Recent accumulation variability and change on the Antarctic Peninsula from the ERA40 reanalysis

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 28: (2008) Published online 26 November 2007 in Wiley InterScience ( Recent accumulation variability and change on the Antarctic Peninsula from the ERA40 reanalysis Georgina M. Miles, a Gareth J. Marshall, a * Joseph R. McConnell b and Alberto J. Aristarain c a British Antarctic Survey, Natural Environment Research Council, Cambridge, UK b Desert Research Institute, Nevada System of Higher Education, Reno, NV USA c Laboratorio de Estratigrafía Glaciar y Geoquímica del Agua y de la Nieve, Instituto Antártico Argentino, Centro Regional de Investigaciones Cientifícas y Teconológicas, Mendoza, Argentina ABSTRACT: The Antarctic Peninsula has displayed significant climate change over recent decades. Understanding contemporaneous changes in accumulation is made difficult because the region s complex orography means that icecore data are not necessarily representative of a wider area. In this paper, the patterns of regional spatial accumulation variability across the Antarctic Peninsula region are presented, based on an Empirical Orthogonal Function (EOF) analysis of European Centre for Medium Range Forecasts Reanalysis (ERA40) data over the 23-year period from 1979 through Annual and seasonal trends in the sign and strength of these patterns are identified, as is their relationship with mean sea level pressure, temperature and indices of large-scale circulation variability. The results reveal that the first pattern of accumulation variability on the Peninsula is primarily related to pressure in the circumpolar trough and the second pattern to temperature: together the two EOFs explain 45 65% of the annual/seasonal accumulation. The strongest positive trend in an EOF occurs with EOF2 in the austral autumn March-April-May (MAM). This is highly correlated with the Southern Annular Mode (SAM) in this season, suggesting stronger westerly winds have caused an increase in orographic precipitation along the west Antarctic Peninsula. A significant correlation with ENSO occurs only in the winter EOF1, associated with blocking in the Bellingshausen Sea. Inter-annual ERA40 accumulation is shown to compare favourably with an ice core in the south of the Peninsula, but, for a variety of reasons, correlates poorly with accumulation as measured in an ice core from the northern tip. Opposite trends in accumulation at these two sites can be explained by the spatial pattern and trend of EOF2 in MAM and thus by recent changes in the SAM. The results of this study will aid in the understanding of temporal accumulation changes observed in the regional ice-core record. Copyright 2007 Royal Meteorological Society KEY WORDS climate change; Antarctica; precipitation Received 25 September 2007; Accepted 27 September Introduction The Antarctic Peninsula comprises a mountain chain running approximately North South from S, extending 1300 km in length and reaching altitudes above 2 km a.s.l. The magnitude of this orographic barrier means that the western and eastern sides of the Peninsula are influenced by very different circulation and precipitation regimes. Both the relatively dense accumulation observations (Turner et al., 2002) and high-resolution regional climate models (van Lipzig et al., 2004; van de Berg et al., 2006) reveal that the western side has significantly more accumulation than the east. This is primarily because the orography forces air masses in the prevailing westerly flow to rise, leading to adiabatic cooling and precipitation over the up-wind slope. In addition, the blocking effect of the Peninsula means that depressions often * Correspondence to: Gareth J. Marshall, British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK. GJMA@bas.ac.uk stagnate west of the barrier, and that region of the Southern Ocean, the Bellingshausen Sea, has been described as a cyclone graveyard (e.g. Turner et al., 1998). Thus, the Peninsula separates climatologically maritime air west of the Peninsula from colder, drier continental air masses in the lee of the mountains to the east. The Antarctic Peninsula is a region of rapid climate change (e.g. Vaughan et al., 2001, 2003; King et al., 2003). For example at Faraday/Vernadsky station on the western Peninsula (Figure 1), the mean annual temperature has increased by 2.9 C from , possibly the largest recorded warming anywhere on Earth during the past half-century. The warming at Faraday is dominated by temperature increases in winter, but in recent decades the greatest Peninsula warming has been during summer in the north-east of the region. The temperature rise has led to the retreat and eventual collapse of several floating ice shelves from the west and north-east coasts of the Peninsula (e.g. Vaughan and Doake, 1996; Scambos et al., 2000; Marshall et al., 2006). Turner et al. (1997) reported an increase in the number of precipitation events Copyright 2007 Royal Meteorological Society

2 1410 G. M. MILES ET AL. observed at Faraday contemporaneous with the warming. This result suggests an increase in the number of cyclonic systems passing through the region: however, the study of Simmonds and Keay (2000), based on the output from an automatic depression tracking algorithm on National Centers for Environmental Prediction reanalysis data, indicates fewer but more intense cyclones in the region. This dichotomy reflects the challenges of reconciling measurement and model in this remote region. Moreover, both observations and accurate regional reanalyses provide relatively short-time series so statistical significance in any apparent trend is difficult to establish. Longer time series of accumulation can be obtained using regional ice-core records. Data from two ice cores obtained recently from the Antarctic Peninsula are shown in Figure 2. The first is located at James Ross Island (JRI) (64.2 S, 57.7 W), in the extreme north-east of the Peninsula (Aristarain et al., 2004), and the second at the base of the Peninsula, west of the Ellsworth Mountains: this latter core forms part of an International Trans- Antarctic Scientific Expedition (ITASE) traverse (Frey et al., 2006), and is named ITASE 01-5 (77.1 S, 89.2 W) (Figure 1). We note the significant variability in both records, particularly at JRI. The annual accumulation at the two sites from the beginning of the JRI record (1830) until about 100 years later was broadly similar. Subsequently, accumulation at JRI increased while that at the ITASE 01-5 site decreased. However, in the last three Figure 1. Map of the Antarctic Peninsula region showing locations mentioned in the text. James Ross Island and ITASE 01-5 are the sites of the two ice cores used in this study. Figure 2. Annual accumulation derived from two Antarctic Peninsula ice cores: ITASE 01-5 (full line) and James Ross Island (dotted line).

3 ANTARCTIC PENINSULA ACCUMULATION VARIABILITY AND CHANGE 1411 decades there has been a downward (upward) trend in accumulation at JRI (ITASE01-5) such that annual values are once again similar. One of the principal aims of this paper is to help explain such marked regional differences in accumulation variability and trends. Snow accumulation in the Antarctic Peninsula is governed by a number of processes, the most dominant being precipitation mass in coastal or sheltered regions. Subsequently, snow may be lost or gained at a particular site through re-distribution by the near-surface wind, termed blowing snow. There may also be a loss of water vapour flux from the surface through sublimation and evaporation, together with some surface melting in the northern Peninsula and at lower elevations in the south. A highresolution regional model of the Peninsula suggested that surface sublimation and wind transport of snow across the coast towards the sea comprise 9 and 6% of the total Peninsula precipitation, respectively (van Lipzig et al., 2004). Accumulation in Antarctica is notoriously difficult to measure accurately (e.g. Bromwich, 1988). While there is a higher density of measurements from ice cores and stake arrays in the Peninsula than across the rest of the continent, the region s complex orography and consequent changes in the near-surface wind field mean that the area over which these accumulation observations can be considered representative is limited. Furthermore, a highly nonlinear relationship exists between wind speed and blowing snow transport (King et al., 2004). Such factors contribute to local, depositional noise. Thus, the map of Peninsula precipitation produced by Turner et al. (2002), that is highly dependent on defined regional accumulation height relationships, is likely to have significant errors away from the in situ data. Gridded data from reanalyses and climate models used in combination with surface observations probably represent the best way to study the spatial relationships in accumulation variability across the Antarctic Peninsula (Turner et al., 1999). As discussed in King and Turner (1997), there is a strong correlation between the spatial variability of temperature and accumulation across the Antarctic Plateau, attributed to the relationship of saturation vapour pressure and air temperature. However, for coastal regions, accumulation is primarily dependent on the position and strength of cyclones within the circumpolar trough (CPT), the zonal band of low-pressure that rings Antarctica. For the Peninsula, the pressure in the Amundsen- Bellingshausen Seas (ABS) to the west is of greatest importance (Genthon et al., 2003). At inter-seasonal timescales the Semi-Annual Oscillation (SAO), the twiceyearly contraction and intensification of the CPT, dominates (e.g. Turner et al., 1998). The Southern Hemisphere (SH) Annular Mode (SAM) significantly affects interannual precipitation variability. This is the principal mode of atmospheric variability across the SH extra-tropics, describing 35% of total pressure variability. The SAM has a quasi-annular structure with synchronous anomalies of opposite sign in the mid- and high-latitudes. In recent decades, the SAM has trended towards its positive phase, defined as negative pressure anomalies over Antarctica, particularly, in autumn and summer (e.g. Marshall, 2003). Physically, this means stronger westerlies impinging on the western Peninsula. Not surprisingly, given the dominance of orographic precipitation, van den Broeke and van Lipzig (2004) found that model simulations indicated a positive unit change in the SAM caused 30% more precipitation on the western side of the Peninsula but had no effect to the east. Turner et al. (2005) showed a strong relationship between the SAM and precipitation events at Faraday, particularly, in summer. Turner (2004) reviewed the relationships between the El-Niño-Southern Oscillation (ENSO) and the Antarctic climate. A weak teleconnection exists between ENSO and the ABS region of low pressure, and is thought to arise from Rossby waves generated from convection at lower latitudes being propagated southwards. While there is some evidence of ENSO having an impact on accumulation over West Antarctica (Bromwich et al., 2000), Genthon et al. (2003) demonstrated that the strength of this relationship is reduced on moving east towards the Peninsula. A modelling study by Lachlan-Cope and Connolley (2006) indicates that the strength of the teleconnection is weak because the relationship between upper level divergence and tropical SSTs via deep convection is complex and natural variation in the zonal flow of SH high-latitudes can swamp the ENSO signal. Thus, the strength of the ENSO signal west of the Peninsula can be modified by the SAM (e.g. Fogt and Bromwich, 2006). Bromwich et al. (2000) considered the ENSO signal in West Antarctic precipitation in the earlier European Centre for Medium range Weather Forecasts (ECMWF) reanalysis (ERA15) data. They found that the modelled accumulation is strongly affected by the moisture flux within the reanalysis model, which is sensitive to the wind fields: in ERA15 these were in error because an incorrect height for one of the sparse Antarctic radiosonde stations propagated across much of Antarctica. In this work, we utilize the ECMWF 40-year reanalysis (ERA40) dataset to produce an internally consistent pattern analysis of the accumulation regimes of the Antarctic Peninsula. This is currently the global reanalysis that best represents the Antarctic climate system (e.g. Marshall, 2003; Bromwich and Fogt, 2004). Accumulation rates in this work, as derived from the ERA40 data, are defined as precipitation minus evaporation (P E) and are given in mm water equivalent. Other terms such as blowing snow and sublimation can be locally important (e.g. van Lipzig et al., 2004) but here we primarily focus on regional patterns. Genthon et al. (2005) presented a comparison of ice-core data and the ERA40 dataset for mass balance in West Antarctica, including ITASE 01-5 in the southern Peninsula. At this site, the correlation between annual accumulation derived from the two datasets for was 0.6, which improved to 0.7 fora more recent period ( ). EOFs are used to identify sub-regions of the Peninsula where accumulation variability is highest within the

4 1412 G. M. MILES ET AL. reanalysis data on seasonal and annual timescales. A similar study was reported in Matulla et al. (2003) whereby regionality of Austrian precipitation was identified using EOF and other pattern analysis techniques. The mountainous topography was identified as profoundly affecting the seasonal influence of the prevailing air masses upon precipitation. EOF analysis has been widely used to statistically decompose atmospheric process variability in time and space (e.g. Walsh et al., 1982; Hannachi et al., 2006). For example, Genthon et al. (2003) presented an analysis of Antarctic-wide (south of 60 S) circulation (500 hpa geopotential height) and precipitation in which EOFs were used to identify principal modes of variability. The principal modes of precipitation variability varied jointly with the SAM and ENSO although they shared a common spatial structure. It was noted that EOFs of precipitation were noisy compared to circulation, as precipitation characteristics vary widely over the continent. In this study, the question of whether accumulation in different regions of the Peninsula is changing in response to documented warming and/or changes in the SAM is addressed. Examining the trends in these EOF patterns and how they co-vary with pressure and temperature provides an indication of causality or simultaneous effects. This work will be presented with the following structure. After the ERA40 data are described in Section 2, the methodology used for constructing EOFs from ERA40 data is presented in Section 3, with some discussion of quantifying the mutual independence of the EOF patterns, and the effect of rotation. The leading annual and seasonal patterns austral autumn is MAM, winter is June-July-August (JJA), spring is September- October-November (SON) and summer is December- January-February (DJF) of precipitation variability are considered in Section 4, in particular features they share and those that are unique. Physical explanations for the patterns are examined by correlating the EOF projections with the ERA40 mean sea level pressure (MSLP) and near-surface temperature time series to study their covariability. Similarly, the pattern loading time series are regressed with indices of the SAM and ENSO and the results presented. Section 5 compares the ERA40 accumulation series with data from the two ice cores and explains how trends in the principal patterns of accumulation variability across the Peninsula are responsible for the recent opposing trends seen at the two sites. Finally, the key findings are summarized in Section Data 2.1. Data pre-processing The ERA40 reanalysis is thought to be reliable at high southern latitudes only after 1979 when satellite observations from TIROS Operational Vertical Sounder (TOVS) began to be assimilated (Marshall, 2002, 2003). The dataset used here encompasses 23 years from 1979 to 2001: the latter is the final complete year of ERA40. Note that precipitation values in ERA40 are thought to improve slightly further in 1984 when improvements were made to the TOVS retrieval (A. Simmons, personal communication, 2006). The dataset of P E (minus) is evaluated by taking the raw difference between precipitation and evaporation. Precipitation and evaporation are not parameters inherently analysed within the ERA40 model, but may be accessed as model forecasts. The fields used here are derived from daily forecast that comprise the latter 12-h from two 24-h forecasts made 12 h apart to minimize possible errors due to model spin-up (Marshall, 2000). It is important to note some unavoidable limitations with the ERA40 accumulation. The steep topography of the Antarctic Peninsula is necessarily relaxed (smoothed) to prevent instabilities developing within the model so the Peninsula is both too wide and flat. Furthermore, the spatial resolution ( 120 km) around the sharp coastal margins results in some land areas being classed as ocean. The effects of this are deemed to be small except when analysing individual grid points, but this clearly limits the spatial bounds with which one may attribute meteorological properties with confidence. The data are converted from a T106 reduced spectral grid to a1 1 regular grid. The annual and seasonal totals of P E are calculated from summing the daily forecasts while seasonal and annual means for MSLP and nearsurface temperature are calculated from 6-h data. The spatial domain for the EOF analysis extends in latitude from 55 S to85 S and 60 W to 105 W in longitude. This region was selected to isolate the Antarctic Peninsula, with the intention of capturing regional accumulation disparities between the western and eastern sides. It also allows an examination of land/sea differences in accumulation Mean accumulation and trends in P E derived from ERA40 ( ) The mean annual and seasonal accumulation fields in the Peninsula region (shown in Figure 3(a) and (c)) are characterized by having a clear maximum in accumulation on the western side, particularly, north of Alexander Island (Figure 1). This contrasts with a drier region on the eastern side that extends northward from the main Antarctic continent across the Weddell Sea. Recent accumulation trends in ERA40 are shown in Figure 3(b) and (d). The largest changes in annual Peninsula correspond closely to the western side maximum where the rate of increase is 5 mm yr 1, but due to the limited period of the dataset this is not statistically significant. On the eastern side there has been a general reduction in accumulation. The seasonal trends for the same period are most pronounced in austral autumn (MAM) and winter (JJA), where accumulation has markedly increased and decreased, respectively, in the west and south-west of the Peninsula. The region of maximum accumulation change is centred around the northern part of Alexander Island ( 70 S, 70 W), with an increase exceeding 5.5 mm yr 1 in MAM. Figure 3(b) indicates that the location of the maximum change in annual accumulation arises

5 ANTARCTIC PENINSULA ACCUMULATION VARIABILITY AND CHANGE 1413 (a) (c) (b) Figure 3. (a) Mean annual accumulation; (b) trends in annual accumulation; (c) seasonal mean accumulation; (d) trends in seasonal accumulation. Data are derived from ERA40 for the period This figure is available in colour online at because the region of strong accumulation increase in MAM extends further north than the region of significant decrease in JJA. Trends in the other two seasons are less coherent and smaller in magnitude. Owing to the complexity of the Peninsula orography and its representation in the analysis model, absolute values of accumulation trend in this region are subject to a degree of uncertainty. 3. Methodology EOF analysis is a powerful tool for diagnosing low-order spatial patterns of variability within data (Richman, 1986; Hannachi et al., 2006). It seeks to reduce the dimensionality of the data while retaining enough information to explain the observed patterns. EOFs are ordered according to the percentage of the original data variability they explain. The time series projection associated with each EOF pattern (which are themselves stationary static constructions of arbitrary magnitude), provides information about the relative magnitude and phase of the patterns and can indicate any trend towards a particular phase of the pattern. The initial dataset used here to construct the EOFs is in the form of a covariance rather than a correlation matrix, and as such maintains the magnitudes of the initial variances. Note that this may not be reasonable if the study were to include the rest of the Antarctic continent, the majority of which has significantly less accumulation than the Peninsula. The EOFs are evaluated from the accumulation anomalies, and the EOF calculation uses area weights, an important feature when using regularly gridded data at high-latitudes. Time series of these EOFs are regressed onto the analysis fields to produce an output in units of mm yr 1 per standard deviation. EOFs are by definition orthogonal, although some physical patterns they are thought to represent are often not (Richman, 1986). As such, a degree of degeneracy may exist between the EOFs. If the uncertainty bounds of one eigenvalue (associated with each EOF) overlap those of a neighbouring eigenvalue there is some likelihood that the two values are degenerate. That is, they may both be describing some aspect of the same pattern and cannot be considered independent. In general, the eigenvalue spectrum will fall to a point that all the remaining eigenvalues are degenerate (and not statistically significant), each one explaining a very small portion of the total variance. There are a few methods of selecting this cut-off (see Richman and Gong (1999) for a discussion). In this study, we initially assume that for

6 1414 G. M. MILES ET AL. (d) Figure 3. (Continued). asampleofn realizations this is likely to occur at a point of the order of n. Thus, for the 23-year time series used here this suggests that only the first four EOFs are viable (James, 1995) before an analysis of error. There is considerable debate within the literature as to whether and under what circumstances EOFs should be rotated (Dyer, 1975; Richman and Lamb, 1985). The effect of rotation is to redistribute the variance using a mathematical transform resulting in a different pattern. Many considerations are involved and are presented in a comprehensive review by Richman (1986). Under some circumstances rotation may eliminate artefacts that result from the spatial domain of the dataset, and produce patterns more closely related to those that physically exist (Richman, 1986). Alternatively, if little change is observed upon rotation, the EOF patterns may be considered to contain robust features of the physical pattern it attempts to explain (Walsh et al., 1982; Richman and Lamb, 1985; Dommenget and Latif, 2002). In this study, the leading EOFs did not change appreciably upon orthogonal rotation with the widely used VARIMAX criteria (as shown in Figure 4). Thus, they were retained in their unrotated form for the purpose of clarity. With care, interpretation of EOFs can illuminate physical patterns of variability: however, their application may not always be appropriate (Ambaum et al., 2001; Dommenget and Latif, 2002). They are often more useful when used in conjunction with raw data of other variables and analyses of how these co-vary with the EOFs. This also provides some validation as to whether the physical patterns captured are sensible. Hence in this study, we compare the accumulation EOFs to regional variability of MSLP and near-surface temperatures as derived from ERA40.

7 ANTARCTIC PENINSULA ACCUMULATION VARIABILITY AND CHANGE Results EOFs by their nature capture variability only within the data region, but that variability may reflect wider physical mechanisms. Initially, rather than examining external mechanisms themselves, we examine accumulation variability within the selected region. Subsequently, by regressing the EOF projections with time series of atmospheric indices we link some causal or co-varying mechanisms to this variability EOF patterns As discussed above, there are limits to interpreting the physical meaningfulness of EOF patterns. Particularly, pertinent to this case is the extent to which the input data may be considered physically reliable, given the caveats regarding the reanalysis model. Typically EOF3 and EOF4 are found to explain less than 10% of the total variability and, in the majority of cases, are mutually degenerate. They all show incoherent and low correlation to the time series of physical variables. EOF1 and EOF2 typically each explain 20% or greater fraction of the variability, and are the only ones discussed here. EOF1 and EOF2 for the annual accumulation are shown in Figure 4 and for the seasonal data in Figure 5. The EOFs as shown in these figures are considered to be in their positive phase. According to North s rule of thumb (North et al., 1982), the EOF1 and EOF2 accumulation patterns are degenerate in austral summer (DJF) and winter (JJA). That is, the estimated sampling error for the two eigenvalues overlaps. However, they are shown here for completeness and their broad similarity to equivalent EOFs from the other two seasons suggests that any degeneracy has not had a major impact on their spatial patterns. We note that the EOF1 pattern of accumulation is broadly similar in the annual data and all four seasons (cf. Figure 4. (a) Annual accumulation unrotated EOF patterns 1 and 2. (b) Orthogonally rotated annual accumulation EOF patterns 1 and 2. This figure is available in colour online at

8 1416 G. M. MILES ET AL. Figures 4 and 5). Accumulation is positively correlated across the whole of the Peninsula itself, with the exception of the southwest of the region in DJF and MAM. The annual, JJA and SON EOF1 have high variability along the entire western Antarctic Peninsula (WAP). The proportion of the total variance explained by this pattern varies from 24.4% in DJF to 51.6% in SON (41.8% for the annual data). In all cases, EOF2 has an enhancement feature along the WAP opposite in sign to the northern tip and eastern side of the Peninsula. In DJF, MAM and to a lesser extent JJA, a dipole of accumulation anomalies exists in the WAP, where the sign of the anomaly at the western base of the Peninsula is of the opposite sign to that in the north (Figure 5). This is clearly reflected in both the seasonal mean and recent trends for P E in ERA40 (Figure 3). The region north of 70 S tends towards increased annual accumulation and is notably decoupled from that further south, an observation also suggested by the climatological cyclone activity (Jones and Simmonds, 1993). This area is also characterized as having maximum variability in the leading annual and seasonal EOF patterns. Using the methodology summarized by Santer et al. (2000), no statistically significant trends in the phase of the EOF patterns were found. However, there are qualitative indications of marked trends of two particular EOFs in the period examined. The MAM EOF2 is tending towards a more positive phase (Figure 6(a)), which is a state of enhanced accumulation across most of the WAP, centred on Alexander Island. Also, JJA EOF2 (Figure 6(b)) is tending towards a more negative phase, meaning a suppression of accumulation in the same area. Both are in accordance with the apparent seasonal trends in accumulation in ERA40 (Figure 3). In both cases no such trend is found in EOF1, the mode that explains more variability Regression of EOF patterns and meteorological fields A regression of the P E time series onto MSLP and near-surface temperature time series is performed point Figure 5. Unrotated EOF1 and 2 of seasonal accumulation. (a) MAM; (b) JJA; (c) SON; (d) DJF. Units are mm water equivalent per standard deviation. This figure is available in colour online at

9 ANTARCTIC PENINSULA ACCUMULATION VARIABILITY AND CHANGE 1417 Figure 5. (Continued). by point to indicate how the patterns of accumulation vary with physical processes. This provides some estimate of the statistical significance of their relationships. Both sets of data were de-trended for these purposes. Regressions with MSLP and temperature are subject to the accuracy of the ERA40 fields themselves and, as such, they may be considered internally consistent to some degree. The results of correlating MSLP and temperature fields from ERA40 with the EOFs of P E show contrasting correlations for the first two EOFs. In Figure 7, MAM is shown as an example. The region showing the greatest (negative) regression values between MSLP and EOF1 of accumulation encompasses the Peninsula and extends west into the Bellingshausen Sea, with a minimum value of 4.2 hpa per standard deviation. This is thought to arise chiefly from the characteristic blocking features that can persist in the ABS. This highly significant relationship suggests that the different MSLP fields associated with high and low accumulation are statistically robust, which is therefore illustrative of a key underlying mechanism of Peninsula accumulation variability: as expected regional-precipitation variability is dominated by the strength and position of low-pressure systems in the CPT west of the Peninsula. Conversely, regression values between EOF2 and MSLP along the Peninsula are small, but stronger to the north and south, and suggest a relationship between regional accumulation and the strength of the zonal westerlies, as described by the SAM (cf. Section 4.3). The regression of EOF1 with near-surface temperature is quite different, with a clear East West divide. This divide occurs at around W (Figure 7), with the exception of an eastward protuberance at the base of the Peninsula at 75 S. In the eastern (western) sector, which encompasses most of the northern (southern) Peninsula, temperature is positively (negatively) correlated with

10 1418 G. M. MILES ET AL. Figure 6. EOF2 linear trends for (a) MAM and (b) JJA. Dot-dashed horizontal lines indicate 1 standard deviation of the EOF time series. Vertical bars show the standard error of the linear fit through which they pass. enhanced accumulation. However, these relationships are not statistically significant anywhere in the Peninsula itself. The strongest regression with near-surface temperature (2.0 C per standard deviation) is apparent with the WAP enhancement of EOF2. This positive pattern is present in all seasons and annually. The region of broadest and strongest regression values occurs for the MAM EOF2 pattern (in the given example, Figure 6), indicating that the accumulation pattern in EOF2 is strongly related to temperature. The relationship between the first pattern of accumulation variability and pressure is expected. The strength and location of the CPT in relation to the Peninsula affects the location and amount of snowfall. The strength of both the prevailing and synoptic winds associated with weather systems are governed by the depth of the low and will affect the longevity and location of any precipitation. Moreover, the variance explained by EOF1 is highest in the equinoctial seasons, when the CPT is deepest and closest to Antarctica. In the WAP, ERA40 temperature and pressure correlate strongly and negatively in all seasons except DJF, although the pattern is not uniform (results not shown). This is thought to result from warm advection of air from the north by low pressure centred in the ABS. In the ABS itself, the correlation is positive as the low pressure draws cold air off the continent. In spring (SON), the correlation between MSLP and near-surface temperatures is much weaker along the WAP, suggesting other factors such as sea-ice extent and concentration are influencing temperature Correlation with the SAM A correlation test was performed between each season EOF projection and a station-based SAM index (Marshall, 2003), defined as SAM = P 40S P 65S where P is the normalized monthly zonal MSLP at the subscripted latitudes. Two positive correlation coefficients ( 0.45) stand out from the rest. These were for the first and second MAM EOFs, and both are significant at <5% level. EOF1 and EOF2 together account for 61% of the observed accumulation variability in this season. MAM EOF1 (Figure 4(a)) is a dipole pattern with enhanced P E at the south-western base of the Peninsula. This is suggestive of a strong SAM influence on the accumulation patterns in autumn. Note that this is the season when the largest (positive) changes in the SAM have been observed and hence explains the reason for the more positive MAM EOF2 pattern Correlation with ENSO A correlation test between EOF projections and the Southern Oscillation Index (SOI) was also performed for each season. The SOI index was obtained from the Australian Bureau of Meteorology (Australian Bureau of Meteorology, 2006). A correlation of 0.5 is found between the first EOF for accumulation in JJA and the mean SOI for that season, significant at <5% level. This EOF pattern accounts for 34% of the total variability. Generally, during an El-Niño event (negative SOI) pressures are higher in the Bellingshausen Sea leading to an outflow of cold air from West Antarctica, immediately west of the Peninsula, over the ocean and reducing precipitation in the region. This suggests an apparent change in the seasonal influence of ENSO on Peninsula precipitation patterns to that described on larger spatial scales by Renwick and Revell (1999). A second correlation of 0.4 was found between SON EOF4 and the SOI, significant at <5%. The EOF accounts for only 5% of the total accumulation variability but is distinguishable from noise according to the estimated error of its eigenvalue. This is interpreted as a higher order response of the circulation in austral spring, the season when blocking in the ABS is most frequent (Renwick and Revell, 1999) Ice core-era40 accumulation comparison ERA40 accumulation data exhibit disparate climatologies and trends in the extreme north and southern base of the Peninsula, as do two ice cores from these regions, JRI and ITASE 01-5, respectively. The accumulation from both cores for the period of overlap with ERA40 used in this study is shown in Figure 8. Summer chemical markers (maxima in the ratio of non-sea salt sulphate to sodium concentration and maxima in hydrogen peroxide

11 ANTARCTIC PENINSULA ACCUMULATION VARIABILITY AND CHANGE 1419 Figure 7. Regression of the MAM accumulation EOF1 and EOF2 time series against (a) MSLP (units are hpa per standard deviation) and (b) temperature time series (units are C per standard deviation). The 5% significance level is shown. This figure is available in colour online at concentration) were used to distinguish annual layers of accumulation in the ice cores (Frey et al., 2006). The analogous time series of accumulation from ERA40 was derived for the core locations, interpolated from the nearest model grid points are plotted in Figure 8 for comparison against the ice-core data. The accumulation exhibits a recent small increase in the south, in the ITASE 01-5 core, and a marked decrease in accumulation in the north at JRI, as also demonstrated in the ERA40 data (Figure 3(a)). We note that the recent accumulation trends in the cores are of the order of the decadal variability observed in the ice core over the last 100 years, which is higher at the JRI site (cf. Figure 2). Figure 8 shows that the annual accumulation in the reanalysis approximates that from the ice cores. A statistically significant (<5% level) positive correlation of 0.66 exists between the annual accumulation in the ITASE core and ERA40 for (as also demonstrated by Genthon et al., 2005). In contrast, no significant correlation is found between the JRI core and ERA40. Inter-annual variability in accumulation at this site is higher in the ice core than in ERA40 and also at the ITASE site. The comparison was repeated using mid-winter markers (e.g. minima rather than maxima of the chemical markers described previously) to establish winter-to-winter accumulation in the ice cores: a poorer correlation was found. This suggests that in this case the summer-to-summer accumulation measurements are more temporally consistent or that their inherent definition noise is less (e.g. Peel, 1992).

12 1420 G. M. MILES ET AL. has been shown to be strongly related to temperature across the Peninsula region as a whole. In addition, it is positively correlated with the SAM, which also had high positive values at the end of the twentieth century. As the accumulation at JRI is negatively correlated to the MAM EOF2 (Figure 5(a)) the same processes have contributed to the recent decline in accumulation there. Hence, it seems that SAM variability has played a significant role in driving regional accumulation trends across the Antarctic Peninsula. 5. Summary Figure 8. Annual accumulation measured in ice cores collected at James Ross Island and the ITASE 01-5 site at the base of the Antarctic Peninsula, compared to ERA40 estimates of accumulation at each site, fitted with linear trends. Correlation between ice-core accumulation and the ERA40 EOF time series was examined to ascertain whether any variability in the former could be directly related to the latter. The correlation between annual summer-to-summer accumulation in the ITASE 01-5 core and EOF1 of the ERA40 accumulation is 0.46, significant at <5% level. This EOF captures 42% of the overall annual accumulation variability in the Antarctic Peninsula region. This indicates that (1) the ERA40 pattern of variability is capturing the real variability at the ITASE site, and (2) that this site reflects accumulation variability across the entire southern Peninsula region. Note that the correlation between annual winter-to-winter accumulation in the JRI ice core and EOF3 is 0.51, significant at <5% level: however, this EOF accounts for only 7.5% of the variability over the region and is degenerate with EOF4. The much better agreement between the ITASE 01-5 core accumulation observations and ERA40 than at the JRI site is thought to be primarily a product of the relatively simple orography at the former site that can be better portrayed by the ERA40 model. James Ross Island is depicted as less than 200 m high in this model, whereas the core site is above 1600 m a.s.l. Furthermore, at the lower-latitude JRI site there is far more summer melting and perhaps some percolation, resulting in migration of the glaciochemical profile and so mixing of the accumulation record between adjacent years. Models with higher spatial resolution and more realistic orography may give improved results at this location (e.g. van Lipzig et al., 2004). As the contrasting accumulation trends from the two ice cores are correctly reproduced by the ERA40 data, we can have some confidence that the relationships between accumulation and physical processes suggested by this study are operating in reality. Thus, the increase in accumulation at the ITASE 01-5 site can be primarily attributed to an increase in the magnitude of the second EOF of accumulation in austral autumn (MAM) at the end of the 1990s (cf. Figure 6(a)). This EOF pattern ECMWF ERA40 accumulation (P E) data are used to construct EOFs of annual and seasonal variability for the Antarctic Peninsula region based on a 23-year time period ( ). Only the first two EOFs are examined as higher order EOFs were shown to be degenerate. The first two EOFs account for 45 65% of the total annual/seasonal accumulation variability over the Peninsula. The primary spatial patterns in EOF1 are similar for all seasons to that of the annual data (cf. Figures 4 and 5). However, there are greater seasonal differences in EOF2, both in the spatial pattern and the trends. For example, a meridional dipole of accumulation anomalies along the WAP is particularly pronounced in autumn (MAM) and winter (JJA). While MAM EOF2 is trending towards an increasingly positive phase, meaning higher (lower) accumulation in the southern (northern) WAP, the opposite is occurring in JJA. These findings are also shown in the raw data. No clear trends were observed in the other seasons. A robust relationship between the EOF1 of accumulation and MSLP (higher pressure is associated with less accumulation) is shown to exist in both the annual and seasonal data. Thus EOF1 reflects the strength of lowpressure systems within the CPT. A similarly strong association exists between EOF2 and near-surface temperature (warmer temperatures affiliated with more accumulation). The advection of warm air into or cold air out of the Peninsula region is dependent on the exact longitudinal position of weather systems within the CPT (Marshall et al., 1998): a low in the Bellingshausen Sea will cause warm air and moisture to reach the Peninsula while the opposite will occur if there is high pressure (blocking) west of the Peninsula. Thus, one hypothesis is that EOF2 reflects changes in the position of the long-wave pattern in the South Pacific region. However, there is no evidence of this in the correlation with MSLP EOF2 is not statistically related to pressure in the ABS and the observed relationship appears to simply indicate a preference for higher precipitation on warmer days throughout the year. MAM EOF1 and EOF2 correlate positively with the SAM. The shape of this pattern of variability suggests a strong SAM influence on accumulation in austral autumn. This is the season when the SAM has become most positive in recent decades: the associated stronger

13 ANTARCTIC PENINSULA ACCUMULATION VARIABILITY AND CHANGE 1421 westerly winds have led to an increase in orographic precipitation in the WAP. The SOI index (of ENSO) is negatively correlated with JJA EOF1 as expected from previous work, with a possible higher order response in SON. Despite the challenges of complex orography, the ERA40 accumulation is similar in terms of annual accumulation compared to ice-core measurements taken from northern and southern regions of the Peninsula. The trends for the last 23 years are of a similar sign in ERA40 and the ice cores but more pronounced in the latter, particularly in the north at James Ross Island but on a year-to-year basis ERA40 accumulation at this site does not correlate with ice-core measurements. A better inter-annual correlation is identified with the southern core, located in a region of smoother orography. Results from this study suggest that recent changes in the SAM believed to be primarily caused by anthropogenic activity (e.g. Thompson and Solomon, 2002; Marshall et al., 2004; Arblaster and Meehl, 2006) have played a significant role in driving the opposing trends in accumulation between the southwest and North-East Antarctic Peninsula, as revealed in ice cores, through its positive relationship with the second EOF of accumulation in austral autumn. This study provides a first-order description of the patterns of accumulation variability across the Antarctic Peninsula against which data obtained from regional ice cores can be compared. Future ice cores, together with regional models having higher spatial resolution and more realistic orography than ERA40 (e.g. van Lipzig et al., 2004) will enable the refinement of the broadscale patterns revealed here. 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