) has a much smaller influence on annual anomalies in Mk3, except for some cooling around 50 S. There is some similarity to the less-certain ERA40 pattern for HLM (Figure 3(e)), but not LLM (Figure 3(f)). This may be in part due to theuseofera402mt, as the correlations for 2 m T in Mk3 over ocean are a little weaker. Watterson (2000), Sen Gupta and England (2006) and others have shown that both surface and ocean heat fluxes contribute to the SST anomalies of HLM/SAM. The surface components associated with HLM are illustrated in Figure 4. Positive flux from reduced evaporation (Figure 4) occurs under easterly wind anomalies along 40 S, but not at all longitudes in this lag-0 result. Net short-wave (solar) radiation (Figure 4) warms the surface over S. Sensible heat flux (Figure 4(c)) Figure 2. Correlations between annual mean sea-level pressure and annual means of indices: for Mk3 ENSO, HLM, (c) LLM and for ERA40 (d) ENSO (e) HLM and (f) LLM. The Mk3 coastline is shown, as in subsequent figures. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at

5 COMPONENTS OF PRECIPITATION AND TEMPERATURE ANOMALIES 813 Figure 3. Correlations between annual mean temperature and annual means of indices: for Mk3 ENSO, HLM, (c) LLM and for ERA40 (d) ENSO (e) HLM and (f) LLM. The surface T is used for Mk3, but 2 m T is used for ERA40. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at (c) (d) Figure 4. Surface heat flux anomalies (in W m 2 ) associated with +1 SD of the annual mean HLM index in Mk3: evaporative flux, short-wave radiation, (c) sensible heat and (d) downward long-wave radiation. In each case a positive value indicates a heating of the surface. The +0.3 W m 2 contour is shown, to aid interpretation of the B/W print. This figure is available in colour online at and downward long-wave radiation (Figure 4(d)) help drive the surface anomalies in some parts of the ocean. The continents tend to be cooler in Mk3 during positive HLM (Figure 3). The dimensional surface temperature anomaly (for +1 SD)reaches 0.3 K in eastern Australia. (Note that very similar results hold for surface air temperature.) This is still smaller than the ENSO anomaly, for this time scale. Temperatures over New Zealand (NZ) and southern South America are warmer, but by less than 0.15 K. The correlation pattern for HLM agrees well with that shown by Gillett et al. (2006), including the signs of anomalies at Antarctic stations. The

6 814 I. G. WATTERSON ERA40 result (Figure 3(e)) is rather consistent, except for South Africa. The mechanism for land temperature anomalies is relatively simple, as the net surface flux, consisting of the above four components plus upward long-wave radiation, is near-zero on these time scales (because of the relatively small depth of interacting soil, see Watterson, 2000). The latter flux is, naturally, almost proportional to the temperature anomaly (e.g. Watterson and Dix, 1996). The evaporative and solar fluxes drive the temperature, for most mid-latitude land (Figure 4). We turn to the correlations between the indices and annual mean precipitation, shown in Figure 5. The correlations for ENSO in Mk3 (Figure 5) are strongly positive (coloured blue) over the equatorial Pacific, and negative farther west over negative SST anomalies. The rainfall pattern is shifted westward in comparison with the observational field depicted by [their Figure 3] Meinke et al. (2005), as well as that from ERA40 (Figure 5(d)). The shift appears smaller than that for equatorial SST (Figure 3), which is helpful to the SH simulation, given the role of latent heating in driving teleconnections. The reduction in rainfall over western Australia in Mk3 is too strong. Nevertheless there is considerable realism in the pattern over South America, and some also for southern Africa and NZ (Ummenhofer and England, 2007). Rainfall in Mk3 is negatively correlated with HLM at most grid points within the band S, as shown in Figure 5. Correlations reach 0.5onthe Chilean coast. To the north and south are (partial) bands of enhanced rainfall, with r reaching 0.3 in central Argentina and southeastern Australia. Contrasting signs of rainfall anomalies between the North and South Islands of NZ are seen in the model as in observations shown (for SAM) by Ummenhofer and England (2007). Two bands are evident in the pattern for LLM (Figure 5(c)), but with limited impact on land points. Encouragingly, the ERA40 results (Figure 5(f) and (g)) show very similar bands. The patterns of T (Figure 3) and P (Figure 5) from Mk3 are spatially uncorrelated over the whole hemisphere. However, it is evident that for mid-latitude land the fields are anti-correlated the warmer red colours of Figure 3 tend to coincide with the drier reds in Figure 5. Over land grid points between 60 S and 10 S, the spatial correlation coefficient for Mk3 ranges from 0.52 for ENSO to 0.68 for HLM. This is consistent with an influence of rainfall and related clouds on both the evaporative and short-wave fluxes. The band of lower rainfall under HLM occurs around the node of the EOF at 47 S (Figure 1), where there is a divergence of low-level wind in the zonal mean (Watterson, 2007, Figure 4(c)). Likewise, Seager et al. (2005) related ENSO rainfall to the atmospheric horizontal moisture flux, in particular the component derived from the product of monthly mean wind vectors and specific humidity. This mean-flow flux has been evaluated for each month of the 20C data set and vertically integrated over the model levels following Watterson (1996). The dimensional anomalies of the flux components, from the regression of annual means with the ENSO and HLM indices, are shown by the vectors in Figure 6. The convergence of this vector field is also shown. The ENSO result is similar to Figure 8 of Seager et al. (2005). The vectors reflect the winds of the humid lower troposphere, where the zonal wind anomaly is towards lower pressure (Figure 2). The flux takes moisture away from Australia, and the convergence field relates closely to rainfall anomalies in the other continents, also. Note that the mean-flow moisture convergence is partly balanced by a transient-flow term (not shown) as well as evaporation. For HLM (Figure 6), the mid-latitude mean-flow Figure 5. Correlations between annual mean rainfall and annual means of indices: for Mk3 ENSO, HLM, (c) LLM and for ERA40 (d) ENSO (e) HLM and (f) LLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at

7 COMPONENTS OF PRECIPITATION AND TEMPERATURE ANOMALIES Figure 6. Moisture flux anomalies in Mk3 associated with +1 SD of the annual mean: ENSO and HLM. Shown as vectors are the components of vertically integrated mean-flow flux, from a reduced grid, with the scale indicated, in the unit mm m s 1, and omitting vectors under length 1 unit. The shading is the associated convergence field in mm d 1. The mm unit refers to the depth of condensed water. This figure is available in colour online at zonal fluxes are consistent in sign with the EOF winds, whereas the negative convergence field confirms the role of low-level meridional wind anomalies. The magnitudes of the convergence and rainfall anomalies around 60 S are relatively small. At some coastal regions, in particular southeastern Australia, the rainfall anomalies relate to the advection of moisture by the zonal wind Seasonal The rainfall and (surface air) temperature anomalies associated with the three large-scale modes can have a considerable seasonal dependence. This follows from both the seasonal cycle of the quantities, and also any variation in the latitude of a mode. Given the focus on impacts, we examine the three inhabited southern continents individually. Maps of all 72 cases (four seasons, three modes and two variables for each region) were considered, but, for brevity, only a selection of the Mk3 results is shown. To indicate the importance of the modes in each case, a single statistic, the mean magnitude of the correlation coefficient r, is given in Table II. Values relate to the land between 60 S and 10 S, including nearby islands such as are represented on the model grid, as shown in the maps. If there is a predominant sign of r (see Table II caption), this is given. Simple continental area means of the magnitudes of the r values give ENSO as the most important, followed by HLM then LLM, for each continent. The influence of each mode is strongest in Australia NZ, then South America and then southern Africa. A similar comparison holds for the annual cases (not shown). The area means in Table II can be compared with the expected mean of r for a sample of 100 pairs of values of uncorrelated variables, which is The results are compared with those from other studies. The seasonal correlations from the ERA40 data set (not shown) are broadly consistent with those shown from Mk3, but some differences will be mentioned. Table II. Statistics of correlations between seasonal indices of HLM, LLM and ENSO and seasonal means of surface air temperature T and precipitation P over land grid points in Mk3 between 60 S and 10 S, for three large regions: The number is the area mean of the magnitude of r 100. A sign (+ or ) indicates that of the area mean of r itself, and of the value of largest magnitude, where these agree. Mode DJF MAM JJA SON ANZ (T) ENSO HLM LLM ANZ (P) ENSO HLM LLM SAM (T) ENSO HLM LLM SAM (P) ENSO HLM LLM SAF (T) ENSO HLM LLM SAF (P) ENSO HLM LLM Australia New Zealand, ANZ; South America, SAM; and Southern Africa, SAF Australia NZ Consistent with Table II, there is a considerable seasonal variation in the temperature anomalies associated with the modes over both Australia and NZ.

8 816 I. G. WATTERSON (c) (e) (d) (f) Figure 7. Correlations between seasonal mean temperature over Australia NZ and seasonal means of indices: DJF ENSO, JJA ENSO, (c) DJF HLM, (d) JJA HLM, (e) DJF LLM and (f) JJA LLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at Maps for both December January February (DJF) (summer) and JJA (winter) are shown in Figure 7, as these differ for each mode. Aside from the Australian warmth in the ENSO DJF case [and for September October November (SON), not shown], a striking result is the coolness of the south for (positive) HLM in winter, with r = 0.59 at 36 S, 141 E. NZ is strongly cooled by ENSO in SON. The HLM pattern for SON is like that for DJF. The HLM patterns are similar to those from Australian observations under SAM shown by Hendon et al. (2007) (averaging their daily max and min values). Seasonal influences of LLM (Figure 7(e) and (f)) can be significant. High pressure is maintained over the Tasman Sea (as in Figure 2(c)) in each season, but the influence of LLM on temperature varies. The P anomalies show somewhat smaller seasonal variation (Table II), and only three cases are shown in Figure 8. ENSO brings reduced rainfall to Australia in each season, peaking in SON (Figure 8). As for the annual case, the western values are too high compared with ERA40. In JJA (Figure 8), HLM relates to more rain except in the far south of both countries. The impact in March April May (MAM) is weak. Interestingly, the LLM pattern in JJA (not shown) is similar to that of HLM and for the far southwest of Australia exceeds it, with r reaching This similarity is partly explained by the modest correlation between the indices. The ERA40 LLM JJA result is weak, however. The LLM DJF case for Mk3 (Figure 8(c)), and also ERA40, has positive values in the interior of Western Australia. Some of the observed increasing trends in this region (Smith, 2004, Figure 5(c)) could be related to a jet shift similar to LLM. The signs of the HLM rainfall anomaly patterns over Australia are very much like those observed (Hendon et al., 2007), in each of the four seasons. The southern decreases in JJA (and SON) relate to a northward shift of several degrees, relative to the annual case, in the pressure and wind anomalies associated with the mode, in both the model and observations (Watterson, 2007). The rainfall pattern contrasts with that in temperature in JJA, but this is partly explained by examination of the surface heat flux components (not shown). Around 30 S, rainfall increases match the increased evaporative cooling and decreased solar radiation, as in the annual case. These components are smaller in the far south, where southerly wind anomalies (for positive HLM) lead to cooling by sensible heat flux. In fact, the temperature variability for JJA is small, and the HLM anomalies are typically only ±0.3 K. In summary, ENSO is the most important influence on northern Australia in Mk3. HLM is as important as (c) Figure 8. Correlations between seasonal mean rainfall over Australia NZ and seasonal means of indices: SON ENSO, JJA HLM and (c) DJF LLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at

9 COMPONENTS OF PRECIPITATION AND TEMPERATURE ANOMALIES 817 (c) Figure 9. Correlations between seasonal mean temperature over South America and seasonal means of indices: JJA ENSO, DJF HLM and (c) MAM LLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at (c) Figure 10. Correlations between seasonal mean rainfall over South America and seasonal means of indices: JJA ENSO, DJF HLM and (c) JJA HLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at ENSO in southeastern Australia, where a positive mode results in higher rainfall and lower temperatures. In the far south of Australia in winter, both HLM and LLM lead to lower rainfall, and lower temperatures South America The averages in Table II indicate a considerable influence of the three modes over South America. In most cases there are significant correlations of both signs, in part due to the large latitudinal range. A small selection of the maps is shown for temperature (Figure 9) and rainfall (Figure 10). The ENSO influence includes low pressure that extends into the continent from the Pacific (Figure 2), except in the far south, as in observations presented by Schneider and Gies (2004). Temperatures are higher in the tropics, in each season, but lower in the far south in JJA (Figure 9) and also SON. Rainfall forms a banded pattern in the annual case, but the northern dryness is shifted to the east coast in JJA (Figure 10). The midlatitude anomalies of rain and temperature are rather weak in DJF and MAM (not shown). An exception is the increased rain around Uruguay in summer (and the annual case Figure 5), consistent with Silvestri and Vera (2003), and also ERA40. The HLM impacts on temperatures in bands, most strongly in DJF (Figure 9) and SON (when r reaches at 55 S). The influence on rainfall is also banded [e.g. for DJF and JJA, Figure 10 and (c)], with some movement with season, as found for SAM by Silvestri and Vera (2003). The LLM impacts are also banded, and despite the small annual results, r can exceed ±0.3, as for T in MAM, the case shown in Figure 9(c) Southern Africa Equatorial Africa is warmed by El Niño, and in Mk3 the south is also generally warmer, as seen for DJF and JJA in Figure 11 and. The far south west is weakly cooled in summer, and there is little anomaly in SON for either T or P (not shown). The largest correlations for rainfall are in JJA, shown in Figure 12. The seasonal contrast for rainfall is, however, reversed from that in ERA40 (not shown). Cook (2001) and Tennant and Reason (2005) show how teleconnections from the Pacific can result in modified rainfall over southern Africa. A comprehensive assessment is not possible here, but it seems likely that the westward bias in the Pacific can lead to biases over Africa, in both annual (Figure 2) and seasonal patterns. Correlations of the HLM index with temperature vary, with cooling in the south in DJF (Figure 11(c)) and modest warming farther north in JJA (Figure 11(d)). In Mk3 (Figure 12) and also ERA40, rainfall is enhanced in the southeast in JJA. Reason and Rouault (2005) found

10 818 I. G. WATTERSON (c) (d) Figure 11. Correlations between seasonal mean temperature over southern Africa and seasonal means of indices: DJF ENSO, JJA ENSO, (c) DJF HLM and (d) JJA HLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at Figure 12. Correlations between seasonal mean rainfall over southern Africa and seasonal means of indices: JJA ENSO and JJA HLM. The +0.2 ( 0.2) contour is shown by the solid (dashed) line. This figure is available in colour online at that observed winter rainfall anomalies in western South Africa could also be related to shifts in zonal winds and moisture fluxes. For the HLM index, though, the band of negative correlations remains just south of the continent [as in Figure 5 and (e)]. The LLM seasonal impacts are small. A weak enhancement of rain in all seasons contributes to the most notable result the annual case shown in Figure 5(c). 4. Components of climate change We turn now to the changes in rainfall and precipitation under global warming, and the components related to the three modes. In response to the prescribed SRES A1B scenario of GHG concentrations rising through to 2100, Mk3 simulates global mean temperatures that increase at a rate a little lower than that of the CMIP3 multimodel mean (Meehl et al., 2007). Simulating to 2200 with stabilized GHG, the warming continues at a slower rate. To provide a full 100-year averaging period for the warmer climate, we focus on the period , denoted 22C. The difference in global mean temperature for 22C relative to 20C is 2.79 K. As discussed by Watterson (2007) the forced response is estimated at 0.3 K higher, after comparison is made with an unforced or control simulation that cools a little Mean changes in fields and indices Watterson (2007) showed zonal means of the surface temperature change (22C minus 20C) in the SH, which were 2.8 K at the equator, but below 1 K around 55 S. The changes at grid points over the hemisphere for the annual case are shown here in Figure 13, relative to the global mean. Land temperatures generally rise more than the mean, except in the southern mid-latitudes, where there is also small oceanic warming. The change in the equatorial ocean is close to the mean, peaking in the west Pacific as does the model El Niño (Figure 3). The change in precipitation over the hemisphere is shown in Figure 14. This has much in common with

11 COMPONENTS OF PRECIPITATION AND TEMPERATURE ANOMALIES 819 (c) Figure 13. Annual change in temperature, in Kelvin: net change (minus the global mean 2.79 K) and components associated with ENSO and (c) HLM. The +0.1 K contour is shown. This figure is available in colour online at the CMIP3 multi-model mean change over the 21st century presented by [Meehl et al., 2007, their Figure 10.12]. Tropical SH land increases in Mk3 are confined to the northeast and far west of South America, and East Africa (around 15 S). The Australian changes are less positive than in CMIP3, while the Indonesian changes are much more negative, and of the opposite sign in some places. Changes in the mid- and high latitudes are very similar in pattern, and even magnitude, to CMIP3. Zonal mean surface pressure is 4 hpa lower in the Antarctic, and 2 hpa higher around 45 S (Figure 15 of Watterson, 2007). This is a positive SAM-like change in the mean, consistent with increases in the SAM index by 2100 from the CMIP3 models analysed by Miller et al. (2006). Watterson (2007) showed that the change in the vertically integrated zonal wind U at the high latitudes was similar to a +1 SD HLM change. Furthermore, changes in winds from different tropospheric levels have a similar, zonally symmetric, structure, like HLM. Ring and Plumb (2007) argue that the eddy-feedback mechanisms that support the existence of the HLM may also lead to it being a substantial component of forced change. Consistent with the PC analysis, the change U can be represented as a sum of the EOFs u i, with coefficient a i given by applying the projection operator (with latitude φ in degrees): 0 0 a i = Uu i cos(φ)dφ/ u i u i cos(φ)dφ (1) For the annual case, these coefficients are for HLM (i = 1), and only for LLM (i = 2), given in Watterson (2007), and repeated here in Table III (case U ). The zonal mean zonal wind increases at around 50 S through the troposphere, hence consistent with a positive LLM change. However, at around 30 S, the change ranges from a small decrease at lower levels, to a large increase near the tropopause, hence the nearzero LLM change. Stratospheric wind changes provide a negative contribution to the HLM index change, unlike the mode itself. Projecting the EOFs on seasonal mean changes of U leads to considerable variation, as seen in Table III. Stratospheric winds strongly diminish the HLM index in JJA and SON. The LLM index changes even vary in sign. With respect to impacts on rainfall and temperature near the surface, the discussion on mechanisms points to the role of low-level winds in driving anomalies in surface heat flux, surface stress and moisture flux. For the purpose of estimating the components of change in the mid- and low latitudes that can be related to the mode-like meridional shifts of winds, the inclusion of upper-level winds in the projection is questionable. An alternative is to use u at 843 hpa (which is associated with the fifth model level). Winds at that level associated with the modes are very similar to the U EOFs, so it is convenient to apply the same projection operation. The results are given in Table III (case a, for alternative), for both annual and seasonal cases. The HLM-a index change has less seasonal variation than that for U, and an annual change 13% larger. The LLMa change is dominated by a positive change in JJA. This is 1.3 times the inter-annual SD for JJA (Table I).

12 820 I. G. WATTERSON (c) Figure 14. Annual change in rainfall, in mm d 1 : net change and components associated with ENSO and (c) HLM. The +0.1 mm d 1 contour is shown. This figure is available in colour online at Figure 15. Change in seasonal mean temperature (in K) over Australia NZ associated with change in index: DJF HLM and JJA HLM. The +0.2 K ( 0.2 K) contour is shown by the solid (dashed) line. This figure is available in colour online at The overall wind changes at the 843 hpa level are rather zonal through the mid-latitudes, so the impact patterns based on the HLM and LLM in the 20C analysis remain relevant. We can use the a index changes as factors to these patterns. The resulting components can be described as mode-like. Because HLM and LLM variability is so barotropic, the components would be very similar to those based on EOFs of u at 843 hpa (and likely also SAM). A projection approach can be used to provide an index factor representative of the ENSO-like component. A reasonable domain for the projection is the ocean within 30 S and 30 N. The ENSO temperature pattern in Figure 3, normalized by multiplying by the local SDs and dividing by the index SD, is an appropriate mode function. Let us assume that ENSO influences the distribution of SST change, relative to the mean over the domain (which is 2.37 K). Applying the projection operation with these fields produces the change coefficient (+0.49 K) given in Table III. This represents a change of 0.81 SD of 20C annual variability, a smaller factor than for the HLM-a case (1.8 SD). Since the seasonal temperature patterns for ENSO have been shown (at least in part), and will be used again shortly in maps, these have been used in a similar fashion to get the seasonal change coefficients. Note that the average of these four is 0.51, a little larger than the annual result. In addition, given that the mean of the SST mode function in each case is not quite zero (0.14 K in the annual case) it could be argued that this field should be offset by the mean, before applying Equation (1). The effect would be a 21% increase in the annual index change. In fact, this corresponds to a spatial regression form of Equation (1), which appears to

13 COMPONENTS OF PRECIPITATION AND TEMPERATURE ANOMALIES 821 be similar to that used by Yamaguchi and Noda (2006) and also Collins et al. (2005), in defining ENSO-like change. These alterative factors for the ENSO index change, as well as those for the wind modes, could be used to rescale to dimensional results that follow, if so desired. Since we also make the common assumption that the inter-annual variability associated with the indices in 20C can be applied to long-term change (see Hendon et al., 2007 and our later Discussion), the following components of change should be considered somewhat qualitative. Note that the index changes apply to the means of the two 100-year periods. The HLM index slowly declines over the 22nd century (see Watterson, 2007), and there is a considerable decadal variation, so that somewhat larger changes would apply for the period around Annual Given the ENSO, HLM-a and LLM-a index changes shown in Table III, it is straightforward to depict the components of change from 20C to 22C associated with the three modes. We simply multiply the dimensional form of the regression patterns presented in Section 3 by the index change. Matching the cases shown in Section 3, have firstly the change in T associated with the Mk3 ENSO pattern, shown in Figure 13. This is most positive (nearly 1 K) east of New Guinea, while tropical South America is also warmed. Australia is warmed by an average 0.25 K. Cooling peaks in the seas around Indonesia, but aside from the Antarctic Peninsula, the magnitude of relative cooling over land, including the far south of South America, is less than 0.1 K. The Ross Sea region is warmed. The large gradient of equatorial warming from 120 E to 170 E (Figure 13), compared with that in CMIP3, is consistent with the above-average magnitude of the El Niño-like change in Mk3, as diagnosed by Yamaguchi and Noda, (2006, their Table II). The annual temperature change associated with HLM is plotted in Figure 13(c). There is a cooling of over 0.5 K in parts of Australia, with the average ( 0.24 K) almost offsetting the ENSO change. There is widespread cooling from HLM over South America. Mid-latitude warming is always small, even where the correlations in Figure 3 Table III. Changes in indices (22C minus 20C) for different time periods. Period ENSO HLM-U LLM-U HLM-a LLM-a Annual DJF MAM JJA SON The ENSO change (unit K) is determined using a temperature pattern that varies with the period. Two values are given for both the HLM and LLM index changes, determined from changes in either U (HLM-U, LLM-U) oru at 843 hpa (HLM-a, LLM-a). are high. The change associated with LLM is very small in the annual case and not shown. Returning to the precipitation change, we see from Figure 14 that much of the tropical pattern, particularly the strong dipole north of Australia, is associated with ENSO. The decreases in northwestern Australia, and small percentage decreases over much of the continent, can be attributed to the model s El Niño-like response. Little of the overall rainfall decrease in western and southern Africa appears attributable to the modes, however. Rainfall is increased over much of Australia by the HLM change. Bands of change occur over South America. Evidently, at higher latitudes changes are dominated by the effect of enhanced moisture in the warmer air rather than the modes Seasonal As for the 20C correlations, presentation of the seasonal results is limited to selected regional maps. A statistic representing the mean magnitude of the component in each case is given in Table IV. For rainfall the changes at each grid point are expressed as a percentage of the 20C mean amounts. If there is a predominant sign of change over a land region this is given (Table IV Table IV. Statistics of changes in seasonal means of surface air temperature T (in K 100) and precipitation P (in % of present) over land grid points in Mk3, attributed to changes in indices, for the three regions of Table II:. Mode DJF MAM JJA SON ANZ (T) ENSO HLM LLM ANZ (P) ENSO HLM LLM SAM (T) ENSO HLM LLM SAM (P) ENSO HLM LLM SAF (T) ENSO HLM LLM SAF (P) ENSO HLM LLM The number is the area mean of the magnitude of the value. A sign indicates that of the area mean of the value itself, and of the value of largest magnitude, where these agree. Australia New Zealand, ANZ; South America, SAM; and Southern Africa, SAF.

14 822 I. G. WATTERSON (c) Figure 16. Change in seasonal mean rainfall over Australia NZ associated with change in index, as a percentage of 20C mean rainfall: JJA ENSO, JJA HLM, and (c) JJA LLM. The +15% ( 15%) contour is shown by the solid (dashed) line. This figure is available in colour online at caption), and these signs are very similar (except for smaller LLM changes) to those in Table II (which were based on r). Averaging the eight results for each region and mode (disregarding the units) gives the same ordering in overall importance as in the 20C case, led by ENSO over Australia NZ. The HLM averages are only a little less than the ENSO averages, however Australia NZ The model s El Niño-like change warms Australia in each season, similarly to the annual case, except for JJA which has some cooling in the north [as in Figure 7]. The HLM change mostly warms NZ, especially in DJF shown in Figure 15, and mostly cools Australia. Despite the large correlations in JJA in the south, the actual change (Figure 15) is only around 0.3 K. Combined with cooling in the south from the LLM change (Figure 7(f)), the net change there from the modes in JJA is negative. All three contributions to rainfall in JJA are shown in Figure 16. These largely offset each other. While the correlations for 20C are significant in the far south of Australia, the magnitudes of change, even expressed in percentage terms, are small. In this analysis LLM produces the largest component of the decrease in the far southwest. The northern changes are relative to a small base in JJA. The HLM change increases rainfall along the east coast in each season South America The ENSO and HLM components of temperature change over South America are similar in each season to the annual case. An exception is a warming of 0.5 K around 40 S, 70 W in DJF from HLM. Changes from LLM in JJA reach ±0.1 K. Despite some moderate correlations in 20C the ENSO change to rainfall reaches 10% of the mean in only a few small regions, in each season. Changes from HLM vary with season, as seen in Figure 17. Note that the JJA changes in the northeast (Figure 17) are from a very small base. Changes from LLM in JJA are comparable, while those for SON on the subtropical west coast reach 20% at 20 S and+20% at 30 S Southern Africa Africa is mostly warmed by the ENSO component, with the DJF result shown in Figure 18. The change from HLM is a weak cooling in the far south (Figure 13(c)). It is quite seasonal elsewhere, with warming in MAM (Figure 18) reaching 0.3 K, where there is cooling in summer. The LLM-like change provides a weak warming overall. The largest percentage rainfall change associated Figure 17. Change in seasonal mean rainfall over South America associated with change in index, in %: DJF HLM and JJA HLM. The +15% ( 15%) contour is shown by the solid (dashed) line. This figure is available in colour online at